llama-index-agent-agentmesh 0.2.0

AgentMesh trust layer integration for LlamaIndex agents

LlamaIndex AgentMesh Integration

AgentMesh trust layer integration for LlamaIndex - enabling cryptographic identity verification and trust-gated agent workflows.

Overview

This integration provides:

• TrustedAgentWorker: Agent worker with cryptographic identity and trust verification
• TrustGatedQueryEngine: Query engines with access control based on trust
• Secure Data Access: Governance layer for RAG pipelines with identity-based policies

Installation

pip install llama-index-agent-agentmesh

Quick Start

Creating a Trusted Agent

from llama_index.agent.agentmesh import TrustedAgentWorker, CMVKIdentity

# Generate cryptographic identity
identity = CMVKIdentity.generate(
    agent_name="research-agent",
    capabilities=["document_search", "summarization"],
)

# Create trusted agent worker
worker = TrustedAgentWorker.from_tools(
    tools=[search_tool, summarize_tool],
    identity=identity,
    llm=llm,
)

# Create agent with trust verification
agent = worker.as_agent()

Trust-Gated Query Engine

from llama_index.agent.agentmesh import TrustGatedQueryEngine, TrustPolicy

# Wrap query engine with trust policy
trusted_engine = TrustGatedQueryEngine(
    query_engine=base_engine,
    policy=TrustPolicy(
        min_trust_score=0.8,
        required_capabilities=["document_access"],
        audit_queries=True,
    ),
)

# Query requires verified identity
response = trusted_engine.query(
    "What are the quarterly results?",
    invoker_card=requester_card,
)

Multi-Agent Trust Handoffs

from llama_index.agent.agentmesh import TrustHandshake, TrustedAgentCard

# Create agent card for discovery
card = TrustedAgentCard(
    name="research-agent",
    description="Performs document research",
    capabilities=["search", "summarize"],
    identity=identity,
)
card.sign(identity)

# Verify peer before task handoff
handshake = TrustHandshake(my_identity=identity)
result = handshake.verify_peer(peer_card)

if result.trusted:
    # Safe to delegate task
    pass

Features

TrustedAgentWorker

An agent worker that:

• Has cryptographic identity for authentication
• Verifies peer agents before accepting tasks
• Signs outputs for verification by recipients
• Supports capability-based access control

TrustGatedQueryEngine

A query engine wrapper that:

• Requires identity verification for queries
• Enforces trust score thresholds
• Restricts access based on capabilities
• Provides audit logging of all queries

Data Access Governance

Control access to your RAG pipeline:

from llama_index.agent.agentmesh import DataAccessPolicy

policy = DataAccessPolicy(
    allowed_collections=["public", "internal"],
    denied_collections=["confidential"],
    require_audit=True,
    max_results_per_query=100,
)

# Apply policy to index
trusted_index = TrustedVectorStoreIndex(
    index=base_index,
    policy=policy,
)

Security Model

AgentMesh uses Ed25519 cryptography for:

• Identity Generation: Unique DID per agent
• Request Signing: All queries are signed
• Response Verification: Outputs can be verified

API Reference

Class	Description
CMVKIdentity	Cryptographic agent identity
TrustedAgentWorker	Agent worker with trust verification
TrustGatedQueryEngine	Query engine with access control
TrustHandshake	Peer verification protocol
TrustedAgentCard	Agent discovery card
DataAccessPolicy	RAG access governance

License

MIT License