agentmesh-platform 2.0.0

Community Edition — The Secure Nervous System for Cloud-Native Agent Ecosystems - Identity, Trust, Reward, Governance

AgentMesh

SSL for AI Agents

The trust, identity, and governance layer for production AI agent systems

Identity · Trust · Reward · Governance

⭐ If this project helps you, please star it! It helps others discover AgentMesh.

🔗 Part of the Agent Governance Ecosystem — Works with Agent OS (kernel), Agent Hypervisor (runtime), and Agent SRE (reliability)

📦 Install the full stack: pip install ai-agent-governance[full] — PyPI | GitHub

Quick Start • MCP Proxy • Examples • Agent OS • Agent Hypervisor

Trusted By

Featured In

1,300+ Tests Passing

6 Framework Integrations

170K+ Combined Stars of Integrated Projects

4 Protocol Bridges (A2A · MCP · IATP · AI Card)

<1ms p99 Full Governance Pipeline

🏢 Production Integrations

Framework	Stars	Status	What We Ship
Dify	65K ⭐	✅ Merged	Trust verification plugin in Dify Marketplace
LlamaIndex	47K ⭐	✅ Merged	TrustedAgentWorker + TrustGatedQueryEngine
Microsoft Agent-Lightning	15K ⭐	✅ Merged	Governance kernel for RL training safety
LangGraph	24K ⭐	📦 PyPI	Trust-scored state transitions
OpenAI Agents SDK	—	📦 PyPI	Tool-level governance guardrails
Haystack	22K ⭐	🔄 In Review	GovernancePolicyChecker + TrustGate components

AgentMesh is "SSL for AI Agents" — the trust and identity layer that makes multi-agent systems enterprise-ready. Every agent gets a cryptographic identity. Every interaction is verified. Every action is audited.

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Overview

AgentMesh is the first platform purpose-built for the Governed Agent Mesh — the cloud-native, multi-vendor network of AI agents that will define enterprise operations.

The protocols exist (A2A, MCP, IATP). The agents are shipping. The trust layer does not. AgentMesh fills that gap.

┌─────────────────────────────────────────────────────────────────────────────┐
│                           AGENTMESH ARCHITECTURE                            │
├─────────────────────────────────────────────────────────────────────────────┤
│  LAYER 4  │  Reward & Learning Engine                                       │
│           │  Per-agent trust scores · Multi-dimensional rewards · Adaptive  │
├───────────┼─────────────────────────────────────────────────────────────────┤
│  LAYER 3  │  Governance & Compliance Plane                                  │
│           │  Policy engine · EU AI Act / SOC2 / HIPAA · Merkle audit logs   │
├───────────┼─────────────────────────────────────────────────────────────────┤
│  LAYER 2  │  Trust & Protocol Bridge                                        │
│           │  A2A · MCP · IATP · Protocol translation · Capability scoping   │
├───────────┼─────────────────────────────────────────────────────────────────┤
│  LAYER 1  │  Identity & Zero-Trust Core                                     │
│           │  Agent CA · Ephemeral creds · SPIFFE/SVID · Human sponsors      │
└───────────┴─────────────────────────────────────────────────────────────────┘

Why AgentMesh?

The Problem

• 40:1 to 100:1 — Non-human identities now outnumber human identities in enterprises
• AI agents are the fastest-growing, least-governed identity category
• A2A gives agents a common language. MCP gives agents tools. Neither enforces trust.

The Solution

AgentMesh provides:

Capability	Description
Agent Identity	First-class identity with human sponsor accountability
Ephemeral Credentials	15-minute TTL by default, auto-rotation
Protocol Bridge	Native A2A, MCP, IATP with unified trust model
Reward Engine	Continuous behavioral scoring, not static rules
Compliance Automation	EU AI Act, SOC 2, HIPAA, GDPR mapping

Quick Start

Option 1: Secure Claude Desktop (Recommended)

# Install AgentMesh
pip install agentmesh-platform

# Set up Claude Desktop to use AgentMesh governance
agentmesh init-integration --claude

# Restart Claude Desktop - all MCP tools are now secured!

Claude will now route tool calls through AgentMesh for policy enforcement and trust scoring.

