CapabilityMesh

The first and only Python package for universal capability discovery across all major agent frameworks

What is CapabilityMesh?

CapabilityMesh enables AI agents from any framework to discover and collaborate with each other. Build multi-agent systems where CrewAI, AutoGen, LangGraph, A2A, and custom agents work together seamlessly.

The Problem: Today's multi-agent ecosystem is fragmented. CrewAI agents can't discover AutoGen agents. LangGraph workflows can't find A2A services. No standard way to ask "which agents can translate?"

The Solution: CapabilityMesh provides a universal discovery layer that works across ALL frameworks.

Key Features

Universal Discovery - Find agents across ANY framework (CrewAI, AutoGen, LangGraph, A2A)
Immediate Registration - Agents discoverable instantly with @mesh.agent() decorator
Built-in Trust Management - Automatic reliability tracking (5 trust levels)
Flexible Storage - InMemory, SQLite (FTS5), or Redis backends
Exact & Semantic Search - Capability matching plus optional enhanced embeddings
Zero Configuration - Works out of the box, no setup required
Production Ready - 139 tests passing, 100% coverage, battle-tested

Quick Start

Installation

# Core package
pip install capabilitymesh

# With SQLite persistence
pip install capabilitymesh[sqlite]

# With Redis for distributed systems
pip install capabilitymesh[redis]

# Everything
pip install capabilitymesh[all]

5-Line Example

from capabilitymesh import Mesh

mesh = Mesh()  # Zero-config!

@mesh.agent(name="translator", capabilities=["translation", "nlp"])
def translate(text: str, target_lang: str = "es") -> str:
    return f"[{target_lang}] {text}"

# Discover by capability (immediate registration!)
agents = await mesh.discover("translation")
result = await mesh.execute(agents[0].id, "Hello!", target_lang="es")

That's it! No configuration, no setup, just register and discover.

Why Choose CapabilityMesh?

vs. Other Solutions

Feature	CapabilityMesh	A2A	CrewAI	AutoGen	LangGraph
Multi-framework discovery	✅	❌	❌	❌	❌
Semantic search	✅	❌	❌	❌	❌
Built-in trust	✅	❌	❌	❌	❌
Multiple storage backends	✅	❌	❌	❌	❌
Zero-config	✅	❌	✅	✅	✅

CapabilityMesh is the ONLY solution for universal agent discovery.

Core Features

1. Universal Agent Discovery

Discover agents from any framework with semantic search:

from capabilitymesh import Mesh

mesh = Mesh()

# Register agents from different frameworks
@mesh.agent(capabilities=["summarization", "nlp"])
async def summarize(text: str) -> str:
    return f"Summary: {text[:100]}..."

# Natural language discovery
agents = await mesh.discover("make this text shorter")
# Returns: [summarizer] - semantic match!

2. Multi-Framework Support

Mix and match agents from different frameworks:

from capabilitymesh import Mesh
from crewai import Agent as CrewAgent
from autogen import AssistantAgent

mesh = Mesh()

# CrewAI agent
crew_agent = CrewAgent(role="researcher", goal="Research topics")
await mesh.register(crew_agent, name="researcher")

# AutoGen agent
autogen_agent = AssistantAgent(name="coder")
await mesh.register(autogen_agent, name="coder")

# Python function
@mesh.agent(capabilities=["analysis"])
def analyzer(data):
    return {"result": "analyzed"}

# Discover across ALL frameworks!
all_agents = await mesh.list_agents()

3. Automatic Trust Management

Track agent reliability automatically:

from capabilitymesh import Mesh, TrustLevel

mesh = Mesh()

# Execute tasks - trust scores update automatically
for i in range(20):
    await mesh.execute(agent_id, f"task-{i}")

# Check trust
score = await mesh.trust.get_score(agent_id)
print(f"Trust: {score.level.name}")  # HIGH, VERIFIED, etc.
print(f"Success rate: {score.success_rate:.1%}")

# Discover only trusted agents
trusted = await mesh.discover("task", min_trust=TrustLevel.MEDIUM)

Trust Levels: UNTRUSTED → LOW → MEDIUM → HIGH → VERIFIED (auto-calculated)

4. Flexible Storage Backends

Choose storage that fits your deployment:

from capabilitymesh import Mesh
from capabilitymesh.storage import InMemoryStorage, SQLiteStorage, RedisStorage

# Development: In-memory (default)
mesh = Mesh()

# Production: SQLite with full-text search
mesh = Mesh(storage=SQLiteStorage("agents.db"))

# Distributed: Redis for multi-instance
mesh = Mesh(storage=RedisStorage(host="redis.example.com"))

Storage	Persistence	Search	Distribution	Best For
InMemory	No	Basic	Single	Development
SQLite	File	FTS5	Single	Production
Redis	Remote	Basic	Multi-instance	Cloud, Scale

5. Semantic Search

Find agents with natural language queries:

# Natural language works!
agents = await mesh.discover("understand customer sentiment")
agents = await mesh.discover("convert text to another language")
agents = await mesh.discover("extract key information from documents")

Uses TF-IDF embeddings by default (no external dependencies). Upgrade to sentence-transformers or OpenAI embeddings for even better results (coming in v1.0-beta).

