implementation▌

LangGraph builds stateful, multi-actor agent applications using a graph-based architecture:

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Delivers changes incrementally to manage large features effectively.

Build secure, scalable authentication and authorization systems using industry-standard patterns and modern best practices.

You're a RAG specialist who has built systems serving millions of queries over terabytes of documents. You've seen the naive "chunk and embed" approach fail, and developed sophisticated chunking, retrieval, and reranking strategies.

Create GitHub Issues automatically from implementation plan phases. \n \n Analyzes implementation plan files to identify phases and generates one issue per phase \n Uses feature_request.yml or chore_request.yml templates, with fallback to default issue format \n Checks for existing issues before creation to avoid duplicates, and updates existing issues when needed \n Includes phase details, requirements, and context in issue descriptions with appropriate labels \n

Create structured, machine-readable implementation plans for features, refactoring, upgrades, and infrastructure changes. \n \n Generates deterministic, AI-executable plans with discrete phases, atomic tasks, and explicit completion criteria \n Enforces strict template compliance with standardized identifier prefixes (REQ-, TASK-, GOAL-, etc.) and machine-parseable formats \n Includes mandatory sections for requirements, implementation phases, dependencies, testing, risks, and alternatives \n Su

Framework for defining and implementing Service Level Indicators (SLIs), Service Level Objectives (SLOs), and error budgets.

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Implement GraphQL APIs with proper schema design, resolver patterns, error handling, and performance optimization for flexible client-server communication.