Langchain plugins

Build production LLM applications with LangGraph agents, RAG pipelines, hybrid search, and advanced prompt engineering. Automate agent architecture design, vector index optimization, and prompt refinement for deploying reliable AI systems.

Build and evaluate production-grade AI agents using LangGraph, RAG systems, MCP servers, and prompt engineering patterns—with behavioral testing and reliability monitoring.

Accelerate LLM application development with production-ready patterns for context window management, RAG pipelines, prompt caching, observability via Langfuse, and agent architectures.

Add persistent memory to AI agents by storing, retrieving, and searching user-specific learnings, decisions, and session states across Claude workflows using the Mem0 platform.

Evaluate and improve LLM applications by instrumenting agents, chatbots, and RAG pipelines with DeepEval tracing, generating test suites, running evaluations, and exporting traces to Confident AI for observability and iterative refinement.

Run a complete AI-assisted software development workflow inside Claude Code: structured planning, context engineering, milestone management, code review, automated testing, and documentation — all governed by spec-driven and meta-prompting principles.

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.

Set up end-to-end Langfuse LLM observability: trace calls via OpenAI/LangChain wrappers, evaluate prompts with scores/feedback, monitor costs/latency/security, integrate into CI/CD pipelines, deploy to Vercel/AWS/Docker, troubleshoot errors/migrations, and optimize for production scale in Node.js/Python apps.

Optimize Python deep learning models using Adam, SGD optimizers, learning rate schedulers, and regularization to improve accuracy and reduce training time. Generate production-ready AI/ML code from context analysis, including validation, error handling, performance metrics, insights, artifacts, and documentation.

Build recommendation engines by generating Python code for collaborative, content-based, or hybrid filtering using scikit-learn, TensorFlow, or PyTorch to personalize movies, products, or content. Analyze context to produce complete AI/ML tasks with validation, error handling, performance metrics, insights, artifacts, and documentation.

Explain machine learning model predictions using SHAP, LIME, and feature importance to identify influential features and debug behavior. Generate production-ready AI/ML code from context, including validation, error handling, performance metrics, insights, artifacts, and documentation.

Build and orchestrate AI agents using LangChain, LangGraph, and Deep Agents — scaffold, develop, deploy, and manage stateful agent workflows with memory, RAG pipelines, human-in-the-loop approval, and parallel task execution.

Build and validate LLM evaluation pipelines: design judge prompts, calibrate against human labels, generate synthetic test data, audit pipeline trustworthiness, analyze failure modes, evaluate RAG systems, and collect human annotations via a browser UI.

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.

Analyze AI prompts for clarity, specificity, completeness, and issues with 1-10 scores and targeted fixes, then optimize by rewriting with structured best practices like sectioning, examples, chain-of-thought, and guardrails for superior LLM results.

Build and deploy AI agents to trade crypto, stocks, forex, and derivatives on Kraken via bash CLI: monitor markets, execute strategies like DCA, grid bots, basis trades, portfolio rebalancing; manage risks, staking, subaccounts with paper trading default and live opt-in safeguards. Integrates with Claude, Cursor, VSCode for stdio tool calls.

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.

Rapidly implement production-ready AI/ML features in apps, including LLM integrations with prompt engineering, ML pipelines for recommendations, computer vision for visual search, and intelligent automation, using a specialized agent.

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.

Apply 33 structured thinking frameworks for strategic reasoning, decision-making, research, and problem-solving — including Bayesian analysis, kill criteria, layered reasoning, and premortems — plus specialized agents for fantasy sports, household finance, Substack growth, scientific writing, and GraphRAG system design.

Instrument Python and JavaScript LLM apps with LangSmith tracing using LangChain auto-tracing, decorators, or OpenTelemetry; create, manage, and upload evaluation datasets; build custom evaluators like LLM-as-Judge; run evaluations locally via SDK or CLI, and query/export traces.

Provides agent skills for comprehensive Neo4j database management: querying, modeling, data ingestion, AI/ML pipelines (GraphRAG, embeddings), graph algorithms, provisioning, security, and performance tuning.

Build, deploy, and troubleshoot the full UiPath automation lifecycle — RPA workflows, coded apps, AI agents, BPMN orchestration, document extraction, test management, and governance — directly from Claude Code. Manage organizations, discover automation opportunities, design solutions from PDDs, and run diagnostics across all UiPath products.

