diff --git a/AGENTS.md b/AGENTS.md index 546f2997bf5..d330961793a 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -212,6 +212,8 @@ When opening issues or pull requests, follow these templates: Using helpers like `supports_reasoning` (which read from `model_prices_and_context_window.json` / `get_model_info`) allows future model updates to "just work" without code changes. +9. **Never close HTTP/SDK clients on cache eviction**: Do not add `close()`, `aclose()`, or `create_task(close_fn())` inside `LLMClientCache._remove_key()` or any cache eviction path. Evicted clients may still be held by in-flight requests; closing them causes `RuntimeError: Cannot send a request, as the client has been closed.` in production after the cache TTL (1 hour) expires. Connection cleanup is handled at shutdown by `close_litellm_async_clients()`. See PR #22247 for the full incident history. + ## HELPFUL RESOURCES - Main documentation: https://docs.litellm.ai/ diff --git a/CLAUDE.md b/CLAUDE.md index c1eb75d2515..104a751ecaf 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -114,4 +114,17 @@ LiteLLM is a unified interface for 100+ LLM providers with two main components: ### Enterprise Features - Enterprise-specific code in `enterprise/` directory - Optional features enabled via environment variables -- Separate licensing and authentication for enterprise features \ No newline at end of file +- Separate licensing and authentication for enterprise features + +### HTTP Client Cache Safety +- **Never close HTTP/SDK clients on cache eviction.** `LLMClientCache._remove_key()` must not call `close()`/`aclose()` on evicted clients — they may still be used by in-flight requests. Doing so causes `RuntimeError: Cannot send a request, as the client has been closed.` after the 1-hour TTL expires. Cleanup happens at shutdown via `close_litellm_async_clients()`. + +### Troubleshooting: DB schema out of sync after proxy restart +`litellm-proxy-extras` runs `prisma migrate deploy` on startup using **its own** bundled migration files, which may lag behind schema changes in the current worktree. Symptoms: `Unknown column`, `Invalid prisma invocation`, or missing data on new fields. + +**Diagnose:** Run `\d "TableName"` in psql and compare against `schema.prisma` — missing columns confirm the issue. + +**Fix options:** +1. **Create a Prisma migration** (permanent) — run `prisma migrate dev --name ` in the worktree. The generated file will be picked up by `prisma migrate deploy` on next startup. +2. **Apply manually for local dev** — `psql -d litellm -c "ALTER TABLE ... ADD COLUMN IF NOT EXISTS ..."` after each proxy start. Fine for dev, not for production. +3. **Update litellm-proxy-extras** — if the package is installed from PyPI, its migration directory must include the new file. Either update the package or run the migration manually until the next release ships it. \ No newline at end of file diff --git a/docs/my-website/blog/gpt_5_4/index.md b/docs/my-website/blog/gpt_5_4/index.md new file mode 100644 index 00000000000..de099736f00 --- /dev/null +++ b/docs/my-website/blog/gpt_5_4/index.md @@ -0,0 +1,97 @@ +--- +slug: gpt_5_4 +title: "Day 0 Support: GPT-5.4" +date: 2026-03-05T10:00:00 +authors: + - name: Sameer Kankute + title: SWE @ LiteLLM (LLM Translation) + url: https://www.linkedin.com/in/sameer-kankute/ + image_url: https://pbs.twimg.com/profile_images/2001352686994907136/ONgNuSk5_400x400.jpg + - name: Krrish Dholakia + title: "CEO, LiteLLM" + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaff + title: "CTO, LiteLLM" + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg +description: "GPT-5.4 model support in LiteLLM" +tags: [openai, gpt-5.4, completion] +hide_table_of_contents: false +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +LiteLLM now supports fully GPT-5.4! + +## Docker Image + +```bash +docker pull ghcr.io/berriai/litellm:v1.81.14-stable.gpt-5.4_patch +``` + +## Usage + + + + +**1. Setup config.yaml** + +```yaml +model_list: + - model_name: gpt-5.4 + litellm_params: + model: openai/gpt-5.4 + api_key: os.environ/OPENAI_API_KEY +``` + +**2. Start the proxy** + +```bash +docker run -d \ + -p 4000:4000 \ + -e OPENAI_API_KEY=$OPENAI_API_KEY \ + -v $(pwd)/config.yaml:/app/config.yaml \ + ghcr.io/berriai/litellm:v1.81.14-stable.gpt-5.4_patch \ + --config /app/config.yaml +``` + +**3. Test it** + +```bash +curl -X POST "http://0.0.0.0:4000/chat/completions" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_KEY" \ + -d '{ + "model": "gpt-5.4", + "messages": [ + {"role": "user", "content": "Write a Python function to check if a number is prime."} + ] + }' +``` + + + + +```python +from litellm import completion + +response = completion( + model="openai/gpt-5.4", + messages=[ + {"role": "user", "content": "Write a Python function to check if a number is prime."} + ], +) + +print(response.choices[0].message.content) +``` + + + + +## Notes + +- Restart your container to get the cost tracking for this model. +- Use `/responses` for better model performance. +- GPT-5.4 supports reasoning, function calling, vision, and tool-use — see the [OpenAI provider docs](../../docs/providers/openai) for advanced usage. diff --git a/docs/my-website/docs/a2a_agent_headers.md b/docs/my-website/docs/a2a_agent_headers.md new file mode 100644 index 00000000000..457893b3b66 --- /dev/null +++ b/docs/my-website/docs/a2a_agent_headers.md @@ -0,0 +1,252 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# A2A Agent Authentication Headers + +Forward authentication credentials (Bearer tokens, API keys, etc.) from clients to backend A2A agents. + +## Overview + +When LiteLLM proxies a request to a backend A2A agent, the agent may require its own authentication headers. There are three ways to supply them: + +| Method | Who configures | How it works | +|---|---|---| +| **Static headers** | Admin (UI / API) | Always sent, regardless of client request | +| **Forward client headers** | Admin (UI / API) | Header names to extract from client request and forward | +| **Convention-based** | Client (no admin config) | Client sends `x-a2a-{agent_name}-{header}` — automatically routed | + +All three methods can be combined. **Static headers always win** on key conflicts. + +--- + +## Method 1 — Static Headers + +Admin-configured headers that are always sent to the backend agent. Use this for server-to-server tokens or internal credentials that clients should never see or override. + + + + +1. Go to **Agents** in the LiteLLM dashboard. +2. Create or edit an agent. +3. Open the **Authentication Headers** panel. +4. Under **Static Headers**, click **Add Static Header** and fill in the header name and value. + + + + +```bash +curl -X POST http://localhost:4000/v1/agents \ + -H "Authorization: Bearer sk-admin" \ + -H "Content-Type: application/json" \ + -d '{ + "agent_name": "my-agent", + "agent_card_params": { ... }, + "static_headers": { + "Authorization": "Bearer internal-server-token", + "X-Internal-Service": "litellm-proxy" + } + }' +``` + +To update an existing agent: + +```bash +curl -X PATCH http://localhost:4000/v1/agents/{agent_id} \ + -H "Authorization: Bearer sk-admin" \ + -H "Content-Type: application/json" \ + -d '{ + "static_headers": { + "Authorization": "Bearer new-token" + } + }' +``` + + + + +**Client call — no special headers needed:** + +```bash +curl -X POST http://localhost:4000/a2a/my-agent \ + -H "Authorization: Bearer sk-client-key" \ + -H "Content-Type: application/json" \ + -d '{ + "jsonrpc": "2.0", "id": "1", "method": "message/send", + "params": { "message": { "role": "user", "parts": [{"kind": "text", "text": "Hello"}], "messageId": "msg-1" } } + }' +``` + +The backend agent receives `Authorization: Bearer internal-server-token` without the client ever knowing the value. + +--- + +## Method 2 — Forward Client Headers + +Admin specifies a list of header **names**. When the client sends a request that includes those headers, LiteLLM extracts their values and forwards them to the backend agent. The client controls the values; the admin controls which headers are eligible to be forwarded. + + + + +1. Go to **Agents** in the LiteLLM dashboard. +2. Create or edit an agent. +3. Open the **Authentication Headers** panel. +4. Under **Forward Client Headers**, type header names and press **Enter** (e.g. `x-api-key`, `Authorization`). + + + + +```bash +curl -X POST http://localhost:4000/v1/agents \ + -H "Authorization: Bearer sk-admin" \ + -H "Content-Type: application/json" \ + -d '{ + "agent_name": "my-agent", + "agent_card_params": { ... }, + "extra_headers": ["x-api-key", "x-user-token"] + }' +``` + + + + +**Client call — include the forwarded headers:** + +```bash +curl -X POST http://localhost:4000/a2a/my-agent \ + -H "Authorization: Bearer sk-client-key" \ + -H "x-api-key: user-secret-value" \ + -H "Content-Type: application/json" \ + -d '{ ... }' +``` + +The backend agent receives `x-api-key: user-secret-value`. + +:::note +Header name matching is **case-insensitive**. If the client sends `X-API-Key` and `extra_headers` lists `x-api-key`, they match. +::: + +--- + +## Method 3 — Convention-Based Forwarding + +Clients can forward headers to a specific agent without any admin pre-configuration by using the naming convention: + +``` +x-a2a-{agent_name_or_id}-{header_name}: value +``` + +LiteLLM parses these headers automatically and routes them to the matching agent only. + +**Examples:** + +| Client header sent | Agent name/ID | Forwarded as | +|---|---|---| +| `x-a2a-my-agent-authorization: Bearer tok` | `my-agent` | `authorization: Bearer tok` | +| `x-a2a-my-agent-x-api-key: secret` | `my-agent` | `x-api-key: secret` | +| `x-a2a-abc123-authorization: Bearer tok` | agent ID `abc123` | `authorization: Bearer tok` | + +```bash +curl -X POST http://localhost:4000/a2a/my-agent \ + -H "Authorization: Bearer sk-client-key" \ + -H "x-a2a-my-agent-authorization: Bearer agent-specific-token" \ + -H "Content-Type: application/json" \ + -d '{ ... }' +``` + +The `x-a2a-other-agent-authorization` header sent in the same request is **not** forwarded to `my-agent` — it is silently ignored. + +:::tip Matches both agent name and agent ID +Both the human-readable name (e.g. `my-agent`) and the UUID (e.g. `abc123-...`) are valid. Use whichever is convenient for the client. +::: + +--- + +## Merge Precedence + +When multiple methods supply the same header name, **static headers win**: + +``` +dynamic (forwarded/convention) → merged ← static (overlays, wins) +``` + +Example: + +| Source | `Authorization` value | +|---|---| +| Client sends (via `extra_headers` or convention) | `Bearer client-token` | +| Admin-configured `static_headers` | `Bearer server-token` | +| **What the backend agent receives** | **`Bearer server-token`** | + +This ensures admin-controlled credentials cannot be overridden by client requests. + +--- + +## Combining All Three Methods + +```bash +# Register agent with static + forwarded headers +curl -X POST http://localhost:4000/v1/agents \ + -H "Authorization: Bearer sk-admin" \ + -H "Content-Type: application/json" \ + -d '{ + "agent_name": "my-agent", + "agent_card_params": { ... }, + "static_headers": { + "X-Internal-Token": "secret123" + }, + "extra_headers": ["x-user-id"] + }' + +# Client call using all three mechanisms +curl -X POST http://localhost:4000/a2a/my-agent \ + -H "Authorization: Bearer sk-client-key" \ + -H "x-user-id: user-42" \ + -H "x-a2a-my-agent-x-request-id: req-abc" \ + -H "Content-Type: application/json" \ + -d '{ ... }' +``` + +The backend agent receives: + +``` +X-Internal-Token: secret123 ← static header (always) +x-user-id: user-42 ← forwarded (in extra_headers) +x-request-id: req-abc ← convention-based (x-a2a-my-agent-*) +X-LiteLLM-Trace-Id: ← LiteLLM internal +X-LiteLLM-Agent-Id: ← LiteLLM internal +``` + +--- + +## Header Isolation + +Each agent invocation uses an isolated HTTP connection. Headers configured for agent A are **never** sent to agent B, even if both agents are running and receiving requests simultaneously. + +--- + +## API Reference + +### `POST /v1/agents` / `PATCH /v1/agents/{agent_id}` + +| Field | Type | Description | +|---|---|---| +| `static_headers` | `object` | `{"Header-Name": "value"}` — always forwarded | +| `extra_headers` | `string[]` | Header names to extract from client request and forward | + +### Agent Response + +Both fields are returned in `GET /v1/agents` and `GET /v1/agents/{agent_id}`: + +```json +{ + "agent_id": "...", + "agent_name": "my-agent", + "static_headers": { "X-Internal-Token": "secret123" }, + "extra_headers": ["x-user-id"], + ... +} +``` + +:::caution +`static_headers` values are stored in the database and returned by the API. Treat them as you would any credential — do not store sensitive long-lived tokens here if your API is publicly accessible. Consider using short-lived tokens or environment-injected secrets instead. +::: diff --git a/docs/my-website/docs/anthropic_unified/messages_to_responses_mapping.md b/docs/my-website/docs/anthropic_unified/messages_to_responses_mapping.md new file mode 100644 index 00000000000..87188c363bc --- /dev/null +++ b/docs/my-website/docs/anthropic_unified/messages_to_responses_mapping.md @@ -0,0 +1,120 @@ +# v1/messages → /responses Parameter Mapping + +When you send a request to `/v1/messages` targeting an OpenAI or Azure model, LiteLLM internally routes it through the OpenAI Responses API. This page documents exactly how every parameter gets translated in both directions. + +The transformation lives in `litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py`. + + +## Request: Anthropic → Responses API + +### Top-level parameters + +| Anthropic (`/v1/messages`) | Responses API | Notes | +|---|---|---| +| `model` | `model` | Passed through as-is | +| `messages` | `input` | Structurally transformed — see the messages section below | +| `system` (string) | `instructions` | Passed as a plain string | +| `system` (list of content blocks) | `instructions` | Text blocks are joined with `\n`; non-text blocks are ignored | +| `max_tokens` | `max_output_tokens` | Renamed | +| `temperature` | `temperature` | Passed through as-is | +| `top_p` | `top_p` | Passed through as-is | +| `tools` | `tools` | Format-translated — see the tools section below | +| `tool_choice` | `tool_choice` | Type-remapped — see the tool_choice section below | +| `thinking` | `reasoning` | Budget tokens mapped to effort level — see the thinking section below | +| `output_format` or `output_config.format` | `text` | Wrapped as `{"format": {"type": "json_schema", "name": "structured_output", "schema": ..., "strict": true}}` | +| `context_management` | `context_management` | Converted from Anthropic dict to OpenAI array format — see the context_management section below | +| `metadata.user_id` | `user` | Extracted from the metadata object and truncated to 64 characters | +| `stop_sequences` | ❌ Not mapped | Dropped silently | +| `top_k` | ❌ Not mapped | Dropped silently | +| `speed` | ❌ Not mapped | Only used to set Anthropic beta headers on the native path | + + +### How messages get converted + +Each Anthropic message is expanded into one or more Responses API input items. The key difference is that `tool_result` and `tool_use` blocks become **top-level items** in the input array rather than being nested inside a message. + +| Anthropic message | Responses API input item | +|---|---| +| `user` role, string content | `{"type": "message", "role": "user", "content": [{"type": "input_text", "text": "..."}]}` | +| `user` role, `{"type": "text"}` block | `{"type": "input_text", "text": "..."}` inside a user message | +| `user` role, `{"type": "image", "source": {"type": "base64"}}` | `{"type": "input_image", "image_url": "data:;base64,"}` inside a user message | +| `user` role, `{"type": "image", "source": {"type": "url"}}` | `{"type": "input_image", "image_url": ""}` inside a user message | +| `user` role, `{"type": "tool_result"}` block | Top-level `{"type": "function_call_output", "call_id": "...", "output": "..."}` — pulled out of the message entirely | +| `assistant` role, string content | `{"type": "message", "role": "assistant", "content": [{"type": "output_text", "text": "..."}]}` | +| `assistant` role, `{"type": "text"}` block | `{"type": "output_text", "text": "..."}` inside an assistant message | +| `assistant` role, `{"type": "tool_use"}` block | Top-level `{"type": "function_call", "call_id": "", "name": "...", "arguments": ""}` — pulled out of the message entirely | +| `assistant` role, `{"type": "thinking"}` block | `{"type": "output_text", "text": ""}` inside an assistant message | + + +### tools + +| Anthropic tool | Responses API tool | +|---|---| +| Any tool where `type` starts with `"web_search"` or `name == "web_search"` | `{"type": "web_search_preview"}` | +| All other tools | `{"type": "function", "name": "...", "description": "...", "parameters": }` | + + +### tool_choice + +| Anthropic `tool_choice.type` | Responses API `tool_choice` | +|---|---| +| `"auto"` | `{"type": "auto"}` | +| `"any"` | `{"type": "required"}` | +| `"tool"` | `{"type": "function", "name": ""}` | + + +### thinking → reasoning + +The `budget_tokens` value is mapped to a string effort level. `summary` is always set to `"detailed"`. + +| `thinking.budget_tokens` | `reasoning.effort` | +|---|---| +| >= 10000 | `"high"` | +| >= 5000 | `"medium"` | +| >= 2000 | `"low"` | +| < 2000 | `"minimal"` | + +If `thinking.type` is anything other than `"enabled"`, the `reasoning` field is not sent at all. + + +### context_management + +Anthropic uses a nested dict with an `edits` array. OpenAI uses a flat array of compaction objects. + +``` +Anthropic input: +{ + "edits": [ + { + "type": "compact_20260112", + "trigger": {"type": "input_tokens", "value": 150000} + } + ] +} + +Responses API output: +[ + {"type": "compaction", "compact_threshold": 150000} +] +``` + + +## Response: Responses API → Anthropic + +When the Responses API reply comes back, LiteLLM converts it into an Anthropic `AnthropicMessagesResponse`. + +| Responses API field | Anthropic response field | Notes | +|---|---|---| +| `response.id` | `id` | | +| `response.model` | `model` | Falls back to `"unknown-model"` if missing | +| `ResponseReasoningItem` — `summary[*].text` | `content` block `{"type": "thinking", "thinking": "..."}` | Each non-empty summary text becomes a thinking block | +| `ResponseOutputMessage` — `content[*]` where `type == "output_text"` | `content` block `{"type": "text", "text": "..."}` | | +| `ResponseFunctionToolCall` — `{call_id, name, arguments}` | `content` block `{"type": "tool_use", "id": "...", "name": "...", "input": {...}}` | `arguments` is JSON-parsed back into a dict | +| Any `function_call` present in output | `stop_reason: "tool_use"` | | +| `response.status == "incomplete"` | `stop_reason: "max_tokens"` | Takes precedence over the default | +| Everything else | `stop_reason: "end_turn"` | Default | +| `response.usage.input_tokens` | `usage.input_tokens` | | +| `response.usage.output_tokens` | `usage.output_tokens` | | +| *(hardcoded)* | `type: "message"` | Always set | +| *(hardcoded)* | `role: "assistant"` | Always set | +| *(hardcoded)* | `stop_sequence: null` | Always null on this path | diff --git a/docs/my-website/docs/mcp.md b/docs/my-website/docs/mcp.md index fcbb31c07d3..57bc1d57ffd 100644 --- a/docs/my-website/docs/mcp.md +++ b/docs/my-website/docs/mcp.md @@ -336,175 +336,9 @@ litellm_settings: ## Converting OpenAPI Specs to MCP Servers -LiteLLM can automatically convert OpenAPI specifications into MCP servers, allowing you to expose any REST API as MCP tools. This is useful when you have existing APIs with OpenAPI/Swagger documentation and want to make them available as MCP tools. +LiteLLM can convert OpenAPI specifications into MCP servers, exposing any REST API as MCP tools without writing custom server code. -**Benefits:** - -- **Rapid Integration**: Convert existing APIs to MCP tools without writing custom MCP server code -- **Automatic Tool Generation**: LiteLLM automatically generates MCP tools from your OpenAPI spec -- **Unified Interface**: Use the same MCP interface for both native MCP servers and OpenAPI-based APIs -- **Easy Testing**: Test and iterate on API integrations quickly - -**Configuration:** - -Add your OpenAPI-based MCP server to your `config.yaml`: - -```yaml title="config.yaml - OpenAPI to MCP" showLineNumbers -model_list: - - model_name: gpt-4o - litellm_params: - model: openai/gpt-4o - api_key: sk-xxxxxxx - -mcp_servers: - # OpenAPI Spec Example - Petstore API - petstore_mcp: - url: "https://petstore.swagger.io/v2" - spec_path: "/path/to/openapi.json" - auth_type: "none" - - # OpenAPI Spec with API Key Authentication - my_api_mcp: - url: "http://0.0.0.0:8090" - spec_path: "/path/to/openapi.json" - auth_type: "api_key" - auth_value: "your-api-key-here" - - # OpenAPI Spec with Bearer Token - secured_api_mcp: - url: "https://api.example.com" - spec_path: "/path/to/openapi.json" - auth_type: "bearer_token" - auth_value: "your-bearer-token" -``` - -**Configuration Parameters:** - -| Parameter | Required | Description | -|-----------|----------|-------------| -| `url` | Yes | The base URL of your API endpoint | -| `spec_path` | Yes | Path or URL to your OpenAPI specification file (JSON or YAML) | -| `auth_type` | No | Authentication type: `none`, `api_key`, `bearer_token`, `basic`, `authorization` | -| `auth_value` | No | Authentication value (required if `auth_type` is set) | -| `authorization_url` | No | For `auth_type: oauth2`. Optional override; if omitted LiteLLM auto-discovers it. | -| `token_url` | No | For `auth_type: oauth2`. Optional override; if omitted LiteLLM auto-discovers it. | -| `registration_url` | No | For `auth_type: oauth2`. Optional override; if omitted LiteLLM auto-discovers it. | -| `scopes` | No | For `auth_type: oauth2`. Optional override; if omitted LiteLLM uses the scopes advertised by the server. | -| `description` | No | Optional description for the MCP server | -| `allowed_tools` | No | List of specific tools to allow (see [MCP Tool Filtering](#mcp-tool-filtering)) | -| `disallowed_tools` | No | List of specific tools to block (see [MCP Tool Filtering](#mcp-tool-filtering)) | - -### Usage Example - -Once configured, you can use the OpenAPI-based MCP server just like any other MCP server: - - - - -```python title="Using OpenAPI-based MCP Server" showLineNumbers -from fastmcp import Client -import asyncio - -# Standard MCP configuration -config = { - "mcpServers": { - "petstore": { - "url": "http://localhost:4000/petstore_mcp/mcp", - "headers": { - "x-litellm-api-key": "Bearer sk-1234" - } - } - } -} - -# Create a client that connects to the server -client = Client(config) - -async def main(): - async with client: - # List available tools generated from OpenAPI spec - tools = await client.list_tools() - print(f"Available tools: {[tool.name for tool in tools]}") - - # Example: Get a pet by ID (from Petstore API) - response = await client.call_tool( - name="getpetbyid", - arguments={"petId": "1"} - ) - print(f"Response:\n{response}\n") - - # Example: Find pets by status - response = await client.call_tool( - name="findpetsbystatus", - arguments={"status": "available"} - ) - print(f"Response:\n{response}\n") - -if __name__ == "__main__": - asyncio.run(main()) -``` - - - - - -```json title="Cursor MCP Configuration for OpenAPI Server" showLineNumbers -{ - "mcpServers": { - "Petstore": { - "url": "http://localhost:4000/petstore_mcp/mcp", - "headers": { - "x-litellm-api-key": "Bearer $LITELLM_API_KEY" - } - } - } -} -``` - - - - - -```bash title="Using OpenAPI MCP Server with OpenAI" showLineNumbers -curl --location 'https://api.openai.com/v1/responses' \ ---header 'Content-Type: application/json' \ ---header "Authorization: Bearer $OPENAI_API_KEY" \ ---data '{ - "model": "gpt-4o", - "tools": [ - { - "type": "mcp", - "server_label": "petstore", - "server_url": "http://localhost:4000/petstore_mcp/mcp", - "require_approval": "never", - "headers": { - "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY" - } - } - ], - "input": "Find all available pets in the petstore", - "tool_choice": "required" -}' -``` - - - - -**How It Works** - -1. **Spec Loading**: LiteLLM loads your OpenAPI specification from the provided `spec_path` -2. **Tool Generation**: Each API endpoint in the spec becomes an MCP tool -3. **Parameter Mapping**: OpenAPI parameters are automatically mapped to MCP tool parameters -4. **Request Handling**: When a tool is called, LiteLLM converts the MCP request to the appropriate HTTP request -5. **Response Translation**: API responses are converted back to MCP format - -**OpenAPI Spec Requirements** - -Your OpenAPI specification should follow standard OpenAPI/Swagger conventions: -- **Supported versions**: OpenAPI 3.0.x, OpenAPI 3.1.x, Swagger 2.0 -- **Required fields**: `paths`, `info` sections should be properly defined -- **Operation IDs**: Each operation should have a unique `operationId` (this becomes the tool name) -- **Parameters**: Request parameters should be properly documented with types and descriptions +See the **[MCP from OpenAPI Specs guide](./mcp_openapi.md)** for full setup, usage examples, and how to override tool names and descriptions. ## MCP OAuth @@ -870,6 +704,63 @@ asyncio.run(main()) [Learn more about customer management →](./proxy/customers) +## Calling the Proxy's /v1/responses Endpoint + +When calling your LiteLLM Proxy's `/v1/responses` endpoint to use MCP tools, **always use `server_url: "litellm_proxy"`** in the tools array. This tells the proxy to use its configured MCP servers. + +:::important Do not use the full proxy URL +Using `server_url: "https://your-proxy.com/mcp"` is incorrect when the request is already going to the proxy. The proxy needs the literal value `litellm_proxy` to route to its configured MCP servers. +::: + +```bash title="Correct: Using litellm_proxy" showLineNumbers +curl --location 'https://your-proxy.com/v1/responses' \ +--header 'Content-Type: application/json' \ +--header "Authorization: Bearer $LITELLM_API_KEY" \ +--data '{ + "model": "gpt-4", + "tools": [ + { + "type": "mcp", + "server_label": "litellm", + "server_url": "litellm_proxy", + "require_approval": "never" + } + ], + "input": "Run available tools", + "tool_choice": "required" +}' +``` + +### Sending Custom Headers to MCP Servers + +To pass custom headers (e.g., API keys, auth tokens) to specific MCP servers, use either: + +**Option 1: Request headers** – Add `x-mcp-{server_alias}-{header_name}` to your request headers. The proxy forwards these to the matching MCP server. + +```bash +# Send Authorization header to the "weather2" MCP server +--header 'x-mcp-weather2-authorization: Bearer your-token' + +# Send custom header to the "github" MCP server +--header 'x-mcp-github-x-api-key: your-api-key' +``` + +**Option 2: Headers in tool config** – Include a `headers` object in the tool definition. These are merged with request headers. + +```json +{ + "type": "mcp", + "server_label": "litellm", + "server_url": "litellm_proxy", + "require_approval": "never", + "headers": { + "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY", + "x-mcp-servers": "Zapier_MCP,dev-group", + "x-mcp-weather2-authorization": "Bearer your-weather-api-token" + } +} +``` + ## Using your MCP with client side credentials Use this if you want to pass a client side authentication token to LiteLLM to then pass to your MCP to auth to your MCP. diff --git a/docs/my-website/docs/mcp_control.md b/docs/my-website/docs/mcp_control.md index 96c71ef9278..ccaa37f9497 100644 --- a/docs/my-website/docs/mcp_control.md +++ b/docs/my-website/docs/mcp_control.md @@ -323,7 +323,7 @@ curl --location '/v1/responses' \ { "type": "mcp", "server_label": "litellm", - "server_url": "/dev_group/mcp", + "server_url": "litellm_proxy", "require_approval": "never", "headers": { "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY" @@ -335,7 +335,7 @@ curl --location '/v1/responses' \ }' ``` -This example uses URL namespacing to access all servers in the "dev_group" access group. +This example uses the `x-mcp-servers` header to access all servers in the "dev_group" access group. Use `server_url: "litellm_proxy"` when calling the proxy's `/v1/responses` endpoint—do not use the full proxy URL. @@ -423,7 +423,7 @@ curl --location '/v1/responses' \ { "type": "mcp", "server_label": "litellm", - "server_url": "/mcp/", + "server_url": "litellm_proxy", "require_approval": "never", "headers": { "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY", @@ -436,7 +436,7 @@ curl --location '/v1/responses' \ }' ``` -This configuration restricts the request to only use tools from the specified MCP servers. +This configuration restricts the request to only use tools from the specified MCP servers. Use `server_url: "litellm_proxy"` when calling the proxy's `/v1/responses` endpoint. diff --git a/docs/my-website/docs/mcp_openapi.md b/docs/my-website/docs/mcp_openapi.md new file mode 100644 index 00000000000..0f18ecc127a --- /dev/null +++ b/docs/my-website/docs/mcp_openapi.md @@ -0,0 +1,226 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import Image from '@theme/IdealImage'; + +# MCP from OpenAPI Specs + +LiteLLM can convert any OpenAPI/Swagger spec into an MCP server — no custom MCP server code required. + +## Step 1 — Add the MCP Server + +Add your OpenAPI-based server in `config.yaml`: + +```yaml title="config.yaml" showLineNumbers +mcp_servers: + petstore_mcp: + url: "https://petstore.swagger.io/v2" + spec_path: "/path/to/openapi.json" + auth_type: "none" + + my_api_mcp: + url: "http://0.0.0.0:8090" + spec_path: "/path/to/openapi.json" + auth_type: "api_key" + auth_value: "your-api-key-here" + + secured_api_mcp: + url: "https://api.example.com" + spec_path: "/path/to/openapi.json" + auth_type: "bearer_token" + auth_value: "your-bearer-token" +``` + +Or from the UI: go to **MCP Servers → Add New MCP Server**, fill in the URL and spec path, and LiteLLM will fetch the spec and load all endpoints as tools. + +**Configuration parameters:** + +| Parameter | Required | Description | +|-----------|----------|-------------| +| `url` | Yes | Base URL of your API | +| `spec_path` | Yes | Path or URL to your OpenAPI spec (JSON or YAML) | +| `auth_type` | No | `none`, `api_key`, `bearer_token`, `basic`, `authorization`, `oauth2` | +| `auth_value` | No | Auth value (required if `auth_type` is set) | +| `description` | No | Optional description | +| `allowed_tools` | No | Allowlist of specific tools | +| `disallowed_tools` | No | Blocklist of specific tools | + +**Supported spec versions:** OpenAPI 3.0.x, 3.1.x, Swagger 2.0. Each operation's `operationId` becomes the tool name — make sure they're unique. + +Once tools are loaded, you'll see them in the Tool Configuration section: + + + +
