How much does DeepSearcher cost?

DeepSearcher is free under the Apache 2.0 license. You only pay for your own LLM API usage and vector database infrastructure costs, as it is a self-hosted framework.

Which LLMs and vector databases are supported?

It supports DeepSeek, OpenAI (o1, o3-mini), Claude, and Llama. For vector storage, it integrates with Milvus, Zilliz Cloud, and other stores with partitioning capabilities.

Is DeepSearcher suitable for non-technical users?

No. It is a library requiring Python glue code and tuning, not a turnkey product. Skip it if you want a no-code hosted SaaS, polished UI, or chatbot without writing code.

How does DeepSearcher handle document loading?

It covers local files out of the box. Web-crawling integrations are currently in progress. The project ships with a CLI and Python entry points rather than a hosted API.

What is the primary use case for DeepSearcher?

It is best for engineering teams needing self-hosted RAG over internal knowledge without sending data to hosted SaaS. It performs multi-step retrieval and reasoning over private documents.

DeepSearcher

Open-source agentic RAG framework for private enterprise data, built by the Zilliz/Milvus team.

Free· Free, Apache 2.0; bring your own LLM and vector DB costsRAGMulti-model (DeepSeek, OpenAI o1/o3-mini, Claude, Llama, others)6.9 / 10

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In short

DeepSearcher is a free, open-source agentic RAG framework by Zilliz that connects LLMs to private data for multi-step retrieval and reasoning.

Best for

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 if

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.

Editor's take

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

enterprise-ragagentic-searchprivate-document-qaresearch-agentsknowledge-base-search

Frequently asked

How much does DeepSearcher cost?: DeepSearcher is free under the Apache 2.0 license. You only pay for your own LLM API usage and vector database infrastructure costs, as it is a self-hosted framework.
Which LLMs and vector databases are supported?: It supports DeepSeek, OpenAI (o1, o3-mini), Claude, and Llama. For vector storage, it integrates with Milvus, Zilliz Cloud, and other stores with partitioning capabilities.
Is DeepSearcher suitable for non-technical users?: No. It is a library requiring Python glue code and tuning, not a turnkey product. Skip it if you want a no-code hosted SaaS, polished UI, or chatbot without writing code.
How does DeepSearcher handle document loading?: It covers local files out of the box. Web-crawling integrations are currently in progress. The project ships with a CLI and Python entry points rather than a hosted API.
What is the primary use case for DeepSearcher?: It is best for engineering teams needing self-hosted RAG over internal knowledge without sending data to hosted SaaS. It performs multi-step retrieval and reasoning over private documents.