AgentBridge serves as a neutral interop and governance layer designed for multi-agent systems, facilitating communication among various protocols seamlessly. This powerful framework allows for any protocol input and any protocol output, integrating essential features such as identity management, budget control, and a tamper-evident audit trail, all embedded within the call path.

Key Features

• N-Protocol Mesh (Any-to-Any): AgentBridge supports multiple protocols including MCP (Anthropic), A2A (Google/LF), ACP (IBM/LF), and OpenAI function calling, among others. Adding a new protocol is simplified to just one adapter instead of complex mappings, as each adapter is validated against the real official SDK of the protocol.

• In-Line Proxy: The architecture allows the bridge to operate between live agents on different protocols, enabling real-time communication and translation.

• Governance Plane: Built-in features for agent identity management using Ed25519 DIDs, individual agent budget controls, and human-in-the-loop approval processes ensure secure and compliant interactions. A hash-chained, tamper-evident audit trail is enforced at every step, storing data in a robust and durable database.

• Enterprise Governance: AgentBridge includes a declarative policy engine for implementing rules such as cost caps and operational hours, alongside role-based access control (RBAC) and integration with single sign-on (SSO) solutions.

• Framework Integrations: The platform effortlessly integrates with various frameworks such as LangChain, CrewAI, AutoGen, and LlamaIndex, allowing them to communicate across any supported protocol without the need for new dependencies.

Usage Example

AgentBridge can be employed to enable communication between agents speaking different protocols. Here is an example of how to use it:

import asyncio
from src.integrations import bridge_tool_call
from src.proxy import transport

async def main():
    result = await bridge_tool_call(
        "add", {"a": 2, "b": 3}, to="mcp",
        invoke=lambda w: transport.call_mcp_tool(
            "python", ["examples/mcp_server_agent.py"], w["params"]["name"], w["params"]["arguments"]),
    )
    print(result)  # Output: OpenAI-shaped tool result: "5"

asyncio.run(main())

This demonstrates how to reach an agent/tool that communicates through a different protocol, showcasing the bridging capabilities of AgentBridge effectively.

Architecture and Security

AgentBridge operates with a robust architecture that ensures secure and reliable operations via its governance gateway. The audit logs are designed to provide the integrity needed for high-stakes environments, and all endpoints are secured with rigorous measures to prevent unauthorized access.

For detailed insights into how to deploy AgentBridge, and for comprehensive documentation, please refer to the respective docs available in the repository.

Conclusion

AgentBridge stands at the forefront of creating a cohesive environment for various agents by simplifying protocol communication while embedding essential governance features. This innovative approach not only supports advanced integration scenarios but also addresses the growing need for compliance in multi-agent frameworks.