Build a LangChain agent with explicit tools, middleware and output expectations.
Editorial note checked 2026-10-29 against this instruction snapshot. Supporting scripts and runtime behavior remain outside this editorial review.
The guide covers agent creation, tool definitions, model configuration and structured responses. It is useful for connecting a small action surface to an existing application.
Developers implementing a basic LangChain agent.
Tool schemas describe inputs but do not authorize side effects. Model output still needs validation and provider calls can incur costs.
Unknown; inspect source requirements and supporting scripts.
Inspect the pinned source snapshot and its supporting files first. The command selects this skill through the open-source skills CLI. Node.js and agent-specific setup may be required. Installation can download code. Catalog ingestion never executes skill instructions or install scripts.
npx skills add https://github.com/langchain-ai/langchain-skills/tree/126c384d8552dcb476b226c244172186cc3f4328/config/skills/langchain-fundamentals --skill langchain-fundamentalsReview pinned skill folder ↗ · CLI documentation ↗
After source review, use npx skills check and npx skills update to inspect and apply upstream updates. Re-check changed instructions before running them.
Static review only. This is not a security audit or a guarantee of safety.
SKILL.md text only; scripts, dependencies and runtime behavior not audited.
No configured text patterns matched. Unmatched or concealed behavior can still exist.
Allowed tools declaration: Unknown. Runtime permissions and network behavior: Unknown.
86/100 · Metadata coverage 100% · Model 1.0
Scores summarize observable discovery signals. Popularity and repository activity apply to the whole repository. Unknown runtime safety earns no certification.
Define a narrow task, expose only required tools and configure error and approval handling before broadening capabilities.
Test malformed tool inputs, unavailable dependencies and invalid structured output, and verify that the application checks results before acting on them.
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Configure a Deep Agents application around its harness and supported extension points.
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Choose Deep Agents storage backends according to persistence and filesystem requirements.
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Coordinate Deep Agents tasks with explicit delegation, planning and approval boundaries.
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