AI Agents and RAG Pipelines Built on LangChain
LangChain provides the orchestration layer for our AI applications — connecting models, tools, memory, and retrieval into coherent, reliable workflows.
How we use LangChain
LangChain is the most widely-used Python/TypeScript framework for building LLM-powered applications. It provides abstractions for prompt management, model switching, memory, RAG pipelines, and agent execution. We use LangChain for its ecosystem maturity and LangGraph for complex stateful agent workflows. Together they handle the plumbing so we can focus on the application logic that creates real business value.
What this means for your project
- ✦Production-ready LangChain integrations, not demos
- ✦Code you own — no black boxes
- ✦Engineers who have shipped real systems with this stack
- ✦Ongoing support and updates after launch
What we build with LangChain
RAG (Retrieval-Augmented Generation) systems
LangChain's document loaders, text splitters, vector store integrations, and retrieval chains make it the fastest way to build production-grade RAG.
Multi-step LLM workflows
Chain multiple model calls, tool uses, and conditional logic into coherent workflows — with proper error handling and retry logic at each step.
LangGraph agent systems
LangGraph (LangChain's agent framework) provides stateful, graph-based agent execution — the most reliable way to build complex multi-step agents.
Model provider abstraction
Switch between OpenAI, Anthropic, and other providers without rewriting your application — LangChain abstracts the API differences.
Common questions
Do you use LangChain or build custom agent systems?+
We use LangChain/LangGraph for most agent work — the ecosystem maturity and reliability are better than custom implementations for production systems. For very simple tasks, we build lightweight custom wrappers.
What is the difference between LangChain and LangGraph?+
LangChain handles individual chains and RAG pipelines. LangGraph adds stateful, graph-based control flow for complex agent systems where the execution path depends on previous results.
Related technologies
Want to build with LangChain?
Tell us what you are building — we scope it for free and reply within 24 hours with a plan and fixed price.
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