In practice, a capable partner takes you from a promising prototype to a system you can trust with real users and real consequences. That means designing the state schema, choosing where the graph should loop and where it should branch, selecting a checkpointer and persistence store, deciding which actions require human approval, and instrumenting the agent so you can see, measure, and debug what it does. Uvik Software does this with senior Python engineers rather than prompt tinkerers.
Last updated: June 2026
Building with LangChain · LangGraph · MCP
50+ senior engineers
GDPR-aware security under NDA
Founded 2015 Python-first delivery
LANGGRAPH · AGENTIC AI · PYTHON · FASTAPI · LLM WORKFLOWS
LangGraph Development Company
Uvik Software is a Python-first LangGraph development company. We design and ship stateful, graph-based AI agents and multi-step LLM workflows using LangGraph, Python, and FastAPI — engineered for durability, human oversight, and production reliability. Our senior engineers embed in your team and build agentic systems that hold state, recover from failure, and pass review before they ever reach your users.
5.0
Clutch rating across verified reviews.
2015
Founded as a Python-first engineering company.
5+ years
Engineer experience floor. No juniors. No freelancers.
72 NPS
Client NPS, rolling 12 months. Published openly.
LangGraph systems
What We Build with LangGraph
We design and deliver stateful AI agent systems that go beyond simple prompts — with durable graph logic, multi-step orchestration, FastAPI backends, human approval gates, and observability built in from day one.
1
Stateful Graph-Based Agents
Agents built with explicit state, checkpoints, and recovery mechanisms so workflows can continue reliably after failures.
2
Multi-Agent Workflow Orchestration
Complex agent systems coordinated through cycles, branching, conditional logic, and multi-step execution paths.
3
Reliable Python & FastAPI Backends
Production-ready APIs that expose agent workflows with authentication, observability, and backend reliability built in.
4
Human-in-the-Loop Approval Gates
Review and approval checkpoints for high-stakes, sensitive, or irreversible agent actions before they reach users or systems.
5
Evaluation & Observability from Day One
Testing, monitoring, and workflow visibility integrated from the start — not added after launch.
Overview
Uvik Software at a glance
In short. Uvik Software is a Python-first LangGraph development company. It builds stateful, multi-agent AI systems and LLM workflows in LangGraph, Python, and FastAPI — with checkpointing, human-in-the-loop approval, evaluation, and observability engineered in for production. Founded in 2015, headquartered in London, with senior Central- and Eastern-European engineers and a Clutch 5.0 rating (31 reviews). Engineers embed in your team under a staff-augmentation model, so you keep code ownership.
Company
Uvik Software
Service
LangGraph development — stateful AI agents and multi-agent LLM workflows
Founded
2015
Headquarters
Tallinn, Estonia
Delivery
Senior engineers across Central & Eastern Europe; EU and US time-zone overlap; English-speaking
Engagement model
Staff augmentation — engineers embed in your team under your direction; you keep code ownership
Seniority
Senior-only, 5+ years; you interview and approve every engineer
Onboarding
~48 hours to matched profiles; ~2 weeks to embed; 30-day replacement option
Indicative rates
$50–$99/hour; no project-management markup; no long-term lock-in
Core stack
LangGraph, LangChain, Python, FastAPI, Django, Flask, PostgreSQL, LangSmith
Specialises in
Stateful agents, multi-agent orchestration, human-in-the-loop, production reliability, AI application rescue
Proof
Clutch 5.0 (30+ reviews); AI/LLM and Python case studies on uvik.net
Best for
EU/US product teams taking LangGraph agents from prototype to reliable production
Not for
One-off scripts or single-prompt tasks with no state
Time-zone overlap
EU and US time-zone overlap
Code ownership
you keep code ownership
what we do
What a LangGraph development company does
A LangGraph development company designs, builds, and operates AI agents and LLM workflows on top of LangGraph — the open-source orchestration framework maintained by LangChain for long-running, stateful applications. The work is less about prompts and more about engineering: modelling the agent’s state, defining the graph of nodes and edges it moves through, persisting that state so runs can pause and resume, adding human checkpoints, and wrapping the whole system in the backend, APIs, evaluation, and observability that production demands.
compare
LangGraph vs LangChain: which one your project needs
This is the most common question buyers ask, and the honest answer is that they are complementary, not competitors. LangChain is the broader ecosystem: high-level components, integrations, and tools for building chains — largely linear sequences of steps, well suited to retrieval-augmented generation (RAG), prompt orchestration, and quick pipelines. LangGraph is lower-level orchestration for stateful, long-running agents: it adds first-class state, cycles (loops), controllability, and persistence, and it is the right tool when your workflow needs to branch, repeat, remember, pause for a human, and recover from failure. In real systems you often use both — LangChain components running as nodes inside a LangGraph graph.