+ +## Step 2 — Optionally Override Tool Names and Descriptions + +By default, tool names and descriptions come from the `operationId` and description fields in your spec. You can rename or rewrite them so MCP clients see something cleaner — without touching the upstream spec. + +### From the UI + +Each tool card has a pencil icon. Click it to open the inline editor: + + + +
+ +- **Display Name** — overrides the name MCP clients see +- **Description** — overrides the description MCP clients see +- Leave a field blank to keep the original from the spec + +After setting overrides, a purple **Custom name** badge appears on the tool card: + + + +
+ +### From the API + +Pass `tool_name_to_display_name` and `tool_name_to_description` in the create or update request: + +```bash title="Create server with tool name overrides" showLineNumbers +curl -X POST http://localhost:4000/v1/mcp/server \ + -H "Authorization: Bearer $LITELLM_MASTER_KEY" \ + -H "Content-Type: application/json" \ + -d '{ + "name": "petstore_mcp", + "url": "https://petstore.swagger.io/v2", + "spec_path": "/path/to/openapi.json", + "tool_name_to_display_name": { + "getPetById": "Get Pet", + "findPetsByStatus": "List Available Pets" + }, + "tool_name_to_description": { + "getPetById": "Look up a pet by its ID", + "findPetsByStatus": "Returns all pets matching a given status (available, pending, sold)" + } + }' +``` + +```bash title="Update overrides on an existing server" showLineNumbers +curl -X PUT http://localhost:4000/v1/mcp/server/{server_id} \ + -H "Authorization: Bearer $LITELLM_MASTER_KEY" \ + -H "Content-Type: application/json" \ + -d '{ + "tool_name_to_display_name": { + "getPetById": "Get Pet" + }, + "tool_name_to_description": { + "getPetById": "Look up a pet by its ID" + } + }' +``` + +The map key is the **original `operationId`** from the spec — not the prefixed tool name. LiteLLM strips the server prefix before doing the lookup. + +For example, if your server is `petstore_mcp`, the tool is exposed as `petstore_mcp-getPetById`. The map key is still `getPetById`. + +**Before and after:** + +``` +# Without overrides +Tool: "petstore_mcp-getPetById" +Description: "Returns a single pet" + +Tool: "petstore_mcp-findPetsByStatus" +Description: "Finds Pets by status" + +# After overrides +Tool: "Get Pet" +Description: "Look up a pet by its ID" + +Tool: "List Available Pets" +Description: "Returns all pets matching a given status (available, pending, sold)" +``` + +## Using the Server + + + + +```python title="Using OpenAPI-based MCP Server" showLineNumbers +from fastmcp import Client +import asyncio + +config = { + "mcpServers": { + "petstore": { + "url": "http://localhost:4000/petstore_mcp/mcp", + "headers": { + "x-litellm-api-key": "Bearer sk-1234" + } + } + } +} + +client = Client(config) + +async def main(): + async with client: + tools = await client.list_tools() + print(f"Available tools: {[tool.name for tool in tools]}") + + response = await client.call_tool( + name="Get Pet", # overridden name + arguments={"petId": "1"} + ) + print(f"Response: {response}") + +if __name__ == "__main__": + asyncio.run(main()) +``` + + + + + +```json title="Cursor MCP Configuration" showLineNumbers +{ + "mcpServers": { + "Petstore": { + "url": "http://localhost:4000/petstore_mcp/mcp", + "headers": { + "x-litellm-api-key": "Bearer $LITELLM_API_KEY" + } + } + } +} +``` + + + + + +```bash title="Using OpenAPI MCP Server with OpenAI" showLineNumbers +curl --location 'https://api.openai.com/v1/responses' \ +--header 'Content-Type: application/json' \ +--header "Authorization: Bearer $OPENAI_API_KEY" \ +--data '{ + "model": "gpt-4o", + "tools": [ + { + "type": "mcp", + "server_label": "petstore", + "server_url": "http://localhost:4000/petstore_mcp/mcp", + "require_approval": "never", + "headers": { + "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY" + } + } + ], + "input": "Find all available pets", + "tool_choice": "required" +}' +``` + + + diff --git a/docs/my-website/docs/providers/azure_ai/azure_model_router.md b/docs/my-website/docs/providers/azure_ai/azure_model_router.md index 16bc1afb70e..9b308b709c7 100644 --- a/docs/my-website/docs/providers/azure_ai/azure_model_router.md +++ b/docs/my-website/docs/providers/azure_ai/azure_model_router.md @@ -2,6 +2,32 @@ Azure Model Router is a feature in Azure AI Foundry that automatically routes your requests to the best available model based on your requirements. This allows you to use a single endpoint that intelligently selects the optimal model for each request. +## Quick Start + +**Model pattern**: `azure_ai/model_router/` + +```python +import litellm + +response = litellm.completion( + model="azure_ai/model_router/model-router", # Replace with your deployment name + messages=[{"role": "user", "content": "Hello!"}], + api_base="https://your-endpoint.cognitiveservices.azure.com/openai/v1/", + api_key="your-api-key", +) +``` + +**Proxy config** (`config.yaml`): + +```yaml +model_list: + - model_name: model-router + litellm_params: + model: azure_ai/model_router/model-router + api_base: https://your-endpoint.cognitiveservices.azure.com/openai/deployments/model-router/chat/completions?api-version=2025-01-01-preview + api_key: your-api-key +``` + ## Key Features - **Automatic Model Selection**: Azure Model Router dynamically selects the best model for your request @@ -229,19 +255,51 @@ Cost is tracked based on the actual model used (e.g., `gpt-4.1-nano`), plus a fl ## Cost Tracking -LiteLLM automatically handles cost tracking for Azure Model Router by: +LiteLLM automatically handles cost tracking for Azure Model Router. Understanding how this works helps you interpret spend and debug billing. + +### How LiteLLM Calculates Cost + +When you use Azure Model Router, LiteLLM computes **two cost components**: + +| Component | Description | When Applied | +|-----------|-------------|--------------| +| **Model Cost** | Token-based cost for the actual model that handled the request (e.g., `gpt-5-nano`, `gpt-4.1-nano`) | Always, when Azure returns the model in the response | +| **Router Flat Cost** | $0.14 per million input tokens (Azure AI Foundry infrastructure fee) | When the **request** was made via a model router endpoint | + +### Cost Calculation Flow + +1. **Request model detection**: LiteLLM records the model you requested (e.g., `azure_ai/model_router/model-router`). If it contains `model_router` or `model-router`, the request is treated as a router request. + +2. **Response model extraction**: Azure returns the actual model used in the response (e.g., `gpt-5-nano-2025-08-07`). LiteLLM uses this for the model cost lookup. + +3. **Model cost**: LiteLLM looks up the response model in its pricing table and computes cost from prompt tokens and completion tokens. + +4. **Router flat cost**: Because the original request was to a model router, LiteLLM adds the flat cost ($0.14 per M input tokens) on top of the model cost. -1. **Detecting the actual model**: When Azure Model Router routes your request to a specific model (e.g., `gpt-4.1-nano-2025-04-14`), LiteLLM extracts this from the response -2. **Calculating accurate costs**: Costs are calculated based on: - - The actual model used (e.g., `gpt-4.1-nano` token costs) - - Plus a flat infrastructure cost of **$0.14 per million input tokens** for using the Model Router -3. **Streaming support**: Cost tracking works correctly for both streaming and non-streaming requests +5. **Total cost**: `Total = Model Cost + Router Flat Cost` + +### Configuration Requirements + +For cost tracking to work correctly: + +- **Use the full pattern**: `azure_ai/model_router/` (e.g., `azure_ai/model_router/model-router`) +- **Proxy config**: When using the LiteLLM proxy, set `model` in `litellm_params` to the full pattern so the request model is correctly identified as a router + +```yaml +# proxy_server_config.yaml +model_list: + - model_name: model-router + litellm_params: + model: azure_ai/model_router/model-router # Required for router cost detection + api_base: https://your-endpoint.cognitiveservices.azure.com/openai/deployments/model-router/chat/completions?api-version=2025-01-01-preview + api_key: your-api-key +``` ### Cost Breakdown When you use Azure Model Router, the total cost includes: -- **Model Cost**: Based on the actual model that handled your request (e.g., `gpt-4.1-nano`) +- **Model Cost**: Based on the actual model that handled your request (e.g., `gpt-5-nano`, `gpt-4.1-nano`) - **Router Flat Cost**: $0.14 per million input tokens (Azure AI Foundry infrastructure fee) ### Example Response with Cost diff --git a/docs/my-website/docs/providers/bedrock_mantle.md b/docs/my-website/docs/providers/bedrock_mantle.md new file mode 100644 index 00000000000..185d9a6e215 --- /dev/null +++ b/docs/my-website/docs/providers/bedrock_mantle.md @@ -0,0 +1,157 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Amazon Bedrock Mantle + +[Amazon Bedrock Mantle](https://docs.aws.amazon.com/bedrock/latest/userguide/bedrock-mantle.html) is Amazon Bedrock's distributed inference engine (Project Mantle) that exposes an **OpenAI-compatible API** for Bedrock-hosted models. + +Use this provider to call Bedrock Mantle models with accurate **AWS Bedrock pricing** instead of OpenAI pricing. + +:::tip + +**We support ALL Bedrock Mantle models, just set `model=bedrock_mantle/` as a prefix when sending litellm requests** + +::: + +## API Key + +```python +# env variable +os.environ['BEDROCK_MANTLE_API_KEY'] = "your-aws-bedrock-api-key" + +# optional: override region (defaults to us-east-1) +os.environ['BEDROCK_MANTLE_REGION'] = "us-east-1" # or use AWS_REGION +``` + +## Supported Models + +| Model | Context Window | Input (per 1M tokens) | Output (per 1M tokens) | +|-------|---------------|----------------------|------------------------| +| `openai.gpt-oss-120b` | 131K | $0.15 | $0.60 | +| `openai.gpt-oss-20b` | 131K | $0.075 | $0.30 | +| `openai.gpt-oss-safeguard-120b` | 131K | $0.15 | $0.60 | +| `openai.gpt-oss-safeguard-20b` | 131K | $0.075 | $0.30 | + +## Sample Usage + + + + +```python +from litellm import completion +import os + +os.environ['BEDROCK_MANTLE_API_KEY'] = "your-bedrock-api-key" + +response = completion( + model="bedrock_mantle/openai.gpt-oss-120b", + messages=[{"role": "user", "content": "hello from litellm"}], +) +print(response) +``` + + + + +```python +from litellm import completion +import os + +os.environ['BEDROCK_MANTLE_API_KEY'] = "your-bedrock-api-key" + +response = completion( + model="bedrock_mantle/openai.gpt-oss-120b", + messages=[{"role": "user", "content": "hello from litellm"}], + stream=True, +) + +for chunk in response: + print(chunk) +``` + + + + +```python +import asyncio +from litellm import acompletion +import os + +os.environ['BEDROCK_MANTLE_API_KEY'] = "your-bedrock-api-key" + +async def main(): + response = await acompletion( + model="bedrock_mantle/openai.gpt-oss-120b", + messages=[{"role": "user", "content": "hello from litellm"}], + ) + print(response) + +asyncio.run(main()) +``` + + + + +## Region Configuration + +The API base URL is `https://bedrock-mantle.{region}.api.aws/v1`. Region is resolved in this order: + +1. `BEDROCK_MANTLE_REGION` env var +2. `AWS_REGION` env var +3. Default: `us-east-1` + +**Supported regions:** `us-east-1`, `us-east-2`, `us-west-2`, `eu-west-1`, `eu-west-2`, `eu-central-1`, `eu-south-1`, `eu-north-1`, `ap-northeast-1`, `ap-south-1`, `ap-southeast-3`, `sa-east-1` + +```python +import os +os.environ['BEDROCK_MANTLE_REGION'] = "eu-west-1" + +# or pass api_base directly +response = completion( + model="bedrock_mantle/openai.gpt-oss-120b", + messages=[{"role": "user", "content": "hello"}], + api_base="https://bedrock-mantle.eu-west-1.api.aws/v1", +) +``` + +## Usage with LiteLLM Proxy + +### 1. Set Bedrock Mantle models on config.yaml + +```yaml +model_list: + - model_name: gpt-oss-120b + litellm_params: + model: bedrock_mantle/openai.gpt-oss-120b + api_key: os.environ/BEDROCK_MANTLE_API_KEY + # optional region override: + api_base: "https://bedrock-mantle.us-east-1.api.aws/v1" + + - model_name: gpt-oss-20b + litellm_params: + model: bedrock_mantle/openai.gpt-oss-20b + api_key: os.environ/BEDROCK_MANTLE_API_KEY +``` + +### 2. Start the proxy + +```shell +litellm --config /path/to/config.yaml +``` + +### 3. Send a request + +```python +import openai + +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000", +) + +response = client.chat.completions.create( + model="gpt-oss-120b", + messages=[{"role": "user", "content": "hello from litellm"}], +) +print(response) +``` diff --git a/docs/my-website/docs/providers/chatgpt.md b/docs/my-website/docs/providers/chatgpt.md index 156bbf99df6..222881953dc 100644 --- a/docs/my-website/docs/providers/chatgpt.md +++ b/docs/my-website/docs/providers/chatgpt.md @@ -4,12 +4,12 @@ Use ChatGPT Pro/Max subscription models through LiteLLM with OAuth device flow a | Property | Details | |-------|-------| -| Description | ChatGPT subscription access (Codex + GPT-5.2 family) via ChatGPT backend API | +| Description | ChatGPT subscription access (Codex + GPT-5.3/5.4 family) via ChatGPT backend API | | Provider Route on LiteLLM | `chatgpt/` | | Supported Endpoints | `/responses`, `/chat/completions` (bridged to Responses for supported models) | | API Reference | https://chatgpt.com | -ChatGPT subscription access is native to the Responses API. Chat Completions requests are bridged to Responses for supported models (for example `chatgpt/gpt-5.2`). +ChatGPT subscription access is native to the Responses API. Chat Completions requests are bridged to Responses for supported models (for example `chatgpt/gpt-5.4`). Notes: - The ChatGPT subscription backend rejects token limit fields (`max_tokens`, `max_output_tokens`, `max_completion_tokens`) and `metadata`. LiteLLM strips these fields for this provider. @@ -31,7 +31,7 @@ ChatGPT subscription access uses an OAuth device code flow: import litellm response = litellm.responses( - model="chatgpt/gpt-5.2-codex", + model="chatgpt/gpt-5.3-codex", input="Write a Python hello world" ) @@ -44,7 +44,7 @@ print(response) import litellm response = litellm.completion( - model="chatgpt/gpt-5.2", + model="chatgpt/gpt-5.4", messages=[{"role": "user", "content": "Write a Python hello world"}] ) @@ -55,16 +55,36 @@ print(response) ```yaml showLineNumbers title="config.yaml" model_list: - - model_name: chatgpt/gpt-5.2 + - model_name: chatgpt/gpt-5.4 model_info: mode: responses litellm_params: - model: chatgpt/gpt-5.2 - - model_name: chatgpt/gpt-5.2-codex + model: chatgpt/gpt-5.4 + - model_name: chatgpt/gpt-5.4-pro model_info: mode: responses litellm_params: - model: chatgpt/gpt-5.2-codex + model: chatgpt/gpt-5.4-pro + - model_name: chatgpt/gpt-5.3-codex + model_info: + mode: responses + litellm_params: + model: chatgpt/gpt-5.3-codex + - model_name: chatgpt/gpt-5.3-codex-spark + model_info: + mode: responses + litellm_params: + model: chatgpt/gpt-5.3-codex-spark + - model_name: chatgpt/gpt-5.3-instant + model_info: + mode: responses + litellm_params: + model: chatgpt/gpt-5.3-instant + - model_name: chatgpt/gpt-5.3-chat-latest + model_info: + mode: responses + litellm_params: + model: chatgpt/gpt-5.3-chat-latest ``` ```bash showLineNumbers title="Start LiteLLM Proxy" diff --git a/docs/my-website/docs/providers/openai.md b/docs/my-website/docs/providers/openai.md index 782c7072e50..6817c32e9b4 100644 --- a/docs/my-website/docs/providers/openai.md +++ b/docs/my-website/docs/providers/openai.md @@ -192,8 +192,12 @@ os.environ["OPENAI_BASE_URL"] = "https://your_host/v1" # OPTIONAL | gpt-5.2-2025-12-11 | `response = completion(model="gpt-5.2-2025-12-11", messages=messages)` | | gpt-5.2-chat-latest | `response = completion(model="gpt-5.2-chat-latest", messages=messages)` | | gpt-5.3-chat-latest | `response = completion(model="gpt-5.3-chat-latest", messages=messages)` | +| gpt-5.4 | `response = completion(model="gpt-5.4", messages=messages)` | +| gpt-5.4-2026-03-05 | `response = completion(model="gpt-5.4-2026-03-05", messages=messages)` | | gpt-5.2-pro | `response = completion(model="gpt-5.2-pro", messages=messages)` | | gpt-5.2-pro-2025-12-11 | `response = completion(model="gpt-5.2-pro-2025-12-11", messages=messages)` | +| gpt-5.4-pro | `response = completion(model="gpt-5.4-pro", messages=messages)` | +| gpt-5.4-pro-2026-03-05 | `response = completion(model="gpt-5.4-pro-2026-03-05", messages=messages)` | | gpt-5.1 | `response = completion(model="gpt-5.1", messages=messages)` | | gpt-5.1-codex | `response = completion(model="gpt-5.1-codex", messages=messages)` | | gpt-5.1-codex-mini | `response = completion(model="gpt-5.1-codex-mini", messages=messages)` | diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index 94619082e88..d919d0412cd 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -1687,6 +1687,20 @@ litellm.vertex_location = "us-central1 # Your Location | gemini-2.5-flash-lite-preview-09-2025 | `completion('gemini-2.5-flash-lite-preview-09-2025', messages)`, `completion('vertex_ai/gemini-2.5-flash-lite-preview-09-2025', messages)` | | gemini-3.1-flash-lite-preview | `completion('gemini-3.1-flash-lite-preview', messages)`, `completion('vertex_ai/gemini-3.1-flash-lite-preview', messages)` | +## PayGo / Priority Cost Tracking + +LiteLLM automatically tracks spend for Vertex AI Gemini models using the correct pricing tier based on the response's `usageMetadata.trafficType`: + +| Vertex AI `trafficType` | LiteLLM `service_tier` | Pricing applied | +|-------------------------|-------------------------|-----------------| +| `ON_DEMAND_PRIORITY` | `priority` | PayGo / priority pricing (`input_cost_per_token_priority`, `output_cost_per_token_priority`) | +| `ON_DEMAND` | standard | Default on-demand pricing | +| `FLEX` / `BATCH` | `flex` | Batch/flex pricing | + +When you use [Vertex AI PayGo](https://cloud.google.com/vertex-ai/generative-ai/pricing) (on-demand priority) or batch workloads, LiteLLM reads `trafficType` from the response and applies the matching cost per token from the [model cost map](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json). No configuration is required — spend tracking works out of the box for both standard and PayGo requests. + +See [Spend Tracking](../proxy/cost_tracking.md) for general cost tracking setup. + ## Private Service Connect (PSC) Endpoints LiteLLM supports Vertex AI models deployed to Private Service Connect (PSC) endpoints, allowing you to use custom `api_base` URLs for private deployments. diff --git a/docs/my-website/docs/proxy/cost_tracking.md b/docs/my-website/docs/proxy/cost_tracking.md index b1e5eae2a62..f28eec287d4 100644 --- a/docs/my-website/docs/proxy/cost_tracking.md +++ b/docs/my-website/docs/proxy/cost_tracking.md @@ -8,6 +8,8 @@ Track spend for keys, users, and teams across 100+ LLMs. LiteLLM automatically tracks spend for all known models. See our [model cost map](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json) +Provider-specific cost tracking (e.g., [Vertex AI PayGo / priority pricing](../providers/vertex.md#paygo--priority-cost-tracking), [Bedrock service tiers](../providers/bedrock.md#usage---service-tier), [Azure base model mapping](./custom_pricing.md#set-base_model-for-cost-tracking-eg-azure-deployments)) is applied automatically when the response includes tier metadata. + :::tip Keep Pricing Data Updated [Sync model pricing data from GitHub](./sync_models_github.md) to ensure accurate cost tracking. ::: diff --git a/docs/my-website/docs/proxy/custom_pricing.md b/docs/my-website/docs/proxy/custom_pricing.md index b61da85bb1d..2a28ddbc454 100644 --- a/docs/my-website/docs/proxy/custom_pricing.md +++ b/docs/my-website/docs/proxy/custom_pricing.md @@ -104,9 +104,18 @@ There are other keys you can use to specify costs for different scenarios and mo - `input_cost_per_video_per_second` - Cost per second of video input - `input_cost_per_video_per_second_above_128k_tokens` - Video cost for large contexts - `input_cost_per_character` - Character-based pricing for some providers +- `input_cost_per_token_priority` / `output_cost_per_token_priority` - Priority/PayGo pricing (Vertex AI Gemini, Bedrock) +- `input_cost_per_token_flex` / `output_cost_per_token_flex` - Batch/flex pricing These keys evolve based on how new models handle multimodality. The latest version can be found at [https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json). +### Service Tier / PayGo Pricing (Vertex AI, Bedrock) + +For providers that support multiple pricing tiers (e.g., Vertex AI PayGo, Bedrock service tiers), LiteLLM automatically applies the correct cost based on the response: + +- **Vertex AI Gemini**: Uses `usageMetadata.trafficType` (`ON_DEMAND_PRIORITY` → priority, `FLEX`/`BATCH` → flex). See [Vertex AI - PayGo / Priority Cost Tracking](../providers/vertex.md#paygo--priority-cost-tracking). +- **Bedrock**: Uses `serviceTier` from the response. See [Bedrock - Usage - Service Tier](../providers/bedrock.md#usage---service-tier). + ## Zero-Cost Models (Bypass Budget Checks) **Use Case**: You have on-premises or free models that should be accessible even when users exceed their budget limits. diff --git a/docs/my-website/docs/proxy/forward_client_headers.md b/docs/my-website/docs/proxy/forward_client_headers.md index 17f813eabee..cf34d4f1074 100644 --- a/docs/my-website/docs/proxy/forward_client_headers.md +++ b/docs/my-website/docs/proxy/forward_client_headers.md @@ -112,6 +112,8 @@ general_settings: forward_llm_provider_auth_headers: true # Enable BYOK ``` +For **Claude Code** with `/login` and your own Anthropic key, see [Claude Code BYOK](../tutorials/claude_code_byok.md). Use `ANTHROPIC_CUSTOM_HEADERS="x-litellm-api-key: sk-12345"` to pass your LiteLLM key while your Anthropic key (from `/login`) is forwarded as `x-api-key`. + Client request: ```bash curl -X POST "http://localhost:4000/v1/messages" \ diff --git a/docs/my-website/docs/proxy/token_auth.md b/docs/my-website/docs/proxy/token_auth.md index e8634f0faf5..7364ae0fb56 100644 --- a/docs/my-website/docs/proxy/token_auth.md +++ b/docs/my-website/docs/proxy/token_auth.md @@ -1054,6 +1054,95 @@ curl -X GET 'http://0.0.0.0:4000/user/info?user_id=user-123' \ -H 'Authorization: Bearer ' ``` +## [BETA] JWT-to-Virtual-Key Mapping + +Map JWT identities to LiteLLM virtual keys so that JWT-authenticated users get per-user budgets, rate limits, model access controls, and spend tracking. + +When a JWT comes in, LiteLLM looks up a configured claim (e.g. `email`, `sub`) in a mapping table. If a mapping exists, the request is treated as if it arrived with the corresponding virtual key — all virtual key features apply. + +### Setup + +Add `virtual_key_claim_field` to your JWT auth config: + +```yaml +general_settings: + enable_jwt_auth: True + litellm_jwtauth: + virtual_key_claim_field: "email" # JWT claim to look up (supports dot notation) + virtual_key_mapping_cache_ttl: 300 # Cache TTL in seconds (default: 300) +``` + +### Managing Mappings + +All endpoints require admin auth (`Authorization: Bearer `). + +**Create a mapping** — link a JWT claim value to an existing virtual key: + +```bash +curl -X POST http://localhost:4000/jwt/key/mapping/new \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "jwt_claim_name": "email", + "jwt_claim_value": "user@example.com", + "key": "sk-virtual-key-from-key-generate" + }' +``` + +**List mappings** (paginated): + +```bash +curl http://localhost:4000/jwt/key/mapping/list?page=1&size=50 \ + -H "Authorization: Bearer sk-1234" +``` + +**Get a specific mapping:** + +```bash +curl "http://localhost:4000/jwt/key/mapping/info?id=" \ + -H "Authorization: Bearer sk-1234" +``` + +**Update a mapping:** + +```bash +curl -X POST http://localhost:4000/jwt/key/mapping/update \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "id": "", + "description": "Updated description", + "is_active": true + }' +``` + +**Delete a mapping:** + +```bash +curl -X POST http://localhost:4000/jwt/key/mapping/delete \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{"id": ""}' +``` + +### How It Works + +1. A request arrives with a JWT bearer token +2. LiteLLM validates the JWT signature +3. Extracts the configured claim (e.g. `email` → `user@example.com`) +4. Looks up the claim value in the `LiteLLM_JWTKeyMapping` table +5. If a mapping exists, the request proceeds as if the mapped virtual key was used — budgets, rate limits, model access, and spend tracking all apply +6. If no mapping exists, falls back to standard JWT auth (team-level controls) + +### Error Codes + +| Code | Meaning | +|------|---------| +| 409 | Duplicate mapping — a mapping for that claim name + value already exists | +| 400 | The provided key does not match an existing virtual key | +| 404 | Mapping not found (for update/delete/info) | +| 403 | Non-admin user attempted a mapping operation | + ## All JWT Params [**See Code**](https://github.com/BerriAI/litellm/blob/b204f0c01c703317d812a1553363ab0cb989d5b6/litellm/proxy/_types.py#L95) diff --git a/docs/my-website/docs/tutorials/claude_code_byok.md b/docs/my-website/docs/tutorials/claude_code_byok.md new file mode 100644 index 00000000000..e1deac623bb --- /dev/null +++ b/docs/my-website/docs/tutorials/claude_code_byok.md @@ -0,0 +1,123 @@ +# Claude Code with Bring Your Own Key (BYOK) + +Use Claude Code with your own Anthropic API key through the LiteLLM proxy. When you use Claude's `/login` with your Anthropic account, your API key is sent as `x-api-key`. With BYOK enabled, LiteLLM forwards your key to Anthropic instead of using proxy-configured keys — so you pay Anthropic directly while still benefiting from LiteLLM's routing, logging, and guardrails. + +## How It Works + +1. **Claude Code `/login`** — You sign in with your Anthropic account; Claude Code sends your Anthropic API key as `x-api-key`. +2. **LiteLLM authentication** — You pass your LiteLLM proxy key via `ANTHROPIC_CUSTOM_HEADERS` so the proxy can authenticate and track your usage. +3. **Key forwarding** — With `forward_llm_provider_auth_headers: true`, LiteLLM forwards your `x-api-key` to Anthropic, giving it precedence over any proxy-configured keys. + +## Prerequisites + +- [Claude Code](https://docs.anthropic.com/en/docs/claude-code/overview) installed +- Anthropic API key (from [console.anthropic.com](https://console.anthropic.com)) +- LiteLLM proxy with a virtual key for authentication + +## Step 1: Configure LiteLLM Proxy + +Enable forwarding of LLM provider auth headers so your Anthropic key takes precedence: + +```yaml title="config.yaml" +model_list: + - model_name: claude-sonnet-4-5 + litellm_params: + model: anthropic/claude-sonnet-4-5 + # No api_key needed — client's key will be used + +litellm_settings: + forward_llm_provider_auth_headers: true # Required for BYOK +``` + +:::info Why `forward_llm_provider_auth_headers`? + +By default, LiteLLM strips `x-api-key` from client requests for security. Setting this to `true` allows client-provided provider keys (like your Anthropic key from `/login`) to be forwarded to Anthropic, overriding any proxy-configured keys. + +::: + +## Step 2: Create a LiteLLM Virtual Key + +Create a virtual key in the LiteLLM UI or via API. +```bash +# Example: Create key via API +curl -X POST "http://localhost:4000/key/generate" \ + -H "Authorization: Bearer sk-your-master-key" \ + -H "Content-Type: application/json" \ + -d '{"key_alias": "claude-code-byok", "models": ["claude-sonnet-4-5"]}' +``` + +## Step 3: Configure Claude Code + +Set environment variables so Claude Code uses LiteLLM and sends your LiteLLM key for proxy auth: + +```bash +# Point Claude Code to your LiteLLM proxy +export ANTHROPIC_BASE_URL="http://localhost:4000" + +# Model name from your config +export ANTHROPIC_MODEL="claude-sonnet-4-5" + +# LiteLLM proxy auth — this is added to every request +# Use x-litellm-api-key so the proxy authenticates you; your Anthropic key goes via x-api-key from /login +export ANTHROPIC_CUSTOM_HEADERS="x-litellm-api-key: sk-12345" +``` + +Replace `sk-12345` with your actual LiteLLM virtual key. + +:::tip Multiple headers + +For multiple headers, use newline-separated values: + +```bash +export ANTHROPIC_CUSTOM_HEADERS="x-litellm-api-key: sk-12345 +x-litellm-user-id: my-user-id" +``` + +::: + +## Step 4: Sign In with Claude Code + +1. Launch Claude Code: + + ```bash + claude + ``` + +2. Use **`/login`** and sign in with your Anthropic account (or use your API key directly). + +3. Claude Code will send: + - `x-api-key`: Your Anthropic API key (from `/login`) + - `x-litellm-api-key`: Your LiteLLM key (from `ANTHROPIC_CUSTOM_HEADERS`) + +4. LiteLLM authenticates you via `x-litellm-api-key`, then forwards `x-api-key` to Anthropic. Your Anthropic key takes precedence over any proxy-configured key. + +## Summary + +| Header | Source | Purpose | +|--------|--------|---------| +| `x-api-key` | Claude Code `/login` (Anthropic key) | Sent to Anthropic for API calls | +| `x-litellm-api-key` | `ANTHROPIC_CUSTOM_HEADERS` | Proxy authentication, tracking, rate limits | + +## Troubleshooting + +### Requests fail with "invalid x-api-key" + +- Ensure `forward_llm_provider_auth_headers: true` is set in `litellm_settings` (or `general_settings`). +- Restart the LiteLLM proxy after changing the config. +- Verify you completed `/login` in Claude Code so your Anthropic key is being sent. + +### Proxy returns 401 + +- Check that `ANTHROPIC_CUSTOM_HEADERS` includes `x-litellm-api-key: `. +- Ensure the LiteLLM key is valid and has access to the model. + +### Proxy key is used instead of my Anthropic key + +- Confirm `forward_llm_provider_auth_headers: true` is in your config. +- The setting can be in `litellm_settings` or `general_settings` depending on your config structure. +- Enable debug logging: `LITELLM_LOG=DEBUG` to see which key is being forwarded. + +## Related + +- [Forward Client Headers](./../proxy/forward_client_headers.md) — Full BYOK and header forwarding docs +- [Claude Code Max Subscription](./claude_code_max_subscription.md) — Using Claude Code with OAuth/Max subscription through LiteLLM diff --git a/docs/my-website/img/claude_code_byok_screenshot.png b/docs/my-website/img/claude_code_byok_screenshot.png new file mode 100644 index 00000000000..2788df95c49 Binary files /dev/null and b/docs/my-website/img/claude_code_byok_screenshot.png differ diff --git a/docs/my-website/img/mcp_openapi_custom_name_badge.png b/docs/my-website/img/mcp_openapi_custom_name_badge.png new file mode 100644 index 00000000000..11f94c1e68c Binary files /dev/null and b/docs/my-website/img/mcp_openapi_custom_name_badge.png differ diff --git a/docs/my-website/img/mcp_openapi_tool_edit_panel.png b/docs/my-website/img/mcp_openapi_tool_edit_panel.png new file mode 100644 index 00000000000..f826fb1f176 Binary files /dev/null and b/docs/my-website/img/mcp_openapi_tool_edit_panel.png differ diff --git a/docs/my-website/img/mcp_openapi_tools_loaded.png b/docs/my-website/img/mcp_openapi_tools_loaded.png new file mode 100644 index 00000000000..bb9f6be2719 Binary files /dev/null and b/docs/my-website/img/mcp_openapi_tools_loaded.png differ diff --git a/docs/my-website/package-lock.json b/docs/my-website/package-lock.json index 5113cd3e381..1ffc267557c 100644 --- a/docs/my-website/package-lock.json +++ b/docs/my-website/package-lock.json @@ -9,10 +9,10 @@ "version": "0.0.0", "dependencies": { "@docusaurus/core": "3.8.1", - 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"node_modules/yaml": { - "version": "1.10.2", - "resolved": "https://registry.npmjs.org/yaml/-/yaml-1.10.2.tgz", - "integrity": "sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==", - "license": "ISC", - "engines": { - "node": ">= 6" - } - }, "node_modules/yocto-queue": { "version": "1.2.2", "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-1.2.2.tgz", diff --git a/docs/my-website/package.json b/docs/my-website/package.json index a864301d77f..acdd7463a36 100644 --- a/docs/my-website/package.json +++ b/docs/my-website/package.json @@ -15,10 +15,10 @@ }, "dependencies": { "@docusaurus/core": "3.8.1", - "@docusaurus/plugin-google-gtag": "^3.5.2", + "@docusaurus/plugin-google-gtag": "3.8.1", "@docusaurus/plugin-ideal-image": "3.8.1", - "@docusaurus/preset-classic": "^3.5.2", - "@docusaurus/theme-mermaid": "^3.5.2", + "@docusaurus/preset-classic": "3.8.1", + "@docusaurus/theme-mermaid": "3.8.1", "@inkeep/cxkit-docusaurus": "^0.5.89", "@mdx-js/react": "^3.0.0", "clsx": "^1.2.1", diff --git a/docs/my-website/release_notes/v1.81.14.md b/docs/my-website/release_notes/v1.81.14.md index 3a133f092ae..c342bc47ee9 100644 --- a/docs/my-website/release_notes/v1.81.14.md +++ b/docs/my-website/release_notes/v1.81.14.md @@ -1,5 +1,5 @@ --- -title: "[Preview] v1.81.14 - New Gateway Level Guardrails & Compliance Playground" +title: "v1.81.14 - New Gateway Level Guardrails & Compliance Playground" slug: "v1-81-14" date: 2026-02-21T00:00:00 authors: @@ -27,7 +27,7 @@ import Image from '@theme/IdealImage'; docker run \ -e STORE_MODEL_IN_DB=True \ -p 4000:4000 \ -ghcr.io/berriai/litellm:main-v1.81.14.rc.1 +ghcr.io/berriai/litellm:main-v1.81.14-stable ``` diff --git a/docs/my-website/release_notes/v1.82.0.md b/docs/my-website/release_notes/v1.82.0.md index beb2451dd5c..b2491875217 100644 --- a/docs/my-website/release_notes/v1.82.0.md +++ b/docs/my-website/release_notes/v1.82.0.md @@ -1,5 +1,5 @@ --- -title: "v1.82.0 - Realtime Guardrails, Projects Management, and 10+ Performance Optimizations" +title: "[Preview] v1.82.0 - Realtime Guardrails, Projects Management, and 10+ Performance Optimizations" slug: "v1-82-0" date: 2026-02-28T00:00:00 authors: @@ -46,6 +46,11 @@ pip install litellm==1.82.0 - **Guardrail ecosystem expansion** — [Noma v2, Lakera v2 post-call, Singapore regulatory policies (PDPA + MAS), employment discrimination blockers, code execution blocker, guardrail policy versioning, and production monitoring](../../docs/proxy/guardrails) - [PR #21400](https://github.com/BerriAI/litellm/pull/21400), [PR #21783](https://github.com/BerriAI/litellm/pull/21783), [PR #21948](https://github.com/BerriAI/litellm/pull/21948) - **OpenAI Codex 5.3 — day 0** — [Full support for `gpt-5.3-codex` on OpenAI and Azure, plus `gpt-audio-1.5` and `gpt-realtime-1.5` model coverage](../../docs/providers/openai) - [PR #22035](https://github.com/BerriAI/litellm/pull/22035) - **10+ performance optimizations** — Streaming hot-path fixes, Redis pipeline batching, database task batching, ModelResponse init skip, and router cache improvements — lower latency and CPU on every request +- **`/v1/messages` → `/responses` routing** — `/v1/messages` requests are now routed to the [Responses API](../../docs/response_api) by default for OpenAI/Azure models + +:::danger v1/messages routing change +This version starts routing `/v1/messages` requests to the `/responses` API by default. To opt out and continue using chat/completions, set `LITELLM_USE_CHAT_COMPLETIONS_URL_FOR_ANTHROPIC_MESSAGES=true` or `litellm_settings.use_chat_completions_url_for_anthropic_messages: true` in your config. +::: --- diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index f7487d24b12..91edd3118cd 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -154,6 +154,7 @@ const sidebars = { items: [ "tutorials/claude_responses_api", "tutorials/claude_code_max_subscription", + "tutorials/claude_code_byok", "tutorials/claude_code_customer_tracking", "tutorials/claude_code_prompt_cache_routing", "tutorials/claude_code_websearch", @@ -538,6 +539,7 @@ const sidebars = { items: [ "a2a", "a2a_invoking_agents", + "a2a_agent_headers", "a2a_cost_tracking", "a2a_agent_permissions" ], @@ -608,6 +610,7 @@ const sidebars = { items: [ "mcp", "mcp_usage", + "mcp_openapi", "mcp_oauth", "mcp_public_internet", "mcp_semantic_filter", @@ -623,6 +626,7 @@ const sidebars = { items: [ "anthropic_unified/index", "anthropic_unified/structured_output", + "anthropic_unified/messages_to_responses_mapping", ] }, "anthropic_count_tokens", @@ -794,6 +798,7 @@ const sidebars = { "providers/bedrock_realtime_with_audio", "providers/aws_polly", "providers/bedrock_vector_store", + "providers/bedrock_mantle", ] }, "providers/litellm_proxy", diff --git a/docs/my-website/src/pages/index.md b/docs/my-website/src/pages/index.md index 91215b33c5d..296a06bd7e9 100644 --- a/docs/my-website/src/pages/index.md +++ b/docs/my-website/src/pages/index.md @@ -7,42 +7,41 @@ https://github.com/BerriAI/litellm ## **Call 100+ LLMs using the OpenAI Input/Output Format** -- Translate inputs to provider's `completion`, `embedding`, and `image_generation` endpoints -- [Consistent output](https://docs.litellm.ai/docs/completion/output), text responses will always be available at `['choices'][0]['message']['content']` +- Translate inputs to provider's endpoints (`/chat/completions`, `/responses`, `/embeddings`, `/images`, `/audio`, `/batches`, and more) +- [Consistent output](https://docs.litellm.ai/docs/supported_endpoints) - same response format regardless of which provider you use - Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing) - Track spend & set budgets per project [LiteLLM Proxy Server](https://docs.litellm.ai/docs/simple_proxy) ## How to use LiteLLM -You can use litellm through either: -1. [LiteLLM Proxy Server](#litellm-proxy-server-llm-gateway) - Server (LLM Gateway) to call 100+ LLMs, load balance, cost tracking across projects -2. [LiteLLM python SDK](#basic-usage) - Python Client to call 100+ LLMs, load balance, cost tracking -### **When to use LiteLLM Proxy Server (LLM Gateway)** - -:::tip - -Use LiteLLM Proxy Server if you want a **central service (LLM Gateway) to access multiple LLMs** - -Typically used by Gen AI Enablement / ML PLatform Teams - -::: - - - LiteLLM Proxy gives you a unified interface to access multiple LLMs (100+ LLMs) - - Track LLM Usage and setup guardrails - - Customize Logging, Guardrails, Caching per project - -### **When to use LiteLLM Python SDK** - -:::tip - - Use LiteLLM Python SDK if you want to use LiteLLM in your **python code** - -Typically used by developers building llm projects - -::: - - - LiteLLM SDK gives you a unified interface to access multiple LLMs (100+ LLMs) - - Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing) +You can use LiteLLM through either the Proxy Server or Python SDK. Both gives you a unified interface to access multiple LLMs (100+ LLMs). Choose the option that best fits your needs: + + + + + + + + + + + + + + + + + + + + + + + + + + +
LiteLLM Proxy ServerLiteLLM Python SDK
Use CaseCentral service (LLM Gateway) to access multiple LLMsUse LiteLLM directly in your Python code
Who Uses It?Gen AI Enablement / ML Platform TeamsDevelopers building LLM projects
Key Features• Centralized API gateway with authentication & authorization
• Multi-tenant cost tracking and spend management per project/user
• Per-project customization (logging, guardrails, caching)
• Virtual keys for secure access control
• Admin dashboard UI for monitoring and management		• Direct Python library integration in your codebase
• Router with retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - Router
• Application-level load balancing and cost tracking
• Exception handling with OpenAI-compatible errors
• Observability callbacks (Lunary, MLflow, Langfuse, etc.)
## **LiteLLM Python SDK** @@ -67,7 +66,7 @@ import os os.environ["OPENAI_API_KEY"] = "your-api-key" response = completion( - model="gpt-3.5-turbo", + model="openai/gpt-5", messages=[{ "content": "Hello, how are you?","role": "user"}] ) ``` @@ -83,13 +82,27 @@ import os os.environ["ANTHROPIC_API_KEY"] = "your-api-key" response = completion( - model="claude-2", + model="anthropic/claude-sonnet-4-5-20250929", messages=[{ "content": "Hello, how are you?","role": "user"}] ) ``` + + +```python +from litellm import completion +import os +## set ENV variables +os.environ["XAI_API_KEY"] = "your-api-key" + +response = completion( + model="xai/grok-2-latest", + messages=[{ "content": "Hello, how are you?","role": "user"}] +) +``` + ```python @@ -97,11 +110,11 @@ from litellm import completion import os # auth: run 'gcloud auth application-default' -os.environ["VERTEX_PROJECT"] = "hardy-device-386718" -os.environ["VERTEX_LOCATION"] = "us-central1" +os.environ["VERTEXAI_PROJECT"] = "hardy-device-386718" +os.environ["VERTEXAI_LOCATION"] = "us-central1" response = completion( - model="chat-bison", + model="vertex_ai/gemini-1.5-pro", messages=[{ "content": "Hello, how are you?","role": "user"}] ) ``` @@ -212,8 +225,61 @@ response = completion( + + +```python +from litellm import completion +import os + +## set ENV variables. Visit https://vercel.com/docs/ai-gateway#using-the-ai-gateway-with-an-api-key for instructions on obtaining a key +os.environ["VERCEL_AI_GATEWAY_API_KEY"] = "your-vercel-api-key" + +response = completion( + model="vercel_ai_gateway/openai/gpt-5", + messages=[{ "content": "Hello, how are you?","role": "user"}] +) +``` + + + +### Response Format (OpenAI Chat Completions Format) + +```json +{ + "id": "chatcmpl-565d891b-a42e-4c39-8d14-82a1f5208885", + "created": 1734366691, + "model": "gpt-5", + "object": "chat.completion", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Hello! As an AI language model, I don't have feelings, but I'm operating properly and ready to assist you with any questions or tasks you may have. How can I help you today?", + "role": "assistant", + "tool_calls": null, + "function_call": null + } + } + ], + "usage": { + "completion_tokens": 43, + "prompt_tokens": 13, + "total_tokens": 56, + "completion_tokens_details": null, + "prompt_tokens_details": { + "audio_tokens": null, + "cached_tokens": 0 + }, + "cache_creation_input_tokens": 0, + "cache_read_input_tokens": 0 + } +} +``` + ### Responses API Use `litellm.responses()` for advanced models that support reasoning content like GPT-5, o3, etc. @@ -265,11 +331,11 @@ from litellm import responses import os # auth: run 'gcloud auth application-default' -os.environ["VERTEX_PROJECT"] = "jr-smith-386718" -os.environ["VERTEX_LOCATION"] = "us-central1" +os.environ["VERTEXAI_PROJECT"] = "jr-smith-386718" +os.environ["VERTEXAI_LOCATION"] = "us-central1" response = responses( - model="chat-bison", + model="vertex_ai/gemini-1.5-pro", messages=[{ "content": "What is the capital of France?","role": "user"}] ) ``` @@ -314,7 +380,7 @@ import os os.environ["OPENAI_API_KEY"] = "your-api-key" response = completion( - model="gpt-3.5-turbo", + model="openai/gpt-5", messages=[{ "content": "Hello, how are you?","role": "user"}], stream=True, ) @@ -331,14 +397,29 @@ import os os.environ["ANTHROPIC_API_KEY"] = "your-api-key" response = completion( - model="claude-2", + model="anthropic/claude-sonnet-4-5-20250929", messages=[{ "content": "Hello, how are you?","role": "user"}], stream=True, ) ``` + + +```python +from litellm import completion +import os +## set ENV variables +os.environ["XAI_API_KEY"] = "your-api-key" + +response = completion( + model="xai/grok-2-latest", + messages=[{ "content": "Hello, how are you?","role": "user"}], + stream=True, +) +``` + ```python @@ -346,11 +427,11 @@ from litellm import completion import os # auth: run 'gcloud auth application-default' -os.environ["VERTEX_PROJECT"] = "hardy-device-386718" -os.environ["VERTEX_LOCATION"] = "us-central1" +os.environ["VERTEXAI_PROJECT"] = "hardy-device-386718" +os.environ["VERTEXAI_LOCATION"] = "us-central1" response = completion( - model="chat-bison", + model="vertex_ai/gemini-1.5-pro", messages=[{ "content": "Hello, how are you?","role": "user"}], stream=True, ) @@ -370,7 +451,7 @@ os.environ["NVIDIA_NIM_API_BASE"] = "nvidia_nim_endpoint_url" response = completion( model="nvidia_nim/", - messages=[{ "content": "Hello, how are you?","role": "user"}] + messages=[{ "content": "Hello, how are you?","role": "user"}], stream=True, ) ``` @@ -466,22 +547,74 @@ response = completion( ``` + + + +```python +from litellm import completion +import os + +## set ENV variables. Visit https://vercel.com/docs/ai-gateway#using-the-ai-gateway-with-an-api-key for instructions on obtaining a key +os.environ["VERCEL_AI_GATEWAY_API_KEY"] = "your-vercel-api-key" + +response = completion( + model="vercel_ai_gateway/openai/gpt-5", + messages = [{ "content": "Hello, how are you?","role": "user"}], + stream=True, +) +``` + + + +### Streaming Response Format (OpenAI Format) + +```json +{ + "id": "chatcmpl-2be06597-eb60-4c70-9ec5-8cd2ab1b4697", + "created": 1734366925, + "model": "claude-sonnet-4-5-20250929", + "object": "chat.completion.chunk", + "system_fingerprint": null, + "choices": [ + { + "finish_reason": null, + "index": 0, + "delta": { + "content": "Hello", + "role": "assistant", + "function_call": null, + "tool_calls": null, + "audio": null + }, + "logprobs": null + } + ] +} +``` + ### Exception handling LiteLLM maps exceptions across all supported providers to the OpenAI exceptions. All our exceptions inherit from OpenAI's exception types, so any error-handling you have for that, should work out of the box with LiteLLM. ```python -from openai.error import OpenAIError +import litellm from litellm import completion +import os os.environ["ANTHROPIC_API_KEY"] = "bad-key" try: - # some code - completion(model="claude-instant-1", messages=[{"role": "user", "content": "Hey, how's it going?"}]) -except OpenAIError as e: - print(e) + completion(model="anthropic/claude-instant-1", messages=[{"role": "user", "content": "Hey, how's it going?"}]) +except litellm.AuthenticationError as e: + # Thrown when the API key is invalid + print(f"Authentication failed: {e}") +except litellm.RateLimitError as e: + # Thrown when you've exceeded your rate limit + print(f"Rate limited: {e}") +except litellm.APIError as e: + # Thrown for general API errors + print(f"API error: {e}") ``` ### Logging Observability - Log LLM Input/Output ([Docs](https://docs.litellm.ai/docs/observability/callbacks)) @@ -502,7 +635,7 @@ os.environ["OPENAI_API_KEY"] litellm.success_callback = ["lunary", "mlflow", "langfuse", "helicone"] # log input/output to lunary, mlflow, langfuse, helicone #openai call -response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]) +response = completion(model="openai/gpt-5", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]) ``` ### Track Costs, Usage, Latency for streaming @@ -527,7 +660,7 @@ litellm.success_callback = [track_cost_callback] # set custom callback function # litellm.completion() call response = completion( - model="gpt-3.5-turbo", + model="openai/gpt-5", messages=[ { "role": "user", @@ -584,7 +717,7 @@ Example `litellm_config.yaml` ```yaml model_list: - - model_name: gpt-3.5-turbo + - model_name: gpt-5 litellm_params: model: azure/ api_base: os.environ/AZURE_API_BASE # runs os.getenv("AZURE_API_BASE") @@ -621,7 +754,7 @@ docker run \ import openai # openai v1.0.0+ client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:4000") # set proxy to base_url # request sent to model set on litellm proxy, `litellm --model` -response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [ +response = client.chat.completions.create(model="gpt-5", messages = [ { "role": "user", "content": "this is a test request, write a short poem" diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260305000000_add_agent_headers/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260305000000_add_agent_headers/migration.sql new file mode 100644 index 00000000000..acb35baba96 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260305000000_add_agent_headers/migration.sql @@ -0,0 +1,5 @@ +-- Add static_headers and extra_headers to LiteLLM_AgentsTable + +ALTER TABLE "LiteLLM_AgentsTable" + ADD COLUMN IF NOT EXISTS "static_headers" JSONB DEFAULT '{}', + ADD COLUMN IF NOT EXISTS "extra_headers" TEXT[] DEFAULT ARRAY[]::TEXT[]; diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index 5abe7a0a2b1..6d20a71f039 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -277,6 +277,7 @@ model LiteLLM_MCPServerTable { alias String? description String? url String? + spec_path String? transport String @default("sse") auth_type String? credentials Json? @default("{}") diff --git a/litellm/__init__.py b/litellm/__init__.py index 57e9cb25f43..d75f47567a5 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -593,6 +593,7 @@ def identify(event_details): aws_polly_models: Set = set() gigachat_models: Set = set() llamagate_models: Set = set() +bedrock_mantle_models: Set = set() def is_bedrock_pricing_only_model(key: str) -> bool: @@ -855,6 +856,8 @@ def add_known_models(model_cost_map: Optional[Dict] = None): gigachat_models.add(key) elif value.get("litellm_provider") == "llamagate": llamagate_models.add(key) + elif value.get("litellm_provider") == "bedrock_mantle": + bedrock_mantle_models.add(key) add_known_models() @@ -962,6 +965,7 @@ def add_known_models(model_cost_map: Optional[Dict] = None): | ovhcloud_models | lemonade_models | docker_model_runner_models + | bedrock_mantle_models | set(clarifai_models) ) @@ -1065,6 +1069,7 @@ def add_known_models(model_cost_map: Optional[Dict] = None): "aws_polly": aws_polly_models, "gigachat": gigachat_models, "llamagate": llamagate_models, + "bedrock_mantle": bedrock_mantle_models } # mapping for those models which have larger equivalents @@ -1426,6 +1431,7 @@ def set_global_gitlab_config(config: Dict[str, Any]) -> None: from .llms.topaz.image_variations.transformation import TopazImageVariationConfig as TopazImageVariationConfig from litellm.llms.openai.completion.transformation import OpenAITextCompletionConfig as OpenAITextCompletionConfig from .llms.groq.chat.transformation import GroqChatConfig as GroqChatConfig + from .llms.bedrock_mantle.chat.transformation import BedrockMantleChatConfig as BedrockMantleChatConfig from .llms.a2a.chat.transformation import A2AConfig as A2AConfig from .llms.voyage.embedding.transformation import VoyageEmbeddingConfig as VoyageEmbeddingConfig from .llms.voyage.embedding.transformation_contextual import VoyageContextualEmbeddingConfig as VoyageContextualEmbeddingConfig diff --git a/litellm/_lazy_imports_registry.py b/litellm/_lazy_imports_registry.py index 4bb336a4d77..9e0453102d0 100644 --- a/litellm/_lazy_imports_registry.py +++ b/litellm/_lazy_imports_registry.py @@ -214,6 +214,7 @@ "TopazImageVariationConfig", "OpenAITextCompletionConfig", "GroqChatConfig", + "BedrockMantleChatConfig", "A2AConfig", "GenAIHubOrchestrationConfig", "VoyageEmbeddingConfig", @@ -858,6 +859,7 @@ "OpenAITextCompletionConfig", ), "GroqChatConfig": (".llms.groq.chat.transformation", "GroqChatConfig"), + "BedrockMantleChatConfig": (".llms.bedrock_mantle.chat.transformation", "BedrockMantleChatConfig"), "A2AConfig": (".llms.a2a.chat.transformation", "A2AConfig"), "GenAIHubOrchestrationConfig": ( ".llms.sap.chat.transformation", diff --git a/litellm/a2a_protocol/main.py b/litellm/a2a_protocol/main.py index 485b57e311b..0067af3c7db 100644 --- a/litellm/a2a_protocol/main.py +++ b/litellm/a2a_protocol/main.py @@ -9,6 +9,7 @@ import uuid from typing import TYPE_CHECKING, Any, AsyncIterator, Coroutine, Dict, Optional, Union +import httpx import litellm from litellm._logging import verbose_logger, verbose_proxy_logger from litellm.a2a_protocol.streaming_iterator import A2AStreamingIterator @@ -162,6 +163,49 @@ async def _send_message_via_completion_bridge( return LiteLLMSendMessageResponse.from_dict(response_dict) +async def _execute_a2a_send_with_retry( + a2a_client: Any, + request: Any, + agent_card: Any, + card_url: Optional[str], + api_base: Optional[str], + agent_name: Optional[str], +) -> Any: + """Send an A2A message with retry logic for localhost URL errors.""" + a2a_response = None + for _ in range(2): # max 2 attempts: original + 1 retry + try: + a2a_response = await a2a_client.send_message(request) + break # success, exit retry loop + except A2ALocalhostURLError as e: + a2a_client = handle_a2a_localhost_retry( + error=e, + agent_card=agent_card, + a2a_client=a2a_client, + is_streaming=False, + ) + card_url = agent_card.url if agent_card else None + except Exception as e: + try: + map_a2a_exception(e, card_url, api_base, model=agent_name) + except A2ALocalhostURLError as localhost_err: + a2a_client = handle_a2a_localhost_retry( + error=localhost_err, + agent_card=agent_card, + a2a_client=a2a_client, + is_streaming=False, + ) + card_url = agent_card.url if agent_card else None + continue + except Exception: + raise + if a2a_response is None: + raise RuntimeError( + "A2A send_message failed: no response received after retry