sm-bridge 0.3.1

A Python library for building NANDA-compatible AI agent registries

SM Bridge

A Python library for building NANDA-compatible AI agent registries.

NANDA (Network of AI Agents in Decentralized Architecture) is the protocol for federated AI agent discovery and communication. This library provides the primitives needed to make your agent registry interoperable with the NANDA ecosystem.

Features

• NANDA AgentFacts Models - Pydantic models implementing the projnanda/agentfacts-format specification
• FastAPI Router - Drop-in endpoints for /nanda/index, /nanda/resolve, /nanda/deltas
• Delta Store - Change tracking for registry synchronization
• Converter Interface - Abstract pattern for integrating with your existing registry

Installation

pip install git+https://github.com/Sharathvc23/sm-bridge.git

Or install from source:

git clone https://github.com/Sharathvc23/sm-bridge
cd sm-bridge
pip install -e .

Quick Start

Basic Usage

from fastapi import FastAPI
from sm_bridge import SmBridge, SimpleAgent

# Create the bridge
bridge = SmBridge(
    registry_id="my-registry",
    provider_name="My Company",
    provider_url="https://example.com",
    base_url="https://registry.example.com"
)

# Register agents
bridge.register_agent(SimpleAgent(
    id="my-agent",
    name="My Agent",
    description="An agent that does things",
    namespace="production",
    labels=["chat", "tool-use"],
    skills=[
        {"id": "summarize", "description": "Summarizes text"},
        {"id": "translate", "description": "Translates between languages"}
    ]
))

# Mount routers
app = FastAPI()
app.include_router(bridge.router)            # /nanda/* endpoints
app.include_router(bridge.wellknown_router)   # /.well-known/nanda.json (RFC 8615)

This gives you:

• GET /nanda/index - List all public agents (with correct total_count for pagination)
• GET /nanda/resolve?agent=my-agent - Resolve a single agent
• GET /nanda/deltas?since=0 - Get changes for sync
• GET /.well-known/nanda.json - Registry discovery (RFC 8615 compliant)

Custom Registry Integration

For existing registries with their own data models:

from sm_bridge import (
    AbstractAgentConverter,
    SmAgentFacts,
    SmProvider,
    SmEndpoints,
    SmCapabilities,
    SmSkill,
    DeltaStore,
    create_sm_router,
)
from typing import Iterator

class MyRegistryConverter(AbstractAgentConverter):
    def __init__(self, db_connection):
        super().__init__(
            registry_id="my-registry",
            provider_name="My Company",
            provider_url="https://example.com"
        )
        self.db = db_connection
    
    def to_sm(self, agent) -> SmAgentFacts:
        return SmAgentFacts(
            id=f"did:web:example.com:agents:{agent.id}",
            handle=self.build_handle(agent.namespace, agent.id),
            agent_name=agent.display_name,
            label=agent.category,
            description=agent.description,
            version=agent.version,
            provider=self.build_provider(),
            endpoints=SmEndpoints(static=[agent.endpoint_url]),
            capabilities=SmCapabilities(modalities=agent.capabilities),
            skills=[SmSkill(id=s.id, description=s.desc) for s in agent.skills],
            metadata={
                "x_my_registry": {
                    "internal_id": agent.internal_id,
                    "created_at": agent.created_at.isoformat(),
                }
            }
        )
    
    def list_agents(self, limit: int, offset: int) -> Iterator:
        return self.db.query_agents(limit=limit, offset=offset)
    
    def get_agent(self, agent_id: str):
        return self.db.get_agent(agent_id)
    
    def is_public(self, agent) -> bool:
        return agent.visibility == "public"

# Create router with custom converter
converter = MyRegistryConverter(db_connection)
delta_store = DeltaStore()

nanda_router, wellknown_router = create_sm_router(
    converter=converter,
    delta_store=delta_store,
    registry_id="my-registry",
    base_url="https://registry.example.com",
    provider_name="My Company",
    provider_url="https://example.com"
)

app = FastAPI()
app.include_router(nanda_router)
app.include_router(wellknown_router)

Models

SmAgentFacts

The core data structure for agent metadata:

from sm_bridge import SmAgentFacts

facts = SmAgentFacts(
    id="did:web:example.com:agents:my-agent",
    handle="@my-registry:production/my-agent",
    agent_name="My Agent",
    label="assistant",
    description="An AI assistant",
    version="1.0.0",
    provider=SmProvider(
        name="My Company",
        url="https://example.com"
    ),
    endpoints=SmEndpoints(
        static=["https://api.example.com/agents/my-agent"]
    ),
    capabilities=SmCapabilities(
        modalities=["text", "tool-use"],
        authentication=SmAuthentication(methods=["did-auth"])
    ),
    skills=[
        SmSkill(
            id="urn:my-registry:cap:summarize:v1",
            description="Summarizes long documents",
            inputModes=["text"],
            outputModes=["text"]
        )
    ],
    metadata={
        "x_my_registry": {
            "custom_field": "custom_value"
        }
    }
)

