Langchain Plugins

Add persistent memory to Claude Code tasks and AI apps via Mem0: retrieve relevant past decisions, strategies, and session states on new tasks; store user data for personalization; enable semantic search across long-term memories using Python/TS SDKs, hooks, and MCP tools.

Rapidly implement production-ready AI/ML features in apps: integrate LLMs with prompt engineering and response handling, build ML pipelines for recommendation systems, add computer vision for visual search, and enable intelligent automation using OpenAI, Anthropic, LangChain, Hugging Face, or Ollama.

Rapidly implement production-ready AI/ML features in apps: integrate LLMs via prompt engineering and response handling, build ML pipelines for user behavior-based recommendations, add computer vision for photo-based product search, and deploy intelligent automations.

Delegate complex AI and data tasks to specialized agents that proactively build LLM applications with RAG and orchestration, design scalable ETL pipelines and warehouses, deploy MLOps workflows, optimize prompts, analyze datasets, manage context, and decompose goals into actionable hierarchies.

Orchestrate 36 specialized AI agents and 281 skills to automate full-stack development workflows: plan/implement features with parallel subagents, generate/run tests, review PRs, enforce code quality/security via hooks, coordinate git worktrees, and produce demos/docs in React/Python/FastAPI stacks.

Generate importable n8n workflow JSON files from natural language descriptions, designing complex automations with loops, branching, error handling, retries, notifications, AI content pipelines, lead qualification, document processing, and OpenAI/JavaScript integrations.

Equip LangGraph and LangChain projects with Deep Agents skills to build complex multi-agent systems, enabling task planning and decomposition, multi-agent coordination patterns, error recovery with retries and human-in-loop, state schema design with checkpointers, agent testing via pytest/vitest and LangSmith, trace analysis for debugging, project scaffolding, and deployment to LangSmith.

Build robust LLM evaluation pipelines by auditing setups for issues, conducting error analysis on traces, generating synthetic test data, designing and validating LLM-as-judge prompts, evaluating RAG with custom metrics, and creating browser-based UIs for human annotation and labeling.

Build sophisticated AI agents using LangChain, LangGraph, and Deep Agents skills that enable task orchestration with subagents, memory and filesystem persistence, RAG pipelines, human-in-the-loop interactions, and framework selection in Python and TypeScript workflows.

Invoke 24 elite skills in Claude Code to enforce disciplined engineering workflows: strict TDD for changes, step-by-step design and implementation plans, multi-agent task dispatching, domain expertise in ML/embedded/AI/frontend, git worktrees and branch management, root-cause debugging, rigorous code reviews, and context optimization for long sessions.

Integrate You.com tools for web search, synthesized research with citations, and web content extraction into AI agents built with Vercel AI SDK, Claude Agent SDK, OpenAI Agents SDK, crewAI, LangChain, Microsoft Teams.ai, direct REST API calls, or bash CLI scripts.

Instrument LLM apps in Python or TypeScript with OpenInference tracing for Phoenix observability, debug traces and spans using CLI tools to fetch, filter, and analyze performance, and build evaluation workflows with code/LLM judges, datasets, and experiments.