langchain vs openai-agents-python
Two of the top projects in Agent frameworks and SDKs, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
langchain
Python framework for building LLM agents and apps from interoperable components
openai-agents-python
Python SDK for multi-agent workflows with handoffs, guardrails and tracing
Verdict
langchain ranks higher, 88 to 77. Of the 4 score parts we measure for both, langchain leads on 1, openai-agents-python on 2 and 1 is even.
- Adoptionlangchain leads by 64 points, 98 to 34: a wide gap.
- FreshnessEven at 100.
- Maintenanceopenai-agents-python leads by 14 points, 99 to 85.
- Agent-readyopenai-agents-python leads by 40 points, 70 to 30: a wide gap.
Pick langchain if you are
Python developers building LLM agents and applications in code
- Common interface across model providers, embeddings and vector stores
- Large integrations library covering tools, retrievers and vector stores
Pick openai-agents-python if you are
Python developers building tool-using or multi-agent LLM apps
- Small API: Agent and Runner cover a text agent in a few lines
- Tools can be Python functions, MCP servers or hosted tools
| What we compare | langchain | openai-agents-python |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 98, widely used | 34, known |
| Freshness | 100, active | 100, active |
| Maintenance | 85, healthy | 99, healthy |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 30, minimal | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 147.8k | 30k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Python | Python |
| Docker | No | No |
| GPU | Not stated | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
langchain
LangChain is a Python library that gives a standard interface to chat models, embeddings, vector stores and tools, so providers can be swapped with little code change. Models load through init_chat_model with a provider:model string. For lower-level agent orchestration it points to LangGraph, and for tracing and evals to LangSmith.
Who it is for: Python developers building LLM agents and applications in code
Strengths
- Common interface across model providers, embeddings and vector stores
- Large integrations library covering tools, retrievers and vector stores
- Companions: LangGraph for orchestration, Deep Agents for planning and subagents
- MIT license; JS/TS counterpart available as LangChain.js
Weaknesses
- Observability, evals and managed deployment sit in LangSmith, a separate product
- Controllable stateful agent workflows need LangGraph, not core LangChain
- README gives no hardware requirements or list of supported providers
- Abstraction layer is another API to learn on top of provider SDKs
- Needs Model provider API or other model backend
- Models: OpenAI (README example), Other providers via integrations
openai-agents-python
Python library from OpenAI for building agents with instructions, tools, guardrails and handoffs between agents. It supports the OpenAI Responses and Chat Completions APIs and, per the README, 100+ other LLMs. It also includes sandbox agents, realtime and voice agents, sessions for conversation history, human-in-the-loop controls and built-in tracing.
Who it is for: Python developers building tool-using or multi-agent LLM apps
Strengths
- Small API: Agent and Runner cover a text agent in a few lines
- Tools can be Python functions, MCP servers or hosted tools
- Built-in tracing, guardrails, sessions and human-in-the-loop support
- Includes sandbox, realtime and voice agents; MIT license, Python 3.10+
Weaknesses
- Examples assume OPENAI_API_KEY; non-OpenAI models go through optional LiteLLM or any-llm
- Pull requests limited to repository collaborators
- Python only; JS/TS version is a separate repository
- Realtime agents use gpt-realtime-2.1, an OpenAI-hosted model
- no GPU
- Needs OpenAI API key, Python 3.10+, optional: Redis, optional: SQLAlchemy, optional: websockets, optional: LiteLLM or any-llm, optional: Docker
- Models: OpenAI Responses API, OpenAI Chat Completions API, 100+ other LLMs via LiteLLM or any-llm
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