head to head · open source
llama_index vs Beam
llama_index has 52,776 GitHub stars, 8,495 forks, 770 open issues and last shipped 8 days ago. Beam has 1,841 stars, 175 forks, 21 open issues and last shipped yesterday. llama_index leads on adoption by 2,767% (52,776 vs 1,841 stars). llama_index is written in Python under MIT; Beam is written in Go under AGPL-3.0. llama_index has attracted 16% as many forks as stars, Beam 10%. Beam was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 2 topic tags (fine-tuning, llm), 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 | Beam | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 1.8K |
| License | MIT | AGPL-3.0 |
| Written in | Python | Go |
| Last push | 2026-10-26 | 2026-11-02 |
| Forks | ⑂ 8.5K | ⑂ 175 |
| 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 Beam if
- You want the Beam feature set and don't need the biggest community
- You prefer the AGPL-3.0 license terms
- Your stack matches Go
- You evaluated both and Beam 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 Beam
Run AI Workloads at Scale Beam is a fast, open source runtime for serverless AI workloads. It gives you a Pythonic interface to deploy and scale AI applications with zero infrastructure overhead. ✨ Features Fast Cold Starts : Launch containers in under a second using a custom container runtime, scheduler, and embedded caching Parallelization and Concurrency : Fan out workloads to 100s of containers First Class Developer Experience : Hot reloading, webhooks, and scheduled jobs Scale to Zero : Workloads are serverless by default Volume Storage : Mount distributed storage volumes GPU Support : Run on our cloud…
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Frequently asked questions
Is llama_index or Beam more popular?
llama_index has 52,776 GitHub stars and Beam has 1,841. llama_index has the larger community by that measure.
Are llama_index and Beam free?
Both are open source. llama_index is licensed under MIT and Beam under AGPL-3.0. Neither carries a licence fee.
What is the difference between llama_index and Beam?
llama_index is written in Python and Beam in Go. 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 Beam?
Choose llama_index if you want the larger community (52,776 stars) or its MIT licence terms. Choose Beam if its feature set, stack or AGPL-3.0 licence fits better. Both are self-hostable.