agentfield vs OpenRath
Open-source control plane that runs AI agents as microservices: write plain Python/Go/TS functions, get REST endpoints with routing, queues, retries, memory and tracing — one laptop to 10k agents. — versus — PyTorch-shaped multi-agent framework: Session is the tensor, Agent the layer, Workflow the module — plus a v2 durable runtime with checkpoints, leases, an effect ledger and human interrupts.
Both are control planes making agent code durable with queues, retries and tracing. AgentField exposes plain functions as REST microservices; OpenRath keeps composition inside the Python object model and only crosses the wire at the Agent Server boundary.
| agentfield | OpenRath | |
|---|---|---|
| Stars | 2.5k | 1.1k |
| Forks | 411 | 59 |
| Language | Go | Python |
| License | Apache-2.0 | BSD-3-Clause |
| Last activity | today | 1 months ago |
| Topics | agents, orchestration | agents, orchestration |
| Curated connections | 9 | 7 |
agentfield — the curator's take
The 'agents as a backend' play: write plain functions (no DSL, no graph wiring), and the Go control plane turns each into a REST endpoint any service can call — with fan-out to thousands of parallel agents, queues, retries, versioned deploys, observability and identity/audit built in. Reach for it when agents must be production infrastructure callable by frontends, cron jobs and other services — not a chat window. NOT for notebook experiments or a single local agent (a control plane + SDK is real operational commitment), and it won't give you reasoning-pattern libraries — you still design the agent logic it hosts. Its prompt-to-backend flow (/agentfield in Claude Code/Cursor) is a nice on-ramp, but evaluate the runtime, not the demo.
OpenRath — the curator's take
The abstraction is the pitch. Starting from Session rather than the agent loop is what lets you branch, trace and compose many agents across many sessions without hand-rolling a state machine — and v2's effect ledger (idempotency keys, ambiguous effects parked in NEEDS_REVIEW instead of blindly replayed) is the part most 'production-ready' agent frameworks quietly skip. The cost is conceptual weight plus Postgres, Redis and S3 for anything durable. For one agent with five tools this is far more machinery than the job needs, and the Agent Server HTTP surface is still Beta.