Add comprehensive documentation and initialize project structure

agents/__init__.py

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+"""Agent system for the LinkedIn Automation Platform."""

agents/coordinator.py

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+"""Coordinator agent for orchestrating the multi-agent workflow."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from .sub_agents.browser import BrowserAgent
+from .sub_agents.classifier import ClassifierAgent
+from .sub_agents.enricher import EnricherAgent
+from .sub_agents.scorer import ScorerAgent
+from .sub_agents.scraper import ScraperAgent
+from .sub_agents.searcher import SearcherAgent
+
+
+class CoordinatorAgent:
+    """High level agent responsible for coordinating sub-agents."""
+
+    def __init__(self) -> None:
+        self.scraper = ScraperAgent()
+        self.classifier = ClassifierAgent()
+        self.searcher = SearcherAgent()
+        self.enricher = EnricherAgent()
+        self.scorer = ScorerAgent()
+        self.browser = BrowserAgent()
+
+    async def start_workflow(self, startup_url: str) -> Any:
+        """Execute the full investor discovery workflow.
+
+        The implementation is simplified and primarily demonstrates how the
+        various sub-agents are expected to interact.
+        """
+
+        scraped = await self.scraper.scrape(startup_url)
+        classification = await self.classifier.classify(scraped.get("text", ""))
+        provider_results = await self.searcher.parallel_search(classification)
+        enriched = await self.enricher.enrich([])
+        scored = self.scorer.score(enriched)
+        return scored

agents/sub_agents/__init__.py

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+"""Specialised sub-agents used by the coordinator."""

agents/sub_agents/browser.py

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+"""Browser automation agent using Model Context Protocol (MCP)."""
+
+from __future__ import annotations
+
+
+class BrowserAgent:
+    async def navigate(self, url: str) -> str:
+        """Navigate to a URL and return page content."""
+
+        # Placeholder for MCP browser automation
+        return ""

agents/sub_agents/classifier.py

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+"""Classifier agent for industry, stage and geography detection."""
+
+from __future__ import annotations
+
+from typing import Dict
+
+
+class ClassifierAgent:
+    async def classify(self, text: str) -> Dict:
+        """Return a lightweight classification for the provided text."""
+
+        # Placeholder for AI classification
+        return {}

agents/sub_agents/enricher.py

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+"""Enricher agent that augments investor data using Apollo.io."""
+
+from __future__ import annotations
+
+from typing import Dict, List
+
+
+class EnricherAgent:
+    async def enrich(self, investors: List[Dict]) -> List[Dict]:
+        """Enrich investor records with contact information."""
+
+        # Placeholder for Apollo integration
+        return investors

agents/sub_agents/scorer.py

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+"""Scorer agent implementing the Smart-Money Radar formula."""
+
+from __future__ import annotations
+
+from typing import Dict, List
+
+
+class ScorerAgent:
+    def score(self, investors: List[Dict]) -> List[Dict]:
+        """Apply the custom scoring algorithm to investors."""
+
+        # Placeholder scoring - returns investors unchanged
+        return investors

agents/sub_agents/scraper.py

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+"""Scraper agent using Apify to extract website content."""
+
+from __future__ import annotations
+
+from typing import Dict
+
+
+class ScraperAgent:
+    async def scrape(self, url: str) -> Dict[str, str]:
+        """Scrape the startup homepage and return HTML and text."""
+
+        # Placeholder for Apify integration
+        return {"html": "", "text": ""}

agents/sub_agents/searcher.py

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+"""Searcher agent responsible for querying multiple AI providers."""
+
+from __future__ import annotations
+
+import asyncio
+from typing import Dict, List
+
+
+class SearcherAgent:
+    async def parallel_search(self, classification: Dict) -> Dict[str, Dict]:
+        """Run searches across all available providers in parallel."""
+
+        search_prompt = ""  # would be generated from the classification
+        tasks = {
+            "claude": self._search_claude(search_prompt),
+            "gemini": self._search_gemini(search_prompt),
+            "grok": self._search_grok(search_prompt),
+        }
+        results = await asyncio.gather(*tasks.values(), return_exceptions=True)
+        return {name: result for name, result in zip(tasks.keys(), results)}
+
+    async def _search_claude(self, prompt: str) -> Dict:
+        return {}
+
+    async def _search_gemini(self, prompt: str) -> Dict:
+        return {}
+
+    async def _search_grok(self, prompt: str) -> Dict:
+        return {}
+
+    def _deduplicate_investors(self, investors: List[Dict]) -> List[Dict]:
+        """Name normalization and similarity matching."""
+
+        return investors

