LangChain & LangGraph

LangChain & LangGraph Development

We build complex AI applications with LangChain, the most popular framework for LLM development. From RAG pipelines to multi-agent systems with LangGraph, we implement AI workflows that go beyond a simple prompt, with complete observability via LangSmith.

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LangChain is the leading open source framework for building applications with language models that go beyond simple prompt-response. It enables chaining multiple LLM calls, connecting to external data sources, implementing conversational memory and creating autonomous agents that use tools. LangGraph extends these capabilities for complex multi-step workflows with state graphs, enabling multi-agent systems with fine control over execution flow, conditional branching and human-in-the-loop. LCEL (LangChain Expression Language) provides a declarative syntax for composing complex chains in a readable and maintainable way. With LangSmith, you get complete pipeline observability: detailed traces of each step, latency and cost metrics, automated quality evaluations and production debugging tools. The LangChain ecosystem supports multiple LLM providers (OpenAI, Anthropic, open source models), vector stores and tools, enabling component swaps without rewriting the application. Soamee specializes in LangChain development for businesses that need RAG pipelines, multi-agent systems and complex AI workflows in production.

Framework

What we build with LangChain

We master the entire LangChain ecosystem to build robust and maintainable AI applications in production.

Chains & LCEL

Composition of processing chains with LangChain Expression Language. Declarative pipelines combining prompts, LLMs, parsers and transformations into reusable, testable flows with strong typing.

Autonomous agents

Agents that reason, plan and execute actions using tools. From simple ReAct agents to complex multi-tool systems that interact with APIs, databases and external services.

Tools & integrations

Definition and integration of custom tools that agents can invoke. Web search, database queries, code execution, API calls and any business function as a tool.

Memory & state

Conversational memory systems to maintain context between interactions. Buffer memory, summary memory, entity memory and persistent memory with databases for long conversations.

LangGraph workflows

State graphs for complex multi-step workflows. Conditional branching, loops, parallelism, checkpoints and human-in-the-loop. Ideal for processes requiring multiple coordinated decisions and actions.

LangSmith observability

Complete AI pipeline monitoring: detailed traces of each step, latency and cost metrics per chain, automated quality evaluations and production debugging tools.

Retrieval chains (RAG)

Complete RAG pipelines: document ingestion, intelligent chunking, embeddings, semantic search, re-ranking and generation with relevant context and source citation.

Structured output

Structured data extraction from free text using Pydantic models. Typed outputs, automatic validation, retry with correction and robust parsing to feed downstream systems.

Multi-agent systems

Architectures with multiple specialized AI agents that collaborate. Supervisor agents, tool-calling agents, planning agents and execution agents orchestrated with LangGraph for maximum control.

Casi d'uso

Real-world LangChain scenarios

Production RAG pipelines

Complete Retrieval Augmented Generation systems that let users query extensive document bases in natural language. Automatic ingestion, adaptive chunking, re-ranking and responses with cited sources. Ideal for internal knowledge bases, technical documentation and support.

Multi-agent systems

Architectures with multiple specialized AI agents that collaborate to solve complex tasks. One agent plans, another researches, another executes actions and another verifies results. Orchestrated with LangGraph for maximum control and observability.

Complex AI workflows

Automated business processes with multiple AI steps: classification, extraction, enrichment, validation and action. Conditional branching based on content, human-in-the-loop for critical decisions and checkpoints for recovery.

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Process

How we develop with LangChain

Iterative development with testing and observability from day one.

Pipeline design

We map your business process to a LangChain/LangGraph graph. Define chains, tools, memory and decision points. Select optimal LLMs and vector stores.

Development with evaluations

We implement the pipeline with automated tests at each step. LangSmith datasets for regression testing, quality evaluations with LLM-as-judge and custom metrics.

Integration & deploy

Connect with your systems (APIs, databases, file storage), configure LangSmith for production and deploy with cost and latency monitoring.

Data-driven iteration

Real trace analysis in LangSmith, failure identification, prompt and chain optimization based on production data. Measurable continuous improvement.

Tecnologie

LangChain stack

LangChain LangGraph LangSmith LCEL LangServe Python TypeScript OpenAI Anthropic Pinecone pgvector Chroma Weaviate FAISS Pydantic FastAPI Redis PostgreSQL Docker Kubernetes

Related services

Tutte le integrazioni RAG Knowledge Base AI Agents AI Automation Vector Databases

FAQ

Domande frequenti about LangChain

What is LangChain and why use it?

LangChain is an open source framework that provides abstractions and components for building LLM applications. Instead of making direct calls to OpenAI or Claude APIs, LangChain gives you tools to chain multiple steps, connect to data sources, implement memory and create agents. This accelerates development, improves maintainability and enables switching LLM providers without rewriting the application.

What's the difference between LangChain and LangGraph?

LangChain is ideal for linear chains and simple agents. LangGraph extends LangChain for workflows with complex state: graphs with conditional branching, loops, parallelism and human-in-the-loop. Use LangGraph when your flow isn't linear, when you need checkpoints for recovery, or when multiple agents must coordinate.

Does LangChain add overhead or latency?

LangChain's overhead is minimal compared to LLM call latency. The framework adds milliseconds while a GPT-4 call takes seconds. The benefits in maintainability, observability and development productivity far outweigh the negligible technical overhead. Plus, with native streaming and async, LangChain is optimized for production.

Do I need LangSmith in production?

LangSmith is highly recommended for production. It lets you see exactly what your pipeline is doing on each request: what prompts were sent, what the LLM responded, how much it cost and how long it took. It's essential for debugging, cost optimization and continuous quality improvement. Without observability, a production AI pipeline is an unmaintainable black box.

Iniziamo

Build AI pipelines with LangChain

We help you design and develop complex AI applications with LangChain and LangGraph, with observability and testing from day one.

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