LangChain agents, built for production
LangChain and LangGraph are how we turn a model into a working agent — tool calls, retrieval, memory, and an explicit control graph you can inspect, checkpoint, and resume. We build them to ship, not to demo.
- LangGraph state machines
- Tool & function calling
- Retrieval-augmented agents
- LangSmith / OTel tracing
An orchestration layer, not a model
LangChain doesn't think. It coordinates the things that do.
LangChain is a set of composable abstractions — chat models, tools, retrievers, and output parsers — that give every provider a common interface. That sounds mundane until you've maintained an agent that calls three models, four APIs, and a vector store across two clouds. The value is the seam: swap a model, add a tool, or change a prompt without rewriting everything downstream.
LangGraph is the part we lean on hardest. It models an agent as an explicit graph of nodes and edges with persisted state, so the loop isn't a black box — it's a checkpointed state machine you can pause, inspect, branch, and resume. That's what makes a LangChain agent safe to run unattended.
What we assemble with it
The pieces that turn a prompt into an agent that gets work done in your systems.
LangGraph control flow
Explicit nodes, edges, and conditional routing — the agent loop as a state machine, not a while-loop you can't debug.
Tools & function calling
Typed tools wrap your APIs, databases, and internal services so the agent acts through a governed surface, not raw HTTP.
Retrieval pipelines
Retrievers and document loaders ground the agent in your knowledge base, with re-ranking and citation back to source.
Model abstraction
One chat-model interface over Anthropic, OpenAI, or open models — chosen per node for cost and capability.
Memory & checkpoints
Persisted graph state means long-running, resumable agents that survive restarts and pick up exactly where they stopped.
Tracing & evals
LangSmith or OpenTelemetry traces every step, so you can debug a bad run and measure quality, not guess at it.
From graph sketch to running agent
A measured path that keeps the orchestration honest and the domain logic portable.
Model the graph
We map the workflow to LangGraph nodes and edges, deciding where the agent loops, branches, or hands off to a human.
Wire the tools
Your APIs and data become typed tools and retrievers behind interfaces we own — not LangChain-specific glue.
Instrument it
We add checkpoints, tracing, and evals so every run is observable and regressions are caught before users see them.
Harden & ship
Approval gates, retries, and timeouts go in; the graph deploys to your VPC or on-prem with full lineage.
We use the framework, not its gravity
LangChain moves fast and its surface area is wide — which is exactly why we treat it as a layer, not a foundation. Your domain logic, prompts, and data contracts live behind interfaces we define, so the framework stays replaceable.
If LangGraph stops being the right tool, the same agent can move to Temporal for durable execution, AutoGen or CrewAI for multi-agent patterns, or a lean custom runtime. The orchestrator is a decision, not a dependency you're stuck with.
- Domain logic isolated from framework APIs
- Portable to Temporal, CrewAI, or custom runtimes
- No proprietary lock-in, no surprise rewrites
Where LangChain fits — and where it doesn't
We reach for it on purpose, not by default.
| Reach for LangChain | Reach for something else | |
|---|---|---|
| Single agent, rich tools | LangGraph with typed tool calling | — |
| Retrieval-heavy workflows | LangChain retrievers + re-rank | — |
| Many cooperating agents | Workable, but verbose | CrewAI or AutoGen roles |
| Long-running, exactly-once jobs | Checkpoints help | Temporal durable execution |
| Deterministic data pipelines | Overkill | n8n or Airflow DAGs |
Frequently asked questions
LangChain or LangGraph — which do you actually build on?
Usually LangGraph for anything that runs in production. LangChain gives us the model, tool, and retriever abstractions; LangGraph gives us the explicit state machine — nodes, edges, and checkpoints — that we need for durable, resumable, human-in-the-loop agents.
Does using LangChain lock us into LangChain?
No, and we design to keep it that way. LangChain is a thin orchestration layer over your models, tools, and data. We isolate business logic behind our own interfaces so a graph can be ported to Temporal, a custom runtime, or a different framework without rewriting your domain code.
Do we have to use LangSmith?
It helps, but it's optional. We can route traces to LangSmith for evaluation and debugging, or emit OpenTelemetry spans to your own observability stack. Either way, every agent decision is traceable end to end.
Which models work with a LangChain agent?
Whatever fits the task. LangChain's chat-model interface lets us run Anthropic, OpenAI, or open-source models behind the same graph, and swap them per-node — a cheap model for routing, a stronger one for reasoning — without touching orchestration.
Bring a workflow. We'll graph it.
One working session to map your highest-leverage automation to a LangGraph agent and the path to production.