langchain-ai/deepagents▌

Access LangGraph documentation to build stateful agents and multi-agent workflows. \n \n Fetches official LangGraph Python docs covering state machines, graph-based agent design, and human-in-the-loop patterns \n Prioritizes relevant documentation by query type: implementation guides for how-to questions, concept pages for theory, tutorials for end-to-end examples, and API references for technical details \n Automatically selects 2–4 most relevant documentation URLs and retrieves their content t

Drafts platform-specific social media posts with research-backed content and generated companion images. \n \n Supports LinkedIn posts (1,300 characters with professional tone) and Twitter/X threads (280 characters per tweet with 1/🧵 format) \n Requires delegating research to a subagent before writing, then reading findings to ensure accuracy and relevance \n Generates eye-catching social images automatically using generate_social_image tool with bold, high-contrast compositions optimized for s

Orchestrates multi-source web research by delegating to subagents, synthesizing findings, and producing cited reports. \n \n Breaks research questions into 2–5 distinct subtopics, creates a research plan file, and spawns up to 3 subagents in parallel for efficient investigation \n Each subagent performs 3–5 web searches per subtopic and writes findings to local files with key facts, quotes, and source URLs \n Synthesizes results by reading local findings files, integrating insights across subtop

Search arXiv for preprints and academic papers by topic with abstract retrieval. \n \n Query-based search across physics, mathematics, computer science, biology, statistics, and related fields \n Configurable result limit (default 10 papers) with results sorted by relevance \n Returns title and abstract for each matching paper \n Requires the arxiv Python package; install via pip if not already present \n

For straightforward questions about a single table:

Use sql_db_list_tables tool to see all available tables in the database.

The deepagents CLI loads skills from five sources, listed here from lowest to highest precedence:

Long-form blog post writing with research delegation, structured content templates, and AI-generated cover images. \n \n Delegates research to subagents before writing, storing findings in markdown for reference and context \n Enforces a five-part post structure: hook, context, main content (3–5 sections), practical application, and conclusion with call-to-action \n Generates SEO-optimized cover images using detailed prompts covering subject, style, composition, color, and lighting \n Outputs po