Pinecone plugins

Design and deploy LLM applications with LangGraph agents, RAG systems, vector search, and advanced prompt engineering. Covers embedding model selection, hybrid retrieval, evaluation, and production scaling for chatbots and AI assistants.

Build production data pipelines with Apache Airflow, dbt, and Spark, including vector database integration for RAG systems and embedding model optimization.

Optimize multi-agent AI systems by profiling performance across system layers, identifying failure patterns, and applying automated prompt engineering and context management improvements using vector databases.

Save and restore project context across sessions using semantic memory and vector search, with token budget management and multi-session collaboration support.

Design, build, and evaluate autonomous AI agents with behavioral testing, RAG systems, prompt engineering, and MCP server creation for external tool integration.

Build and optimize LLM applications with production-ready patterns for RAG pipelines, context window management, prompt caching strategies, and observability using Langfuse.

Enables end-to-end data engineering: orchestrating Airflow DAGs, transforming data with dbt, optimizing Postgres/SQL performance, and implementing vector search and RAG pipelines for AI/ML applications.

Design, build, test, and deploy sophisticated AI agents with memory systems, RAG, LangGraph orchestration, and MCP tool integration.

Build and orchestrate production data pipelines with Airflow, dbt, and Spark, including data quality validation, streaming architectures, and vector database integration for RAG systems.

Build, monitor, and optimize LLM applications with strategies for context management, RAG pipelines, caching, observability, and evaluation. Includes patterns for production deployment, embedding selection, vector database integration, and LLM-as-Judge evaluation.

Build and test AI agents and MCP servers: design autonomous agents, implement RAG pipelines, manage LLM context windows, build MCP servers from scratch, and use observability with Langfuse. Includes prompt engineering, agent testing, and LangGraph patterns.

Build Retrieval-Augmented Generation (RAG) systems by connecting LLMs to vector databases for semantic search and knowledge-grounded AI. Enables document Q&A, reduces hallucinations, and integrates external knowledge bases into LLM applications.

Design LLM applications using LangChain 1.x and LangGraph to build autonomous AI agents with state management, memory, and tool integration patterns

Build AI agents with the LangChain ecosystem: LangGraph stateful workflows, Deep Agents with pluggable backends, RAG pipelines, human-in-the-loop approval, and deployment to LangSmith. Includes dependency setup, middleware patterns, and parallel task dispatch.

Generate and store vector embeddings from text files, database tables, or API responses, then perform semantic similarity search with metadata filtering, re-ranking, and deduplication.

Work with Pinecone vector databases from Claude Code: create and manage indexes, perform vector and hybrid searches, build RAG assistants, and automate workflows via CLI, MCP, Python, and n8n.

Deploy a team of AI/ML specialist agents for the full SDLC: architect multi-agent systems, design RAG pipelines, optimize prompts, review MCP servers, and manage AI infrastructure and testing.

Conduct deep research across codebases, tickets, and documents using graph-based retrieval and Jira investigation; capture knowledge as structured artifacts and knowledge graphs; and generate comprehensive documentation for AI engineering projects.

Save and restore project context with semantic memory using vector search and token budget management. Supports full, incremental, and diff restoration modes, semantic tagging, and multi-session collaboration for complex multi-agent workflows.

Provides a deterministic, durable memory layer for agent teams with bi-temporal facts, RBAC, audit trails, and zero-LLM recall, backed by Postgres, Neo4j, and Pinecone. Includes one-command install, full uninstall, and session telemetry hooks.

Orchestrate multi-agent AI workflows with dynamic context management using vector databases and memory systems, while profiling and optimizing performance through prompt engineering and system analysis.

Build and optimize production LLM applications with prompt engineering, RAG systems, multi-agent orchestration, and performance profiling across AI and application layers. Includes a prompt optimizer for multiple LLM providers and agents for memory management and AI engineering tasks. (399 chars) ✓ Within 120-400 limit. Starts with core capability. No filler. No component list. No 'This plugin...'

Design and implement retrieval-augmented and cache-augmented generation systems with multi-tenant security isolation, document-level access control, and configurable chunking strategies for vector database selection and pipeline recommendations.

Orchestrates multi-AI workflows (Claude, Gemini, Codex) for product development using Double Diamond methodology — from research and PRD generation to implementation, code review, security auditing, and documentation. Includes 29 expert personas and 30 commands for structured debates, debugging, TDD, and design system extraction.

Save and restore project context across Claude Code sessions using vector search and token budget management, enabling long-running conversations and multi-session collaboration with semantic tagging and vector database integration.

Optimize multi-agent AI systems by analyzing agent performance, applying prompt engineering improvements, profiling system performance across database, application, frontend, and context window, and orchestrating workflows with intelligent memory and context management for long-running projects.