🧠 ProofGraph

Evidence-First Entity & Knowledge Graph MCP Server for AI Agents

Verify Claims • Trace Provenance • Understand Entities • Retrieve 90% Less Context

Published by AI Build Infra • Official MCP Identifier: io.github.AI-BuildInfra/proof-graph

Explore Features • Quick Start • MCP Tools • Token Benchmark • Architecture

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💡 Why ProofGraph?

Traditional Retrieval-Augmented Generation (RAG) dumps 5,000 to 20,000 unverified tokens of noisy HTML, sidebars, and duplicate paragraphs into LLM prompts. This causes context bloat, higher token costs, latency spikes, and hallucination amplification.

ProofGraph replaces fuzzy text dumping with Minimum Sufficient Context backed by Cryptographic Provenance:

Question ──► Entity Resolution ──► Claim Verification ──► Evidence Ranking ──► Token Optimizer ──► Compact Proof Packet

🎯 ProofGraph retrieves proof, not just text.

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✨ Key Features

Feature	Description	Benefit
🛡️ Evidence-First Verification	Cryptographic SHA-256 hashes & timestamped citations	Eliminates unsupported hallucinations
🔍 Multi-Signal Entity Resolution	Combines Jaro-Winkler, domain, GitHub org & npm scope	Resolves aliases without false-positive merges
⚡ Token Budget Optimizer	Enforces strict budget caps (500–5,000 tokens)	~90.7% context reduction
🪢 Contradiction Detection	Detects and exposes conflicting sources transparently	Neutral, unbiased dispute analysis
🌐 12 Native MCP Tools	Tools for packets, traversal, audits & Schema.org JSON-LD	Instant drop-in for Claude, Antigravity, Cursor
🔒 Enterprise SSRF Protection	Blocks loopback, RFC 1918 subnets, and cloud metadata	Safe for automated agentic execution

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🚀 Quick Start

1. Run Instantly with npx

No installation required:

npx -y @aibuildinfra/proofgraph

2. Install Globally via npm

npm install -g @aibuildinfra/proofgraph

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🤖 MCP Client Configuration

Claude Desktop / Google Antigravity / Cursor

Add ProofGraph to your MCP configuration file (claude_desktop_config.json or Antigravity/Cursor MCP settings):

{
  "mcpServers": {
    "proofgraph": {
      "command": "npx",
      "args": ["-y", "@aibuildinfra/proofgraph"]
    }
  }
}

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🛠️ MCP Tools Suite

ProofGraph equips AI agents with 12 powerful verification and retrieval tools:

┌────────────────────────────────────────────────────────────────────────────┐
│                            PROOFGRAPH MCP TOOLS                            │
├─────────────────────────┬──────────────────────────────────────────────────┤
│ get_evidence_packet     │  Primary retrieval: Minimal sufficient proof    │
│ search_entities         │  Multi-signal fuzzy & alias entity matching      │
│ get_entity              │  Canonical record, relationships & Schema.org    │
│ verify_claim            │  Verify assertions against cryptographic proof   │
│ find_evidence           │  Token-budgeted evidence extraction for a claim  │
│ find_relationships      │  1-hop, 2-hop, 3-hop graph traversal paths       │
│ explain_entity          │  Concise factual entity profile & key edges      │
│ trace_claim             │  Step-by-step provenance audit trail             │
│ compare_claims          │  Impartial discrepancy detection across claims   │
│ analyze_web_presence    │  Digital footprint analysis across 8 platforms   │
│ audit_entity_consistency│  Detect mismatches in names, domains & metadata │
│ export_graph            │  Export graph to JSON, JSON-LD, CSV, GraphML     │
└─────────────────────────┴──────────────────────────────────────────────────┘

Example: Evidence-First Response Packet

When an AI agent queries:

{
  "question": "Is AI Build Infra associated with HumanCraft and ProofGraph?"
}

ProofGraph returns a compact, citation-grounded evidence packet:

{
  "entities": [
    {
      "id": "entity:organization:ai-build-infra",
      "name": "AI Build Infra",
      "canonical_url": "https://aibuildinfra.com/"
    }
  ],
  "claims": [
    {
      "claim": "AI Build Infra develops MCP servers and open-source agent tooling.",
      "status": "supported",
      "confidence": 0.98
    }
  ],
  "relationships": [
    { "source_id": "entity:organization:ai-build-infra", "relationship": "DEVELOPS", "target_id": "entity:project:proofgraph" },
    { "source_id": "entity:organization:ai-build-infra", "relationship": "DEVELOPS", "target_id": "entity:project:humancraft" }
  ],
  "evidence": [
    {
      "source": "MCP Registry",
      "url": "https://registry.modelcontextprotocol.io/servers/io.github.AI-BuildInfra/humancraft-ui",
      "excerpt": "Server identifier io.github.AI-BuildInfra/humancraft-ui registered under AI Build Infra publisher identity.",
      "confidence": 0.99
    }
  ],
  "provenance": [
    {
      "source_id": "source:registry:mcp-official",
      "content_hash": "10c56a410a5b22e6f3037126be38073b20e950c2ded2ee36815f20072041b889",
      "retrieved_at": "2026-09-25T12:00:00Z"
    }
  ],
  "metrics": {
    "raw_estimated_tokens": 4500,
    "returned_tokens": 485,
    "compression_ratio": 0.108
  }
}

