Merge remote-tracking branch 'origin/metaagent' into metaagent

Author: LinxinS97

.github/workflows/contrib-tests.yml

@@ -638,3 +638,39 @@ jobs:
         with:
           file: ./coverage.xml
           flags: unittests
+
+  CohereTest:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest, macos-latest, windows-latest]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          lfs: true
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install packages and dependencies for all tests
+        run: |
+          python -m pip install --upgrade pip wheel
+          pip install pytest-cov>=5
+      - name: Install packages and dependencies for Cohere
+        run: |
+          pip install -e .[cohere,test]
+      - name: Set AUTOGEN_USE_DOCKER based on OS
+        shell: bash
+        run: |
+          if [[ ${{ matrix.os }} != ubuntu-latest ]]; then
+            echo "AUTOGEN_USE_DOCKER=False" >> $GITHUB_ENV
+          fi
+      - name: Coverage
+        run: |
+          pytest test/oai/test_cohere.py --skip-openai
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v3
+        with:
+          file: ./coverage.xml
+          flags: unittests

README.md

@@ -66,7 +66,12 @@
 
 ## What is AutoGen
 
-AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools.
+AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. AutoGen aims to streamline the development and research of agentic AI, much like PyTorch does for Deep Learning. It offers features such as agents capable of interacting with each other, facilitates the use of various large language models (LLMs) and tool use support, autonomous and human-in-the-loop workflows, and multi-agent conversation patterns.
+
+**Open Source Statement**: The project welcomes contributions from developers and organizations worldwide. Our goal is to foster a collaborative and inclusive community where diverse perspectives and expertise can drive innovation and enhance the project's capabilities. Whether you are an individual contributor or represent an organization, we invite you to join us in shaping the future of this project. Together, we can build something truly remarkable.
+
+The project is currently maintained by a [dynamic group of volunteers](https://butternut-swordtail-8a5.notion.site/410675be605442d3ada9a42eb4dfef30?v=fa5d0a79fd3d4c0f9c112951b2831cbb&pvs=4) from several different organizations. Contact project administrators Chi Wang and Qingyun Wu via [email protected] if you are interested in becoming a maintainer.
+
 
 ![AutoGen Overview](https://github.com/microsoft/autogen/blob/main/website/static/img/autogen_agentchat.png)
 

autogen/agentchat/contrib/agent_eval/README.md

@@ -1,7 +1,9 @@
-Agents for running the AgentEval pipeline.
+Agents for running the [AgentEval](https://microsoft.github.io/autogen/blog/2023/11/20/AgentEval/) pipeline.
 
 AgentEval is a process for evaluating a LLM-based system's performance on a given task.
 
 When given a task to evaluate and a few example runs, the critic and subcritic agents create evaluation criteria for evaluating a system's solution. Once the criteria has been created, the quantifier agent can evaluate subsequent task solutions based on the generated criteria.
 
 For more information see: [AgentEval Integration Roadmap](https://github.com/microsoft/autogen/issues/2162)
+
+See our [blog post](https://microsoft.github.io/autogen/blog/2024/06/21/AgentEval) for usage examples and general explanations.

autogen/agentchat/contrib/llamaindex_conversable_agent.py

@@ -8,15 +8,14 @@
 
 try:
     from llama_index.core.agent.runner.base import AgentRunner
+    from llama_index.core.base.llms.types import ChatMessage
     from llama_index.core.chat_engine.types import AgentChatResponse
-    from llama_index_client import ChatMessage
 except ImportError as e:
     logger.fatal("Failed to import llama-index. Try running 'pip install llama-index'")
     raise e
 
 
 class LLamaIndexConversableAgent(ConversableAgent):
-
     def __init__(
         self,
         name: str,

autogen/logger/file_logger.py

@@ -18,6 +18,7 @@
 if TYPE_CHECKING:
     from autogen import Agent, ConversableAgent, OpenAIWrapper
     from autogen.oai.anthropic import AnthropicClient
+    from autogen.oai.cohere import CohereClient
     from autogen.oai.gemini import GeminiClient
     from autogen.oai.groq import GroqClient
     from autogen.oai.mistral import MistralAIClient
@@ -205,7 +206,16 @@ def log_new_wrapper(
 
     def log_new_client(
         self,
-        client: AzureOpenAI | OpenAI | GeminiClient | AnthropicClient | MistralAIClient | TogetherClient | GroqClient,
+        client: (
+            AzureOpenAI
+            | OpenAI
+            | GeminiClient
+            | AnthropicClient
+            | MistralAIClient
+            | TogetherClient
+            | GroqClient
+            | CohereClient
+        ),
         wrapper: OpenAIWrapper,
         init_args: Dict[str, Any],
     ) -> None:

autogen/logger/sqlite_logger.py

@@ -19,6 +19,7 @@
 if TYPE_CHECKING:
     from autogen import Agent, ConversableAgent, OpenAIWrapper
     from autogen.oai.anthropic import AnthropicClient
+    from autogen.oai.cohere import CohereClient
     from autogen.oai.gemini import GeminiClient
     from autogen.oai.groq import GroqClient
     from autogen.oai.mistral import MistralAIClient
@@ -392,7 +393,16 @@ def log_function_use(self, source: Union[str, Agent], function: F, args: Dict[st
 
     def log_new_client(
         self,
-        client: Union[AzureOpenAI, OpenAI, GeminiClient, AnthropicClient, MistralAIClient, TogetherClient, GroqClient],
+        client: Union[
+            AzureOpenAI,
+            OpenAI,
+            GeminiClient,
+            AnthropicClient,
+            MistralAIClient,
+            TogetherClient,
+            GroqClient,
+            CohereClient,
+        ],
         wrapper: OpenAIWrapper,
         init_args: Dict[str, Any],
     ) -> None:

autogen/oai/client.py

@@ -77,6 +77,13 @@
 except ImportError as e:
     groq_import_exception = e
 
+try:
+    from autogen.oai.cohere import CohereClient
+
+    cohere_import_exception: Optional[ImportError] = None
+except ImportError as e:
+    cohere_import_exception = e
+
 logger = logging.getLogger(__name__)
 if not logger.handlers:
     # Add the console handler.
@@ -497,6 +504,11 @@ def _register_default_client(self, config: Dict[str, Any], openai_config: Dict[s
                     raise ImportError("Please install `groq` to use the Groq API.")
                 client = GroqClient(**openai_config)
                 self._clients.append(client)
+            elif api_type is not None and api_type.startswith("cohere"):
+                if cohere_import_exception:
+                    raise ImportError("Please install `cohere` to use the Groq API.")
+                client = CohereClient(**openai_config)
+                self._clients.append(client)
             else:
                 client = OpenAI(**openai_config)
                 self._clients.append(OpenAIClient(client))