OpenRathPyTorch-shaped multi-agent framework: Session is the tensor, Agent the layer, Workflow the module — plus a v2 durable runtime with checkpoints, leases, an effect ledger and human interrupts.
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 → trueforgeTrueFoundry's open agent harness: the runtime loop — model calls, MCP tools, SKILL.md packs, sandboxing, approvals, compaction — behind a chat UI, HTTP API, TypeScript SDK and embeddable UI.
Why switchBoth ship the batteries — planning, subagents, filesystem, skills, human-in-the-loop. deepagents is a Python library on LangGraph you compose in code; TrueForge is a TypeScript server with a chat UI, HTTP API and catalogs you configure once.
Full comparison → CrewAIOrchestrate role-playing, autonomous AI agents that collaborate on tasks.
Why switchSame job — a framework for multi-agent systems — different philosophy: CrewAI models role-playing crews; deepagents is one deep agent that plans and delegates to sub-agents with isolated contexts.
Full comparison → OpenManusOpen-source general autonomous agent from the MetaGPT team — the 'Manus without an invite code': browsing, tool use and multi-step task execution from a simple Python core.
Why switchSame job — a batteries-included general autonomous agent that plans, browses and executes multi-step tasks. deepagents is the actively maintained, LangGraph-backed harness; OpenManus is the leaner, quieter reference implementation.
Full comparison → AutoGenMulti-agent conversation framework for building LLM applications with cooperating agents.
Why switchBoth are batteries-included multi-agent frameworks; AutoGen centers on agent-to-agent conversation, deepagents on a single planner with sub-agents, filesystem and context management.
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 switchSame 'library, not product' position, opposite scope. DeepAgents ships planning, sub-agents with isolated context and a virtual filesystem on LangGraph; AgentDock ships a loop and expects the consuming app to own tools, prompts, auth and persistence.
Full comparison → harness-sdkStrands Agents: open-source Python and TypeScript SDK for building and running agent harnesses in your process — model-agnostic, with lifecycle controls, hooks, MCP, multi-agent and tracing.
Why switchBoth give you a batteries-included agent harness you can drop below; deepagents assembles planning, sub-agents and a filesystem on LangGraph, Strands its own model-driven loop in Python and TypeScript.
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 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.
Full comparison → labs-OO-AgentsNVIDIA's object-oriented agent framework: an agent is a Python class — fields are state, docstrings are prompts, and a `...` method body becomes an LLM loop that acts by writing Python.
Why switchBoth are 'write an agent in Python' frameworks with opposite ergonomics: deepagents is batteries-included on LangGraph, labs-oo-agents makes the class itself the agent â fields are state, docstrings are prompts.
Full comparison →