AgentRPC

Exposes functions written in any programming language (Python, JavaScript, Go, Rust, etc.) as callable RPC endpoints without requiring language-specific bindings or serialization boilerplate. AgentRPC uses a language-agnostic protocol layer that wraps native function signatures and marshals arguments/returns across process and network boundaries, enabling seamless cross-language function invocation.

Enables calling remote functions as if they were local by handling all network transport, serialization, and error propagation transparently. The client-side implementation uses a proxy/stub pattern that intercepts function calls, serializes arguments, sends them over the network (HTTP, WebSocket, or custom transport), deserializes responses, and returns results or throws exceptions as if the function executed locally.

Manages connection pools to remote services, reusing connections across multiple function calls to reduce overhead and improve throughput. Handles connection lifecycle (creation, reuse, cleanup), connection failures, and resource limits, allowing applications to efficiently manage connections to many remote services.

Allows functions to be executed locally when available, with automatic fallback to remote execution if the local implementation is unavailable or outdated. Enables hybrid deployments where functions can run locally for performance or offline capability, with transparent fallback to remote services.

Automatically marshals typed function arguments and return values across process and network boundaries using a schema definition system. AgentRPC defines function signatures with explicit type information, validates arguments against schemas at call time, and handles serialization/deserialization of primitives, objects, arrays, and custom types without requiring manual encoding logic.

Maintains a registry of exposed functions with metadata (signatures, descriptions, tags, capabilities) that agents can query to discover available functions and their contracts. The registry supports semantic search and filtering, allowing AI agents to find relevant functions based on natural language descriptions or capability tags, then invoke them with validated arguments.

Abstracts the underlying transport layer (HTTP, WebSocket, gRPC, custom protocols) behind a unified client/server interface, allowing the same function to be called over different transports without code changes. The transport layer is pluggable; developers can switch between HTTP for simplicity, WebSocket for bidirectional communication, or gRPC for performance without modifying function definitions or calling code.

Enables composing multiple remote function calls into workflows where output from one function feeds into another, with automatic argument passing and error handling. Supports sequential chaining, conditional branching, and parallel execution of remote functions, allowing complex distributed workflows to be expressed as function compositions without explicit orchestration code.

Captures exceptions thrown in remote functions, serializes them with stack traces and context, transmits them over the network, and re-throws them as native exceptions in the calling language. Preserves error types, messages, and stack traces across language boundaries, enabling consistent error handling patterns in distributed systems.

Provides hooks to intercept and modify requests before they're sent to remote functions and responses before they're returned to callers. Middleware can log calls, add authentication headers, measure latency, validate responses, or transform data, allowing cross-cutting concerns to be handled separately from function logic.

Supports functions that return results incrementally (streaming) or take a long time to complete, with mechanisms for partial results, progress updates, and cancellation. Clients can receive streamed responses as they arrive rather than waiting for the entire result, and can cancel long-running operations mid-execution.

Automatically propagates context (request IDs, user identity, tracing spans, custom metadata) across chained remote function calls, ensuring that all functions in a call chain share the same context. Enables distributed tracing, user tracking, and custom context without requiring explicit parameter passing.