attempts." + ) + return a2a_response + + @client async def asend_message( a2a_client: Optional["A2AClientType"] = None, @@ -169,6 +213,7 @@ async def asend_message( api_base: Optional[str] = None, litellm_params: Optional[Dict[str, Any]] = None, agent_id: Optional[str] = None, + agent_extra_headers: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> LiteLLMSendMessageResponse: """ @@ -250,9 +295,12 @@ async def asend_message( "Either a2a_client or api_base is required for standard A2A flow" ) trace_id = trace_id or str(uuid.uuid4()) - extra_headers = {"X-LiteLLM-Trace-Id": trace_id} + extra_headers: Dict[str, str] = {"X-LiteLLM-Trace-Id": trace_id} if agent_id: extra_headers["X-LiteLLM-Agent-Id"] = agent_id + # Overlay agent-level headers (agent headers take precedence over LiteLLM internal ones) + if agent_extra_headers: + extra_headers.update(agent_extra_headers) a2a_client = await create_a2a_client( base_url=api_base, extra_headers=extra_headers ) @@ -279,44 +327,17 @@ async def asend_message( if getattr(message, "context_id", None) is None: message.context_id = context_id - # Retry loop: if connection fails due to localhost URL in agent card, retry with fixed URL - a2a_response = None - for _ in range(2): # max 2 attempts: original + 1 retry - try: - a2a_response = await a2a_client.send_message(request) - break # success, exit retry loop - except A2ALocalhostURLError as e: - # Localhost URL error - fix and retry - a2a_client = handle_a2a_localhost_retry( - error=e, - agent_card=agent_card, - a2a_client=a2a_client, - is_streaming=False, - ) - card_url = agent_card.url if agent_card else None - except Exception as e: - # Map exception - will raise A2ALocalhostURLError if applicable - try: - map_a2a_exception(e, card_url, api_base, model=agent_name) - except A2ALocalhostURLError as localhost_err: - # Localhost URL error - fix and retry - a2a_client = handle_a2a_localhost_retry( - error=localhost_err, - agent_card=agent_card, - a2a_client=a2a_client, - is_streaming=False, - ) - card_url = agent_card.url if agent_card else None - continue - except Exception: - # Re-raise the mapped exception - raise + a2a_response = await _execute_a2a_send_with_retry( + a2a_client=a2a_client, + request=request, + agent_card=agent_card, + card_url=card_url, + api_base=api_base, + agent_name=agent_name, + ) verbose_logger.info(f"A2A send_message completed, request_id={request.id}") - # a2a_response is guaranteed to be set if we reach here (loop breaks on success or raises) - assert a2a_response is not None - # Wrap in LiteLLM response type for _hidden_params support response = LiteLLMSendMessageResponse.from_a2a_response(a2a_response) @@ -426,6 +447,7 @@ async def asend_message_streaming( agent_id: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None, proxy_server_request: Optional[Dict[str, Any]] = None, + agent_extra_headers: Optional[Dict[str, str]] = None, ) -> AsyncIterator[Any]: """ Async: Send a streaming message to an A2A agent. @@ -507,7 +529,17 @@ async def asend_message_streaming( raise ValueError( "Either a2a_client or api_base is required for standard A2A flow" ) - a2a_client = await create_a2a_client(base_url=api_base) + # Mirror the non-streaming path: always include trace and agent-id headers + streaming_extra_headers: Dict[str, str] = { + "X-LiteLLM-Trace-Id": str(request.id), + } + if agent_id: + streaming_extra_headers["X-LiteLLM-Agent-Id"] = agent_id + if agent_extra_headers: + streaming_extra_headers.update(agent_extra_headers) + a2a_client = await create_a2a_client( + base_url=api_base, extra_headers=streaming_extra_headers + ) # Type assertion: a2a_client is guaranteed to be non-None here assert a2a_client is not None @@ -621,17 +653,17 @@ async def create_a2a_client( verbose_logger.info(f"Creating A2A client for {base_url}") - # Use LiteLLM's cached httpx client - http_handler = get_async_httpx_client( - llm_provider=httpxSpecialProvider.A2A, - params={"timeout": timeout}, + # Always create a fresh httpx client per A2A call so that per-agent auth + # headers (extra_headers) are never shared across agents or requests. + # Mutating a cached shared client would cause headers from one agent to + # bleed into requests made to a different agent. + httpx_client = httpx.AsyncClient( + timeout=httpx.Timeout(timeout), + headers=extra_headers or {}, ) - httpx_client = http_handler.client - if extra_headers: - httpx_client.headers.update(extra_headers) verbose_proxy_logger.debug( - f"A2A client created with extra_headers={extra_headers}" + f"A2A client created with extra_headers={list(extra_headers.keys())}" ) # Resolve agent card diff --git a/litellm/batches/main.py b/litellm/batches/main.py index e69c5a5c377..723b59c6b46 100644 --- a/litellm/batches/main.py +++ b/litellm/batches/main.py @@ -33,6 +33,7 @@ from litellm.types.llms.openai import ( CancelBatchRequest, CreateBatchRequest, + FileExpiresAfter, RetrieveBatchRequest, ) from litellm.types.router import GenericLiteLLMParams @@ -219,7 +220,7 @@ def create_batch( # noqa: PLR0915 extra_body=extra_body, ) if output_expires_after is not None: - _create_batch_request["output_expires_after"] = output_expires_after + _create_batch_request["output_expires_after"] = cast(FileExpiresAfter, output_expires_after) if model is not None: provider_config = ProviderConfigManager.get_provider_batches_config( model=model, diff --git a/litellm/caching/llm_caching_handler.py b/litellm/caching/llm_caching_handler.py index 331aa8f51cd..c2274713bb9 100644 --- a/litellm/caching/llm_caching_handler.py +++ b/litellm/caching/llm_caching_handler.py @@ -3,36 +3,21 @@ """ import asyncio -from typing import Set from .in_memory_cache import InMemoryCache class LLMClientCache(InMemoryCache): - # Background tasks must be stored to prevent garbage collection, which would - # trigger "coroutine was never awaited" warnings. See: - # https://docs.python.org/3/library/asyncio-task.html#creating-tasks - # Intentionally shared across all instances as a global task registry. - _background_tasks: Set[asyncio.Task] = set() + """Cache for LLM HTTP clients (OpenAI, Azure, httpx, etc.). - def _remove_key(self, key: str) -> None: - """Close async clients before evicting them to prevent connection pool leaks.""" - value = self.cache_dict.get(key) - super()._remove_key(key) - if value is not None: - close_fn = getattr(value, "aclose", None) or getattr(value, "close", None) - if close_fn and asyncio.iscoroutinefunction(close_fn): - try: - task = asyncio.get_running_loop().create_task(close_fn()) - self._background_tasks.add(task) - task.add_done_callback(self._background_tasks.discard) - except RuntimeError: - pass - elif close_fn and callable(close_fn): - try: - close_fn() - except Exception: - pass + IMPORTANT: This cache intentionally does NOT close clients on eviction. + Evicted clients may still be in use by in-flight requests. Closing them + eagerly causes ``RuntimeError: Cannot send a request, as the client has + been closed.`` errors in production after the TTL (1 hour) expires. + + Clients that are no longer referenced will be garbage-collected normally. + For explicit shutdown cleanup, use ``close_litellm_async_clients()``. + """ def update_cache_key_with_event_loop(self, key): """ diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index c29b755681b..babb575ee32 100644 --- a/litellm/completion_extras/litellm_responses_transformation/transformation.py +++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py @@ -1028,12 +1028,16 @@ def translate_responses_chunk_to_openai_stream( # noqa: PLR0915 if provider_specific_fields: tool_call_chunk.provider_specific_fields = provider_specific_fields # type: ignore + # Do NOT emit finish_reason here — response.completed handles the terminal + # finish_reason. Emitting "tool_calls" here would prematurely terminate + # the stream before subsequent tool calls arrive (same fix as #17246 for + # the message-type branch). return ModelResponseStream( choices=[ StreamingChoices( index=0, - delta=Delta(tool_calls=[tool_call_chunk]), - finish_reason="tool_calls", + delta=Delta(), + finish_reason=None, ) ] ) diff --git a/litellm/constants.py b/litellm/constants.py index c1bb7da1b73..2ae365300ef 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -1242,6 +1242,11 @@ LITELLM_METADATA_FIELD = "litellm_metadata" OLD_LITELLM_METADATA_FIELD = "metadata" LITELLM_TRUNCATED_PAYLOAD_FIELD = "litellm_truncated" +LITELLM_TRUNCATION_DB_SAFEGUARD_NOTE = ( + "Truncation is a DB storage safeguard. " + "Full, untruncated data is logged to logging callbacks (OTEL, Datadog, etc.). " + "To increase the truncation limit, set `MAX_STRING_LENGTH_PROMPT_IN_DB` in your env." +) ########################### LiteLLM Proxy Specific Constants ########################### ######################################################################################## diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 6354bf44943..75d45af86e6 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -272,6 +272,8 @@ def cost_per_token( # noqa: PLR0915 ### SERVICE TIER ### service_tier: Optional[str] = None, # for OpenAI service tier pricing response: Optional[Any] = None, + ### REQUEST MODEL ### + request_model: Optional[str] = None, # original request model for router detection ) -> Tuple[float, float]: # type: ignore """ Calculates the cost per token for a given model, prompt tokens, and completion tokens. @@ -520,7 +522,7 @@ def cost_per_token( # noqa: PLR0915 return dashscope_cost_per_token(model=model, usage=usage_block) elif custom_llm_provider == "azure_ai": return azure_ai_cost_per_token( - model=model, usage=usage_block, response_time_ms=response_time_ms + model=model, usage=usage_block, response_time_ms=response_time_ms, request_model=request_model ) else: model_info = _cached_get_model_info_helper( @@ -1457,6 +1459,11 @@ def completion_cost( # noqa: PLR0915 text=completion_string ) + # Get the original request model for router detection + request_model_for_cost = None + if litellm_logging_obj is not None: + request_model_for_cost = litellm_logging_obj.model + ( prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar, @@ -1479,6 +1486,7 @@ def completion_cost( # noqa: PLR0915 rerank_billed_units=rerank_billed_units, service_tier=service_tier, response=completion_response, + request_model=request_model_for_cost, ) # Get additional costs from provider (e.g., routing fees, infrastructure costs) diff --git a/litellm/fine_tuning/main.py b/litellm/fine_tuning/main.py index db77fa32919..e6f2a6f86db 100644 --- a/litellm/fine_tuning/main.py +++ b/litellm/fine_tuning/main.py @@ -126,6 +126,19 @@ async def acreate_fine_tuning_job( raise e +def _resolve_fine_tuning_timeout( + timeout: Any, + custom_llm_provider: str, +) -> Union[float, httpx.Timeout]: + """Normalise a raw timeout value to a float (seconds) or httpx.Timeout for fine-tuning calls.""" + timeout = timeout or 600.0 + if isinstance(timeout, httpx.Timeout): + if not supports_httpx_timeout(custom_llm_provider): + return float(timeout.read or 600) + return timeout + return float(timeout) + + @client def create_fine_tuning_job( model: str, @@ -164,21 +177,10 @@ def create_fine_tuning_job( _oai_hyperparameters: Hyperparameters = Hyperparameters( **hyperparameters ) # Typed Hyperparameters for OpenAI Spec - ### TIMEOUT LOGIC ### - timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600 - # set timeout for 10 minutes by default - - if ( - timeout is not None - and isinstance(timeout, httpx.Timeout) - and supports_httpx_timeout(custom_llm_provider) is False - ): - read_timeout = timeout.read or 600 - timeout = read_timeout # default 10 min timeout - elif timeout is not None and not isinstance(timeout, httpx.Timeout): - timeout = float(timeout) # type: ignore - elif timeout is None: - timeout = 600.0 + timeout = _resolve_fine_tuning_timeout( + optional_params.timeout or kwargs.get("request_timeout", 600), + custom_llm_provider, + ) # OpenAI if custom_llm_provider == "openai": diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index 7cdd338c4f7..9fe27ed41cf 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -735,13 +735,16 @@ def _handle_success(self, kwargs, response_obj, start_time, end_time): self._maybe_log_raw_request( kwargs, response_obj, start_time, end_time, span ) - # Ensure proxy-request parent span is annotated with the actual operation kind + # Set only minimal identification attributes on the proxy-request parent + # to avoid duplicating all attributes that already exist on litellm_request if ( parent_span is not None and hasattr(parent_span, "name") and parent_span.name == LITELLM_PROXY_REQUEST_SPAN_NAME ): - self.set_attributes(parent_span, kwargs, response_obj) + self._set_proxy_span_minimal_attributes( + parent_span, kwargs, response_obj + ) else: # Do not create primary span (keep hierarchy shallow when parent exists) from opentelemetry.trace import Status, StatusCode @@ -779,6 +782,42 @@ def _handle_success(self, kwargs, response_obj, start_time, end_time): ): parent_span.end(end_time=self._to_ns(end_time)) + def _set_proxy_span_minimal_attributes( + self, span: "Span", kwargs, response_obj + ): + """ + Set only minimal identification attributes on the proxy-request parent span. + + This avoids duplicating the full attribute set that already exists on the + child litellm_request span, reducing storage and search noise. + """ + from litellm.proxy._types import SpanAttributes + + standard_logging_payload: Optional[StandardLoggingPayload] = kwargs.get( + "standard_logging_object" + ) + + if kwargs.get("model"): + self.safe_set_attribute( + span=span, + key=SpanAttributes.LLM_REQUEST_MODEL.value, + value=kwargs.get("model"), + ) + + if standard_logging_payload: + self.safe_set_attribute( + span=span, + key=SpanAttributes.LLM_REQUEST_TYPE.value, + value=standard_logging_payload.get("call_type"), + ) + + if response_obj and response_obj.get("id"): + self.safe_set_attribute( + span=span, + key="gen_ai.response.id", + value=response_obj.get("id"), + ) + def _start_primary_span( self, kwargs, @@ -1149,8 +1188,17 @@ def _create_guardrail_span( self, kwargs: Optional[dict], context: Optional[Context] ): """ - Creates a span for Guardrail, if any guardrail information is present in standard_logging_object + Creates a span for Guardrail, if any guardrail information is present in standard_logging_object. + + Only creates spans when a valid parent context exists to prevent orphaned + root-level guardrail traces in the "All Spans" view. """ + if context is None: + verbose_logger.debug( + "OpenTelemetry: Skipping guardrail span creation - no parent context (would create orphaned trace)" + ) + return + # Create span for guardrail information kwargs = kwargs or {} standard_logging_payload: Optional[StandardLoggingPayload] = kwargs.get( @@ -1579,12 +1627,18 @@ def set_attributes( # noqa: PLR0915 value=optional_params.get("user"), ) - # The unique identifier for the completion. - if response_obj and response_obj.get("id"): + # The unique identifier for the LLM call. + # Falls back to litellm_call_id for response types without an id field + # (e.g. EmbeddingResponse, ImageResponse). + _response_id = ( + (response_obj.get("id") if response_obj else None) + or kwargs.get("litellm_call_id") + ) + if _response_id: self.safe_set_attribute( span=span, key="gen_ai.response.id", - value=response_obj.get("id"), + value=_response_id, ) # The model used to generate the response. @@ -1808,8 +1862,6 @@ def _transform_choices_to_otel_semantic_conventions( def set_raw_request_attributes(self, span: Span, kwargs, response_obj): try: - self.set_attributes(span, kwargs, response_obj) - kwargs.get("optional_params", {}) litellm_params = kwargs.get("litellm_params", {}) or {} custom_llm_provider = litellm_params.get("custom_llm_provider", "Unknown") diff --git a/litellm/integrations/websearch_interception/handler.py b/litellm/integrations/websearch_interception/handler.py index bef8925e8e9..c31140d44d8 100644 --- a/litellm/integrations/websearch_interception/handler.py +++ b/litellm/integrations/websearch_interception/handler.py @@ -7,6 +7,7 @@ """ import asyncio +import math from typing import Any, Dict, List, Optional, Tuple, Union, cast import litellm @@ -481,6 +482,56 @@ async def async_run_chat_completion_agentic_loop( response_format=response_format, ) + @staticmethod + def _resolve_max_tokens( + optional_params: Dict, + kwargs: Dict, + ) -> int: + """Extract max_tokens and validate against thinking.budget_tokens. + + Anthropic API requires ``max_tokens > thinking.budget_tokens``. + If the constraint is violated, auto-adjust to ``budget_tokens + 1024``. + """ + max_tokens: int = optional_params.get( + "max_tokens", + kwargs.get("max_tokens", 1024), + ) + thinking_param = optional_params.get("thinking") + if thinking_param and isinstance(thinking_param, dict): + budget_tokens = thinking_param.get("budget_tokens") + if ( + budget_tokens is not None + and isinstance(budget_tokens, (int, float)) + and math.isfinite(budget_tokens) + and budget_tokens > 0 + ): + if max_tokens <= budget_tokens: + adjusted = math.ceil(budget_tokens) + 1024 + verbose_logger.debug( + "WebSearchInterception: max_tokens=%s <= thinking.budget_tokens=%s, " + "adjusting to %s to satisfy Anthropic API constraint", + max_tokens, budget_tokens, adjusted, + ) + max_tokens = adjusted + return max_tokens + + @staticmethod + def _prepare_followup_kwargs(kwargs: Dict) -> Dict: + """Build kwargs for the follow-up call, excluding internal keys. + + ``litellm_logging_obj`` MUST be excluded so the follow-up call creates + its own ``Logging`` instance via ``function_setup``. Reusing the + initial call's logging object triggers the dedup flag + (``has_logged_async_success``) which silently prevents the initial + call's spend from being recorded — the root cause of the + SpendLog / AWS billing mismatch. + """ + _internal_keys = {'litellm_logging_obj'} + return { + k: v for k, v in kwargs.items() + if not k.startswith('_websearch_interception') and k not in _internal_keys + } + async def _execute_agentic_loop( self, model: str, @@ -504,7 +555,7 @@ async def _execute_agentic_loop( ) search_tasks.append(self._execute_search(query)) else: - verbose_logger.warning( + verbose_logger.debug( f"WebSearchInterception: Tool call {tool_call['id']} has no query" ) # Add empty result for tools without query @@ -531,7 +582,7 @@ async def _execute_agentic_loop( final_search_results.append(cast(str, result)) else: # Should never happen, but handle for type safety - verbose_logger.warning( + verbose_logger.debug( f"WebSearchInterception: Unexpected result type {type(result)} at index {i}" ) final_search_results.append(str(result)) @@ -557,13 +608,18 @@ async def _execute_agentic_loop( f"WebSearchInterception: Last message (tool_result): {user_message}" ) + # Correlation context for structured logging + _call_id = ( + getattr(logging_obj, "litellm_call_id", None) + or kwargs.get("litellm_call_id", "unknown") + ) + + full_model_name = model # safe default before try block + # Use anthropic_messages.acreate for follow-up request try: - # Extract max_tokens from optional params or kwargs - # max_tokens is a required parameter for anthropic_messages.acreate() - max_tokens = anthropic_messages_optional_request_params.get( - "max_tokens", - kwargs.get("max_tokens", 1024) # Default to 1024 if not found + max_tokens = self._resolve_max_tokens( + anthropic_messages_optional_request_params, kwargs ) verbose_logger.debug( @@ -576,16 +632,10 @@ async def _execute_agentic_loop( if k != 'max_tokens' } - # Remove internal websearch interception flags from kwargs before follow-up request - # These flags are used internally and should not be passed to the LLM provider - kwargs_for_followup = { - k: v for k, v in kwargs.items() - if not k.startswith('_websearch_interception') - } + kwargs_for_followup = self._prepare_followup_kwargs(kwargs) # Get model from logging_obj.model_call_details["agentic_loop_params"] # This preserves the full model name with provider prefix (e.g., "bedrock/invoke/...") - full_model_name = model if logging_obj is not None: agentic_params = logging_obj.model_call_details.get("agentic_loop_params", {}) full_model_name = agentic_params.get("model", model) @@ -609,7 +659,10 @@ async def _execute_agentic_loop( return final_response except Exception as e: verbose_logger.exception( - f"WebSearchInterception: Follow-up request failed: {str(e)}" + "WebSearchInterception: Follow-up request failed " + "[call_id=%s model=%s messages=%d searches=%d]: %s", + _call_id, full_model_name, len(follow_up_messages), + len(final_search_results), str(e), ) raise @@ -620,7 +673,7 @@ async def _execute_search(self, query: str) -> str: try: from litellm.proxy.proxy_server import llm_router except ImportError: - verbose_logger.warning( + verbose_logger.debug( "WebSearchInterception: Could not import llm_router from proxy_server, " "falling back to direct litellm.asearch() with perplexity" ) @@ -643,7 +696,7 @@ async def _execute_search(self, query: str) -> str: f"with provider '{search_provider}'" ) else: - verbose_logger.warning( + verbose_logger.debug( f"WebSearchInterception: Search tool '{self.search_tool_name}' not found in router, " "falling back to first available or perplexity" ) @@ -717,7 +770,7 @@ async def _execute_chat_completion_agentic_loop( # noqa: PLR0915 ) search_tasks.append(self._execute_search(query)) else: - verbose_logger.warning( + verbose_logger.debug( f"WebSearchInterception: Tool call {tool_call.get('id')} has no query" ) # Add empty result for tools without query @@ -742,7 +795,7 @@ async def _execute_chat_completion_agentic_loop( # noqa: PLR0915 elif isinstance(result, str): final_search_results.append(cast(str, result)) else: - verbose_logger.warning( + verbose_logger.debug( f"WebSearchInterception: Unexpected result type {type(result)} at index {i}" ) final_search_results.append(str(result)) diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index 82ae5a9ff0a..d1ee17fdd2e 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -561,6 +561,13 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915 ) = litellm.GroqChatConfig()._get_openai_compatible_provider_info( api_base, api_key ) + elif custom_llm_provider == "bedrock_mantle": + ( + api_base, + dynamic_api_key, + ) = litellm.BedrockMantleChatConfig()._get_openai_compatible_provider_info( + api_base, api_key + ) elif custom_llm_provider == "nvidia_nim": # nvidia_nim is openai compatible, we just need to set this to custom_openai and have the api_base be https://api.endpoints.anyscale.com/v1 api_base = ( diff --git a/litellm/litellm_core_utils/get_supported_openai_params.py b/litellm/litellm_core_utils/get_supported_openai_params.py index 4b40f44cbc4..773dca101b3 100644 --- a/litellm/litellm_core_utils/get_supported_openai_params.py +++ b/litellm/litellm_core_utils/get_supported_openai_params.py @@ -88,6 +88,8 @@ def get_supported_openai_params( # noqa: PLR0915 return litellm.VolcEngineConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "groq": return litellm.GroqChatConfig().get_supported_openai_params(model=model) + elif custom_llm_provider == "bedrock_mantle": + return litellm.BedrockMantleChatConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "hosted_vllm": return litellm.HostedVLLMChatConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "vllm": diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py index 125f2585a33..d59b8d88714 100644 --- a/litellm/litellm_core_utils/prompt_templates/common_utils.py +++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py @@ -20,6 +20,7 @@ cast, ) +from litellm import verbose_logger from litellm.router_utils.batch_utils import InMemoryFile from litellm.types.llms.openai import ( AllMessageValues, @@ -1278,16 +1279,76 @@ def extract_images_from_message(message: AllMessageValues) -> List[str]: return images +def _attempt_json_repair(s: str) -> Optional[Any]: + """ + Attempt to repair truncated JSON produced by LLM tool calls. + + Handles the most common truncation patterns where the model generates + valid JSON that is cut short (missing closing brackets/braces). + + Returns the parsed value on success, or None if repair fails. + """ + import json + + stripped = s.rstrip() + if not stripped: + return None + + # Track the stack of unmatched openers to respect nesting order + opener_stack: list = [] + in_string = False + escape_next = False + + for ch in stripped: + if escape_next: + escape_next = False + continue + if ch == "\\": + if in_string: + escape_next = True + continue + if ch == '"': + in_string = not in_string + continue + if in_string: + continue + if ch == "{": + opener_stack.append("}") + elif ch == "[": + opener_stack.append("]") + elif ch in ("}", "]"): + if opener_stack and opener_stack[-1] == ch: + opener_stack.pop() + + if not opener_stack: + return None + + # Remove trailing comma before we close brackets + candidate = stripped.rstrip(",") + + # Close in reverse order of opening (respects nesting) + candidate += "".join(reversed(opener_stack)) + + try: + return json.loads(candidate) + except json.JSONDecodeError: + pass + + return None + + def parse_tool_call_arguments( arguments: Optional[str], tool_name: Optional[str] = None, context: Optional[str] = None, -) -> Dict[str, Any]: +) -> Any: """ Parse tool call arguments from a JSON string. - This function handles malformed JSON gracefully by raising a ValueError - with context about what failed and what the problematic input was. + When the JSON is malformed (e.g. truncated by the model), this function + attempts a lightweight repair (closing unmatched brackets/braces) before + raising an error. A warning is logged whenever repair succeeds so that + callers are aware the arguments were not perfectly formed. Args: arguments: The JSON string containing tool arguments, or None. @@ -1295,19 +1356,34 @@ def parse_tool_call_arguments( context: Optional context string (e.g., "Anthropic Messages API"). Returns: - Parsed arguments as a dictionary. Returns empty dict if arguments is None or empty. + Parsed arguments (usually a dict, but may be any JSON-deserializable + type such as list, str, int, float, or None). Returns empty dict if + arguments is None or empty. Raises: - ValueError: If the arguments string is not valid JSON. + ValueError: If the arguments string is not valid JSON and cannot be repaired. """ import json - if not arguments: + if not arguments or not arguments.strip(): return {} try: return json.loads(arguments) - except json.JSONDecodeError as e: + except json.JSONDecodeError as original_error: + repaired = _attempt_json_repair(arguments) + if repaired is not None: + verbose_logger.warning( + "Repaired truncated tool call arguments for tool '%s' (%s). " + "Original (%d chars): %.200s%s", + tool_name or "", + context or "unknown context", + len(arguments), + arguments, + "..." if len(arguments) > 200 else "", + ) + return repaired + error_parts = ["Failed to parse tool call arguments"] if tool_name: @@ -1316,10 +1392,11 @@ def parse_tool_call_arguments( error_parts.append(f"({context})") error_message = ( - " ".join(error_parts) + f". Error: {str(e)}. Arguments: {arguments}" + " ".join(error_parts) + + f". Error: {str(original_error)}. Arguments: {arguments}" ) - raise ValueError(error_message) from e + raise ValueError(error_message) from original_error def split_concatenated_json_objects(raw: str) -> List[Dict[str, Any]]: diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index 796223ff8e1..a694cec7d66 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -1035,9 +1035,13 @@ def convert_to_anthropic_tool_invoke_xml(tool_calls: list) -> str: parsed_args = parse_tool_call_arguments( tool_arguments, tool_name=tool_name, context="Anthropic XML tool invoke" ) - parameters = "".join( - f"<{param}>{val}\n" for