head to head · open source
llama_index vs opencode-mem
llama_index has 52,483 GitHub stars, 8,334 forks, 770 open issues and last shipped 2 days ago. opencode-mem has 1,739 stars, 175 forks, 6 open issues and last shipped yesterday. llama_index leads on adoption by 2,918% (52,483 vs 1,739 stars). llama_index is written in Python under MIT; opencode-mem is written in TypeScript under MIT. llama_index has attracted 16% as many forks as stars, opencode-mem 10%. opencode-mem was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (vector-database), 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.
← all 20902 open source comparisons
Side by side
| llama_index | opencode-mem | |
|---|---|---|
| GitHub stars | ★ 52K | ★ 1.7K |
| License | MIT | MIT |
| Written in | Python | TypeScript |
| Last push | 2026-10-12 | 2026-10-13 |
| Forks | ⑂ 8.3K | ⑂ 175 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick llama_index if
- You weight community size — 52K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick opencode-mem if
- You want the opencode-mem feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches TypeScript
- You evaluated both and opencode-mem fits your workflow better
About llama_index
LlamaIndex is an MIT licensed, open source Python framework for building agentic applications — retrieval augmented generation systems, agents and multi agent workflows — on top of private documents and data, and it is aimed at AI engineers and teams who need to connect large language models to their own sources of context.
read the full llama_index overview →
About opencode-mem
opencode mem is an MIT licensed, TypeScript plugin for OpenCode that gives AI coding agents persistent memory across sessions by storing and searching vector embeddings in an embedded local Turso/libSQL database.
read the full opencode-mem 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 opencode-mem more popular?
llama_index has 52,483 GitHub stars and opencode-mem has 1,739. llama_index has the larger community by that measure.
Are llama_index and opencode-mem free?
Both are open source. llama_index is licensed under MIT and opencode-mem under MIT. Neither carries a licence fee.
What is the difference between llama_index and opencode-mem?
llama_index is written in Python and opencode-mem 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 opencode-mem?
Choose llama_index if you want the larger community (52,483 stars) or its MIT licence terms. Choose opencode-mem if its feature set, stack or MIT licence fits better. Both are self-hostable.