We won the Open Agentic AI Innovation Award at the first AAIF Technical Excellence Awards.We won the Open Agentic AI Innovation Award Read why

LangChain development

Your workflows on LangChain and LangGraph, yours to run

Workflows that run on LangChain and LangGraph, tested on your real cases at every change, in code your engineers can debug, upgrade and extend in your repository.

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30+ production deployments since 2017
Why LangChain projects stall

LangChain makes the demo fast. The months after it are the hard part.

Someone has to debug it, upgrade it and hand it to your engineers.

  1. 01

    Where it stallsA demo chain nobody can debug.

    What we do A LangGraph state graph, every step traced.

  2. 02

    Where it stallsEvery upgrade breaks something.

    What we do Pinned versions, tested chains, planned upgrades.

  3. 03

    Where it stallsThe chain is tied to one provider.

    What we do The model stays replaceable, the graph yours.

How we work

From a demo chain to a graph your engineers extend

One team maps the process, models it in LangGraph and hands over the repository.

  1. 0103

    Your workflow as a graph

    We map the process as it runs and model the redesigned version as a LangGraph state graph.

    • Today's process mapped
    • Human checkpoints marked
    • Framework choice, with reasons
    Mixam's three-agent product advisor lifted conversion to a final sale from about 20% to about 40%.
  2. 0203

    Built to survive upgrades

    We pin versions, trace every run and catch regressions before an upgrade reaches users.

    • LangChain or LangGraph app
    • Retrieval tuned to your data
    • Tracing and evaluation suites
    Rotwand Rotwand's news data platform, built on LangChain and LlamaIndex, pulls unstructured data from news sources.
  3. 0303

    Yours, framework included

    Code, graphs, prompts and evaluation suites live in your repositories; tracing runs in your environment.

    • Pinned lockfile and graphs
    • Hosted or open-weight model
    • Training for your engineers
Engineering in the open

We help build what agentic AI runs on

Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.

Shaping agentic AI

Agentic AI Foundation

Vstorm was the first agentic AI engineering company to join the Agentic AI Foundation and received its 2026 Open Agentic AI Innovation Award.

Read the announcement

Open source

We build open-source agent tooling that other engineering teams install

9M+ PyPI downloads
3,900+ GitHub stars
31 public repositories

Framework contributors

When the framework breaks, we fix it at the source

We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase.

Research

Our engineers and PhDs publish peer-reviewed research

Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.

Track record

Frontier AI teams now build on what we shipped first

Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day.

Case studies

More LangChain builds: Rotwand news data platform Senetic email replies Self-hosted team LLM workspace

All case studies
Engagement models

Ways to start

Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold.

Start with one workflow

We map one workflow with your experts and agree a success threshold before any build.

Best whenYou want the success test agreed before you commit to a build.

Book a free consultation
  1. Process redesign and build Best whenA new LangChain or LangGraph workflow, one accountable team.
  2. Stalled pilot recovery Best whenA LangChain app that works in a demo but is hard to change.
  3. Forward Deployed Engineering Best whenYour team builds on LangChain and wants senior engineers next to it.
FAQ

Questions about LangChain development

Should we build on LangChain, LangGraph or another framework? +
We choose per case and write down why. LangChain structures most retrieval pipelines, LangGraph fits stateful multi-step agents, and LlamaIndex covers more complex retrieval. Other frameworks, Pydantic AI among them, win on some projects, and the decision record says when and why.
What is LangGraph, and how does it relate to LangChain? +
LangGraph is a library from the LangChain team for building stateful workflows as graphs: each node is a step, each edge a decision, and the graph holds the state of the run in one place. It suits agents that loop, retry, branch or wait for a person, where a linear chain becomes hard to follow.
When should a LangChain chain become a LangGraph graph? +
When the chain that grew from the demo keeps adding branches, when nobody can say which step produced a wrong answer, or when one failed step ends the whole run. In a graph, a failed step retries without stopping the run, people sit at the checkpoints where judgment matters, and every step is traced and tested.
Can LangChain be used for building multi-agent systems? +
Yes. LangGraph is the usual base: each agent is a node or a subgraph, and the graph decides which agent runs next and what state it sees. We add tracing and an evaluation suite for each agent, so a regression in one does not hide behind the others.
Is LangChain an open-source framework? +
Yes. LangChain and LangGraph are open source, so nothing in the stack ties the system to Vstorm. Code, graphs, prompts, evaluation suites and the pinned lockfile live in your repositories.
Can you take over or audit an existing LangChain project? +
Yes. A LangChain project audit reviews the code and the running system and lists what to improve, what to optimize and which risks to reduce, ranked by cost and effect. We then fix it with your team or hand the plan over.
How do you test and monitor LangChain and LangGraph agents in production? +
With an evaluation suite built from your real cases that reruns on every prompt, model or retrieval change, and tracing of every run with OpenTelemetry, sent to Logfire or the tools you already run, such as Datadog or Grafana. Framework versions are pinned, and upgrades are tested against the same suite before they reach users.
Have you built client systems on LangChain? +
Yes. For Rotwand we built a data platform on LangChain and LlamaIndex that pulls unstructured data from news sources with LLMs. For Senetic, a RAG pipeline answers customer emails in each customer's own language. Both are published as case studies.
Do we own the LangChain code you build? +
Yes. Code, graphs, prompts and evaluation sets live in your repositories, the model stays replaceable, and there is no license or platform fee to Vstorm.
LangChain consultation

Bring the chain your team is stuck on

We trace one run with you and show where it breaks.

Book a free consultationRead case studies
From the blog

Reading on LangChain and LangGraph

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Engineering

Pydantic Deep Agents vs LangChain Deep Agents: Which Python AI Agent Framework Should You Choose in 2026?

Two open-source Python frameworks for agents that run for hours, plan work and spawn subagents. Compared on architecture, control and production fit.
Engineering

How to choose the right LangChain developer?

How does LangChain help companies develop Large Language Models (LLMs) applications? Check out our insights as a LangChain developer.
Engineering

What is LangGraph and how to use it?

LangGraph combines graph structures with LLMs to simplify data analysis, uncover patterns, and deliver actionable insights.