head to head · open source
txtai vs vearch
txtai has 13,062 GitHub stars, 962 forks, 9 open issues and last shipped 2 days ago. vearch has 2,333 stars, 366 forks, 170 open issues and last shipped 3 months ago. txtai leads on adoption by 460% (13,062 vs 2,333 stars). txtai is written in Python under Apache-2.0; vearch is written in Python under Apache-2.0. txtai has attracted 7% as many forks as stars, vearch 16%. txtai was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 3 topic tags (embeddings, rag, retrieval-augmented-generation), 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
| txtai | vearch | |
|---|---|---|
| GitHub stars | ★ 13K | ★ 2.3K |
| License | Apache-2.0 | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-11-03 | 2026-07-27 |
| Forks | ⑂ 962 | ⑂ 366 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick txtai if
- You weight community size — 13K stars and counting
- You want the Apache-2.0 license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick vearch if
- You want the vearch 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 vearch fits your workflow better
About txtai
All in one AI framework txtai is an all in one AI framework for semantic search, LLM orchestration and language model workflows. The key component of txtai is an embeddings database, which is a union of vector indexes (sparse and dense), graph networks and relational databases. This foundation enables vector search and/or serves as a powerful knowledge source for large language model (LLM) applications. Build autonomous agents, retrieval augmented generation (RAG) processes, multi model workflows and more. Summary of txtai features: 🔎 Vector search with SQL, object storage, topic modeling, graph analysis and…
read the full txtai overview →
About vearch
简体中文 English Overview Vearch is a cloud native distributed vector database for efficient similarity search of embedding vectors in your AI applications. Key features Hybrid search : Both vector search and scalar filtering. Performance : Fast vector retrieval search from millions of objects in milliseconds. Scalability & Reliability : Replication and elastic scaling out. Document Restful APIs Tutorial 参考文档 OpenAPIs API Documentation SDK Usage Cases Use Vearch as a Memory Backend Vearch integrates with popular AI frameworks: Real world Demos VisualSearch : Vearch can be leveraged to build a complete visual search…
read the full vearch overview →
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Frequently asked questions
Is txtai or vearch more popular?
txtai has 13,062 GitHub stars and vearch has 2,333. txtai has the larger community by that measure.
Are txtai and vearch free?
Both are open source. txtai is licensed under Apache-2.0 and vearch under Apache-2.0. Neither carries a licence fee.
What is the difference between txtai and vearch?
txtai is written in Python and vearch 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, txtai or vearch?
Choose txtai if you want the larger community (13,062 stars) or its Apache-2.0 licence terms. Choose vearch if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.