behive

Your AI assistant → BeHive → Verified, structured, scored knowledge
                              ├── 363 claims (avg quality 0.77)
                              ├── 42 entities with relationships
                              └── Synthesized report with citations

pip install behive

# Set your LLM API key (you use YOUR OWN subscription — BeHive costs nothing)
export ANTHROPIC_API_KEY=your-key  # or OPENAI_API_KEY, or AWS creds for Bedrock

# PostgreSQL required for storage (or use Docker below)
export BEHIVE_DB_URL=postgresql://user:***@localhost:5432/behive

# Start the server
behive serve

git clone https://github.com/qa10devteam/behive && cd behive
echo "ANTHROPIC_API_KEY=your-key" > .env
docker compose up -d
# API at http://localhost:8091

pip install "behive[all]"

pip install "behive[stealth]"   # curl_cffi, primp, nodriver, patchright
pip install "behive[harvest]"   # trafilatura, newspaper4k, PyMuPDF, crawl4ai
pip install "behive[process]"   # rapidfuzz, spacy, litellm, tiktoken
pip install "behive[mcp,api]"   # MCP server + REST API

# Apply a preset
behive config --preset balanced   # Haiku collects, Sonnet synthesizes (~$1.50/mission)
behive config --preset budget     # Haiku everywhere (~$0.30/mission)
behive config --preset quality    # Sonnet everywhere (~$4.00/mission)
behive config --preset local      # Your own LLM server ($0.00/mission)

behive config --quick    # Pick one model for everything
behive config --full     # Choose model per stage (interactive)

# Set a single stage
behive config --stage synth --model claude-opus
behive config --stage scout --model ollama/deepseek-r1

# Check current config
behive config --show

export BEHIVE_MODEL_SCOUT=ollama/llama3.1
export BEHIVE_MODEL_SYNTH=anthropic/claude-sonnet-4-20250514
behive serve

pip install behive
export ANTHROPIC_API_KEY=*** # your own key
behive serve

{
  "mcpServers": {
    "behive": {
      "url": "http://localhost:8090/mcp",
      "transport": "streamable-http"
    }
  }
}

You ask Claude a question
    ↓
Claude calls BeHive MCP tool "research_topic"
    ↓
BeHive scouts 70+ APIs, fetches 1000+ URLs via stealth drones
    ↓
Your LLM key extracts claims (Claude Haiku = ~$0.50 per mission)
    ↓
BeHive scores, deduplicates, builds knowledge graph
    ↓
Returns structured report to Claude
    ↓
Claude presents findings with confidence scores and source links

pip install behive
export OPENAI_API_KEY=*** # your own key
behive serve --host 0.0.0.0

# Expose with a tunnel (for testing):
# npx cloudflared tunnel --url http://localhost:8091

openapi: 3.1.0
info:
  title: BeHive Research API
  version: 0.3.0
servers:
  - url: https://*** paths:
  /research:
    post:
      operationId: startResearch
      summary: Start a deep research mission
      requestBody:
        required: true
        content:
          application/json:
            schema:
              type: object
              required: [query]
              properties:
                query:
                  type: string
                  description: Research topic or question
                depth:
                  type: integer
                  default: 3
                  description: 1=quick, 3=standard, 5=deep
      responses:
        '200':
          description: Mission started successfully
  /research/{mission_id}:
    get:
      operationId: getResearchResults
      summary: Get completed research with scored claims
      parameters:
        - name: mission_id
          in: path
          required: true
          schema:
            type: string
      responses:
        '200':
          description: Research results with claims and report
  /claims/search:
    get:
      operationId: searchKnowledge
      summary: Search across all previously researched knowledge
      parameters:
        - name: q
          in: query
          required: true
          schema:
            type: string
        - name: limit
          in: query
          schema:
            type: integer
            default: 20
      responses:
        '200':
          description: Matching claims with scores

{
  "mcpServers": {
    "behive": {
      "url": "http://localhost:8090/mcp",
      "transport": "streamable-http"
    }
  }
}

# BeHive skill auto-loads when you ask Hermes to research anything
# Just ensure behive serve is running on the same machine
behive serve

# Install from integrations directory
cp integrations/openclaw/SKILL.md ~/.openclaw/skills/behive-research.md

Layer 1 │ DIRECT          — aiohttp + full Chrome 131 headers
Layer 2 │ UA ROTATION     — 10 browser fingerprints (Chrome/Firefox/Safari/Edge)
Layer 3 │ curl_cffi       — TLS impersonation (JA3/JA4 fingerprint matching)
Layer 4 │ primp           — Rust-native TLS, newer fingerprints than curl_cffi
Layer 5 │ nodriver        — Headless Chrome via CDP, passes Cloudflare Bot Management
Layer 6 │ patchright      — Stealth Playwright (no Runtime.enable/Console.enable leak)
Layer 7 │ Jina relay      — r.jina.ai proxy (paywall + captcha bypass)
Layer 8 │ Archives        — Wayback Machine + archive.org fallback

