ai-iq-passport 0.4.0

Portable AI agent identity & reputation layer for cross-framework agent collaboration

AI-IQ Passport

Give your AI agent a verifiable CV.

Portable AI agent identity and reputation layer that works across A2A, MCP, CrewAI, and any framework.

The Problem

AI agents have no portable identity or reputation. When an agent joins a new swarm, mesh, or framework, it starts from zero:

• No way to prove what it's capable of
• No track record of task completion
• No verifiable credentials or skills
• No reputation that travels between systems

The Solution

AI-IQ Passport creates a cryptographically signed, portable identity card for AI agents. It captures:

• Skills with confidence scores, FSRS stability, and automatic decay based on age
• Reputation based on feedback, predictions, task completion, and skill quality
• Predictions with confidence, deadlines, and immutable resolution tracking
• Task log (append-only) providing a complete audit trail of work
• Verifiable signatures using Ed25519 public-key cryptography

Export to any format: A2A Agent Cards, MCP resources, or plain JSON. Your agent's reputation travels with it.

What's New in v0.3.0 - Tamper-Resistant Features

Four features that make passports genuinely hard to game (tested with real AI-IQ data):

1. FSRS Integration: Skills include stability scores from spaced repetition

   • High confidence + low stability = suspicious (detectable in audit)
   • Imported from real AI-IQ memory database (158+ skills verified)

2. Skill Decay: Confidence automatically decays 1%/week after 30 days of inactivity

   • Prevents inflated old skills (can't claim expert in unused tech)
   • Uses actual last_reviewed timestamps from AI-IQ

3. Prediction Detail: Full prediction tracking with outcomes, deadlines, and accuracy

   • Can't hide bad predictions (reviewers see full audit trail)
   • Structure ready for AI-IQ predictions table

4. Task Log: Immutable append-only log of all tasks with outcomes and tags

   • Reviewers judge task difficulty (can't cherry-pick only easy wins)
   • Imported from AI-IQ feedback + memories (85+ tasks verified)

Why this matters: These features create cross-verifying evidence. An agent can't game the system by claiming high confidence without practice (FSRS will be low), cherry-picking successful tasks (task log is append-only), or hiding failed predictions (full detail is auditable).

See TAMPER_RESISTANCE.md for full documentation and REVIEWER_GUIDE.md for audit instructions.

Quick Start

Install:

pip install ai-iq-passport

Generate a passport in 5 lines:

# Generate a passport
ai-iq-passport generate --name "MyAgent" --output agent.json

# Add a skill
ai-iq-passport skill add "Python development" --passport agent.json --confidence 0.9

# Export to A2A format
ai-iq-passport export agent.json --format a2a --output agent-a2a.json

Or import from AI-IQ memory system:

ai-iq-passport generate --name "MyAgent" --from-ai-iq ~/.ai-iq/memories.db

CLI Reference

Generate passport

ai-iq-passport generate --name "AgentName" [OPTIONS]

Options:
  --agent-id ID          Custom agent ID (auto-generated if omitted)
  --from-ai-iq PATH      Import skills/reputation from AI-IQ database
  --output PATH          Output file (default: passport.json)
  --traits KEY=VALUE     Add custom traits (repeatable)

Manage skills

ai-iq-passport skill add "skill_name" [OPTIONS]

Options:
  --passport PATH        Passport file (default: passport.json)
  --confidence N         Confidence 0.0-1.0 (default: 0.7)
  --evidence N           Evidence count (default: 0)
  --tags TAG1,TAG2       Comma-separated tags

Sign and verify

# Generate signing keys
ai-iq-passport keygen --output-dir ./keys

# Sign passport
ai-iq-passport sign passport.json --key ./keys/agent.key

# Verify signature
ai-iq-passport verify passport.json --pubkey ./keys/agent.pub

Export formats

ai-iq-passport export passport.json --format [a2a|mcp|json] --output out.json

View passport

ai-iq-passport show passport.json
ai-iq-passport show passport.json --full  # Show full JSON

Refresh from AI-IQ

ai-iq-passport refresh --passport passport.json --from-ai-iq ~/.ai-iq/memories.db

Peer Exchange

Share and verify agent passports across machines over HTTP. Perfect for agent-to-agent discovery and trust networks.