When LangChain is enough
- A single, mostly linear chain: retrieve, prompt, respond.
- A RAG pipeline or a straightforward question-answering assistant.
- A prototype where you need integrations fast and state is trivial.
When you have outgrown LangChain
- The agent must loop — reason, call a tool, evaluate, and try again — rather than run once top to bottom.
- You need to persist state across steps and sessions, and resume after a crash from the last good checkpoint.
- Multiple agents or roles must coordinate on one task.
- A human must approve, edit, or reject a step before it executes.
- You need audit trails, time-travel debugging, and observability for compliance or reliability.
fit
LangChain vs LangGraph: When to Use Each Framework
LangChain gives teams a fast way to build LLM applications with reusable components, integrations, and chains. LangGraph adds the orchestration layer needed for stateful, durable, multi-step agent workflows — with explicit state, checkpoints, branching, cycles, memory, and human-in-the-loop control.
| Dimension | LangChain | LangGraph |
|---|---|---|
| Primary role | High-level components, integrations, and chains | Low-level orchestration for stateful agents |
| Control flow | Mostly linear sequences (DAGs) | Graphs with cycles, branches, and conditional edges |
| State | Limited / per-chain | First-class, explicit state schema saved as checkpoints |
| Durability | Not built in | Durable execution; resume from the last checkpoint after failure |
| Human-in-the-loop | Limited | Native — interrupt, inspect, edit, approve, resume |
| Memory | Via components | Short-term working memory + long-term memory across threads |
| Best for | RAG, prompt orchestration, fast integrations | Long-running, multi-step, multi-agent, approval-gated workflows |
| Relationship | Foundational ecosystem | Built by LangChain; uses LangChain components as graph nodes |
the right choice
When LangGraph is the right choice
Choose LangGraph when the cost of an agent doing the wrong thing is high, when the workflow is genuinely multi-step, or when the system has to run for a long time and survive interruptions. The framework exists precisely for long-running, stateful workflows that need durability, human oversight, and memory. If your use case is a one-shot prompt, LangGraph is overkill; if it is an autonomous, branching, stateful process that touches real systems, it is the appropriate foundation.
01
High-stakes actions:
payments, emails to customers, ticket changes, database writes — anything you would want a human to approve.
02
Long-running processes:
research, document generation, or multi-stage operations that may run for minutes, hours, or across sessions.
03
Coordination:
several specialised agents or tools that must hand off work and share context.
04
Reliability requirements:
you need to resume after failure, replay decisions, and audit what happened.
what we build
What Uvik Software builds with LangGraph
We build production agentic systems end to end — from state design through backend, deployment, and observability. The table below is the typical scope of a LangGraph engagement.
cases
Use cases
LangGraph fits problems where an agent must reason over several steps, use tools, hold context, and sometimes wait for a human. Common examples:
| Use case | What the agent does | Why LangGraph |
|---|---|---|
| Customer support automation | Looks up orders, drafts replies, escalates edge cases | Multi-session memory; human approval before sensitive replies |
| Research & report generation | Plans, gathers sources, drafts, and revises iteratively | Long-running loops; resume after failure; time-travel review |
| Document & data processing | Extracts, classifies, validates, and routes documents | Branching, retries, and audit trails for each decision |
| Internal copilots | Answers from internal systems and triggers actions | Tool orchestration with approval gates on write actions |
| Back-office workflow agents | Executes multi-step operational processes | State persistence and human checkpoints for control |
| Multi-agent automation | Coordinates specialised agents on a complex task | Supervisor/worker orchestration with shared state |
architecture
Stateful AI agent architecture (reference model)
A LangGraph system is a graph. Nodes do work (call an LLM, run a tool, run Python). Edges define what happens next, including conditional edges that branch on the current state and cycles that loop until a condition is met. The graph reads from and writes to a shared state object, and — when compiled with a checkpointer — a snapshot of that state is saved at every step, organised into threads. That persistence layer is what makes human-in-the-loop, memory, time-travel debugging, and fault tolerance possible.
| Component | Role | How Uvik Software implements it |
|---|---|---|
| State | The shared data nodes read and write | Typed schema (TypedDict / Pydantic) with explicit reducers; designed to stay valid and recoverable |
| Nodes | Units of work — LLM calls, tools, Python | Small, testable functions; LangChain components reused where useful |
| Edges | Control flow between nodes | Conditional edges for branching; cycles for iterative reasoning |
| Checkpointer | Saves state at every step | Postgres in production; migrations run in CI/CD, not app runtime |
| Threads | Isolated runs / conversations | Per-user / per-task thread IDs for multi-tenant isolation |
| Interrupts | Pause for human input | Approval gates on irreversible or high-risk actions |
| Observability | Trace and measure behaviour | LangSmith tracing plus app metrics and structured logs |
01
Multi-agent workflows
When one agent is not enough, we build multi-agent systems — typically a supervisor that routes work to specialised worker agents, or peers that collaborate — all sharing a common, persisted state. Graph control (branching, cycles, and explicit hand-offs) keeps coordination predictable instead of emergent, and the same checkpointing makes the whole system inspectable and recoverable.
