langchain vs metagpt
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
metagpt
Python multi-agent framework that turns one-line requirements into a software repo
Verdict
langchain ranks higher, 88 to 33. Of the 4 score parts we measure for both, langchain leads on 4, metagpt on none.
- Adoptionlangchain leads by 40 points, 97 to 57: a wide gap.
- Freshnesslangchain leads by 75 points, 100 to 25: a wide gap.
- Maintenancelangchain leads by 59 points, 86 to 27: a wide gap.
- Agent-readylangchain leads by 30 points, 30 to 0: 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 metagpt if you are
Developers experimenting with multi-agent LLM workflows for code generation
- Role and SOP model produces requirements, design docs and code repo from one prompt
- Usable as CLI or Python library; pip install metagpt
| What we compare | langchain | metagpt |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 97, widely used | 57, popular |
| Freshness | 100, active | 25, quiet |
| Maintenance | 86, healthy | 27, weak |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 147.8k | 70.8k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Jan 2026 |
| Last release | Oct 2026 | Apr 2024 |
| Language | Python | Python |
| Docker | No | Yes |
| 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
metagpt
MetaGPT assigns LLM-backed roles (product manager, architect, project manager, engineer) to follow predefined SOPs, turning a one-line requirement into user stories, designs, APIs and code. It runs as a CLI (`metagpt "Create a 2048 game"`) or a Python library, and also ships a Data Interpreter role for data analysis. LLM providers such as OpenAI, Azure, Ollama and Groq are set in `~/.metagpt/config2.yaml`.
Who it is for: Developers experimenting with multi-agent LLM workflows for code generation
Strengths
- Role and SOP model produces requirements, design docs and code repo from one prompt
- Usable as CLI or Python library; pip install metagpt
- Provider set in one YAML file: OpenAI, Azure, Ollama, Groq and others
- MIT license with tutorials, agent and multi-agent guides, and research papers
Weaknesses
- Python 3.9 to below 3.12 only
- Requires Node and pnpm installed before actual use
- Last release was 2024-04-22; the PyPI package may lag the main branch
- README calls the software-company schematic 'gradually implementing'
- no GPU
- Docker
- Needs Python 3.9-3.11, Node.js, pnpm, LLM API key or local LLM endpoint
- Models: OpenAI, Azure, Ollama, Groq
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