Does AutoGen support graphs?

AutoGen 0.4 introduced GroupChat and Selector patterns, which approximate graph control flow. Agentmatic's StateGraph is more explicit and gives you checkpointing + time-travel.

Can I use AutoGen tools with Agentmatic?

Yes via the LangChain bridge in either direction, or via MCP for process-isolated tools.

Which has better observability?

Agentmatic ships OpenTelemetry tracing by default with per-node and per-tool-call spans. AutoGen integrates with multiple observability backends but doesn't ship OTel natively.

Comparison

Agentmatic vs AutoGen — conversation-driven vs state-graph orchestration

Microsoft AutoGen orchestrates conversations between agents. Agentmatic orchestrates state graphs. Different paradigms, different sweet spots — both viable for multi-agent systems.

Published May 12, 2026

Two paradigms

AutoGen is built around the metaphor of agents in a conversation. You create agents (UserProxy, AssistantAgent, GroupChatManager), they message each other, and the orchestrator routes the chat. Humans can join the chat.

Agentmatic is built around state graphs. Every transition is explicit. State is a typed structure that mutates as nodes execute. The framework owns control flow.

Where AutoGen shines

Conversational UX. When the workflow really is “multiple agents in a chat with a human,” AutoGen’s abstractions fit naturally.
Microsoft/Azure ecosystem. Tight integration with Semantic Kernel, Azure AI, and AutoGen Studio.
Group chat patterns. Selector / Round Robin / Manual selection are first-class.

Where Agentmatic shines

Determinism. Same input, same graph, same output. Critical for testing and reproducing prod bugs.
Checkpointing. Every superstep is a save point. Time-travel debugging and HITL are free.
Performance. Rust runtime, 10–15× faster graph traversal.
Multi-language. Same engine in Python, TypeScript, Rust, Go, Java — your polyglot team uses one framework.
Resilience. Circuit breakers, retry with backoff, dead-letter queues — first-class, MIT-licensed.
Distributed. Self-hosted clusters in the open-source core.

Side-by-side: same problem

A multi-agent research → write → review workflow:

# AutoGen
researcher = AssistantAgent("researcher", llm_config=cfg)
writer = AssistantAgent("writer", llm_config=cfg)
reviewer = AssistantAgent("reviewer", llm_config=cfg)
manager = GroupChatManager(GroupChat([researcher, writer, reviewer]))
user_proxy.initiate_chat(manager, message="...")

# Agentmatic
graph = StateGraph(State)
graph.add_node("research", researcher)
graph.add_node("write", writer)
graph.add_node("review", reviewer)
graph.add_conditional_edges("review", verify, {"ok": END, "revise": "write"})
agent = graph.compile(checkpointer=PostgresSaver.from_env())

The AutoGen version reads like a setup. The Agentmatic version reads like a control flow. Both work. The right choice depends on whether you think more in conversations or in state machines.

Pick Agentmatic

You need deterministic, checkpointable graphs (every run reproducible).
You want production resilience primitives (CB, retry, DLQ) in the box.
You need polyglot SDKs (Python + TS + Go + Java + Rust).
You'd rather work in StateGraph than chat-loop abstractions.

Pick AutoGen

Your mental model is 'agents have a conversation, sometimes with humans in the chat.'
You're already using Microsoft Semantic Kernel / Azure AI heavily — AutoGen lives in that ecosystem.
You want the AutoGen Studio visual builder specifically.

Pricing notes

AutoGen is MIT-licensed and free. AutoGen Studio adds a visual builder. Agentmatic is also MIT, with first-class multi-language SDKs and built-in resilience primitives.