langchain-ai/langgraph vs microsoft/autogen
langchain-ai/langgraph scores higher overall: 82/100 (A-tier) against 75/100 (A-tier). langchain-ai/langgraph leads on reliability, skill leverage, documentation; microsoft/autogen leads on no dimension. Scored 2026-07-07, methodology v1.0.
Dimension by dimension
| Dimension | langgraph | autogen |
|---|---|---|
| Reliability | 80 | 74 |
| Skill Leverage | 82 | 78 |
| Documentation | 84 | 82 |
| Maintenance | 92 | 60 |
| Safety / Governance | 72 | 68 |
| Evaluation Readiness | 76 | 72 |
| Composability | 80 | 76 |
| Adoption (capped) | 90 | 90 |
| Overall | 82 · A | 75 · A |
The entries
The most adopted graph-based agent orchestration framework: workflows as explicit state machines with checkpointing, human-in-the-loop…
Microsoft's agentic programming framework: conversation-based multi-agent patterns from the original research lineage, in transition toward…
Frequently asked questions
langchain-ai/langgraph or microsoft/autogen?
langchain-ai/langgraph scores higher overall (82 vs 75, methodology v1.0). But they are the same category, so the dimension table below is the real answer.
How is this comparison generated?
Both scorecards come from the same public rubric with evidence notes, scored by the same editorial process. This page presents them side by side; it adds no new judgments beyond the scores themselves.