AgentRPC vs Inngest
Detailed side-by-side comparison to help you choose the right tool
AgentRPC
🔴DeveloperIntegrations
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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Starting Price
FreeInngest
🔴DeveloperAI Agents
Durable-execution platform for AI workflows and agents — write step-functions in TypeScript or Python, get retries, scheduling and observability for free.
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FreeFeature Comparison
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💡 Our Take
Choose AgentRPC for simple cross-network function exposure to AI agents with long-polling and MCP support. Choose Inngest if you need event-driven workflow orchestration with durable state, scheduled jobs, and retry semantics for background processing.
AgentRPC - 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
Inngest - Pros & Cons
Pros
- ✓Durable execution survives crashes and resumes mid-workflow
- ✓AgentKit framework purpose-built for multi-step AI agents
- ✓Generous free tier: 50k runs/month with full features
- ✓Beautiful dashboard with traces, logs, and replay
- ✓Works on Vercel, Cloudflare Workers, Lambda, and containers
Cons
- ✗TypeScript-first — Python SDK is less mature
- ✗Step-function programming model has a learning curve
- ✗Self-hosted Inngest available but most teams use the cloud
- ✗Pricing jumps from $30 Basic to $150 Pro tier feel steep mid-stage
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