Diagnose and fix ML training failures (OOM, NaN, divergence), generate citation-grounded implementation plans for fine-tuning and inference pipelines, and verify code/configs against official framework docs before running GPU jobs.

Instrument, trace, evaluate, and improve AI agents using MLflow. Covers the full agent improvement loop: instrumenting Python/TypeScript code with MLflow Tracing, debugging individual traces and multi-turn chat sessions, searching and querying trace metrics, evaluating GenAI output quality with MLflow APIs, and onboarding to MLflow for GenAI or traditional ML use cases.

Integrate Honcho memory library into Python or TypeScript codebases to enable stateful AI agents using OpenAI, Anthropic, or LangChain setups with peers, sessions, and dialectic chat endpoints. Migrate Honcho SDK code from v1.6.0 to v2.0.0, updating async accessors, observations to conclusions, config methods, and casing conventions.

Integrate You.com's web search, research with citations, and content extraction into AI agents built with frameworks like Vercel AI SDK, Claude Agent SDK, OpenAI Agents SDK, crewAI, LangChain, and Microsoft Teams.ai. Also accessible via direct REST API or bash CLI.

Orchestrate multi-agent AI teams for collaborative software development workflows: bootstrap projects, create and manage roles, decompose tasks, dispatch workers, inspect status, ensure QA processes, and recover sessions via CLI commands.

Build and review AI agent applications using PydanticAI, LangGraph, DeepAgents, and Vercel AI SDK. Guides architectural decisions, implements agents, reviews code for bugs and anti-patterns, and builds streaming chat interfaces.

Analyze AI agent execution traces in OTEL JSON or Claude Code JSONL format to detect issues like goal drift, grounding failures, missed actions, guardrail violations, and instruction following errors. Triage findings with specialized agents, generate and review reports, remediate context via diffs to prompts and tools, and enable autosync for ongoing monitoring from LangSmith or LangFuse sources.

Delegate SDLC tasks to 14 specialized AI agents that design multi-agent architectures, engineer prompts and RAG systems, orchestrate workflows, optimize LLM infrastructure, handle AI DevOps, testing, and quality assurance for production-ready AI/ML applications.

Establish an AI-powered architecture documentation framework that creates ADRs, orchestrates multi-perspective reviews from specialist agents (security, performance, maintainability, domain design, etc.), and enforces YAGNI simplicity via pragmatic guard mode.

End-to-end AI/ML workflows on DataRobot: train models with AutoML, feature engineer, deploy to production with CI/CD, monitor for drift, explain predictions via SHAP, and build AI agents — all from Claude

Turn AI coding agents into production-grade n8n workflow builders — architect, validate, debug, and ship automations with secure credentials, error handling, binary data management, sub-workflows, and MCP tool integration for agent-driven development cycles.

Bootstrap Spring Boot projects from scratch with Maven/Gradle, generate full CRUD REST APIs with entities/services/tests, configure Redis caching, JWT/OAuth security, Resilience4J patterns, JPA optimizations, OpenAPI docs, logging best practices, and Spring AI integrations. Review code via skills/agent, scan routes, and start apps locally.

Build and develop LangChain LLM applications in TypeScript/JavaScript and Python: initialize projects with Bun/NPM and core deps, create chains/agents/RAG with OpenAI/Anthropic/Zod, construct stateful LangGraph workflows with checkpoints/human-in-loop, and assemble hierarchical deep agents for multi-step orchestration, file context, subagents, and persistent memory.

Optimize LLM agent code performance through automated evolution loops. Runs multi-agent proposals, LangSmith evaluations, and git worktrees to iteratively improve agents, with built-in evaluator auditing, dataset quality checks, stagnation detection, and architecture analysis when progress stalls.

Quickly reference LangChain 1.0 core concepts including Agents, Tools, Memory, Middleware, and runtime context to build AI agents, define tools, manage memory, and integrate with OpenAI or Anthropic models in Python projects.

Add persistent memory and personalization to AI applications with semantic search, guided memory recall, and automatic context management for Claude workflows.

Build secure backend services by designing REST/GraphQL APIs, implementing OAuth/JWT authentication, integrating LLMs with RAG pipelines and prompt engineering, and conducting OWASP Top 10 security reviews with threat modeling and vulnerability fixes.