param, val in parsed_args.items() - ) + if isinstance(parsed_args, dict): + parameters = "".join( + f"<{param}>{val}\n" + for param, val in parsed_args.items() + ) + else: + parameters = f"{parsed_args}\n" invokes += ( "\n" f"{tool_name}\n" diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index 1f17a3da4bb..317f1037686 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -1099,7 +1099,14 @@ def chunk_creator(self, chunk: Any): # type: ignore # noqa: PLR0915 and self.custom_llm_provider in litellm._custom_providers ): if self.received_finish_reason is not None: - if "provider_specific_fields" not in chunk: + _chunk_has_content = isinstance(chunk, dict) and ( + bool(chunk.get("text", "")) + or chunk.get("tool_use") is not None + ) + if not _chunk_has_content and ( + not isinstance(chunk, dict) + or "provider_specific_fields" not in chunk + ): raise StopIteration anthropic_response_obj: GChunk = cast(GChunk, chunk) completion_obj["content"] = anthropic_response_obj["text"] diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index b9d07d7c544..5227d369027 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -194,6 +194,7 @@ def get_supported_openai_params(self, model: str): "web_search_options", "speed", "context_management", + "cache_control", ] if ( @@ -1031,6 +1032,9 @@ def map_openai_params( # noqa: PLR0915 elif param == "speed" and isinstance(value, str): # Pass through Anthropic-specific speed parameter for fast mode optional_params["speed"] = value + elif param == "cache_control" and isinstance(value, dict): + # Pass through top-level cache_control for automatic prompt caching + optional_params["cache_control"] = value ## handle thinking tokens self.update_optional_params_with_thinking_tokens( diff --git a/litellm/llms/azure/batches/handler.py b/litellm/llms/azure/batches/handler.py index aaefe801687..0e474a468e5 100644 --- a/litellm/llms/azure/batches/handler.py +++ b/litellm/llms/azure/batches/handler.py @@ -35,7 +35,7 @@ async def acreate_batch( create_batch_data: CreateBatchRequest, azure_client: Union[AsyncAzureOpenAI, AsyncOpenAI], ) -> LiteLLMBatch: - response = await azure_client.batches.create(**create_batch_data) + response = await azure_client.batches.create(**create_batch_data) # type: ignore[arg-type] return LiteLLMBatch(**response.model_dump()) def create_batch( @@ -73,7 +73,7 @@ def create_batch( return self.acreate_batch( # type: ignore create_batch_data=create_batch_data, azure_client=azure_client ) - response = cast(Union[AzureOpenAI, OpenAI], azure_client).batches.create(**create_batch_data) + response = cast(Union[AzureOpenAI, OpenAI], azure_client).batches.create(**create_batch_data) # type: ignore[arg-type] return LiteLLMBatch(**response.model_dump()) async def aretrieve_batch( @@ -81,7 +81,7 @@ async def aretrieve_batch( retrieve_batch_data: RetrieveBatchRequest, client: Union[AsyncAzureOpenAI, AsyncOpenAI], ) -> LiteLLMBatch: - response = await client.batches.retrieve(**retrieve_batch_data) + response = await client.batches.retrieve(**retrieve_batch_data) # type: ignore[arg-type] return LiteLLMBatch(**response.model_dump()) def retrieve_batch( diff --git a/litellm/llms/azure/chat/gpt_5_transformation.py b/litellm/llms/azure/chat/gpt_5_transformation.py index 2a2955fca37..ffdd52c30ab 100644 --- a/litellm/llms/azure/chat/gpt_5_transformation.py +++ b/litellm/llms/azure/chat/gpt_5_transformation.py @@ -28,8 +28,8 @@ def is_model_gpt_5_model(cls, model: str) -> bool: def get_supported_openai_params(self, model: str) -> List[str]: """Get supported parameters for Azure OpenAI GPT-5 models. - Azure OpenAI GPT-5.2 models support logprobs, unlike OpenAI's GPT-5. - This overrides the parent class to add logprobs support back for gpt-5.2. + Azure OpenAI GPT-5.2/5.4 models support logprobs, unlike OpenAI's GPT-5. + This overrides the parent class to add logprobs support back for gpt-5.2+. Reference: - Tested with Azure OpenAI GPT-5.2 (api-version: 2025-01-01-preview) @@ -43,10 +43,10 @@ def get_supported_openai_params(self, model: str) -> List[str]: if "tool_choice" not in params: params.append("tool_choice") - # Only gpt-5.2 has been verified to support logprobs on Azure. + # Only gpt-5.2+ has been verified to support logprobs on Azure. # The base OpenAI class includes logprobs for gpt-5.1+, but Azure - # hasn't verified support for gpt-5.1, so remove them unless gpt-5.2. - if self.is_model_gpt_5_1_model(model) and not self.is_model_gpt_5_2_model(model): + # hasn't verified support for gpt-5.1, so remove them unless gpt-5.2/5.4+. + if self._supports_reasoning_effort_level(model, "none") and not self.is_model_gpt_5_2_model(model): params = [p for p in params if p not in ["logprobs", "top_logprobs"]] elif self.is_model_gpt_5_2_model(model): azure_supported_params = ["logprobs", "top_logprobs"] @@ -67,11 +67,11 @@ def map_openai_params( or optional_params.get("reasoning_effort") ) - # gpt-5.1 supports reasoning_effort='none', but other gpt-5 models don't + # gpt-5.1/5.2/5.4 support reasoning_effort='none', but other gpt-5 models don't # See: https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/reasoning - is_gpt_5_1 = self.is_model_gpt_5_1_model(model) + supports_none = self._supports_reasoning_effort_level(model, "none") - if reasoning_effort_value == "none" and not is_gpt_5_1: + if reasoning_effort_value == "none" and not supports_none: if litellm.drop_params is True or ( drop_params is not None and drop_params is True ): @@ -101,8 +101,8 @@ def map_openai_params( drop_params=drop_params, ) - # Only drop reasoning_effort='none' for non-gpt-5.1 models - if result.get("reasoning_effort") == "none" and not is_gpt_5_1: + # Only drop reasoning_effort='none' for models that don't support it + if result.get("reasoning_effort") == "none" and not supports_none: result.pop("reasoning_effort") return result diff --git a/litellm/llms/azure_ai/anthropic/messages_transformation.py b/litellm/llms/azure_ai/anthropic/messages_transformation.py index a4dc88f9c68..8e60e84391b 100644 --- a/litellm/llms/azure_ai/anthropic/messages_transformation.py +++ b/litellm/llms/azure_ai/anthropic/messages_transformation.py @@ -1,7 +1,7 @@ """ Azure Anthropic messages transformation config - extends AnthropicMessagesConfig with Azure authentication """ -from typing import TYPE_CHECKING, Any, List, Optional, Tuple +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple from litellm.llms.anthropic.experimental_pass_through.messages.transformation import ( AnthropicMessagesConfig, @@ -114,3 +114,53 @@ def get_complete_url( return api_base + def _remove_scope_from_cache_control( + self, anthropic_messages_request: Dict + ) -> None: + """ + Remove `scope` field from cache_control for Azure AI Foundry. + + Azure AI Foundry's Anthropic endpoint does not support the `scope` field + (e.g., "global" for cross-request caching). Only `type` and `ttl` are supported. + + Processes both `system` and `messages` content blocks. + """ + def _sanitize(cache_control: Any) -> None: + if isinstance(cache_control, dict): + cache_control.pop("scope", None) + + def _process_content_list(content: list) -> None: + for item in content: + if isinstance(item, dict) and "cache_control" in item: + _sanitize(item["cache_control"]) + + if "system" in anthropic_messages_request: + system = anthropic_messages_request["system"] + if isinstance(system, list): + _process_content_list(system) + + if "messages" in anthropic_messages_request: + for message in anthropic_messages_request["messages"]: + if isinstance(message, dict) and "content" in message: + content = message["content"] + if isinstance(content, list): + _process_content_list(content) + + def transform_anthropic_messages_request( + self, + model: str, + messages: List[Dict], + anthropic_messages_optional_request_params: Dict, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Dict: + anthropic_messages_request = super().transform_anthropic_messages_request( + model=model, + messages=messages, + anthropic_messages_optional_request_params=anthropic_messages_optional_request_params, + litellm_params=litellm_params, + headers=headers, + ) + self._remove_scope_from_cache_control(anthropic_messages_request) + return anthropic_messages_request + diff --git a/litellm/llms/azure_ai/cost_calculator.py b/litellm/llms/azure_ai/cost_calculator.py index 999f94da182..6fb29962677 100644 --- a/litellm/llms/azure_ai/cost_calculator.py +++ b/litellm/llms/azure_ai/cost_calculator.py @@ -61,7 +61,10 @@ def calculate_azure_model_router_flat_cost(model: str, prompt_tokens: int) -> fl def cost_per_token( - model: str, usage: Usage, response_time_ms: Optional[float] = 0.0 + model: str, + usage: Usage, + response_time_ms: Optional[float] = 0.0, + request_model: Optional[str] = None, ) -> Tuple[float, float]: """ Calculate the cost per token for Azure AI models. @@ -71,9 +74,10 @@ def cost_per_token( - Plus the cost of the actual model used (handled by generic_cost_per_token) Args: - model: str, the model name without provider prefix + model: str, the model name without provider prefix (from response) usage: LiteLLM Usage block response_time_ms: Optional response time in milliseconds + request_model: Optional[str], the original request model name (to detect router usage) Returns: Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd @@ -84,7 +88,13 @@ def cost_per_token( """ prompt_cost = 0.0 completion_cost = 0.0 - + + # Determine if this was a model router request + # Check both the response model and the request model + is_router_request = _is_azure_model_router(model) or ( + request_model is not None and _is_azure_model_router(request_model) + ) + # Calculate base cost using generic cost calculator # This may raise an exception if the model is not in the cost map try: @@ -103,19 +113,21 @@ def cost_per_token( verbose_logger.debug( f"Azure AI Model Router: model '{model}' not in cost map, calculating routing flat cost only. Error: {e}" ) - + # Add flat cost for Azure Model Router # The flat cost is defined in model_prices_and_context_window.json for azure_ai/model_router - if _is_azure_model_router(model): - router_flat_cost = calculate_azure_model_router_flat_cost(model, usage.prompt_tokens) - + if is_router_request: + # Use the request model for flat cost calculation if available, otherwise use response model + router_model_for_calc = request_model if request_model else model + router_flat_cost = calculate_azure_model_router_flat_cost(router_model_for_calc, usage.prompt_tokens) + if router_flat_cost > 0: verbose_logger.debug( f"Azure AI Model Router flat cost: ${router_flat_cost:.6f} " f"({usage.prompt_tokens} tokens × ${router_flat_cost / usage.prompt_tokens:.9f}/token)" ) - + # Add flat cost to prompt cost prompt_cost += router_flat_cost - + return prompt_cost, completion_cost diff --git a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py index dfab81123fd..e5698843e8f 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py @@ -6,7 +6,10 @@ from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import ( AmazonInvokeConfig, ) -from litellm.llms.bedrock.common_utils import get_anthropic_beta_from_headers +from litellm.llms.bedrock.common_utils import ( + get_anthropic_beta_from_headers, + remove_custom_field_from_tools, +) from litellm.types.llms.anthropic import ANTHROPIC_TOOL_SEARCH_BETA_HEADER from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse @@ -108,6 +111,12 @@ def transform_request( if "anthropic_version" not in _anthropic_request: _anthropic_request["anthropic_version"] = self.anthropic_version + # Remove `custom` field from tools (Bedrock doesn't support it) + # Claude Code sends `custom: {defer_loading: true}` on tool definitions, + # which causes Bedrock to reject the request with "Extra inputs are not permitted" + # Ref: https://github.com/BerriAI/litellm/issues/22847 + remove_custom_field_from_tools(_anthropic_request) + tools = optional_params.get("tools") tool_search_used = self.is_tool_search_used(tools) programmatic_tool_calling_used = self.is_programmatic_tool_calling_used(tools) diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index b779c892c67..8e944988a95 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -49,6 +49,27 @@ def get_cached_model_info(): return _get_model_info +def remove_custom_field_from_tools(request_body: dict) -> None: + """ + Remove ``custom`` field from each tool in the request body. + + Claude Code (v2.1.69+) sends ``custom: {defer_loading: true}`` on tool + definitions, which Anthropic's API accepts but Bedrock rejects with + ``"Extra inputs are not permitted"``. + + Args: + request_body: The request dictionary to modify in-place. + + Ref: https://github.com/BerriAI/litellm/issues/22847 + """ + tools = request_body.get("tools") + if not tools or not isinstance(tools, list): + return + for tool in tools: + if isinstance(tool, dict): + tool.pop("custom", None) + + class AmazonBedrockGlobalConfig: def __init__(self): pass diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py index 03885ff2080..9fae5fd2a17 100644 --- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py @@ -26,6 +26,7 @@ from litellm.llms.bedrock.common_utils import ( get_anthropic_beta_from_headers, is_claude_4_5_on_bedrock, + remove_custom_field_from_tools, ) from litellm.types.llms.anthropic import ANTHROPIC_TOOL_SEARCH_BETA_HEADER from litellm.types.llms.openai import AllMessageValues @@ -118,10 +119,13 @@ def _remove_ttl_from_cache_control( self, anthropic_messages_request: Dict, model: Optional[str] = None ) -> None: """ - Remove `ttl` field from cache_control in messages. - Bedrock doesn't support the ttl field in cache_control. + Remove unsupported fields from cache_control for Bedrock. - Update: Bedock supports `5m` and `1h` for Claude 4.5 models. + Bedrock only supports `type` and `ttl` in cache_control. It does NOT support: + - `scope` (e.g., "global") - always removed + - `ttl` - removed for older models; Claude 4.5+ supports "5m" and "1h" + + Processes both `system` and `messages` content blocks. Args: anthropic_messages_request: The request dictionary to modify in-place @@ -131,23 +135,36 @@ def _remove_ttl_from_cache_control( if model: is_claude_4_5 = self._is_claude_4_5_on_bedrock(model) + def _sanitize_cache_control(cache_control: dict) -> None: + if not isinstance(cache_control, dict): + return + # Bedrock doesn't support scope (e.g., "global" for cross-request caching) + cache_control.pop("scope", None) + # Remove ttl for models that don't support it + if "ttl" in cache_control: + ttl = cache_control["ttl"] + if is_claude_4_5 and ttl in ["5m", "1h"]: + return + cache_control.pop("ttl", None) + + def _process_content_list(content: list) -> None: + for item in content: + if isinstance(item, dict) and "cache_control" in item: + _sanitize_cache_control(item["cache_control"]) + + # Process system (list of content blocks) + if "system" in anthropic_messages_request: + system = anthropic_messages_request["system"] + if isinstance(system, list): + _process_content_list(system) + + # Process messages if "messages" in anthropic_messages_request: for message in anthropic_messages_request["messages"]: if isinstance(message, dict) and "content" in message: content = message["content"] if isinstance(content, list): - for item in content: - if isinstance(item, dict) and "cache_control" in item: - cache_control = item["cache_control"] - if ( - isinstance(cache_control, dict) - and "ttl" in cache_control - ): - ttl = cache_control["ttl"] - if is_claude_4_5 and ttl in ["5m", "1h"]: - continue - - cache_control.pop("ttl", None) + _process_content_list(content) def _supports_extended_thinking_on_bedrock(self, model: str) -> bool: """ @@ -402,6 +419,12 @@ def transform_anthropic_messages_request( anthropic_messages_request=anthropic_messages_request, ) + # 5a. Remove `custom` field from tools (Bedrock doesn't support it) + # Claude Code sends `custom: {defer_loading: true}` on tool definitions, + # which causes Bedrock to reject the request with "Extra inputs are not permitted" + # Ref: https://github.com/BerriAI/litellm/issues/22847 + remove_custom_field_from_tools(anthropic_messages_request) + # 6. AUTO-INJECT beta headers based on features used anthropic_model_info = AnthropicModelInfo() tools = anthropic_messages_optional_request_params.get("tools") diff --git a/litellm/llms/bedrock_mantle/chat/transformation.py b/litellm/llms/bedrock_mantle/chat/transformation.py new file mode 100644 index 00000000000..e413bb22b2d --- /dev/null +++ b/litellm/llms/bedrock_mantle/chat/transformation.py @@ -0,0 +1,80 @@ +""" +Amazon Bedrock Mantle - OpenAI-compatible inference engine in Amazon Bedrock. + +API docs: https://docs.aws.amazon.com/bedrock/latest/userguide/bedrock-mantle.html + +Base URL: https://bedrock-mantle.{region}.api.aws/v1 +Auth: AWS Bedrock API key as Bearer token (set via BEDROCK_MANTLE_API_KEY env var) + or region-aware key via BEDROCK_MANTLE_{REGION}_API_KEY. +""" + +from typing import Iterator, AsyncIterator, Any, Optional, Tuple, Union + +import litellm +from litellm._logging import verbose_logger +from litellm.secret_managers.main import get_secret_str + +from ...openai_like.chat.transformation import OpenAILikeChatConfig + + +BEDROCK_MANTLE_DEFAULT_REGION = "us-east-1" + + +class BedrockMantleChatConfig(OpenAILikeChatConfig): + """ + Transformation config for Amazon Bedrock Mantle OpenAI-compatible API. + """ + + @property + def custom_llm_provider(self) -> Optional[str]: + return "bedrock_mantle" + + @classmethod + def get_config(cls): + return super().get_config() + + def _get_openai_compatible_provider_info( + self, api_base: Optional[str], api_key: Optional[str] + ) -> Tuple[Optional[str], Optional[str]]: + region = ( + get_secret_str("BEDROCK_MANTLE_REGION") + or get_secret_str("AWS_REGION") + or BEDROCK_MANTLE_DEFAULT_REGION + ) + api_base = ( + api_base + or get_secret_str("BEDROCK_MANTLE_API_BASE") + or f"https://bedrock-mantle.{region}.api.aws/v1" + ) + dynamic_api_key = api_key or get_secret_str("BEDROCK_MANTLE_API_KEY") + return api_base, dynamic_api_key + + def get_supported_openai_params(self, model: str) -> list: + base_params = super().get_supported_openai_params(model) + try: + if litellm.supports_reasoning( + model=model, custom_llm_provider=self.custom_llm_provider + ): + if "reasoning_effort" not in base_params: + base_params.append("reasoning_effort") + except Exception as e: + verbose_logger.debug( + f"BedrockMantleChatConfig: error checking reasoning support: {e}" + ) + return base_params + + def get_model_response_iterator( + self, + streaming_response: Union[Iterator[str], AsyncIterator[str], Any], + sync_stream: bool, + json_mode: Optional[bool] = False, + ) -> Any: + from litellm.llms.openai.chat.gpt_transformation import ( + OpenAIChatCompletionStreamingHandler, + ) + + return OpenAIChatCompletionStreamingHandler( + streaming_response=streaming_response, + sync_stream=sync_stream, + json_mode=json_mode, + ) diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index b6fcf853ab5..1cef3e9ce15 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -4454,8 +4454,11 @@ async def _call_agentic_completion_hooks( return agentic_response except Exception as e: + _call_id = getattr(logging_obj, "litellm_call_id", "unknown") verbose_logger.exception( - f"LiteLLM.AgenticHookError: Exception in agentic completion hooks: {str(e)}" + "LiteLLM.AgenticHookError: Exception in agentic completion hooks " + "[call_id=%s model=%s]: %s", + _call_id, model, str(e), ) # Check if we need to convert response to fake stream diff --git a/litellm/llms/openai/chat/gpt_5_transformation.py b/litellm/llms/openai/chat/gpt_5_transformation.py index 014e80f0a3a..358a94016ae 100644 --- a/litellm/llms/openai/chat/gpt_5_transformation.py +++ b/litellm/llms/openai/chat/gpt_5_transformation.py @@ -3,6 +3,7 @@ from typing import Optional import litellm +from litellm.utils import _supports_factory from .gpt_transformation import OpenAIGPTConfig @@ -41,40 +42,24 @@ def is_model_gpt_5_codex_model(cls, model: str) -> bool: return "gpt-5-codex" in model @classmethod - def is_model_gpt_5_1_codex_max_model(cls, model: str) -> bool: - """Check if the model is the gpt-5.1-codex-max variant.""" - model_name = model.split("/")[-1] # handle provider prefixes - return model_name == "gpt-5.1-codex-max" - - @classmethod - def is_model_gpt_5_1_model(cls, model: str) -> bool: - """Check if the model is a gpt-5.1 or gpt-5.2 chat variant. - - gpt-5.1/5.2 support temperature when reasoning_effort="none", - unlike base gpt-5 which only supports temperature=1. Excludes - pro variants which keep stricter knobs and gpt-5.2-chat variants - which only support temperature=1. - """ + def is_model_gpt_5_2_model(cls, model: str) -> bool: + """Check if the model is a gpt-5.2 variant (including pro).""" model_name = model.split("/")[-1] - is_gpt_5_1 = model_name.startswith("gpt-5.1") - is_gpt_5_2 = ( - model_name.startswith("gpt-5.2") - and "pro" not in model_name - and not model_name.startswith("gpt-5.2-chat") - ) - return is_gpt_5_1 or is_gpt_5_2 + return model_name.startswith("gpt-5.2") or model_name.startswith("gpt-5.4") @classmethod - def is_model_gpt_5_2_pro_model(cls, model: str) -> bool: - """Check if the model is the gpt-5.2-pro snapshot/alias.""" - model_name = model.split("/")[-1] - return model_name.startswith("gpt-5.2-pro") + def _supports_reasoning_effort_level(cls, model: str, level: str) -> bool: + """Check if the model supports a specific reasoning_effort level. - @classmethod - def is_model_gpt_5_2_model(cls, model: str) -> bool: - """Check if the model is a gpt-5.2 variant (including pro).""" - model_name = model.split("/")[-1] - return model_name.startswith("gpt-5.2") + Looks up ``supports_{level}_reasoning_effort`` in the model map via + the shared ``_supports_factory`` helper. + Returns False for unknown models (safe fallback). + """ + return _supports_factory( + model=model, + custom_llm_provider=None, + key=f"supports_{level}_reasoning_effort", + ) def get_supported_openai_params(self, model: str) -> list: if self.is_model_gpt_5_search_model(model): @@ -114,7 +99,7 @@ def get_supported_openai_params(self, model: str) -> list: ] # gpt-5.1/5.2 support logprobs, top_p, top_logprobs when reasoning_effort="none" - if not self.is_model_gpt_5_1_model(model): + if not self._supports_reasoning_effort_level(model, "none"): non_supported_params.extend(["logprobs", "top_p", "top_logprobs"]) return [ @@ -147,16 +132,13 @@ def map_openai_params( or optional_params.get("reasoning_effort") ) if reasoning_effort is not None and reasoning_effort == "xhigh": - if not ( - self.is_model_gpt_5_1_codex_max_model(model) - or self.is_model_gpt_5_2_model(model) - ): + if not self._supports_reasoning_effort_level(model, "xhigh"): if litellm.drop_params or drop_params: non_default_params.pop("reasoning_effort", None) else: raise litellm.utils.UnsupportedParamsError( message=( - "reasoning_effort='xhigh' is only supported for gpt-5.1-codex-max and gpt-5.2 models." + "reasoning_effort='xhigh' is only supported for gpt-5.1-codex-max, gpt-5.2, and gpt-5.4+ models." ), status_code=400, ) @@ -171,7 +153,8 @@ def map_openai_params( ) # gpt-5.1/5.2 support logprobs, top_p, top_logprobs only when reasoning_effort="none" - if self.is_model_gpt_5_1_model(model): + supports_none = self._supports_reasoning_effort_level(model, "none") + if supports_none: sampling_params = ["logprobs", "top_logprobs", "top_p"] has_sampling = any(p in non_default_params for p in sampling_params) if has_sampling and reasoning_effort not in (None, "none"): @@ -181,7 +164,7 @@ def map_openai_params( else: raise litellm.utils.UnsupportedParamsError( message=( - "gpt-5.1/5.2 only support logprobs, top_p, top_logprobs when " + "gpt-5.1/5.2/5.4 only support logprobs, top_p, top_logprobs when " "reasoning_effort='none'. Current reasoning_effort='{}'. " "To drop unsupported params set `litellm.drop_params = True`" ).format(reasoning_effort), @@ -191,10 +174,8 @@ def map_openai_params( if "temperature" in non_default_params: temperature_value: Optional[float] = non_default_params.pop("temperature") if temperature_value is not None: - is_gpt_5_1 = self.is_model_gpt_5_1_model(model) - - # gpt-5.1 supports any temperature when reasoning_effort="none" (or not specified, as it defaults to "none") - if is_gpt_5_1 and (reasoning_effort == "none" or reasoning_effort is None): + # models supporting reasoning_effort="none" also support flexible temperature + if supports_none and (reasoning_effort == "none" or reasoning_effort is None): optional_params["temperature"] = temperature_value elif temperature_value == 1: optional_params["temperature"] = temperature_value diff --git a/litellm/llms/openai/chat/o_series_transformation.py b/litellm/llms/openai/chat/o_series_transformation.py index 6ef43ec5bfd..0c5ee90b332 100644 --- a/litellm/llms/openai/chat/o_series_transformation.py +++ b/litellm/llms/openai/chat/o_series_transformation.py @@ -131,7 +131,10 @@ def map_openai_params( def is_model_o_series_model(self, model: str) -> bool: model = model.split("/")[-1] # could be "openai/o3" or "o3" - return model.startswith(("o1", "o3", "o4")) and model in litellm.open_ai_chat_completion_models + return ( + len(model) > 1 and model[0] == "o" and model[1].isdigit() + and model in litellm.open_ai_chat_completion_models + ) @overload def _transform_messages( diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py index 7020f796bb7..5a8b4aafe01 100644 --- a/litellm/llms/openai/openai.py +++ b/litellm/llms/openai/openai.py @@ -1938,7 +1938,7 @@ async def acreate_batch( create_batch_data: CreateBatchRequest, openai_client: AsyncOpenAI, ) -> LiteLLMBatch: - response = await openai_client.batches.create(**create_batch_data) + response = await openai_client.batches.create(**create_batch_data) # type: ignore[arg-type] return LiteLLMBatch(**response.model_dump()) def create_batch( @@ -1974,7 +1974,7 @@ def create_batch( return self.acreate_batch( # type: ignore create_batch_data=create_batch_data, openai_client=openai_client ) - response = cast(OpenAI, openai_client).batches.create(**create_batch_data) + response = cast(OpenAI, openai_client).batches.create(**create_batch_data) # type: ignore[arg-type] return LiteLLMBatch(**response.model_dump()) @@ -1984,7 +1984,7 @@ async def aretrieve_batch( openai_client: AsyncOpenAI, ) -> LiteLLMBatch: verbose_logger.debug("retrieving batch, args= %s", retrieve_batch_data) - response = await openai_client.batches.retrieve(**retrieve_batch_data) + response = await openai_client.batches.retrieve(**retrieve_batch_data) # type: ignore[arg-type] return LiteLLMBatch(**response.model_dump()) def retrieve_batch( @@ -2020,7 +2020,7 @@ def retrieve_batch( return self.aretrieve_batch( # type: ignore retrieve_batch_data=retrieve_batch_data, openai_client=openai_client ) - response = cast(OpenAI, openai_client).batches.retrieve(**retrieve_batch_data) + response = cast(OpenAI, openai_client).batches.retrieve(**retrieve_batch_data) # type: ignore[arg-type] return LiteLLMBatch(**response.model_dump()) async def acancel_batch( diff --git a/litellm/llms/openrouter/image_edit/transformation.py b/litellm/llms/openrouter/image_edit/transformation.py index ed5e6ae67d5..7a4cef1798d 100644 --- a/litellm/llms/openrouter/image_edit/transformation.py +++ b/litellm/llms/openrouter/image_edit/transformation.py @@ -91,9 +91,9 @@ def map_openai_params( if key == "size": if "image_config" not in mapped_params: mapped_params["image_config"] = {} - mapped_params["image_config"]["aspect_ratio"] = self._map_size_to_aspect_ratio(value) + mapped_params["image_config"]["aspect_ratio"] = self._map_size_to_aspect_ratio(cast(str, value)) elif key == "quality": - image_size = self._map_quality_to_image_size(value) + image_size = self._map_quality_to_image_size(cast(str, value)) if image_size: if "image_config" not in mapped_params: mapped_params["image_config"] = {} diff --git a/litellm/llms/sap/chat/handler.py b/litellm/llms/sap/chat/handler.py index c24cf3d279f..1390b2a4785 100755 --- a/litellm/llms/sap/chat/handler.py +++ b/litellm/llms/sap/chat/handler.py @@ -181,7 +181,7 @@ class AsyncSAPStreamIterator: def __init__( self, - response:AsyncIterator, + response: AsyncIterator, event_prefix: str = "data: ", final_msg: str = "[DONE]", ): diff --git a/litellm/llms/sap/chat/models.py b/litellm/llms/sap/chat/models.py index d8039ff5618..1b09ce9a756 100644 --- a/litellm/llms/sap/chat/models.py +++ b/litellm/llms/sap/chat/models.py @@ -45,9 +45,21 @@ class FunctionObj(BaseModel): class FunctionTool(BaseModel): description: str = "" name: str - parameters: dict = {} + parameters: dict = {"type": "object", "properties": {}} strict: bool = False + @field_validator("parameters", mode="before") + @classmethod + def ensure_object_type(cls, v: dict) -> dict: + """Ensure parameters has type='object' as required by SAP Orchestration Service.""" + if not v: + return {"type": "object", "properties": {}} + if "type" not in v: + v = {"type": "object", **v} + if "properties" not in v: + v["properties"] = {} + return v + class ChatCompletionTool(BaseModel): type_: Literal["function"] = Field(default="function", alias="type") diff --git a/litellm/llms/sap/chat/transformation.py b/litellm/llms/sap/chat/transformation.py index 2b1573bf4ed..a019ba1767a 100755 --- a/litellm/llms/sap/chat/transformation.py +++ b/litellm/llms/sap/chat/transformation.py @@ -157,9 +157,9 @@ def get_supported_openai_params(self, model): "response_format", "timeout", ] + # Remove response_format for providers that don't support it on SAP GenAI Hub if ( - model.startswith('anthropic') - or model.startswith("amazon") + model.startswith("amazon") or model.startswith("cohere") or model.startswith("alephalpha") or model == "gpt-4" @@ -169,6 +169,7 @@ def get_supported_openai_params(self, model): params.remove("tool_choice") return params + def validate_environment( self, headers: dict, @@ -203,8 +204,18 @@ def transform_request( litellm_params: dict, headers: dict, ) -> dict: + # Filter out parameters that are not valid model params for SAP Orchestration API + # - tools, model_version, deployment_url: handled separately + excluded_params = {"tools", "model_version", "deployment_url"} + + # Filter strict for GPT models only - SAP AI Core doesn't accept it as a model param + # LangChain agents pass strict=true at top level, which fails for GPT models + # Anthropic models accept strict, so preserve it for them + if model.startswith("gpt"): + excluded_params.add("strict") + model_params = { - k: v for k, v in optional_params.items() if k not in {"tools", "model_version", "deployment_url"} + k: v for k, v in optional_params.items() if k not in excluded_params } model_version = optional_params.pop("model_version", "latest") @@ -286,7 +297,37 @@ def transform_response( original_response=raw_response.text, additional_args={"complete_input_dict": request_data}, ) - return ModelResponse.model_validate(raw_response.json()["final_result"]) + response = ModelResponse.model_validate(raw_response.json()["final_result"]) + + # Strip markdown code blocks if JSON response_format was used with Anthropic models + # SAP GenAI Hub with Anthropic models sometimes wraps JSON in ```json ... ``` + # based on prompt phrasing. GPT/Gemini models don't exhibit this behavior, + # so we gate the stripping to avoid accidentally modifying valid responses. + response_format = optional_params.get("response_format", {}) + if response_format.get("type") in ("json_object", "json_schema"): + if model.startswith("anthropic"): + response = self._strip_markdown_json(response) + + return response + + def _strip_markdown_json(self, response: ModelResponse) -> ModelResponse: + """Strip markdown code block wrapper from JSON content if present. + + SAP GenAI Hub with Anthropic models sometimes returns JSON wrapped in + markdown code blocks (```json ... ```) depending on prompt phrasing. + This method strips that wrapper to ensure consistent JSON output. + """ + import re + + for choice in response.choices or []: + if choice.message and choice.message.content: + content = choice.message.content.strip() + # Match ```json ... ``` or ``` ... ``` + match = re.match(r'^```(?:json)?\s*\n?(.*?)\n?```$', content, re.DOTALL) + if match: + choice.message.content = match.group(1).strip() + + return response def get_model_response_iterator( self, @@ -295,6 +336,6 @@ def get_model_response_iterator( json_mode: Optional[bool] = False, ): if sync_stream: - return SAPStreamIterator(response=streaming_response) # type: ignore + return SAPStreamIterator(response=streaming_response) # type: ignore else: - return AsyncSAPStreamIterator(response=streaming_response) # type: ignore + return AsyncSAPStreamIterator(response=streaming_response) # type: ignore diff --git a/litellm/llms/searchapi/search/transformation.py b/litellm/llms/searchapi/search/transformation.py index 30571b468f6..826f2436cb7 100644 --- a/litellm/llms/searchapi/search/transformation.py +++ b/litellm/llms/searchapi/search/transformation.py @@ -3,7 +3,7 @@ SearchAPI.io API Reference: https://www.searchapi.io/docs/google """ -from typing import Dict, List, Literal, Optional, TypedDict, Union +from typing import Dict, List, Literal, Optional, TypedDict, Union, cast from urllib.parse import urlencode import httpx @@ -164,7 +164,7 @@ def transform_search_request( if "country" in optional_params: # Map to gl parameter - result_data["gl"] = optional_params["country"].lower() + result_data["gl"] = cast(str, optional_params["country"]).lower() # Pass through all other SearchAPI.io-specific parameters for param, value in optional_params.items(): diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index b8343d735b4..57889284a8c 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -595,6 +595,8 @@ def _transform_request_body( safety_settings: Optional[List[SafetSettingsConfig]] = optional_params.pop( "safety_settings", None ) # type: ignore + # Drop output_config as it's not supported by Vertex AI + optional_params.pop("output_config", None) config_fields = GenerationConfig.__annotations__.keys() # If the LiteLLM client sends Gemini-supported parameter "labels", add it diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py index eb2d5ad51cb..e28b755be75 100644 --- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py +++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py @@ -800,9 +800,10 @@ def _map_reasoning_effort_to_thinking_level( GeminiThinkingConfig with thinkingLevel and includeThoughts """ # Check if this is gemini-3-flash which supports MINIMAL thinking level + # Covers gemini-3-flash, gemini-3-flash-preview, gemini-3.1-flash, gemini-3.1-flash-lite-preview, etc. is_gemini3flash = model and ( - "gemini-3-flash-preview" in model.lower() - or "gemini-3-flash" in model.lower() + "gemini-3-flash" in model.lower() + or "gemini-3.1-flash" in model.lower() ) is_gemini31pro = model and ( "gemini-3.1-pro-preview" in model.lower() diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py index e05e64988d4..6bede1a2352 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py @@ -152,4 +152,8 @@ def transform_anthropic_messages_request( "output_format", None ) # do not pass output_format in request body to vertex ai - vertex ai does not support output_format as yet + anthropic_messages_request.pop( + "output_config", None + ) # do not pass output_config in request body to vertex ai - vertex ai does not support output_config + return anthropic_messages_request diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py index 78418799eb1..4e2c2895f9e 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py @@ -107,6 +107,9 @@ def transform_request( # VertexAI doesn't support output_format parameter, remove it if present data.pop("output_format", None) + + # VertexAI doesn't support output_config parameter, remove it if present + data.pop("output_config", None) tools = optional_params.get("tools") tool_search_used = self.is_tool_search_used(tools) diff --git a/litellm/main.py b/litellm/main.py index 794d30ed384..364519e1fe3 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -2241,6 +2241,32 @@ def completion( # type: ignore # noqa: PLR0915 logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements client=client, ) + elif custom_llm_provider == "bedrock_mantle": + api_base = api_base or litellm.api_base or get_secret("BEDROCK_MANTLE_API_BASE") + api_key = api_key or litellm.api_key or get_secret("BEDROCK_MANTLE_API_KEY") + headers = headers or litellm.headers + config = litellm.BedrockMantleChatConfig.get_config() + for k, v in config.items(): + if k not in optional_params: + optional_params[k] = v + response = base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider=custom_llm_provider, + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, + client=client, + ) elif custom_llm_provider == "a2a": # A2A (Agent-to-Agent) Protocol # Resolve agent configuration from registry if model format is "a2a/" diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index fc8c90ad773..93fb78d76da 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -1239,7 +1239,7 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 346 }, - "apac.anthropic.claude-sonnet-4-6": { + "au.anthropic.claude-sonnet-4-6": { "cache_creation_input_token_cost": 4.125e-06, "cache_creation_input_token_cost_above_200k_tokens": 8.25e-06, "cache_read_input_token_cost": 3.3e-07, @@ -5817,6 +5817,15 @@ ], "source": "https://devblogs.microsoft.com/foundry/whats-new-in-azure-ai-foundry-august-2025/#mistral-document-ai-(ocr)-%E2%80%94-serverless-in-foundry" }, + "azure_ai/mistral-document-ai-2512": { + "litellm_provider": "azure_ai", + "ocr_cost_per_page": 0.003, + "mode": "ocr", + "supported_endpoints": [ + "/v1/ocr" + ], + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/" + }, "azure_ai/doc-intelligence/prebuilt-read": { "litellm_provider": "azure_ai", "ocr_cost_per_page": 0.0015, @@ -14740,13 +14749,12 @@ "max_tokens": 65535, "max_video_length": 1, "max_videos_per_prompt": 10, - "mode": "chat", + "mode": "realtime", "output_cost_per_audio_token": 1.2e-05, "output_cost_per_token": 2e-06, "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ - "/v1/chat/completions", - "/v1/completions" + "/vertex_ai/live" ], "supported_modalities": [ "text", @@ -14785,14 +14793,13 @@ "max_tokens": 65535, "max_video_length": 1, "max_videos_per_prompt": 10, - "mode": "chat", + "mode": "realtime", "output_cost_per_audio_token": 1.2e-05, "output_cost_per_token": 2e-06, "rpm": 100000, "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ - "/v1/chat/completions", - "/v1/completions" + "/v1/realtime" ], "supported_modalities": [ "text", @@ -18430,6 +18437,93 @@ "max_tokens": 8191, "mode": "embedding" }, + "chatgpt/gpt-5.4": { + "litellm_provider": "chatgpt", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_vision": true + }, + "chatgpt/gpt-5.4-pro": { + "litellm_provider": "chatgpt", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "supported_endpoints": [ + "/v1/responses" + ], + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_vision": true + }, + "chatgpt/gpt-5.3-codex": { + "litellm_provider": "chatgpt", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "supported_endpoints": [ + "/v1/responses" + ], + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_vision": true + }, + "chatgpt/gpt-5.3-codex-spark": { + "litellm_provider": "chatgpt", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "supported_endpoints": [ + "/v1/responses" + ], + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_vision": true + }, + "chatgpt/gpt-5.3-instant": { + "litellm_provider": "chatgpt", + "max_input_tokens": 128000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "responses", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_vision": true + }, + "chatgpt/gpt-5.3-chat-latest": { + "litellm_provider": "chatgpt", + "max_input_tokens": 128000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "responses", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_vision": true + }, "chatgpt/gpt-5.2-codex": { "litellm_provider": "chatgpt", "max_input_tokens": 128000, @@ -20499,7 +20593,9 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_service_tier": true, - "supports_vision": true + "supports_vision": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-5.1": { "cache_read_input_token_cost": 1.25e-07, @@ -20535,7 +20631,10 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_service_tier": true, - "supports_vision": true + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": false }, "gpt-5.1-2025-11-13": { "cache_read_input_token_cost": 1.25e-07, @@ -20571,7 +20670,10 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_service_tier": true, - "supports_vision": true + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": false }, "gpt-5.1-chat-latest": { "cache_read_input_token_cost": 1.25e-07, @@ -20606,7 +20708,10 @@ "supports_response_schema": true, "supports_system_messages": true, "supports_tool_choice": false, - "supports_vision": true + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": false }, "gpt-5.2": { "cache_read_input_token_cost": 1.75e-07, @@ -20643,7 +20748,10 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_service_tier": true, - "supports_vision": true + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true }, "gpt-5.2-2025-12-11": { "cache_read_input_token_cost": 1.75e-07, @@ -20680,7 +20788,10 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_service_tier": true, - "supports_vision": true + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true }, "gpt-5.2-chat-latest": { "cache_read_input_token_cost": 1.75e-07, @@ -20714,7 +20825,10 @@ "supports_response_schema": true, "supports_system_messages": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-5.3-chat-latest": { "cache_read_input_token_cost": 1.75e-07, @@ -20748,7 +20862,10 @@ "supports_response_schema": true, "supports_system_messages": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-5.2-pro": { "input_cost_per_token": 2.1e-05, @@ -20779,7 +20896,9 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_vision": true, - "supports_web_search": true + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": true }, "gpt-5.2-pro-2025-12-11": { "input_cost_per_token": 2.1e-05, @@ -20810,8 +20929,226 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": true + }, + "gpt-5.4": { + "cache_read_input_token_cost": 2.5e-07, + "cache_read_input_token_cost_priority": 5e-07, + "input_cost_per_token": 2.5e-06, + "input_cost_per_token_priority": 5e-06, + "litellm_provider": "openai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_priority": 2.25e-05, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true + }, + "gpt-5.4-2026-03-05": { + "cache_read_input_token_cost": 2.5e-07, + "cache_read_input_token_cost_priority": 5e-07, + "input_cost_per_token": 2.5e-06, + "input_cost_per_token_priority": 5e-06, + "litellm_provider": "openai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_priority": 2.25e-05, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true + }, + "gpt-5.4-pro": { + "cache_read_input_token_cost": 2e-06, + "input_cost_per_token": 2e-05, + "litellm_provider": "openai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 0.00012, + "supported_endpoints": [ + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true + }, + "gpt-5.4-pro-2026-03-05": { + "cache_read_input_token_cost": 2e-06, + "input_cost_per_token": 2e-05, + "litellm_provider": "openai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 0.00012, + "supported_endpoints": [ + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, "supports_web_search": true }, + "gpt-5.4-pro": { + "cache_read_input_token_cost": 3e-06, + "cache_read_input_token_cost_priority": 6e-06, + "input_cost_per_token": 3e-05, + "input_cost_per_token_priority": 6e-05, + "litellm_provider": "openai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 1.8e-04, + "output_cost_per_token_priority": 2.7e-04, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": true + }, + "gpt-5.4-pro-2026-03-05": { + "cache_read_input_token_cost": 3e-06, + "cache_read_input_token_cost_priority": 6e-06, + "input_cost_per_token": 3e-05, + "input_cost_per_token_priority": 6e-05, + "litellm_provider": "openai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 1.8e-04, + "output_cost_per_token_priority": 2.7e-04, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": true + }, "gpt-5-pro": { "input_cost_per_token": 1.5e-05, "input_cost_per_token_batches": 7.5e-06, @@ -20843,7 +21180,9 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_vision": true, - "supports_web_search": true + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-5-pro-2025-10-06": { "input_cost_per_token": 1.5e-05, @@ -20876,7 +21215,9 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_vision": true, - "supports_web_search": true + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-5-2025-08-07": { "cache_read_input_token_cost": 1.25e-07, @@ -20915,7 +21256,9 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_service_tier": true, - "supports_vision": true + "supports_vision": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-5-chat": { "cache_read_input_token_cost": 1.25e-07, @@ -20947,7 +21290,9 @@ "supports_response_schema": true, "supports_system_messages": true, "supports_tool_choice": false, - "supports_vision": true + "supports_vision": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-5-chat-latest": { "cache_read_input_token_cost": 1.25e-07, @@ -20979,7 +21324,9 @@ "supports_response_schema": true, "supports_system_messages": true, "supports_tool_choice": false, - "supports_vision": true + "supports_vision": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-5-codex": { "cache_read_input_token_cost": 1.25e-07, @@ -21009,7 +21356,9 @@ "supports_response_schema": true, "supports_system_messages": false, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-5.1-codex": { "cache_read_input_token_cost": 1.25e-07, @@ -21042,7 +21391,9 @@ "supports_response_schema": true, "supports_system_messages": false, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-5.1-codex-max": { "cache_read_input_token_cost": 1.25e-07, @@ -21072,7 +21423,9 @@ "supports_response_schema": true, "supports_system_messages": false, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": true }, "gpt-5.1-codex-mini": { "cache_read_input_token_cost": 2.5e-08, @@ -21105,7 +21458,9 @@ "supports_response_schema": true, "supports_system_messages": false, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-5.2-codex": { "cache_read_input_token_cost": 1.75e-07, @@ -21138,7 +21493,9 @@ "supports_response_schema": true, "supports_system_messages": false, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": true }, "gpt-5.3-codex": { "cache_read_input_token_cost": 1.75e-07, @@ -21171,7 +21528,9 @@ "supports_response_schema": true, "supports_system_messages": false, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-5-mini": { "cache_read_input_token_cost": 2.5e-08, @@ -21210,7 +21569,9 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_service_tier": true, - "supports_vision": true + "supports_vision": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.5e-08, @@ -21249,7 +21610,9 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_service_tier": true, - "supports_vision": true + "supports_vision": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-5-nano": { "cache_read_input_token_cost": 5e-09, @@ -21285,7 +21648,9 @@ "supports_response_schema": true, "supports_system_messages": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-5-nano-2025-08-07": { "cache_read_input_token_cost": 5e-09, @@ -21320,7 +21685,9 @@ "supports_response_schema": true, "supports_system_messages": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-image-1": { "cache_read_input_image_token_cost": 2.5e-06, @@ -23211,6 +23578,19 @@ "max_input_tokens": 200000, "max_output_tokens": 8192 }, + "mistral.devstral-2-123b": { + "input_cost_per_token": 4e-07, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 256000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 2e-06, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "mistral.magistral-small-2509": { "input_cost_per_token": 5e-07, "litellm_provider": "bedrock_converse", @@ -34639,6 +35019,20 @@ "supports_tool_choice": true, "source": "https://aws.amazon.com/bedrock/pricing/" }, + "zai.glm-4.7-flash": { + "input_cost_per_token": 7e-08, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4e-07, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "zai/glm-5": { "cache_creation_input_token_cost": 0, "cache_read_input_token_cost": 2e-07, @@ -38453,7 +38847,9 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_vision": true, - "supports_web_search": true + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-5-search-api-2025-10-14": { "cache_read_input_token_cost": 1.25e-07, @@ -38472,7 +38868,9 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_vision": true, - "supports_web_search": true + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": false }, "gpt-realtime-mini-2025-10-06": { "cache_creation_input_audio_token_cost": 3e-07, @@ -39115,5 +39513,59 @@ "metadata": { "notes": "DuckDuckGo Instant Answer API is free and does not require an API key." } + }, + "bedrock_mantle/openai.gpt-oss-120b": { + "input_cost_per_token": 1.5e-07, + "output_cost_per_token": 6e-07, + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "bedrock_mantle/openai.gpt-oss-20b": { + "input_cost_per_token": 7.5e-08, + "output_cost_per_token": 3e-07, + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "bedrock_mantle/openai.gpt-oss-safeguard-120b": { + "input_cost_per_token": 1.5e-07, + "output_cost_per_token": 6e-07, + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 131072, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "bedrock_mantle/openai.gpt-oss-safeguard-20b": { + "input_cost_per_token": 7.5e-08, + "output_cost_per_token": 3e-07, + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 131072, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true } } diff --git a/litellm/passthrough/main.py b/litellm/passthrough/main.py index df4737cec85..e76a222b2ed 100644 --- a/litellm/passthrough/main.py +++ b/litellm/passthrough/main.py @@ -289,10 +289,10 @@ def llm_passthrough_route( request = client.client.build_request( method=method, url=updated_url, - content=signed_json_body, - data=data if signed_json_body is None else None, + content=signed_json_body if signed_json_body is not None else content, + data=data if (signed_json_body is None and content is None) else None, files=files, - json=json if signed_json_body is None else None, + json=json if (signed_json_body is None and content is None) else None, params=params, headers=headers, cookies=cookies, @@ -410,8 +410,9 @@ async def _async_streaming( litellm_logging_obj: "LiteLLMLoggingObj", provider_config: "BasePassthroughConfig", ): + iter_response = await response try: - iter_response = await response + iter_response.raise_for_status() raw_bytes: List[bytes] = [] async for chunk in iter_response.aiter_bytes(): # type: ignore @@ -425,5 +426,9 @@ async def _async_streaming( provider_config=provider_config, ) ) - except Exception as e: - raise e + except Exception: + try: + await iter_response.aclose() + except Exception: + pass + raise diff --git a/litellm/proxy/_experimental/mcp_server/byok_oauth_endpoints.py b/litellm/proxy/_experimental/mcp_server/byok_oauth_endpoints.py new file mode 100644 index 00000000000..db18885721a --- /dev/null +++ b/litellm/proxy/_experimental/mcp_server/byok_oauth_endpoints.py @@ -0,0 +1,786 @@ +""" +BYOK (Bring Your Own Key) OAuth 2.1 Authorization Server endpoints for MCP servers. + +When an MCP client connects to a BYOK-enabled server and no stored credential exists, +LiteLLM runs a minimal OAuth 2.1 authorization code flow. The "authorization page" is +just a form that asks the user for their API key — not a full identity-provider OAuth. + +Endpoints implemented here: + GET /.well-known/oauth-authorization-server — OAuth authorization server metadata + GET /.well-known/oauth-protected-resource — OAuth protected resource metadata + GET /v1/mcp/oauth/authorize — Shows HTML form to collect the API key + POST /v1/mcp/oauth/authorize — Stores temp auth code and redirects + POST /v1/mcp/oauth/token — Exchanges code for a bearer JWT token +""" + +import base64 +import hashlib +import html as _html_module +import time +import uuid +from typing import Dict, Optional, cast +from urllib.parse import urlencode, urlparse + +import jwt +from fastapi import APIRouter, Form, HTTPException, Request +from fastapi.responses import HTMLResponse, JSONResponse, RedirectResponse + +from litellm._logging import verbose_proxy_logger +from litellm.proxy._experimental.mcp_server.db import store_user_credential +from litellm.proxy._experimental.mcp_server.discoverable_endpoints import ( + get_request_base_url, +) + +# --------------------------------------------------------------------------- +# In-memory store for pending authorization codes. +# Each entry: {code: {api_key, server_id, code_challenge, redirect_uri, user_id, expires_at}} +# --------------------------------------------------------------------------- +_byok_auth_codes: Dict[str, dict] = {} + +# Authorization codes expire after 5 minutes. +_AUTH_CODE_TTL_SECONDS = 300 +# Hard cap to prevent memory exhaustion from incomplete OAuth flows. +_AUTH_CODES_MAX_SIZE = 1000 + +router = APIRouter(tags=["mcp"]) + + +# --------------------------------------------------------------------------- +# PKCE helper +# --------------------------------------------------------------------------- + + +def _verify_pkce(code_verifier: str, code_challenge: str) -> bool: + """Return True iff SHA-256(code_verifier) == code_challenge (base64url, no padding).""" + digest = hashlib.sha256(code_verifier.encode()).digest() + computed = base64.urlsafe_b64encode(digest).rstrip(b"=").decode() + return computed == code_challenge + + +# --------------------------------------------------------------------------- +# Cleanup of expired auth codes (called lazily on each request) +# --------------------------------------------------------------------------- + + +def _purge_expired_codes() -> None: + now = time.time() + expired = [k for k, v in _byok_auth_codes.items() if v["expires_at"] < now] + for k in expired: + del _byok_auth_codes[k] + + +def _build_authorize_html( + server_name: str, + server_initial: str, + client_id: str, + redirect_uri: str, + code_challenge: str, + code_challenge_method: str, + state: str, + server_id: str, + access_items: list, + help_url: str, +) -> str: + """Build the 2-step BYOK OAuth authorization page HTML.""" + + # Escape all user-supplied / externally-derived values before interpolation + e = _html_module.escape + server_name = e(server_name) + server_initial = e(server_initial) + client_id = e(client_id) + redirect_uri = e(redirect_uri) + code_challenge = e(code_challenge) + code_challenge_method = e(code_challenge_method) + state = e(state) + server_id = e(server_id) + + # Build access checklist rows + access_rows = "".join( + f'
✓{e(item)}
' + for item in access_items + ) + access_section = "" + if access_rows: + access_section = f""" +
+
+ ▮ + Requested Access +
+ {access_rows} +
""" + + # Help link for step 2 + help_link_html = "" + if help_url: + help_link_html = f'Where do I find my API key? ↗' + + return f""" + + + + +Connect {server_name} — LiteLLM + + + +
+ + +
+
+
+
+
+ + +
+ LiteLLM + → + +
+ +

