openai/openai-agents-python vs langchain-ai/langgraph
langchain-ai/langgraph scores higher overall: 82/100 (A-tier) against 79/100 (A-tier). openai/openai-agents-python leads on no dimension; langchain-ai/langgraph leads on reliability, skill leverage, evaluation readiness. Scored 2026-07-07, methodology v1.0.
Dimension by dimension
| Dimension | openai-agents-python | langgraph |
|---|---|---|
| Reliability | 78 | 80 |
| Skill Leverage | 76 | 82 |
| Documentation | 84 | 84 |
| Maintenance | 92 | 92 |
| Safety / Governance | 72 | 72 |
| Evaluation Readiness | 70 | 76 |
| Composability | 78 | 80 |
| Adoption (capped) | 86 | 90 |
| Overall | 79 · A | 82 · A |
The entries
OpenAI's first-party multi-agent framework: agents, handoffs, guardrails, and tracing in a deliberately small API surface.
The most adopted graph-based agent orchestration framework: workflows as explicit state machines with checkpointing, human-in-the-loop…
Frequently asked questions
openai/openai-agents-python or langchain-ai/langgraph?
langchain-ai/langgraph scores higher overall (82 vs 79, 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.