Option 2: Create a Governed Agent

# Initialize a governed agent in 30 seconds
agentmesh init --name my-agent --sponsor [e-mail]

# Register with the mesh
agentmesh register

# Start with governance enabled
agentmesh run

Option 3: Wrap Any MCP Server

# Proxy any MCP server with governance
agentmesh proxy --target npx --target -y \
  --target @modelcontextprotocol/server-filesystem \
  --target /path/to/directory

# Use strict policy (blocks writes/deletes)
agentmesh proxy --policy strict --target <your-mcp-server>

Installation

pip install agentmesh-platform

Or install with extra dependencies:

pip install agentmesh-platform[server]  # FastAPI server
pip install agentmesh-platform[dev]     # Development tools

Or from source:

git clone https://github.com/imran-siddique/agent-mesh.git
cd agent-mesh
pip install -e .

Examples & Integrations

Real-world examples to get started quickly:

Example	Use Case	Key Features
Registration Hello World	Agent registration walkthrough	Identity, DID, sponsor handshake
MCP Tool Server	Secure MCP server with governance	Rate limiting, output sanitization, audit logs
Multi-Agent Customer Service	Customer support automation	Delegation chains, trust handshakes, A2A
Healthcare HIPAA	HIPAA-compliant data analysis	Compliance automation, PHI protection, Merkle audit
DevOps Automation	Just-in-time DevOps credentials	Ephemeral creds, capability scoping
GitHub PR Review	Code review agent	Output policies, shadow mode, trust decay

Framework integrations:

• Claude Desktop - Secure MCP tools with one command
• LangChain Integration - Secure LangChain agents with policies
• CrewAI Integration - Multi-agent crew governance
• LangGraph - Trust checkpoints for graph workflows (built-in)
• OpenAI Swarm - Trust-verified handoffs (built-in)
• Dify - Trust middleware for Dify workflows

📚 Browse all examples →

Trust Visualization Dashboard

Interactive Streamlit dashboard with 5 tabs:

cd examples/06-trust-score-dashboard
pip install -r requirements.txt
streamlit run trust_dashboard.py

Tabs: Trust Network | Trust Scores | Credential Lifecycle | Protocol Traffic | Compliance

The AgentMesh Proxy: "SSL for AI Agents"

Problem: AI agents like Claude Desktop have unfettered access to your filesystem, database, and APIs through MCP servers. One hallucination could be catastrophic.

Solution: AgentMesh acts as a transparent governance proxy:

# Before: Unsafe direct access
{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/me"]
    }
  }
}

# After: Protected by AgentMesh
{
  "mcpServers": {
    "filesystem": {
      "command": "agentmesh",
      "args": [
        "proxy", "--policy", "strict",
        "--target", "npx", "--target", "-y",
        "--target", "@modelcontextprotocol/server-filesystem",
        "--target", "/Users/me"
      ]
    }
  }
}

What you get:

• 🔒 Policy Enforcement - Block dangerous operations before they execute
• 📊 Trust Scoring - Behavioral monitoring (800-1000 scale)
• 📝 Audit Logs - Tamper-evident record of every action
• ✅ Verification Footers - Visual confirmation in outputs

Set it up in 10 seconds:

agentmesh init-integration --claude
# Restart Claude Desktop - done!

Learn more: Claude Desktop Integration Guide

Core Concepts

1. Agent Identity

Every agent gets a unique, cryptographically bound identity:

from agentmesh import AgentIdentity

identity = AgentIdentity.create(
    name="data-analyst-agent",
    sponsor="[e-mail]",  # Human accountability
    capabilities=["read:data", "write:reports"],
)

2. Delegation Chains

Agents can delegate to sub-agents, but scope always narrows:

# Parent agent delegates to child
child_identity = parent_identity.delegate(
    name="summarizer-subagent",
    capabilities=["read:data"],  # Subset of parent's capabilities
)

3. Trust Handshakes (IATP)

Cross-agent communication requires trust verification:

from agentmesh import TrustBridge

bridge = TrustBridge()

# Verify peer before communication
verification = await bridge.verify_peer(
    peer_id="did:mesh:other-agent",
    required_trust_score=700,
)

if verification.verified:
    await bridge.send_message(peer_id, message)

4. Reward Scoring

Every action is scored across multiple dimensions:

from agentmesh import RewardEngine

engine = RewardEngine()

# Actions are automatically scored
score = engine.get_agent_score("did:mesh:my-agent")
# {
#   "total": 847,
#   "dimensions": {
#     "policy_compliance": 95,
#     "resource_efficiency": 82,
#     "output_quality": 88,
#     "security_posture": 91,
#     "collaboration_health": 84
#   }
# }

