agent-framework/python/packages/core/AGENTS.md

Core Package (agent-framework-core)

The foundation package containing all core abstractions, types, and built-in OpenAI/Azure OpenAI support.

Module Structure

agent_framework/
├── __init__.py          # Public API exports
├── _agents.py           # Agent implementations
├── _clients.py          # Chat client base classes and protocols
├── _types.py            # Core types (ChatMessage, ChatResponse, Content, etc.)
├── _tools.py            # Tool definitions and function invocation
├── _middleware.py       # Middleware system for request/response interception
├── _threads.py          # AgentThread and message store abstractions
├── _memory.py           # Context providers for memory/RAG
├── _mcp.py              # Model Context Protocol support
├── _workflows/          # Workflow orchestration (sequential, concurrent, handoff, etc.)
├── openai/              # Built-in OpenAI client
├── azure/               # Lazy-loading entry point for Azure integrations
└── <provider>/          # Other lazy-loading provider folders

Core Classes

Agents (_agents.py)

• SupportsAgentRun - Protocol defining the agent interface
• BaseAgent - Abstract base class for agents
• ChatAgent - Main agent class wrapping a chat client with tools, instructions, and middleware

Chat Clients (_clients.py)

• ChatClientProtocol - Protocol for chat client implementations
• BaseChatClient - Abstract base class with middleware support; subclasses implement _inner_get_response() and _inner_get_streaming_response()

Types (_types.py)

• ChatMessage - Represents a chat message with role, content, and metadata
• ChatResponse - Response from a chat client containing messages and usage
• ChatResponseUpdate - Streaming response update
• AgentResponse / AgentResponseUpdate - Agent-level response wrappers
• Content - Base class for message content (text, function calls, images, etc.)
• ChatOptions - TypedDict for chat request options

Tools (_tools.py)

• ToolProtocol - Protocol for tool definitions
• FunctionTool - Wraps Python functions as tools with JSON schema generation
• @tool decorator - Converts functions to tools
• use_function_invocation() - Decorator to add automatic function calling to chat clients

Middleware (_middleware.py)

• AgentMiddleware - Intercepts agent run() calls
• ChatMiddleware - Intercepts chat client get_response() calls
• FunctionMiddleware - Intercepts function/tool invocations
• AgentContext / ChatContext / FunctionInvocationContext - Context objects passed through middleware

Threads (_threads.py)

• AgentThread - Manages conversation history for an agent
• ChatMessageStoreProtocol - Protocol for persistent message storage
• ChatMessageStore - Default in-memory implementation

Memory (_memory.py)

• ContextProvider - Protocol for providing additional context to agents (RAG, memory systems)
• Context - Container for context data

Workflows (_workflows/)

• Workflow - Graph-based workflow definition
• WorkflowBuilder - Fluent API for building workflows
• Orchestrators: SequentialOrchestrator, ConcurrentOrchestrator, GroupChatOrchestrator, MagenticOrchestrator, HandoffOrchestrator

Built-in Providers

OpenAI (openai/)

• OpenAIChatClient - Chat client for OpenAI API
• OpenAIResponsesClient - Client for OpenAI Responses API

Azure OpenAI (azure/)

• AzureOpenAIChatClient - Chat client for Azure OpenAI
• AzureOpenAIResponsesClient - Client for Azure OpenAI Responses API

Key Patterns

Creating an Agent

from agent_framework import ChatAgent
from agent_framework.openai import OpenAIChatClient

agent = ChatAgent(
    chat_client=OpenAIChatClient(),
    instructions="You are helpful.",
    tools=[my_function],
)
response = await agent.run("Hello")

Using as_agent() Shorthand

agent = OpenAIChatClient().as_agent(
    name="Assistant",
    instructions="You are helpful.",
)

Middleware Pipeline

from agent_framework import ChatAgent, AgentMiddleware, AgentContext

class LoggingMiddleware(AgentMiddleware):
    async def process(self, context: AgentContext, call_next) -> None:
        print(f"Input: {context.messages}")
        await call_next(context)
        print(f"Output: {context.result}")

agent = ChatAgent(..., middleware=[LoggingMiddleware()])

Custom Chat Client

from agent_framework import BaseChatClient, ChatResponse, ChatMessage

class MyClient(BaseChatClient):
    async def _inner_get_response(self, *, messages, options, **kwargs) -> ChatResponse:
        # Call your LLM here
        return ChatResponse(messages=[ChatMessage(role="assistant", text="Hi!")])

    async def _inner_get_streaming_response(self, *, messages, options, **kwargs):
        yield ChatResponseUpdate(...)