LangChain / LangGraph

LangChain & LangGraph Development

LangChain and LangGraph are the most widely adopted frameworks for building production AI agents and RAG systems — but using them well requires deep familiarity with their patterns, pitfalls, and the right tradeoffs. SaTekk's engineers have shipped production LangChain applications, LangGraph stateful agents, and LCEL-based pipelines. We build the architecture correctly the first time, so you don't spend months debugging runnable-chain spaghetti.

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LangChain ecosystem builds we deliver

01

LangGraph Stateful Agents

Stateful, graph-based AI agents with branching logic, conditional edges, human-in-the-loop gates, and persistent memory — built with LangGraph's full capabilities.

02

LCEL Chains & Pipelines

Clean LangChain Expression Language (LCEL) chains for document processing, RAG, summarization, and extraction — typed, composable, and streaming-ready.

03

LangChain RAG Pipelines

Full RAG implementations using LangChain's retrieval components — document loaders, text splitters, embeddings, vector stores, and retrieval chains.

04

Tool Use & ReAct Agents

ReAct-pattern agents with custom tool definitions, structured output parsers, and multi-step reasoning loops built for reliable production behavior.

05

LangSmith Integration

Full LangSmith tracing and evaluation setup — so you can observe every chain step, debug failures, run regression evals, and monitor production quality.

06

Migration & Refactoring

We migrate legacy LangChain v0.0.x codebases to modern LCEL and LangGraph patterns — or refactor over-engineered chains into simpler, maintainable code.

Free
30-min strategy call
100%
source-code ownership
Fixed
timeline & pricing
30d
post-launch support

Frequently asked questions

When should I use LangGraph vs. plain LangChain?+
Use LangChain (LCEL chains) for linear, stateless pipelines: RAG, summarization, extraction, classification. Use LangGraph when you need stateful execution, conditional branching, cycles (the agent can loop), or human-in-the-loop checkpoints. LangGraph is the right tool once your agent needs to make decisions and potentially backtrack — not just execute a fixed sequence.
How does LangGraph compare to CrewAI?+
LangGraph is lower-level and more flexible — you define the graph structure explicitly, giving you precise control over agent behavior. CrewAI is higher-level and opinionated — faster to get started, but harder to customize for complex workflows. We use LangGraph when you need reliability and custom logic in production, and CrewAI when you want faster prototyping for well-defined crew patterns.
Is LangChain production-ready?+
LangChain has had stability issues in the past, but the LCEL and LangGraph APIs are now stable and battle-tested. The key is using the framework's patterns correctly — avoiding the anti-patterns that made early LangChain codebases painful. Our engineers know which parts of the ecosystem are solid and which to build around or replace.
Can you help us debug or improve an existing LangChain codebase?+
Yes — this is one of the most common engagements we do. We audit your existing LangChain code, identify performance bottlenecks, reliability issues, and architectural problems, then refactor iteratively. We've rescued several LangChain projects that were stuck or underperforming, and we add LangSmith tracing so you can actually see what's happening.
~/satekk $ ./start-project

Get your LangChain build right.

Book a free technical call. Share your use case and we'll tell you whether LangChain, LangGraph, or something simpler is the right tool — and how to build it.

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