5. Policy Engine

Declarative governance policies:

# policy.yaml
version: "1.0"
agent: "data-analyst-agent"

rules:
  - name: "no-pii-export"
    condition: "action.type == 'export' and data.contains_pii"
    action: "deny"
    
  - name: "rate-limit-api"
    condition: "action.type == 'api_call'"
    limit: "100/hour"
    
  - name: "require-approval-for-delete"
    condition: "action.type == 'delete'"
    action: "require_approval"
    approvers: ["security-team"]

Protocol Support

Protocol	Status	Description
AI Card	✅ Alpha	Cross-protocol identity standard (src/agentmesh/integrations/ai_card/)
A2A	✅ Alpha	Agent-to-agent coordination (full adapter in src/agentmesh/integrations/a2a/)
MCP	✅ Alpha	Tool and resource binding (trust-gated server/client in src/agentmesh/integrations/mcp/)
IATP	✅ Alpha	Trust handshakes (via agent-os, graceful fallback if unavailable)
ACP	🔜 Planned	Lightweight messaging (protocol bridge supports routing, adapter not yet implemented)
SPIFFE	✅ Alpha	Workload identity

Architecture

agentmesh/
├── identity/           # Layer 1: Identity & Zero-Trust
│   ├── agent_id.py     # Agent identity management (DIDs, Ed25519 keys)
│   ├── credentials.py  # Ephemeral credential issuance (15-min TTL)
│   ├── delegation.py   # Cryptographic delegation chains
│   ├── spiffe.py       # SPIFFE/SVID integration
│   ├── risk.py         # Continuous risk scoring
│   └── sponsor.py      # Human sponsor accountability
│
├── trust/              # Layer 2: Trust & Protocol Bridge
│   ├── bridge.py       # Multi-protocol trust bridge (A2A/MCP/IATP/ACP)
│   ├── handshake.py    # IATP trust handshakes
│   ├── cards.py        # Trusted agent cards
│   └── capability.py   # Capability scoping
│
├── governance/         # Layer 3: Governance & Compliance
│   ├── policy.py       # Declarative policy engine (YAML/JSON)
│   ├── compliance.py   # Compliance mapping (EU AI Act, SOC2, HIPAA, GDPR)
│   ├── audit.py        # Merkle-chained audit logs
│   └── shadow.py       # Shadow mode for policy testing
│
├── reward/             # Layer 4: Reward & Learning
│   ├── engine.py       # Multi-dimensional reward engine
│   ├── scoring.py      # 5-dimension trust scoring
│   └── learning.py     # Adaptive learning & weight optimization
│
├── integrations/       # Protocol & framework adapters
│   ├── ai_card/        # AI Card standard (cross-protocol identity)
│   ├── a2a/            # Google A2A protocol support
│   ├── mcp/            # Anthropic MCP trust-gated server/client
│   ├── langgraph/      # LangGraph trust checkpoints
│   └── swarm/          # OpenAI Swarm trust-verified handoffs
│
├── cli/                # Command-line interface
│   ├── main.py         # agentmesh init/register/status/audit/policy
│   └── proxy.py        # MCP governance proxy
│
├── core/               # Low-level services
│   └── identity/ca.py  # Certificate Authority (SPIFFE/SVID)
│
├── storage/            # Storage abstraction (memory, Redis, PostgreSQL)
│
├── observability/      # OpenTelemetry tracing & Prometheus metrics
│
└── services/           # Service wrappers (registry, audit, reward)

Compliance

AgentMesh automates compliance mapping for:

• EU AI Act — Risk classification, transparency requirements
• SOC 2 — Security, availability, processing integrity
• HIPAA — PHI handling, audit controls
• GDPR — Data processing, consent, right to explanation

from agentmesh import ComplianceEngine, ComplianceFramework

compliance = ComplianceEngine(frameworks=[ComplianceFramework.SOC2, ComplianceFramework.HIPAA])

# Check an action for violations
violations = compliance.check_compliance(
    agent_did="did:agentmesh:healthcare-agent",
    action_type="data_access",
    context={"data_type": "phi", "encrypted": True},
)

# Generate compliance report
from datetime import datetime, timedelta
report = compliance.generate_report(
    framework=ComplianceFramework.SOC2,
    period_start=datetime.utcnow() - timedelta(days=30),
    period_end=datetime.utcnow(),
)

