Agent Search

AgentSearch

Self-hosted search API for AI agents. 17 endpoints. Kill-chain content extraction with optional browser rendering. Optional Tor-anonymized stack. No third-party search API keys, no per-query fees, no vendor lock-in. Optional local bearer auth is supported.

[](LICENSE) [](https://pypi.org/project/agentsearch-client/)

git clone https://github.com/brcrusoe72/agent-search.git
cd agent-search
./scripts/prepare-searxng.sh
docker compose up -d
curl "http://localhost:3939/search?q=distributed+consensus+algorithms"

You now have a deduplicated, multi-engine search API running on :3939.

If you enable auth, pass the token on all non-health endpoints:

export AGENT_SEARCH_TOKEN="change-me"
curl -H "Authorization: Bearer $AGENT_SEARCH_TOKEN" \
  "http://localhost:3939/search?q=distributed+consensus+algorithms"

Prefer not to use Docker for the API server?

git clone https://github.com/brcrusoe72/agent-search.git
cd agent-search
./scripts/install-native.sh
./scripts/run-native.sh

Native mode requires Python 3.11+ and a reachable SearXNG instance with JSON output enabled. It stores AgentSearch state in ./data. See [Native Install](docs/native-install.md).

Verify

Run the self-contained test suite:

python -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt pytest requests
pip install -e sdk -e mcp-server
./scripts/prepare-searxng.sh
pytest tests -q
python -m compileall app adapters mcp-server/agent_search_mcp scripts sdk -q
docker compose -f docker-compose.yml config --quiet
docker compose -f docker-compose.yml -f examples/compose.private.yml config --quiet
docker build -t agent-search-api:test .

Those tests mock SearXNG, so they do not require Docker or a running local service.

Run the optional live localhost check:

AGENTSEARCH_INTEGRATION=1 pytest tests -q

If your local instance requires auth:

AGENT_SEARCH_TOKEN="change-me" AGENTSEARCH_INTEGRATION=1 pytest tests -q

Run the optional Docker smoke tests against a running direct/private stack:

AGENT_SEARCH_TOKEN="change-me" \
AGENTSEARCH_DOCKER_INTEGRATION=1 \
pytest tests/test_live_docker.py -q

---

What it does

AgentSearch wraps SearXNG with a FastAPI layer that adds everything LLM agents actually need: deduplication, cross-engine scoring, content extraction, query expansion, domain trust scoring, prompt injection scrubbing, and self-improvement.

Standard stack — docker compose up gives you search on :3939.

Private stack — docker compose -f docker-compose.yml -f examples/compose.private.yml up adds an anonymized instance on :3940 that routes all traffic through Tor with Snowflake obfuscation. Encrypted DNS via CoreDNS → Cloudflare DoT. Network-level isolation — the private stack physically cannot egress without Tor.

Search engines

AgentSearch delegates engine support to the connected SearXNG instance. The authoritative list for a running stack is:

curl "http://localhost:3939/engines"

The bundled searxng/settings.example.yml explicitly enables 25 engines, including best-effort Google/Startpage/Yahoo entries plus Brave, Bing, DuckDuckGo, Google Scholar, Semantic Scholar, arXiv, Crossref, OpenAlex, PubMed, Bing News, Reuters, Wikinews, Wikipedia, Wikidata, Hugging Face, Reddit, Hacker News, Stack Overflow, GitHub, Docker Hub, and Lobsters.

Run ./scripts/prepare-searxng.sh to create ignored local runtime files at searxng/settings.yml and searxng/settings.tor.yml with generated SearXNG instance secrets. Do not commit those generated files.

Because SearXNG is configured with use_default_settings: true, your live instance may expose additional enabled engines from the installed SearXNG catalog. Use the engines= query parameter to request specific engines, and use /engines to verify what is available in that deployment.