Serve your passport

Expose your passport over HTTP for other agents to fetch:

ai-iq-passport serve --port 8500
# Server running at: http://0.0.0.0:8500
# Endpoints:
#   GET  /health   - Server health check
#   GET  /passport - Get full passport
#   GET  /verify   - Check signature status
#   POST /exchange - Exchange passports

Fetch a remote passport

Fetch and display a remote agent's passport:

ai-iq-passport fetch http://machine-a:8500
# Agent: MyAgent
# ID: agent-123
# Reputation: 0.85
# Top Skills: Python (0.9), Testing (0.8), API Design (0.75)

# Save to local peers directory
ai-iq-passport fetch http://machine-a:8500 --save

Trust a peer

Mark a peer as trusted in your local registry:

ai-iq-passport trust agent-123

List known peers

View all peers you've collected:

ai-iq-passport peers
# Known Peers (3):
# ================================================================================
#
# [TRUSTED] MyAgent (agent-123...)
#   Reputation: 0.85
#   Top Skills: Python, Testing, API Design
#   File: ~/.ai-iq-passport/peers/agent-123.json
#
# [untrusted] RemoteAgent (remote-456...)
#   Reputation: 0.78
#   Top Skills: JavaScript, WebDev, React
#   File: ~/.ai-iq-passport/peers/remote-456.json

Full exchange handshake

Mutual passport exchange (both agents send and receive):

# Machine A: Start server
ai-iq-passport serve --port 8500

# Machine B: Exchange passports
ai-iq-passport exchange http://machine-a:8500
# Exchange successful!
# Remote Agent: MyAgent
# Reputation: 0.85
# Top Skills: Python, Testing, API Design
# Saved peer to: ~/.ai-iq-passport/peers/agent-123.json
# Use 'ai-iq-passport trust agent-123' to mark as trusted

For complete documentation including programmatic API, security considerations, and use cases, see PEER_EXCHANGE.md.

Export Formats

A2A (Agent-to-Agent)

Exports to Google's A2A Agent Card format for multi-agent collaboration:

{
  "@context": "https://a2aproject.org/schema",
  "@type": "AgentCard",
  "id": "agent-123",
  "name": "MyAgent",
  "capabilities": [
    {
      "name": "Python development",
      "confidence": 0.9,
      "evidence_count": 45
    }
  ],
  "reputation": {
    "score": 0.82,
    "task_completion_rate": 0.95
  }
}

MCP (Model Context Protocol)

Exports as MCP resource for Claude Desktop and other MCP clients:

{
  "uri": "passport://agent-123",
  "name": "Agent Passport: MyAgent",
  "description": "AI Agent: MyAgent | Skills: Python, API design | Reputation: 0.82",
  "mimeType": "application/json",
  "annotations": {
    "agent_id": "agent-123",
    "verified": true,
    "reputation_score": 0.82
  }
}

Plain JSON

Standard JSON format for custom integrations.

How Reputation Works

Reputation is calculated from four weighted factors:

1. Feedback score (35%): Ratio of good/bad/meh feedback from users or other agents
2. Prediction accuracy (25%): Percentage of confirmed vs refuted predictions
3. Task completion (25%): Ratio of completed vs failed tasks
4. Consistency (15%): Regularity of activity over time

Overall score: 0.0 (worst) to 1.0 (best).

When importing from AI-IQ, reputation is calculated from:

• feedback table: good/bad/meh ratings
• predictions table: confirmed/refuted outcomes
• memories table with category='pending': task completion tracking

Integration Examples

With CrewAI

from passport import AgentCard
from crewai import Agent

# Load passport
card = AgentCard.load("agent.json")

# Create CrewAI agent with passport context
agent = Agent(
    role=card.name,
    goal=f"Leverage my {len(card.skills)} skills to complete tasks",
    backstory=f"I have a reputation score of {card.reputation.overall_score:.2f}",
    verbose=True
)

With A2A

from passport import AgentCard
from passport.adapters import export_a2a

card = AgentCard.load("agent.json")
a2a_card = export_a2a(card.to_dict())

# Use in A2A protocol for agent discovery and capability matching

With MCP Server (Claude Code Integration)

AI-IQ Passport includes a native MCP (Model Context Protocol) server that exposes agent passports as resources and tools.