Connect {server_name} MCP

+

LiteLLM needs access to {server_name} to complete your request.

+ +
+ + + +
+

How it works

+

LiteLLM acts as a secure bridge. Your requests are routed through our MCP client directly to {server_name}’s API.

+
+
+ + {access_section} + + + +
+ + +
+
+ +
+
+
+
+ +
+ +
+ +
+

Provide API Key

+

Enter your {server_name} API key to authorize this connection.

+ +
+ + + + + + + + + + + + {help_link_html} + +
+
+ Save key for future use + +
+ +
+ +
+ + + +

Your key is stored securely and transmitted over HTTPS. It is never shared with third parties.

+
+ + +
+
+ +
+ + +""" + + +# --------------------------------------------------------------------------- +# OAuth metadata discovery endpoints +# --------------------------------------------------------------------------- + + +@router.get("/.well-known/oauth-authorization-server", include_in_schema=False) +async def oauth_authorization_server_metadata(request: Request) -> JSONResponse: + """RFC 8414 Authorization Server Metadata for the BYOK OAuth flow.""" + base_url = get_request_base_url(request) + return JSONResponse( + { + "issuer": base_url, + "authorization_endpoint": f"{base_url}/v1/mcp/oauth/authorize", + "token_endpoint": f"{base_url}/v1/mcp/oauth/token", + "response_types_supported": ["code"], + "grant_types_supported": ["authorization_code"], + "code_challenge_methods_supported": ["S256"], + } + ) + + +@router.get("/.well-known/oauth-protected-resource", include_in_schema=False) +async def oauth_protected_resource_metadata(request: Request) -> JSONResponse: + """RFC 9728 Protected Resource Metadata pointing back at this server.""" + base_url = get_request_base_url(request) + return JSONResponse( + { + "resource": base_url, + "authorization_servers": [base_url], + } + ) + + +# --------------------------------------------------------------------------- +# Authorization endpoint — GET (show form) and POST (process form) +# --------------------------------------------------------------------------- + + +@router.get("/v1/mcp/oauth/authorize", include_in_schema=False) +async def byok_authorize_get( + request: Request, + client_id: Optional[str] = None, + redirect_uri: Optional[str] = None, + response_type: Optional[str] = None, + code_challenge: Optional[str] = None, + code_challenge_method: Optional[str] = None, + state: Optional[str] = None, + server_id: Optional[str] = None, +) -> HTMLResponse: + """ + Show the BYOK API-key entry form. + + The MCP client navigates the user here; the user types their API key and + clicks "Connect & Authorize", which POSTs back to this same path. + """ + if response_type != "code": + raise HTTPException(status_code=400, detail="response_type must be 'code'") + if not redirect_uri: + raise HTTPException(status_code=400, detail="redirect_uri is required") + if not code_challenge: + raise HTTPException(status_code=400, detail="code_challenge is required") + + # Resolve server metadata (name, description items, help URL). + server_name = "MCP Server" + access_items: list = [] + help_url = "" + if server_id: + try: + from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( + global_mcp_server_manager, + ) + + registry = global_mcp_server_manager.get_registry() + if server_id in registry: + srv = registry[server_id] + server_name = srv.server_name or srv.name + access_items = list(srv.byok_description or []) + help_url = srv.byok_api_key_help_url or "" + except Exception: + pass + + server_initial = (server_name[0].upper()) if server_name else "S" + + html = _build_authorize_html( + server_name=server_name, + server_initial=server_initial, + client_id=client_id or "", + redirect_uri=redirect_uri, + code_challenge=code_challenge, + code_challenge_method=code_challenge_method or "S256", + state=state or "", + server_id=server_id or "", + access_items=access_items, + help_url=help_url, + ) + return HTMLResponse(content=html) + + +@router.post("/v1/mcp/oauth/authorize", include_in_schema=False) +async def byok_authorize_post( + request: Request, + client_id: str = Form(default=""), + redirect_uri: str = Form(...), + code_challenge: str = Form(...), + code_challenge_method: str = Form(default="S256"), + state: str = Form(default=""), + server_id: str = Form(default=""), + api_key: str = Form(...), +) -> RedirectResponse: + """ + Process the BYOK API-key form submission. + + Stores a short-lived authorization code and redirects the client back to + redirect_uri with ?code=...&state=... query parameters. + """ + _purge_expired_codes() + + # Validate redirect_uri scheme to prevent open redirect + parsed_uri = urlparse(redirect_uri) + if parsed_uri.scheme not in ("http", "https"): + raise HTTPException(status_code=400, detail="Invalid redirect_uri scheme") + + # Reject new codes if the store is at capacity (prevents memory exhaustion + # from a burst of abandoned OAuth flows). + if len(_byok_auth_codes) >= _AUTH_CODES_MAX_SIZE: + raise HTTPException(status_code=503, detail="Too many pending authorization flows") + + if code_challenge_method != "S256": + raise HTTPException( + status_code=400, detail="Only S256 code_challenge_method is supported" + ) + + auth_code = str(uuid.uuid4()) + _byok_auth_codes[auth_code] = { + "api_key": api_key, + "server_id": server_id, + "code_challenge": code_challenge, + "redirect_uri": redirect_uri, + "user_id": client_id, # external client passes LiteLLM user-id as client_id + "expires_at": time.time() + _AUTH_CODE_TTL_SECONDS, + } + + params = urlencode({"code": auth_code, "state": state}) + separator = "&" if "?" in redirect_uri else "?" + location = f"{redirect_uri}{separator}{params}" + return RedirectResponse(url=location, status_code=302) + + +# --------------------------------------------------------------------------- +# Token endpoint +# --------------------------------------------------------------------------- + + +@router.post("/v1/mcp/oauth/token", include_in_schema=False) +async def byok_token( + request: Request, + grant_type: str = Form(...), + code: str = Form(...), + redirect_uri: str = Form(default=""), + code_verifier: str = Form(...), + client_id: str = Form(default=""), +) -> JSONResponse: + """ + Exchange an authorization code for a short-lived BYOK session JWT. + + 1. Validates the authorization code and PKCE challenge. + 2. Stores the API key via store_user_credential(). + 3. Issues a signed JWT with type="byok_session". + """ + from litellm.proxy.proxy_server import master_key, prisma_client + + _purge_expired_codes() + + if grant_type != "authorization_code": + raise HTTPException(status_code=400, detail="unsupported_grant_type") + + record = _byok_auth_codes.get(code) + if record is None: + raise HTTPException(status_code=400, detail="invalid_grant") + + if time.time() > record["expires_at"]: + del _byok_auth_codes[code] + raise HTTPException(status_code=400, detail="invalid_grant") + + # PKCE verification + if not _verify_pkce(code_verifier, record["code_challenge"]): + raise HTTPException(status_code=400, detail="invalid_grant") + + # Consume the code (one-time use) + del _byok_auth_codes[code] + + server_id: str = record["server_id"] + api_key_value: str = record["api_key"] + # Prefer the user_id that was stored when the code was issued; fall back to + # whatever client_id the token request supplies (they should match). + user_id: str = record.get("user_id") or client_id + + if not user_id: + raise HTTPException( + status_code=400, + detail="Cannot determine user_id; pass LiteLLM user id as client_id", + ) + + # Persist the BYOK credential + if prisma_client is not None: + try: + await store_user_credential( + prisma_client=prisma_client, + user_id=user_id, + server_id=server_id, + credential=api_key_value, + ) + # Invalidate any cached negative result so the user isn't blocked + # for up to the TTL period after completing the OAuth flow. + from litellm.proxy._experimental.mcp_server.server import ( + _invalidate_byok_cred_cache, + ) + _invalidate_byok_cred_cache(user_id, server_id) + except Exception as exc: + verbose_proxy_logger.error( + "byok_token: failed to store user credential for user=%s server=%s: %s", + user_id, + server_id, + exc, + ) + raise HTTPException(status_code=500, detail="Failed to store credential") + else: + verbose_proxy_logger.warning( + "byok_token: prisma_client is None — credential not persisted" + ) + + if master_key is None: + raise HTTPException( + status_code=500, detail="Master key not configured; cannot issue token" + ) + + now = int(time.time()) + payload = { + "user_id": user_id, + "server_id": server_id, + # "type" distinguishes this from regular proxy auth tokens. + # The proxy's SSO JWT path uses asymmetric keys (RS256/ES256), so an + # HS256 token signed with master_key cannot be accepted there. + "type": "byok_session", + "iat": now, + "exp": now + 3600, + } + access_token = jwt.encode(payload, cast(str, master_key), algorithm="HS256") + + return JSONResponse( + { + "access_token": access_token, + "token_type": "bearer", + "expires_in": 3600, + } + ) diff --git a/litellm/proxy/_experimental/mcp_server/db.py b/litellm/proxy/_experimental/mcp_server/db.py index a9734233a61..4c6735bacd3 100644 --- a/litellm/proxy/_experimental/mcp_server/db.py +++ b/litellm/proxy/_experimental/mcp_server/db.py @@ -13,6 +13,7 @@ ) from litellm.proxy.common_utils.encrypt_decrypt_utils import ( _get_salt_key, + decrypt_value_helper, encrypt_value_helper, ) from litellm.proxy.utils import PrismaClient @@ -60,8 +61,18 @@ def _prepare_mcp_server_data( if data.env is not None: data_dict["env"] = safe_dumps(data.env) + # Handle tool name override serialization + if data.tool_name_to_display_name is not None: + data_dict["tool_name_to_display_name"] = safe_dumps(data.tool_name_to_display_name) + if data.tool_name_to_description is not None: + data_dict["tool_name_to_description"] = safe_dumps(data.tool_name_to_description) + # mcp_access_groups is already List[str], no serialization needed + # Force include is_byok even when False (exclude_none=True would not drop it, + # but be explicit to ensure a False value is always written to the DB). + data_dict["is_byok"] = getattr(data, "is_byok", False) + return data_dict @@ -369,3 +380,74 @@ async def rotate_mcp_server_credentials_master_key( "updated_by": touched_by, }, ) + + +async def store_user_credential( + prisma_client: PrismaClient, + user_id: str, + server_id: str, + credential: str, +) -> None: + """Store a user credential for a BYOK MCP server.""" + import base64 + + encoded = base64.urlsafe_b64encode(credential.encode()).decode() + await prisma_client.db.litellm_mcpusercredentials.upsert( + where={"user_id_server_id": {"user_id": user_id, "server_id": server_id}}, + data={ + "create": { + "user_id": user_id, + "server_id": server_id, + "credential_b64": encoded, + }, + "update": {"credential_b64": encoded}, + }, + ) + + +async def get_user_credential( + prisma_client: PrismaClient, + user_id: str, + server_id: str, +) -> Optional[str]: + """Return credential for a user+server pair, or None.""" + import base64 + + row = await prisma_client.db.litellm_mcpusercredentials.find_unique( + where={"user_id_server_id": {"user_id": user_id, "server_id": server_id}} + ) + if row is None: + return None + try: + return base64.urlsafe_b64decode(row.credential_b64).decode() + except Exception: + # Fall back to nacl decryption for credentials stored by older code + return decrypt_value_helper( + value=row.credential_b64, + key="byok_credential", + exception_type="debug", + return_original_value=False, + ) + + +async def has_user_credential( + prisma_client: PrismaClient, + user_id: str, + server_id: str, +) -> bool: + """Return True if the user has a stored credential for this server.""" + row = await prisma_client.db.litellm_mcpusercredentials.find_unique( + where={"user_id_server_id": {"user_id": user_id, "server_id": server_id}} + ) + return row is not None + + +async def delete_user_credential( + prisma_client: PrismaClient, + user_id: str, + server_id: str, +) -> None: + """Delete the user's stored credential for a BYOK MCP server.""" + await prisma_client.db.litellm_mcpusercredentials.delete( + where={"user_id_server_id": {"user_id": user_id, "server_id": server_id}} + ) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index b7c013e9f20..7c17da36bb7 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -644,6 +644,15 @@ async def build_mcp_server_from_table( ), created_at=getattr(mcp_server, "created_at", None), updated_at=getattr(mcp_server, "updated_at", None), + tool_name_to_display_name=_deserialize_json_dict( + getattr(mcp_server, "tool_name_to_display_name", None) + ), + tool_name_to_description=_deserialize_json_dict( + getattr(mcp_server, "tool_name_to_description", None) + ), + is_byok=bool(getattr(mcp_server, "is_byok", False)), + byok_description=getattr(mcp_server, "byok_description", None) or [], + byok_api_key_help_url=getattr(mcp_server, "byok_api_key_help_url", None), ) return new_server @@ -2651,6 +2660,9 @@ def _build_mcp_server_table(self, server: MCPServer) -> LiteLLM_MCPServerTable: registration_url=server.registration_url, allow_all_keys=server.allow_all_keys, available_on_public_internet=server.available_on_public_internet, + is_byok=server.is_byok, + byok_description=server.byok_description, + byok_api_key_help_url=server.byok_api_key_help_url, ) async def get_all_mcp_servers_unfiltered(self) -> List[LiteLLM_MCPServerTable]: diff --git a/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py b/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py index 21d39c97d7c..6ad391a5008 100644 --- a/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py +++ b/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py @@ -3,6 +3,7 @@ """ import asyncio +import contextvars import json import os from pathlib import PurePosixPath @@ -22,6 +23,13 @@ BASE_URL = "" HEADERS: Dict[str, str] = {} +# Per-request auth header override for BYOK servers. +# Set this ContextVar before calling a local tool handler to inject the user's +# stored credential into the HTTP request made by the tool function closure. +_request_auth_header: contextvars.ContextVar[Optional[str]] = contextvars.ContextVar( + "_request_auth_header", default=None +) + def _sanitize_path_parameter_value(param_value: Any, param_name: str) -> str: """Ensure path params cannot introduce directory traversal.""" @@ -116,6 +124,8 @@ def extract_parameters(operation: Dict[str, Any]) -> tuple: # OpenAPI 3.x and 2.x parameters if "parameters" in operation: for param in operation["parameters"]: + if "name" not in param: + continue param_name = param["name"] if param.get("in") == "path": path_params.append(param_name) @@ -139,6 +149,8 @@ def build_input_schema(operation: Dict[str, Any]) -> Dict[str, Any]: # Process parameters if "parameters" in operation: for param in operation["parameters"]: + if "name" not in param: + continue param_name = param["name"] param_schema = param.get("schema", {}) param_type = param_schema.get("type", "string") @@ -211,6 +223,15 @@ async def tool_function(**kwargs: Any) -> str: The function safely handles parameter names that aren't valid Python identifiers by using **kwargs instead of named parameters. """ + # Allow per-request auth override (e.g. BYOK credential set via ContextVar). + # The ContextVar holds the full Authorization header value, including the + # correct prefix (Bearer / ApiKey / Basic) formatted by the caller in + # server.py based on the server's configured auth_type. + effective_headers = dict(headers) + override_auth = _request_auth_header.get() + if override_auth: + effective_headers["Authorization"] = override_auth + # Build URL from base_url and path url = base_url + path @@ -263,20 +284,20 @@ async def tool_function(**kwargs: Any) -> str: client = get_async_httpx_client(llm_provider=httpxSpecialProvider.MCP) if original_method == "get": - response = await client.get(url, params=params, headers=headers) + response = await client.get(url, params=params, headers=effective_headers) elif original_method == "post": response = await client.post( - url, params=params, json=json_body, headers=headers + url, params=params, json=json_body, headers=effective_headers ) elif original_method == "put": response = await client.put( - url, params=params, json=json_body, headers=headers + url, params=params, json=json_body, headers=effective_headers ) elif original_method == "delete": - response = await client.delete(url, params=params, headers=headers) + response = await client.delete(url, params=params, headers=effective_headers) elif original_method == "patch": response = await client.patch( - url, params=params, json=json_body, headers=headers + url, params=params, json=json_body, headers=effective_headers ) else: return f"Unsupported HTTP method: {original_method}" diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index cdb26acb658..5c063839304 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -5,6 +5,7 @@ import asyncio import contextlib +import time import traceback import uuid from datetime import datetime @@ -40,6 +41,8 @@ LITELLM_MCP_SERVER_DESCRIPTION, LITELLM_MCP_SERVER_NAME, LITELLM_MCP_SERVER_VERSION, + add_server_prefix_to_name, + get_server_prefix, ) from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.auth.ip_address_utils import IPAddressUtils @@ -52,6 +55,32 @@ from litellm.types.utils import CallTypes, StandardLoggingMCPToolCall from litellm.utils import Rules, client, function_setup +# Short-lived in-memory cache for BYOK credentials. +# Keyed by (user_id, server_id); value is (credential_or_None, monotonic_timestamp). +# Storing the credential value (not just a bool) means _get_byok_credential and +# _check_byok_credential share a single DB round-trip per TTL window. +_byok_cred_cache: Dict[Tuple[str, str], Tuple[Optional[str], float]] = {} +_BYOK_CRED_CACHE_TTL = 60 # seconds +_BYOK_CRED_CACHE_MAX_SIZE = 4096 # cap to prevent unbounded growth + + +def _invalidate_byok_cred_cache(user_id: str, server_id: str) -> None: + """Remove a (user_id, server_id) entry from the BYOK credential cache. + + Call this after storing or deleting a credential so subsequent calls + see the fresh value rather than a stale cached result. + """ + _byok_cred_cache.pop((user_id, server_id), None) + + +def _write_byok_cred_cache( + user_id: str, server_id: str, credential: Optional[str] +) -> None: + """Write a credential value to the cache, evicting all entries if at capacity.""" + if len(_byok_cred_cache) >= _BYOK_CRED_CACHE_MAX_SIZE: + _byok_cred_cache.clear() + _byok_cred_cache[(user_id, server_id)] = (credential, time.monotonic()) + # Check if MCP is available # "mcp" requires python 3.10 or higher, but several litellm users use python 3.8 # We're making this conditional import to avoid breaking users who use python 3.8. @@ -116,6 +145,9 @@ from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( global_mcp_server_manager, ) + from litellm.proxy._experimental.mcp_server.openapi_to_mcp_generator import ( + _request_auth_header, + ) from litellm.proxy._experimental.mcp_server.sse_transport import SseServerTransport from litellm.proxy._experimental.mcp_server.tool_registry import ( global_mcp_tool_registry, @@ -737,6 +769,29 @@ def filter_tools_by_allowed_tools( return tools_to_return + def apply_tool_overrides( + tools: List[MCPTool], + mcp_server: MCPServer, + ) -> List[MCPTool]: + """Apply admin-configured display name/description overrides to tools. + + Overrides are keyed by the unprefixed tool name, same convention as + allowed_tools configuration. + """ + display_name_map = mcp_server.tool_name_to_display_name or {} + description_map = mcp_server.tool_name_to_description or {} + if not display_name_map and not description_map: + return tools + + for tool in tools: + unprefixed, _ = split_server_prefix_from_name(tool.name) + lookup_key = unprefixed or tool.name + if lookup_key in display_name_map: + tool.name = display_name_map[lookup_key] + if lookup_key in description_map: + tool.description = description_map[lookup_key] + return tools + def _get_client_ip_from_context() -> Optional[str]: """ Extract client_ip from auth context. @@ -991,6 +1046,10 @@ async def _fetch_and_filter_server_tools( user_api_key_auth=user_api_key_auth, ) + # Apply display-name/description overrides last so that + # permission filtering always works against original names. + filtered_tools = apply_tool_overrides(filtered_tools, server) + verbose_logger.debug( f"Successfully fetched {len(tools)} tools from server {server.name}, {len(filtered_tools)} after filtering" ) @@ -1449,6 +1508,142 @@ async def _list_mcp_resource_templates( return managed_resource_templates + def _resolve_display_name_to_original( + name: str, + allowed_mcp_servers: List[MCPServer], + ) -> str: + """Translate a display-name override back to the original prefixed tool name. + + When a client received a customised display name from tools/list (e.g. + "Get Pet") it will call tools/call with that same string. We need to + reverse-map it to the original prefixed name (e.g. + "petstore_mcp-getPetById") before any routing or permission logic runs. + """ + for server in allowed_mcp_servers: + display_map = server.tool_name_to_display_name or {} + for unprefixed_name, display_name in display_map.items(): + if display_name == name: + return add_server_prefix_to_name( + unprefixed_name, get_server_prefix(server) + ) + return name + + async def _get_byok_credential( + mcp_server: MCPServer, + user_api_key_auth: Optional[UserAPIKeyAuth], + ) -> Optional[str]: + """Retrieve the stored BYOK credential for a user+server pair. + + Uses the shared _byok_cred_cache to avoid a DB round-trip on every + tool call within the TTL window. + """ + if not mcp_server.is_byok: + return None + user_id = (user_api_key_auth.user_id if user_api_key_auth else None) or "" + if not user_id: + return None + + cache_key = (user_id, mcp_server.server_id) + cached = _byok_cred_cache.get(cache_key) + if cached is not None: + credential, ts = cached + if time.monotonic() - ts < _BYOK_CRED_CACHE_TTL: + return credential + + from litellm.proxy._experimental.mcp_server.db import get_user_credential + from litellm.proxy.proxy_server import prisma_client + + if prisma_client is None: + return None + credential = await get_user_credential( + prisma_client=prisma_client, + user_id=user_id, + server_id=mcp_server.server_id, + ) + _write_byok_cred_cache(user_id, mcp_server.server_id, credential) + return credential + + async def _check_byok_credential( + mcp_server: MCPServer, + user_api_key_auth: Optional[UserAPIKeyAuth], + ) -> None: + """ + If the MCP server is BYOK-enabled, verify that the requesting user has a + stored credential. When no credential is found, raise an HTTP 401 with a + WWW-Authenticate header that points the MCP client to our OAuth metadata + endpoint so it can drive the authorization flow. + """ + if not mcp_server.is_byok: + return + + user_id = (user_api_key_auth.user_id if user_api_key_auth else None) or "" + if not user_id: + raise HTTPException( + status_code=401, + detail={ + "error": "byok_auth_required", + "server_id": mcp_server.server_id, + "server_name": mcp_server.server_name or mcp_server.name, + "message": "User identity is required for BYOK servers", + }, + headers={ + "WWW-Authenticate": 'Bearer resource_metadata="/.well-known/oauth-protected-resource"' + }, + ) + + # Check shared credential cache before hitting the DB. + cache_key = (user_id, mcp_server.server_id) + cached = _byok_cred_cache.get(cache_key) + if cached is not None: + cached_cred, ts = cached + if time.monotonic() - ts < _BYOK_CRED_CACHE_TTL: + if cached_cred is None: + raise HTTPException( + status_code=401, + detail={ + "error": "byok_auth_required", + "server_id": mcp_server.server_id, + "server_name": mcp_server.server_name or mcp_server.name, + "message": ( + "No stored credential found for this BYOK server. " + "Complete the OAuth authorization flow to provide your API key." + ), + }, + headers={ + "WWW-Authenticate": 'Bearer resource_metadata="/.well-known/oauth-protected-resource"' + }, + ) + return + + from litellm.proxy._experimental.mcp_server.db import get_user_credential + from litellm.proxy.proxy_server import prisma_client + + if prisma_client is None: + return + + credential = await get_user_credential( + prisma_client=prisma_client, + user_id=user_id, + server_id=mcp_server.server_id, + ) + _write_byok_cred_cache(user_id, mcp_server.server_id, credential) + if credential is None: + raise HTTPException( + status_code=401, + detail={ + "error": "byok_auth_required", + "server_id": mcp_server.server_id, + "server_name": mcp_server.server_name or mcp_server.name, + "message": ( + "No stored credential found for this BYOK server. " + "Complete the OAuth authorization flow to provide your API key." + ), + }, + headers={ + "WWW-Authenticate": 'Bearer resource_metadata="/.well-known/oauth-protected-resource"' + }, + ) + async def execute_mcp_tool( name: str, arguments: Dict[str, Any], @@ -1485,6 +1680,10 @@ async def execute_mcp_tool( # Track resolved MCP server for both permission checks and dispatch mcp_server: Optional[MCPServer] = None + # If the client called with a display-name override (e.g. "Get Pet"), + # translate it back to the original prefixed name before any routing. + name = _resolve_display_name_to_original(name, allowed_mcp_servers) + # Remove prefix from tool name for logging and processing original_tool_name, server_name = split_server_prefix_from_name(name) @@ -1520,57 +1719,99 @@ async def execute_mcp_tool( "mcp_tool_call_metadata" ] = standard_logging_mcp_tool_call litellm_logging_obj.model = f"MCP: {name}" + # Resolve the MCP server early so BYOK checks and credential injection + # apply to ALL dispatch paths (local tool registry AND managed MCP server). + if mcp_server is None: + mcp_server = global_mcp_server_manager._get_mcp_server_from_tool_name(name) + + if mcp_server: + standard_logging_mcp_tool_call["mcp_server_cost_info"] = ( + mcp_server.mcp_info or {} + ).get("mcp_server_cost_info") + if litellm_logging_obj: + litellm_logging_obj.model_call_details[ + "mcp_tool_call_metadata" + ] = standard_logging_mcp_tool_call + + # BYOK: retrieve the stored per-user credential. A single DB call + # both checks existence and fetches the value, avoiding a double query. + if mcp_server.is_byok and not mcp_auth_header: + byok_cred = await _get_byok_credential(mcp_server, user_api_key_auth) + if byok_cred is None: + raise HTTPException( + status_code=401, + detail={ + "error": "byok_auth_required", + "server_id": mcp_server.server_id, + "server_name": mcp_server.server_name or mcp_server.name, + "message": ( + "No stored credential found for this BYOK server. " + "Complete the OAuth authorization flow to provide your API key." + ), + }, + headers={ + "WWW-Authenticate": 'Bearer resource_metadata="/.well-known/oauth-protected-resource"' + }, + ) + mcp_auth_header = byok_cred + elif mcp_server.is_byok: + # External auth header supplied; still enforce user-identity check. + await _check_byok_credential(mcp_server, user_api_key_auth) + # Check if tool exists in local registry first (for OpenAPI-based tools) # These tools are registered with their prefixed names ######################################################### local_tool = global_mcp_tool_registry.get_tool(name) if local_tool: verbose_logger.debug(f"Executing local registry tool: {name}") - local_content = await _handle_local_mcp_tool(name, arguments) + # For BYOK servers the credential must be injected via a ContextVar + # because the tool function has headers baked into its closure. + # Pre-format the full Authorization header value using the server's + # configured auth_type so the generator doesn't need to know the prefix. + auth_header_value: Optional[str] = None + if mcp_auth_header: + server_auth_type = getattr(mcp_server, "auth_type", None) if mcp_server else None + if server_auth_type == MCPAuth.api_key: + auth_header_value = f"ApiKey {mcp_auth_header}" + elif server_auth_type == MCPAuth.basic: + auth_header_value = f"Basic {mcp_auth_header}" + else: + auth_header_value = f"Bearer {mcp_auth_header}" + _auth_token = _request_auth_header.set(auth_header_value) + try: + local_content = await _handle_local_mcp_tool(name, arguments) + finally: + _request_auth_header.reset(_auth_token) response = CallToolResult(content=cast(Any, local_content), isError=False) # Try managed MCP server tool (pass the full prefixed name) # Primary and recommended way to use external MCP servers ######################################################### - else: - # If we haven't already resolved the server, do it now for dispatch - if mcp_server is None: - mcp_server = global_mcp_server_manager._get_mcp_server_from_tool_name( - name - ) - if mcp_server: - standard_logging_mcp_tool_call["mcp_server_cost_info"] = ( - mcp_server.mcp_info or {} - ).get("mcp_server_cost_info") - # Update model_call_details with the cost info - if litellm_logging_obj: - litellm_logging_obj.model_call_details[ - "mcp_tool_call_metadata" - ] = standard_logging_mcp_tool_call - response = await _handle_managed_mcp_tool( - server_name=server_name, - name=original_tool_name, # Pass the full name (potentially prefixed) - arguments=arguments, - user_api_key_auth=user_api_key_auth, - mcp_auth_header=mcp_auth_header, - mcp_server_auth_headers=mcp_server_auth_headers, - oauth2_headers=oauth2_headers, - raw_headers=raw_headers, - litellm_logging_obj=litellm_logging_obj, - host_progress_callback=host_progress_callback, - ) + elif mcp_server: + response = await _handle_managed_mcp_tool( + server_name=server_name, + name=original_tool_name, # Pass the full name (potentially prefixed) + arguments=arguments, + user_api_key_auth=user_api_key_auth, + mcp_auth_header=mcp_auth_header, + mcp_server_auth_headers=mcp_server_auth_headers, + oauth2_headers=oauth2_headers, + raw_headers=raw_headers, + litellm_logging_obj=litellm_logging_obj, + host_progress_callback=host_progress_callback, + ) - # Fall back to local tool registry with original name (legacy support) - ######################################################### - # Deprecated: Local MCP Server Tool - ######################################################### - else: - local_content = await _handle_local_mcp_tool( - original_tool_name, arguments - ) - response = CallToolResult( - content=cast(Any, local_content), isError=False - ) + # Fall back to local tool registry with original name (legacy support) + ######################################################### + # Deprecated: Local MCP Server Tool + ######################################################### + else: + local_content = await _handle_local_mcp_tool( + original_tool_name, arguments + ) + response = CallToolResult( + content=cast(Any, local_content), isError=False + ) return response diff --git a/litellm/proxy/_experimental/out/404/index.html b/litellm/proxy/_experimental/out/404/index.html index 0f45eb1aa33..37a8fe100a8 100644 --- a/litellm/proxy/_experimental/out/404/index.html +++ b/litellm/proxy/_experimental/out/404/index.html @@ -1 +1 @@ -404: This page could not be found.LiteLLM Dashboard

