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LangChain and LlamaIndex compared side by side on GitHub stars, downloads, language, license, capabilities, strengths and trade-offs.
Composable building blocks for LLM apps — chains, agents, retrievers, and integrations.
Data-grounded agents and RAG pipelines, with deep indexing primitives.
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Comparing agent frameworks is the process of evaluating two or three competing libraries side by side on live community signals, technical capabilities, language, license, and trade-offs so a team can pick the one that fits its stack and roadmap.
Start with the constraints that are not negotiable: the language your team already ships in, the license your legal team will sign off on, and the deployment model you can support. A framework that fails any of those three is not really an option, no matter how popular it is.
Then look at live signals: GitHub stars and contributors show whether the project is gathering momentum, npm and PyPI downloads show whether teams are actually shipping with it, and last-commit date shows whether the maintainers are still around. Capability flags like multi-agent, streaming, tool use, human-in-the-loop, memory, and evaluations narrow the field to frameworks that match your job-to-be-done.
Finish by reading the strengths and trade-offs columns side by side. The smallest framework that covers your real requirements almost always beats the most popular one. Copy the share link once you have a comparison you trust so you can revisit it during planning.
Decisions about agent frameworks rarely happen in isolation. Pair this comparator with the directories and benchmarks that ground the rest of the stack.
Live data from GitHub and the package registries, refreshed every day.
GitHub stars
Contributors
Forks
Star history sampled from the GitHub stargazer timeline and extended by our daily sync.
Open and closed model rankings with benchmarks, context windows, modalities, and live API prices.
Straight answers to the questions we hear most often.
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LangChain is more popular on GitHub with 140.3K stars, against 52.4K for LlamaIndex. Stars measure developer interest, so also compare downloads and contributors above.
LangChain has 3.7K contributors and its last commit was 3 mo ago. LlamaIndex has 2.0K contributors and its last commit was 4 days ago.
LangChain has Human in the Loop built in. LlamaIndex does not, based on each project’s documentation. See the capabilities table above for the full list.
LangChain is a Mixed framework from LangChain Inc., released under the MIT license. LlamaIndex is a Mixed framework from LlamaIndex Inc., released under the MIT license. The table above shows where their capabilities and community activity differ.
Start with the language your team already ships in. Then check which capabilities you need, such as multi-agent workflows, human approval steps or memory. Community size matters too: a larger project means more examples, integrations and answers when your team gets stuck.
npm downloads per week
PyPI downloads per month
