Agentfacts Py AI Agent

<p align="center"> <img src=".github/workflows/AgentFacts.svg" alt="AgentFacts Logo" width="140" /> </p> <h1 align="center">AgentFacts</h1> <p align="center"><strong>The open source SDK for AI agents Identity.</strong><br>

Overview

AgentFacts is an open source SDK that creates a signed, verifiable profile for any AI agent. It captures identity, base model, tools, policy, and provenance in a tamper-evident JSON card. Verify offline with Ed25519 + did:key, or auto-introspect popular frameworks for instant metadata. AgentFacts brings trust and transparency to AI agents without heavy lifting by developers.

Quick Start 🚀

Installation

pip install agentfacts

Optional extras:

pip install agentfacts[cli]         # CLI tools (agentfacts, af commands)
pip install agentfacts[middleware]  # FastAPI/Flask middleware
pip install agentfacts[all]         # All extras

Requires Python 3.10+.

Quickstart Example

from agentfacts import AgentFacts

# Suppose you have an existing LangChain agent (e.g., an AgentExecutor or chain)
agent = MyLangChainAgent(...)

# Generate an AgentFacts profile from the agent (introspects model, tools, etc.)
af = AgentFacts.from_agent(agent, name="CustomerSupportBot", description="Answers support FAQs")

# Sign the agent’s metadata (generates a DID and Ed25519 key pair under the hood)
af.sign()

# The AgentFacts metadata can be obtained as a dict or JSON
profile_json = af.to_json()
print("AgentFacts Profile:", profile_json)

# Verify the AgentFacts signature (returns a VerificationResult object)
result = af.verify()
assert result.valid, f"Verification failed: {result.errors}"

This will produce a signed JSON profile (you can save it as agent_profile.json) containing the agent’s details and a cryptographic signature. Anyone with the agent’s public DID can call AgentFacts.verify(...) to validate that profile. ✅

Key Features ✨

• Verifiable identity: Ed25519 signatures with did:key DIDs.
• Structured metadata: Model, tools, policy, and compliance in one schema.
• Tamper evidence: Merkle log proofs for audit trails.
• Framework introspection: Auto-detects popular agent frameworks.
• Policy checks: Zero-trust rules for required capabilities and attestations.
• Lightweight: Fast signing and RFC 8785 canonical JSON for cross-language parity.

Supported Frameworks

• LangChain
• LlamaIndex
• Hugging Face Agents
• OpenAgents
• CrewAI
• AutoGen
• Manual/Custom

AgentFacts Schema

{
  "spec_version": "v0.1",
  "agent": {"id": "did:key:...", "name": "Agent", "model": {"name": "gpt-4", "provider": "openai"}},
  "publisher": {"id": "did:key:...", "keys": [{"id": "did:key:...#sig-1", "type": "Ed25519VerificationKey2020", "public_key": "BASE64"}]},
  "policy": {"compliance": {"frameworks": []}, "constraints": {}},
  "issued_at": "2025-02-12T00:00:00Z",
  "signature": {"alg": "ed25519", "key_id": "did:key:...#sig-1", "value": "BASE64_SIGNATURE"},
  "log_proof": {"log_id": "local", "leaf_hash": "LEAF_HASH", "root_hash": "ROOT_HASH", "inclusion": [{"hash": "SIBLING_HASH_1", "position": "left"}]}
}

Roadmap

• Core SDK: Sign/Verify
• LangChain/CrewAI/HuggingFace integrations
• CLI Wizard
• Attestation plugins
• Web playground
• Multi-party signing

Contributing

We welcome issues and PRs. See CONTRIBUTING.md.

License

MIT. See LICENSE.