diff --git a/agents/nemo-agent-local-spec/AGENT-SPEC.md b/agents/nemo-agent-local-spec/AGENT-SPEC.md new file mode 100644 index 0000000000..347fe2eb32 --- /dev/null +++ b/agents/nemo-agent-local-spec/AGENT-SPEC.md @@ -0,0 +1,113 @@ +--- +name: nemo-agent-local +created_timestamp: 2026-07-28T20:09:49Z +author: Danielle Ali and Codex +--- + +# Agent Spec: nemo-agent-local + +> This file is the durable contract for the local NeMo Platform assistant. +> Keep it aligned with the implementation under `agents/nemo-agent-local/`. + +## Role + +Help NeMo Platform developers inspect and operate their current workspace through Studio using the NeMo Platform SDK. + +## Purpose + +This agent provides a conversational backend for NeMo Studio so developers can confirm that a packaged NVIDIA NeMo Agent Toolkit (NAT) agent can be built, deployed, and used to interact with NeMo Platform. It should make routine discovery and operational tasks faster without requiring users to translate their intent into CLI commands or raw API requests. + +The mission is grounded in the current deployment proof of concept and its implementation: answer simple read-only questions immediately, carry out explicit multi-step platform tasks through supported SDK operations, verify consequential results, and request missing context rather than guessing or entering an unbounded reasoning loop. + +## Scope + +- Audience: NeMo Platform developers and internal operators using local Studio or a development environment +- Categories: platform resource discovery; workspace-scoped resource management; agent and job status checks; evaluation and data operations; deployment troubleshooting +- In scope: list and inspect supported platform resources; create, update, or delete resources when explicitly requested; check jobs and deployments; perform multi-step SDK workflows and report verified results; ask for missing workspace, resource, or operation details +- Out of scope: invoking the NeMo CLI or arbitrary subprocesses; bypassing NeMo Platform APIs; silently choosing an ambiguous workspace or destructive target; claiming success without a successful SDK response or verification + +## Tools + +| Tool or source | Purpose | Credentials/scopes | Side effects | Freshness / expected failures | +|---|---|---|---|---| +| NeMo Platform Python SDK (`nemo_api`) | Access supported platform resources and actions through dot-separated SDK resource paths | Uses the deployment's platform base URL and active workspace; upstream secrets remain managed by NeMo Platform | Read and write operations depend on the requested SDK action | Workspace-scoped calls fail when no workspace is supplied; unavailable plugin resources or invalid SDK paths must be reported without repeated retries | +| Platform status helper (`check_status`) | Check evaluation, customization, audit, and Data Designer jobs | Same platform access as the SDK client | Read-only | A service may expose different status subresources; report when no supported status method exists | +| Deterministic list fast paths | Return names for common read-only list requests without an LLM round trip | Same platform access as the SDK client | Read-only | Must use the active workspace and return a direct clarification or error if required context is absent; must not fall through to the full agent loop after a recognized request fails | +| Packaged agent skills | Supply task-specific playbooks when spec-compliant skills are included in the image | No separate credentials | Depends on the selected playbook and SDK action | The agent must log which skills are loaded; an empty or malformed skills directory means no playbooks are available | + +## Model + +- Mode: cloud +- Family: NVIDIA Nemotron 3 Super 120B A12B + +## Framework + +- Resolution: langgraph-nat +- Notes: LangGraph Deep Agent wrapped as a custom NAT workflow + +## Harness + +- Description: A LangGraph Deep Agent with SDK tools, optional packaged skills, deterministic read-only fast paths, and a custom NAT wrapper compatible with Studio and evaluation request shapes +- Agent loop: Deep Agents orchestrates model and tool turns; recognized list requests bypass the model; complex runs must have explicit iteration, retry, and wall-clock limits +- Tool dispatch: The model calls typed LangChain tools that resolve NeMo Platform SDK resources and return serialized results or concise errors +- Context management: Studio conversation messages are normalized into LangChain messages; the model receives a concise system prompt and packaged skills when available +- State management: LangGraph state holds the conversation and scratch data; packaged skill files and temporary files use routed filesystem backends +- Guardrails: API-only operation; no CLI or arbitrary subprocess route; ambiguous workspace or destructive target requires clarification +- Observability: Structured application logs for fast-path selection, tool failures, model requests, retries, health probes, and final workflow errors; agent-specific telemetry exporters are disabled +- Verification: Consequential multi-step requests should read back final state when the SDK supports it; unit tests and the NAT evaluation configuration validate core behavior +- Runtime: Custom NAT workflow served in a Docker deployment and consumed by local NeMo Studio +- Notes: A recognized fast-path failure must terminate with a useful clarification or error instead of falling through to the model-driven graph + +## Behavior + +- Be concise and action-oriented. Distinguish completed work from instructions or proposed work. +- Use the active request workspace automatically when it is available. +- When a required workspace, resource name, target, or other consequential parameter is missing or ambiguous, ask one focused clarification question and stop that run. +- Do not interpret missing context as permission to choose a destructive target. +- For recognized deterministic requests, return the SDK result or a direct actionable failure; never use the full agent as an error-recovery fallback. +- Use only NeMo Platform SDK tools. Never invoke the CLI, shell, or subprocesses. +- Attempt reasonable equivalent SDK operations when a method name differs, but bound retries and do not loop over equivalent failures. +- Report upstream model, SDK, and service failures honestly. Never claim that a mutation or deployment succeeded without verification. +- Avoid exposing API keys or secret values in prompts, logs, or responses. + +## Success Criteria + +- Common read-only list requests, including workspaces, models, providers, filesets, datasets, benchmarks, and metrics, complete through one deterministic SDK operation and normally return within a few seconds. +- Missing workspace or target context produces a clear clarification question rather than an exception, guessed value, or model-driven recovery loop. +- Explicit multi-step operations use the correct workspace, execute only requested side effects, verify the final state when possible, and return a concise summary. +- Complex requests have bounded model calls, retries, and execution time. A failed upstream decode must surface promptly instead of keeping Studio busy through repeated ten-minute retries. +- Studio receives incremental, nonduplicated streaming output and reaches a terminal success or error state. +- The agent never routes through the CLI, leaks managed credentials, or silently reaches a different NeMo Platform environment. + +## Evaluation Setup + +Unit coverage lives in `agents/nemo-agent-local/tests/test_nemo_agent.py`. Run it with: + +```bash +uv run --frozen pytest agents/nemo-agent-local/tests/test_nemo_agent.py -v +``` + +The NAT evaluation workflow is `agents/nemo-agent-local/src/nemo_agent/nemo-eval.yml`, with inputs in `nemo-eval-data.json`. It currently evaluates workspace creation, workspace listing, and model/provider listing with a tunable RAG judge weighted for coverage (0.5), correctness (0.3), and relevance (0.2). No formal aggregate pass threshold is defined. + +Manual Studio validation is documented in `agents/nemo-agent-local/tests/smoke_test.md`. Current coverage gaps include fileset listing, missing-workspace clarification, fast-path failure containment, destructive-action ambiguity, iteration limits, retry limits, cancellation, and end-to-end latency thresholds. + +## Change Scope + +- System prompt: yes +- Tools: yes +- Middleware: yes +- Inference params: yes +- Model swap (within mode): yes +- Skills: yes +- Fine-tuning: no +- Notes: Preserve API-only operation, managed secrets, and disabled agent telemetry; require human approval before broadening destructive capabilities or changing deployment mode + +## Signals + +Prioritize fast-path hit rate, per-request model-call count, tool-error repetition, total latency, upstream decode timeouts, retry count, clarification quality, and verified task completion. Treat repeated identical SDK errors or dozens of model calls for a simple request as runaway behavior. Routine container health probes are operational noise and should not be interpreted as user traffic. Until a dedicated agent telemetry pipeline is added, use container and platform logs for diagnosis. + +## Open Questions + +- Should `default` always be assumed when Studio does not propagate a workspace, or should the agent ask whenever more than one workspace exists? +- Which mutation categories require an explicit confirmation step even when the target is unambiguous? +- What maximum model-call count and wall-clock limit should apply to the complex path? diff --git a/agents/nemo-agent-local/.dockerignore b/agents/nemo-agent-local/.dockerignore new file mode 100644 index 0000000000..bad91b0534 --- /dev/null +++ b/agents/nemo-agent-local/.dockerignore @@ -0,0 +1,21 @@ +# Managed by `nemo agents package` — safe to delete if you take ownership. +.env +.env.* +*.pem +*.key +credentials.json +.git/ +.gitignore +__pycache__/ +*.pyc +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ +*.egg-info/ +dist/ +build/ +.venv/ +node_modules/ +tests/ +src/nemo_agent/nemo-eval.yml +src/nemo_agent/nemo-eval-data.json diff --git a/agents/nemo-agent-local/Dockerfile b/agents/nemo-agent-local/Dockerfile new file mode 100644 index 0000000000..fd3bb88d5b --- /dev/null +++ b/agents/nemo-agent-local/Dockerfile @@ -0,0 +1,72 @@ +# Managed by `nemo agents package` — safe to delete if you take ownership. +ARG BASE_IMAGE_URL=nvcr.io/nvidia/base/ubuntu +ARG BASE_IMAGE_TAG=noble-20260217 +ARG PYTHON_VERSION=3.13 +ARG NAT_VERSION=1.8.0 +FROM ${BASE_IMAGE_URL}:${BASE_IMAGE_TAG} +ARG PYTHON_VERSION +ARG NAT_VERSION + +COPY --from=ghcr.io/astral-sh/uv:0.8.15 /uv /uvx /bin/ + +ENV PYTHONDONTWRITEBYTECODE=1 + +# Keep the uv-managed Python in a world-readable location and use copy +# link-mode so the venv is self-contained (no cross-directory symlinks), +# letting the non-root runtime user exec it without needing /root access. +ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python \ + UV_LINK_MODE=copy + +RUN apt-get update && \ + apt-get install -y --no-install-recommends g++ gcc ca-certificates curl && \ + update-ca-certificates && \ + rm -rf /var/lib/apt/lists/* + +ENV REQUESTS_CA_BUNDLE=/etc/ssl/certs/ca-certificates.crt +ENV SSL_CERT_FILE=/etc/ssl/certs/ca-certificates.crt + +WORKDIR /workspace + +COPY ./ /workspace + +# Project mode. ``pyproject.toml`` is the single source of truth: it must +# declare ``nvidia-nat[...]`` (for the ``nat`` CLI), a concrete ``version`` +# (or a container-resolvable dynamic version), and every runtime dep. +# ``uv sync`` is deliberately not used because it honors +# ``[tool.uv.sources]`` path overrides pointing at sibling workspace +# packages that typically do not exist inside the build context. +RUN --mount=type=cache,id=uv_cache,target=/root/.cache/uv,sharing=locked \ + uv venv --python ${PYTHON_VERSION} /workspace/.venv && \ + . /workspace/.venv/bin/activate && \ + uv pip install . && \ + chmod -R a+rX /opt/uv /workspace/.venv + +LABEL org.opencontainers.image.title="builtin-nemo-agent" \ + org.opencontainers.image.version="0.1.0" \ + org.opencontainers.image.authors="NVIDIA" \ + org.opencontainers.image.created="2026-07-22T20:51:42.179931+00:00" \ + org.opencontainers.image.description="Built-in NeMo Platform agent — a LangChain Deep Agent that uses the NeMo SDK to perform platform tasks." \ + org.opencontainers.image.revision="" \ + org.opencontainers.image.source="" \ + com.nemo.agent.id="790bd61b98d0" \ + com.nemo.agent.framework="nemo_agent_toolkit" \ + com.nemo.agent.nat-version="1.8.0" \ + com.nemo.agent.contract-version="0.0.0" + +ENV NAT_CONFIG_FILE=/workspace/src/nemo_agent/nemo-agent.yml + +ENV PATH="/workspace/.venv/bin:$PATH" + +# Some modern base images (notably Ubuntu 24.04 "noble" and the NVIDIA base +# images derived from it) ship with a default unprivileged user at +# uid=1000/gid=1000. Reclaim 1000 for ``agent`` *by id, not by name* so this +# layer is portable across older base images (where 1000 is free; the guarded +# delete is a no-op) and across future base images that might rename the +# default user. +RUN if getent passwd 1000 >/dev/null; then userdel -rf "$(getent passwd 1000 | cut -d: -f1)" 2>/dev/null || true; fi && \ + if getent group 1000 >/dev/null; then groupdel -f "$(getent group 1000 | cut -d: -f1)" 2>/dev/null || true; fi && \ + groupadd -g 1000 agent && useradd -u 1000 -g agent -m agent && \ + chown -R agent:agent /workspace +USER agent + +ENTRYPOINT ["sh", "-c", "exec nat serve --config_file=$NAT_CONFIG_FILE --host 0.0.0.0"] diff --git a/agents/nemo-agent-local/pyproject.toml b/agents/nemo-agent-local/pyproject.toml new file mode 100644 index 0000000000..28d1a90f95 --- /dev/null +++ b/agents/nemo-agent-local/pyproject.toml @@ -0,0 +1,33 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "builtin-nemo-agent" +version = "0.1.0" +description = "Built-in NeMo Platform agent — a LangChain Deep Agent that uses the NeMo SDK to perform platform tasks." +requires-python = ">=3.11,<3.14" +dependencies = [ + "nvidia-nat-core>=1.8.0,<1.9", + "nvidia-nat-langchain>=1.8.0,<1.9", + "nemo-platform", + "deepagents", + "langchain-openai", + "langchain-core", + "httpx", +] + +[project.entry-points."nat.components"] +nemo_agents_nemo_agent = "nemo_agent.register" +# Custom NAT workflow type that wraps the nemo-agent graph with permissive +# input/output schemas. See src/nemo_agent/wrapper.py for the full rationale — +# in short, NAT 1.6.0's stock `langgraph_wrapper` doesn't accept the +# `{"input_message": ...}` shape that `nvidia-nat-eval` posts and crashes on +# deep-agent state deltas that don't carry `messages`. +nemo_agents_nemo_agent_wrapper = "nemo_agent.wrapper" + +[tool.hatch.build.targets.wheel] +packages = ["src/nemo_agent"] diff --git a/agents/nemo-agent-local/src/nemo_agent/nemo-agent.remote.yml b/agents/nemo-agent-local/src/nemo_agent/nemo-agent.remote.yml new file mode 100644 index 0000000000..1170127829 --- /dev/null +++ b/agents/nemo-agent-local/src/nemo_agent/nemo-agent.remote.yml @@ -0,0 +1,22 @@ +# Remote NeMo Platform deployment configuration. +# +# Keep the model workspace-qualified: the agent is deployed in a personal +# workspace while inference is served by the shared, platform-managed route. +# The platform injects a pod-reachable Inference Gateway base URL at deploy +# time; no upstream URL or credential belongs in this file. + +llms: + agent: + _type: openai + api_key: not-used + model_name: default/nvidia-nemotron-3-super-120b-a12b + do_auto_retry: false + num_retries: 1 + retry_on_status_codes: [599] + retry_on_errors: ["__never_retry__"] + max_retries: 0 + request_timeout: 120 + +workflow: + _type: nemo_agent_wrapper + description: NeMo Platform assistant using LangChain Deep Agents diff --git a/agents/nemo-agent-local/src/nemo_agent/nemo-agent.yml b/agents/nemo-agent-local/src/nemo_agent/nemo-agent.yml new file mode 100644 index 0000000000..2637cba157 --- /dev/null +++ b/agents/nemo-agent-local/src/nemo_agent/nemo-agent.yml @@ -0,0 +1,81 @@ +# NeMo Platform Agent — NAT workflow configuration +# +# A LangChain Deep Agent that uses the NeMo SDK to perform platform tasks. +# Deliberately configured as the "expensive baseline" for the optimization +# pipeline to compress. +# +# Deploy: +# uv pip install -e plugins/nemo-agents +# uv pip install -e plugins/nemo-agents/builtin_agents/nemo-agent +# nemo agents create --name nemo-agent \ +# --agent-config plugins/nemo-agents/builtin_agents/nemo-agent/src/nemo_agent/nemo-agent.yml +# nemo agents deploy --agent nemo-agent +# +# Invoke: +# nemo agents invoke --agent nemo-agent --input "Create a workspace called demo" + +llms: + agent: + _type: openai + # All AUT model traffic routes through the platform Inference Gateway + # (IGW). Both `nemo agents deploy` (production) and the agentic-use + # benchmark runner (tests/agentic-use/nat_runner.py via + # _prepare_aut_config_for_runtime → inject_gateway_url) inject the IGW + # base_url at runtime. Do NOT set `base_url` here — `inject_gateway_url` + # uses setdefault, so an explicit value would silently bypass IGW. + # `api_key: not-used` is a sentinel; IGW retrieves the real upstream + # credential from the platform secrets service. + api_key: not-used + # Model entity name. IGW resolves this to the upstream provider that + # owns it (each entity is owned by exactly one provider, so no provider + # field is needed in this yml). Use entity (dash) form — IGW handles + # the mapping to the served slash form internally. + # + # The dash-form name encodes the upstream catalog. nvidia-build serves + # NIM models as `nvidia/` (single namespace), so its entities + # look like `nvidia-`. nvidia-inference-api proxies LiteLLM, + # which serves the same NIM catalog as `nvidia/nvidia/` and the + # bedrock catalog as `aws/anthropic/`, so its entities look like + # `nvidia-nvidia-` / `aws-anthropic-`. The double prefix + # is therefore a reliable signal that an entity routes through + # nvidia-inference-api. + # + # Examples (each empirically validated end-to-end): + # model_name: nvidia-nvidia-nemotron-3-super-v3 # via nvidia-inference-api + # model_name: aws-anthropic-claude-opus-4-5 # via nvidia-inference-api + # model_name: nvidia-llama-3-3-nemotron-super-49b-v1-5 # via nvidia-build + # + # For the full list of currently-discovered entities, hit + # ${NMP_BASE_URL}/v1/models on the running platform, or run the + # optional "list available models" cell in the playground notebook. + model_name: nvidia-nemotron-3-super-120b-a12b + # One total attempt: a retryable upstream decode timeout must not restart + # another long-running generation and keep Studio busy indefinitely. + do_auto_retry: false + num_retries: 1 + # NAT 1.8 currently patches LangChain methods even when do_auto_retry is + # false. Some layers replace empty lists with their defaults, so use + # impossible sentinels to prevent nested wrappers from matching 429. + retry_on_status_codes: [599] + retry_on_errors: ["__never_retry__"] + # Disable the OpenAI client's independent retry loop. Without this, one + # model call can issue eleven requests before NAT sees the final error. + max_retries: 0 + # Bound the HTTP model call below the Inference Gateway's 600-second + # decode wall-clock timeout so the agent can return a terminal error. + request_timeout: 120 + + # model_name: vm-opus-nemotron-random + # AWS-hosted Claude Opus 4.5 rejects Switchyard's Anthropic translation + # fallback of 128k output tokens. Keep this below the 64k AWS route limit + # while leaving room for prompt/tool tokens on longer benchmark tasks. + # Only necessary for Switchyard virtual models. + # max_tokens: 49152 + +workflow: + # Custom workflow type registered by `nemo-agents-nemo-agent`. Wraps the same + # deep-agent graph as `langgraph_wrapper` would, but with input/output schemas + # that survive both the eval client's `{"input_message": ...