deepagents vs hive
LangChain's batteries-included agent harness on LangGraph — planning, sub-agents with isolated context, filesystem, shell, skills, human-in-the-loop and persistent memory out of the box. — 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.
Both are batteries-included harnesses for long-running multi-agent work: deepagents gives you explicit LangGraph composition — you own the plan; Hive compiles the DAG from a stated objective and adds self-healing, cost caps and audit trails.
| deepagents | hive | |
|---|---|---|
| Stars | 27k | 11k |
| Forks | 3.8k | 5.7k |
| Language | Python | Python |
| License | MIT | Apache-2.0 |
| Last activity | yesterday | 2 months ago |
| Topics | agents, orchestration | agents, orchestration |
| Curated connections | 7 | 2 |
deepagents — the curator's take
The fastest route to a serious long-horizon agent if you accept LangChain's stack: planning, sub-agents, context offloading and HITL gates work out of the box, and any LangGraph graph plugs in as a sub-agent, so custom orchestration composes instead of forking. NOT for simple tool-calling loops — LangChain's create_agent is lighter — and the opinions run deep: if you're fighting the harness, you wanted LangGraph directly. Model-agnostic in theory; tuned around frontier tool-callers in practice.
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.