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eR;eE(this,c,"m",T).call(this)}[(d=new WeakMap,u=new WeakMap,m=new WeakMap,p=new WeakMap,h=new WeakMap,f=new WeakMap,g=new WeakMap,y=new WeakMap,x=new WeakMap,b=new WeakMap,v=new WeakMap,w=new WeakMap,_=new WeakMap,j=new WeakMap,k=new WeakMap,c=new WeakSet,S=function(){if(0===this.receivedMessages.length)throw new eP("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},N=function(){if(0===this.receivedMessages.length)throw new eP("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new eP("stream ended without producing a content block with type=text");return e.join(" ")},E=function(){this.ended||ek(this,d,void 0,"f")},C=function(e){if(this.ended)return;let t=eE(this,c,"m",A).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":tL(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:tU(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":ek(this,d,t,"f")}},T=function(){if(this.ended)throw new eP("stream has ended, this shouldn't happen");let e=eE(this,d,"f");if(!e)throw new eP("request ended without sending any chunks");return ek(this,d,void 0,"f"),e},A=function(e){let t=eE(this,d,"f");if("message_start"===e.type){if(t)throw new eP(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eP(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.container=e.delta.container,t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(s.text+=e.delta.text);break;case"citations_delta":s?.type==="text"&&(s.citations??(s.citations=[]),s.citations.push(e.delta.citation));break;case"input_json_delta":if(s&&tL(s)){let t=s[tM]||"";if(Object.defineProperty(s,tM,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{s.input=tI(t)}catch(s){let e=new eP(`Unable to parse tool parameter JSON from model. Please retry your request or adjust your prompt. Error: ${s}. JSON: ${t}`);eE(this,k,"f").call(this,e)}}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tU(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new ts(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tU(e){}let tD={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},tB={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tq extends tj{constructor(){super(...arguments),this.batches=new tO(this._client)}create(e,t){let{betas:s,...r}=e;r.model in tB&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${tB[r.model]} -Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let a=this._client._options.timeout;if(!r.stream&&null==a){let e=tD[r.model]??void 0;a=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:a??6e5,...t,headers:tk([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return t$.createMessage(this,e,t)}countTokens(e,t){let{betas:s,...r}=e;return this._client.post("/v1/messages/count_tokens?beta=true",{body:r,...t,headers:tk([{"anthropic-beta":[...s??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tq.Batches=tO;class tz extends tj{constructor(){super(...arguments),this.models=new tA(this._client),this.messages=new tq(this._client),this.files=new tT(this._client)}}tz.Models=tA,tz.Messages=tq,tz.Files=tT;class tF extends tj{create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:tk([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let tW="__json_buf";function tH(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tJ{constructor(){P.add(this),this.messages=[],this.receivedMessages=[],O.set(this,void 0),this.controller=new AbortController,R.set(this,void 0),I.set(this,()=>{}),M.set(this,()=>{}),L.set(this,void 0),$.set(this,()=>{}),U.set(this,()=>{}),D.set(this,{}),B.set(this,!1),q.set(this,!1),z.set(this,!1),F.set(this,!1),W.set(this,void 0),H.set(this,void 0),V.set(this,e=>{if(ek(this,q,!0,"f"),eT(e)&&(e=new eR),e instanceof eR)return ek(this,z,!0,"f"),this._emit("abort",e);if(e instanceof eP)return this._emit("error",e);if(e instanceof Error){let t=new eP(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new eP(String(e)))}),ek(this,R,new Promise((e,t)=>{ek(this,I,e,"f"),ek(this,M,t,"f")}),"f"),ek(this,L,new Promise((e,t)=>{ek(this,$,e,"f"),ek(this,U,t,"f")}),"f"),eE(this,R,"f").catch(()=>{}),eE(this,L,"f").catch(()=>{})}get response(){return eE(this,W,"f")}get request_id(){return eE(this,H,"f")}async withResponse(){let e=await eE(this,R,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tJ;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new tJ;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...s,headers:{...s?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},eE(this,V,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,s){let r=s?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),eE(this,P,"m",K).call(this);let{response:a,data:n}=await e.create({...t,stream:!0},{...s,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(a),n))eE(this,P,"m",X).call(this,e);if(n.controller.signal?.aborted)throw new eR;eE(this,P,"m",Y).call(this)}_connected(e){this.ended||(ek(this,W,e,"f"),ek(this,H,e?.headers.get("request-id"),"f"),eE(this,I,"f").call(this,e),this._emit("connect"))}get ended(){return eE(this,B,"f")}get errored(){return eE(this,q,"f")}get aborted(){return eE(this,z,"f")}abort(){this.controller.abort()}on(e,t){return(eE(this,D,"f")[e]||(eE(this,D,"f")[e]=[])).push({listener:t}),this}off(e,t){let s=eE(this,D,"f")[e];if(!s)return this;let r=s.findIndex(e=>e.listener===t);return r>=0&&s.splice(r,1),this}once(e,t){return(eE(this,D,"f")[e]||(eE(this,D,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{ek(this,F,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){ek(this,F,!0,"f"),await eE(this,L,"f")}get currentMessage(){return eE(this,O,"f")}async finalMessage(){return await this.done(),eE(this,P,"m",J).call(this)}async finalText(){return await this.done(),eE(this,P,"m",G).call(this)}_emit(e,...t){if(eE(this,B,"f"))return;"end"===e&&(ek(this,B,!0,"f"),eE(this,$,"f").call(this));let s=eE(this,D,"f")[e];if(s&&(eE(this,D,"f")[e]=s.filter(e=>!e.once),s.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];eE(this,F,"f")||s?.length||Promise.reject(e),eE(this,M,"f").call(this,e),eE(this,U,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];eE(this,F,"f")||s?.length||Promise.reject(e),eE(this,M,"f").call(this,e),eE(this,U,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",eE(this,P,"m",J).call(this))}async _fromReadableStream(e,t){let s=t?.signal;s&&(s.aborted&&this.controller.abort(),s.addEventListener("abort",()=>this.controller.abort())),eE(this,P,"m",K).call(this),this._connected(null);let r=ts.fromReadableStream(e,this.controller);for await(let e of r)eE(this,P,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new eR;eE(this,P,"m",Y).call(this)}[(O=new WeakMap,R=new WeakMap,I=new WeakMap,M=new WeakMap,L=new WeakMap,$=new WeakMap,U=new WeakMap,D=new WeakMap,B=new WeakMap,q=new WeakMap,z=new WeakMap,F=new WeakMap,W=new WeakMap,H=new WeakMap,V=new WeakMap,P=new WeakSet,J=function(){if(0===this.receivedMessages.length)throw new eP("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},G=function(){if(0===this.receivedMessages.length)throw new eP("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new eP("stream ended without producing a content block with type=text");return e.join(" ")},K=function(){this.ended||ek(this,O,void 0,"f")},X=function(e){if(this.ended)return;let t=eE(this,P,"m",Q).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":tH(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:tG(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":ek(this,O,t,"f")}},Y=function(){if(this.ended)throw new eP("stream has ended, this shouldn't happen");let e=eE(this,O,"f");if(!e)throw new eP("request ended without sending any chunks");return ek(this,O,void 0,"f"),e},Q=function(e){let t=eE(this,O,"f");if("message_start"===e.type){if(t)throw new eP(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eP(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(s.text+=e.delta.text);break;case"citations_delta":s?.type==="text"&&(s.citations??(s.citations=[]),s.citations.push(e.delta.citation));break;case"input_json_delta":if(s&&tH(s)){let t=s[tW]||"";Object.defineProperty(s,tW,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t&&(s.input=tI(t))}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tG(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new ts(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tG(e){}class tV extends tj{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(tC`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",tu,{query:e,...t})}delete(e,t){return this._client.delete(tC`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(tC`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let s=await this.retrieve(e);if(!s.results_url)throw new eP(`No batch \`results_url\`; Has it finished processing? ${s.processing_status} - ${s.id}`);return this._client.get(s.results_url,{...t,headers:tk([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tP.fromResponse(t.response,t.controller))}}class tK extends tj{constructor(){super(...arguments),this.batches=new tV(this._client)}create(e,t){e.model in tX&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${tX[e.model]} -Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let s=this._client._options.timeout;if(!e.stream&&null==s){let t=tD[e.model]??void 0;s=this._client.calculateNonstreamingTimeout(e.max_tokens,t)}return this._client.post("/v1/messages",{body:e,timeout:s??6e5,...t,stream:e.stream??!1})}stream(e,t){return tJ.createMessage(this,e,t)}countTokens(e,t){return this._client.post("/v1/messages/count_tokens",{body:e,...t})}}let tX={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};tK.Batches=tV;class tY extends tj{retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(tC`/v1/models/${e}`,{...s,headers:tk([{...r?.toString()!=null?{"anthropic-beta":r?.toString()}:void 0},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/models",tu,{query:r,...t,headers:tk([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers])})}}let tQ=e=>void 0!==globalThis.process?globalThis.process.env?.[e]?.trim()??void 0:void 0!==globalThis.Deno?globalThis.Deno.env?.get?.(e)?.trim():void 0;class tZ{constructor({baseURL:e=tQ("ANTHROPIC_BASE_URL"),apiKey:t=tQ("ANTHROPIC_API_KEY")??null,authToken:s=tQ("ANTHROPIC_AUTH_TOKEN")??null,...r}={}){Z.set(this,void 0);const a={apiKey:t,authToken:s,...r,baseURL:e||"https://api.anthropic.com"};if(!a.dangerouslyAllowBrowser&&"u">typeof window&&void 0!==window.document&&"u">typeof navigator)throw new eP("It looks like you're running in a browser-like environment.\n\nThis is disabled by default, as it risks exposing your secret API credentials to attackers.\nIf you understand the risks and have appropriate mitigations in place,\nyou can set the `dangerouslyAllowBrowser` option to `true`, e.g.,\n\nnew Anthropic({ apiKey, dangerouslyAllowBrowser: true });\n");this.baseURL=a.baseURL,this.timeout=a.timeout??t0.DEFAULT_TIMEOUT,this.logger=a.logger??console;const n="warn";this.logLevel=n,this.logLevel=eV(a.logLevel,"ClientOptions.logLevel",this)??eV(tQ("ANTHROPIC_LOG"),"process.env['ANTHROPIC_LOG']",this)??n,this.fetchOptions=a.fetchOptions,this.maxRetries=a.maxRetries??2,this.fetch=a.fetch??function(){if("u">typeof fetch)return fetch;throw Error("`fetch` is not defined as a global; Either pass `fetch` to the client, `new Anthropic({ fetch })` or polyfill the global, `globalThis.fetch = fetch`")}(),ek(this,Z,e7,"f"),this._options=a,this.apiKey=t,this.authToken=s}withOptions(e){return new this.constructor({...this._options,baseURL:this.baseURL,maxRetries:this.maxRetries,timeout:this.timeout,logger:this.logger,logLevel:this.logLevel,fetchOptions:this.fetchOptions,apiKey:this.apiKey,authToken:this.authToken,...e})}defaultQuery(){return this._options.defaultQuery}validateHeaders({values:e,nulls:t}){if(!(this.apiKey&&e.get("x-api-key")||t.has("x-api-key")||this.authToken&&e.get("authorization"))&&!t.has("authorization"))throw Error('Could not resolve authentication method. 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t,i,o=((t={}).AUDIO_SPEECH="audio_speech",t.AUDIO_TRANSCRIPTION="audio_transcription",t.IMAGE_GENERATION="image_generation",t.VIDEO_GENERATION="video_generation",t.CHAT="chat",t.RESPONSES="responses",t.IMAGE_EDITS="image_edits",t.ANTHROPIC_MESSAGES="anthropic_messages",t.EMBEDDING="embedding",t),r=((i={}).IMAGE="image",i.VIDEO="video",i.CHAT="chat",i.RESPONSES="responses",i.IMAGE_EDITS="image_edits",i.ANTHROPIC_MESSAGES="anthropic_messages",i.EMBEDDINGS="embeddings",i.SPEECH="speech",i.TRANSCRIPTION="transcription",i.A2A_AGENTS="a2a_agents",i.MCP="mcp",i.REALTIME="realtime",i);let a={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"};e.s(["EndpointType",()=>r,"getEndpointType",0,e=>{if(console.log("getEndpointType:",e),Object.values(o).includes(e)){let t=a[e];return console.log("endpointType:",t),t}return"chat"}],785913),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:i,accessToken:o,apiKey:a,inputMessage:n,chatHistory:s,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedPolicies:u,selectedMCPServers:p,mcpServers:m,mcpServerToolRestrictions:g,selectedVoice:f,endpointType:h,selectedModel:_,selectedSdk:v,proxySettings:b}=e,x="session"===i?o:a,y=window.location.origin,w=b?.LITELLM_UI_API_DOC_BASE_URL;w&&w.trim()?y=w:b?.PROXY_BASE_URL&&(y=b.PROXY_BASE_URL);let S=n||"Your prompt here",j=S.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),k=s.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),C={};l.length>0&&(C.tags=l),c.length>0&&(C.vector_stores=c),d.length>0&&(C.guardrails=d),u.length>0&&(C.policies=u);let O=_||"your-model-name",N="azure"===v?`import openai + +client = openai.AzureOpenAI( + api_key="${x||"YOUR_LITELLM_API_KEY"}", + azure_endpoint="${y}", + api_version="2024-02-01" +)`:`import openai + +client = openai.OpenAI( + api_key="${x||"YOUR_LITELLM_API_KEY"}", + base_url="${y}" +)`;switch(h){case r.CHAT:{let e=Object.keys(C).length>0,i="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let o=k.length>0?k:[{role:"user",content:S}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.chat.completions.create( + model="${O}", + messages=${JSON.stringify(o,null,4)}${i} +) + +print(response) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.chat.completions.create( +# model="${O}", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "${j}" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} +# } +# } +# ] +# } +# ]${i} +# ) +# print(response_with_file) +`;break}case r.RESPONSES:{let e=Object.keys(C).length>0,i="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let o=k.length>0?k:[{role:"user",content:S}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.responses.create( + model="${O}", + input=${JSON.stringify(o,null,4)}${i} +) + +print(response.output_text) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.responses.create( +# model="${O}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${j}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${i} +# ) +# print(response_with_file.output_text) +`;break}case r.IMAGE:t="azure"===v?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${O}", + prompt="${n}", + n=1 +) + +json_response = json.loads(result.model_dump_json()) + +# Set the directory for the stored image +image_dir = os.path.join(os.curdir, 'images') + +# If the directory doesn't exist, create it +if not os.path.isdir(image_dir): + os.mkdir(image_dir) + +# Initialize the image path +image_filename = f"generated_image_{int(time.time())}.png" +image_path = os.path.join(image_dir, image_filename) + +try: + # Retrieve the generated image + if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): + image_url = json_response["data"][0]["url"] + generated_image = requests.get(image_url).content + with open(image_path, "wb") as image_file: + image_file.write(generated_image) + + print(f"Image saved to {image_path}") + # Display the image + image = Image.open(image_path) + image.show() + else: + print("Could not find image URL in response.") + print("Full response:", json_response) +except Exception as e: + print(f"An error occurred: {e}") + print("Full response:", json_response) +`:` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${O}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case r.IMAGE_EDITS:t="azure"===v?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${O}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${O}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case r.EMBEDDINGS:t=` +response = client.embeddings.create( + input="${n||"Your string here"}", + model="${O}", + encoding_format="base64" # or "float" +) + +print(response.data[0].embedding) +`;break;case r.TRANSCRIPTION:t=` +# Open the audio file +audio_file = open("path/to/your/audio/file.mp3", "rb") + +# Make the transcription request +response = client.audio.transcriptions.create( + model="${O}", + file=audio_file${n?`, + prompt="${n.replace(/"/g,'\\"')}"`:""} +) + +print(response.text) +`;break;case r.SPEECH:t=` +# Make the text-to-speech request +response = client.audio.speech.create( + model="${O}", + input="${n||"Your text to convert to speech here"}", + voice="${f}" # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer +) + +# Save the audio to a file +output_filename = 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LLM Cost Estimate Report

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