Overview
Introduction Authentication Rate Limits
Signals API
GET /signals GET /signals/:id GET /graph GET /trends
Intelligence API v2
GET /v2/world-state GET /v2/causal GET /v2/brief GET /v2/causal (planned)
Agent Integration
Quick Start Schema Reference Code Examples

Signal Market API

The Signal Market API provides programmatic access to the world's most structured AI technology intelligence layer. Designed for both human developers and AI agents that need machine-readable world state.

Base URL: https://signal-market.vercel.app/api  ·  Local: http://localhost:3001/api

# Get all signals in 30 seconds
curl https://signal-market.vercel.app/api/signals

# Get intelligence brief
curl https://signal-market.vercel.app/api/v2/brief

# Get WorldStateObject for a signal
curl https://signal-market.vercel.app/api/v2/world-state/evt_001

Authentication

Pass your API key in the Authorization header or as a query parameter.

Your API Key (demo)
sm_founder_95d12139a6c22bd5bdd33720462ad743
Replace with your personal key from the dashboard. Get a production key →
# Header (recommended)
curl -H "Authorization: Bearer sm_your_key_here" /api/signals

# Query parameter
curl /api/signals?api_key=sm_your_key_here

Rate Limits

PlanRequests/minRequests/dayHistory
Free101,0007 days
Pro6050,00090 days
Agent300UnlimitedFull

Rate limit headers are returned with every response: X-RateLimit-Remaining, X-RateLimit-Reset.

GET /api/signals

Returns all active signals, sorted by confidence. The primary intelligence feed.

GET /api/signals All active signals
QUERY PARAMETERS
stagestringFilter by stage: accelerating | forming | emerging | peak | fading
limitintegerMax results (default: 50, max: 200)
min_confidencefloatMinimum confidence threshold (0–1)
# Response
{
  "signals": [
    {
      "signal_id": "evt_001",
      "topic": "AI Agents",
      "stage": "accelerating",
      "confidence": 0.97,
      "impact_score": 0.92,
      "sources": ["arxiv:cs.AI", "huggingface:trending"],
      "cross_validated": true
    }
  ],
  "meta": { "total": 9, "sources_monitored": 6 }
}

GET /api/v2/world-state

Returns WorldStateObject v2 — the structured, agent-native representation of each signal. Includes event classification, related domains, causal phase, and propagation metadata.

GET /api/v2/world-state/:signal_id WorldStateObject v2
signal_idpath paramSignal ID (e.g. evt_001)
state_versionresponseSchema version ("2.0.0")
event.event_typeresponseresearch_breakthrough | adoption_signal | technology_emergence | technology_acceleration
event.related_domainsresponseAdjacent topics from graph topology
null fieldsresponseactors, propagation, scenario_sensitivity (P1/P2, not yet implemented)

GET /api/v2/brief

Compiled intelligence brief — structured for both human reading and agent consumption. Includes narrative, sections by stage, graph topology insight, and agent action items.

# Python: fetch brief for agent
import requests

brief = requests.get("https://signal-market.vercel.app/api/v2/brief").json()

# Accelerating signals
for section in brief["sections"]:
  if section["title"] == "Accelerating":
    for s in section["signals"]:
      print(s["topic"], s["confidence"])

Agent Quick Start

Signal Market is designed as a world-state interface for AI agents. Agents can query the current state of technology trends and receive structured, confidence-scored intelligence.

// LangChain / agent tool definition
const signalTool = {
  name: "get_technology_signals",
  description: "Get current AI technology trend signals with confidence scores",
  func: async (stage) => {
    const r = await fetch(`/api/signals?stage=${stage}`);
    return r.json();
  }
};

// WorldState for deep context
const wsTools = await fetch("/api/v2/world-state").then(r => r.json());
// Returns: event_type, related_domains, causal phase, agent action items

WorldStateObject v2 Schema

The v2 schema is designed to be agent-parseable: typed fields, explicit nulls with explanation, versioned.

{
  "signal_id": "evt_001", // stable identifier
  "state_version": "2.0.0", // schema version
  "event": {
    "event_type": "research_breakthrough", // derived from sources
    "entities": ["AI Agents"],
    "related_domains": ["LLM Infrastructure", "AI Coding"], // from graph
    "first_order_effects": ["tooling demand", "talent surge"],
    "actors": null, // P1: requires actor data pipeline
  },
  "confidence": 0.97,
  "causal_explanation": {
    "current_phase": "growing",
    "core_drivers": null // P0-B: implementing next
  },
  "propagation": null, // P1: blocked on actor data
  "lifecycle_stage": "accelerating",
  "time_horizon": "months"
}

Null fields are intentionally null — not missing. Each null has a documented implementation milestone. This is a commitment to honesty over fake completeness.

GET /api/v2/causal/:signal_id

Returns a CausalChainObject for a signal — why it's happening, what's driving it, what could stop it, and how urgent the decision window is.

GET /api/v2/causal all signals
GET /api/v2/causal/:signal_id single signal
# Causal analysis for AI Agents signal
curl /api/v2/causal/evt_001 -H "x-api-key: YOUR_KEY"

CausalChainObject schema:

{
  "primary_cause": "LLM capability crossing tool-use threshold",
  "mechanism": "GPT-4 demonstrated reliable function calling...",
  "enabling_factors": ["string"],
  "accelerants": ["string"], // what's making it faster
  "inhibitors": ["string"], // what could slow or reverse it
  "causal_chain": [{ "step": 1, "label": "...", "description": "..." }],
  "causal_confidence": 0.88,
  "causal_model_source": "domain_knowledge", // or "derived"
  "why_now": {
    "timing_factors": ["string"],
    "urgency": "high | medium | low",
    "window": "3–12 months"
  },
  "engine": "P0-B"
}