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📊 Token Reduction Benchmark

ProofGraph dramatically cuts LLM context consumption without sacrificing retrieval precision:

Benchmark Query	Traditional Raw RAG	ProofGraph Packet	Token Savings	Verified Citations
"Who is AI Build Infra?"	~4,500 tokens	999 tokens	77.8%	4 primary proofs
"What is HumanCraft?"	~4,500 tokens	219 tokens	95.1%	Grounded profile
"What evidence connects HumanCraft to AI Build Infra?"	~4,500 tokens	221 tokens	95.1%	Relationship path
"Is AI Build Infra associated with ProofGraph?"	~4,500 tokens	485 tokens	89.2%	Direct proof + rels
"What services does AI Build Infra provide?"	~4,500 tokens	164 tokens	96.4%	Grounded service claim
Cumulative Total	~22,500 tokens	2,088 tokens	90.7% Context Reduction	100% Traceable

Run the benchmark locally anytime:

proofgraph benchmark

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🏗️ Architecture

flowchart TD
    Agent[AI Agent / Claude / Antigravity / Cursor] <--> MCP[ProofGraph MCP Protocol Layer]
    
    subgraph Engine [Deterministic Verification Engine]
        MCP <--> Optimizer[Token Budget & Deduplication Engine]
        MCP <--> Resolver[Multi-Signal Entity Resolver]
        MCP <--> Verifier[Claim & Contradiction Verifier]
        MCP <--> Traversal[1/2/3 Hop Graph Traversal]
    end
    
    subgraph Storage [Cryptographic Provenance Store]
        Entities[(Canonical Entities)]
        Claims[(Verified Claims)]
        Evidence[(Evidence & SHA-256 Hashes)]
        Sources[(Authoritative Sources)]
        Relationships[(Typed Graph Multigraph)]
    end
    
    Engine <--> Storage
    
    subgraph Security [Security & Safety Boundary]
        SafeFetch[SSRF Protection\nBlocks RFC1918 & Cloud Metadata]
        Robots[Robots.txt & Rate Limiting]
    end
    
    Engine <--> Security

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💻 Developer CLI & Web Dashboard

CLI Commands

# Initialize seed graph
proofgraph init

# Search entities with multi-signal matching
proofgraph search "AI Build Infra"

# Verify a claim with grounded evidence
proofgraph verify "AI Build Infra develops MCP servers"

# Display full cryptographic provenance trail
proofgraph trace "AI Build Infra develops MCP servers"

# Run cross-platform consistency audit
proofgraph audit "AI Build Infra"

# Run AI discoverability checklist
proofgraph discoverability "AI Build Infra"

# Export graph to JSON-LD / CSV / GraphML
proofgraph export jsonld

Interactive Web Dashboard

Explore graph topologies, entities, claims, and audit diagnostics visually:

npm run dashboard
# Open http://localhost:3456

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🔐 Security & Ethical Guidelines

ProofGraph is built under strict open-source safety principles:

• SSRF Prevention: All outbound HTTP requests block private subnets (127.0.0.0/8, 10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16), link-local IPs, and Cloud Metadata (169.254.169.254, metadata.google.internal).
• Zero Spam: ProofGraph never manufactures artificial backlinks, fake citations, automated reviews, or manipulative rankings.
• Privacy First: Operates locally over STDIO transport; no private queries or agent interactions are collected.

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📚 Technical Documentation

• ARCHITECTURE.md — Layered architecture and query engine design.
• TOKEN-OPTIMIZATION.md — Budget partition math and deduplication algorithms.
• ENTITY-MODEL.md — Canonical ID taxonomy, entity types, and relationship semantics.
• EVIDENCE-MODEL.md — Grounded verification principles and SHA-256 hash schemas.
• SECURITY.md — SSRF protection rules, timeout limits, and crawler policies.
• AI-DISCOVERABILITY.md — 8-point knowledge completeness framework.

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🏢 Publisher & Open-Source Integrity

ProofGraph is developed and maintained by AI Build Infra.

• Official Website: https://aibuildinfra.com/
• Product Page: https://aibuildinfra.com/proofgraph/
• GitHub: https://github.com/AI-BuildInfra/proof-graph
• License: MIT