Handle Format

NANDA handles follow the format @registry:namespace/agent-id:

handle = SmAgentFacts.create_handle(
    registry="my-registry",
    namespace="production", 
    agent_id="my-agent"
)
# Returns: "@my-registry:production/my-agent"

Delta Store

Track changes for registry synchronization:

from sm_bridge import DeltaStore, SmAgentFacts

store = DeltaStore()

# Record an agent creation/update
delta = store.add("upsert", agent_facts)
print(f"Recorded delta with seq={delta.seq}")

# Get all changes since seq 0
deltas = store.since(0)

# Get next sequence number for polling
next_seq = store.next_seq

For production, extend PersistentDeltaStore to persist to a database:

from sm_bridge import PersistentDeltaStore

class PostgresDeltaStore(PersistentDeltaStore):
    def __init__(self, dsn: str):
        super().__init__()
        self.conn = psycopg2.connect(dsn)
    
    def _persist(self, delta):
        # INSERT INTO nanda_deltas ...
        pass
    
    def _load_since(self, seq):
        # SELECT * FROM nanda_deltas WHERE seq > ...
        pass

MCP Tools

Advertise MCP tools that agents can use:

from sm_bridge import SmBridge, SmTool

bridge = SmBridge(
    registry_id="my-registry",
    provider_name="My Company",
    provider_url="https://example.com",
    tools=[
        SmTool(
            tool_id="search",
            description="Search the web",
            endpoint="https://api.example.com/mcp/search",
            params=["query", "limit"]
        ),
        SmTool(
            tool_id="calculate",
            description="Perform calculations",
            endpoint="https://api.example.com/mcp/calculate",
            params=["expression"]
        )
    ]
)

Registry Discovery

The library serves /.well-known/nanda.json at the domain root (RFC 8615) via the separate wellknown_router:

{
  "registry_id": "my-registry",
  "registry_did": "did:web:registry.example.com",
  "namespaces": ["did:web:example.com:*"],
  "index_url": "https://registry.example.com/nanda/index",
  "resolve_url": "https://registry.example.com/nanda/resolve",
  "deltas_url": "https://registry.example.com/nanda/deltas",
  "tools_url": "https://registry.example.com/nanda/tools",
  "provider": {
    "name": "My Company",
    "url": "https://example.com"
  },
  "capabilities": ["agentfacts", "deltas", "mcp-tools"]
}

Federating with NANDA

To join the NANDA network:

1. Deploy your registry with the NANDA bridge endpoints
2. Ensure /.well-known/nanda.json is accessible
3. Contact the MIT NANDA team to register as a federated peer
4. (Optional) Implement incremental sync or gossip mechanisms for real-time or near-real-time federation

AI Catalog gateway

create_gateway_router serves the AI Catalog discovery endpoints for the agents in a DeltaStore, alongside the /nanda/* router (the two surfaces are independent):

from fastapi import FastAPI
from sm_bridge import DeltaStore, create_gateway_router

delta_store = DeltaStore()

app = FastAPI()
app.include_router(
    create_gateway_router(delta_store, base_url="https://reg.example.com", domain="example.com")
)

Endpoint	Returns
GET /.well-known/ai-catalog.json	CatalogDocument — all agents
GET /agents/{slug}	CatalogEntry — a pointer to the agent's card
GET /cards/{slug}.json	A2A AgentCard (full SmAgentFacts included under _meta)

Each SmAgentFacts is translated into a CatalogEntry and an A2A AgentCard whose url is the agent's runtime. Point a NANDA index at base_url (as a hosting_path=registry entry) to make the agents discoverable through the AI Catalog flow.

Related Packages

Package	Question it answers
sm-model-provenance	"Where did this model come from?" (identity, versioning, provider, NANDA serialization)
sm-model-card	"What is this model?" (unified metadata schema — type, status, risk level, metrics, weights hash)
sm-model-integrity-layer	"Does this model's metadata meet policy?" (rule-based checks)
sm-model-governance	"Has this model been cryptographically approved for deployment?" (approval flow with signatures, quorum, scoping, revocation)
sm-bridge (this package)	"How do I expose this to the NANDA network?" (FastAPI router, AgentFacts models, delta sync)

License

MIT

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Personal research contributions aligned with Project NANDA standards. Stellarminds.ai