LangGraph agents
LangGraph Development Services
DartSol builds stateful AI agents with LangGraph: multi-step workflows that use your tools and data, pause for human approval, survive failures and can be traced from start to finish.
In short: what is LangGraph?
LangGraph is LangChain's framework for orchestrating long-running, stateful agents. Work is modeled as a graph of steps that share state, with built-in support for durable execution, human-in-the-loop review, memory and streaming.
What we build with LangGraph
Document processing agents
Read incoming documents, extract and check the data, ask a person when something is unclear, and file the result.
Support triage agents
Classify requests, look up account details through your APIs and draft replies for approval.
Research assistants
Gather information from approved sources across several steps and produce a sourced summary.
Multi-agent workflows
A supervisor coordinates specialized agents, such as a researcher, a writer and a reviewer, on one task.
Operations agents with approvals
Agents that prepare changes in your systems and pause for a person to approve before acting.
RAG-powered agents
Agents that search your knowledge base as one of their tools, then decide what to do next.
How we build production LangGraph agents
Explicit graph and state
Each step is a node with a clear job, connected by edges and conditions, sharing a typed state you can inspect.
Narrow, validated tools
Agents act only through tools with specific purposes and checked inputs, with the permissions each task needs and no more.
Durable execution
State is persisted, so long-running workflows survive failures and resume where they left off.
Human-in-the-loop
The workflow pauses at approval points so a person can review, edit the state or stop it.
Streaming
Progress and partial results stream to your interface, so users can see what the agent is doing.
Tracing and evaluation
Runs are traced (for example with LangSmith) and tested against scenario sets before and after each change.
When LangGraph is the right choice
Good fit
- The task takes several steps, branches, or needs retries and loops
- You need a person to approve or correct the work at certain points
- The workflow runs for a long time or must survive failures
- Several agents or tools need to share state and hand work to each other
Probably not needed
- A single prompt or a fixed workflow already does the job reliably
- Every step is predictable, so a standard automation is cheaper and simpler
How a project runs
- 1
Map the workflow
Agree the steps, decisions, tools, approval points and what the agent must never do.
- 2
Design the graph
Define the state, nodes and edges, failure handling and how humans step in.
- 3
Build and evaluate
Implement iteratively against a scenario test set, with tracing from day one.
- 4
Deploy and monitor
Run in your cloud or a managed platform, with logging, alerts and regular review.
Frequently asked questions
What is LangGraph?
LangGraph is a framework from LangChain for building agents as graphs: steps (nodes) connected by edges, sharing state. It's designed for long-running, stateful agents, with durable execution, human-in-the-loop controls, memory and streaming. It can be used with or without LangChain.
How is LangGraph different from LangChain?
LangChain provides building blocks for working with language models, such as model interfaces, prompts and integrations. LangGraph is the lower-level orchestration layer for controlling how an agent moves through steps, keeps state, pauses for people and recovers from failures. Many projects use both.
When should we use a LangGraph agent instead of a normal automation?
Use an agent when the work needs judgment over unstructured input, such as emails, documents or open-ended requests, and the path changes from case to case. If every step is predictable, a standard automation is usually cheaper and more reliable, and we'll tell you so.
Which language models can it use?
LangGraph works with many model providers. We choose a model based on the task, your data policies and cost, and keep the design flexible so the model can be changed later.
Is a LangGraph agent safe to run in production?
With the right engineering, yes: narrow tools, scoped permissions, approval points for consequential actions, persisted state, tracing and evaluation. Language models can still make mistakes, so we don't promise fully autonomous or error-free operation.
How much does a LangGraph project cost?
It depends on the number of steps, tools, integrations and approval points. After a short consultation we provide a scoped written quote.
Discuss Your Project
What happens next
- 1We review your request.
- 2We reply by email with any questions, or suggest a short call.
- 3If it's a good fit, you get a written quote for the agreed scope.
Prefer another way? Email info@dartsol.com or pick a time on Calendly (opens in a new tab).
DartSol LLC is registered in Austin, Texas, and works remotely with businesses across the United States, as well as in Canada and Australia.