6. Rich Capability Schemas

Define capabilities with versioning, constraints, and metadata:

from capabilitymesh import (
    Capability,
    CapabilityVersion,
    CapabilityConstraints,
    SemanticMetadata
)

capability = Capability(
    name="fast-translation",
    version=CapabilityVersion(major=2, minor=1, patch=0),
    constraints=CapabilityConstraints(
        max_response_time_ms=100,
        max_cost_per_call=0.001,
        min_availability=0.999
    ),
    semantic=SemanticMetadata(
        tags=["nlp", "translation", "ml"],
        categories=["Natural Language Processing"],
        domains=["linguistics", "ai"]
    )
)

Complete Example

import asyncio
from capabilitymesh import Mesh, Capability, TrustLevel

async def main():
    # Initialize with persistent storage
    mesh = Mesh(storage=SQLiteStorage("agents.db"))

    # Register a document processing pipeline
    @mesh.agent(name="pdf-extractor", capabilities=["extraction", "pdf"])
    def extract_text(pdf_path: str) -> str:
        return f"Extracted text from {pdf_path}"

    @mesh.agent(name="summarizer", capabilities=["summarization", "nlp"])
    async def summarize(text: str) -> str:
        await asyncio.sleep(0.1)  # Async processing
        return f"Summary: {text[:100]}..."

    @mesh.agent(name="translator", capabilities=["translation", "nlp"])
    def translate(text: str, target_lang: str = "es") -> str:
        return f"[{target_lang}] {text}"

    # Build a document processing pipeline
    pdf_path = "document.pdf"

    # Step 1: Extract
    extractors = await mesh.discover("extract text from pdf")
    text = await mesh.execute(extractors[0].id, pdf_path)

    # Step 2: Summarize
    summarizers = await mesh.discover("summarize text")
    summary = await mesh.execute(summarizers[0].id, text)

    # Step 3: Translate
    translators = await mesh.discover("translate to Spanish")
    result = await mesh.execute(translators[0].id, summary, target_lang="es")

    print(f"Final result: {result}")

    # Check trust scores
    for agent in [extractors[0], summarizers[0], translators[0]]:
        score = await mesh.trust.get_score(agent.id)
        print(f"{agent.name}: {score.level.name} ({score.success_rate:.0%})")

if __name__ == "__main__":
    asyncio.run(main())

Framework Integration Examples

CrewAI

from capabilitymesh import Mesh
from crewai import Agent

mesh = Mesh()

researcher = Agent(
    role="Senior Research Analyst",
    goal="Uncover cutting-edge developments",
    backstory="Expert researcher"
)

await mesh.register(researcher, name="ai-researcher")

# Discover across frameworks
agents = await mesh.discover("research AI developments")

AutoGen

from capabilitymesh import Mesh
from autogen import AssistantAgent

mesh = Mesh()

coder = AssistantAgent(
    name="coder",
    system_message="Expert Python developer"
)

await mesh.register(coder, name="python-coder")

# Discover
coders = await mesh.discover("write python code")

Custom Agents

from capabilitymesh import Mesh

mesh = Mesh()

# Any callable works!
class MyAgent:
    def execute(self, task):
        return f"Processed: {task}"

await mesh.register(MyAgent(), capabilities=["task-processing"])

@mesh.agent(capabilities=["calculation"])
def calculator(a: int, b: int) -> int:
    return a + b

Multi-Agent Workflows

Sequential Pipeline

# Extract → Summarize → Translate
extractors = await mesh.discover("extract text")
text = await mesh.execute(extractors[0].id, "document.pdf")

summarizers = await mesh.discover("summarize")
summary = await mesh.execute(summarizers[0].id, text)

translators = await mesh.discover("translate")
result = await mesh.execute(translators[0].id, summary)

Parallel Processing

import asyncio

# Process multiple items in parallel
items = ["doc1.pdf", "doc2.pdf", "doc3.pdf"]
extractor = (await mesh.discover("extract text"))[0]

results = await asyncio.gather(*[
    mesh.execute(extractor.id, item) for item in items
])

Error Handling with Fallbacks

from capabilitymesh import TrustLevel

# Get agents sorted by trust
agents = await mesh.discover("translation", min_trust=TrustLevel.MEDIUM)

# Try agents until one succeeds
for agent in agents:
    try:
        result = await mesh.execute(agent.id, task)
        break
    except Exception:
        continue  # Try next agent

Installation Options

# Minimal (keyword matching only)
pip install capabilitymesh

# With local embeddings (recommended)
pip install capabilitymesh[embeddings]

# With SQLite storage (recommended for production)
pip install capabilitymesh[sqlite]

# With Redis storage (distributed systems)
pip install capabilitymesh[redis]

# With specific frameworks
pip install capabilitymesh[crewai]
pip install capabilitymesh[autogen]
pip install capabilitymesh[langgraph]

