epstein-db/agents/pattern-finder

Pattern Finder Agent

An AI agent specialized in discovering non-obvious connections, patterns, and relationships within the Epstein Files database.

Purpose

While the extraction pipeline identifies explicit entities and relationships, the Pattern Finder looks for:

1. Indirect Connections — Entities that appear in similar contexts but are never directly linked
2. Temporal Patterns — Activities that cluster around specific dates or events
3. Financial Flows — Money movement patterns across entities
4. Network Anomalies — Unusually dense or sparse connection patterns
5. Cross-Reference Insights — What PPP/FEC/Grants matches reveal about entities

How It Works

The agent runs periodically (or on-demand) and:

1. Samples the Graph — Pulls subgraphs around high-degree or interesting entities
2. Generates Hypotheses — Uses LLM to identify potential patterns
3. Validates Hypotheses — Checks evidence in the actual documents
4. Reports Findings — Stores validated patterns with evidence chains

Agent Architecture

┌─────────────────────────────────────────────────────────────────┐
│                     Pattern Finder Agent                         │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  1. Sampling Module                                              │
│     • Random walk from high-degree nodes                         │
│     • Temporal window sampling                                   │
│     • Cross-reference focused sampling                           │
│                                                                  │
│  2. Hypothesis Generator (LLM)                                   │
│     • Pattern recognition prompts                                │
│     • Anomaly detection prompts                                  │
│     • Connection inference prompts                               │
│                                                                  │
│  3. Evidence Validator                                           │
│     • Document retrieval                                         │
│     • Citation extraction                                        │
│     • Confidence scoring                                         │
│                                                                  │
│  4. Report Generator                                             │
│     • Pattern summary                                            │
│     • Evidence chain                                             │
│     • Visualization data                                         │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Pattern Types

Financial Patterns

• Money flows between entities
• Unusual transaction timing
• Shell company connections
• Donation clustering

Travel Patterns

• Co-location events
• Flight log correlations
• Property connections
• Event attendance

Organizational Patterns

• Board memberships
• Foundation connections
• Employment relationships
• Legal representation

Temporal Patterns

• Activity clustering around dates
• Gaps in documentation
• Correlated timelines

Usage

# Run a pattern discovery session
npm run agent:pattern-finder

# Focus on specific entity
npm run agent:pattern-finder -- --entity "Ghislaine Maxwell"

# Focus on date range
npm run agent:pattern-finder -- --from "2005-01-01" --to "2010-12-31"

# Focus on pattern type
npm run agent:pattern-finder -- --type financial

Output

Patterns are stored in the pattern_findings table with:

• Title and description
• Involved entities
• Evidence (documents, relationships)
• Confidence score
• Status (hypothesis, validated, rejected)

Integration with OpenClaw

This agent can be spawned as a sub-agent from OpenClaw:

sessions_spawn({
  task: "Analyze the network around Les Wexner for financial patterns",
  label: "pattern-finder-wexner",
})