Add LLM observability to agents: trace execution flows, log lifecycle events, evaluate performance metrics, and control session telemetry — all through Opik's SDK integrations and auto-detection of frameworks like OpenAI, LangChain, Anthropic, and LlamaIndex

Use the LangSmith CLI to inspect traces, debug AI chains, analyze token costs, and manage datasets, runs, and prompts from the terminal.

Build production-grade LLM apps in Python: implement RAG pipelines with embeddings and hybrid search, design LangChain/LangGraph agents, optimize prompts, tune vector indexes, and evaluate performance using AI agents, skills, and commands for architecture, code gen, and benchmarking.

Pulls any URL into Claude Code as formatted, indexed Markdown—no browser or API key needed. Instantly caches and retrieves docs for RAG, dependency-aware lookups, and version-sensitive tool behavior without leaving the terminal.

Fetch, index, and search static documentation from any URL or library alias (e.g., react, nextjs, fastapi) directly into Claude Code sessions. Use conditional-GET caching for instant reloads of live docs, regex grep for context-aware searches, refresh stale caches (>7 days), manage sources, and expose as local MCP tools—no browser, API keys, or external services needed.

Build, optimize, and deploy DSPy programs for a wide range of AI tasks — chatbots, RAG, agents, classification, extraction — with guided skills covering every stage: signatures, modules, data handling, evaluation, optimizers, adapters, tools, retrieval, and deployment as web APIs.

Build production RAG pipelines, vector search systems, LLM integrations from OpenAI and Anthropic, and agent orchestrations for chatbots and AI features. Diagnose issues in prompts, system instructions, and agent behaviors, then iteratively refine them via structured analysis. Conduct multi-dimensional analyses of topics covering problems, contexts, options, and recommendations.

Generate idiomatic Python code for OpenGradient SDK to build decentralized AI inference workflows: run verified LLM calls from OpenAI, Anthropic, and Google via TEE security with x402 payments, enable streaming and tool calling, perform on-chain ONNX model inference, integrate LangChain agents, manage model hub operations, and create digital twins chats.

Script and automate YugabyteDB Anywhere’s control-plane REST API for managing providers, universes, backups, KMS, DR, alerts, HA, RBAC, and Prometheus metrics, with Python and PowerShell wrappers.

Build and manage Twilio-based communication solutions: send/receive SMS, voice calls, WhatsApp messages, build AI voice agents, manage email via SendGrid, and ensure compliance, deliverability, and observability.

Analyze executive director responsibilities for trade associations and nonprofits, score automation potential with a 6-factor algorithm, generate production-ready LangGraph workflows with agents, and deploy to dev/staging/production environments with monitoring and rollback.

Build and integrate agents using the A2A (Agent-to-Agent) protocol—create Agent Cards for discovery, handle JSON-RPC task lifecycle with streaming and push notifications, configure authentication, and orchestrate multi-agent systems with framework integrations like LangGraph and CrewAI.

Run spec-driven PDCA DevOps sprints with role-based AI agents that clarify requirements, generate PRDs/specs/diagrams, manage Markdown sprint boards, scaffold projects/tests, perform code reviews/security audits/QA, trace execution flows, create PRs/releases, and orchestrate isolated git worktree executions.

Scaffold, integrate, and validate generative UI apps with OpenUI, supporting any LLM provider and backend language. Automatically detect project stack, generate streaming adapters, and run a 9-step validation pipeline.

Build, manage, and deploy production-ready LangGraph multi-agent AI workflows using CLI commands for nodes, edges, memory, MCP integration, testing, and cloud platforms like Docker, Kubernetes, AWS, GCP.

Build production-ready LLM applications by delegating to expert AI agents that engineer prompts, manage dynamic contexts with vector DBs and knowledge graphs, optimize single and multi-agent performance, and orchestrate RAG, multimodal, and enterprise AI workflows.

Convert docs, repos, PDFs, videos, and more into AI-ready skill packages for LLM platforms like Claude, OpenAI, and Gemini, with auto-detection of source types and configurable preset levels.

Deploy LangGraph agents on AWS Bedrock AgentCore with multi-agent orchestration, persistent short/long-term memory, Gateway MCP tools, and CLI workflows for observability and scaling in production.