What it does

AgentSearch is a Python framework that connects LLMs to search engines so you can build agents that search, summarize, and generate follow-up queries. It wraps Bing, SERP API, and its own hosted “AgentSearch” engine behind one client, and supports models from OpenAI, Anthropic, HuggingFace, and SciPhi’s own Sensei-7B.

The interesting bit

The project ships with a dedicated dataset (AgentSearch-V1) and a search-specialized 7B model, suggesting the authors want you to run the whole stack locally rather than just piping Bing results to GPT-4. That’s the unusual angle: it’s not merely glue code, it’s a vertically-integrated bet that search agents need their own data and smaller, tuned models.

Key highlights

• One client handles multiple search providers (Bing, SERP API, AgentSearch) and multiple LLM backends
• Includes a custom 7B model (Sensei-7B-V1) fine-tuned for search tasks
• Provides a public dataset for building local search engines
• Returns structured output: summaries, related queries, and raw search results in one call
• Free API key available through SciPhi signup

Caveats

• README mentions a “User Guide coming soon” — documentation is currently thin beyond quickstart snippets
• Heavy emphasis on SciPhi’s own API and model; third-party setup details are sparse

Verdict

Worth a look if you’re building search agents and want a unified interface with a local/self-hosted option. Skip it if you already have a stable RAG pipeline you like and don’t need another abstraction layer.