AutoGen Starter

Implements a three-layer architecture (autogen-core runtime, autogen-agentchat API, autogen-ext integrations) that enables multiple LLM-powered agents to collaborate through structured message passing and subscription-based routing. Uses AgentRuntime protocol with SingleThreadedAgentRuntime and GrpcWorkerAgentRuntime implementations to coordinate agent lifecycle, message delivery, and state management across autonomous or human-supervised workflows. BaseGroupChat abstraction provides pre-built patterns for round-robin, sequential, and custom agent selection strategies.

Provides CodeExecutorAgent and code execution extensions that enable agents to write, execute, and debug Python code within isolated sandboxed environments. Integrates with the AgentRuntime system to capture code output, errors, and side effects as structured messages that feed back into agent reasoning loops. Supports both local execution and remote execution via worker processes, with configurable timeouts and resource limits.

Provides a collection of sample projects and templates (in the /samples directory) demonstrating common multi-agent patterns: group chat, code execution, RAG-augmented agents, teachable agents, and human-in-the-loop workflows. Each sample includes runnable code, configuration examples, and documentation showing how to compose agents, configure LLM providers, and implement specific patterns. Serves as both learning resource and starting point for new projects.

Enables fine-grained agent customization through composition of components: AssistantAgent (LLM-powered), CodeExecutorAgent (code execution), and custom agents extending BaseAgent protocol. Agents are configured with specific LLM clients, tools, system prompts, and memory systems, allowing different agents in the same system to have different capabilities and behaviors. Configuration is declarative (via dictionaries or config files) or programmatic (via Python code).

Implements memory systems (part of autogen-ext) that enable agents to retrieve and inject relevant context from external knowledge bases, vector stores, or file systems before generating responses. Integrates with the ChatCompletionClient abstraction to augment LLM prompts with retrieved documents or embeddings-based search results. Supports both in-memory and persistent storage backends, with configurable retrieval strategies (semantic search, keyword matching, hybrid).

Provides ChatCompletionClient protocol and implementations for OpenAI, Azure OpenAI, and other LLM providers, enabling agents to switch between models or providers without code changes. Supports model-specific parameters (temperature, top_p, max_tokens) and handles provider-specific API differences (authentication, endpoint formats, response schemas). Includes fallback and retry logic for resilience.

Implements BaseTool interface and tool registry system enabling agents to call external functions, APIs, and Model Context Protocol (MCP) tools through structured function calling. Supports schema-based tool definition with automatic validation, parameter mapping, and error handling. Integrates with LLM function-calling APIs (OpenAI, Anthropic) and includes MCP client implementations for connecting to external tool servers.

Provides specialized agent patterns (in autogen-agentchat) that enable agents to learn from human feedback, corrections, and examples during conversations. Implements memory mechanisms to store learned facts, preferences, and correction patterns, which are injected into subsequent agent reasoning. Supports interactive human-in-the-loop workflows where agents pause for feedback and adapt behavior based on corrections.

Provides configurable termination conditions (max turns, token limits, custom predicates) that control when multi-agent conversations end. Integrates with the AgentRuntime to monitor conversation state and trigger termination events. Supports both hard stops (immediate termination) and soft stops (agent notification to wrap up), enabling graceful conversation closure.

Provides GraphFlow abstraction (mentioned in DeepWiki) for defining multi-agent workflows as directed acyclic graphs where nodes represent agents or tasks and edges represent data flow and dependencies. Enables specification of complex orchestration patterns (conditional routing, parallel execution, sequential pipelines) without manual state management. Integrates with the AgentRuntime to execute graph nodes and route results between agents.

Implements MagenticOne, a specialized multi-agent system (documented in DeepWiki) designed for autonomous web browsing, file manipulation, and complex task execution. Combines multiple specialized agents (web navigator, file manager, code executor, etc.) coordinated through group chat patterns to accomplish complex real-world tasks. Integrates with web interaction tools and file system APIs to enable agents to interact with external systems autonomously.

Implements GrpcWorkerAgentRuntime enabling agents to run in separate processes or machines and communicate via gRPC, supporting both Python and .NET ecosystems. Enables polyglot agent systems where Python agents coordinate with .NET agents or vice versa. Handles serialization, message routing, and lifecycle management across language boundaries.