Orchestration framework · in regular use
LangChain agency in Nice
The tool that chains model calls, document retrieval and your own functions.
What we do with it
LangChain at our clients, concretely
Used with Python and a document store on a sentiment-tracking tool for AI models.
The limits
When we recommend something else
For a single model call, the tool adds a dependency with nothing in return.
Our work
Projects we delivered with LangChain
LLM Monitor
SaaS that measures how visible brands are in AI answers.
LangChain: Common layer for querying several model providers in the same way.
Read the LLM Monitor case study
Method
How we bring LangChain into a project
Before writing any code, we map the chain step by step with you: what triggers the process, which data is retrieved, when the model comes in, which of your functions it may call and what brings the process to a close. Steps that need no model, such as a calculation or a database read, remain ordinary code. That map then anchors the documentation.
Each step is tested on its own, then the full chain is evaluated on representative cases, including deliberately malformed ones. Functions made available to the model get only the permissions they strictly need: read-only by default, with any write to your software requiring explicit approval. A step-by-step record of every run is kept in logs hosted on your own server.
The application is delivered as a standalone Python service, deployed on hosting you control. Since a single process may call the model several times, we count calls per chain and set a maximum number of iterations, so a loop that goes wrong cannot inflate the bill. The repository, tests and prompts are handed over to you.
For your business
What it changes in practice
Some tasks cannot be reduced to one question put to a model: a file has to be found, a detail cross-checked against your business software, a document prepared and then checked for consistency. Today a member of staff does that back-and-forth across several screens. A chain built with LangChain carries out those steps in order, and the person only steps in at the decision point that belongs to them.
Because each step is logged separately, an error can be pinned down: you know whether the problem lies in retrieval, in the model or in data from your software, rather than facing a black box. The code is open source and belongs to you, with no subscription to a proprietary platform. On the other hand, changing a chain's logic requires a developer, which slows down adjustments a team would like to make on its own. We point this out during scoping so the choice is made with full knowledge.
Our reading of it
What we take away from LangChain
LangChain becomes useful at the point where one call to a model is no longer enough: documents have to be retrieved, then the model called, then the shape of the answer checked, possibly the whole thing retried differently. Writing that sequence by hand is perfectly possible; the trouble starts when it has to be traced and tested.
We use it mainly as an abstraction over vendors and as an execution trace — seeing which step produced which output changes what debugging feels like. The code is Python, like most of our model work, and the main use case remains document search. Our reservation is firm: the library moves fast and adds layers. For a single stable call we would rather talk straight to the OpenAI API, in twenty lines anyone can read.
Decision guide
Should you choose LangChain?
LangChain or direct calls to the model provider's SDK?
If your application sends one question to a model and displays the answer, call the provider's SDK directly: less code, fewer dependencies, and behaviour that is easier to debug. LangChain becomes useful when processing chains several steps, such as searching a document store, rephrasing, calling one of your own functions and then checking the result. It then supplies standard building blocks for those chains instead of hand-writing everything.
LangChain or a no-code automation platform for our AI workflows?
A no-code platform suits a short sequence that someone in-house must be able to change without a developer. Once the workflow touches your databases, applies precise business logic or needs automated testing, LangChain, written in Python, gives more control: the code is versioned, reviewed and hosted wherever you decide, including within the European Union. You are not bound by a platform vendor's pricing or limits.
Does LangChain make it easier to move from one model to another?
Partly. LangChain offers a common interface to many providers and to open models hosted on your own servers, which reduces the changes needed when you switch, for instance to bring sensitive processing back into Europe. Prompts often still need adjusting, though, because each model responds differently. So we test every switch against a reference set of questions before it reaches production.
Client questions
What clients ask us about LangChain
Could another provider take over our LangChain project?
Yes, and that is one of its strengths. LangChain is a widely used open-source framework in the Python ecosystem, publicly documented and known to many developers. We hand over the source code, a description of each processing chain and the prompts sent to the model. Any developer comfortable with Python and language models can therefore pick up maintenance without starting again from scratch.
LangChain changes quickly: could our application break?
The framework does change often, and some releases alter its interfaces. We pin the versions in use, rely only on the components we need and cover the main chains with tests. Updates are applied deliberately, after checking, never automatically. Your application therefore keeps working exactly as it is until you choose to move it forward.
Does LangChain send our data anywhere?
LangChain is a library that runs on your server and stores nothing by itself. Data only goes to the services we configure, such as the model provider or the document store. Some monitoring tools linked to its ecosystem are separate online services; we only switch them on with your agreement, and a model hosted on your own infrastructure keeps everything in-house.
Can a LangChain agent be left to act on its own in our software?
Technically yes; in practice we restrict it. An agent decides for itself which actions to take next, which makes it less predictable than a fixed chain. We let it look things up freely, but any action that changes data, sends a message or commits the business waits for a person's approval. Where a fixed sequence covers the need, we prefer that to an agent.
Will we be able to change the prompts sent to the model without a developer?
Up to a point. We keep prompts in files separate from the code, written in plain language, which a trained person can adjust. A change can still alter how the whole chain behaves, though, so it goes through the reference tests before reaching production. The logic of the steps themselves remains development work.
Going further
LangChain sits within our Artificial intelligence
A technology does not make a project. The method around it decides the outcome: scoping, prototypes, short cycles, and a clean way out.
Neighbouring technologies
A LangChain project in mind?
The first conversation is free. We will tell you plainly whether this technology is the right one, and if not, which is.
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