LangChain Development
Framework for production LLM applications
Why We Use LangChain
We build production LLM applications with LangChain — from RAG pipelines and conversational agents to complex multi-step chains. Deep expertise in LangChain, LangGraph, and LangSmith.
LangChain provides the abstractions we need for production LLM apps: document loaders, vector store integrations, chain composition, memory management, and agent tooling — all with proper observability via LangSmith.
What We Build With LangChain
RAG Pipelines
Production RAG with chunking strategies, hybrid search (BM25 + semantic), re-ranking, and citation tracking for enterprise knowledge bases.
AI Agents
LangGraph-based agents with tool calling, multi-step planning, human-in-the-loop, and state management for complex workflows.
LangSmith Observability
Full tracing, evaluation, and monitoring of LLM chains in production — debug issues, track costs, and measure quality.
Vector Stores
Integration with Pinecone, Weaviate, Qdrant, pgvector, and Supabase for scalable semantic search and retrieval.
Use Cases
Related Services
Frequently Asked Questions
When should I use LangChain vs direct API calls?
Do you use LangGraph for agents?
Can LangChain work with open-source models?
Ready to build with LangChain?
Let's discuss how LangChain fits into your AI product. Book a free 30-minute call with our founder.