agentfield vs scale-agentex
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 — Scale AI's open agent platform: scaffold agents with a CLI, run them behind the ACP protocol with a dev UI, and graduate from sync chat to durable Temporal-backed long-running workflows.
Both are open control planes for building and deploying agents as long-running services with routing, queues and durable state. AgentField wraps plain Python/Go/TS functions as microservice agents; Agentex standardizes on the ACP protocol with Temporal underneath and a scaffolding CLI + dev UI.
| agentfield | scale-agentex | |
|---|---|---|
| Stars | 2.4k | 455 |
| Forks | 374 | 51 |
| Language | Go | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | yesterday | yesterday |
| Topics | agents, orchestration | agents, orchestration |
| Curated connections | 4 | 2 |
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.
scale-agentex — the curator's take
Pick it when agents outgrow request/response: the async tier runs on Temporal, so long-running autonomous work gets durability, retries and resumability without changing your agent code's architecture — that L1→L5 'same framework at every level' pitch is the real differentiator. The local story is genuinely turnkey: ./dev.sh boots Postgres, Redis, Mongo and Temporal plus a dev UI, and `agentex init` scaffolds a working agent. NOT for a simple chatbot — that stack is heavy, and Python 3.12+/Docker are hard requirements. If you want multi-agent conversation patterns rather than deploy-and-scale infrastructure, a framework like CrewAI or AutoGen is the lighter tool. The enterprise 'zero-ops' path funnels into Scale's hosted SGP platform — fine, but know it.