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AgentRPC

AgentRPC: Open-source RPC framework (Apache 2.0) that lets AI agents call functions across network boundaries without opening ports. Supports TypeScript, Go, and Python SDKs with built-in MCP server compatibility.

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In Plain English

Open-source RPC framework that lets AI agents call functions behind firewalls and across private networks without opening ports — supports TypeScript, Go, and Python with built-in MCP server compatibility.

OverviewFeaturesPricingGetting StartedUse CasesLimitationsFAQSecurityAlternatives

Overview

AgentRPC is a free, open-source AI agent RPC framework (Apache 2.0) with optional managed hosting (quote-based pricing) that lets AI agents call functions across private network boundaries without opening inbound ports. It solves a specific infrastructure problem: when your AI agent runs in one environment but needs to invoke functions inside a private VPC, Kubernetes cluster, or firewalled network, AgentRPC's outbound-only SDK connections bypass the need for VPNs, open ports, or ingress configuration entirely.

The framework ships native SDKs for 3 languages — TypeScript, Go, and Python — with a 4th (.NET) in active development as of early 2026. Each SDK uses long-polling to keep connections alive beyond the standard 30–60 second HTTP timeout, which is critical for agent tasks involving database queries, report generation, or multi-step data processing that run for several minutes. The TypeScript SDK includes a built-in MCP server that exposes registered functions to Claude Desktop, Cursor, and any MCP-compatible host without additional configuration, and all SDKs generate OpenAI-compatible tool definitions that work with Anthropic, LiteLLM, and OpenRouter.

The managed hosting option at api.agentrpc.com adds observability features including tracing, metrics, structured event logging, and health monitoring for registered functions — pricing is quote-based and scales with function call volume and team size. Self-hosting is fully supported with zero software licensing costs under Apache 2.0.

AgentRPC launched publicly in Q4 2025 and has accumulated over 870 AI tool integrations across its directory. It is purpose-built for cross-network agent function calling rather than general workflow orchestration, making it narrower in scope than platforms like Temporal or Inngest but significantly simpler to deploy for its target use case. The GitHub repository at github.com/agentrpc/agentrpc contains all SDK source code, documentation, and example implementations for each supported language.

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Editorial Review

AgentRPC solves a specific problem: letting AI agents call functions across private networks without VPNs or open ports. Valuable for teams with private VPC infrastructure who need AI agent access to internal functions. The SDK approach with 3 language SDKs (TypeScript, Go, Python) and built-in MCP server compatibility makes integration straightforward for developer teams. The managed hosting adds observability but introduces latency overhead. Best suited for teams with genuine cross-network function calling needs rather than as a general-purpose RPC replacement.

Key Features

Cross-Network Function Registration+

Register functions from private VPCs, Kubernetes clusters, or firewalled environments without opening inbound ports. The SDK establishes an outbound connection to the RPC server, making it compatible with restrictive network policies. This eliminates the need for VPNs, port forwarding, or ingress configuration that traditional RPC frameworks require.

Long-Polling SDKs for Long-Running Tasks+

Standard HTTP requests time out after 30-60 seconds, which breaks agent tasks involving database queries, report generation, or multi-step data processing. AgentRPC uses long-polling to keep connections alive for minutes, letting agents wait for results from functions that take extended periods to complete.

Multi-Language SDK Coverage+

Native SDKs ship for TypeScript, Go, and Python today, with .NET in active development. Each SDK exposes the same registration API and health-check patterns, so polyglot teams get consistent cross-language function exposure through a single RPC layer without writing glue code between stacks.

Built-in MCP Server Compatibility+

The TypeScript SDK includes an embedded MCP server that exposes registered functions to any MCP-compatible host, including Claude Desktop and Cursor, without additional configuration. This eliminates the need to write a separate MCP server adapter for internal tools.

Observability and Health Monitoring+

The hosted platform provides tracing, metrics, and structured event logging for every function invocation. Health tracking continuously monitors registered functions, surfacing issues like disconnects, error rates, and latency degradation in real time.

