aden-hive

hive

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.

10,786 5,668 Python Apache-2.0updated 2 months ago

agents orchestration

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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.

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README.md

The agent harness for production workloads — state management, failure recovery, observability, and human oversight so your agents actually run.

Overview

OpenHive is a zero-setup, model-agnostic execution harness that dynamically generates multi-agent topologies to tackle complex, long-running business workflows without requiring any orchestration boilerplate. By simply defining your objective, the runtime compiles a strict, graph-based execution DAG that safely coordinates specialized agents to execute concurrent tasks in parallel. Backed by persistent, role-based memory that intelligently evolves with your project's context, OpenHive ensures deterministic fault tolerance, deep state observability, and seamless asynchronous execution across whichever underlying LLMs you choose to plug in.

Features

✅ Multi-Agent Coordination for parallel task execution
✅ Graph-based execution for recurring and complex processes
✅ Role-based memory that evolves with your projects
✅ Zero Setup - No technical configuration required
✅ General Compute Use and Browser Use with Native Extension
✅ Custom Model Support

Visit adenhq.com for complete documentation, examples, and guides.

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deepagents

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Why it fitsBoth 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.

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Why it fitsTwo 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.