}` payload and the + # deep-agent's wider state shape. See src/nemo_agent/wrapper.py. + _type: nemo_agent_wrapper + description: NeMo Platform assistant using LangChain Deep Agents diff --git a/agents/nemo-agent-local/src/nemo_agent/nemo-eval-data.json b/agents/nemo-agent-local/src/nemo_agent/nemo-eval-data.json new file mode 100644 index 0000000000..216af98d3f --- /dev/null +++ b/agents/nemo-agent-local/src/nemo_agent/nemo-eval-data.json @@ -0,0 +1,14 @@ +[ + { + "input_message": "Show the details for the default workspace without making any changes.", + "expected_output": "details for the default workspace" + }, + { + "input_message": "List all workspaces on the platform", + "expected_output": "list of workspaces" + }, + { + "input_message": "List the available models and inference providers using the platform API.", + "expected_output": "list of available models and inference providers" + } +] diff --git a/agents/nemo-agent-local/src/nemo_agent/nemo-eval.yml b/agents/nemo-agent-local/src/nemo_agent/nemo-eval.yml new file mode 100644 index 0000000000..dc6695093d --- /dev/null +++ b/agents/nemo-agent-local/src/nemo_agent/nemo-eval.yml @@ -0,0 +1,98 @@ +# Eval config for the built-in nemo-agent. +# +# Uses the custom `nemo_agent_wrapper` workflow type (defined in +# src/nemo_agent/wrapper.py) so the same config works whether the agent runs +# inline (no --agent) or against the deployed agent's `/generate/full` endpoint +# (--agent nemo-agent). The wrapper accepts both the `{"messages": [...]}` +# shape used by chat clients and the `{"input_message": "..."}` shape that +# `nvidia-nat-eval`'s remote workflow client posts to `/generate/full`. +# +# base_url and api_key for both LLMs are auto-injected by EvaluateAgentJob, +# routing through the platform inference gateway. See: +# plugins/nemo-agents/src/nemo_agents_plugin/utils.py::inject_gateway_url +# +# Run inline (in-process, no deployed agent needed): +# nemo agents evaluate run \ +# --eval-config plugins/nemo-agents/builtin_agents/nemo-agent/src/nemo_agent/nemo-eval.yml \ +# --output ./eval-out +# +# Run against the deployed agent (requires `nemo agents deploy nemo-agent`): +# nemo agents evaluate run \ +# --eval-config plugins/nemo-agents/builtin_agents/nemo-agent/src/nemo_agent/nemo-eval.yml \ +# --agent nemo-agent \ +# --output ./eval-out + +llms: + agent: + _type: openai + # `base_url` and `api_key` are auto-injected by EvaluateAgentJob unless + # already set, routing this LLM through the platform Inference Gateway. + # See plugins/nemo-agents/src/nemo_agents_plugin/utils.py::inject_gateway_url + model_name: nvidia-nemotron-3-super-120b-a12b + + judge_llm: + _type: openai + model_name: aws-anthropic-bedrock-claude-opus-4-7 + # NOTE: do not set `temperature` for this model — Bedrock claude-opus-4-7 + # returns 400 with "`temperature` is deprecated for this model." for any + # value (including 0.0 / 0.001). + max_tokens: 1024 + + # ─── Bypassing the Inference Gateway ────────────────────────────────────── + # To run the eval against NVIDIA's hosted inference directly (no platform + # Inference Gateway in the loop), set `base_url` and `api_key` explicitly + # on each LLM. Because the auto-injection uses setdefault semantics, fields + # you set here are left alone. Use the *served* model name (slashes) — the + # gateway-mapped entity name (`aws-anthropic-bedrock-claude-opus-4-7`) is + # only meaningful when something resolves it back to the upstream model. + # + # Caveat: the agent's tools (`nemo_api`, `check_status`) still call platform + # APIs via the Python SDK. The current `nemo-eval-data.json` exercises + # platform resources, including workspace and model/provider operations, so + # bypassing the gateway is not an LLM-only smoke test. A fully self-contained + # bypass run requires a separate pure-LLM dataset that does not invoke + # platform tools. + # + # agent: + # _type: openai + # base_url: https://inference-api.nvidia.com/v1 + # api_key: ${INFERENCE_NVIDIA_API_KEY} # `sk-…` LiteLLM virtual key + # model_name: aws/anthropic/bedrock-claude-opus-4-7 + # + # judge_llm: + # _type: openai + # base_url: https://inference-api.nvidia.com/v1 + # api_key: ${INFERENCE_NVIDIA_API_KEY} + # model_name: aws/anthropic/bedrock-claude-opus-4-7 + # max_tokens: 1024 + +workflow: + _type: nemo_agent_wrapper + description: NeMo Platform assistant using LangChain Deep Agents + +eval: + general: + max_concurrency: 1 + output_dir: eval/nemo-agent + dataset: + _type: json + file_path: nemo-eval-data.json + structure: + question_key: input_message + answer_key: expected_output + evaluators: + accuracy: + _type: tunable_rag_evaluator + llm_name: judge_llm + default_scoring: true + default_score_weights: + coverage: 0.5 + correctness: 0.3 + relevance: 0.2 + judge_llm_prompt: > + You are an evaluator. Score whether the generated answer correctly + addresses the question compared to the expected answer description. + Rules: + - Score is a float between 0.0 and 1.0. + - 1.0 means the answer fully satisfies the expected answer criteria. + - Provide a 1-2 sentence reasoning. diff --git a/agents/nemo-agent-local/src/nemo_agent/register.py b/agents/nemo-agent-local/src/nemo_agent/register.py new file mode 100644 index 0000000000..e5cc5a6a43 --- /dev/null +++ b/agents/nemo-agent-local/src/nemo_agent/register.py @@ -0,0 +1,801 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +import asyncio +import json +import logging +import os +import re +import types +import uuid +from pathlib import Path +from typing import Any +from urllib.parse import urlencode + +import httpx +from deepagents import create_deep_agent # ty: ignore[unresolved-import] +from deepagents.backends import ( # ty: ignore[unresolved-import] + CompositeBackend, + FilesystemBackend, + StateBackend, +) +from langchain_core.messages import AIMessage, AIMessageChunk, HumanMessage +from langchain_core.runnables import RunnableConfig +from langchain_core.tools import tool +from nemo_platform import NeMoPlatform +from pydantic import BaseModel + +logger = logging.getLogger(__name__) + +SYSTEM_PROMPT = """You are a NeMo Platform assistant executing benchmark tasks. + +Complete every numbered requirement in the user instruction before returning. +Do not return a plan or instructions for a human to run. Execute the required +operations yourself with available tools, then report concise results. + +Do not claim tools are insufficient until you have attempted reasonable +equivalent operations with `nemo_api`. Use `check_status` for platform jobs +and deployments. Do not attempt to invoke the NeMo CLI or other subprocesses. + +Use the deployment's active workspace unless the user explicitly names another +one. If a required workspace, resource name, target, or consequential parameter +is missing or ambiguous, ask one focused clarification question and stop. Do +not repeat the same failing tool call or guess a destructive target. +""" +SKILLS_DIR = Path(__file__).parent / "skills" +DEFAULT_WORKSPACE = "default" +DEFAULT_RECURSION_LIMIT = 24 +STUDIO_CALLBACK_PATH = "/studio/api/coding-agents/mcp/{session_id}" +STUDIO_CALLBACK_TIMEOUT_SECONDS = 3600.0 +_READ_ONLY_SDK_ACTIONS = frozenset( + { + "get", + "get_logs", + "get_status", + "list", + "read", + "retrieve", + "search", + } +) + +_DIRECT_LIST_REQUEST = re.compile(r"^\s*(list|show|what|which)\b|\bavailable\b", re.IGNORECASE) +_DIRECT_FILESET_DELETE = re.compile( + r"^\s*(?:(?:can|could|would)\s+you\s+)?(?:please\s+)?" + r"delete\s+(?:the\s+)?(?P[a-z0-9][a-z0-9._-]*)\s+fileset\s*[?.!]?\s*$", + re.IGNORECASE, +) +_FAST_PATH_MUTATION = re.compile( + r"\b(" + r"add|cancel|create|delete|deploy|edit|evaluate|invoke|optimi[sz]e|" + r"remove|run|set|submit|undeploy|update|upload|write" + r")\b", + re.IGNORECASE, +) +_FAST_PATH_ANALYSIS = re.compile(r"\b(analy[sz]e|audit|compare|explain|investigate|why)\b", re.IGNORECASE) +_DIRECT_LIST_RESOURCES = ( + (re.compile(r"\bworkspaces?\b", re.IGNORECASE), "workspaces"), + (re.compile(r"\bmodels?\b", re.IGNORECASE), "models"), + (re.compile(r"\bproviders?\b", re.IGNORECASE), "inference.providers"), + (re.compile(r"\bfilesets?\b", re.IGNORECASE), "files.filesets"), + (re.compile(r"\bdatasets?\b", re.IGNORECASE), "datasets"), + (re.compile(r"\bbenchmarks?\b", re.IGNORECASE), "evaluation.benchmarks"), + (re.compile(r"\bmetrics?\b", re.IGNORECASE), "evaluation.metrics"), +) + +_client: NeMoPlatform | None = None + + +def _latest_user_text(messages: list[Any]) -> str: + latest_user_text = "" + for message in reversed(messages): + if isinstance(message, HumanMessage): + latest_user_text = message.text + break + return latest_user_text + + +def _direct_list_resource(messages: list[Any]) -> str | None: + """Resolve safe list-by-name requests to a known SDK resource.""" + latest_user_text = _latest_user_text(messages) + if not latest_user_text or not _DIRECT_LIST_REQUEST.search(latest_user_text): + return None + if _FAST_PATH_MUTATION.search(latest_user_text) or _FAST_PATH_ANALYSIS.search(latest_user_text): + return None + for pattern, resource_path in _DIRECT_LIST_RESOURCES: + if pattern.search(latest_user_text): + return resource_path + return None + + +def _direct_fileset_delete_name(messages: list[Any]) -> str | None: + """Resolve an explicit single-fileset deletion request.""" + latest_user_text = _latest_user_text(messages) + if not latest_user_text: + return None + match = _DIRECT_FILESET_DELETE.fullmatch(latest_user_text) + if match is None: + return None + name = match.group("name") + if name.lower() in {"a", "all", "an", "every", "my", "the"}: + return None + return name + + +def _active_workspace() -> str: + """Return the workspace injected by the deployment runtime.""" + return os.environ.get("NMP_WORKSPACE") or DEFAULT_WORKSPACE + + +def _direct_list_error(resource_path: str, exc: Exception) -> str: + """Turn a recognized fast-path failure into a terminal user response.""" + resource_name = resource_path.rsplit(".", maxsplit=1)[-1] + if isinstance(exc, ValueError) and "workspace" in str(exc).lower(): + return f"Which workspace should I use to list {resource_name}?" + return f"I couldn't list {resource_name} in workspace '{_active_workspace()}': {type(exc).__name__}: {exc}" + + +def _list_resource_names(resource_path: str) -> str: + """List one SDK resource and return names or a terminal actionable error.""" + try: + resource = _resolve_resource(_get_client(), resource_path) + serialized = _serialize(_call_sdk_method(resource, "list")) + except Exception as exc: + logger.exception("nemo-agent: direct list fast path failed for resource=%s", resource_path) + return _direct_list_error(resource_path, exc) + + if isinstance(serialized, dict): + serialized = serialized.get("data") + if not isinstance(serialized, list): + return f"I couldn't list {resource_path}: the platform returned an unexpected response." + names = [item.get("name") for item in serialized if isinstance(item, dict) and isinstance(item.get("name"), str)] + if not names: + return f"No {resource_path.rsplit('.', maxsplit=1)[-1]} found in workspace '{_active_workspace()}'." + logger.info("nemo-agent: direct list fast path resource=%s count=%d", resource_path, len(names)) + return "\n".join(names) + + +def _delete_fileset(name: str) -> str: + """Delete one explicitly named fileset and verify that it is absent.""" + workspace = _active_workspace() + try: + resource = _resolve_resource(_get_client(), "files.filesets") + _call_sdk_method(resource, "delete", {"name": name}) + serialized = _serialize(_call_sdk_method(resource, "list")) + except Exception as exc: + logger.exception("nemo-agent: direct fileset delete failed for name=%s", name) + if isinstance(exc, ValueError) and "workspace" in str(exc).lower(): + return f"Which workspace should I use to delete fileset '{name}'?" + return f"I couldn't delete fileset '{name}' in workspace '{workspace}': {type(exc).__name__}: {exc}" + + if isinstance(serialized, dict): + serialized = serialized.get("data") + if not isinstance(serialized, list): + return ( + f"The delete request for fileset '{name}' completed, but I couldn't verify " + f"the result in workspace '{workspace}'." + ) + remaining_names = { + item.get("name") for item in serialized if isinstance(item, dict) and isinstance(item.get("name"), str) + } + if name in remaining_names: + return f"I couldn't verify deletion: fileset '{name}' still exists in workspace '{workspace}'." + + logger.info("nemo-agent: direct fileset delete name=%s workspace=%s", name, workspace) + return f"Deleted fileset '{name}' from workspace '{workspace}'." + + +def _get_client() -> NeMoPlatform: + global _client + if _client is None: + base_url = os.environ.get("NMP_BASE_URL") or os.environ.get("NEMO_BASE_URL") + kwargs: dict[str, Any] = {} + if base_url: + kwargs["base_url"] = base_url + kwargs["workspace"] = _active_workspace() + _client = NeMoPlatform(**kwargs) + return _client + + +def _serialize(obj: Any) -> Any: + """Convert SDK response objects to JSON-serializable form.""" + if obj is None or isinstance(obj, (str, int, float, bool)): + return obj + if isinstance(obj, dict): + return {k: _serialize(v) for k, v in obj.items()} + if isinstance(obj, list): + return [_serialize(item) for item in obj] + if isinstance(obj, BaseModel): + return obj.model_dump() + return str(obj) + + +def _resolve_resource(client: NeMoPlatform, resource_path: str) -> Any: + """Navigate a dot-separated path on the SDK client. + + Example: "inference.providers" -> client.inference.providers + """ + current = client + for part in resource_path.split("."): + current = getattr(current, part) + return current + + +def _call_sdk_method(resource: Any, action: str, params: dict[str, Any] | None = None) -> Any: + method = getattr(resource, action) + if params: + return method(**params) + return method() + + +def _trusted_studio_session_id(config: RunnableConfig) -> str | None: + configurable = config.get("configurable") + if not isinstance(configurable, dict): + return None + value = configurable.get("studio_session_id") + if not isinstance(value, str): + return None + try: + return str(uuid.UUID(value)) + except ValueError: + return None + + +def _request_mutation_approval( + config: RunnableConfig, + *, + tool_name: str, + tool_input: dict[str, Any], +) -> str | None: + session_id = _trusted_studio_session_id(config) + if session_id is None: + return "Denied: mutating operations require a trusted Studio approval context" + approval = _call_studio_tool( + session_id, + "approval_prompt", + { + "tool_name": tool_name, + "input": tool_input, + }, + ) + if approval.get("behavior") != "allow": + return f"Denied by user: {approval.get('message') or 'operation was not approved'}" + return None + + +@tool +def nemo_api( + resource: str, + action: str, + config: RunnableConfig, + params: str | None = None, +) -> str: + """Call any NeMo Platform SDK method. + + Args: + resource: Dot-separated SDK resource path. Examples: + workspaces, inference.providers, inference.deployments, + evaluation.metrics, evaluation.metric_jobs, + evaluation.benchmarks, evaluation.benchmark_jobs, + files, files.filesets, guardrail, secrets, customization, audit, + data_designer, models, datasets, inference.virtual_models + action: Method to call on the resource. Common actions: + create, list, retrieve, get, delete, update, + get_status, get_logs, cancel, evaluate, upload_content + params: JSON string of keyword arguments to pass. Example: + '{"name": "my-workspace", "description": "test"}' + '{"name": "my-secret", "value": "secret-payload"}' + '{"content": "hello", "remote_path": "verify.txt", "fileset": "my-fileset"}' + Useful paths: + - Fileset CRUD: resource="files.filesets" + - In-memory upload: resource="files", action="upload_content" + - Secret CRUD: resource="secrets" + + Returns: + JSON string of the result. + """ + client = _get_client() + try: + normalized_action = action.strip().lower() + if normalized_action not in _READ_ONLY_SDK_ACTIONS: + denial = _request_mutation_approval( + config, + tool_name="nemo_api", + tool_input={ + "resource": resource, + "action": action, + "params": params, + }, + ) + if denial is not None: + return denial + + resolved = _resolve_resource(client, resource) + parsed_params = json.loads(params) if params else None + result = _call_sdk_method(resolved, action, parsed_params) + return json.dumps(_serialize(result), indent=2, default=str) + except Exception as e: + return f"Error: {type(e).