Agent-Skills.md

Agent Skills: LangChain Open Deep Research

Run LangChain Open Deep Research or LangGraph-based iterative research workflows with configurable retrieval, reflection, and server-backed execution. Use when a benchmark or task needs inspectable graph orchestration rather than a single hosted research API.

UncategorizedID: closedloop-technologies/awesome-deep-researchers/deep-research-langchain

Repository

closedloop-technologies/awesome-deep-researchers
closedloop-technologiesLicense: MIT
71

Install this agent skill to your local

pnpm dlx add-skill https://github.com/closedloop-technologies/climbhill/tree/HEAD/.agents/skills/deep-research-langchain

Skill Files

Browse the full folder contents for deep-research-langchain.

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.agents/skills/deep-research-langchain/SKILL.md

Skill Metadata

Name
deep-research-langchain
Description
Run LangChain Open Deep Research or LangGraph-based iterative research workflows with configurable retrieval, reflection, and server-backed execution. Use when a benchmark or task needs inspectable graph orchestration rather than a single hosted research API.

LangChain Open Deep Research

Use this skill when you need an inspectable graph workflow with iterative research, reflection, and configurable retrieval.

Environment

op run --env-file .env.climbhill -- python .agents/skills/deep-research-langchain/scripts/research.py \
  --query "AI for Science agents: current systems and evidence" \
  --max-iterations 2

Common 1Password-backed variables:

  • OPENAI_API_KEY or ANTHROPIC_API_KEY
  • TAVILY_API_KEY

Cost Controls

  • Keep --max-iterations at 1 or 2 for benchmark tasks.
  • Use a low-cost model for reflection and summarization.
  • Skip this benchmark when the local LangGraph server is not running.

Source Link

  • https://github.com/langchain-ai/open_deep_research