01
LangGraph Agent Orchestration
Build stateful, multi-step AI agents using LangGraph — with conditional edges, memory, interrupts, and human-in-the-loop nodes.
LangChain Development
Expert LangChain AI Development
We build production-grade LangChain and LangGraph applications — from autonomous agents and RAG systems to multi-model pipelines and enterprise AI backends. Expert developers, battle-tested code.
LangChain · LangGraph · LangSmith · Python · GPT-4o · Claude · Starting at $1,500
LangChainLangGraphLangSmithGPT-4oClaude 3Llama 3PineconePython
01Capabilities
What We Build with LangChain
02
RAG Pipeline Engineering
Production-grade retrieval systems using LangChain's document loaders, text splitters, vector stores, and retrieval chains.
03
Multi-Model LLM Integration
Connect GPT-4o, Claude 3, Gemini, Llama 3, and Mistral via LangChain's unified interface. Switch models without rewriting logic.
04
Tool-Calling & Function Use
Build agents that use web search, code execution, database queries, API calls, and custom tools to complete real-world tasks.
05
Production-Grade Reliability
We build with LangSmith tracing, error handling, rate limiting, fallback chains, and structured output parsing for production stability.
06
Streaming & Real-Time Output
Implement streaming responses, token-by-token output, and real-time tool-call visibility for end-user-facing LangChain applications.
02Use Cases
LangChain Use Cases We Deliver
01Custom AI research agents that search the web and compile structured reports
02Enterprise RAG systems trained on SharePoint, Confluence, and internal databases
03AI code assistants trained on your proprietary codebase using LangChain + Chroma
04Document analysis pipelines for contracts, invoices, and compliance documents
05Multi-agent systems where specialized agents collaborate to complete complex tasks
06AI backends for SaaS products — from prototype to scalable production API
03FAQ Index
LangChain Development FAQs
Why use LangChain instead of calling the OpenAI API directly?
LangChain provides memory management, tool-calling abstractions, retrieval chains, agent orchestration, and production tooling (LangSmith) that would take months to build from scratch. It's the right foundation for any serious AI application.
What's the difference between LangChain and LangGraph?
LangChain is the core framework for building LLM applications. LangGraph is built on top of it and adds stateful graph-based agent orchestration — essential for complex multi-step agents with memory, branching, and human-in-the-loop.
Do you build the frontend for LangChain applications too?
Yes — we build full-stack LangChain applications with Next.js frontends, FastAPI Python backends, and streaming UI components. You get a complete, production-ready application.
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Location
Remote team serving US, UK, Europe & Australia
Reply Time
Within 24 hours
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