JIRO — Search API, Web Scraping & Query Graph Engine

9 search engines. 12 social platforms. 22 MCP tools. One self-hosted API.

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What is JIRO?

JIRO is an open-source, self-hosted search API and web scraping platform. It aggregates 9 search engines and 12 social platforms behind one HTTP API, and adds a query graph engine that plans, executes and cites multi-step research — every answer traces back to the pages that produced it.

It runs entirely on your own infrastructure. No per-query vendor bill, no data leaving your network.

Answer engine optimization note for the curious: the Sourced answers section below is the part that replaces a search API. Engines return links; JIRO returns claims plus the source ids behind them.

Quick Start

pip install jirosearch
jiro serve

curl -X POST http://localhost:8000/search \
  -H "Content-Type: application/json" \
  -d '{"q": "latest AI research", "engine": "google"}'

Running: 9 engines, hybrid ranking, caching and structured extraction, all local.

No server? Use the cloud API:

jiro auth login   # device code flow, opens your browser

curl -X POST https://searchjiro.vercel.app/api/proxy/search \
  -H "Authorization: Bearer jsk_live_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{"q": "latest AI research", "engine": "google"}'

Sourced answers (0.4.0)

Most search APIs return a list of links and stop. JIRO 0.4.0 runs a query graph: it plans the research shape, executes nodes in parallel, and returns an evidence packet where every claim carries the source_ids it rests on.

curl -X POST http://localhost:8000/v1/graph/search \
  -H "Content-Type: application/json" \
  -d '{"query": "compare vector databases", "fetch_top": 3, "max_tokens": 2000}'

{
  "graph_id": "g_8f2c1a",
  "intent": "comparison",
  "status": "ok",
  "duration_ms": 1840,
  "engine_attribution": { "google": 2, "bing": 1 },
  "gaps": [],
  "budget": { "requests_used": 5, "bytes_used": 284113, "nodes_used": 9, "tokens_used": 1780 },
  "packet": {
    "packet_id": "p_41d9",
    "used_tokens": 1780,
    "claims": [
      { "id": "c1", "text": "Qdrant is written in Rust.", "source_ids": ["s2"] }
    ],
    "sources": [{ "id": "s2", "url": "https://qdrant.tech/documentation/" }],
    "contradictions": []
  }
}

Plan without paying. /v1/graph/plan is a dry run — it validates the graph and returns a cost estimate without touching the network.

What the graph engine buys you

Passing work between agents

session_handoff is an MCP tool that packages a finished run into a brief another agent can act on — resolvable evidence ids, open questions taken from gaps, contradictions, remaining budget, and the next node to run. Roles: planner, retriever, verifier, writer, observer.

Search engines

Social platforms

Reddit · Twitter/X · YouTube · LinkedIn · TikTok · Instagram · Facebook · Threads · Hacker News · Bluesky · Telegram · Pinterest

Official SDKs

All three expose the 0.4.0 query graph and are versioned in lockstep with the server.

Python

pip install jiro-sdk

from jiro_sdk import JiroClient

client = JiroClient(api_key="your-key")

# Classic search + scrape
results = client.search("python web scraping")
content = client.scrape("https://example.com")

# Query graph (0.4.0)
plan = client.plan_search("compare vector databases")          # dry run, no network
run  = client.search_graph("compare vector databases", fetch_top=3)
for claim in run["packet"]["claims"]:
    print(claim["text"], claim["source_ids"])

JavaScript / TypeScript

npm install jiro-sdk

import { JiroClient } from 'jiro-sdk';

const client = new JiroClient({ apiKey: 'your-key' });

const results = await client.search('python web scraping');
const run = await client.searchGraph('compare vector databases', { fetchTop: 3 });

// WebSocket streaming
const ws = client.createSearchStream('AI news');
ws.onmessage = (event) => console.log(JSON.parse(event.data));

Go

go get github.com/DevAnimecx/jiro/sdk/go

import "github.com/DevAnimecx/jiro/sdk/go"

client := jiro.NewClient(jiro.WithAPIKey("your-key"))

results, _ := client.Search("python web scraping", nil)
run, _ := client.GraphSearch("compare vector databases", &jiro.GraphOptions{FetchTop: 3}, false)

CLI

# Search
jiro search web "python web scraping"
jiro search web "AI news" --parallel --engines 3
jiro search web -i                      # interactive

# Scrape
jiro scrape https://example.com
jiro scrape "free SaaS directories"     # search + scrape top result

# AI
jiro ai ask "What is Python?"
jiro ai setup --provider openai -k sk-...

# Auth (cloud)
jiro auth login && jiro auth whoami

# License (self-hosted)
jiro license activate JIRO-PRO-A1B2-C3D4-E5F6

# System
jiro status && jiro doctor && jiro serve --port 8000

MCP integration

Works with any MCP-compatible client — Claude Desktop, Cursor, Windsurf, and others.

