LangChain Development Services

Custom LangChain development for AI applications. Chains, agents, RAG pipelines, tool use, memory management. Production-grade AI systems. Freelance developer in Barcelona. From EUR 3,000.

TL;DR

LangChain is the framework for building complex AI applications: multi-step chains, autonomous agents, RAG pipelines, and tool-using AI. I build production LangChain applications that go beyond demos. From EUR 3,000.

What Is LangChain and When to Use It

LangChain is a Python framework for building applications powered by language models. It provides abstractions for common patterns: chaining multiple LLM calls, connecting AI to external tools, managing conversation memory, and building retrieval-augmented generation (RAG) systems.

You need LangChain when your AI application requires more than a single API call: multi-step reasoning, tool use (search, calculate, query database), document retrieval, or autonomous agent behavior. For simple API integration, direct LLM integration is simpler and faster.

LangChain Applications I Build

RAG pipelines: Document loading, chunking, embedding, vector storage, and retrieval chains. See my RAG development services for details.

AI agents: Autonomous agents that use tools (web search, database queries, API calls, calculations) to accomplish complex tasks. See my AI agent development services.

Multi-step chains: Sequential AI processing: extract data from document, validate against rules, generate report, send notification. Each step uses the output of the previous step.

Conversational AI with memory: Chatbots that remember conversation history, user preferences, and context across sessions. Buffer memory, summary memory, and vector-based memory depending on use case.

LangChain Development Pricing

Simple Chain (EUR 3,000-5,000): RAG pipeline or multi-step chain with 1-2 tools. 3-4 weeks.

Agent System (EUR 6,000-10,000): Autonomous agent with multiple tools, memory, and error handling. 5-8 weeks.

Enterprise Platform (EUR 12,000+): Multi-agent system with orchestration, monitoring, and custom evaluation. 8-12 weeks.

Frequently Asked Questions

Do I need LangChain or can I use the OpenAI API directly?

For a single LLM call (chatbot, content generation, data extraction), use the OpenAI/Claude API directly -- LangChain adds unnecessary complexity. Use LangChain when you need: multi-step chains, tool use, RAG with document retrieval, conversation memory across sessions, or agent autonomy. LangChain saves significant development time for these complex patterns.

LangChain vs LlamaIndex: which should I use?

LlamaIndex is focused specifically on RAG (document retrieval and Q&A). LangChain is broader: chains, agents, tools, memory, and RAG. I often use both together: LlamaIndex for the RAG pipeline and LangChain for the agent/chain orchestration layer. For pure document Q&A, LlamaIndex alone may be sufficient.

Build Your LangChain Application

Describe the AI workflow you want to automate. I will recommend the right architecture.

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