{"data":{"slug":"zilliztech-deep-searcher","name":"deep-searcher","tagline":"Open Source Deep Research Alternative to Reason and Search on Private Data.","github_url":"https://github.com/zilliztech/deep-searcher","owner":"zilliztech","repo":"deep-searcher","owner_avatar_url":"https://avatars.githubusercontent.com/u/18416694?v=4","primary_language":"Python","stars":8060,"forks":775,"topics":["agent","agentic-rag","claude","deep-research","deepseek","deepseek-r1","grok","grok3","llama4","llm","milvus","openai","qwen3","rag","reasoning-models","vector-database","zilliz"],"archived":false,"github_pushed_at":"2025-11-19T06:04:16+00:00","maintenance_label":"Slowing","stars_delta_30d":59,"url":"https://www.graphcanon.com/tools/zilliztech-deep-searcher","markdown_url":"https://www.graphcanon.com/tools/zilliztech-deep-searcher.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/zilliztech-deep-searcher","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=zilliztech-deep-searcher","description":"Open Source Deep Research Alternative to Reason and Search on Private Data. Written in Python.","homepage_url":"https://zilliztech.github.io/deep-searcher/","license":"Apache-2.0","open_issues":53,"watchers":59,"ai_summary":"An AI tool for reasoning and searching on private data, leveraging multiple vector databases and LLM integrations.","readme_excerpt":"### Installation\nInstall DeepSearcher using one of the following methods:\n\n#### Option 1: Using pip\nCreate and activate a virtual environment(Python 3.10 version is recommended).\n```bash\npython -m venv .venv\nsource .venv/bin/activate\n```\nInstall DeepSearcher\n```bash\npip install deepsearcher\n```\n\nFor optional dependencies, e.g., ollama:\n```bash\npip install \"deepsearcher[ollama]\"\n```\n\n#### Option 2: Install in Development Mode\nWe recommend using [uv](https://github.com/astral-sh/uv) for faster and more reliable installation. Follow the [offical installation instructions](https://docs.astral.sh/uv/getting-started/installation/) to install it.\n\nClone the repository and navigate to the project directory:\n```shell\ngit clone https://github.com/zilliztech/deep-searcher.git && cd deep-searcher\n```\nSynchronize and install dependencies:\n```shell\nuv sync\nsource .venv/bin/activate\n```\n\nFor more detailed development setup and optional dependency installation options, see [CONTRIBUTING.md](CONTRIBUTING.md#development-environment-setup-with-uv).\n\n---\n\n### Quick start demo\n\nTo 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.\n\n```python\nfrom deepsearcher.configuration import Configuration, init_config\nfrom deepsearcher.online_query import query\n\nconfig = Configuration()\n\n---\n\n### Deployment\n\n#### Configure modules\n\nYou can configure all arguments by modifying [config.yaml](./config.yaml) to set up your system with default modules.\nFor example, set your `OPENAI_API_KEY` in the `llm` section of the YAML file.\n\n#### Start service\nThe main script will run a FastAPI service with default address `localhost:8000`.\n\n```shell\n$ python main.py\n```\n\n#### Access via browser\n\nYou can open url http://localhost:8000/docs in browser to access the web service.\nClick on the button \"Try it out\", it allows you to fill the parameters and directly interact with the API.\n\n\n---\n\n---\n\n## 📌 Future Plans\n- Enhance web crawling functionality\n- Support more vector databases (e.g., FAISS...)\n- Add support for additional large models\n- Provide RESTful API interface (**DONE**)\n\nWe welcome contributions! Star & Fork the project and help us build a more powerful DeepSearcher! 🎯","github_created_at":"2025-02-08T02:07:05+00:00","created_at":"2026-07-07T17:37:24.411667+00:00","updated_at":"2026-08-18T12:01:16.122207+00:00","categories":[{"slug":"ai-agents","name":"AI Agents","url":"https://www.graphcanon.com/categories/ai-agents","markdown_url":"https://www.graphcanon.com/categories/ai-agents.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/ai-agents"},{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"},{"slug":"vector-databases","name":"Vector Databases","url":"https://www.graphcanon.com/categories/vector-databases","markdown_url":"https://www.graphcanon.com/categories/vector-databases.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/vector-databases"}],"tags":[{"slug":"agent","name":"agent"},{"slug":"agentic-rag","name":"agentic-rag"},{"slug":"deep-research","name":"deep-research"},{"slug":"llm","name":"llm"},{"slug":"vector-database","name":"vector-database"}],"trust":{"provenance":{"is_fork":false,"github_id":929200745,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-18T12:01:14.848Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":272,"last_release_at":"2025-02-20T02:19:59Z","stars_delta_30d":59,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:09:29.960Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-18T12:01:15.357Z"},"deploy":{"source":"dockerfile:Dockerfile","self_host":true,"observed_at":"2026-08-18T12:01:15.357Z","managed_saas":false},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-08-18T12:01:15.357Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-18T12:01:15.357Z"},"has_docker":{"value":true,"source":"dockerfile:Dockerfile","observed_at":"2026-08-18T12:01:15.357Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-18T12:01:15.357Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["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."],"when_not_to_use":["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."],"source":"enrich:decision_facts","observed_at":"2026-07-12T11:27:28.172Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"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."}]}}