Open-source tools for AI-native workflows — by Ramacharan Reddy Kasireddy.
LangModule is a collection of open-source projects focused on making AI agent infrastructure more accessible. The projects here solve real problems encountered while building production LLM systems — from durable state management for agentic workflows to personal knowledge management powered by AI.
A LangGraph checkpoint saver for Azure Cosmos DB with sync and async support. Drop-in persistence backend for LangGraph workflows with keyless DefaultAzureCredential authentication, tip-document optimization for O(1) latest-checkpoint access, and transactional batch consistency.
pip install langgraph-checkpoint-cosmos
Production-ready PostgreSQL memory for LangGraph agents. Short-term memory (checkpointer) and long-term memory (store) with one-line setup — connection pooling, lifecycle management, retry with backoff, thread cleanup, TTL auto-expiry, and optional semantic search with any embedding provider. No boilerplate.
pip install langgraph-postgres-memory
A local-first personal knowledge base implementing the LLM Wiki pattern. Feed it URLs, PDFs, YouTube videos, or audio — an LLM organizes everything into a structured, cross-referenced wiki. Query it through any AI tool via MCP (21 tools), browse in Obsidian, or search with full-text SQLite FTS5.
pip install wikinow
AI Engineer with 3+ years of experience building production ML and GenAI systems end-to-end. MS in Computer Science from the University at Buffalo (SUNY).
Day job: Building multi-agent systems, RAG pipelines, and AI-powered automation at scale using LangGraph, AWS Bedrock, Azure OpenAI, and cloud-native infrastructure.
Core areas:
- LLM agents & orchestration (LangGraph, LangChain, MCP, ReAct, tool calling)
- RAG systems (hybrid search, embeddings, guardrails, observability)
- Cloud AI infrastructure (AWS Bedrock, Azure OpenAI, Terraform, Kubernetes)
- Deep learning (PyTorch, fine-tuning, LoRA/QLoRA, quantization, distillation)