__name__}: {e}" + + +def _studio_callback_url( + session_id: str, + *, + workspace: str | None = None, + studio_base_url: str | None = None, +) -> str: + """Build the Studio callback URL using the deployment-reachable platform origin.""" + base_url = (os.environ.get("NMP_BASE_URL") or os.environ.get("NEMO_BASE_URL") or "").rstrip("/") + if not base_url: + raise RuntimeError("NMP_BASE_URL is required for Studio UI callbacks") + + query = { + key: value + for key, value in { + "workspace": workspace or _active_workspace(), + "studio_base_url": studio_base_url, + }.items() + if value + } + url = f"{base_url}{STUDIO_CALLBACK_PATH.format(session_id=session_id)}" + return f"{url}?{urlencode(query)}" if query else url + + +def _json_rpc_content(payload: dict[str, Any]) -> dict[str, Any]: + error = payload.get("error") + if isinstance(error, dict): + raise RuntimeError(str(error.get("message") or "Studio callback failed")) + + result = payload.get("result") + if not isinstance(result, dict): + raise RuntimeError("Studio callback response did not include a result") + content = result.get("content") + if not isinstance(content, list) or not content: + return {} + first = content[0] + if not isinstance(first, dict) or not isinstance(first.get("text"), str): + return {} + decoded = json.loads(first["text"]) + if not isinstance(decoded, dict): + raise RuntimeError("Studio callback tool returned a non-object result") + return decoded + + +def _parse_studio_callback_response(response: httpx.Response) -> dict[str, Any]: + response.raise_for_status() + if response.headers.get("content-type", "").startswith("text/event-stream"): + for line in reversed(response.text.splitlines()): + if not line.startswith("data:"): + continue + payload = json.loads(line.removeprefix("data:").strip()) + if isinstance(payload, dict): + return _json_rpc_content(payload) + raise RuntimeError("Studio callback stream ended without a tool result") + + payload = response.json() + if not isinstance(payload, dict): + raise RuntimeError("Studio callback returned a non-object response") + return _json_rpc_content(payload) + + +def _call_studio_tool( + session_id: str, + tool_name: str, + arguments: dict[str, Any], + *, + workspace: str | None = None, + studio_base_url: str | None = None, +) -> dict[str, Any]: + """Call one of Studio's blocking UI tools from a deployed agent.""" + body = { + "jsonrpc": "2.0", + "id": str(uuid.uuid4()), + "method": "tools/call", + "params": {"name": tool_name, "arguments": arguments}, + } + with httpx.Client(timeout=STUDIO_CALLBACK_TIMEOUT_SECONDS) as client: + response = client.post( + _studio_callback_url( + session_id, + workspace=workspace, + studio_base_url=studio_base_url, + ), + json=body, + ) + return _parse_studio_callback_response(response) + + +@tool +def select_agent( + studio_session_id: str, + title: str = "Select agent", + description: str = "", + default_agent: str | None = None, +) -> str: + """Render Studio's agent selector and return the user's selected agent.""" + return json.dumps( + _call_studio_tool( + studio_session_id, + "select_agent", + { + "title": title, + "description": description, + "default_agent": default_agent, + }, + ) + ) + + +@tool +def select_model( + studio_session_id: str, + title: str = "Select model", + description: str = "", + default_model: str | None = None, + output_key: str = "model", +) -> str: + """Render Studio's model selector and return the user's selected model.""" + return json.dumps( + _call_studio_tool( + studio_session_id, + "select_model", + { + "title": title, + "description": description, + "default_model": default_model, + "output_key": output_key, + }, + ) + ) + + +@tool +def select_dataset_file( + studio_session_id: str, + title: str = "Select dataset", + description: str = "", + accepted_file_types: list[str] | None = None, +) -> str: + """Render Studio's fileset/dataset picker and return the selected file.""" + return json.dumps( + _call_studio_tool( + studio_session_id, + "select_dataset_file", + { + "title": title, + "description": description, + "accepted_file_types": accepted_file_types or [], + }, + ) + ) + + +@tool +def select_eval_config( + studio_session_id: str, + title: str = "Select evaluation config", + description: str = "", + agent: str | None = None, +) -> str: + """Render Studio's evaluation-config picker and return the selected config.""" + return json.dumps( + _call_studio_tool( + studio_session_id, + "select_eval_config", + { + "title": title, + "description": description, + "agent": agent, + }, + ) + ) + + +@tool +def job_progress( + studio_session_id: str, + job_name: str, + job_type: str | None = None, + source: str | None = None, + title: str | None = None, + description: str | None = None, +) -> str: + """Render a Studio progress card for a platform job that was just launched.""" + return json.dumps( + _call_studio_tool( + studio_session_id, + "job_progress", + { + "job_name": job_name, + "job_type": job_type, + "source": source, + "title": title, + "description": description, + }, + ) + ) + + +@tool +def studio_link( + studio_session_id: str, + destination: str, + name: str | None = None, + label: str | None = None, + studio_base_url: str | None = None, +) -> str: + """Build a feature-flag-aware link to a NeMo Studio page.""" + return json.dumps( + _call_studio_tool( + studio_session_id, + "studio_link", + { + "destination": destination, + "name": name, + "label": label, + }, + studio_base_url=studio_base_url, + ) + ) + + +@tool +def check_status(service: str, job_name: str) -> str: + """Check the status of a platform job or deployment. + + Args: + service: The service that owns the job. One of: + evaluation, customization, audit, data_designer + job_name: The name or ID of the job to check. + + Returns: + JSON string with the job status. + """ + client = _get_client() + try: + svc = getattr(client, service) + status_methods = [ + ("metric_jobs", "get_status"), + ("benchmark_jobs", "get_status"), + ("jobs", "get_status"), + ] + last_error: Exception | None = None + for sub_resource, method_name in status_methods: + if hasattr(svc, sub_resource): + resource = getattr(svc, sub_resource) + if hasattr(resource, method_name): + try: + result = getattr(resource, method_name)(name=job_name) + return json.dumps(_serialize(result), indent=2, default=str) + except Exception as exc: + last_error = exc + if last_error: + return f"Error: {type(last_error).__name__}: {last_error}" + return f"Error: no status method found for service '{service}'" + except Exception as e: + return f"Error: {type(e).__name__}: {e}" + + +_nemo_tools = [ + nemo_api, + check_status, + select_agent, + select_model, + select_dataset_file, + select_eval_config, + job_progress, + studio_link, +] + + +class _StreamSafeGraph: + """Wraps a CompiledStateGraph with NAT-compatible incremental streaming. + + The custom ``nemo_agent_wrapper`` accepts Deep Agent state deltas that do + not contain ``messages``. Delegate to the graph's real ``astream`` so NAT + can forward chunks as each graph step completes instead of buffering the + entire run behind ``ainvoke``. + """ + + def __init__(self, graph): + self._graph = graph + + def __getattr__(self, name): + return getattr(self._graph, name) + + @staticmethod + def _bounded_config(config: dict[str, Any] | None) -> dict[str, Any]: + return { + "recursion_limit": DEFAULT_RECURSION_LIMIT, + **(config or {}), + } + + @staticmethod + async def _direct_response(input_data: Any, config: RunnableConfig) -> str | None: + if not isinstance(input_data, dict): + return None + messages = input_data.get("messages") + if not isinstance(messages, list): + return None + fileset_name = _direct_fileset_delete_name(messages) + if fileset_name is not None: + denial = await asyncio.to_thread( + _request_mutation_approval, + config, + tool_name="nemo_api", + tool_input={ + "resource": "files.filesets", + "action": "delete", + "params": json.dumps({"name": fileset_name}), + }, + ) + if denial is not None: + return denial + return await asyncio.to_thread(_delete_fileset, fileset_name) + resource_path = _direct_list_resource(messages) + if resource_path is None: + return None + return await asyncio.to_thread(_list_resource_names, resource_path) + + async def ainvoke(self, input_data, config=None, **kwargs): + bounded_config = self._bounded_config(config) + direct_response = await self._direct_response(input_data, bounded_config) + if direct_response is not None: + return {"messages": [AIMessage(content=direct_response)]} + return await self._graph.ainvoke(input_data, config=bounded_config, **kwargs) + + async def astream(self, input_data, config=None, **kwargs): + bounded_config = self._bounded_config(config) + direct_response = await self._direct_response(input_data, bounded_config) + if direct_response is not None: + yield {"messages": [AIMessageChunk(content=direct_response)]} + return + + if "stream_mode" not in kwargs: + async for message, _metadata in self._graph.astream( + input_data, + config=bounded_config, + stream_mode="messages", + **kwargs, + ): + if isinstance(message, AIMessage): + yield {"messages": [message]} + return + + async for chunk in self._graph.astream(input_data, config=bounded_config, **kwargs): + yield chunk + + async def astream_events(self, *args, **kwargs): + async for event in self._graph.astream_events(*args, **kwargs): + yield event + + +def _get_model(): + """Get the LLM from NAT's builder context (YAML llms: section).""" + from nat.builder.framework_enum import LLMFrameworkEnum + from nat.builder.sync_builder import SyncBuilder + + model = SyncBuilder.current().get_llm("agent", wrapper_type=LLMFrameworkEnum.LANGCHAIN) + return _disable_nat_method_retries(model) + + +def _disable_nat_method_retries(model: Any) -> Any: + """Remove NAT's instance-level retry decorators from the model. + + NAT 1.8's FastAPI worker round-trips its validated config through + ``model_dump`` before starting the serving process. RetryMixin fields are + lost in that round trip, so the worker silently restores its five-attempt + defaults even when the source YAML disables retries. ``patch_with_retry`` + uses ``functools.wraps``, which leaves the original method available as + ``__wrapped__``. Restore only wrappers whose code comes from NAT's + ``automatic_retries.py``; leave every other LangChain decorator intact. + """ + disabled: list[str] = [] + for name in dir(model): + if name.startswith("_"): + continue + try: + bound_method = getattr(model, name) + except Exception: + continue + function = getattr(bound_method, "__func__", bound_method) + wrapped = getattr(function, "__wrapped__", None) + code = getattr(function, "__code__", None) + if wrapped is None or code is None or Path(code.co_filename).name != "automatic_retries.py": + continue + object.__setattr__(model, name, types.MethodType(wrapped, model)) + disabled.append(name) + if disabled: + logger.info("nemo-agent: disabled NAT automatic retry wrappers for model methods: %s", ", ".join(disabled)) + return model + + +def _discover_skills(skills_dir: Path) -> list[str]: + """Return the list of well-formed skills under ``skills_dir``. + + deepagents' ``SkillsMiddleware`` follows the Agent Skills specification + (https://agentskills.io/specification): each skill must be a subdirectory + containing a ``SKILL.md`` file whose first line is ``---`` (start of YAML + frontmatter). We mirror that shape here so we can log a clear count and + warn loudly when the layout drifts — silently empty skill loads have + already cost us one Stage 4 baseline run. + """ + if not skills_dir.is_dir(): + return [] + discovered: list[str] = [] + for child in sorted(skills_dir.iterdir()): + if not child.is_dir(): + continue + skill_md = child / "SKILL.md" + if not skill_md.is_file(): + continue + try: + with skill_md.open("r", encoding="utf-8") as fp: + first_line = fp.readline().strip() + except OSError: + continue + if first_line == "---": + discovered.append(child.name) + return discovered + + +def _build_backend() -> CompositeBackend: + """Build the agent filesystem backend. + + DeepAgents resolves both skill files and file-tool reads through this + backend. Route packaged skills and `/tmp` to the real filesystem; keep all + other scratchpad paths in graph state. + + Use virtual mode for routed filesystem roots because CompositeBackend strips + the route prefix before delegation: `/tmp/foo` reaches the `/tmp/` backend + as `/foo`, which should resolve under `/tmp`, not the container root. + """ + routes = { + f"{str(SKILLS_DIR).rstrip('/')}/": FilesystemBackend(root_dir=str(SKILLS_DIR), virtual_mode=True), + "/tmp/": FilesystemBackend(root_dir="/tmp", virtual_mode=True), + } + return CompositeBackend(default=StateBackend(), routes=routes) + + +def create_nemo_agent(config=None): + """Create the NeMo Platform Deep Agent. + + Args: + config: Optional configuration for NAT langgraph_wrapper compatibility. + """ + model = _get_model() + # Only pass ``skills`` when at least one spec-compliant skill is actually + # discovered. Driving ``skills`` purely off directory existence makes + # ``create_deep_agent`` behavior depend on whatever its (downstream) + # parser does with an empty layout, which is something we'd rather not + # rely on when the layout drifts. + discovered = _discover_skills(SKILLS_DIR) + skills = [str(SKILLS_DIR)] if discovered else None + if SKILLS_DIR.is_dir() and not discovered: + logger.warning( + "nemo-agent: SKILLS_DIR=%s is configured but no spec-compliant " + "skills were discovered (each skill must be a subdirectory with " + "a SKILL.md starting with '---' YAML frontmatter). Agent will " + "run without playbook scaffolding.", + SKILLS_DIR, + ) + elif discovered: + logger.info("nemo-agent: loaded %d skills: %s", len(discovered), ", ".join(discovered)) + backend = _build_backend() + graph = create_deep_agent( + model=model, + tools=_nemo_tools, + system_prompt=SYSTEM_PROMPT, + skills=skills, + backend=backend, + ) + return _StreamSafeGraph(graph) + + +# Module-level graph factory. The active workflow type is `nemo_agent_wrapper` +# (see ./wrapper.py), which calls ``create_nemo_agent`` directly. This alias is +# kept so the graph can also be loaded via ``_type: langgraph_wrapper`` with +# ``graph: .../register.py:agent`` for ad-hoc debugging — note that path will +# hit the NAT 1.6.0 input/output schema bugs that ``nemo_agent_wrapper`` +# exists to work around. +agent = create_nemo_agent diff --git a/agents/nemo-agent-local/src/nemo_agent/skills/.gitkeep b/agents/nemo-agent-local/src/nemo_agent/skills/.gitkeep new file mode 100644 index 0000000000..e69de29bb2 diff --git a/agents/nemo-agent-local/src/nemo_agent/skills/auditor/SKILL.md b/agents/nemo-agent-local/src/nemo_agent/skills/auditor/SKILL.md new file mode 100644 index 0000000000..83d37091a2 --- /dev/null +++ b/agents/nemo-agent-local/src/nemo_agent/skills/auditor/SKILL.md @@ -0,0 +1,17 @@ +--- +name: auditor +description: NeMo Platform auditor playbook for audit target and config CRUD through the platform SDK. Use when the task involves audit targets, audit configs, or probes. +--- +Auditor tasks + +- Use `nemo_api` with `audit.targets` for target CRUD and `audit.configs` + for config CRUD. +- Use the standard SDK actions: `create`, `list`, `retrieve`, `update`, and + `delete`. +- Pass the target or config fields as compact JSON in `params`. +- For config create, prefer minimal valid JSON: + - `plugins`: `{"probe_spec":"dan.AutoDANCached"}` (or requested probe) + - `reporting`: `{}` + - `run`: `{}` + - `system`: `{"lite": true}` +- Follow full lifecycle: create temp resource, verify/list/update/delete, then create final verification resource. diff --git a/agents/nemo-agent-local/src/nemo_agent/skills/benchmark-execution/SKILL.md b/agents/nemo-agent-local/src/nemo_agent/skills/benchmark-execution/SKILL.md new file mode 100644 index 0000000000..0e3cb559ea --- /dev/null +++ b/agents/nemo-agent-local/src/nemo_agent/skills/benchmark-execution/SKILL.md @@ -0,0 +1,20 @@ +--- +name: benchmark-execution +description: "Benchmark task execution contract: complete every numbered requirement, execute tool calls directly (never plan-only), and verify final state with a direct retrieve/list before responding. Use for every agentic-use benchmark task." +--- +# Benchmark execution contract + +This skill defines the execution requirements that every nemo-agent run under +`tests/agentic-use/` must satisfy so the canonical gate +(`tests/agentic-use/passrate_token_policy_gate.py`) can score the run on +verifier pass-rate and token totals. See +[`tests/agentic-use/README.md`](../../../../../../tests/agentic-use/README.md) +for the full Run -> Gate -> Optimize loop these tasks plug into. + +- Treat `instruction.md` as the task contract: finish all numbered requirements. +- Execute tool calls yourself; do not end with a plan-only response. +- Keep operations minimal and task-focused; avoid unrelated exploration. +- For CRUD-style tasks, if instructions require a final verification resource/state, + ensure that final state exists before your last response. +- Before final response, run one direct verification call that checks the required + end state from the instruction (for example: retrieve/list/get status). diff --git a/agents/nemo-agent-local/src/nemo_agent/skills/entities/SKILL.md b/agents/nemo-agent-local/src/nemo_agent/skills/entities/SKILL.md new file mode 100644 index 0000000000..0e32b7acec --- /dev/null +++ b/agents/nemo-agent-local/src/nemo_agent/skills/entities/SKILL.md @@ -0,0 +1,10 @@ +--- +name: entities +description: NeMo Platform model and dataset CRUD lifecycle through the platform SDK, with strict field matching on final verification entities. +--- +Entity tasks + +- Use `nemo_api` with the `models` or `datasets` resource as appropriate. +- Use the exact entity type and name from the instruction. +- For model/dataset CRUD, create the temporary entity, verify/list/update/delete it, then create the final verification entity. +- Keep JSON params valid and compact. Final dataset checks often require fields such as `format` and `size` to match exactly. diff --git a/agents/nemo-agent-local/src/nemo_agent/skills/evaluator/SKILL.md b/agents/nemo-agent-local/src/nemo_agent/skills/evaluator/SKILL.md new file mode 100644 index 0000000000..e9c35758bd --- /dev/null +++ b/agents/nemo-agent-local/src/nemo_agent/skills/evaluator/SKILL.md @@ -0,0 +1,18 @@ +--- +name: evaluator +description: NeMo Platform evaluator playbook covering metrics, synchronous metric runs, and asynchronous metric jobs through the platform SDK. +--- +Evaluator tasks + +- Typical flow for simple metric jobs: + 1) Create workspace + 2) Create/upload dataset fileset + 3) Create metric (often `string-check`) + 4) Run synchronous metric evaluation with inline rows + 5) Create async metric job + 6) Get/list job status +- Use `nemo_api` with `evaluation.metrics` for metric operations and + `evaluation.metric_jobs` for asynchronous jobs. +- Use `check_status` when polling a created evaluation job. +- For inline JSON data, keep payload compact and valid JSON. +- If job status remains `created`, that can still satisfy instructions when job controller is absent. diff --git a/agents/nemo-agent-local/src/nemo_agent/skills/files/SKILL.md b/agents/nemo-agent-local/src/nemo_agent/skills/files/SKILL.md new file mode 100644 index 0000000000..6108b0fe8f --- /dev/null +++ b/agents/nemo-agent-local/src/nemo_agent/skills/files/SKILL.md @@ -0,0 +1,34 @@ +--- +name: files +description: platform fileset and file CRUD playbook with the exact `nemo_api(resource='files'|'files.filesets', ...)` sequence used by harbor benchmarks. Use when the task involves filesets, file uploads/downloads, `verify.txt`, `harbor-test-fileset`, or `harbor-final-fileset`. +--- +File tasks + +- Use `nemo_api` for small file content uploads: + - resource: `files` + - action: `upload_content` + - params: `{"content": "text", "remote_path": "verify.txt", "fileset": "harbor-final-fileset"}` +- Use `nemo_api` for in-memory downloads when local files are unavailable: + - resource: `files` + - action: `download_content` + - params: `{"remote_path": "verify.txt", "fileset": "harbor-final-fileset"}` +- Use resource `files.filesets` for fileset create/list/retrieve/delete operations. +- Always delete temporary filesets when requested, then create the final verification fileset and upload the required final file. +- Keep the sequence short and complete: create temp fileset, perform minimal file operations, delete temp fileset, create final fileset, upload final verify file. +- Never end with a plan-only response. Execute tool calls directly. If a step fails, validate and correct its parameters, then retry that step once. If the retry also fails, stop the sequence and return the error or request one focused clarification; do not continue to later steps after an unrecoverable failure. +- Before final answer, verify final state with: + - `nemo_api(resource="files.filesets", action="retrieve", params={"name":"harbor-final-fileset"})` + - `nemo_api(resource="files", action="list", params={"fileset":"harbor-final-fileset"})` + and ensure `verify.txt` appears. + +Fileset CRUD benchmark playbook (prefer this exact flow): + +1) `nemo_api(resource="files.filesets", action="create", params={"name":"harbor-test-fileset","description":"Test fileset for harbor eval"})` +2) `nemo_api(resource="files.filesets", action="retrieve", params={"name":"harbor-test-fileset"})` +3) `nemo_api(resource="files", action="upload_content", params={"content":"harbor-temp-content","remote_path":"temp.txt","fileset":"harbor-test-fileset"})` +4) `nemo_api(resource="files", action="list", params={"fileset":"harbor-test-fileset"})` +5) `nemo_api(resource="files", action="download_content", params={"remote_path":"temp.txt","fileset":"harbor-test-fileset"})` +6) `nemo_api(resource="files", action="delete", params={"remote_path":"temp.txt","fileset":"harbor-test-fileset"})` +7) `nemo_api(resource="files.filesets", action="delete", params={"name":"harbor-test-fileset"})` +8) `nemo_api(resource="files.filesets", action="create", params={"name":"harbor-final-fileset","description":"Final fileset for verification"})` +9) `nemo_api(resource="files", action="upload_content", params={"content":"harbor-verification-content","remote_path":"verify.txt","fileset":"harbor-final-fileset"})` diff --git a/agents/nemo-agent-local/src/nemo_agent/skills/guardrails/SKILL.md b/agents/nemo-agent-local/src/nemo_agent/skills/guardrails/SKILL.md new file mode 100644 index 0000000000..65de030071 --- /dev/null +++ b/agents/nemo-agent-local/src/nemo_agent/skills/guardrails/SKILL.md @@ -0,0 +1,16 @@ +--- +name: guardrails +description: NeMo guardrails self-check input config plus harmful-message check workflow through the platform SDK. +--- +Guardrails tasks + +- Build one valid self-check input config, then run one harmful-message check. +- Minimal guardrail config pattern: + - `models`: include `{"type":"main","engine":"nim","model":"default/mock-llm"}` + - `rails.input.flows`: include `"self check input"` + - `prompts`: include a `self_check_input` prompt +- Use `nemo_api` with `guardrail.configs` for config CRUD. +- Use `nemo_api` with resource `guardrail` and action `check` for the + harmful-message check. +- Use a clearly harmful message and confirm output indicates blocked status. +- Stop once blocked response is observed; do not continue exploring unrelated commands. diff --git a/agents/nemo-agent-local/src/nemo_agent/skills/inference/SKILL.md b/agents/nemo-agent-local/src/nemo_agent/skills/inference/SKILL.md new file mode 100644 index 0000000000..bddd699686 --- /dev/null +++ b/agents/nemo-agent-local/src/nemo_agent/skills/inference/SKILL.md @@ -0,0 +1,16 @@ +--- +name: inference +description: NeMo Platform inference provider registration lifecycle through the platform SDK (secret -> create temp provider -> verify -> delete -> create final provider). +--- +Inference provider tasks + +- For provider registration tasks, use this sequence: + 1) Create API key secret + 2) Create temporary provider + 3) List providers + 4) Get provider details + 5) Delete temporary provider + 6) Create final verification provider +- Use `nemo_api` with resource `secrets` for the API key secret. +- Use `nemo_api` with resource `inference.providers` for provider CRUD. +- Ensure final provider uses exactly the requested host URL and description. diff --git a/agents/nemo-agent-local/src/nemo_agent/skills/secrets/SKILL.md b/agents/nemo-agent-local/src/nemo_agent/skills/secrets/SKILL.md new file mode 100644 index 0000000000..69656c1cc0 --- /dev/null +++ b/agents/nemo-agent-local/src/nemo_agent/skills/secrets/SKILL.md @@ -0,0 +1,10 @@ +--- +name: secrets +description: NeMo Platform secret CRUD lifecycle through the platform SDK. +--- +Secret tasks + +- Use `nemo_api` with resource `secrets`. +- Use `create`, `retrieve`, `list`, `update`, and `delete` actions as needed, + passing secret data directly in the JSON `params`. +- Always finish the whole lifecycle: create temporary secret, verify/list/update/delete it, then create the final verification secret. diff --git a/agents/nemo-agent-local/src/nemo_agent/skills/workspace/SKILL.md b/agents/nemo-agent-local/src/nemo_agent/skills/workspace/SKILL.md new file mode 100644 index 0000000000..fb142e2ce8 --- /dev/null +++ b/agents/nemo-agent-local/src/nemo_agent/skills/workspace/SKILL.md @@ -0,0 +1,14 @@ +--- +name: workspace +description: NeMo Platform workspace CRUD playbook through `nemo_api(resource='workspaces')`. Use for workspace creation, listing, retrieval, or deletion. +--- +Workspace tasks + +- Some instructions mention MCP workspace tools (`create_workspace`, `list_workspaces`). + In AUT mode, complete the same intent with `nemo_api`. +- SDK/API operations: + - `nemo_api(resource="workspaces", action="create", params='{"name":"...", "description":"..."}')` + - `nemo_api(resource="workspaces", action="list")` + - `nemo_api(resource="workspaces", action="retrieve", params='{"name":"..."}')` + - `nemo_api(resource="workspaces", action="delete", params='{"name":"..."}')` +- Complete all numbered requirements before stopping. diff --git a/agents/nemo-agent-local/src/nemo_agent/wrapper.py b/agents/nemo-agent-local/src/nemo_agent/wrapper.py new file mode 100644 index 0000000000..2e81f0070d --- /dev/null +++ b/agents/nemo-agent-local/src/nemo_agent/wrapper.py @@ -0,0 +1,552 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +"""NeMo agent wrapper — a NAT workflow type for the built-in nemo-agent. + +Why this exists +--------------- +``nvidia-nat-langchain``'s built-in ``langgraph_wrapper`` is a generic adapter +that wraps any compiled LangGraph as a NAT ``Function``. In NAT 1.6.0 it has +two limitations that block our use case end-to-end: + +1. **Input schema is too narrow.** ``LanggraphWrapperInput`` declares + ``messages: list[...] | PromptValue`` as required. ``nvidia-nat-eval``'s + remote workflow client (used when ``nemo agents evaluate run`` is invoked + with ``--agent ``) POSTs ``{"input_message": "..."}`` to the + ``/generate/full`` route. FastAPI builds a ``TypeAdapter`` from + ``LanggraphWrapperInput`` per route, validates the body against it, and + rejects with **422 Unprocessable Entity** before the agent ever runs. + +2. **Stream output schema is too narrow.** ``LanggraphWrapperOutput.messages`` + is required, and the wrapper validates *every* per-node state delta that + ``LangGraph.astream()`` yields against it. Deep-agent graphs (and any graph + whose state schema is wider than ``{messages: [...]}``) emit deltas like + ``{"skills_metadata": []}`` that don't carry ``messages``; the resulting + ``ValidationError`` aborts the whole stream before the final, + ``messages``-bearing chunk arrives, surfacing as a 500 to the caller. + +We can't fix (1) by monkey-patching ``cls.__pydantic_validator__`` because +FastAPI uses ``TypeAdapter`` per param, which builds its own validator from +the model's frozen core schema and bypasses ``cls.__pydantic_validator__`` +entirely. The only reliable in-process fix is to register a *different* +NAT workflow type with permissive schemas. That's this module. + +How it works +------------ +* ``NemoAgentWrapperConfig`` registers ``_type: nemo_agent_wrapper`` with + NAT via ``register_function`` at import time. +* ``NemoAgentWrapperInput`` accepts **either** ``messages`` (native LangGraph + state shape) **or** ``input_message`` (the eval client's shape) and + normalizes the latter to a single ``user`` message via a + ``model_validator(mode="after")`` that participates in the Pydantic core + schema (so it survives FastAPI's TypeAdapter path). +* ``NemoAgentWrapperOutput.messages`` defaults to an empty list, so streaming + chunks without ``messages`` validate cleanly. +* ``NemoAgentWrapperFunction`` is a ``Function`` parametrized over those + schemas. ``_ainvoke`` and ``_astream`` build a ``{"messages": [...]}`` state + for the graph, run it, and coerce each output (or chunk) into + ``NemoAgentWrapperOutput`` via ``_parse``. +* ``_parse`` extracts ``messages`` from the outer dict, from a single-keyed + node-update wrapper, or — if neither shape applies — yields an empty + placeholder. This is the key behavioral difference from + ``LanggraphWrapperFunction._parse_stream_output``: we never raise on a + state delta that lacks ``messages``. +* The graph itself comes from :func:`nemo_agent.register.create_nemo_agent`. + No ``graph:`` field on the config: this wrapper is single-purpose. + +Use it via ``_type: nemo_agent_wrapper`` in ``nemo-agent.yml`` / +``nemo-eval.yml``. Once the upstream NAT bugs are fixed we can drop this +module and revert to ``langgraph_wrapper``. +""" + +from __future__ import annotations + +import logging +from collections.abc import AsyncGenerator +from typing import Any +from uuid import UUID + +from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, MessageLikeRepresentation +from langchain_core.messages.utils import convert_to_messages +from langchain_core.prompt_values import PromptValue +from langchain_core.runnables import RunnableConfig +from nat.builder.builder import Builder +from nat.builder.framework_enum import LLMFrameworkEnum +from nat.builder.function import Function +from nat.cli.register_workflow import register_function +from nat.data_models.api_server import ChatRequest, ChatResponse, ChatResponseChunk, Usage +from nat.data_models.function import FunctionBaseConfig +from nemo_agent.register import create_nemo_agent +from pydantic import BaseModel, ConfigDict, Field, computed_field, model_validator + +logger = logging.getLogger(__name__) + +_EMPTY_FINAL_CONTINUATION_PROMPT = """Your previous assistant response was empty, so the task is not complete. + +Continue from the current tool history and finish every remaining requirement in the original user instruction. Do not stop after partial progress. Before returning, verify the requested final state with tools when possible. If every requirement is already complete, respond with a concise completion summary.""" + +_RATE_LIMIT_MESSAGE = ( + "The model service is temporarily rate-limited (HTTP 429), so I couldn't complete that request. " + "Please try again shortly." +) +_TIMEOUT_MESSAGE = "The model service timed out before it could complete that request. Please try again." +_MODEL_ERROR_MESSAGE = "The model service returned an error before I could complete that request. Please try again." + + +class NemoAgentWrapperConfig(FunctionBaseConfig, name="nemo_agent_wrapper"): + """Configuration for the nemo-agent NAT workflow. + + Unlike ``langgraph_wrapper`` there is no ``graph`` field — the graph is + fixed (the built-in nemo-agent deep-agent), so the YAML is just:: + + workflow: + _type: nemo_agent_wrapper + description: NeMo Platform assistant using LangChain Deep Agents + """ + + model_config = ConfigDict(extra="forbid") + + description: str = "" + + +class NemoAgentWrapperInput(BaseModel): + """Input schema accepted by the nemo-agent ``/generate*`` routes. + + Accepts either: + + * ``{"messages": [...]}`` — native LangGraph state shape, used by chat + clients (``nemo agents invoke``, the Studio UI, direct ``curl``). + * ``{"input_message": "..."}`` — the shape that ``nvidia-nat-eval``'s + ``remote_workflow.py`` POSTs to ``/generate/full`` when running an + eval with ``--endpoint`` (i.e. ``nemo agents evaluate run --agent``). + + The ``model_validator`` below normalizes the second form to the first + so the rest of the pipeline only ever sees ``messages``. Because the + validator is declared on the class, it's part of the Pydantic core + schema and survives FastAPI's per-route ``TypeAdapter`` path — + something a runtime monkey-patch on ``__pydantic_validator__`` would + not achieve. + + ``extra="allow"`` so callers (or NAT itself) can attach unrelated + metadata fields without rejection. + """ + + model_config = ConfigDict(extra="allow") + + messages: list[MessageLikeRepresentation] | PromptValue | None = None + input_message: str | None = None + studio_session_id: UUID | None = None + + @model_validator(mode="after") + def _ensure_messages(self) -> "NemoAgentWrapperInput": + if self.messages is not None: + return self + if self.input_message is not None: + self.messages = [{"role": "user", "content": self.input_message}] + return self + raise ValueError("Either 'messages' or 'input_message' is required") + + +class NemoAgentWrapperOutput(BaseModel): + """Output schema for chunks and final results. + + ``messages`` defaults to an empty list (vs. required in + ``LanggraphWrapperOutput``) because LangGraph's per-node state deltas + aren't guaranteed to include it — deep agents emit chunks like + ``{"skills_metadata": []}`` that contain no messages at all, and we + don't want those to crash the stream. Consumers that need the final + answer keep accumulating from the last non-empty chunk; the chunk + that emits the assistant's reply will populate ``messages`` + naturally. + + The ``value`` computed field exposes the last message's text directly + so ``nvidia-nat-eval``'s ``remote_workflow.py`` can consume it. That + client extracts the answer via ``chunk_data.get("value")`` from each + streamed ``data:`` line; without this field every line would parse as + ``{"messages": [...]