Setup:

1. Add to your Claude Code MCP config (~/.config/claude/mcp.json or similar):

{
  "mcpServers": {
    "ai-iq-passport": {
      "command": "python",
      "args": ["-m", "passport.mcp_server"]
    }
  }
}

2. Restart Claude Code. The passport server will be available.

Resources:

• passport://current - Get current agent's passport
• passport://{agent_id} - Get specific agent's passport

Tools:

• passport_generate - Generate a new passport (with optional AI-IQ import)
• passport_verify - Verify passport signature
• passport_skills - List top skills with confidence scores
• passport_reputation - Get reputation breakdown

Example usage in Claude Code:

Read passport://current

Use passport_generate to create a passport for "MyAgent" with AI-IQ import from ~/.ai-iq/memories.db

Show me the top 5 skills using passport_skills

The MCP server automatically stores passports at ~/.ai-iq-passport/passport.json and maintains a registry at ~/.ai-iq-passport/registry/ for multi-agent scenarios.

As MCP Resource (Programmatic)

from passport import AgentCard
from passport.adapters import export_mcp

card = AgentCard.load("agent.json")
mcp_resource = export_mcp(card.to_dict())

# Returns MCP-compatible resource dict

Programmatic API

from passport import AgentCard, Skill

# Create passport
card = AgentCard.create(name="MyAgent", agent_id="agent-123")

# Add skills
card.add_skill(Skill(
    name="Python development",
    confidence=0.9,
    evidence_count=45,
    tags=["programming", "backend"]
))

# Add traits
card.add_trait("framework", "CrewAI")
card.add_trait("model", "claude-sonnet-4.5")

# Save
card.save("agent.json")

# Load and verify
loaded = AgentCard.load("agent.json")
print(loaded.summary())

What AI-IQ Passport Adds

Feature	A2A	MCP	CrewAI	AI-IQ Passport
Agent identity	Yes	No	No	Yes
Capability declaration	Yes	Via tools	No	Yes
Reputation tracking	No	No	No	Yes
Task history	No	No	No	Yes
Cryptographic signing	No	No	No	Yes
Cross-framework portability	No	No	No	Yes
Feedback-based scoring	No	No	No	Yes
Prediction accuracy tracking	No	No	No	Yes

AI-IQ Passport provides the identity and reputation layer that other frameworks lack. It's designed to work alongside A2A, MCP, and CrewAI, not replace them.

Development

Install with dev dependencies:

pip install -e ".[dev]"

Run tests:

pytest tests/ -v
pytest tests/ --cov=passport --cov-report=html

Format code:

black passport/ tests/

Type check:

mypy passport/

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes with tests
4. Run the test suite (pytest tests/)
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

License

MIT License - see LICENSE file for details.

MCP Server Installation

To use the MCP server with Claude Code or other MCP clients:

# Install with MCP support
pip install ai-iq-passport[mcp]

# Or if already installed
pip install mcp>=1.0.0

Add to your MCP config (see mcp_config.json for example):

{
  "mcpServers": {
    "ai-iq-passport": {
      "command": "python",
      "args": ["-m", "passport.mcp_server"]
    }
  }
}

Or use the entry point:

{
  "mcpServers": {
    "ai-iq-passport": {
      "command": "ai-iq-passport-mcp"
    }
  }
}

Restart your MCP client to load the server.

Links

• GitHub: https://github.com/kobie3717/ai-iq-passport
• PyPI: https://pypi.org/project/ai-iq-passport/ (coming soon)
• Issues: https://github.com/kobie3717/ai-iq-passport/issues
• AI-IQ Memory System: https://github.com/kobie3717/ai-iq
• MCP (Model Context Protocol): https://modelcontextprotocol.io/

Author

Built by @kobie3717