Strategy modes can also use direct no-key providers for vertical search. These do not require paid search APIs: GitHub repository search, MDN search, Docker Hub search, PyPI package metadata, Wikipedia, Wikidata, Hacker News, arXiv, Crossref, OpenAlex, and Semantic Scholar are called directly when a mode selects them. SearXNG remains the broad-web provider for Google/Bing/Brave/DuckDuckGo-style engines, but Google, Startpage, Yahoo, and Reddit are best-effort explicit sources rather than defaults because they are commonly blocked or empty.

Why not just use SearXNG directly?

SearXNG finds pages. AgentSearch finds pages, reads them, scores them, deduplicates them, caches them, scrubs prompt injections out of them, detects paywalls, falls back through escalating extraction strategies when the first one fails, and gets better at it over time. One API call.

| | AgentSearch | Tavily | Exa | SerpAPI | Raw SearXNG | |---|---|---|---|---|---| | Cost | Free | $0.005/query | $0.001/query | $50/mo | Free | | Self-hosted | ✅ | ❌ | ❌ | ❌ | ✅ | | Content extraction | Kill chain + browser renderer | Basic | Basic | ❌ | ❌ | | Deduplication | Cross-engine | ❌ | ❌ | ❌ | ❌ | | Prompt injection scrubbing | ✅ | ❌ | ❌ | ❌ | ❌ | | Self-improving | ✅ (evolver) | ❌ | ❌ | ❌ | ❌ | | Tor anonymization | Optional | ❌ | ❌ | ❌ | Manual |

Endpoints

Search

| Endpoint | Method | What it does | |---|---|---| | /search | GET | Multi-engine web search with deduplication and scoring | | /search/strategy | GET | Named search modes: general, code, academic, news, private, reference, community | | /search/deep | GET | Server-side query expansion — runs variations in parallel, fuses results | | /search/extract | GET | Search + inline content extraction in one call | | /search/jobs | GET | Job search across LinkedIn, Indeed, Glassdoor, ZipRecruiter | | /search/policy | GET | Policy and regulatory document search | | /search/sources | GET | Source discovery with institutional filtering | | /search/sources/institutions | GET | List source registry institutions | | /search/stats | GET | Query statistics and cache metrics | | /news | GET | Structured multi-source news with reliable defaults and explicit engine overrides |

Content extraction

| Endpoint | Method | What it does | |---|---|---| | /read | GET | Kill chain extraction for any URL | | /read/batch | POST | Concurrent multi-URL extraction in one request | | /providers/browser/fetch | GET | Ephemeral browser render/extract for JS-rendered target pages |

The kill chain escalates through strategies until one succeeds:

1. Direct fetch + smart content selectors
2. Readability scoring (paragraph density vs link density)
3. User-agent rotation (Chrome/Safari/Firefox/Edge signatures)
4. Browser render/extract for JS-rendered target pages
5. Wayback Machine (CDX API → latest snapshot)
6. Google Cache
7. Search-about fallback (find coverage elsewhere)
8. Custom adapters (pluggable Python modules from disk)
9. PDF extraction (pdfplumber)
10. YouTube transcript (yt-dlp)

Every request gets SSRF protection, prompt injection detection, paywall detection, and content length caps automatically. The browser renderer uses an ephemeral context, blocks high-cost resource types by default, and reports CAPTCHA/challenge pages instead of trying to bypass them. It is for rendering target pages, not for scraping blocked search-result pages.

Self-improvement

| Endpoint | Method | What it does | |---|---|---| | /adapt/report | POST | Report a fetch failure for a URL | | /adapt/stats | GET | View adaptation metrics and failure patterns | | /adapt/evolve | POST | Trigger self-improvement cycle — analyzes failures, tunes config |

Infrastructure

| Endpoint | Method | What it does | |---|---|---| | /health | GET | Health check (API + SearXNG status) | | /engines | GET | List available search engines and their status | | /providers/health | GET | Summarize provider health from recorded live attempts | | /providers/stats | GET | Rolling provider/SearXNG attempt telemetry |

Quick examples

Search with content extraction

curl "http://localhost:3939/search/extract?q=python+async+patterns&count=3"

Returns search results with extracted content inline — no second round-trip to /read.

