Framework
LangChain development
LangChain is a toolkit for assembling applications that use language models together with your own information and tools. We use it where it saves real work, and we write plain code where a framework would only add weight.
- 1,200+ clients helped
- Building since 2010
- 15 services, one team
- 50+ technologies
At a glance
What it is
LangChain is an open-source toolkit for building applications around language models, handling steps such as retrieving documents, calling tools, and chaining prompts.
Problems it solves
An AI feature that answers from your own information needs more than a model: it needs retrieval, memory, tool use, and checks, and wiring these by hand is slow.
Who it suits
Teams building AI assistants, document search, or workflow agents, and technical leads deciding whether to use a framework.
How to choose
Use a framework when the application has several moving parts. For a single model call, plain code is simpler and easier to maintain.
What it means
Plumbing for language-model applications
LangChain is an open-source framework with ready-made parts for common steps in language-model applications: loading documents, searching them, building prompts, calling models, and using tools.
A model alone only produces text. Useful applications surround it with your information and your rules. A framework supplies those pieces so a team does not write each from scratch.
The trade-off is that frameworks in this area change quickly and add layers between you and the model. Used carefully, they speed up the first version. Used everywhere, they can make later changes harder.
What DECHY can do
Working with LangChain
Build
- Document search and question answering with sources.
- Assistants that use tools and your records.
Check
- Test sets that measure answer quality.
- Tracing, so every step can be reviewed.
Control
- Limits on what the system may do.
- Approval steps for sensitive actions.
Keep flexible
- A design where the model provider can be changed.
- Documentation of the choices and the reasons.
How we build it
From idea to a checked AI feature
Define what good looks like
We write down example questions and the answers you would accept, before choosing any tool.
What it produces
- Example set
- Acceptance rules
Build the simplest version
We start with the least machinery that could work, and add framework parts only where they help.
What it produces
- First working version
Test and tighten
We score results against the example set, fix the weakest cases, and add limits on what the system may do.
What it produces
- Test results
- Limits
Release carefully
We start with a small group, review real use, and widen as quality holds.
What it produces
- Pilot
- Quality reports
Where it fits
Framework or plain code?
The honest answer depends on how many moving parts the application has.
How complex is the AI feature?
Choose this when
Summarize a document, classify a message, or draft a reply.
- What you get
- A few lines of plain code against the provider's own tools. Easier to read and to change.
- Keep in mind
- A framework here is usually unnecessary weight.
Choose this when
Answer questions from your own documents, with sources shown.
- What you get
- Either approach works. A framework helps with loading, splitting, and searching documents.
- Keep in mind
- Whatever is used, plan how answers are checked and how documents stay current.
Choose this when
An assistant that looks up records, calls other systems, and decides what to do next.
- What you get
- A framework's agent and tool support can save time, along with tools for tracing what happened.
- Keep in mind
- Limit what the system may do, and keep a person in the loop for steps that matter.
Options
LangChain compared with other approaches
LangChain
This pageSuits
Applications with several steps, tools, and models.
- Trade-off
- Fast-changing, with extra layers to learn.
LlamaIndex
Suits
Applications centered on searching your documents.
- Trade-off
- Narrower scope for general workflows.
Semantic Kernel
Suits
Teams working in Microsoft's ecosystem.
- Trade-off
- Smaller community outside it.
Plain code against a provider
Suits
Simple features and teams who want full control.
- Trade-off
- You write the plumbing yourself.
Common questions
Questions about LangChain
Do we need LangChain to build an AI feature?
No. Many features are simpler and clearer in plain code. We recommend a framework only when the number of steps and tools justifies it.
Will we be locked in to it?
We keep the framework behind clear boundaries and document the design, so replacing it later is a contained job.
How do you know the answers are right?
We test against example questions, show sources with answers, and review samples of real use. No system is right every time, so doubtful cases go to people.
Which model do you use?
The one that suits the task, the data rules, and the cost. We design so the choice can change.
Have a different question?
Tell us what you are weighing up and we will answer it plainly.
Ask a questionAlready a client? Email support@dechytech.com
Technical depth
How we build with LangChain
What we settle early on LangChain work: retrieval, models and providers, and evaluation and safety, so nothing surprises your team later.
For document search we split content sensibly, create embeddings, store them in a vector database such as Pinecone or PostgreSQL with a vector extension, and test retrieval quality before tuning prompts.
We keep model calls behind a thin layer so OpenAI, Anthropic Claude, Google Gemini, or an open model can be swapped by configuration.
- A fixed set of example questions with expected answers, run on every change.
- Tracing of prompts, tool calls, and outputs.
- Guards against prompt injection and limits on tool permissions.
Tell us about the AI feature
Describe what it should do and which information it should use. We will say whether a framework helps and how we would check the results.