head to head · open source
llama_index vs Compartment
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. Compartment has 580 stars, 8 forks, 1 open issues and last shipped 14 days ago. llama_index leads on adoption by 9,000% (52,779 vs 580 stars). llama_index is written in Python under MIT; Compartment is written in Python under Apache-2.0. llama_index has attracted 16% as many forks as stars, Compartment 1%. llama_index was the more recently maintained of the two, and both are self-hostable with no licence fee.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
← all 20902 open source comparisons
Side by side
| llama_index | Compartment | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 580 |
| License | MIT | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-10-26 | 2026-10-20 |
| Forks | ⑂ 8.5K | ⑂ 8 |
| 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 Compartment if
- You want the Compartment feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Python
- You evaluated both and Compartment 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 Compartment
Compartment Encrypted, fully offline memory for AI agents. One vault on your own computer, read and written by Claude Code, Claude Desktop, Hermes Agent, OpenClaw, Cursor, Codex and any other MCP client. No API key, no account, no network, no telemetry. One click install (after pip install compartment && compartment init ): Claude Code, Claude Desktop, Hermes Agent and OpenClaw are wired by one command instead: compartment integrate claude , hermes or openclaw . Compartment is persistent memory for AI agents, stored on your own computer. What an agent learns in one session is available in every later session, in…
read the full Compartment overview →
More in AI & Machine Learning
Related comparisons
More Machine Learning Infrastructure projects
Compare either of these against the rest of the Machine Learning Infrastructure field.
Frequently asked questions
Is llama_index or Compartment more popular?
llama_index has 52,779 GitHub stars and Compartment has 580. llama_index has the larger community by that measure.
Are llama_index and Compartment free?
Both are open source. llama_index is licensed under MIT and Compartment under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and Compartment?
llama_index is written in Python and Compartment in Python. 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 Compartment?
Choose llama_index if you want the larger community (52,779 stars) or its MIT licence terms. Choose Compartment if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.