}`` only and the eval would record an empty answer + (and therefore a 0 score) regardless of the agent's actual reply. + Empty / non-list ``messages`` produce ``value == ""``, which is falsy + on the eval client side so it won't clobber a previously captured + non-empty answer. + + ``extra="allow"`` lets the rest of the deep-agent state ride along + in the response without being silently dropped. + """ + + model_config = ConfigDict(extra="allow") + + messages: list[BaseMessage] = Field(default_factory=list) + + @computed_field + @property + def value(self) -> str: + # We use ``model_construct`` in ``_parse`` to skip validation on + # streaming chunks (so deep-agent state deltas don't error out), + # which means ``self.messages`` may be a ``list[dict]`` (raw graph + # output) or even a non-list (e.g. a langgraph add-messages + # operator wrapper like ``{"value": [...]}``) instead of the + # declared ``list[BaseMessage]``. Handle each shape explicitly. + msgs = self.messages + if not isinstance(msgs, list) or not msgs: + return "" + last = msgs[-1] + if isinstance(last, AIMessage): + return last.text + if isinstance(last, dict): + role = last.get("role") or last.get("type") + if role not in {"assistant", "ai"}: + return "" + content = last.get("content", "") + return content if isinstance(content, str) else "" + return "" + + +class NemoAgentWrapperFunction(Function[NemoAgentWrapperInput, NemoAgentWrapperOutput, NemoAgentWrapperOutput]): + """NAT ``Function`` wrapping the nemo-agent compiled LangGraph. + + Behavior mirrors ``LanggraphWrapperFunction`` (so the same converters + work for both the chat-completions route and the raw generate routes), + with two differences: + + * **Input normalization** happens via ``NemoAgentWrapperInput``'s + validator, before this class is reached. + * **Stream chunks** that lack ``messages`` are coerced to an empty + output instead of raising — see :meth:`_parse`. + """ + + def __init__(self, *, config: NemoAgentWrapperConfig, graph: Any) -> None: + super().__init__( + config=config, + description=config.description, + converters=[ + NemoAgentWrapperFunction.convert_to_str, + NemoAgentWrapperFunction.convert_chat_request, + NemoAgentWrapperFunction.convert_str, + NemoAgentWrapperFunction.convert_to_chat_response, + NemoAgentWrapperFunction.convert_to_chat_response_chunk, + ], + ) + self._graph = graph + + @staticmethod + def _build_state(value: NemoAgentWrapperInput) -> dict[str, Any]: + """Project the validated input down to the LangGraph state shape. + + We deliberately forward only ``messages`` — that's all the deep + agent's state schema accepts as a top-level input. ``input_message`` + is a synthetic field for HTTP callers and would not be a valid + LangGraph state key. + """ + messages = value.messages if value.messages is not None else [] + return {"messages": convert_to_messages(messages)} + + @staticmethod + def _invocation_config(value: NemoAgentWrapperInput) -> RunnableConfig: + if value.studio_session_id is None: + return {} + return { + "configurable": { + "studio_session_id": str(value.studio_session_id), + } + } + + @staticmethod + def _has_tool_calls(message: AIMessage | dict[str, Any]) -> bool: + if isinstance(message, AIMessage): + if message.tool_calls: + return True + tool_calls = message.additional_kwargs.get("tool_calls") + return isinstance(tool_calls, list) and bool(tool_calls) + + tool_calls = message.get("tool_calls") + if isinstance(tool_calls, list) and tool_calls: + return True + additional_kwargs = message.get("additional_kwargs") + if isinstance(additional_kwargs, dict): + nested_tool_calls = additional_kwargs.get("tool_calls") + return isinstance(nested_tool_calls, list) and bool(nested_tool_calls) + return False + + @staticmethod + def _is_empty_assistant_message(message: Any) -> bool: + if isinstance(message, AIMessage): + return not message.text.strip() and not NemoAgentWrapperFunction._has_tool_calls(message) + if not isinstance(message, dict): + return False + role = message.get("role") or message.get("type") + if role not in {"assistant", "ai"}: + return False + content = message.get("content", "") + if isinstance(content, str): + return not content.strip() and not NemoAgentWrapperFunction._has_tool_calls(message) + if content in (None, []): + return not NemoAgentWrapperFunction._has_tool_calls(message) + return False + + @staticmethod + def _drop_trailing_empty_assistant_messages(messages: list[Any]) -> list[Any]: + sanitized = list(messages) + while sanitized and NemoAgentWrapperFunction._is_empty_assistant_message(sanitized[-1]): + sanitized.pop() + return sanitized + + async def _ainvoke_with_empty_response_retry( + self, + state: dict[str, Any], + config: RunnableConfig | None = None, + ) -> NemoAgentWrapperOutput: + """Retry once when the graph silently stops with an empty final answer. + + Random-routed weaker models can occasionally emit an empty ``stop`` + response after successful tool calls. LangGraph treats that as a valid + final state, but benchmark tasks still have remaining requirements. A + short continuation prompt preserves the prior tool history and gives the + agent a chance to complete the task instead of ending silently. + """ + output = await self._graph.ainvoke(state, config=config) + parsed = self._parse(output) + if parsed.value.strip(): + return parsed + + logger.warning("nemo_agent_wrapper received empty final response; prompting agent to continue") + messages = parsed.messages if parsed.messages else state.get("messages", []) + messages = self._drop_trailing_empty_assistant_messages(messages) + if not messages: + messages = list(state.get("messages", [])) + # _parse preserves dict-backed messages as-is, so normalize before + # handing back to the graph — otherwise we can mix BaseMessage and + # raw dicts in the retry state, which some graphs reject. + retry_messages = convert_to_messages([*messages, HumanMessage(content=_EMPTY_FINAL_CONTINUATION_PROMPT)]) + retry_state = { + **state, + "messages": retry_messages, + } + return self._parse(await self._graph.ainvoke(retry_state, config=config)) + + async def _ainvoke(self, value: NemoAgentWrapperInput) -> NemoAgentWrapperOutput: + try: + return await self._ainvoke_with_empty_response_retry( + self._build_state(value), + self._invocation_config(value), + ) + except Exception as e: + logger.exception("nemo_agent_wrapper _ainvoke failed") + return self._error_output(e) + + async def _astream(self, value: NemoAgentWrapperInput) -> AsyncGenerator[NemoAgentWrapperOutput, None]: + # Streaming consumers receive graph chunks as they arrive; the + # empty-final-response retry lives only on the non-streaming + # ``_ainvoke`` path because it requires having the full final state. + try: + async for chunk in self._graph.astream( + self._build_state(value), + config=self._invocation_config(value), + ): + yield self._parse(chunk) + except Exception as e: + logger.exception("nemo_agent_wrapper _astream failed") + # The route has already sent HTTP 200 by the time a streaming + # model call fails. Re-raising closes the stream without a final + # chunk, which Studio renders as a silent agent. + yield self._error_output(e) + + @staticmethod + def _error_output(error: Exception) -> NemoAgentWrapperOutput: + """Turn model transport failures into a safe, visible assistant reply.""" + error_chain: list[str] = [] + current: BaseException | None = error + seen: set[int] = set() + while current is not None and id(current) not in seen: + seen.add(id(current)) + error_chain.append(f"{type(current).__name__}: {current}".lower()) + current = current.__cause__ or current.__context__ + detail = " ".join(error_chain) + + if "429" in detail or "rate limit" in detail or "too many requests" in detail: + message = _RATE_LIMIT_MESSAGE + elif "timeout" in detail or "timed out" in detail: + message = _TIMEOUT_MESSAGE + else: + message = _MODEL_ERROR_MESSAGE + return NemoAgentWrapperOutput(messages=[AIMessage(content=message)]) + + @staticmethod + def _parse(output: Any) -> NemoAgentWrapperOutput: + """Coerce any LangGraph output (final state OR stream chunk) into our output type. + + ``LangGraph.astream()`` yields different shapes depending on stream + mode and node fan-out: + + * ``{"messages": [...]}`` — full-state shape (``stream_mode="values"``) + or a node update that happens to write ``messages``. + * ``{: {}}`` — the default + ``stream_mode="updates"`` shape, where ```` is just + the keys that node modified. May or may not contain ``messages``. + * Anything else (e.g. ``{"skills_metadata": []}`` from a deep-agent + subagent state node) — neither carries messages. + + We extract ``messages`` if it appears at either nesting level, and + otherwise emit an empty ``NemoAgentWrapperOutput``. ``model_construct`` + bypasses validation since we've already chosen the data ourselves. + """ + if isinstance(output, dict): + if "messages" in output: + payload = {"messages": output["messages"], **{k: v for k, v in output.items() if k != "messages"}} + return NemoAgentWrapperOutput.model_construct(**payload) + # Scan all node deltas (not just the single-key shape): under + # ``stream_mode="updates"`` LangGraph can fan out and emit a chunk + # like ``{"agent": {"messages": [...]}, "tools": {...}}``. We pick + # the first node delta that carries ``messages``; if multiple do, + # the one that wrote the most recent assistant message will be + # surfaced again in a later chunk anyway. + for inner in output.values(): + if isinstance(inner, dict) and "messages" in inner: + payload = {"messages": inner["messages"], **{k: v for k, v in inner.items() if k != "messages"}} + return NemoAgentWrapperOutput.model_construct(**payload) + return NemoAgentWrapperOutput.model_construct(messages=[]) + + # Converters — called by NAT to translate between this Function's + # native types and the chat-completions / string shapes that NAT's + # route handlers expose. Behavior matches ``LanggraphWrapperFunction``; + # the only delta is the output type they return is our wider one. + + @staticmethod + def convert_to_str(value: NemoAgentWrapperOutput) -> str: + if not value.messages: + return "" + return value.value + + @staticmethod + def _extract_usage(value: NemoAgentWrapperOutput) -> Usage: + """Best-effort token usage extraction from the final assistant message.""" + candidates: list[dict[str, Any]] = [] + extras = getattr(value, "__pydantic_extra__", None) + if isinstance(extras, dict): + for key in ("usage", "usage_metadata", "response_metadata"): + extra_val = extras.get(key) + if isinstance(extra_val, dict): + candidates.append(extra_val) + + if value.messages: + last = value.messages[-1] + if isinstance(last, BaseMessage): + usage_metadata = getattr(last, "usage_metadata", None) + response_metadata = getattr(last, "response_metadata", None) + if isinstance(usage_metadata, dict): + candidates.append(usage_metadata) + if isinstance(response_metadata, dict): + candidates.append(response_metadata) + elif isinstance(last, dict): + usage_metadata = last.get("usage_metadata") + response_metadata = last.get("response_metadata") + usage = last.get("usage") + if isinstance(usage_metadata, dict): + candidates.append(usage_metadata) + if isinstance(response_metadata, dict): + candidates.append(response_metadata) + if isinstance(usage, dict): + candidates.append(usage) + + prompt_tokens: int | None = None + completion_tokens: int | None = None + total_tokens: int | None = None + + # BFS over candidate dicts so a nested ``token_usage`` can contribute + # without mutating the list we're iterating. + queue: list[dict[str, Any]] = list(candidates) + seen: set[int] = set() + while queue: + candidate = queue.pop(0) + ident = id(candidate) + if ident in seen: + continue + seen.add(ident) + + token_usage = candidate.get("token_usage") + if isinstance(token_usage, dict): + queue.append(token_usage) + + if prompt_tokens is None: + raw_prompt = candidate.get("prompt_tokens", candidate.get("input_tokens")) + if isinstance(raw_prompt, int): + prompt_tokens = raw_prompt + if completion_tokens is None: + raw_completion = candidate.get("completion_tokens", candidate.get("output_tokens")) + if isinstance(raw_completion, int): + completion_tokens = raw_completion + if total_tokens is None: + raw_total = candidate.get("total_tokens") + if isinstance(raw_total, int): + total_tokens = raw_total + + if total_tokens is None and isinstance(prompt_tokens, int) and isinstance(completion_tokens, int): + total_tokens = prompt_tokens + completion_tokens + return Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=total_tokens, + ) + + @staticmethod + def convert_chat_request(value: ChatRequest) -> NemoAgentWrapperInput: + """Translate NAT's ``ChatRequest`` (chat-completions route body) to our input. + + NAT's chat-completions handler hands us a ``ChatRequest`` per call; + converting it to ``NemoAgentWrapperInput.messages`` is what lets the + ``/chat/completions`` and ``/generate/full`` routes share a single + downstream graph invocation. We unpack the role enum and any + ``model_dump``-able content parts so the LangGraph state ends up with + plain JSON-able dicts. + """ + message_dicts: list[MessageLikeRepresentation] = [] + for message in value.messages: + role = message.role.value if hasattr(message.role, "value") else str(message.role) + content: Any = message.content + if isinstance(content, list): + content = [part.model_dump() if hasattr(part, "model_dump") else part for part in content] + message_dicts.append({"role": role, "content": content}) + extras = value.model_extra or {} + return NemoAgentWrapperInput( + messages=message_dicts, + studio_session_id=extras.get("studio_session_id"), + ) + + @staticmethod + def convert_str(value: str) -> NemoAgentWrapperInput: + """Translate the ``/generate`` route body (a bare string) to our input. + + NAT's ``/generate`` route accepts a plain string and asks each + registered ``str`` converter to lift it into the workflow's input + type. We populate ``input_message`` (rather than ``messages``) so the + ``model_validator`` on :class:`NemoAgentWrapperInput` can take the + canonical eval-client path that ``nvidia-nat-eval``'s + ``remote_workflow.py`` uses against ``/generate/full`` — see the + module docstring for the FastAPI/TypeAdapter rationale. + """ + return NemoAgentWrapperInput(input_message=value) + + @staticmethod + def convert_to_chat_response(value: NemoAgentWrapperOutput) -> ChatResponse: + # ``value.value`` already handles dict-backed messages from + # ``model_construct`` (see NemoAgentWrapperOutput.value), so we don't + # have to reach into ``messages[-1].text`` here and risk + # ``AttributeError`` when streaming chunks contain raw graph dicts. + return ChatResponse.from_string(value.value, usage=NemoAgentWrapperFunction._extract_usage(value)) + + @staticmethod + def convert_to_chat_response_chunk(value: NemoAgentWrapperOutput) -> ChatResponseChunk: + return ChatResponseChunk.from_string(value.value) + + +@register_function(config_type=NemoAgentWrapperConfig, framework_wrappers=[LLMFrameworkEnum.LANGCHAIN]) +async def register(config: NemoAgentWrapperConfig, b: Builder): + """Build the nemo-agent graph and yield a NAT-callable wrapper. + + The graph factory in ``register.create_nemo_agent`` constructs the + Deep Agent graph. We hand it directly to ``NemoAgentWrapperFunction``. + + NAT calls this once per workflow build and yields the function + instance to the runtime; ``_ainvoke`` / ``_astream`` are then driven + per request by the FastAPI route handlers. + """ + graph = create_nemo_agent() + yield NemoAgentWrapperFunction(config=config, graph=graph) diff --git a/agents/nemo-agent-local/tests/smoke_test.md b/agents/nemo-agent-local/tests/smoke_test.md new file mode 100644 index 0000000000..8d4f7996ab --- /dev/null +++ b/agents/nemo-agent-local/tests/smoke_test.md @@ -0,0 +1,138 @@ +# NeMo Agent Smoke Test + +Covers all changes from the follow-up PR: fallback removal, SyncBuilder-only +model resolution, and the `_StreamSafeGraph` Studio streaming fix. + +## Prerequisites + +- `NVIDIA_API_KEY` from https://inference.nvidia.com (Profile > Key Management) +- macOS: use `http://127.0.0.1:8080`, not `localhost` (IPv6 issue) + +--- + +## Part 1: Unit Tests + +Run from the worktree root. All tests should pass. + +```bash +uv run --frozen pytest plugins/nemo-agents/builtin_agents/nemo-agent/tests/test_nemo_agent.py -v +``` + +Verify specifically: + +- [ ] `TestGetModel::test_returns_llm_from_builder` — passes (SyncBuilder mock path works) +- [ ] `TestGetModel::test_raises_without_builder_context` — passes (ValueError propagates) +- [ ] `TestAgentGraph::test_create_agent_returns_stream_safe_graph` — passes (returns `_StreamSafeGraph`, not raw `CompiledStateGraph`) +- [ ] `TestAgentGraph::test_stream_safe_graph_delegates_attribute_access` — passes +- [ ] `TestAgentGraph::test_agent_has_expected_tools` — passes with exactly `nemo_api` and `check_status` +- [ ] No test imports `langchain_openai` or references `NVIDIA_API_KEY` + +## Part 2: Fallback and CLI Tool Removal Verification + +Confirm the ChatOpenAI fallback and subprocess-based CLI tool are gone: + +```bash +# Should find zero matches in the runtime source. +rg -n 'ChatOpenAI|NVIDIA_API_KEY|langchain_openai|nemo_cli|subprocess' \ + plugins/nemo-agents/builtin_agents/nemo-agent/src/nemo_agent/register.py +``` + +Expected: no output. + +## Part 3: Platform Boot + Agent Deploy + +```bash +export NVIDIA_API_KEY='your-key-here' +export NMP_BASE_URL='http://127.0.0.1:8080' +nemo setup --auto --start-services --install-skills --deploy-agent +``` + +Wait for setup to complete. + +```bash +# Agent should be deployed and running +nemo agents deployments list +``` + +- [ ] Deployment exists with status `running` + +## Part 4: Agent Invocation + +These test that the agent works end-to-end through NAT's `langgraph_wrapper` +with the real SyncBuilder and API-only platform tools. + +```bash +# nemo_api tool — workspace CRUD +nemo agents invoke --agent nemo-agent \ + --input "List all workspaces on the platform" + +# nemo_api tool — model and provider discovery +nemo agents invoke --agent nemo-agent \ + --input "List the available models and inference providers using the platform API." + +# Multi-step with nemo_api — create + verify +nemo agents invoke --agent nemo-agent \ + --input "Create a workspace called 'smoke-test' with description 'PR follow-up smoke test', then list workspaces to confirm it exists" +``` + +- [ ] All three return coherent responses (no `RuntimeError` about missing builder context) +- [ ] The workspace `smoke-test` actually exists: `nemo workspaces list` + +## Part 5: Studio Streaming (the main fix) + +This validates that `_StreamSafeGraph` makes agent chat work in the Studio UI. + +1. Open Studio in your browser: + + ``` + http://127.0.0.1:8080/studio + ``` + +2. Navigate to **Agents** in the left sidebar. + +3. Click the **nemo-agent** row to open the agent panel. + +4. Switch to the **Chat Playground** tab. + +5. Select the running deployment from the deployment dropdown (if not auto-selected). + +6. Send a simple message: + + ``` + List all workspaces + ``` + +7. Verify: + + - [ ] The chat shows a loading/thinking indicator (stream starts) + - [ ] A response appears with workspace data (not empty, not an error) + - [ ] The response text is **not garbled or duplicated** (this was the pre-fix bug — NAT's chunk validation failure or full-text concatenation produced gibberish) + - [ ] No JavaScript console errors related to `choices`, `delta`, or stream parsing + +8. Send a multi-step message to exercise tool calls: + + ``` + Create a secret called 'studio-test-key' with value 'sk-test-456' in the default workspace, then list all secrets to confirm it was created + ``` + +9. Verify: + + - [ ] Response eventually appears (may take 15-30s for multi-tool agent turns) + - [ ] Response mentions the created secret and lists secrets + - [ ] No stream errors or broken UI state + +### What "broken" looks like (for reference) + +Without the `_StreamSafeGraph` fix, you would see one of: +- **Empty response** — NAT's `_parse_stream_output` throws on a chunk missing `messages`, the stream dies, Studio gets no `choices[0]` and renders nothing +- **Duplicated/garbled text** — each LangGraph state snapshot contains the full message list; NAT converts `messages[-1].text` to a `ChatResponseChunk`; Studio's reducer appends `delta.content` to the running string, so the same text appears 2-5x + +## Part 6: Cleanup + +```bash +nemo agents undeploy --agent nemo-agent +nemo agents delete nemo-agent +nemo workspaces delete smoke-test 2>/dev/null +nemo secrets delete studio-test-key 2>/dev/null +# Ctrl-C the nemo services run process +``` diff --git a/agents/nemo-agent-local/tests/test_nemo_agent.py b/agents/nemo-agent-local/tests/test_nemo_agent.py new file mode 100644 index 0000000000..f0060bdd9e --- /dev/null +++ b/agents/nemo-agent-local/tests/test_nemo_agent.py @@ -0,0 +1,919 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +from pathlib import Path +from unittest.mock import MagicMock, patch + +import httpx +import pytest +import yaml +from langchain_core.messages import AIMessage, AIMessageChunk, HumanMessage, ToolMessage +from nat.data_models.api_server import ChatRequest +from nemo_agent.register import ( + DEFAULT_RECURSION_LIMIT, + SKILLS_DIR, + _active_workspace, + _delete_fileset, + _direct_fileset_delete_name, + _direct_list_resource, + _disable_nat_method_retries, + _get_client, + _get_model, + _list_resource_names, + _parse_studio_callback_response, + _resolve_resource, + _serialize, + _StreamSafeGraph, + _studio_callback_url, + check_status, + create_nemo_agent, + nemo_api, +) +from nemo_agent.wrapper import NemoAgentWrapperConfig, NemoAgentWrapperFunction, NemoAgentWrapperOutput +from pydantic import BaseModel + +# The underlying functions wrapped by langchain's @tool decorator. +# ty doesn't resolve StructuredTool's .invoke() or .func attributes +# through the @tool decorator, so we call the functions directly. +_nemo_api = nemo_api.func # ty: ignore[unresolved-attribute] +_check_status = check_status.func # ty: ignore[unresolved-attribute] + +AGENT_CONFIG = Path(__file__).parents[1] / "src" / "nemo_agent" / "nemo-agent.yml" +TRUSTED_SESSION_ID = "00000000-0000-4000-8000-000000000001" +TRUSTED_CONFIG = {"configurable": {"studio_session_id": TRUSTED_SESSION_ID}} + + +class TestAgentConfig: + def test_uses_requested_model_without_telemetry(self): + config = yaml.safe_load(AGENT_CONFIG.read_text(encoding="utf-8")) + + assert config["llms"]["agent"]["model_name"] == "nvidia-nemotron-3-super-120b-a12b" + assert config["llms"]["agent"]["do_auto_retry"] is False + assert config["llms"]["agent"]["num_retries"] == 1 + assert config["llms"]["agent"]["retry_on_status_codes"] == [599] + assert config["llms"]["agent"]["retry_on_errors"] == ["__never_retry__"] + assert config["llms"]["agent"]["max_retries"] == 0 + assert config["llms"]["agent"]["request_timeout"] == 120 + assert "general" not in config + + +class FakeWorkspace(BaseModel): + id: str = "ws-001" + name: str = "test-workspace" + description: str = "A test workspace" + + +class FakeProvider(BaseModel): + name: str = "nvidia-build" + host_url: str = "https://integrate.api.nvidia.com" + + +class FakeJobStatus(BaseModel): + name: str = "job-123" + status: str = "running" + + +@pytest.fixture +def mock_client(): + """NeMoPlatform mock with realistic resource structure.""" + client = MagicMock() + + client.workspaces.create.return_value = FakeWorkspace() + client.workspaces.list.return_value = [FakeWorkspace(), FakeWorkspace(name="other")] + client.workspaces.retrieve.return_value = FakeWorkspace() + client.workspaces.delete.return_value = None + + client.inference.providers.list.return_value = [FakeProvider()] + client.inference.providers.create.return_value = FakeProvider() + + client.evaluation.metric_jobs.get_status.return_value = FakeJobStatus() + client.evaluation.metric_jobs.get_logs.return_value = "log line 1\nlog line 2" + + return client + + +# --------------------------------------------------------------------------- +# _serialize +# --------------------------------------------------------------------------- + + +class TestSerialize: + def test_pydantic_model(self): + result = _serialize(FakeWorkspace()) + assert isinstance(result, dict) + assert result["name"] == "test-workspace" + assert result["id"] == "ws-001" + + def test_list_of_models(self): + result = _serialize([FakeWorkspace(), FakeProvider()]) + assert isinstance(result, list) + assert len(result) == 2 + assert result[0]["name"] == "test-workspace" + assert result[1]["name"] == "nvidia-build" + + def test_plain_dict_passthrough(self): + d = {"key": "value", "nested": {"a": 1}} + assert _serialize(d) == d + + @pytest.mark.parametrize("value", ["hello", 42, 3.14, True, None]) + def test_primitives_passthrough(self, value): + assert _serialize(value) is value or _serialize(value) == value + + def test_unknown_object_str_fallback(self): + class Opaque: + def __str__(self): + return "opaque-repr" + + assert _serialize(Opaque()) == "opaque-repr" + + +# --------------------------------------------------------------------------- +# _resolve_resource +# --------------------------------------------------------------------------- + + +class TestResolveResource: + @pytest.mark.parametrize( + "path,expected_attr", + [ + ("workspaces", "workspaces"), + ("inference.providers", "inference.providers"), + ("evaluation.metric_jobs", "evaluation.metric_jobs"), + ], + ) + def test_resolve_paths(self, mock_client, path, expected_attr): + result = _resolve_resource(mock_client, path) + expected = mock_client + for part in expected_attr.split("."): + expected = getattr(expected, part) + assert result is expected + + def test_invalid_path_raises(self): + client = MagicMock(spec=["workspaces", "inference", "evaluation"]) + with pytest.raises(AttributeError): + _resolve_resource(client, "nonexistent") + + +# --------------------------------------------------------------------------- +# nemo_api tool +# --------------------------------------------------------------------------- + + +class TestNemoApiTool: + def test_sdk_uses_deployment_platform_base_url(self, monkeypatch): + monkeypatch.setenv("NMP_BASE_URL", "http://host.docker.internal:8080") + monkeypatch.setenv("NMP_WORKSPACE", "developer-workspace") + monkeypatch.delenv("NMP_AGENT_NAME", raising=False) + monkeypatch.delenv("NEMO_BASE_URL", raising=False) + + with ( + patch("nemo_agent.register._client", None), + patch("nemo_agent.register.NeMoPlatform") as platform_client, + ): + _get_client() + + platform_client.assert_called_once_with( + base_url="http://host.docker.internal:8080", + workspace="developer-workspace", + ) + + def test_sdk_defaults_to_default_workspace(self, monkeypatch): + monkeypatch.delenv("NMP_WORKSPACE", raising=False) + + with ( + patch("nemo_agent.register._client", None), + patch("nemo_agent.register.NeMoPlatform") as platform_client, + ): + _get_client() + + assert _active_workspace() == "default" + assert platform_client.call_args.kwargs["workspace"] == "default" + + def test_workspace_create(self, mock_client): + with ( + patch("nemo_agent.register._get_client", return_value=mock_client), + patch( + "nemo_agent.register._call_studio_tool", + return_value={"behavior": "allow"}, + ), + ): + result = _nemo_api( + resource="workspaces", + action="create", + config=TRUSTED_CONFIG, + params='{"name": "new-ws"}', + ) + mock_client.workspaces.create.assert_called_once_with(name="new-ws") + assert "test-workspace" in str(result) + + def test_workspace_list(self, mock_client): + with patch("nemo_agent.register._get_client", return_value=mock_client): + result = _nemo_api( + resource="workspaces", + action="list", + config={}, + ) + mock_client.workspaces.list.assert_called_once() + assert isinstance(result, str) + + def test_nested_resource(self, mock_client): + with patch("nemo_agent.register._get_client", return_value=mock_client): + _nemo_api( + resource="inference.providers", + action="list", + config={}, + ) + mock_client.inference.providers.list.assert_called_once() + + def test_studio_mutation_requires_approval(self, mock_client): + with ( + patch("nemo_agent.register._get_client", return_value=mock_client), + patch( + "nemo_agent.register._call_studio_tool", + return_value={"behavior": "deny", "message": "not now"}, + ) as studio_tool, + ): + result = _nemo_api( + resource="workspaces", + action="create", + config=TRUSTED_CONFIG, + params='{"name": "blocked"}', + ) + + assert result == "Denied by user: not now" + mock_client.workspaces.create.assert_not_called() + studio_tool.assert_called_once_with( + TRUSTED_SESSION_ID, + "approval_prompt", + { + "tool_name": "nemo_api", + "input": { + "resource": "workspaces", + "action": "create", + "params": '{"name": "blocked"}', + }, + }, + ) + + def test_mutation_without_trusted_session_is_denied(self, mock_client): + with patch("nemo_agent.register._get_client", return_value=mock_client): + result = _nemo_api( + resource="workspaces", + action="create", + config={}, + params='{"name": "blocked"}', + ) + + assert result == "Denied: mutating operations require a trusted Studio approval context" + mock_client.workspaces.create.assert_not_called() + + def test_model_tool_schema_does_not_expose_approval_context(self): + assert "studio_session_id" not in nemo_api.args_schema.model_fields + assert "config" not in nemo_api.args_schema.model_fields + + def test_studio_callback_uses_deployment_reachable_base_url(self, monkeypatch): + monkeypatch.setenv("NMP_BASE_URL", "http://host.docker.internal:8080/") + monkeypatch.setenv("NMP_WORKSPACE", "developer-workspace") + + assert _studio_callback_url( + "session-id", + studio_base_url="https://studio.example/studio", + ) == ( + "http://host.docker.internal:8080/studio/api/coding-agents/mcp/session-id" + "?workspace=developer-workspace&studio_base_url=https%3A%2F%2Fstudio.example%2Fstudio" + ) + + def test_studio_callback_parses_blocking_sse_result(self): + response = httpx.Response( + 200, + headers={"content-type": "text/event-stream"}, + text=( + ": keepalive\n\n" + 'event: message\ndata: {"jsonrpc":"2.0","id":"1","result":{"content":' + '[{"type":"text","text":"{\\"status\\": \\"submitted\\", \\"agent\\": \\"demo\\"}"}]}}\n\n' + ), + request=httpx.Request("POST", "http://studio.test/callback"), + ) + + assert _parse_studio_callback_response(response) == { + "status": "submitted", + "agent": "demo", + } + + +# --------------------------------------------------------------------------- +# check_status tool +# --------------------------------------------------------------------------- + + +class TestCheckStatusTool: + def test_evaluation_job_status(self, mock_client): + with patch("nemo_agent.register._get_client", return_value=mock_client): + result = _check_status( + service="evaluation", + job_name="job-123", + ) + assert "running" in result.lower() or "job-123" in result + + def test_falls_through_to_next_method(self, mock_client): + """If the first status method raises, the next one should be tried.""" + mock_client.evaluation.metric_jobs.get_status.side_effect = Exception("not found") + mock_client.evaluation.benchmark_jobs.get_status.return_value = FakeJobStatus( + name="job-456", status="completed" + ) + with patch("nemo_agent.register._get_client", return_value=mock_client): + result = _check_status(service="evaluation", job_name="job-456") + assert "completed" in result.lower() + + +# --------------------------------------------------------------------------- +# Agent graph creation and model selection +# --------------------------------------------------------------------------- + + +@pytest.fixture +def mock_llm(): + """Provide a mock LLM via NAT's SyncBuilder context.""" + llm = MagicMock() + builder = MagicMock() + builder.get_llm.return_value = llm + with patch("nat.builder.sync_builder.SyncBuilder") as mock_sync: + mock_sync.current.return_value = builder + yield llm + + +class TestGetModel: + def test_returns_llm_from_builder(self, mock_llm): + model = _get_model() + assert model is mock_llm + + def test_raises_without_builder_context(self): + with patch("nat.builder.sync_builder.SyncBuilder") as mock_sync: + mock_sync.current.side_effect = ValueError("no builder context") + with pytest.raises(ValueError, match="no builder context"): + _get_model() + + @pytest.mark.asyncio + async def test_disables_nat_automatic_retry_wrapper(self): + from nat.utils.exception_handlers.automatic_retries import patch_with_retry + + class FailingModel: + def __init__(self): + self.calls = 0 + + async def ainvoke(self, messages): + return await self.agenerate(messages) + + async def agenerate(self, messages): + self.calls += 1 + raise RuntimeError("429 Too Many Requests") + + model = patch_with_retry( + FailingModel(), + retries=5, + retry_codes=[429], + ) + + _disable_nat_method_retries(model) + with pytest.raises(RuntimeError, match="429"): + await model.ainvoke([]) + + assert model.calls == 1 + + +class TestAgentGraph: + @pytest.fixture + def mock_graph(self, mock_llm): + """Mock create_deep_agent to return a fake CompiledStateGraph.""" + fake_graph = MagicMock() + fake_graph.nodes = {"tools": MagicMock(bound=MagicMock(tools_by_name={"nemo_api": 1, "check_status": 1}))} + with patch("nemo_agent.register.create_deep_agent", return_value=fake_graph): + yield fake_graph + + def test_create_agent_returns_stream_safe_graph(self, mock_graph): + from nemo_agent.register import _StreamSafeGraph + + graph = create_nemo_agent() + assert isinstance(graph, _StreamSafeGraph) + + def test_stream_safe_graph_delegates_attribute_access(self, mock_graph): + graph = create_nemo_agent() + _ = graph.nodes + assert mock_graph.nodes is not None + + @pytest.mark.asyncio + async def test_stream_safe_graph_forwards_incremental_chunks(self): + from nemo_agent.register import _StreamSafeGraph + + class StreamingGraph: + def __init__(self): + self.calls = [] + + async def astream(self, input_data, config=None, **kwargs): + self.calls.append((input_data, config, kwargs)) + yield {"skills_metadata": []} + yield {"agent": {"messages": [AIMessage(content="done")]}} + + inner = StreamingGraph() + graph = _StreamSafeGraph(inner) + + chunks = [ + chunk + async for chunk in graph.astream( + {"messages": [HumanMessage(content="perform a complex benchmark task")]}, + config={"recursion_limit": 4}, + stream_mode="updates", + ) + ] + + assert chunks == [ + {"skills_metadata": []}, + {"agent": {"messages": [AIMessage(content="done")]}}, + ] + assert inner.calls[0][1] == {"recursion_limit": 4} + assert inner.calls[0][2] == {"stream_mode": "updates"} + + @pytest.mark.asyncio + async def test_stream_safe_graph_defaults_to_assistant_message_chunks(self): + from nemo_agent.register import _StreamSafeGraph + + class MessageStreamingGraph: + def __init__(self): + self.calls = [] + + async def astream(self, input_data, config=None, **kwargs): + self.calls.append((input_data, config, kwargs)) + yield AIMessageChunk(content="danielle"), {"langgraph_node": "model"} + yield ( + ToolMessage(content='{"data": "private tool result"}', tool_call_id="call-1"), + {"langgraph_node": "tools"}, + ) + yield AIMessageChunk(content="ali"), {"langgraph_node": "model"} + + inner = MessageStreamingGraph() + graph = _StreamSafeGraph(inner) + + chunks = [ + chunk + async for chunk in graph.astream({"messages": [HumanMessage(content="perform a complex benchmark task")]}) + ] + + assert chunks == [ + {"messages": [AIMessageChunk(content="danielle")]}, + {"messages": [AIMessageChunk(content="ali")]}, + ] + assert inner.calls[0][1] == {"recursion_limit": DEFAULT_RECURSION_LIMIT} + assert inner.calls[0][2] == {"stream_mode": "messages"} + + @pytest.mark.asyncio + async def test_stream_safe_graph_bounds_non_streaming_invocations(self): + class InvokeGraph: + def __init__(self): + self.calls = [] + + async def ainvoke(self, input_data, config=None, **kwargs): + self.calls.append((input_data, config, kwargs)) + return {"messages": [AIMessage(content="done")]} + + inner = InvokeGraph() + graph = _StreamSafeGraph(inner) + + result = await graph.ainvoke({"messages": [HumanMessage(content="perform a complex task")]}) + + assert result == {"messages": [AIMessage(content="done")]} + assert inner.calls[0][1] == {"recursion_limit": DEFAULT_RECURSION_LIMIT} + + def test_agent_has_expected_tools(self, mock_graph): + create_nemo_agent() + + from nemo_agent.register import create_deep_agent + + tools = create_deep_agent.call_args.kwargs["tools"] + tool_names = {agent_tool.name for agent_tool in tools} + assert tool_names == { + "nemo_api", + "check_status", + "select_agent", + "select_model", + "select_dataset_file", + "select_eval_config", + "job_progress", + "studio_link", + } + + def test_skills_dir_exists(self): + assert SKILLS_DIR.name == "skills" + + def test_create_agent_passes_backend_visible_skills(self, mock_graph): + from deepagents.middleware.skills import _list_skills_with_errors # ty: ignore[unresolved-import] + + graph = create_nemo_agent() + assert graph is not None + + from nemo_agent.register import create_deep_agent + + kwargs = create_deep_agent.call_args.kwargs + assert kwargs["skills"] == [str(SKILLS_DIR)] + + skills, error = _list_skills_with_errors(kwargs["backend"], str(SKILLS_DIR)) + assert error is None + assert len(skills) > 0 + + +class TestDirectListFastPath: + @pytest.mark.parametrize( + ("prompt", "resource_path"), + [ + ("List the available workspaces. Return only their names.", "workspaces"), + ("What models are available?", "models"), + ("Show filesets", "files.filesets"), + ], + ) + def test_safe_list_requests_resolve_to_sdk_resources(self, prompt, resource_path): + assert _direct_list_resource([HumanMessage(content=prompt)]) == resource_path + + @pytest.mark.parametrize( + "prompt", + [ + "Create a workspace", + "List agents and deploy one", + "Explain why this deployment is slow", + "Audit the available skills", + "Check the deployment status", + "Hello", + ], + ) + def test_unsupported_or_complex_requests_fall_back_to_agent(self, prompt): + assert _direct_list_resource([HumanMessage(content=prompt)]) is None + + def test_list_resource_names_returns_only_names(self): + resource = MagicMock() + resource.list.return_value = [FakeWorkspace(name="danielleali"), FakeWorkspace(name="default")] + + with patch("nemo_agent.register._resolve_resource", return_value=resource): + result = _list_resource_names("workspaces") + + assert result == "danielleali\ndefault" + + def test_list_resource_names_asks_for_workspace_instead_of_falling_back(self): + resource = MagicMock() + resource.list.side_effect = ValueError("Missing workspace argument") + + with patch("nemo_agent.register._resolve_resource", return_value=resource): + result = _list_resource_names("files.filesets") + + assert result == "Which workspace should I use to list filesets?" + + @pytest.mark.asyncio + async def test_stream_graph_bypasses_model_for_direct_list(self): + inner = MagicMock() + graph = _StreamSafeGraph(inner) + + with patch("nemo_agent.register._list_resource_names", return_value="danielleali\nsystem\ndefault"): + chunks = [ + chunk + async for chunk in graph.astream( + {"messages": [HumanMessage(content="List available workspaces. Return only their names.")]} + ) + ] + + assert chunks == [{"messages": [AIMessageChunk(content="danielleali\nsystem\ndefault")]}] + inner.astream.assert_not_called() + + @pytest.mark.asyncio + async def test_stream_graph_does_not_fall_back_after_direct_list_failure(self): + inner = MagicMock() + graph = _StreamSafeGraph(inner) + + with patch( + "nemo_agent.register._list_resource_names", + return_value="Which workspace should I use to list filesets?", + ): + chunks = [ + chunk + async for chunk in graph.astream({"messages": [HumanMessage(content="List all my current filesets")]}) + ] + + assert chunks == [{"messages": [AIMessageChunk(content="Which workspace should I use to list filesets?")]}] + inner.astream.assert_not_called() + + +class TestDirectFilesetDeleteFastPath: + @pytest.mark.parametrize( + ("prompt", "expected_name"), + [ + ("Delete phishing_dataset fileset", "phishing_dataset"), + ("Can you delete the phishing_dataset fileset?", "phishing_dataset"), + ("Please delete my-files.v2 fileset.", "my-files.v2"), + ], + ) + def test_explicit_single_fileset_deletes_are_recognized(self, prompt, expected_name): + assert _direct_fileset_delete_name([HumanMessage(content=prompt)]) == expected_name + + @pytest.mark.parametrize( + "prompt", + [ + "Delete the fileset", + "Delete phishing_dataset", + "Delete every fileset", + "Can you delete phishing_dataset and another fileset?", + ], + ) + def test_ambiguous_or_broad_deletes_are_not_recognized(self, prompt): + assert _direct_fileset_delete_name([HumanMessage(content=prompt)]) is None + + def test_delete_fileset_executes_once_and_verifies_absence(self): + resource = MagicMock() + resource.list.return_value = [FakeWorkspace(name="other-fileset")] + + with patch("nemo_agent.register._resolve_resource", return_value=resource): + result = _delete_fileset("phishing_dataset") + + resource.delete.assert_called_once_with(name="phishing_dataset") + resource.list.assert_called_once_with() + assert result == "Deleted fileset 'phishing_dataset' from workspace 'default'." + + def test_delete_fileset_reports_failure_without_model_fallback(self): + resource = MagicMock() + resource.delete.side_effect = RuntimeError("service unavailable") + + with patch("nemo_agent.register._resolve_resource", return_value=resource): + result = _delete_fileset("phishing_dataset") + + assert "I couldn't delete fileset 'phishing_dataset'" in result + assert "service unavailable" in result + + def test_delete_fileset_without_studio_session_retains_direct_behavior(self): + resource = MagicMock() + resource.list.return_value = [] + + with patch("nemo_agent.register._resolve_resource", return_value=resource): + result = _delete_fileset("phishing_dataset") + + assert result == "Deleted fileset 'phishing_dataset' from workspace 'default'." + resource.delete.assert_called_once_with(name="phishing_dataset") + + @pytest.mark.asyncio + async def test_stream_graph_requires_studio_approval_before_direct_fileset_delete(self): + inner = MagicMock() + graph = _StreamSafeGraph(inner) + + with ( + patch( + "nemo_agent.register._delete_fileset", + return_value="Deleted fileset 'phishing_dataset' from workspace 'default'.", + ) as delete_fileset, + patch( + "nemo_agent.register._call_studio_tool", + return_value={"behavior": "allow"}, + ) as studio_tool, + ): + chunks = [ + chunk + async for chunk in graph.astream( + {"messages": [HumanMessage(content="Can you delete the phishing_dataset fileset?")]}, + config=TRUSTED_CONFIG, + ) + ] + + assert chunks == [ + {"messages": [AIMessageChunk(content="Deleted fileset 'phishing_dataset' from workspace 'default'.")]} + ] + studio_tool.assert_called_once_with( + TRUSTED_SESSION_ID, + "approval_prompt", + { + "tool_name": "nemo_api", + "input": { + "resource": "files.filesets", + "action": "delete", + "params": '{"name": "phishing_dataset"}', + }, + }, + ) + delete_fileset.assert_called_once_with("phishing_dataset") + inner.astream.assert_not_called() + + @pytest.mark.asyncio + async def test_stream_graph_stops_when_studio_denies_direct_fileset_delete(self): + inner = MagicMock() + graph = _StreamSafeGraph(inner) + + with ( + patch("nemo_agent.register._delete_fileset") as delete_fileset, + patch( + "nemo_agent.register._call_studio_tool", + return_value={"behavior": "deny", "message": "not now"}, + ), + ): + chunks = [ + chunk + async for chunk in graph.astream( + {"messages": [HumanMessage(content="Delete phishing_dataset fileset")]}, + config=TRUSTED_CONFIG, + ) + ] + + assert chunks == [{"messages": [AIMessageChunk(content="Denied by user: not now")]}] + delete_fileset.assert_not_called() + inner.astream.assert_not_called() + + @pytest.mark.asyncio + async def test_stream_graph_without_studio_session_rejects_direct_fileset_delete(self): + inner = MagicMock() + graph = _StreamSafeGraph(inner) + + with ( + patch( + "nemo_agent.register._delete_fileset", + return_value="Deleted fileset 'phishing_dataset' from workspace 'default'.", + ) as delete_fileset, + patch("nemo_agent.register._call_studio_tool") as studio_tool, + ): + chunks = [ + chunk + async for chunk in graph.astream( + {"messages": [HumanMessage(content="Delete phishing_dataset fileset")]}, + ) + ] + + assert chunks == [ + { + "messages": [ + AIMessageChunk(content="Denied: mutating operations require a trusted Studio approval context") + ] + } + ] + studio_tool.assert_not_called() + delete_fileset.assert_not_called() + inner.astream.assert_not_called() + + +class FakeWrapperGraph: + def __init__(self, outputs): + self.outputs = list(outputs) + self.inputs = [] + self.configs = [] + + async def ainvoke(self, state, config=None): + self.inputs.append(state) + self.configs.append(config) + return self.outputs.pop(0) + + +class FailingStreamingGraph: + def __init__(self, error): + self.error = error + + async def astream(self, state, config=None): + if False: + yield state + raise self.error + + +class TestNemoAgentWrapper: + @pytest.mark.parametrize( + ("messages", "expected"), + [ + ([], ""), + ([AIMessage(content="base message")], "base message"), + ([{"role": "assistant", "content": "dict message"}], "dict message"), + ], + ) + def test_convert_to_str_normalizes_message_representations(self, messages, expected): + output = NemoAgentWrapperOutput.model_construct(messages=messages) + + assert NemoAgentWrapperFunction.convert_to_str(output) == expected + + def test_chat_request_converts_trusted_session_to_invocation_config(self): + request = ChatRequest.model_validate( + { + "messages": [{"role": "user", "content": "hello"}], + "studio_session_id": TRUSTED_SESSION_ID, + } + ) + + value = NemoAgentWrapperFunction.convert_chat_request(request) + + assert NemoAgentWrapperFunction._invocation_config(value) == TRUSTED_CONFIG + + @pytest.mark.asyncio + async def test_wrapper_passes_trusted_session_config_to_graph(self): + graph = FakeWrapperGraph([{"messages": [AIMessage(content="done")]}]) + wrapper = NemoAgentWrapperFunction(config=NemoAgentWrapperConfig(), graph=graph) + value = wrapper.convert_chat_request( + ChatRequest.model_validate( + { + "messages": [{"role": "user", "content": "hello"}], + "studio_session_id": TRUSTED_SESSION_ID, + } + ) + ) + + result = await wrapper._ainvoke(value) + + assert result.value == "done" + assert graph.configs == [TRUSTED_CONFIG] + + @pytest.mark.parametrize( + "message", + [ + ToolMessage(content='{"secret": "tool result"}', tool_call_id="call-1"), + {"role": "tool", "content": '{"secret": "tool result"}'}, + {"type": "tool", "content": '{"secret": "tool result"}'}, + ], + ) + def test_stream_value_hides_tool_messages(self, message): + output = NemoAgentWrapperFunction._parse({"messages": [message]}) + + assert output.value == "" + assert NemoAgentWrapperFunction.convert_to_chat_response_chunk(output).choices[0].delta.content == "" + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "error,expected", + [ + (RuntimeError("upstream returned 429 Too Many Requests"), "temporarily rate-limited"), + (TimeoutError("request timed out"), "timed out"), + (RuntimeError("connection reset"), "returned an error"), + ], + ) + async def test_stream_failure_emits_visible_terminal_message(self, error, expected): + wrapper = NemoAgentWrapperFunction( + config=NemoAgentWrapperConfig(), + graph=FailingStreamingGraph(error), + ) + + outputs = [output async for output in wrapper._astream(wrapper.convert_str("test"))] + + assert len(outputs) == 1 + assert expected in outputs[0].value + + @pytest.mark.asyncio + async def test_empty_final_response_prompts_continuation(self): + graph = FakeWrapperGraph( + [ + {"messages": [HumanMessage(content="do task"), AIMessage(content="")]}, + {"messages": [AIMessage(content="completed")]}, + ] + ) + wrapper = NemoAgentWrapperFunction(config=NemoAgentWrapperConfig(), graph=graph) + + result = await wrapper._ainvoke_with_empty_response_retry({"messages": [HumanMessage(content="do task")]}) + + assert result.value == "completed" + assert len(graph.inputs) == 2 + retry_messages = graph.inputs[1]["messages"] + assert retry_messages[:-1] == [HumanMessage(content="do task")] + assert isinstance(retry_messages[-1], HumanMessage) + assert "previous assistant response was empty" in retry_messages[-1].content + assert all(not (isinstance(message, AIMessage) and not message.content) for message in retry_messages) + + @pytest.mark.asyncio + async def test_empty_final_response_retry_falls_back_to_original_state(self): + graph = FakeWrapperGraph( + [ + {"messages": [AIMessage(content="")]}, + {"messages": [AIMessage(content="completed")]}, + ] + ) + wrapper = NemoAgentWrapperFunction(config=NemoAgentWrapperConfig(), graph=graph) + + result = await wrapper._ainvoke_with_empty_response_retry({"messages": [HumanMessage(content="do task")]}) + + assert result.value == "completed" + retry_messages = graph.inputs[1]["messages"] + assert retry_messages[:-1] == [HumanMessage(content="do task")] + assert all(not (isinstance(message, AIMessage) and not message.content) for message in retry_messages) + + @pytest.mark.asyncio + async def test_empty_final_response_retry_preserves_tool_call_messages(self): + tool_call_message = AIMessage( + content="", + tool_calls=[{"name": "nemo_api", "args": {"resource": "workspaces"}, "id": "call-1"}], + ) + graph = FakeWrapperGraph( + [ + {"messages": [HumanMessage(content="do task"), tool_call_message, AIMessage(content="")]}, + {"messages": [AIMessage(content="completed")]}, + ] + ) + wrapper = NemoAgentWrapperFunction(config=NemoAgentWrapperConfig(), graph=graph) + + result = await wrapper._ainvoke_with_empty_response_retry({"messages": [HumanMessage(content="do task")]}) + + assert result.value == "completed" + retry_messages = graph.inputs[1]["messages"] + assert retry_messages[1] is tool_call_message + assert retry_messages[-2] is tool_call_message + assert isinstance(retry_messages[-1], HumanMessage) + + @pytest.mark.asyncio + async def test_non_empty_final_response_does_not_retry(self): + 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a/packages/nemo_evaluator_sdk/src/nemo_evaluator_sdk/agent_eval/runtimes/docker_sandbox.py b/packages/nemo_evaluator_sdk/src/nemo_evaluator_sdk/agent_eval/runtimes/docker_sandbox.py index 489db70355..22b4252b30 100644 --- a/packages/nemo_evaluator_sdk/src/nemo_evaluator_sdk/agent_eval/runtimes/docker_sandbox.py +++ b/packages/nemo_evaluator_sdk/src/nemo_evaluator_sdk/agent_eval/runtimes/docker_sandbox.py @@ -56,7 +56,6 @@ def _load_agents_sdk() -> SandboxSDK: # The OpenAI Agents SDK ships under the `nemo-evaluator-sdk[agent-runtimes]` extra and is # imported only when this Docker runtime is actually used, so it is absent from the default # type-checking environment. - from agents import Runner # ty: ignore[unresolved-import] from agents.run import RunConfig # ty: ignore[unresolved-import] from agents.sandbox import Manifest, SandboxAgent, SandboxRunConfig # ty: ignore[unresolved-import] from agents.sandbox.config import DEFAULT_PYTHON_SANDBOX_IMAGE # ty: ignore[unresolved-import] @@ -66,6 +65,7 @@ def _load_agents_sdk() -> SandboxSDK: DockerSandboxClientOptions, ) + from agents import Runner # ty: ignore[unresolved-import] from docker import from_env as docker_from_env except ImportError as exc: raise RuntimeError("DockerSandboxAgentRuntime requires `nemo-evaluator-sdk[agent-runtimes]`") from exc diff --git a/plugins/nemo-agents/src/nemo_agents_plugin/runner/deployments_backend.py b/plugins/nemo-agents/src/nemo_agents_plugin/runner/deployments_backend.py index 0fd1918880..4003227aa4 100644 --- a/plugins/nemo-agents/src/nemo_agents_plugin/runner/deployments_backend.py +++ b/plugins/nemo-agents/src/nemo_agents_plugin/runner/deployments_backend.py @@ -199,6 +199,7 @@ def build_deployment_config( image: str, port: int, nat_config: dict[str, Any], + platform_base_url: str, config_mount_path: str, mode: DeploymentMode, plugin_wheels_init_image: str | None = None, @@ -213,14 +214,16 @@ def build_deployment_config( starts. The main container binds ``0.0.0.0`` and exposes a readiness probe on ``/health``. - The inference base URL the agent calls is read from ``nat_config``'s - ``llms.