10xGraph vs Microsoft AutoGen: Python Compared

This page is written by the 10xGraph team, so read it with that in mind. Claims about AutoGen link to its repository, docs or package metadata, checked on 2026-10-06.

AutoGen is a Microsoft framework for conversational multi-agent systems, dual-licensed under MIT and CC-BY-4.0 per its repository. Its AgentChat layer offers teams such as RoundRobinGroupChat, SelectorGroupChat, Swarm and GraphFlow (teams docs). 10xGraph models work as a typed state graph and ships the production server layer in the open-source install.

Microsoft describes Microsoft Agent Framework as the successor to AutoGen: MIT-licensed, for Python and .NET, with checkpointing and several hosting options. If you are choosing between a maintained Microsoft framework and 10xGraph, compare against that project too.

Production layer compared

The first rows are where the two differ most. Orchestration basics are at the bottom.

Why teams choose 10xGraph over AutoGen for production

1. The server layer is included. AutoGen’s own README points developers to build their own application with authentication and security. 10xGraph generates the endpoints and the auth, ownership and rate-limit settings.
2. Side effects survive crashes. 10xGraph’s tool ledger skips tools that already ran when a run resumes. See replay-safe tools.
3. One state, one history. Every node reads the same AgentState, and the checkpointer stores it per thread.
4. Active development. 10xGraph is pre-1.0 but under active development, while AutoGen is in maintenance mode.

A planner and coder loop in 10xGraph

A two-agent flow with an order of execution fixed by edges:

from tenxgraph.core.graph import Agent, StateGraph
from tenxgraph.core.state import AgentState
from tenxgraph.storage.checkpointer import InMemoryCheckpointer
from tenxgraph.utils import END

planner = Agent(
    model="google/gemini-2.5-flash",
    system_prompt=[{"role": "system", "content": "Break the task into 3 numbered steps."}],
)

coder = Agent(
    model="google/gemini-2.5-flash",
    system_prompt=[{"role": "system", "content": "Use the plan in context to write Python code that implements it."}],
)

graph = StateGraph(AgentState)
graph.add_node("PLAN", planner)
graph.add_node("CODE", coder)
graph.set_entry_point("PLAN")
graph.add_edge("PLAN", "CODE")
graph.add_edge("CODE", END)

app = graph.compile(checkpointer=InMemoryCheckpointer())

To turn this into a loop (plan, code, review, re-plan if the reviewer says no), add a conditional edge from a REVIEW node back to PLAN. For the AutoGen version of a team with termination conditions, see AutoGen’s teams and termination docs.

When you want agents that converse

AutoGen’s selector team lets a model pick the next speaker (teams docs). In 10xGraph, the same idea is a router plus handoff tools:

from tenxgraph.core.graph import Agent, ToolNode
from tenxgraph.prebuilt.tools import create_handoff_tool

author_tools = ToolNode([create_handoff_tool("critic", "Send the draft to the critic")])

author = Agent(
    model="gemini-2.5-flash",
    provider="google",
    system_prompt=[{"role": "system", "content": "Draft and revise the customer reply."}],
    tool_node="AUTHOR_TOOLS",
)

Each handoff is a tool call you can log and cap with recursion_limit. See the handoff how-to for the full graph.

Persistence and resumable threads

from tenxgraph.core.state import Message
from tenxgraph.storage.checkpointer import PgCheckpointer

checkpointer = PgCheckpointer(
    postgres_dsn="postgresql://user:password@localhost:5432/agents",
    redis_url="redis://localhost:6379/0",
)
checkpointer.setup()

app = graph.compile(checkpointer=checkpointer)

app.invoke(
    {"messages": [Message.text_message("Continue from the last revision.")]},
    config={"thread_id": "session-42"},
)

Serving as an API

pip install 10xgraph 10xgraph-api
10xgraph init --yes --template production --auth jwt --rate-limit redis
10xgraph api --host 0.0.0.0 --port 8000
10xgraph build --docker-compose --k8s

Endpoints include:

• POST /v1/graph/invoke: run the graph and return final messages
• POST /v1/graph/stream: server-sent events for streaming
• GET /v1/graph/threads/{thread_id}: fetch persisted state

TypeScript client

import { AgentFlowClient, Message, StreamEventType, bearerAuth } from "10xgraph-client";

const client = new AgentFlowClient({
  baseUrl: "http://127.0.0.1:8000",
  auth: bearerAuth(token),
});

for await (const chunk of client.stream(
  [Message.text_message("Plan and write a script that exports yesterday's refunds.")],
  { config: { thread_id: "ts-stream-1" } },
)) {
  if (chunk.event === StreamEventType.MESSAGE && chunk.message) {
    process.stdout.write(chunk.message.text());
  }
}

Migrating from AutoGen

1. Each AssistantAgent becomes an tenxgraph.core.graph.Agent with the same system_message content as its system_prompt.
2. RoundRobinGroupChat becomes a chain of add_edge calls.
3. SelectorGroupChat becomes a router node, either a plain Python function or an LLM router.
4. TextMentionTermination and similar conditions become a conditional edge that returns END when a flag is set, or a recursion_limit in the invoke config.
5. AutoGen tools become ToolNode([fn, fn, ...]) with regular Python functions.
6. Per-agent message lists become the shared AgentState.messages.

Where AutoGen is the better choice

• Research on multi-agent conversation. AutoGen’s team types, including selector and Magentic-One teams, are built for exploring agent dialogue (teams docs).
• Existing AutoGen code you do not need to change. If it works and you do not need new features, staying put costs nothing.
• A Microsoft stack. If you are on .NET or Azure, look at Microsoft Agent Framework, which supports Python and .NET (repository).
• Python 3.10 or 3.11. 10xGraph requires 3.12 or newer.

Weak spots of 10xGraph

• Smaller community and fewer integrations than the Microsoft ecosystem.
• Pre-1.0: pin versions and read changelogs before upgrading.
• Renamed from Agentflow, so the 10xGraph name has little search history yet.
• No Studio-style visual builder. The playground is a test chat.
• Code-first only, and Python 3.12 or newer.

Sources

Verified on 2026-10-06.

• microsoft/autogen repository (maintenance mode, licenses)
• AutoGen Studio README
• autogen-agentchat on PyPI
• AgentChat teams
• AgentChat state
• AgentChat termination conditions
• microsoft/agent-framework repository

Next steps