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behive

by qa10devteam
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Python
Hybrid

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).

Related MCP server: deep-research

⚡ 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.

You ask Claude → Claude calls BeHive MCP tool → BeHive scouts 70+ APIs
→ fetches 200+ URLs via stealth drones → extracts claims with scores
→ deduplicates → returns structured report to Claude

Cost: Free. The hosted MCP server is provided by the BeHive team.
Privacy: Queries are processed and discarded. No data retention.

⚡ 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"

⚡ 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"
    }
  }
}

⚡ 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

🛠 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

🧠 Model Configuration

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

Stage

What it does

Recommended model

scout

Query generation, source planning

Claude Haiku 4.5 / GPT-4.1-mini / local

harvest

Content triage, relevance filtering

Claude Haiku 4.5 / GPT-4.1-mini / local

process

Claim extraction, entity recognition

Claude Sonnet 4.6 / GPT-4.5 / Gemini 2.5 Flash

synth

Report synthesis, deduplication

Claude Opus 4.6 / GPT-4.5 / Gemini 2.5 Pro

falsifier

Cross-verification, bias detection

Claude Sonnet 5 / o4-mini

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)

Preset

Model String

claude-haiku

anthropic/claude-haiku-4-5-20251001

claude-sonnet

anthropic/claude-sonnet-4-6

claude-opus

anthropic/claude-opus-4-6-v1

claude-sonnet-5

anthropic/claude-sonnet-5

gpt-4.1-mini

openai/gpt-4.1-mini

gpt-4.5

openai/gpt-4.5-preview

o4-mini

openai/o4-mini

gemini-flash

google/gemini-2.5-flash

gemini-pro

google/gemini-2.5-pro

llama-4

ollama/llama4-scout

local

openai/local-model (any OpenAI-compatible endpoint)

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.

Benchmarks

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

Topic

Claims

Avg Quality

Duration

Sources

NVIDIA GPU market 2026

290

0.797

8 min

234

OpenAI o3 capabilities

574

0.789

12 min

174

EU AI Act enforcement

267

0.759

6 min

130

Meta Llama 4 architecture

568

0.821

11 min

198

SpaceX Starship timeline

312

0.804

9 min

187

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.

Drone Arsenal

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

Category

Examples

Method

Web search

DuckDuckGo, Brave, SearXNG

Multi-backend fallthrough

Academic

arXiv, Semantic Scholar, PubMed

Direct API

News

Google News RSS, NewsAPI, GDELT

Real-time feeds

Government

EU Open Data, USPTO, SEC EDGAR

Registry APIs

Code

GitHub, GitLab, npm, PyPI

Package ecosystems

Financial

Yahoo Finance, FRED, ECB

Market data

Social

Reddit, HN (via Algolia), X/Twitter

Community signals

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

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.

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

Tool

Description

research_topic

Start deep research on any topic

mission_status

Check progress of a running mission

get_report

Retrieve the final synthesized report

search_knowledge

Query across all previous research

list_missions

List recent research missions

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}")

Comparison

Feature

BeHive

GPT-Researcher

Tavily

Perplexity

Structured claims

✅ JSON with scores

❌ Text only

❌ Snippets

❌ Text

Knowledge graph

✅ Neo4j entities

❌

❌

❌

Self-hosted

✅ MIT

✅

❌ SaaS only

❌ SaaS only

MCP native

✅

❌

❌

❌

Quality scoring

✅ 5-dimension

❌

❌

❌

Multi-source

70+ APIs

~5

1 (Tavily)

Internal

Cost

Free + your LLM

Free + your LLM

$50-500/mo

$20/mo

Falsifier

✅ Cross-verify

❌

❌

❌

Integrations

Platform

Method

Difficulty

Claude Desktop

MCP (add URL)

10 seconds

ChatGPT

Custom GPT (import URL)

30 seconds

Cursor / Windsurf

MCP config

10 seconds

Claude Code

MCP config

10 seconds

Hermes Agent

Built-in skill

1 command

OpenClaw

Skill file

1 file copy

n8n

Community node

Install in UI

Contributing

See CONTRIBUTING.md. PRs welcome.

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

License

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

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