agentfield vs sim
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 — Visual workspace to build, deploy and orchestrate AI agents — 1,000+ integrations, knowledge bases, built-in tables and files, schedules and run monitoring. Self-host via npx simstudio or Docker.
Same 'run your AI workforce' ambition, opposite audiences: AgentField turns plain Python/Go/TS functions into agent microservices for engineers; Sim gives teams a visual workspace where agents are built and operated without touching the runtime.
| agentfield | sim | |
|---|---|---|
| Stars | 2.4k | 29k |
| Forks | 374 | 3.7k |
| Language | Go | TypeScript |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | yesterday | today |
| 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.
sim — the curator's take
The n8n-for-agents play at real scale (29k stars): build agents visually, conversationally, or in code, wire them to Slack/Notion/Salesforce-class integrations, and keep tables, files, knowledge bases and scheduled runs in the same workspace — it's a platform, not a library. Self-hosting is honest but heavy: Docker with 12GB+ RAM recommended, Postgres + pgvector, and note that the Chat/copilot feature remains a Sim-managed service even self-hosted (you fetch a COPILOT_API_KEY from sim.ai). Ollama/vLLM local models supported. Pick it when non-engineers need to build and operate agents; pick a code-first control plane (agentfield) when engineers do, and skip the platform entirely for a single agent.