qa10devteam/behive

🐝 BeHive

Open-source research engine that extracts structured knowledge from any topic. Feed it a question. Get back scored claims, entity graphs, and a synthesized report — not paragraphs of slop.

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The Problem

You ask an AI to research something. It hallucinates confidently or gives you a 2000-word essay that says nothing specific. No sources. No confidence scores. No way to verify.

BeHive fixes this. It goes out, searches real sources, extracts verifiable claims with confidence scores, and builds a knowledge graph — all through your existing AI (Claude, ChatGPT, Gemini).

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⚡ Use with Claude Desktop (10 seconds)

No install needed. Add this to your Claude Desktop config and you’re done:

File: ~/Library/Application Support/Claude/claude_desktop_config.json (Mac) or %APPDATA%\Claude\claude_desktop_config.json (Windows)

{
  "mcpServers": {
    "behive": {
      "url": "https://api.behive.site/mcp",
      "transport": "streamable-http"
    }
  }
}

Restart Claude Desktop. Ask: “Research the EU AI Act enforcement timeline”

That’s it. Claude calls BeHive automatically.

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⚡ Use with ChatGPT (Custom GPT)

1. Go to chat.openai.com/gpts/editor
2. Create a new GPT
3. Under Actions → click Import from URL → paste:

   https://api.behive.site/openapi.json
   

4. Name it “Deep Researcher” and save

Now ask your GPT: “Research quantum computing breakthroughs 2026”

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⚡ Use with Cursor / Windsurf / Claude Code

Add to your MCP config (.cursor/mcp.json, ~/.claude/settings.json, etc.):

{
  "mcpServers": {
    "behive": {
      "url": "https://api.behive.site/mcp",
      "transport": "streamable-http"
    }
  }
}

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⚡ Use with Hermes Agent / OpenClaw

# Hermes — already has a built-in skill
hermes skill install behive-research

# OpenClaw — copy from integrations/
cp integrations/openclaw/SKILL.md ~/.openclaw/skills/behive.md

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🛠 Self-Host (full control)

Want to run BeHive on your own machine? Three options:

Option A: Zero-setup (DuckDB, no PostgreSQL needed)

pip install behive
export ANTHROPIC_API_KEY=***  # or OPENAI_API_KEY

behive research "NVIDIA GPU market 2026"

Data is stored locally in ~/.behive/research.duckdb. No database setup needed.

Option B: Docker Compose (recommended for teams)

git clone https://github.com/qa10devteam/behive && cd behive
echo "ANTHROPIC_API_KEY=***" > .env
docker compose up -d
# API → http://localhost:8091
# MCP → http://localhost:8090/mcp

Option C: Full production (PostgreSQL + GPU)

pip install "behive[all]"
export BEHIVE_DB_URL=postgresql://user:pass@localhost:5432/behive
export ANTHROPIC_API_KEY=***
behive serve

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🧠 Model Configuration

BeHive works with any LLM. You choose what runs each pipeline stage:

Quick config

behive config --preset budget     # Haiku everywhere (~$0.30/mission)
behive config --preset balanced   # Haiku scout + Sonnet synth (~$1.50)
behive config --preset deep       # Opus synth + Falsifier (~$4.00)

Model presets (2026)

Environment variables

export BEHIVE_MODEL=anthropic/claude-sonnet-4-6          # one model for everything
export BEHIVE_MODEL_SCOUT=ollama/llama4-scout            # per-stage override
export BEHIVE_MODEL_SYNTH=anthropic/claude-opus-4-6-v1   # per-stage override

Priority: BEHIVE_MODEL_{STAGE} > BEHIVE_MODEL > config.yaml > defaults

Or pass any litellm-compatible model string directly.

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Benchmarks

Real results. No cherry-picking. Scale 30 (standard depth).

Quality scoring:

• 0.90+ — Exceptional: specific numbers, dates, fully verifiable
• 0.82+ — Excellent: multi-source corroboration, publication-ready
• 0.75+ — Good: useful intelligence with specifics
• 0.65+ — Acceptable: general facts
• <0.55 — Rejected: too vague, not stored

Honest scoring. No sigmoid rescaling, no artificial inflation.

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Drone Arsenal

BeHive’s scout phase uses 70+ APIs across 37 categories — no single source dependency:

Stealth layer: 8-layer drone architecture bypasses Cloudflare, DataDome, Akamai, PerimeterX. Rotating fingerprints, browser TLS profiles, residential proxy support.

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Architecture

┌─────────────────────────────────────────────────────────────┐
│                      BeHive Pipeline                          │
└─────────────────────────────────────────────────────────────┘
                              │
    ┌───────────┬─────────────┼─────────────┬───────────┐
    ▼           ▼             ▼             ▼           ▼
┌────────┐ ┌────────┐ ┌──────────┐ ┌────────┐ ┌────────┐
│ SCOUT  │ │HARVEST │ │ PROCESS  │ │ SYNTH  │ │ GRAPH  │
│        │ │        │ │          │ │        │ │        │
│ Queen  │ │Parallel│ │ Dual-    │ │ Report │ │ Neo4j  │
│ plans  │ │ fetch  │ │ model    │ │ synth  │ │ build  │
│ 200+   │ │ drones │ │ extract  │ │ dedupe │ │ entity │
│ tasks  │ │ 70 APIs│ │ + score  │ │ FUIR   │ │ link   │
└────────┘ └────────┘ └──────────┘ └────────┘ └────────┘

Key design: The Queen plans all research tasks BEFORE any scout leaves. This means parallel execution, no redundant searches, and smart gap detection.

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API Reference

REST API (port 8091)

# Start a research mission
curl -X POST http://localhost:8091/research \
  -H "Content-Type: application/json" \
  -d '{"topic": "quantum computing 2026", "depth": 3}'

# Check status
curl http://localhost:8091/research/{mission_id}

# Get report
curl http://localhost:8091/research/{mission_id}/report

# Search knowledge base
curl "http://localhost:8091/search?q=quantum+supremacy&limit=20"

MCP Tools

Python SDK

from behive import BeHiveClient

client = BeHiveClient()  # auto-detects local server

# Start research
mission = client.research("EU AI Act penalties", depth=3)

# Wait and get results
report = mission.wait()
print(f"Found {len(report.claims)} claims, avg quality: {report.quality:.2f}")

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Comparison

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Integrations

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Contributing

See CONTRIBUTING.md. PRs welcome.

git clone https://github.com/qa10devteam/behive
cd behive
pip install -e ".[dev]"
pytest

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License

MIT — use it however you want. Star ⭐ if it helps.

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