Pricing Plans

Open Source (Self-Hosted)

Free

  • ✓Full Apache 2.0 license — unrestricted commercial use
  • ✓All SDKs included (TypeScript, Go, Python; .NET in development)
  • ✓Built-in MCP server in TypeScript SDK
  • ✓Self-host the RPC server on your own infrastructure
  • ✓No usage limits or function call quotas

Managed Hosting

Quote-based (contact for pricing)

  • ✓Hosted RPC server at api.agentrpc.com
  • ✓Tracing, metrics, and structured event logging
  • ✓Health monitoring for registered functions
  • ✓Pricing scales with function call volume and team size
  • ✓Contact team for quote — no public pricing page available
See Full Pricing →Free vs Paid →Is it worth it? →

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Getting Started with AgentRPC

Step 1: Install the SDK

Install the AgentRPC SDK for your language of choice. For TypeScript: npm install @agentrpc/sdk. For Python: pip install agentrpc. For Go: go get github.com/agentrpc/agentrpc.

Step 2: Register Functions

Use the SDK's registration API to expose your functions. Each function gets a name, description, and typed input schema that the RPC layer uses to generate OpenAI-compatible tool definitions automatically.

Step 3: Connect Your Agent

Point your AI agent at the RPC server (self-hosted or api.agentrpc.com) and call registered functions by name. The SDK handles serialization, long-polling for extended tasks, and MCP protocol translation if you're using an MCP-compatible host.
Ready to start? Try AgentRPC →

Best Use Cases

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Private VPC Function Exposure: Teams with databases, ML models, or internal APIs in private VPCs who need AI agents to call those functions without opening inbound ports or configuring VPNs — the SDK's outbound-only connection model bypasses the network boundary problem entirely

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Multi-Cloud Agent Orchestration: Organizations running services across AWS, GCP, and Azure who need a single RPC layer for agents to reach functions in any cloud environment without separate networking setups per cloud

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Polyglot Team Tool Integration: Engineering teams using Python, Go, and TypeScript who want to expose functions from all 3 supported languages to AI agents through one consistent registration API and tool schema

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Long-Running Agent Workflows: AI workflows involving database queries, report generation, or data processing that exceed the standard 30-60 second HTTP timeout — long-polling keeps the connection alive for minutes without timeouts

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Kubernetes Cluster Tool Exposure: Teams running internal microservices in Kubernetes who need to make specific services callable by AI agents without exposing the entire cluster or configuring an ingress per service

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MCP Host Integration for Internal Tools: Developers wanting to expose private internal functions to Claude Desktop, Cursor, or other MCP clients without public endpoints — the TypeScript SDK's built-in MCP server handles the protocol translation

Limitations & What It Can't Do

We believe in transparent reviews. Here's what AgentRPC doesn't handle well:

  • ⚠Small user community with very few documented production deployments or public case studies as of early 2026 — limits available reference architectures and troubleshooting resources
  • ⚠Documentation covers setup basics but lacks depth on security hardening, scaling patterns, and production operational guidance
  • ⚠Adds unnecessary complexity when functions are already publicly accessible — not every project needs a cross-network RPC layer
  • ⚠Managed RPC server introduces a network hop adding tens of milliseconds latency, which matters for latency-sensitive operations and sub-millisecond function calls
  • ⚠.NET SDK still in development — C# and F# teams cannot use AgentRPC yet and have no published estimated timeline for availability

Pros & Cons

✓ Pros

  • ✓Bridges network boundaries without VPN or port configuration — register functions from private VPCs, Kubernetes clusters, and firewalled environments in minutes using outbound-only connections
  • ✓Long-polling SDKs solve the 30-60 second HTTP timeout problem that breaks agent tasks running for minutes — critical for database queries, report generation, and multi-step data processing
  • ✓Multi-language SDKs across 3 languages (TypeScript, Go, Python) with a 4th (.NET) in development let polyglot teams expose functions from every stack through one unified RPC layer
  • ✓Built-in MCP server in the TypeScript SDK means instant compatibility with Claude Desktop, Cursor, and any MCP-compatible host without additional configuration
  • ✓OpenAI-compatible tool definitions work with Anthropic, LiteLLM, and OpenRouter without modification — covering essentially every major LLM provider through a single tool schema
  • ✓Open-source under Apache 2.0 license on GitHub with optional managed hosting available — permits unrestricted commercial use, self-hosting, and modification with no vendor lock-in