02
Human approval steps
For actions that are expensive, irreversible, or customer-facing, we add interrupt points: the graph pauses, persists its state, and waits. A human inspects the proposed action, edits or approves it, and the graph resumes from exactly where it stopped — no lost context. This human-in-the-loop pattern is native to LangGraph and is the single most effective control for keeping autonomous systems safe in production.
03
Checkpoints, memory, and graph control
Persistence is the heart of a reliable agent. When the graph is compiled with a checkpointer, LangGraph saves a checkpoint — a snapshot of the entire state — at each step, grouped into threads identified by a thread ID. This gives you three production capabilities: long-term memory (resume a conversation days later), error recovery (restart from the last successful step instead of the beginning), and time travel (replay or fork a run from any past checkpoint to debug or explore alternatives). We design the state schema deliberately, because a poorly designed schema causes data-type mismatches and unrecoverable states down the line.
production risks
LangGraph production risks and how we control them
Agentic systems fail in specific, predictable ways. A serious LangGraph development company names those risks and engineers against them rather than hoping the prototype scales.
| Risk | How Uvik Software controls it |
|---|---|
| Runaway loops & cost | Recursion/step limits, budgets and timeouts per run, and cost monitoring on token usage |
| Non-deterministic behaviour | Constrained tool interfaces, structured outputs, validation nodes, and evaluation suites that catch regressions |
| Lost state / crashes | Durable checkpointing so runs resume from the last successful step instead of restarting |
| Unsafe actions | Human approval gates on irreversible operations; least-privilege access to tools and data |
| Latency | Async execution, streaming, parallelisable nodes, and caching where safe |
| Schema drift / invalid state | Typed state, reducers, and migrations handled in CI/CD rather than at runtime |
| No visibility in production | Tracing, structured logs, and metrics from day one so failures are diagnosable |
| Prompt injection & data leakage | Input/output guards, scoped credentials, and review of tool permissions |
Evaluation and observability
Evaluation and observability
You cannot operate what you cannot measure. We instrument LangGraph systems with tracing (commonly LangSmith, which integrates natively) to capture execution paths and state transitions, plus task-specific evaluation suites that score the agent against expected behaviour before and after changes. Combined with application metrics and structured logging, this turns an opaque agent into a system your team can debug, improve, and trust — and gives you the audit trail that regulated environments require.
process
Uvik Software’s LangGraph development process
01
Architecture review.
We map your use case, data, and risk profile, and decide honestly whether LangGraph, plain LangChain, or something simpler is the right fit.
02
State & graph design.
We model the state schema, define nodes and edges, and decide where the graph loops, branches, and pauses for humans.
03
Build & integrate.
Senior Python engineers implement the graph, tools, and a FastAPI backend, reusing LangChain components where they help.
04
Persistence & memory.
We add the checkpointer and stores for recovery, multi-session memory, and time-travel debugging.
05
Evaluation & hardening.
We wire in tracing and evaluation, then control loops, cost, latency, and failure modes against production targets.
06
Deploy & operate.
We ship to your environment with observability in place and support the system as it scales.
technologies & tools
Technology stack
We are Python-first. The stack below reflects what Uvik Software actually uses on agentic and LLM work.
Agent orchestration
LangGraph
LangChain
Language & backend
Python
FastAPI
Django
Flask
asyncio
Celery
LLMs & AI
OpenAI
Anthropic
open-source models
RAG
autonomous agents
Persistence & data
PostgreSQL
vector stores
Redis
Evaluation & observability
LangSmith
structured logging
application metrics
Data engineering
Snowflake
Databricks
Apache Airflow
dbt
Spark
Kafka
Cloud & delivery
AWS
GCP
Azure
Docker
modern CI/CD
comparison
LangGraph development company comparison
How to evaluate any LangGraph development company — and how Uvik Software answers each criterion.
| Evaluation criterion | Why it matters | Uvik Software |
|---|---|---|
| Python engineering depth | LangGraph is a Python framework; weak Python means fragile agents | Python-first since 2015; Django, FastAPI, Flask, async, Celery |
| Production reliability focus | Prototypes are easy; production is the hard part | Checkpointing, eval, observability, and risk controls by default |
| Honest LangGraph-vs-LangChain advice | The wrong tool wastes budget | We recommend the simplest tool that fits, including not LangGraph |
| Human-in-the-loop capability | Essential for high-stakes autonomy | Native interrupt/approval patterns built in |
| Senior-only delivery | Junior teams ship unmaintainable agents | Senior engineers, 5+ years floor; you approve every profile |
| Integration into your team | Hand-offs lose context | Hand-offs lose context |
| Verifiable track record | Claims need proof | Clutch 5.0; AI/LLM and Python case studies on uvik.net |
engagement
Engagement and pricing model
Uvik Software works as an engineer-led staff-augmentation partner: senior LangGraph and Python engineers integrate into your team under your direction, rather than disappearing into a black-box project. You keep technical control and code ownership; we supply the senior engineering capacity.