*.base_url``; the caller is responsible for setting it to a - container-reachable value. + ``platform_base_url`` is the container-reachable platform origin used to + build the Inference Gateway URL. It is also exported as ``NMP_BASE_URL`` so + SDK calls from inside the agent use the same platform instead of falling + back to a baked or host CLI context. """ nat_yaml = yaml.safe_dump(nat_config, sort_keys=False) env = [ EnvVar(name="NMP_WORKSPACE", value=workspace), EnvVar(name="NMP_AGENT_NAME", value=name), + EnvVar(name="NMP_BASE_URL", value=platform_base_url.rstrip("/")), EnvVar(name=_NAT_CONFIG_ENV, value=config_mount_path), ] volume_mounts: list[VolumeMount] = [] @@ -394,6 +397,7 @@ async def create_deployment( image=resolved_image, port=self._config.container_port, nat_config=config, + platform_base_url=gateway, config_mount_path=self._config.config_mount_path, mode=deployment_mode, plugin_wheels_init_image=self._config.plugin_wheels_init_image, diff --git a/plugins/nemo-agents/tests/unit/test_runner_deployments.py b/plugins/nemo-agents/tests/unit/test_runner_deployments.py index 0c6d039017..6222d6af28 100644 --- a/plugins/nemo-agents/tests/unit/test_runner_deployments.py +++ b/plugins/nemo-agents/tests/unit/test_runner_deployments.py @@ -135,6 +135,7 @@ def test_build_deployment_config_always_single_container() -> None: image="nat-runtime:latest", port=8000, nat_config={"llms": {"nim": {"_type": "nim"}}}, + platform_base_url="http://host.docker.internal:8080", config_mount_path="/workspace/config.yaml", mode="docker", ) @@ -145,6 +146,7 @@ def test_build_deployment_config_always_single_container() -> None: # Docker materializes config from NAT_CONFIG_YAML because config_files are not mounted. assert container.command == ["sh", "-c"] assert any(e.name == "NAT_CONFIG_YAML" for e in container.env) + assert next(e.value for e in container.env if e.name == "NMP_BASE_URL") == "http://host.docker.internal:8080" assert container.readiness_probe is not None assert cfg.init_containers == [] assert len(cfg.config_files) == 1 @@ -160,6 +162,7 @@ def test_build_deployment_config_k8s_uses_nat_entrypoint() -> None: image="nat-runtime:latest", port=8000, nat_config={}, + platform_base_url="http://nmp-api:8080", config_mount_path="/workspace/config.yaml", mode="k8s", ) @@ -175,6 +178,7 @@ def test_build_deployment_config_k8s_option_b_when_image_set() -> None: image="nat-runtime:latest", port=8000, nat_config={}, + platform_base_url="http://nmp-api:8080", config_mount_path="/workspace/config.yaml", mode="k8s", plugin_wheels_init_image="busybox:1.36", @@ -191,6 +195,7 @@ def test_build_deployment_config_docker_never_emits_init_containers() -> None: image="nat-runtime:latest", port=8000, nat_config={}, + platform_base_url="http://host.docker.internal:8080", config_mount_path="/workspace/config.yaml", mode="docker", plugin_wheels_init_image="busybox:1.36", @@ -254,6 +259,9 @@ async def test_create_deployment_docker_rewrites_loopback_base_url() -> None: assert baked["llms"]["llm"]["base_url"] == ( "http://host.docker.internal:8080/apis/inference-gateway/v2/workspaces/default/openai/-/v1" ) + assert next(e.value for e in created_config.containers[0].env if e.name == "NMP_BASE_URL") == ( + "http://host.docker.internal:8080" + ) @pytest.mark.asyncio @@ -283,6 +291,7 @@ async def test_create_deployment_k8s_rewrites_loopback_to_internal() -> None: assert baked["llms"]["llm"]["base_url"] == ( "http://nmp-api:8080/apis/inference-gateway/v2/workspaces/default/openai/-/v1" ) + assert next(e.value for e in created_config.containers[0].env if e.name == "NMP_BASE_URL") == "http://nmp-api:8080" @pytest.mark.asyncio diff --git a/pyproject.toml b/pyproject.toml index 99cd6f423a..989ce52f64 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -544,6 +544,9 @@ extra-paths = [ # nemo-agents plugin is not a workspace member; add its src so ty can # resolve nemo_agents_plugin imports when checking plugin test files. "plugins/nemo-agents/src", + # The built-in NeMo Agent is a standalone deployable with its own lockfile; + # add its src so ty can resolve nemo_agent imports from its tests. + "agents/nemo-agent-local/src", # example-plugin is not a workspace member; add its src so ty can # resolve nmp.example_plugin imports when checking plugin test files. "plugins/example-plugin/src", diff --git a/sdk/python/nemo-platform/src/nemo_platform/beta/evaluator/agent_eval/runtimes/docker_sandbox.py b/sdk/python/nemo-platform/src/nemo_platform/beta/evaluator/agent_eval/runtimes/docker_sandbox.py index 2fcdff1982..9113164c00 100644 --- a/sdk/python/nemo-platform/src/nemo_platform/beta/evaluator/agent_eval/runtimes/docker_sandbox.py +++ b/sdk/python/nemo-platform/src/nemo_platform/beta/evaluator/agent_eval/runtimes/docker_sandbox.py @@ -56,7 +56,6 @@ def _load_agents_sdk() -> SandboxSDK: # The OpenAI Agents SDK ships under the `nemo-evaluator-sdk[agent-runtimes]` extra and is # imported only when this Docker runtime is actually used, so it is absent from the default # type-checking environment. - from agents import Runner # ty: ignore[unresolved-import] from agents.run import RunConfig # ty: ignore[unresolved-import] from agents.sandbox import Manifest, SandboxAgent, SandboxRunConfig # ty: ignore[unresolved-import] from agents.sandbox.config import DEFAULT_PYTHON_SANDBOX_IMAGE # ty: ignore[unresolved-import] @@ -66,6 +65,7 @@ def _load_agents_sdk() -> SandboxSDK: DockerSandboxClientOptions, ) + from agents import Runner # ty: ignore[unresolved-import] from docker import from_env as docker_from_env except ImportError as exc: raise RuntimeError("DockerSandboxAgentRuntime requires `nemo-evaluator-sdk[agent-runtimes]`") from exc diff --git a/services/studio/src/nmp/studio/coding_agent_mcp_tools.py b/services/studio/src/nmp/studio/coding_agent_mcp_tools.py index 634f79795f..4ccd7c9ff2 100644 --- a/services/studio/src/nmp/studio/coding_agent_mcp_tools.py +++ b/services/studio/src/nmp/studio/coding_agent_mcp_tools.py @@ -35,7 +35,7 @@ "Ask the Studio user to choose an agent from a visual dropdown. " "This is the required way to get an agent name in Studio: call it whenever a workflow needs the " "user to name, pick, confirm, or disambiguate an agent (including choosing among deployed agents). " - "Always prefer this over plain-text prompting and over Claude Code's AskUserQuestion tool for agent choices. " + "Always prefer this over asking the user to type an agent name. " "Returns the chosen agent name, or status=skipped / status=error if the user dismisses the dropdown." ), "inputSchema": { @@ -82,7 +82,7 @@ "in Studio (for example an anonymizer or evaluation input, or a CSV/Parquet file). Call it whenever " "a workflow needs the user to pick a fileset or a file inside one, instead of asking for a fileset " "reference, path, or '/#' string in plain text. " - "Always prefer this over plain-text prompting and over AskUserQuestion for file/fileset choices. " + "Always prefer this over asking the user to type a file or fileset reference. " "Pass accepted_file_types (for example ['.csv', '.parquet']) to constrain the picker. " "Returns dataset_fileset and dataset_path, which you can combine as '#' when a tool " "needs a fileset reference. Returns status=skipped / status=error if the user dismisses the picker." @@ -179,7 +179,7 @@ STUDIO_CODING_AGENT_CONTEXT = "\n".join( [ - "You are running inside NeMo Studio's Code Agent chat.", + "You are NeMo Agent, running inside NeMo Studio.", ( "NeMo Studio and the NeMo Platform API are already installed, set up, and running for this " "workspace. Treat the platform as healthy and available." @@ -210,7 +210,7 @@ "plain-text question whenever a suitable tool exists." ), ( - "These needs are mandatory tool calls, not plain-text questions and not AskUserQuestion: " + "These needs are mandatory tool calls, not plain-text questions: " "use mcp__nemo_studio__select_agent whenever you need the user to name, pick, confirm, or " "disambiguate an agent (including among deployed agents); " "mcp__nemo_studio__select_model for model names; " @@ -220,14 +220,13 @@ "and mcp__nemo_studio__select_eval_config for evaluation config files." ), ( - "Never use AskUserQuestion or a plain-text question to choose an agent, model, fileset, " + "Never use a plain-text question to choose an agent, model, fileset, " "dataset or input file, or eval config; those each have a dedicated select_* tool you must " "call instead." ), ( "For clarification, multiple-choice, yes/no, or freeform questions that do NOT map to one of " - "the select_* tools, use Claude Code's AskUserQuestion tool rather than a questionnaire " - "in markdown." + "the select_* tools, ask one concise plain-text question." ), ( "Only fall back to plain chat questions when no suitable UI tool exists, the user already " diff --git a/services/studio/src/nmp/studio/coding_agents.py b/services/studio/src/nmp/studio/coding_agents.py index ff1578f942..e7e67beb0e 100644 --- a/services/studio/src/nmp/studio/coding_agents.py +++ b/services/studio/src/nmp/studio/coding_agents.py @@ -9,16 +9,19 @@ import os import re import shutil +import time import uuid from collections.abc import AsyncIterator, Awaitable, Mapping from dataclasses import dataclass from dataclasses import field as dataclass_field from pathlib import Path from typing import Any -from urllib.parse import urlencode, urlparse +from urllib.parse import quote, urlencode, urlparse +import httpx from fastapi import APIRouter, FastAPI, HTTPException, Request from fastapi.responses import JSONResponse, Response, StreamingResponse +from nmp.common.entities.constants import NAME_PATTERN from nmp.studio import studio_links from nmp.studio.coding_agent_artifacts import ( ChatArtifactsResponse, @@ -48,7 +51,7 @@ permission_prompt_tool, ) from nmp.studio.coding_agent_skills import ClaudeSkillResponse, DuplicateSkillError, list_claude_skill_responses -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field from starlette.routing import NoMatchFound logger = logging.getLogger(__name__) @@ -61,6 +64,11 @@ PUBLIC_MCP_PATH = "/studio/api/coding-agents/mcp/{session_id}" CLAUDE_MCP_TOOL_TIMEOUT_MS = 2_147_483_647 MCP_KEEPALIVE_INTERVAL_SECONDS = 15 +DEFAULT_STUDIO_CODING_AGENT_NAME = "nemo-agent-local-poc" +DEFAULT_STUDIO_CODING_AGENT_BASE_URL = "http://127.0.0.1:8080" +STUDIO_CODING_AGENT_TIMEOUT_SECONDS = 600.0 +MAX_RETAINED_SESSIONS = 100 +MAX_RETAINED_TURNS_PER_SESSION = 50 CLAUDE_PROJECTS_DIR = Path.home() / ".claude" / "projects" SERVER_CWD = Path(os.getcwd()).resolve() @@ -69,6 +77,7 @@ STUDIO_CONTEXT_USER_REQUEST_PREFIX = "User request:" STUDIO_MESSAGE_SUMMARY_START = "<<>>" STUDIO_MESSAGE_SUMMARY_END = "<<>>" +WORKSPACE_NAME_RE = re.compile(NAME_PATTERN) class NewSessionResponse(BaseModel): @@ -83,7 +92,9 @@ class MessageRequest(BaseModel): message: str = Field(min_length=1) studio_base_url: str | None = Field(default=None, min_length=1) studio_pathname: str | None = Field(default=None, min_length=1) - workspace: str | None = Field(default=None, min_length=1) + workspace: str | None = Field(default=None, min_length=1, pattern=NAME_PATTERN) + + model_config = ConfigDict(regex_engine="python-re") class PermissionDecision(BaseModel): @@ -127,9 +138,37 @@ class SessionHistoryResponse(BaseModel): _session_streams: dict[str, asyncio.Queue[tuple[str, Any]]] = {} _pending_permissions: dict[str, tuple[str, asyncio.Future[dict[str, Any]]]] = {} _pending_agent_inputs: dict[str, tuple[str, asyncio.Future[dict[str, Any]]]] = {} +_session_conversations: dict[str, list[dict[str, str]]] = {} +_session_mtimes: dict[str, float] = {} _AGENT_INPUT_RESPONSE_RESERVED_KEYS = frozenset({"message", "status"}) +def _evict_oldest_sessions(*, protected_session_ids: set[str] | None = None) -> None: + """Evict least-recently-updated inactive sessions from in-memory history.""" + protected = (protected_session_ids or set()) | set(_session_streams) + while len(_session_conversations) > MAX_RETAINED_SESSIONS: + candidates = set(_session_conversations) - protected + if not candidates: + break + oldest_session_id = min( + candidates, + key=lambda session_id: (_session_mtimes.get(session_id, 0), session_id), + ) + _session_conversations.pop(oldest_session_id, None) + _session_mtimes.pop(oldest_session_id, None) + _initialized_sessions.discard(oldest_session_id) + + for session_id in set(_session_mtimes) - set(_session_conversations): + _session_mtimes.pop(session_id, None) + + +def _retain_recent_turns(conversation: list[dict[str, str]]) -> None: + """Keep only the most recent complete user/assistant turns.""" + max_messages = MAX_RETAINED_TURNS_PER_SESSION * 2 + if len(conversation) > max_messages: + del conversation[:-max_messages] + + @dataclass class HistorySummary: """Aggregated metadata from a Claude session history file.""" @@ -293,7 +332,7 @@ def _build_studio_system_prompt( current_studio_route = _trimmed_string(studio_pathname) or "unknown" destinations = studio_links.STUDIO_LINK_DESTINATIONS if enabled_destinations is None else enabled_destinations lines = [ - "You are being invoked from inside NeMo Studio's Code Agent chat.", + "You are NeMo Agent, running inside NeMo Studio.", f"Current Studio workspace: {workspace or 'unknown'}", f"Studio UI base URL: {normalized_base_url or 'unknown'}", f"Current Studio route path: {current_studio_route}", @@ -309,9 +348,9 @@ def _build_studio_system_prompt( "If studio_link is unavailable and you must construct a Studio UI link manually, use only a known enabled Studio route and prefer a relative Markdown link that starts with /workspaces/ or /models/.", "Evaluation pages use /workspaces/{workspace}/evaluation/... with singular evaluation; never nest evaluation links under /dashboard/evaluations/.", "Interactive Studio choice behavior:", - "Studio ships dedicated visual picker tools. When a picker fits, you MUST use it instead of plain text and instead of AskUserQuestion.", - "Whenever you need the user to name, pick, confirm, or disambiguate an agent (including choosing among deployed agents), you MUST call mcp__nemo_studio__select_agent to render the agent dropdown. Never ask for an agent in plain text and never use AskUserQuestion for an agent choice.", - "Whenever you need the user to choose a model, you MUST call mcp__nemo_studio__select_model. Never use AskUserQuestion or plain text for a model choice.", + "Studio ships dedicated visual picker tools. When a picker fits, you MUST use it instead of plain text.", + "Whenever you need the user to name, pick, confirm, or disambiguate an agent (including choosing among deployed agents), you MUST call mcp__nemo_studio__select_agent to render the agent dropdown. Never ask for an agent in plain text.", + "Whenever you need the user to choose a model, you MUST call mcp__nemo_studio__select_model. Never ask for a model in plain text.", "Whenever you need a fileset, fileset reference, dataset, or input/source data file (including an anonymizer or evaluation input, or a CSV/Parquet file), you MUST call mcp__nemo_studio__select_dataset_file instead of asking for a fileset reference or '/#' path in plain text; for an evaluation config file, you MUST call mcp__nemo_studio__select_eval_config.", "Treat 'which agent', 'pick an agent', 'choose a model', 'which fileset', and 'what is your fileset reference' as mandatory tool-use requests for the matching select_* tool, exactly like Studio link requests are mandatory studio_link requests.", "Set the picker title and description to match the current workflow, for example title='Select agent to audit'.", @@ -319,11 +358,12 @@ def _build_studio_system_prompt( "A timeout, disconnect, or other interactive-tool error is not permission to continue or repeat the question in plain text. Leave the input unresolved and tell the user the interactive request must be retried.", "A message that needs user input is not complete until you call the matching Studio input tool. Never end a message with only a plain-text question when an interactive tool applies.", "In particular, if you need an agent, model, dataset file, or evaluation config, call the matching select_* tool before completing the message; mentioning the needed selection in prose is not a substitute for the tool call.", - "For finite choices that have no dedicated Studio picker (for example deployments, jobs, or next actions) and for yes/no or multiple-choice clarifications, use Claude Code's AskUserQuestion tool so Studio can render clickable options instead of asking the user to type.", - "For AskUserQuestion, provide input shaped as {'questions': [{'header': '', 'question': '', 'options': [{'label': '