TinySearch

Spend tokens on answers, not webpages.

TinySearch searches, crawls, and reranks the web locally, then gives your agent only the evidence worth putting in its context.

Documentation · Quick start · Python · Discord

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.

TinySearch is part of TinySuite, a suite of focused tools designed to make agentic operations cheaper by minimizing token usage through smart retrieval, selection, and context-management techniques.

Choose a tier

Tier	Use it when	Entry point	Search backend
1. Python library	You are building with TinySuite or Python	pip install tinysuite-search	DDGS
2. One-command MCP	An MCP client should launch TinySearch for you	uvx --from "tinysuite-search[server]" tinysearch	DDGS
3. Docker + SearXNG	You want the full self-hosted stack and HTTP MCP	docker compose ... up -d	Bundled SearXNG

Tiers 1 and 2 need no search service. Tier 3 adds a dedicated SearXNG service, persistent model storage, and a network MCP endpoint. See the installation guide for the Docker setup.

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:

flowchart LR
    A[Question] --> B[Search and crawl]
    B --> C[Local hybrid reranking]
    C --> D[Compact evidence<br/>with source URLs]
    D --> E[Your agent]

That lowers cost in three ways:

• Smaller model context. Only the best-ranked evidence chunks are returned, within a controlled evidence budget.
• No metered search API required by default. TinySearch can search through DDGS without a paid search provider.
• Local retrieval by default. ONNX embeddings and hybrid reranking run on your machine instead of creating embedding API charges.

Search broadly. Read locally. Pay the model only for the evidence that matters.

Actual savings depend on the pages, evidence limits, client model, and provider pricing. TinySearch reduces the web content sent to the model; it does not control what the client does with that evidence afterward.

The naive baseline isn't a strawman product, it's the same pages TinySearch crawled for each query, fed to the model unfiltered, the way a generic "search, then fetch the page" tool (a plain web-search-plus-fetch loop, the kind built into most coding agents) would. Reproduce or rerun it yourself:

python scripts/benchmark_token_savings.py --json-out report.json

Quick start

With uv installed, add TinySearch to any MCP client:

{
  "mcpServers": {
    "tinysearch": {
      "command": "uvx",
      "args": [
        "--python",
        "3.12",
        "--from",
        "tinysuite-search[server]",
        "tinysearch"
      ]
    }
  }
}

The client launches TinySearch over stdio when it needs it. No repository clone, hosted account, or paid search key is required.

On first launch, TinySearch installs Chromium and downloads the local embedding model before it starts accepting requests, a one-time delay of a minute or two. To avoid that delay on the first real query, pre-warm both ahead of time:

uvx --from "tinysuite-search[server]" tinysearch setup

Prefer Docker, a remote MCP endpoint, or a source checkout? Follow the installation guide.

Three focused tools

Tool	Use it when
research(query)	The agent needs to discover and compare relevant sources
scrape_url(url, query)	You already know which page should be inspected
get_current_datetime()	Research depends on the current date or time

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

See the complete MCP tool reference for parameters and response contracts.

What your agent gets

TinySearch does not spend another model call writing the final answer. It gives your existing agent ranked, source-grounded evidence to reason over.

An abridged structured result looks like this:

{
  "schema_version": "1",
  "operation": "research",
  "status": "ok",
  "query": "How does asyncio cancellation work?",
  "sources": [
    {
      "title": "Coroutines and Tasks",
      "url": "https://docs.python.org/3/library/asyncio-task.html",
      "chunks": [
        {
          "text": "Tasks can be cancelled...",
          "tokens": 146,
          "rank": 1,
          "scores": {
            "rrf": 0.91,
            "dense": 0.88,
            "bm25": 0.79
          }
        }
      ]
    }
  ],
  "stats": {
    "search_results": 10,
    "sources_crawled": 4,
    "chunks_considered": 86,
    "chunks_selected": 8
  }
}

MCP tools return the same evidence as a grounded prompt by default, ready for the client model to use. Pass output_format: "json" when you want the structured result directly.

How it works

1. Search the web with DDGS or your own SearXNG instance.
2. Hybrid-rank the search results to choose which pages are worth reading.
3. Crawl those pages in parallel and extract readable content.
4. Chunk, deduplicate, and hybrid-rank the combined evidence pool.
5. Return the best passages with their titles, URLs, ranks, and scores.

Dense and lexical ranking happen before the evidence reaches your model. The model gets a small, relevant research packet rather than a pile of raw pages.

Python library

TinySearch also works as a regular Python package:

pip install tinysuite-search

import asyncio
from tinysearch import research, to_prompt


async def main():
    evidence = await research("How does asyncio cancellation work?")

    print(evidence["sources"])
    print(to_prompt(evidence))


asyncio.run(main())

The Python API returns a stable, JSON-serializable evidence schema. Rendering that evidence into an LLM prompt is explicit, so applications can store, inspect, transform, or budget the result first.

Search backends

TinySearch selects a web-search backend from config, so you can start with no search service and add one later without changing code.

• "ddgs" (native default): queries the ddgs package's automatic backend selection in-process. No SearXNG deployment required.
• "searxng" (Docker default): queries a self-hosted SearXNG instance. Falls back to ddgs on backend failure unless search_backend_fallback is set to false.
• "duckduckgo": skips SearXNG and queries ddgs in DuckDuckGo-only mode.
• "auto": tries SearXNG, then falls back to ddgs on any backend failure.

Set the BRAVE_SEARCH_API_KEY environment variable to add Brave's official Web Search API as a keyed fallback for the ddgs and duckduckgo backends. Brave is only consulted when the primary call errors or returns no results.

Full key reference, SearXNG JSON-output setup, and Compose details live in the configuration reference.

Why TinySearch

• Built around token efficiency. Page selection and passage selection happen before content enters model context.
• Source-grounded by construction. Every evidence group stays attached to its originating URL.
• 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.
• Self-hosted and inspectable. No TinySearch account, analytics service, or hosted scraped-data cache.

Part of TinySuite

TinySuite is a product suite built around one idea: agents should spend tokens on useful work, not operational overhead.

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.

Documentation

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

• TinySearch overview and installation
• Configuration reference
• MCP tools
• Troubleshooting

The repository also contains an annotated example configuration at configs/tinysearch_config.json.

When not to use TinySearch

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

Development

git clone https://github.com/TinySuiteHQ/TinySearch
cd TinySearch
python -m venv .venv
source .venv/bin/activate
pip install -e ".[server]"
python -m unittest discover tests

TinySearch supports Python 3.12 and newer. CI tests Python 3.12, 3.13, and 3.14 across Linux, macOS, and Windows.

Entrypoints

• tinysearch.research and tinysearch.scrape_url: structured Python API
• tinysearch.to_prompt: pure structured-evidence prompt renderer
• tinysearch mcp: stdio MCP server (also the no-argument default)
• tinysearch serve: Streamable HTTP MCP server
• tinysearch.servers.fastapi_server:app: optional FastAPI application

Community

Questions, ideas, and bug reports are welcome:

• Join the TinySearch Discord
• Open a GitHub issue
• Email the maintainer

Privacy and license

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.

TinySearch is available under the MIT License. Downloaded model weights remain subject to their respective model-card licenses. See NOTICE for third-party distribution details.