deepagentsLangChain'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.
Why switchBoth are composable Python agent libraries with sub-agents and durable state. DeepAgents rides LangGraph and its ecosystem; OpenRath rebuilds the primitives around Session and ships its own durable runtime, which is more to learn but fewer layers to fight.
Full comparison → hiveYC-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.
Why switchBoth compile a declared objective into a supervised multi-agent run with crash recovery, cost limits and human-in-the-loop. Hive is harness-shaped — state the objective and let the runtime build the DAG; OpenRath is library-shaped and expects you to write the composition.
Full comparison → ekoProduction JavaScript framework for agentic workflows: one sentence becomes a dependency-aware multi-agent plan that runs in browser, Node, or extension — with pause/resume and snapshot recovery.
Why switchBoth turn multi-agent plans into typed, composable programs rather than prompt chains. Eko is JavaScript-first with a natural-language planner and browser/extension targets; OpenRath is Python-first with explicit PyTorch-style layers and no planner in the way.
Full comparison → agentfieldOpen-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.
Why switchBoth are control planes making agent code durable with queues, retries and tracing. AgentField exposes plain functions as REST microservices; OpenRath keeps composition inside the Python object model and only crosses the wire at the Agent Server boundary.
Full comparison → agentdockTypeScript library giving a backend one agent runtime: session-scoped runs with streaming, approvals and cancel, a tool registry, injectable stores and a factory over nine AI SDK providers.
Why switchBoth answer 'embed an agent runtime in my own backend' rather than 'run a harness'. OpenRath is Python with PyTorch-style composition, a durable runtime, effect ledger and Postgres underneath; AgentDock is a TypeScript loop over the AI SDK small enough to read end to end. Opposite ends of the same decision — reach for OpenRath when runs must survive restarts, AgentDock when they must not add dependencies.
Full comparison →