TinySearch

TinySearch is a self-hosted web-research tool for AI agents. It searches the web, reads the best pages, removes low-value content, and returns compact evidence with source URLs.

Your model receives the useful passages instead of paying to process entire webpages.

The expensive part of agent research is context

A search result is not yet useful evidence. Agents often have to open several pages, ingest navigation and boilerplate, and spend paid input tokens deciding which passages matter.

TinySearch moves that work in front of the model:

TinySearch deliberately stays focused. It is a retrieval layer, not another agent, chat interface, hosted search product, or permanent web index.

TinySearch does not spend another model call writing the final answer. The recommended flow issearchfor lightweight discovery, thenscrape_urlsfor the pages worth reading.

Successful MCP tool-result text is XML. A search result looks like this:

<search_results> <query>Python asyncio cancellation</query> <results> <result index="1"> <title>Coroutines and Tasks</title> <url>https://docs.python.org/3/library/asyncio-task.html</url> <search_preview>Tasks can be cancelled...</search_preview> </result> </results> </search_results>

scrape_urlsreturns each page's selected Markdown chunks under one<url_grounded_answers>batch root, andget_current_datetimereturns<current_datetime>. Dynamic values are escaped so retrieved content cannot forge the XML boundaries around it.

MCP still uses its standard JSON-RPC transport envelope, including protocol-level errors and optionalstructuredContent. Python and FastAPI keep their structured JSON contracts for applications that need to store, inspect, or transform the evidence.
- searchreturns backend-ordered titles, URLs, previews, and upstream dates without starting Chromium or an embedding model.
- scrape_urlsreads one to five known pages concurrently. Omit an item's query or use""to keep clean Markdown in page order within the configured token budget.
- Supply a focused item query when TinySearch should chunk and hybrid-rank that page before returning evidence.

Set theBRAVE_SEARCH_API_KEYenvironment variable to add Brave's official Web Search API as a keyed fallback for theddgsandduckduckgobackends. Brave is only consulted when the primary call errors or returns no results.

TinySearch uses its bundled Playwright Chromium by default. To use a browser that you operate separately, set its Chrome DevTools Protocol endpoint in the config file:

{ "browser_cdp_url": "http://browser:9222" }

Server processes also acceptTINYSEARCH_BROWSER_CDP_URL. When either setting is present, TinySearch connects through Crawl4AI instead of installing or launching the bundled Chromium. The external browser owns its executable, profile, proxy, and fingerprint configuration; TinySearch does not select or install a particular browser backend.

Treat a CDP endpoint as privileged remote control of the browser. Keep it on a private network or loopback interface, require authentication when it crosses a host boundary, and do not expose port 9222 directly to the public internet. When TinySearch itself runs in Docker,localhostrefers to the TinySearch container, so use an endpoint reachable from that container.

The CDP endpoint is operator-managed and cannot be changed through the HTTPPUT /configendpoint, even when configuration writes are enabled. Set it in the startup environment or the file selected byTINYSEARCH_CONFIG_PATH, then restart TinySearch. HTTP clients can continue updating other settings by omittingbrowser_cdp_urlfrom their partial update.

- No vendor in the loop.No TinySearch account, no required API key, no per-request billing, no analytics service or hosted scraped-data cache. The infrastructure you'd otherwise pay a search API for runs on your machine.
- Source-grounded by construction.Every evidence chunk is the original page text, still attached to its originating URL, so a claim in your agent's answer traces back to one specific passage instead of stopping at "the vendor's model said this."
- Built around token efficiency.Page selection and passage selection happen locally, before content enters model context.
- Useful without paid infrastructure.DDGS search and local ONNX embeddings are the defaults.
- Bring your own stack when needed.SearXNG and OpenAI-compatible embedding providers remain optional.
- Works where agents already work.Use MCP over stdio, Streamable HTTP, Python, FastAPI, or Docker.

Each tool focuses on a different part of the agent workflow and uses targeted techniques to reduce unnecessary context before it reaches the model. TinySearch handles the web-research layer by turning pages into a small, ranked, source-grounded evidence packet.

The README is the product overview. Detailed setup and operational material lives in the TinySuite documentation:

TinySearch is intentionally lightweight. Use a commercial search API, persistent crawler, or full search index when you need:

- guaranteed search coverage or an SLA
- large-scale or scheduled indexing
- long-term page storage and change history
- enterprise observability and access controls

TinySearch reads public pages and returns selected excerpts to the calling client. Search, crawling, local embeddings, and reranking can run without sending page content to an embedding provider. If you choose an OpenAI-compatible embedding backend, that provider receives the text sent for vectorization.

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