deep-searcher
Open Source Deep Research Alternative to Reason and Search on Private Data.
GraphCanon updated 2d · GitHub synced 2d · 26 views this month
Decision brief
DeepSearcher is an open-source tool for reasoning and searching on private data, using vector databases and LLM integrations in Python under Apache-2.0 license.
Good fit when
- When you require custom search and reasoning capabilities on your private datasets with integration of multiple LLMs like Claude or Qwen3.
- If your project needs rapid development support through an installation method via UV, enhancing dependency management.
Avoid when
- Avoid if your project demands proprietary solutions, as DeepSearcher is open-source and may not be suitable for closed systems.
- Not ideal when a single vector database suffices; DeepSearcher supports multiple databases which might be overkill and complicate setup unnecessarily.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (272d since push)
- As of 2d
- Provenance
- Not a fork · Organization account
- As of 2d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for Zilliz. Display-only - separate from trust and ranking.
- Company
- Zilliz·GitHub org profile·1mo
- Funding
- $60,000,000 (2022-08)·GraphCanon curated seed (public press)·1mo
- Commercial model
- Open core·GraphCanon curated seed·1mo
Install
pip install deep-searcherHow it fits your stack(22)
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
An AI tool for reasoning and searching on private data, leveraging multiple vector databases and LLM integrations.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Aug 18, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Aug 18, 2026
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 18, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 18, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 18, 2026)
Create and activate a virtual environment(Python 3.10 version is recommended).Source link
Tags
README
Installation
Install DeepSearcher using one of the following methods:
Option 1: Using pip
Create and activate a virtual environment(Python 3.10 version is recommended).
python -m venv .venv
source .venv/bin/activate
Install DeepSearcher
pip install deepsearcher
For optional dependencies, e.g., ollama:
pip install "deepsearcher[ollama]"
Option 2: Install in Development Mode
We recommend using uv for faster and more reliable installation. Follow the offical installation instructions to install it.
Clone the repository and navigate to the project directory:
git clone https://github.com/zilliztech/deep-searcher.git && cd deep-searcher
Synchronize and install dependencies:
uv sync
source .venv/bin/activate
For more detailed development setup and optional dependency installation options, see CONTRIBUTING.md.
Quick start demo
To run this quick start demo, please prepare your OPENAI_API_KEY in your environment variables. If you change the LLM in the configuration, make sure to prepare the corresponding API key.
from deepsearcher.configuration import Configuration, init_config
from deepsearcher.online_query import query
config = Configuration()
---
### Deployment
#### Configure modules
You can configure all arguments by modifying [config.yaml](./config.yaml) to set up your system with default modules.
For example, set your `OPENAI_API_KEY` in the `llm` section of the YAML file.
#### Start service
The main script will run a FastAPI service with default address `localhost:8000`.
```shell
$ python main.py
Access via browser
You can open url http://localhost:8000/docs in browser to access the web service. Click on the button "Try it out", it allows you to fill the parameters and directly interact with the API.
📌 Future Plans
- Enhance web crawling functionality
- Support more vector databases (e.g., FAISS...)
- Add support for additional large models
- Provide RESTful API interface (DONE)
We welcome contributions! Star & Fork the project and help us build a more powerful DeepSearcher! 🎯
For agents
This page has a .md twin and JSON over the API.