{
  "mcpServers": {
    "jiro": { "command": "jiro", "args": ["mcp"] }
  }
}

22 MCP tools

Features

Sourced answers (0.4.0)

• Query graph — plans a research shape, then executes it
• Evidence packets — claims paired with resolvable source ids
• Cost estimation — dry-run a graph before paying for it
• Gap reporting — branches that failed say why
• Session handoff — pass a run to another agent as a structured brief
• Durable runtime — lease-based node execution that survives restarts

Search intelligence

• Hybrid search — keyword + semantic + freshness signals
• Multi-query — parallel query expansion
• Answer synthesis — extractive answers from results
• Pagination — walks SERP pages until num is met or the budget runs out
• Substitution reporting — tells you when a fallback engine answered

Web scraping

• Stealth engine — TLS/JA3 fingerprint rotation
• Anti-bot bypass — Cloudflare, DataDome, PerimeterX
• JS rendering — full browser automation
• Structured extraction — JSON-schema-driven
• Social scraping — 12 platforms, normalized output

Enterprise-ready

• Rate limiting — sliding window, per-tier quotas
• Plugin system — custom engines and scrapers
• Advanced caching — LRU/LFU with analytics
• Batch operations — concurrent search and scrape
• Monitoring — Prometheus metrics, health checks
• WebSocket streaming — real-time results

Deploy

docker-compose up -d                       # Docker
helm install jiro ./helm/jiro              # Kubernetes
pip install jirosearch && jiro serve       # Local

Comparisons

vs SerpAPI

vs ScraperAPI

vs Bright Data

Architecture

jiro/
├── server/routers/      FastAPI endpoints (75+ routes)
├── graph/               Query graph (0.4.0)
│   ├── planner.py       Turns a query into a validated graph
│   ├── executor.py      Ready-driven DAG execution
│   ├── operators.py     SEARCH / FETCH / VERIFY / SYNTHESIZE nodes
│   └── service.py       Plan + run entry points
├── parametric/packet.py Evidence packet assembly
├── search/              Hybrid search, reranking, query expansion
├── scraping/            9 engines, stealth client, 12 social platforms
├── ai/                  LLM integration + MCP tool schemas
├── runtime/             Durable execution, leases, config
├── mcp.py               MCP server (22 tools)
├── ratelimit.py         Rate limiting & quotas
├── cache_advanced.py    Cache with analytics
└── dashboard.py         Web UI

Exit codes

For scripting and CI, the CLI distinguishes outcomes rather than collapsing them:

Pricing

Cloud (managed)

Start free → · Pricing →

Self-hosted (one-time)

Install from GitHub →

FAQ

Do I need API keys for the search engines? No. All 9 engines are scraped directly. No Google, Bing or SerpAPI keys required.

Can I run JIRO fully offline? The server runs locally, but it reaches out to the engines you query. Point it at an internal mirror or disable engines you don't want in the config.

What makes the 0.4.0 answers checkable? Every claim in the evidence packet carries source_ids that resolve against packet.sources[]. A claim whose sources can't be resolved is flagged, not silently dropped.

How is this different from a search API like SerpAPI? SerpAPI returns links from one engine. JIRO searches 9, fetches and extracts the pages, and returns claims with citations — plus it's self-hosted, so there's no per-query bill.

Is it really free? The MIT-licensed core is free forever, self-hosted and unlimited. Paid tiers add AI features, higher rate limits and commercial licensing.

Can I pass research between AI agents? Yes — the session_handoff MCP tool packages a run into a brief with evidence ids, open questions and remaining budget, ready for the next agent to pick up.

Community

• Website — home
• GitHub — source
• Docs — guides
• Blog — articles
• Discord — chat
• Twitter — updates

License

MIT — use it freely, commercially or privately.