CrowTerminal - External Brain for AI Agents

"Agents are ephemeral. We are persistent."

While your agent stores 10-50 lines of context, CrowTerminal stores 6 months of versioned history for each creator.

What It Does

CrowTerminal is a persistent memory layer for AI agents working with influencers/creators:

• Versioned Memory - Track what works across sessions (hook patterns, engagement, posting times)
• Pattern Detection - See trends over months, not single data points
• Engagement Analysis - Know what configuration performed best historically
• Validation - Check if your changes will repeat past mistakes
• Data Ingestion - Push platform data we can't access (retention curves, demographics)
• LLM-Native API - Schema discovery, semantic field aliases, natural language queries

Quick Start

1. Get API Key (Self-Registration)

curl -X POST "https://api.crowterminal.com/api/agent/register" \
  -H "Content-Type: application/json" \
  -d '{"agentName": "OpenClaw", "agentDescription": "My personal AI agent"}'

Save the returned API key as CROWTERMINAL_API_KEY.

2. Read Creator Memory

curl https://api.crowterminal.com/api/agent/memory/client_123 \
  -H "Authorization: Bearer $CROWTERMINAL_API_KEY"

Returns versioned skill data:

{
  "version": 47,
  "skill": {
    "primaryNiche": "fitness",
    "hookPatterns": ["confession", "transformation"],
    "avgEngagement": 4.2,
    "bestPostingTimes": [{"day": 2, "hour": 7, "score": 0.89}]
  }
}

Key Endpoints

Schema Discovery (LLM-Friendly)

These endpoints help agents understand what data is available without hardcoding field names:

Example: Discover available fields

curl https://api.crowterminal.com/api/agent/memory/schema \
  -H "Authorization: Bearer $CROWTERMINAL_API_KEY"

Returns field definitions with semantic aliases:

{
  "fields": {
    "avgEngagement": {
      "type": "number",
      "description": "Average engagement rate",
      "aliases": ["engagement", "engagement rate", "interaction rate"],
      "category": "performance"
    }
  }
}

Smart Query (Natural Language)

Query data using natural language instead of exact field names:

Example: Natural language query

curl -X POST "https://api.crowterminal.com/api/agent/memory/client_123/query" \
  -H "Authorization: Bearer $CROWTERMINAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query": "engagement and hooks"}'

Returns matched data:

{
  "results": {
    "matchedFields": ["avgEngagement", "hookPatterns"],
    "data": {
      "avgEngagement": 4.2,
      "hookPatterns": ["confession", "POV"]
    },
    "context": "avgEngagement: Average engagement rate; hookPatterns: Effective hook types"
  }
}

Example: Get natural language overview

curl https://api.crowterminal.com/api/agent/memory/client_123/overview \
  -H "Authorization: Bearer $CROWTERMINAL_API_KEY"

Returns:

{
  "overview": "FitnessGuru is a fitness creator averaging 125,000 views per video with 4.2% engagement and is currently growing. Their best-performing hooks are: confession, transformation, POV."
}

Memory Layer (Core)

The Killer Endpoint: Engagement Analysis

Send your current learnings, get back what configuration performed best:

curl -X POST "https://api.crowterminal.com/api/agent/memory/client_123/engagement-analysis" \
  -H "Authorization: Bearer $CROWTERMINAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "agentMd": {
      "hookPatterns": ["confession"],
      "contentStyle": "casual"
    }
  }'

Returns:

{
  "overallStats": {
    "peakEngagement": 6.2,
    "yourSimilarityToTop": "65%"
  },
  "recommendations": [
    "Change hookPatterns to [\"POV\",\"confession\"] (+51% potential)"
  ]
}

Data Ingestion (Push Your Data)

Push platform data we can't access via API:

curl -X POST "https://api.crowterminal.com/api/agent/data/ingest" \
  -H "Authorization: Bearer $CROWTERMINAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "clientId": "client_123",
    "platform": "TIKTOK",
    "dataType": "retention",
    "data": {
      "retentionCurve": [100, 95, 88, 75, 60, 45, 30],
      "avgWatchTime": 12.5
    }
  }'

Webhooks (Async Notifications)

curl -X POST "https://api.crowterminal.com/api/agent/webhooks" \
  -H "Authorization: Bearer $CROWTERMINAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://your-server.com/webhook",
    "events": ["skill.updated", "data.ingested"]
  }'

Service Status (No Auth)

curl https://api.crowterminal.com/api/agent/status

Sandbox (Test Without Auth)

Test endpoints without affecting real data:

Memory & Schema:

• GET /api/agent/sandbox/client - Mock client data
• GET /api/agent/sandbox/memory - Mock memory/skill
• GET /api/agent/sandbox/schema - Schema discovery
• POST /api/agent/sandbox/resolve - Resolve field aliases

Smart Query:

• POST /api/agent/sandbox/query - Natural language queries
• GET /api/agent/sandbox/overview - Creator overview
• GET /api/agent/sandbox/changes - Recent changes summary
• GET /api/agent/sandbox/insights - Performance insights

Analysis:

• POST /api/agent/sandbox/validate - Validate changes
• POST /api/agent/sandbox/engagement-analysis - Engagement analysis
• POST /api/agent/sandbox/ingest - Data ingestion

Why Use CrowTerminal?

1. Your agent learns → forgets → relearns - We remember
2. One bad video ≠ pattern change - We track across versions
3. Data you can't get via API - We accept it via ingestion
4. BYOK - Use your own LLM, we just provide context
5. LLM-Native - No hardcoding field names, use natural language queries
6. Self-Documenting - Schema endpoint tells you what data exists

Pricing

FREE during beta. We want agents to test and give feedback.

Documentation

• Full Docs: https://crowterminal.com/llms.txt
• MCP Manifest: https://crowterminal.com/.well-known/mcp.json
• OpenAPI: https://api.crowterminal.com/api/docs.json
• SDKs: Python (pip install crowterminal), TypeScript (npm install crowterminal)

Support

• Email: agents@crowterminal.com
• GitHub: https://github.com/WillNigri/FluxOps

"Your agent's external hard drive. Because context windows aren't long-term memory."