Strategy search

curl "http://localhost:3939/search/strategy?q=fetch+api&mode=code&count=5"
curl "http://localhost:3939/search?q=AI+regulation&mode=academic&count=5"
curl "http://localhost:3939/search?q=Python&mode=reference&count=5"

Modes validate or call only their declared sources instead of falling back silently: general tries Bing first, then uses DuckDuckGo/Brave and direct reference/community providers only when more coverage is needed; code uses direct GitHub, MDN, Docker Hub, and PyPI providers; academic uses direct arXiv, Crossref, OpenAlex, and Semantic Scholar providers; news uses Reuters, Bing News, DuckDuckGo News, and Wikinews through SearXNG; reference uses direct Wikipedia and Wikidata providers; community uses direct Hacker News; private avoids broad general web engines.

Each strategy response includes meta.engine_attempts with source/provider, query, raw result count, latency, and upstream errors so blocked or empty providers stay visible.

Provider telemetry is available without running another probe:

curl "http://localhost:3939/providers/health"
curl "http://localhost:3939/providers/stats"

Telemetry is in-memory and reflects live attempts since the API process started. It tracks attempts, successes, empty-result rate, errors, latency, last error, and last success per direct provider or SearXNG pack.

Deep search (query expansion)

curl "http://localhost:3939/search/deep?q=ethon+industrial+ai+platform&count=10"

Server-side query variation + parallel execution + result fusion. Surfaces results that flat /search misses.

Read a URL (kill chain)

curl "http://localhost:3939/read?url=https://example.com/paywalled-article"

{
  "url": "https://example.com/paywalled-article",
  "content": "Full article text extracted via Wayback Machine...",
  "strategy": "wayback",
  "chars": 4821,
  "cached": false,
  "strategies_tried": ["direct", "readability", "ua_rotation", "wayback"]
}

Browser render a JS page

curl "http://localhost:3939/providers/browser/fetch?url=https://example.com/app&max_links=20"

Returns rendered text, page title, final URL, extracted links, trust metadata, and challenge_detected=true if the page is a CAPTCHA or bot challenge.

Batch read

curl -X POST "http://localhost:3939/read/batch" \
  -H "Content-Type: application/json" \
  -d '{"urls": ["https://a.com", "https://b.com", "https://c.com"]}'

Python SDK

pip install agentsearch-client

from agentsearch import AgentSearch

client = AgentSearch()  # defaults to localhost:3939
results = client.search("manufacturing OEE best practices", count=5)
for r in results.results:
    print(f"{r.title} — {r.url}")

# Content extraction
page = client.read("https://example.com/article")
print(page.content[:500])

# Browser-rendered extraction for JS-heavy target pages
rendered = client.browser_fetch("https://example.com/app", max_links=20)
print(rendered.title, rendered.links[:3])

# Batch read
pages = client.read_batch(["https://a.com", "https://b.com"])
print(f"{pages.successful}/{pages.total} succeeded")

For authenticated instances, pass token=... or use AGENT_SEARCH_TOKEN, AGENTSEARCH_TOKEN, credentials/agent-search-token.txt, or ~/.config/agent-search/token.

LangChain tool

from langchain.tools import tool
import requests

@tool
def web_search(query: str) -> str:
    """Search the web using AgentSearch."""
    resp = requests.get("http://localhost:3939/search", params={"q": query, "count": 5})
    return "\n".join(
        f"- {r['title']}: {r['url']}\n  {r['snippet']}"
        for r in resp.json()["results"]
    )

MCP server (Claude Desktop, Cursor, Windsurf)

pip install mcp httpx
python mcp-server/server.py

For authenticated instances, set AGENT_SEARCH_TOKEN or run:

python mcp-server/server.py --token "change-me"

Add to Claude Desktop config:

{
  "mcpServers": {
    "agent-search": {
      "command": "python",
      "args": ["/path/to/mcp-server/server.py"]
    }
  }
}

See [mcp-server/README.md](mcp-server/README.md) for details.

