head to head · open source
Ollama vs rag_api
Ollama has 181,184 GitHub stars, 17,921 forks, 3,957 open issues and last shipped yesterday. rag_api has 901 stars, 403 forks, 45 open issues and last shipped 1 months ago. Ollama leads on adoption by 20,009% (181,184 vs 901 stars). Ollama is written in Go under MIT; rag_api is written in Python under MIT. Ollama has attracted 10% as many forks as stars, rag_api 45%. Ollama 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.
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Side by side
| Ollama | rag_api | |
|---|---|---|
| GitHub stars | ★ 181K | ★ 901 |
| License | MIT | MIT |
| Written in | Go | Python |
| Last push | 2026-09-17 | 2026-08-15 |
| Forks | ⑂ 18K | ⑂ 403 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick Ollama if
- You weight community size — 181K stars and counting
- You want the MIT license terms
- Your stack matches Go
- You value the larger contributor base for long-term maintenance
pick rag_api if
- You want the rag_api feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches Python
- You evaluated both and rag_api fits your workflow better
About Ollama
Ollama is a Go based, MIT licensed runtime that downloads and runs open source large language models such as DeepSeek, Qwen, Gemma, GLM, MiniMax and gpt oss locally on a user's own machine, and it is aimed at developers and teams that want model inference without routing prompts through a hosted API.
read the full Ollama overview →
About rag_api
ID based RAG FastAPI Overview This project integrates Langchain with FastAPI in an Asynchronous, Scalable manner, providing a framework for document indexing and retrieval, using PostgreSQL/pgvector. Files are organized into embeddings by file id . The primary use case is for integration with LibreChat, but this simple API can be used for any ID based use case. The main reason to use the ID approach is to work with embeddings on a file level. This makes for targeted queries when combined with file metadata stored in a database, such as is done by LibreChat. The API will evolve over time to employ different queryi…
read the full rag_api overview →
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Frequently asked questions
Is Ollama or rag_api more popular?
Ollama has 181,184 GitHub stars and rag_api has 901. Ollama has the larger community by that measure.
Are Ollama and rag_api free?
Both are open source. Ollama is licensed under MIT and rag_api under MIT. Neither carries a licence fee.
What is the difference between Ollama and rag_api?
Ollama is written in Go and rag_api 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, Ollama or rag_api?
Choose Ollama if you want the larger community (181,184 stars) or its MIT licence terms. Choose rag_api if its feature set, stack or MIT licence fits better. Both are self-hostable.