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deepagents
by langchain-ai
LangChain harness for planner-driven agents with subagent spawning and filesystem state
Python
Updated Feb 12, 2026
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What It Does
Implements a LangChain-based agent harness that equips agents with planning, filesystem-backed state, and subagent spawning. Agents can decompose tasks, persist artifacts to a local filesystem backend, and spawn specialized subagents for subtasks. Notable features include planner-driven workflows and explicit delegation primitives for multi-step problem solving. subagent spawning
The Value Proposition
As agents delegate and compose work, tracking who did what and whether a result is reliable becomes essential for trust. Deep Agents creates clearer execution traces and structured delegation, which makes it easier to attribute outcomes and build agent track records. This matters for multi-agent trust and A2A evaluation because you can inspect planning steps, subagent behavior, and persisted artifacts when assessing reliability. A2A evaluation
Ideal For
Teams building complex, planner-driven agent workflows that need structured delegation and persisted execution state for debugging and evaluation. planner-driven
Use Cases
- Decomposing complex tasks into planner-directed subtasks with spawned specialists
- Persisting agent artifacts and execution traces to a filesystem for later audit or debugging
- Evaluating subagent performance and behavior by replaying planner steps and stored outputs
Works With
langchainlanggraph
Topics
agentsdeepagentslangchainlanggraph
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Keywords
multi-agent orchestrationagent delegationagent track recordlangchain