head to head · open source
llama_index vs Laminar
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. Laminar has 3,471 stars, 257 forks, 116 open issues and last shipped yesterday. llama_index leads on adoption by 1,421% (52,779 vs 3,471 stars). llama_index is written in Python under MIT; Laminar is written in TypeScript under Apache-2.0. llama_index has attracted 16% as many forks as stars, Laminar 7%. Laminar was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (agents), so they are genuine substitutes rather than adjacent tools.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
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Side by side
| llama_index | Laminar | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 3.5K |
| License | MIT | Apache-2.0 |
| Written in | Python | TypeScript |
| Last push | 2026-10-26 | 2026-11-02 |
| Forks | ⑂ 8.5K | ⑂ 257 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick llama_index if
- You weight community size — 53K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick Laminar if
- You want the Laminar feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches TypeScript
- You evaluated both and Laminar fits your workflow better
About llama_index
🗂️ LlamaIndex (OSS Framework) 🦙 [!NOTE] The current focus of LlamaIndex is to build the best AI powered engine for document parsing and extraction. LlamaParse is our enterprise platform for agentic OCR, parsing, extraction, indexing and more. LiteParse represents our efforts to build the best free, fast, cheap text parser in the market. ParseBench and ExtractBench represent our commitment towards open benchmarking for parsing and extraction. The company itself has undergone an evolution since when this OSS framework first launched 3 years ago in 2023. Since the early days, the framework has consisted of a…
read the full llama_index overview →
About Laminar
Laminar Laminar is an open source observability platform purpose built for AI agents. [x] Tracing. Docs [x] OpenTelemetry native powerful tracing SDK 1 line of code to automatically trace Vercel AI SDK, Browser Use, Stagehand, LangChain, OpenAI, Anthropic, Gemini, and more . [x] Signals. Docs [x] Describe any behavior of your agent that you want to track in plain English (e.g. "agent is stuck in a loop") [x] Laminar reads every agent run and pings you in Slack when it happens. [x] Evals. Docs [x] Unopinionated, extensible SDK and CLI for running evals locally or in CI/CD pipeline. [x] UI for visualizing evals…
read the full Laminar overview →
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Frequently asked questions
Is llama_index or Laminar more popular?
llama_index has 52,779 GitHub stars and Laminar has 3,471. llama_index has the larger community by that measure.
Are llama_index and Laminar free?
Both are open source. llama_index is licensed under MIT and Laminar under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and Laminar?
llama_index is written in Python and Laminar in TypeScript. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, llama_index or Laminar?
Choose llama_index if you want the larger community (52,779 stars) or its MIT licence terms. Choose Laminar if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.