api_server.py

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+"""FastAPI server for the LinkedIn Automation Platform.
+
+This module exposes the public API used to orchestrate investor
+workflows. The implementation intentionally keeps state in memory and
+focuses on demonstrating the request/response contract described in the
+project documentation.
+"""
+
+from __future__ import annotations
+
+import asyncio
+from typing import Dict, List, Optional
+from uuid import uuid4
+
+from fastapi import BackgroundTasks, FastAPI, HTTPException, WebSocket, WebSocketDisconnect
+from pydantic import BaseModel
+
+app = FastAPI(title="LinkedIn Automation Platform")
+
+# ----------------------------------------------------------------------------
+# In-memory storage used for the demo implementation. A real deployment would
+# persist this information in a database and coordinate with the agent system.
+# ----------------------------------------------------------------------------
+workflows: Dict[str, Dict] = {}
+progress_updates: Dict[str, int] = {}
+budget_config: Dict[str, float] = {}
+
+AVAILABLE_PROVIDERS = ["claude", "openai", "gemini", "grok"]
+TEMPLATES = {
+    "claude": "investor_search_claude",
+    "openai": "investor_search_openai",
+    "gemini": "investor_search_gemini",
+    "grok": "investor_search_grok",
+}
+
+
+class WorkflowRequest(BaseModel):
+    """Request body for starting a workflow."""
+
+    startup_url: str
+    budget_cap: Optional[float] = None
+    providers: List[str] = []
+    max_investors: Optional[int] = None
+
+
+@app.post("/start_workflow")
+async def start_workflow(
+    request: WorkflowRequest, background_tasks: BackgroundTasks
+) -> Dict[str, str]:
+    """Create a new investor discovery workflow.
+
+    The heavy lifting would normally be delegated to the coordinator agent
+    through a background task. Here we only register a stub workflow entry.
+    """
+
+    workflow_id = f"wf_{uuid4().hex}"
+    workflows[workflow_id] = {
+        "id": workflow_id,
+        "status": "running",
+        "stage": "initialized",
+        "progress": 0,
+        "cost_used": 0.0,
+        "investors_found": 0,
+    }
+    progress_updates[workflow_id] = 0
+    # background_tasks.add_task(coordinator.start_workflow, workflow_id, request)
+    return {"id": workflow_id}
+
+
+@app.get("/workflow/{workflow_id}/status")
+async def workflow_status(workflow_id: str) -> Dict:
+    """Return the status of a workflow."""
+
+    wf = workflows.get(workflow_id)
+    if wf is None:
+        raise HTTPException(status_code=404, detail="Workflow not found")
+    return wf
+
+
+@app.post("/workflow/{workflow_id}/stop")
+async def stop_workflow(workflow_id: str) -> Dict:
+    """Stop a running workflow."""
+
+    wf = workflows.get(workflow_id)
+    if wf is None:
+        raise HTTPException(status_code=404, detail="Workflow not found")
+    wf["status"] = "stopped"
+    return wf
+
+
+class ExpansionRequest(BaseModel):
+    """Simple body for an expansion request."""
+
+    query: str
+
+
+@app.post("/expansion/start")
+async def expansion_start(request: ExpansionRequest) -> Dict[str, str]:
+    expansion_id = f"ex_{uuid4().hex}"
+    workflows[expansion_id] = {
+        "id": expansion_id,
+        "status": "running",
+        "stage": "expansion",
+        "progress": 0,
+    }
+    return {"id": expansion_id, "status": "started"}
+
+
+@app.get("/expansion/{expansion_id}/status")
+async def expansion_status(expansion_id: str) -> Dict[str, str]:
+    wf = workflows.get(expansion_id)
+    if wf is None:
+        raise HTTPException(status_code=404, detail="Expansion not found")
+    return wf
+
+
+@app.get("/expansion/{expansion_id}/results")
+async def expansion_results(expansion_id: str) -> Dict:
+    if expansion_id not in workflows:
+        raise HTTPException(status_code=404, detail="Expansion not found")
+    return {"id": expansion_id, "results": []}
+
+
+@app.get("/progress/{job_id}")
+async def get_progress(job_id: str) -> Dict[str, int]:
+    progress = progress_updates.get(job_id, 0)
+    return {"job_id": job_id, "progress": progress}
+
+
+@app.websocket("/ws/progress/{job_id}")
+async def websocket_progress(websocket: WebSocket, job_id: str) -> None:
+    await websocket.accept()
+    try:
+        while True:
+            progress = progress_updates.get(job_id, 0)
+            await websocket.send_json({"job_id": job_id, "progress": progress})
+            await asyncio.sleep(1)
+    except WebSocketDisconnect:
+        pass
+
+
+@app.get("/config/providers")
+async def config_providers() -> Dict[str, List[str]]:
+    return {"providers": AVAILABLE_PROVIDERS}
+
+
+class BudgetConfig(BaseModel):
+    budget: float
+
+
+@app.post("/config/budget")
+async def set_budget(config: BudgetConfig) -> Dict[str, float]:
+    budget_config["budget"] = config.budget
+    return {"budget": config.budget}
+
+
+@app.get("/config/templates")
+async def get_templates() -> Dict[str, Dict]:
+    return {"templates": TEMPLATES}