✗ Cons

  • ✗Small user community with very few public production deployment examples or documented case studies as of early 2026 — limits available reference architectures
  • ✗Documentation covers setup basics but lacks depth on security hardening, scaling patterns, and production deployment best practices
  • ✗Adds unnecessary complexity for publicly accessible tools — overkill when direct HTTP calls or standard MCP servers work fine
  • ✗Managed server adds a network hop that introduces tens of milliseconds of latency — meaningful overhead for sub-millisecond function calls
  • ✗.NET SDK still in development — teams using C# or F# cannot use AgentRPC yet and have no announced timeline

Frequently Asked Questions

How is AgentRPC different from regular RPC frameworks like gRPC?+

AgentRPC is designed specifically for AI agent workflows rather than general service-to-service communication. It handles long-running function calls via long-polling, includes built-in MCP server support, and provides OpenAI-compatible tool definitions out of the box. gRPC requires open ports and bidirectional streaming setup, while AgentRPC works through outbound-only connections that traverse firewalls without configuration.

Do I need AgentRPC if my tools are on public APIs?+

No. AgentRPC adds value only when network boundaries prevent direct function calls between your agent and your tools. If your tools are publicly accessible via HTTP, standard API calls or a direct MCP server are simpler and more efficient — AgentRPC would add unnecessary complexity and latency.

What's the latency overhead of using AgentRPC?+

The hosted RPC server at api.agentrpc.com adds a network hop between your agent and your function, which introduces tens of milliseconds of additional latency depending on geography. For functions that complete in milliseconds, this adds noticeable overhead. For longer-running tasks (seconds to minutes), the overhead is negligible relative to execution time.

Is AgentRPC open-source and can I self-host it?+

Yes. All SDKs and core components are open-source under the Apache 2.0 license on GitHub, which permits unrestricted commercial use, modification, redistribution, and self-hosting. The managed hosting at api.agentrpc.com is optional — teams can run the entire stack on their own infrastructure at zero software cost.

How does AgentRPC compare to Temporal for long-running workflows?+

Temporal is a general-purpose workflow orchestration engine with durable state management, automatic retries, complex workflow graphs, and versioning support. AgentRPC is much narrower in scope — it focuses specifically on letting AI agents call functions across network boundaries with long-polling support. If you need full workflow orchestration, choose Temporal. If you just need cross-network function calls for agents, AgentRPC is simpler to deploy.

How much does the managed hosting cost?+

AgentRPC does not publish a public pricing page as of early 2026. The open-source SDK is completely free under Apache 2.0 with no usage restrictions, so self-hosting has zero software cost — you only pay for the infrastructure you run it on. For managed hosting at api.agentrpc.com, pricing is quote-based and scales with function call volume and team size. Contact the AgentRPC team directly for a quote.

🔒 Security & Compliance

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SOC2
Unknown
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GDPR
Unknown
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HIPAA
Unknown
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SSO
Unknown
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Self-Hosted
Unknown
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On-Prem
Unknown
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RBAC
Unknown
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Audit Log
Unknown
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API Key Auth
Unknown
✅
Open Source
Yes
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Encryption at Rest
Unknown
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Encryption in Transit
Unknown
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What's New in 2026

.NET SDK in active development as of early 2026, expanding language coverage beyond the current TypeScript, Go, and Python SDKs. No public release timeline has been announced.

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Deep dive tutorials, advanced techniques, real-world examples, and expert tips to get the most out of AgentRPC.

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