AgentRPC

AgentRPC is a universal RPC layer designed to seamlessly connect your AI agents and workflows to any function, regardless of programming language or deployment location. It solves the challenge of integrating agents with services running in diverse environments, including those behind private networks, allowing you to leverage your existing infrastructure without complex custom integrations.

How AgentRPC Solves Your Problems

Building AI agents that need to interact with your existing backend services often means grappling with connectivity issues, different programming languages, network boundaries, and the limitations of standard protocols like HTTP. AgentRPC provides the necessary middleware to abstract away these complexities, enabling your agents to reliably call any function as if it were local.

Key Features

AgentRPC delivers robust connectivity and management for your agent's tools:

• 🌐 Multi-Language Compatibility: Connect agents to tools written in TypeScript, Go, Python, and more languages coming soon. This allows you to leverage your existing codebases and teams without needing to rewrite functions.

• 🔒 Secure Private Network Access: Register functions located behind firewalls or in private VPCs without opening inbound ports. AgentRPC uses secure outbound long-polling to connect your tools, enhancing security and simplifying network configuration.

• ⏱️ Support for Long-Running Tasks: Enable agents to call functions that require more time than standard HTTP timeouts allow. The asynchronous polling mechanism reliably handles operations that take minutes or longer, perfect for complex workflows.

• 📊 Comprehensive Observability: Gain full visibility into your agent's tool usage with tracing, metrics, and events via the AgentRPC hosted platform. Understand how your tools are performing and being utilized in real-time.

• ✅ Built-in Automatic Failover: The platform continuously monitors function health and automatically routes requests to healthy replicas in case of an outage. This ensures high availability and reliability for your agent's critical functions without requiring manual intervention.

• 🤝 Framework Compatibility: Out-of-the-box support for industry standards like Model Context Protocol (MCP) and OpenAI SDK compatible agents ensures easy integration with popular agent frameworks.

Use Cases

See how AgentRPC simplifies common AI agent integration challenges:

• Integrating Internal Services: Easily allow your AI agent to interact with proprietary APIs or databases hosted within your private network, without exposing them to the public internet or building custom proxy layers.

• Automating Complex Workflows: Empower agents to initiate and monitor long-running backend processes, such as data analysis jobs or external API calls that exceed typical HTTP request limits, all through a single, reliable interface.

• Leveraging Multi-Language Codebases: Connect agents to functions written in different programming languages across your organization, unifying access to diverse capabilities under a single, agent-friendly interface and maximizing code reuse.

Why Choose AgentRPC?

AgentRPC stands out as a purpose-built solution for the unique challenges of AI agent tool calling:

• Universal Connectivity: Unlike building custom integrations or exposing services via traditional HTTP APIs, AgentRPC provides a single, universal layer designed specifically for AI agent tool calling, abstracting away language and network complexities from the agent.

• Secure Integration with Private Networks: AgentRPC's unique outbound long-polling mechanism allows you to connect agents to functions behind firewalls or in private VPCs securely, without requiring inbound port configurations that can compromise security.

• Reliable Long-Running Tool Execution: Go beyond the limitations of HTTP timeouts. AgentRPC's architecture is built to reliably handle long-running functions, making it suitable for complex tasks that agents might need to trigger.

FAQ

Find quick answers to common questions about AgentRPC:

• Why should I use AgentRPC for my AI agent? AgentRPC simplifies connecting your AI agent to functions deployed anywhere, especially in private networks or different services. Instead of building custom integrations or exposing services, you register functions with AgentRPC's SDK to get an agent-compatible tool definition, making your functions universally discoverable and callable by your agent without hardcoding endpoints.

• How does AgentRPC connect to functions behind a firewall? AgentRPC uses an outbound long-polling connection from the SDK running on your infrastructure to the AgentRPC cloud. This means your function checks periodically for new calls without needing any inbound ports opened on your firewall or private network, ensuring secure access.

• What are the advantages over using standard HTTP APIs? AgentRPC offers built-in service discovery, eliminating the need for agents to hardcode endpoints. It supports long-running functions via its asynchronous polling mechanism, overcoming HTTP timeout limits. It also co-locates tool definitions with the function code and generates agent-compatible schemas (like OpenAI SDK compatible definitions), simplifying integration and management compared to manually creating and distributing API definitions.

Conclusion

AgentRPC simplifies the integration of AI agents with your existing functions, enabling them to leverage the full breadth of your internal capabilities securely and reliably. If you're building AI agents that need to interact with distributed or protected services, AgentRPC provides the robust middleware you need to bridge the gap effectively.

Explore the documentation to learn more about how AgentRPC can empower your AI agents.