AutoGen vs LangChain
Side-by-side comparison built from DeepYard's structured catalog. Content updated Aug 21, 2026.
Direct Answer
AutoGen suits teams needing open-source access, multi agent focus, and 60,568 GitHub stars in current listing data. LangChain suits teams needing open-source access, typescript focus, and 144,745 GitHub stars in current listing data. This summary reflects catalog metadata only for decision support, not independent testing.
What this comparison weighs
- Pricing and free-tier availability
- License model and deployment fit
- GitHub adoption and contributor depth
- Catalog tags, integrations, and supported workflows
| Metric | AutoGen | LangChain |
|---|---|---|
| GitHub Stars | 60.6K | 144.7K |
| Contributors | 444 | 467 |
| Last Commit | Apr 15, 2026 | Aug 22, 2026 |
| Open Issues | 997 | 416 |
| License | open-source | open-source |
| Pricing | open-source | open-source |
| Free Tier | Yes | Yes |
| Category | frameworks | frameworks |
| Trending | No | No |
Choose AutoGen if you need
- • AutoGen is the stronger pick when Multi Agent matters because that capability is listed only on its profile.
- • AutoGen is the cleaner fit if you specifically need Multi Agent workflows from the catalog tags.
Choose LangChain if you need
- • LangChain stands out for Typescript workflows that are not listed for AutoGen.
- • LangChain is the stronger pick when Chain matters because that capability is listed only on its profile.
- • LangChain shows broader GitHub adoption with 144,745 stars versus 60,568 for AutoGen.
Meaningful differences
- • Workflow emphasis: AutoGen highlights Python, while LangChain highlights Typescript.
- • GitHub adoption: AutoGen shows 60,568 stars versus 144,745 for LangChain.
- • Contributor count: AutoGen lists 444 contributors and LangChain lists 467.
Shared capabilities
- • Python
Shared Tags
Only in AutoGen
Only in LangChain
Limitations and evidence
- • DeepYard compares structured public metadata; this is not an independent benchmark unless a test record is shown.
- • Signals such as stars, contributors, and last commit indicate public activity, not purchase fit or runtime quality.
- • Pricing and feature coverage reflect the stored listing snapshot and may lag vendor changes between refreshes.
Source links
About AutoGen
AutoGen is Microsoft's open-source framework for building multi-agent conversational systems where agents can converse with each other and with humans to solve complex tasks. Agents are highly customizable and can use LLMs, tools, and human input in flexible combinations. The framework supports group chats, nested conversations, and code execution sandboxes, making it well-suited for coding assistants, research automation, and enterprise agentic workflows. AutoGen Studio provides a no-code UI for prototyping agent systems visually.
View full listingAbout LangChain
LangChain is the most widely adopted framework for building LLM-powered applications. It provides modular abstractions for chains, agents, memory, retrievers, and tools, enabling developers to compose complex pipelines from reusable components. Available in both Python and JavaScript/TypeScript, LangChain integrates with every major LLM provider, vector store, and data source. Its ecosystem includes LangSmith for observability and LangGraph for stateful, graph-based agent workflows.
View full listing