DeepSearcher
Open-source agentic RAG framework for private enterprise data, built by the Zilliz/Milvus team.
Pick DeepSearcher if you want an open-source, agentic RAG layer over private data and you are comfortable wiring it to your own LLM and vector database.
Skip it if you want a no-code hosted RAG SaaS, a polished UI, or a turnkey chatbot without writing Python.
DeepSearcher is an open-source search-and-reasoning stack from Zilliz (the company behind the Milvus vector database) that wires LLMs to your private documents and then runs multi-step retrieval and reasoning over them. Instead of a single embed-and-answer pass, it decomposes a query, plans sub-searches, hits a vector store, and synthesizes a cited answer, sitting somewhere between classical RAG and a research agent.
It is aimed at engineering teams who want self-hosted RAG over internal knowledge without sending data to a hosted SaaS. DeepSearcher is pluggable on both ends: vector backends include Milvus, Zilliz Cloud and other stores with partitioning, while the LLM layer supports DeepSeek, OpenAI (o1, o3-mini), Claude, Llama and other providers. The framework itself is free under Apache 2.0 - you only pay for whatever model API and infrastructure you run it on.
Document loading covers local files out of the box with web-crawling integrations in progress, and the project ships a CLI plus Python entry points rather than a hosted API. Expect to write some glue code and tune retrieval; this is a library, not a turnkey product, and the natural pairing is Milvus or Zilliz Cloud for the vector layer.
A credible open-source entrant in the agentic-RAG space, and the Milvus pedigree matters - Zilliz knows the retrieval half cold. It is firmly a builder's tool though, closer to LangChain or LlamaIndex than to a product, so judge it as a framework, not a finished app.
— The AI Tool Bible editorial team
Pros
- ✅ Apache 2.0, fully self-hostable for private data
- ✅ Agentic multi-step retrieval, not just one-shot RAG
- ✅ Pluggable LLMs and vector stores including Milvus
- ✅ Backed by Zilliz, the team behind Milvus
Cons
- ⚠️ Library/CLI, no hosted product or managed API
- ⚠️ Web crawling and some loaders still in development
- ⚠️ Requires engineering effort to deploy and tune
- ⚠️ Best experience assumes you already run Milvus/Zilliz
Use cases
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