Implement LangChain callback handlers, streaming, webhooks, Server-Sent Events (SSE), and WebSocket integration.
Integration patterns for LangChain4j with Spring Boot. Auto-configuration, dependency injection, and Spring ecosystem integration.
Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and…
Testing strategies for LangChain4j-powered applications. Mock LLM responses, test retrieval chains, and validate AI workflows. Use when testing AI-powered features reliably.
Configure LangChain4J vector stores for RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j),…
Provides configuration patterns for LangChain4J vector stores in RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone,…
LangChain.js - TypeScript framework for building LLM-powered applications with agents, chains, RAG, tools, memory, and integrations for OpenAI, Anthropic, Google, and hun — from…
LangChain.js - TypeScript framework for building LLM-powered applications with agents, chains, RAG, tools, memory, and integrations for OpenAI, Anthropic, Google, and hun — from…
LangExtract by Google is a Python library for extracting structured information from unstructured text using LLMs with precise source grounding.
A powerful Python-based visual framework for building and deploying AI-powered agents and workflows with Model Context Protocol (MCP) integration, drag-and-drop interface — from…
A powerful Python-based visual framework for building and deploying AI-powered agents and workflows with Model Context Protocol (MCP) integration, drag-and-drop interface — from…
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and — from…
Precisely filter and query Langfuse traces/observations using advanced filter operators for debugging and optimization workflows
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and — from…
Debug AI traces, find exceptions, analyze sessions, and manage prompts via Langfuse MCP. Use when debugging AI pipelines, investigating errors, analyzing latency, managing prompt…
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and — from…
Docker volume backup and restore for self-hosted Langfuse. Use when: backing up a self-hosted Langfuse instance, restoring Langfuse after a crash or migratio...
Configure Langfuse CI/CD integration with GitHub Actions and automated testing. Use when setting up automated testing, configuring CI pipelines, or integrating Langfuse tests into…
Diagnose and fix common Langfuse errors and exceptions. Use when encountering Langfuse errors, debugging missing traces, or troubleshooting integration issues.
Collect Langfuse debug evidence for support tickets and troubleshooting. Use when encountering persistent issues, preparing support tickets, or collecting diagnostic information…
Deploy Langfuse with your application across different platforms. Use when deploying Langfuse to Vercel, AWS, GCP, or Docker, or integrating Langfuse into your deployment…
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and — from…
Extracts traces, observations, and metrics from Langfuse Cloud (EU) API for debugging, telemetry analysis, and regulatory audit trails.
Create a minimal working Langfuse trace example. Use when starting a new Langfuse integration, testing your setup, or learning basic Langfuse tracing patterns.
Troubleshoot and respond to Langfuse-related incidents and outages. Use when experiencing Langfuse outages, debugging production issues, or responding to LLM observability…
Use when adding Langfuse tracing/observability to a Laravel app that uses the laravel/ai SDK (Laravel\\Ai\\ namespace, Agent classes, ->prompt()/->stream(), tools, conversations).
Instrument LLM apps with Langfuse tracing, then use traces, scores, and datasets to test in production, run evaluations on real traffic, catch regressions, and close the loop from…
Set up Langfuse local development workflow with hot reload and debugging. Use when developing LLM applications locally, debugging traces, or setting up a fast iteration loop with…
Configure Langfuse across development, staging, and production environments. Use when setting up multi-environment deployments, configuring per-environment keys, or implementing…
Implement Langfuse rate limiting, batching, and backoff patterns. Use when handling rate limit errors, optimizing trace ingestion, or managing high-volume LLM observability…
Production-grade Langfuse architecture patterns and best practices. Use when designing LLM observability infrastructure, planning Langfuse deployment, or implementing ent — from…
Manage Langfuse instances (cloud or self-hosted) from the CLI — export/import prompts for backup and cross-env migration, send smoke-test traces, check health across multiple…
Integrate Langfuse observability with AWS Strands Agents for comprehensive tracing, monitoring, and debugging of AI agent applications.
Query Langfuse traces for debugging LLM calls, analyzing token usage, and investigating workflow executions.
Guides architectural decisions for LangGraph applications. Use when deciding between LangGraph vs alternatives, choosing state management strategies, designing multi-agent…
LangGraph checkpointing and persistence. Use when implementing fault-tolerant workflows, resuming interrupted executions, debugging with state history, or avoiding re-running…
Reviews LangGraph code for bugs, anti-patterns, and improvements. Use when reviewing code that uses StateGraph, nodes, edges, checkpointing, or other LangGraph features.
Sub-agent pre Antigravity — LangGraph architekt a plánovač. Ovláda celú LangGraph implementáciu od návrhu state schémy cez graf architektúru po produkčné nasadenie.
Prevent and detect code quality issues in LangGraph pipelines. Use when implementing new nodes, debugging state flow issues, troubleshooting empty state keys, or before committing…
A verified skill for LangGraph.js, the graph-based orchestration framework from LangChain. It focuses on controllable agents, persistence, streaming, and deployment entry points.
Multi-agent architectures with LangGraph. Use when building systems with multiple collaborating agents, implementing supervisor or swarm patterns, creating hierarchical agent…
Architect-level development, audit, and migration of multi-agent systems using LangGraph (v1+) and LangChain (v1+).
Persistence and human-in-the-loop patterns for LangGraph. Use when implementing checkpointing, saving/restoring graph state, adding human approval workflows, managing conversation…
Design and build AI agents with LangGraph. Use when building ReAct agents, multi-agent systems, workflow orchestration, human-in-the-loop patterns, or state machine workflows.
Expert guidance for LangGraph Python library. Build stateful, multi-actor applications with LLMs using nodes, edges, and state management.
Expert guidance for LangGraph Python library. Build stateful, multi-actor applications with LLMs using nodes, edges, and state management.
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production — from…
LangSmith Agent Builder - No-code platform for creating AI agents with built-in tools (Gmail, Slack, GitHub, Linear), OAuth integrations, MCP server support, Slack deploy — from…
LangSmith Agent Builder - No-code platform for creating AI agents with built-in tools (Gmail, Slack, GitHub, Linear), OAuth integrations, MCP server support, Slack deploy — from…
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, ch — from…
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, ch — from…
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, ch — from…
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, ch — from…
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production — from…
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production — from…
CRITICAL for RAG: Every RAG query MUST be traced in LangSmith for observability.
LangSmith tracing and debugging setup for LLM applications. Configure observability, capture traces, and enable debugging for LangChain/LangGraph agents.
C# and .NET idioms — .NET 8 LTS, csproj, C# NRT, records, value semantics, switch expressions, async/await, Task, ValueTask, CancellationToken, ConfigureAwait, dependency…
Dart idioms — null safety, async/await, Futures and Streams, widget composition, and Riverpod/Bloc app architecture.
Debug Python and Node.js from the terminal. pdb/breakpoint(), debugpy remote attach, node inspect CLI, Chrome DevTools Protocol for Node. When console.log isn't enough.