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3.6.0

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agentmesh_langchain

AgentMesh trust layer integration for LangChain - cryptographic identity and trust-gated tool execution

agentmeshagentscryptographyidentitylangchainmulti-agenttrust
Published12 May 2026
Direct dependencies5
Transitive dependenciesNot calculated

README

Version 3.6.0 documentation

LangChain AgentMesh Integration

Cryptographic identity verification and trust-gated tool execution for LangChain agents.

Installation

pip install agentmesh-langchain

Features

  • VerificationIdentity: Ed25519-based cryptographic identity for agents
  • TrustGatedTool: Wrap any tool with trust requirements
  • TrustedToolExecutor: Execute tools with automatic verification
  • TrustCallbackHandler: Monitor trust events during chain execution
  • TrustHandshake: Verify peer agents before collaboration
  • DelegationChain: Hierarchical capability delegation

Quick Start

from langchain_agentmesh import VerificationIdentity, TrustGatedTool, TrustedToolExecutor

# Generate agent identity
identity = VerificationIdentity.generate('research-agent', capabilities=['search', 'summarize'])

# Wrap a tool with trust requirements
gated_tool = TrustGatedTool(
    tool=search_tool,
    required_capabilities=['search'],
    min_trust_score=0.8
)

# Execute with verification
executor = TrustedToolExecutor(identity=identity)
result = executor.invoke(gated_tool, 'query')

Use Cases

Multi-Agent Trust Verification

from langchain_agentmesh import TrustHandshake

# Create handshake for peer verification
handshake = TrustHandshake(my_identity)

# Verify peer before collaboration
result = await handshake.verify_peer(peer_identity)
if result.trusted:
    # Safe to delegate task
    response = await peer_agent.invoke(task)

Trust-Gated Tool Execution

from langchain_agentmesh import TrustGatedTool

# Sensitive tool requiring high trust
code_execution_tool = TrustGatedTool(
    tool=python_repl,
    required_capabilities=['code:execute'],
    min_trust_score=0.9,
    audit_logging=True
)

# Only trusted agents can use this tool
result = executor.invoke(code_execution_tool, code)

Callback Integration

from langchain_agentmesh import TrustCallbackHandler

# Monitor trust events
callback = TrustCallbackHandler(
    on_verification=lambda r: print(f"Verified: {r.peer_did}"),
    on_violation=lambda v: alert(f"Violation: {v}")
)

agent = create_agent(callbacks=[callback])

Configuration

ParameterDefaultDescription
min_trust_score0.5Minimum trust score required
required_capabilities[]Required capability list
audit_loggingFalseEnable audit trail
cache_ttl900Verification cache TTL (seconds)

License

MIT

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Declared dependencies 5

Native requirements
5 direct targetsNot calculated transitive count
Package / targetRequirementRelationship
cryptography
Environment
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agentmesh_langchain-3.6.0-py3-none-any.whl

bdist_wheel · 19.04 KB · Python py3

Source file
agentmesh_langchain-3.6.0.tar.gz

sdist · 21.3 KB · Python source

Source file

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microsoft/agent-governance-toolkit

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package_version:a3060f74f357530783dca2c1adc2bbf80fdc2026be5459fde17d4c0992d37953

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