db/schema.sql

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+-- Supabase/PostgreSQL schema for the LinkedIn Automation Platform
+
+-- User Management
+CREATE TABLE IF NOT EXISTS users (
+    id UUID PRIMARY KEY,
+    email TEXT UNIQUE NOT NULL,
+    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
+    subscription_tier TEXT
+);
+
+-- Job Management
+CREATE TABLE IF NOT EXISTS search_jobs (
+    id UUID PRIMARY KEY,
+    user_id UUID REFERENCES users(id),
+    startup_url TEXT NOT NULL,
+    status TEXT NOT NULL,
+    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
+);
+
+CREATE TABLE IF NOT EXISTS analysis_results (
+    id UUID PRIMARY KEY,
+    job_id UUID REFERENCES search_jobs(id),
+    provider TEXT NOT NULL,
+    response_data JSONB,
+    score NUMERIC
+);
+
+-- Smart-Money Radar v4 Tables
+CREATE TABLE IF NOT EXISTS prompt_runs (
+    id UUID PRIMARY KEY,
+    job_id UUID REFERENCES search_jobs(id),
+    provider TEXT NOT NULL,
+    prompt_template TEXT,
+    response JSONB,
+    cost NUMERIC
+);
+
+CREATE TABLE IF NOT EXISTS prompt_templates (
+    id UUID PRIMARY KEY,
+    provider TEXT NOT NULL,
+    template_type TEXT,
+    content TEXT,
+    is_active BOOLEAN DEFAULT TRUE
+);
+
+CREATE TABLE IF NOT EXISTS investor_watchlist (
+    id UUID PRIMARY KEY,
+    investor_name TEXT,
+    firm TEXT,
+    linkedin_url TEXT,
+    added_date DATE DEFAULT CURRENT_DATE
+);
+
+CREATE TABLE IF NOT EXISTS liquidity_events (
+    id UUID PRIMARY KEY,
+    investor_id UUID REFERENCES investor_watchlist(id),
+    event_type TEXT,
+    description TEXT,
+    detected_date DATE
+);
+
+-- Indexes for performance
+CREATE INDEX IF NOT EXISTS idx_search_jobs_user ON search_jobs(user_id);
+CREATE INDEX IF NOT EXISTS idx_prompt_runs_job ON prompt_runs(job_id);
+CREATE INDEX IF NOT EXISTS idx_analysis_results_job ON analysis_results(job_id);
+

integrations/__init__.py

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+"""External integration modules (Apify, Apollo, MCP)."""

integrations/apify.py

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+"""Apify integration helpers."""
+
+from __future__ import annotations
+
+from typing import Dict
+
+
+async def web_scrape(url: str) -> Dict[str, str]:
+    """Sync scrape a startup homepage and return its HTML and text content."""
+
+    # Placeholder for Apify API calls
+    return {"html": "", "text": ""}