Threat Model

Threat	AgentMesh Defense
Prompt Injection	Tool output sanitized at Protocol Bridge
Credential Theft	15-min TTL, instant revocation on trust breach
Shadow Agents	Unregistered agents blocked at network layer
Delegation Escalation	Chains are cryptographically narrowing
Cascade Failure	Per-agent trust scoring isolates blast radius

🗺️ Roadmap

Quarter	Milestone
Q1 2026	✅ Core trust layer, identity, governance engine, 6 framework integrations
Q2 2026	TypeScript SDK, Go SDK, Dashboard UI, Plugin Marketplace
Q3 2026	AI Card spec contribution, CNCF Sandbox application
Q4 2026	Managed cloud service (AgentMesh Cloud), SOC2 Type II

See our full roadmap for details.

Known Limitations & Open Work

Transparency about what's done and what isn't.

Not Yet Implemented

Item	Location	Notes
ACP protocol adapter	trust/bridge.py	Bridge routes ACP messages, but no dedicated ACPAdapter class yet
Service wrapper for audit	services/audit/	Core audit module (governance/audit.py) is complete; service layer wrapper is a TODO
Service wrapper for reward engine	services/reward_engine/	Core reward engine (reward/engine.py) is complete; service layer wrapper is a TODO
Mesh control plane	services/mesh-control-plane/	Placeholder directory; no implementation yet
Delegation chain cryptographic verification	packages/langchain-agentmesh/trust.py	Simulated verification; full cryptographic chain validation not yet implemented

Integration Caveats (Dify)

The Dify integration has these documented limitations:

• Request body signature verification (X-Agent-Signature header) is not yet verified by middleware
• Trust score time decay is not yet implemented (scores don't decay over time)
• Audit logs are in-memory only (not persistent across multi-worker deployments)
• Environment variable configuration requires programmatic initialization (not auto-wired)

Infrastructure

• Redis/PostgreSQL storage providers: Implemented but require real infrastructure for testing (unit tests use in-memory provider)
• Kubernetes Operator: GovernedAgent CRD defined, but no controller/operator to reconcile it
• SPIRE Integration: SPIFFE identity module exists; real SPIRE agent integration is stubbed
• Performance targets: Latency overhead (<5ms) and throughput (10k reg/sec) are design targets, not yet benchmarked

Documentation

• docs/rfcs/ — Directory exists, no RFCs written yet
• docs/architecture/ — Directory exists, no architecture docs yet (see IMPLEMENTATION-NOTES.md for current notes)

Dependencies

AgentMesh builds on:

• Agent OS — IATP protocol, Nexus trust exchange
• Agent Hypervisor — Runtime session governance
• Agent SRE — SLO monitoring, chaos testing
• SPIFFE/SPIRE — Workload identity
• OpenTelemetry — Observability

Frequently Asked Questions

What is an agent mesh? An agent mesh is a trust and communication infrastructure for multi-agent AI systems, analogous to a service mesh for microservices. AgentMesh provides cryptographic identity (DID-based), per-agent trust scoring (0-1000 scale), ephemeral credentials, reward distribution, and automated compliance mapping.

How does AgentMesh handle trust between agents? Every agent gets a trust score from 0 to 1000 based on behavioral history, vouching from other agents, and compliance with governance policies. Trust scores gate what actions agents can perform and which sessions they can join. The score updates in real-time based on agent behavior.

What protocols does AgentMesh bridge? AgentMesh unifies three major protocols: Google's A2A (Agent-to-Agent) for inter-agent communication, Anthropic's MCP (Model Context Protocol) for tool integration, and IATP (Inter-Agent Trust Protocol) for cryptographic trust establishment. This means agents built on different frameworks can communicate through a single trust-verified channel.

Does AgentMesh help with regulatory compliance? Yes. AgentMesh provides automated compliance mapping for EU AI Act, SOC 2, HIPAA, and GDPR. Combined with tamper-evident audit trails and deterministic policy enforcement from Agent OS, it provides the documentation and safety guarantees needed for regulatory compliance.

Contributing

See CONTRIBUTING.md for guidelines.

License

Apache 2.0 — See LICENSE for details.

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Agents shouldn't be islands. But they also shouldn't be ungoverned.

AgentMesh is the trust layer that makes the mesh safe enough to scale.