Langchain plugins

Build and deploy production-grade LLM applications with LangGraph for agent orchestration, advanced RAG pipelines leveraging vector and hybrid search, prompt engineering patterns, and automated evaluation. Covers embedding model selection, vector index optimization, and multi-agent architectures for document Q&A, chatbots, and semantic search over proprietary data.

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.

Build and integrate AI copilot features into web apps using CopilotKit v2, with full support for chat interfaces, agent-to-frontend communication, multiple agent frameworks, and runtime setup in React, Next.js, and other JS frameworks

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.

Orchestrate structured, spec-driven development workflows for AI coding agents — from project initialization and phased planning through autonomous execution, code review, testing, and milestone tracking. Includes context engineering, knowledge graphs, and multi-agent debugging.

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.

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 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.

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.

Run untrusted AI agent code in sandboxed environments with durable workflow orchestration, suspend/resume, and snapshot support — built for Python, TypeScript, and popular LLM frameworks.

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.

Trace, evaluate, and improve AI agents with MLflow's full agent improvement loop. Instrument Python and TypeScript code, debug individual traces and multi-turn sessions, search and query aggregated metrics, and evaluate GenAI output quality using MLflow's native APIs.

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.

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.

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.

Design, optimize, secure, and globally scale Azure Cosmos DB for NoSQL—covering data modeling, partition keys, indexing, vector/full-text search, query and SDK best practices, throughput planning, monitoring, and developer tooling with VS Code and Emulator.

Design, build, and debug production-grade n8n workflows with Claude — from AI agents and file handling to error management, credential security, and exposing workflows as MCP tools for coding agents.

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.

Create and review software architecture documentation using AI specialist agents that assess design decisions, security, performance, maintainability, and simplicity, then record decisions as ADRs while enforcing YAGNI principles to prevent over-engineering.

End-to-end AI/ML workflows on DataRobot: train AutoML models, engineer features, deploy to production with CI/CD, monitor drift, explain predictions, and instrument external agents with OpenTelemetry observability.

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.

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

Keeps Claude Code current with fast-moving SDKs and APIs by pulling server-rendered documentation from any URL, indexing it with conditional-GET caching, and retrieving specific sections via MCP tools—no browser or API keys needed.

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.

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.

Add Opik observability to LLM applications: auto-detect frameworks, trace execution flows, evaluate agent reliability, and monitor production performance with session telemetry controls for Claude Code.

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.

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 orchestrate multi-agent systems using Google's A2A (Agent-to-Agent) protocol—discover agents via Agent Cards, delegate tasks over JSON-RPC with streaming and push notifications, manage full task lifecycles, authenticate agent communication, and integrate with frameworks like LangGraph, CrewAI, and Google ADK.

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.