capabilitymesh 1.0.0a2

Universal capability discovery and negotiation for multi-agent systems - works across all frameworks

CapabilityMesh

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

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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.

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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

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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.

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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.

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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"]
    )
)

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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())

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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

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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

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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]

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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

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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:

1. 01_basic_usage.py - Registration, discovery, execution
2. 02_storage_backends.py - InMemory, SQLite, Redis
3. 03_trust_management.py - Trust tracking and filtering
4. 04_semantic_search.py - Natural language discovery
5. 05_advanced_capabilities.py - Rich schemas and versioning
6. 06_multi_agent_workflow.py - Complex multi-agent coordination

Run any example:

python examples/01_basic_usage.py

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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!

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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

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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.

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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.

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Support & Community

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

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Quick Links

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

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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

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⭐ 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!

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Making agents from any framework work together seamlessly

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