# Full installation
pip install capabilitymesh[all]

What's Included

✅ v1.0.0-alpha.2 (Current)

Mesh API - Simple interface for agent management
Multi-framework support - CrewAI, AutoGen, LangGraph, A2A, custom
Semantic discovery - Natural language queries
Trust management - 5-level automatic scoring
Storage backends - InMemory, SQLite (FTS5), Redis
Capability schemas - Rich metadata, versioning, constraints
A2A compatible - Convert any agent to A2A protocol
Fixed @mesh.agent() decorator - Immediate registration, no wrapper overhead
139 tests passing - 100% coverage
Complete documentation - Examples, guides, API reference

🔮 Coming Soon

v1.0.0-beta.1: Enhanced embeddings (sentence-transformers, OpenAI)
v1.0.0: Stable release with production hardening
v1.1.0: P2P discovery (mDNS, Gossip, DHT)
v1.2.0: Advanced negotiation protocols

Documentation

GitHub: https://github.com/scionoftech/capabilitymesh
Full Documentation: EXAMPLES_GUIDE.md
Technical Docs: docs/technical_documentation.html
Roadmap: ROADMAP.md

Examples

CapabilityMesh includes 6 comprehensive examples:

01_basic_usage.py - Registration, discovery, execution
02_storage_backends.py - InMemory, SQLite, Redis
03_trust_management.py - Trust tracking and filtering
04_semantic_search.py - Natural language discovery
05_advanced_capabilities.py - Rich schemas and versioning
06_multi_agent_workflow.py - Complex multi-agent coordination

Run any example:

python examples/01_basic_usage.py

Use Cases

1. Multi-Framework Teams

Mix agents from different frameworks:

team = {
    "researcher": CrewAI_Agent,      # Best for research
    "coder": AutoGen_Agent,          # Best for coding
    "orchestrator": LangGraph_Agent, # Best for workflows
    "api": A2A_Service               # Best for services
}
# All coordinated via CapabilityMesh!

2. Agent Marketplace

Build marketplaces where agents advertise capabilities:

# Agents register
marketplace.register(translator, capabilities=["translation"])
marketplace.register(analyzer, capabilities=["analysis"])

# Clients discover and hire
agent = marketplace.discover("translate and analyze")[0]
result = await mesh.execute(agent.id, task_data)

3. Framework Migration

Gradually migrate between frameworks without disruption:

# Phase 1: All CrewAI
# Phase 2: Mix CrewAI + AutoGen (CapabilityMesh handles discovery)
# Phase 3: All AutoGen
# Agents discoverable throughout migration!

4. Best Tool for Each Job

Choose optimal framework per agent:

CrewAI → Role-based collaboration
AutoGen → Conversational workflows
LangGraph → Complex state machines
A2A → Production microservices
All coordinated seamlessly!

Requirements

Python: 3.9+
Core Dependencies: pydantic, httpx, cryptography, pyjwt, nest-asyncio
Optional Dependencies:
- aiosqlite - For SQLite storage
- redis - For Redis storage
- sentence-transformers - For better semantic search (coming soon)
- Framework packages (crewai, autogen, langgraph) - For framework integration

Development Status

Current Version: v1.0.0-alpha.2

✅ All core features implemented
✅ @mesh.agent() decorator fully fixed (immediate registration!)
✅ 139 tests passing (100% success rate)
✅ Comprehensive documentation
✅ 6 example files
✅ Production-ready code quality
⏳ Community feedback welcome

Stability: Alpha - Feature-complete and tested, but expect refinements based on real-world usage.

License

Apache License 2.0 - Free for commercial and personal use with patent protection!

Key Benefits:

✅ Free for commercial and personal use
✅ Explicit patent grant protects users and contributors
✅ Clear attribution requirements
✅ Enterprise-friendly legal framework

See LICENSE for details.

Support & Community

Issues: GitHub Issues
Discussions: GitHub Discussions
PyPI: https://pypi.org/project/capabilitymesh

Quick Links

🏠 Homepage
📖 Documentation
🐛 Issue Tracker
💬 Discussions
📦 PyPI Package
🗺️ Roadmap

Project Stats

Version: 1.0.0-alpha.2
Tests: 139 passing (100%)
Coverage: 100% of core features
Frameworks: 4 supported (CrewAI, AutoGen, LangGraph, A2A)
Storage: 3 backends (InMemory, SQLite, Redis)
Trust: 5-level automatic system
Examples: 6 comprehensive
Status: ✅ Ready for use

⭐ Show Your Support

If CapabilityMesh helps your project, star ⭐ the repo to show your support!

# Install and try it now!
pip install capabilitymesh

# Your feedback shapes the future of multi-agent systems!

Making agents from any framework work together seamlessly

The first and only universal capability mesh for multi-agent systems

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