LangChain and LangGraph development
LangChain and LangGraph Development
Retrieval applications and reliable, stateful AI agents built in Python or TypeScript.
LangChain is an open-source framework for building applications with language models, and LangGraph is its lower-level framework for stateful, multi-step agents. We use them to build retrieval applications and agents with tools, memory, human approval and evaluation.
What we cover
- Retrieval (RAG) applications
- Agents with tools
- LangGraph workflows and state
- Evaluation and tracing
- Human approval steps
- Deployment and monitoring
Requirement-led advice · Documented handover
- Advice based on your requirements
- Works with your existing stack
- Documented, maintainable delivery
What Is LangChain?
LangChain provides standard interfaces for chat models, embeddings, vector stores, document loaders and tools, so applications can switch model providers and data stores with little code change. It is available for Python and JavaScript/TypeScript, and its integrations cover the major model providers, including OpenAI, Anthropic, Google, Microsoft Foundry models and open-source models.
Since LangChain 1.0, its agents are built on the LangGraph runtime, which adds durable state, streaming and human-in-the-loop support. LangSmith, a separate product from the same company, provides tracing, evaluation and monitoring for applications built with or without LangChain.
When LangChain and LangGraph are a good fit
- You need code-level control over prompts, retrieval and tools.
- You want to switch or combine model providers.
- An agent must follow multi-step logic with checkpoints and approvals.
- You want tracing and evaluation from development through production.
What Is LangGraph?
LangGraph is an open-source framework for building agents as graphs. Each node is a step, such as a model call, a tool call or a check, and edges decide what happens next, including loops and branches. A shared state object carries information between steps, and checkpoints save that state so a run can pause, recover from failure or continue later.
Those features make LangGraph suited to agents that must be dependable: workflows that wait for a person to approve an action, long-running processes, and systems where several specialised agents work together under a supervisor. LangGraph is available for Python and JavaScript/TypeScript and can be used with or without the rest of LangChain.
When to use LangGraph
- The task has several steps, branches or loops that should be explicit and testable.
- A person must review or approve before the agent acts.
- Runs can be long, so state must survive restarts and failures.
- Several agents or tools need coordination with clear control over who does what.
What We Build With LangChain and LangGraph
Typical applications.
Document question answering
Retrieval over manuals, policies and contracts with citations.
Tool-using agents
Agents that query systems and take approved actions.
Multi-step workflows
Processes broken into reliable, inspectable steps.
Approval workflows
Agents that pause for human review before acting.
Extraction pipelines
Turn documents into structured data.
Assistants with memory
Conversations that remember context across sessions.
Multi-agent systems
Specialised agents coordinated by a supervisor.
Evaluation harnesses
Test suites that track quality across releases.
LangChain and LangGraph Services We Deliver
Each piece of work has a clear output, so you know what you will receive.
Architecture
Choose between a simple chain, an agent or a LangGraph workflow.
Deliverables: architecture, design decisions
Retrieval development
Loaders, chunking, embeddings, vector store and reranking.
Deliverables: retrieval pipeline, quality results
Agent development
LangGraph graphs with tools, state and checkpoints.
Deliverables: agent code, tests
Evaluation and tracing
Datasets, evaluators and tracing for every run.
Deliverables: evaluation suite, dashboards
Deployment
APIs, persistence, scaling and monitoring.
Deliverables: deployment, runbook
Upgrade and review
Review existing LangChain code or upgrade to 1.x.
Deliverables: review report, upgraded code
LangChain Ecosystem Components
Parts of the ecosystem we use.
LangChain
- Chat model and embedding interfaces
- Document loaders and splitters
- Vector store integrations
- Agents and middleware
LangGraph
- State graphs
- Checkpointing and persistence
- Human-in-the-loop interrupts
- Streaming
Quality
- LangSmith tracing
- Evaluation datasets
- Prompt versioning
Integrations
- Model providers
- Vector databases and Azure AI Search
- MCP tools
- Python and TypeScript
Listing a feature here does not mean every project uses it. We recommend what fits your requirements, skills, licensing and running costs.
How LangChain Fits Your Stack
LangChain applications can use models from Microsoft Foundry, Google Gemini and other providers, retrieve from your documents and databases, and call tools exposed through Model Context Protocol servers.
We host them as Python or TypeScript services on Azure or AWS. For low-code agents inside Microsoft 365, Copilot Studio may be simpler; for automation-led AI, n8n builds on LangChain too.
Implementation Considerations
- Start with the simplest design that works; not every task needs an agent.
- Evaluate retrieval quality separately from answer quality.
- Use LangGraph checkpoints and approvals for actions that change data.
- Trace every run so failures can be diagnosed.
- Pin package versions; the ecosystem moves quickly.
- Control token cost with caching, smaller models and limits on agent loops.
How We Work With LangChain and LangGraph
The same four steps we use across our technology work, adapted to your scope.
Discover
Goals, users, constraints and the systems LangChain and LangGraph must work with.
Select
Confirm LangChain and LangGraph is the right fit and agree the architecture and scope.
Build and integrate
Deliver in short iterations with testing, reviews and documentation.
Operate and improve
Monitor, support and improve the solution as your needs change.
LangChain and LangGraph Questions, Answered
What is LangChain?
An open-source framework for building applications with language models, with standard interfaces for models, retrieval and tools.
What is the difference between LangChain and LangGraph?
LangChain provides components and ready-made agents. LangGraph is the lower-level framework for stateful, multi-step agent workflows; LangChain agents run on it.
Do we need LangChain to build AI applications?
No. For simple cases, calling a model SDK directly can be enough. LangChain helps when you need retrieval, tools, several providers or agents.
Can LangGraph agents wait for human approval?
Yes. LangGraph can interrupt a run, wait for a person and resume from saved state.
How do you test LangChain applications?
With evaluation datasets, automated evaluators and tracing in LangSmith or similar tools.
Which languages do you use?
Python most often, and TypeScript where it fits your stack.
Building an AI Agent?
Tell us what the agent should do and which systems it needs, and we will suggest a design.
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