                    ┌─── HEAD sweep (974+ URLs, async semaphore) ───┐
                    │                                                │
                    ▼                                                ▼
          ┌─────────────────┐                            ┌────────────────┐
          │  Resource Router │                            │  Domain Recon  │
          │  (8 resource     │                            │  (tier scoring │
          │   types detected)│                            │   reputation)  │
          └────────┬────────┘                            └───────┬────────┘
                   │                                              │
        ┌──────────┼──────────┬──────────┐                       │
        ▼          ▼          ▼          ▼                       ▼
   api_bee    pdf_drone   std_drone  heavy_drone         domain_score
   (70 APIs)  (VLM parse) (Layer 1-8) (patchright)       (0.0 - 1.0)

                         ┌──────────────────────────────────┐
                         │         BeHive Pipeline           │
                         └──────────────────────────────────┘
                                        │
        ┌───────────┬───────────┬───────┴───────┬───────────┬───────────┐
        ▼           ▼           ▼               ▼           ▼           ▼
   ┌─────────┐ ┌─────────┐ ┌──────────┐ ┌──────────┐ ┌─────────┐ ┌────────┐
   │  SCOUT  │ │ HARVEST │ │ PROCESS  │ │   V4     │ │  SYNTH  │ │ GRAPH  │
   │         │ │         │ │          │ │          │ │         │ │        │
   │ Queen   │ │ Parallel│ │ BeeHive  │ │ Haiku    │ │ Claude  │ │ Neo4j  │
   │ plans   │ │ HTTP    │ │ fast     │ │ extract  │ │ report  │ │ entity │
   │ 5 axes  │ │ 1000+   │ │ extract  │ │ + Sonnet │ │ + cite  │ │ fuse   │
   │ × N     │ │ URLs    │ │ + score  │ │ enrich   │ │         │ │        │
   └─────────┘ └─────────┘ └──────────┘ └──────────┘ └─────────┘ └────────┘
       │              │            │            │            │          │
       │              │            ▼            │            │          │
       │              │    ┌──────────────┐     │            │          │
       │              │    │ Quality Gate │     │            │          │
       │              │    │  conf ≥ 0.55 │     │            │          │
       │              │    │  dedup 0.60  │     │            │          │
       │              │    └──────────────┘     │            │          │
       │              │            │            │            │          │
       └──────────────┴────────────┴────────────┴────────────┴──────────┘
                                        │
                              ┌─────────┴─────────┐
                              │   PostgreSQL       │
                              │   Claims + KG      │
                              │   25K+ records     │
                              └───────────────────┘

curl -X POST http://localhost:8091/research \
  -H "Content-Type: application/json" \
  -d '{
    "query": "SpaceX Starship launch cadence 2026",
    "depth": 3,
    "scale": 30
  }'
# → {"job_id": "hive_1785227949_815112", "status": "started"}

curl -N http://localhost:8091/research/hive_1785227949_815112/events

event: start
data: {"topic": "SpaceX Starship...", "status": "scout"}

event: phase
data: {"phase": "process", "event": "started"}

event: claims
data: {"count": 142, "avg_quality": 0.77, "above_082": 23, "new_since_last": 18}

event: done
data: {"total_claims": 363, "avg_quality": 0.77, "sources": 64}

curl http://localhost:8091/research/hive_1785227949_815112/report
# → {"synthesis": "## SpaceX Starship...", "claims_count": 363, ...}

# Full-text search across all missions
curl "http://localhost:8091/search?query=NVIDIA+revenue&limit=20"

# Entity intelligence
curl http://localhost:8091/intelligence/entity/NVIDIA

# Network graph (2-hop neighborhood)  
curl "http://localhost:8091/intelligence/network/OpenAI?depth=2"

{
  "mcpServers": {
    "behive": {
      "url": "http://localhost:8090/mcp",
      "transport": "streamable-http"
    }
  }
}

git clone https://github.com/qa10devteam/behive.git
cd behive
cp .env.example .env     # add your LLM API key
docker compose up -d     # API ready at localhost:8091

docker compose --profile full up -d

pip install behive[all]

# PostgreSQL
createdb hive
behive db init

# Configure
export BEHIVE_DB_URL="postgresql://user:pass@localhost:5432/hive"
export BEHIVE_LLM=bedrock  # or openai, local

# Start services
behive serve              # Starts both REST API (:8091) + MCP (:8090)

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