AgentSignal

The collective intelligence layer for AI shopping agents.

Every agent that connects makes every other agent smarter. 1,200+ shopping sessions, 95 products, 50 merchants, 10 categories — and growing.

Why this exists: When AI agents shop for users, each agent starts from zero. AgentSignal pools decision signals across all agents so every session benefits from what every other agent has already learned — selection rates, rejection patterns, price intelligence, merchant reliability, and proven constraint matches.

Quick Start (30 seconds)

Remote — zero install, instant intelligence:

{
  "mcpServers": {
    "agent-signal": {
      "url": "https://agent-signal-production.up.railway.app/mcp"
    }
  }
}

Local via npx:

npx agent-signal

Claude Desktop / Claude Code:

{
  "mcpServers": {
    "agent-signal": {
      "command": "npx",
      "args": ["agent-signal"]
    }
  }
}

One Call to Start Shopping Smarter

The smart_shopping_session tool logs your session AND returns all available intelligence in a single call:

smart_shopping_session({
  raw_query: "lightweight running shoes with good cushioning",
  category: "footwear/running",
  budget_max: 200,
  constraints: ["lightweight", "cushioned"]
})

Returns:

Your session ID for subsequent logging
Top picks from other agents in that category
What constraints and factors mattered most
How similar sessions ended (purchased vs abandoned)
Network-wide stats

15 MCP Tools

Smart Combo Tools (recommended)

Tool	What it does
`smart_shopping_session`	Start session + get category intelligence + similar session outcomes — all in one call
`evaluate_and_compare`	Log product evaluation + get product intelligence + deal verdict — all in one call

Read Tools — Leverage Collective Intelligence

Tool	What it tells you
`get_product_intelligence`	Selection rate, rejection reasons, which competitors beat it and why
`get_category_recommendations`	Top picks, decision factors, common requirements, average budgets
`check_merchant_reliability`	Stock accuracy, selection rate, purchase outcomes by merchant
`get_similar_session_outcomes`	What agents with similar constraints ended up choosing
`detect_deal`	Price verdict against historical data — best_price_ever to above_average
`get_warnings`	Stock issues, high rejection rates, abandonment signals
`get_constraint_match`	Products that exactly match your constraints — skip the search

Write Tools — Contribute Back

Tool	What it captures
`log_shopping_session`	Shopping intent, constraints, budget, exclusions
`log_product_evaluation`	Product considered, match score, disposition + rejection reason
`log_comparison`	Products compared, dimensions, winner, deciding factor
`log_outcome`	Final result — purchased, recommended, abandoned, or deferred
`import_completed_session`	Bulk import a completed session retroactively
`get_session_summary`	Retrieve full session details

Example: Full Agent Workflow

# 1. Start smart — one call gets you session ID + intelligence
smart_shopping_session(category: "electronics/headphones", constraints: ["noise-cancelling", "wireless"], budget_max: 400)

# 2. Evaluate products — get intel as you log
evaluate_and_compare(session_id: "...", product_id: "sony-wh1000xm5", price_at_time: 349, disposition: "selected")
evaluate_and_compare(session_id: "...", product_id: "bose-qc45", price_at_time: 279, disposition: "rejected", rejection_reason: "inferior ANC")

# 3. Compare and close
log_comparison(products_compared: ["sony-wh1000xm5", "bose-qc45"], winner: "sony-wh1000xm5", deciding_factor: "noise cancellation quality")
log_outcome(session_id: "...", outcome_type: "purchased", product_chosen_id: "sony-wh1000xm5")

Every step feeds the network. The next agent shopping for headphones benefits from your data.

Categories with Active Intelligence

Category	Sessions
footwear/running	150+
electronics/headphones	140+
gaming/accessories	130+
electronics/tablets	130+
home/furniture/desks	120+
fitness/wearables	118+
electronics/phones	115+
home/smart-home	107+
kitchen/appliances	105+
electronics/laptops	98+

Agent Framework Examples

Ready-to-run examples in /examples:

Framework	File	Description
LangChain	`langchain-shopping-agent.py`	ReAct agent with LangGraph + MCP adapter
CrewAI	`crewai-shopping-crew.py`	Two-agent crew (researcher + shopper)
AutoGen	`autogen-shopping-agent.py`	AutoGen agent with MCP tools
OpenAI Agents	`openai-agents-shopping.py`	OpenAI Agents SDK with Streamable HTTP
Claude	`claude-system-prompt.md`	Optimized system prompt for Claude Desktop/Code

All examples connect to the hosted MCP endpoint — no setup beyond pip install required.

REST API

Merchant-facing analytics at https://agent-signal-production.up.railway.app/api:

Endpoint	Description
`GET /api/products/:id/insights`	Product analytics — consideration rate, rejection reasons
`GET /api/categories/:category/trends`	Category trends — top factors, budgets, attributes
`GET /api/competitive/lost-to?product_id=X`	Competitive losses — what X loses to and why
`GET /api/sessions`	Recent sessions (paginated)
`GET /api/sessions/:id`	Full session detail
`POST /api/admin/aggregate`	Trigger insight computation
`GET /api/health`	Health check

Self-Hosting

git clone https://github.com/dan24ou-cpu/agent-signal.git
cd agent-signal
npm install
cp .env.example .env  # set DATABASE_URL to your PostgreSQL
npm run migrate
npm run seed           # optional: sample data
npm run dev            # starts API + MCP server on port 3100

Architecture

MCP Server — Stdio transport (local) + Streamable HTTP (remote)
REST API — Express on the same port
Database — PostgreSQL (Neon-compatible)
15 MCP tools — 9 read + 6 write

License

MIT

agent-signal

Quick Install