Private stack (Tor + encrypted DNS)

The optional private stack adds a fully anonymized search path:

┌──────────┐    ┌──────────────┐    ┌───────────────┐    ┌──────────┐
│  :3940   │───▶│ api-private  │───▶│ searxng-priv  │───▶│   Tor    │──▶ Internet
│ (agent)  │    │ (FastAPI)    │    │ (SearXNG)     │    │(Snowflake│
└──────────┘    └──────────────┘    └───────────────┘    │ + obfs4) │
                                                          └──────────┘
                 All containers use CoreDNS → Cloudflare DoT
                 tor-internal network: no direct egress possible

What this gives you:

• Your ISP sees TLS to Cloudflare (DNS) and WebRTC-looking traffic (Snowflake). Not search queries.
• The private SearXNG instance lives on an internal-only Docker network with no internet route except through Tor.
• Port 3939 = direct (fast), port 3940 = anonymized (slower, private).

Setup:

./scripts/prepare-searxng.sh
docker compose -f docker-compose.yml -f examples/compose.private.yml up -d --build

All private stack configs live in [examples/](examples/) — copy and customize as needed.

Architecture

Port 3939 (direct)                    Port 3940 (Tor-anonymized)
     │                                      │
     ▼                                      ▼
┌─────────┐                          ┌─────────────┐
│   API   │                          │ api-private  │
│(FastAPI) │                          │  (FastAPI)   │
├─────────┤                          ├─────────────┤
│ dedup   │  ┌─────────────────┐     │ same code   │  ┌───────────────┐
│ scoring │  │    SearXNG      │     │ Tor egress  │  │ SearXNG-priv  │
│ cache   │──│ Google, Bing,   │     │ only        │──│ (tor-internal │
│ scrub   │  │ DDG, Brave,     │     └─────────────┘  │  network)     │
│ killchn │  │ /engines list   │                       └───────┬───────┘
│ trust   │  └─────────────────┘                               │
│ evolver │                                              ┌─────┴─────┐
└─────────┘                                              │    Tor    │
     │                                                   │ Snowflake │
     ▼                                                   │  + obfs4  │
┌─────────┐                                              └───────────┘
│ CoreDNS │──▶ Cloudflare DoT (encrypted DNS)
└─────────┘

Key modules (6,700 LOC)

| Module | LOC | What it does | |---|---|---| | killchain.py | 1016 | Escalating content extraction and browser-render fallback | | browser_renderer.py | 320 | Ephemeral browser rendering, extraction, and challenge detection | | main.py | 920 | FastAPI app, 17 endpoints, auth, rate limiting | | source_tracer.py | 620 | Source provenance tracking and citation chains | | scrubber.py | 539 | Prompt injection detection and content sanitization | | source_library.py | 310 | Curated institutional source registry | | domain_trust.py | 311 | Domain trust scoring (TLD, age, reputation) | | evolver.py | 301 | Self-improvement engine — failure analysis → config tuning | | content_cache.py | 241 | URL-keyed content cache with TTL | | query_expansion.py | 201 | Server-side query variation and fusion |

Plus: 5 pluggable adapters (Cloudflare bypass, Medium, 403 handler, parse error recovery, empty content fallback), MCP server, Python SDK, test suite.

Case study: 0 → 17 frameworks per hunt

A real autonomous research agent ("the wolf") u

…

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: brcrusoe72
• Source: brcrusoe72/agent-search
• License: MIT
• Homepage: https://pypi.org/project/agentsearch-client/

Install and usage instructions live in the source repository linked above.