LangChain and LangGraph development services
LangChain provides building blocks for LLM applications — retrievers, tools, and model integrations — while LangGraph adds stateful, controllable graphs for agents that need memory, branching, and human approval steps.
We use them where they speed up delivery and keep systems maintainable, and write plain code where a framework would add unnecessary abstraction.
What we build with LangChain & LangGraph
RAG pipelines
Retrieval over documents and databases with LangChain retrievers and vector stores.
Stateful agents
LangGraph agents with memory, branching logic, and checkpoints.
Human-in-the-loop flows
Agents that pause for approval before sensitive actions.
Tracing & evaluation
Observability with LangSmith-style tracing and evaluation datasets.
Why teams choose LangChain & LangGraph
- Large ecosystem of model, tool, and vector-store integrations
- LangGraph gives explicit control over agent state and flow
- Works with OpenAI, Claude, Gemini, and open-source models
When we'd suggest something else
For small, single-purpose features, calling a model SDK directly is often simpler; we only add a framework when the system benefits from it.
We're model-agnostic — we recommend what fits your use case, not a favourite vendor.
Related AI services
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