agentfield vs hive
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 — YC-backed multi-agent harness for production: state an objective and the runtime compiles a graph DAG of specialized agents — role-based memory, crash recovery, cost limits, human-in-the-loop.
Two production-runtime bets: AgentField runs agents as microservices — REST, queues, retries, your functions; Hive runs them as a goal-compiled DAG with role-based memory and human-in-the-loop gates.
| agentfield | hive | |
|---|---|---|
| Stars | 2.4k | 11k |
| Forks | 387 | 5.7k |
| Language | Go | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | 4 days ago | 2 months ago |
| Topics | agents, orchestration | agents, orchestration |
| Curated connections | 6 | 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.
hive — the curator's take
The pitch is 'harness, not framework' — you buy determinism, recovery, cost enforcement and audit trails instead of writing orchestration boilerplate, which is the right layer to compete on now that models outrun their scaffolding. The auto-compiled topology is also the risk: when the generated DAG is wrong you're debugging a planner, not your own graph — teams who need to own the execution plan should look at explicit-graph harnesses instead. Their own admission: overkill for simple chains and one-off scripts.