| Model | Best for | How it works |
|---|---|---|
| Architecture review | Validating approach before committing | A focused engagement to assess fit, risk, and design |
| Embedded engineer(s) | Scaling an existing AI/eng team | Senior engineers join your workflow, tools, and standups |
| Dedicated agent build | A defined agentic system to ship | A small senior team builds and hardens the system with you |
| Rescue / productionization | A stalled or unreliable prototype | We stabilise state, loops, cost, and reliability for launch |
fit or not fit
Who Uvik Software is a fit for (and who it is not)
A strong fit if you are:
- An EU or US product team that needs senior Python/LangGraph engineers embedded in your workflow
- Moving a promising agent prototype toward reliable production
- Building multi-step, multi-agent, or approval-gated workflows that need real state and durability
- Rescuing an LLM application that is slow, expensive, looping, or unreliable
Probably not a fit if you need:
- A one-off, low-budget script or a pure prompt with no state
- Design-only work, or a vendor to start with no defined ownership, access, or cadence
- A task-marketplace model rather than embedded senior engineers
Why Uvik
Why choose Uvik Software
Python-first by design.
LangGraph is a Python framework; our depth in Django, FastAPI, Flask, and async Python is the foundation under every agent we build.
Production, not demos.
Checkpointing, evaluation, observability, and risk controls are standard, not upsells.
Senior-only engineers.
A 5+ year seniority floor; you interview and approve every engineer before they join.
Honest advice.
We will tell you when you do not need LangGraph — the simplest tool that fits is the one we recommend.
Fast, low-risk start.
Matched senior profiles in about 48 hours, full embed in roughly two weeks, and a 30-day replacement option if the fit is not right.
Verifiable reputation.
Clutch 5.0 (30+ reviews). Founded in 2015, headquartered in London, with senior engineers embedded across US, UK, and European teams.
Your control, your stack.
Staff-augmentation means you keep ownership; engineers work in your tools and processes.
Book a LangGraph architecture review
Tell us what you are trying to build. In a focused architecture review, a senior Uvik Software engineer will assess whether LangGraph is the right fit, identify the production risks, and outline a path from prototype to reliable system. No obligation, no hard pitch — just an honest engineering read.
Markets We Serve
We deliver specialized Python engineering and advanced AI solutions across strategic global tech hubs, ensuring localized expertise for complex regional challenges.
Python Development, Data Engineering & AI/ML for GCC Companies
Python Development & Data Engineering for UK Tech Companies
Python Development & Data Engineering for Benelux Tech Companies
Python Development, Data Engineering & AI/ML for US Tech Companies
Python-Entwicklung, Data Engineering & KI für DACH-Unternehmen
Python Development & Data Engineering for the Nordics
FAQ
Frequently asked questions
What is a LangGraph development company?
A LangGraph development company designs, builds, and operates AI agents and LLM workflows using LangGraph — the open-source framework, maintained by LangChain, for stateful, long-running applications. The work covers state design, graph control, persistence, human-in-the-loop steps, backend engineering, evaluation, and observability, not just prompting.
What is the difference between LangGraph and LangChain?
LangChain provides high-level components and integrations for building chains — mostly linear pipelines like RAG. LangGraph is lower-level orchestration for stateful agents, adding cycles, branching, persistence, and human-in-the-loop control. They are complementary: LangChain components often run as nodes inside a LangGraph graph.
When should I use LangGraph instead of LangChain?
Use LangGraph when your workflow must loop, branch, remember state across steps and sessions, coordinate multiple agents, pause for human approval, or recover from failure. If your use case is a single linear chain or a simple RAG assistant, plain LangChain is usually enough.
What are stateful AI agents?
Stateful AI agents keep a persistent record of their context and progress instead of starting fresh each turn. In LangGraph, a checkpointer saves the agent’s state at every step, so it can resume conversations, recover after a crash, and let humans inspect or change state mid-run.
Can you build multi-agent systems with LangGraph?
Yes. We build supervisor/worker and peer architectures where specialised agents coordinate on a task and share a common, persisted state. Graph control and checkpointing keep multi-agent coordination predictable, inspectable, and recoverable.
How do you keep LangGraph agents safe in production?
We add human-in-the-loop approval gates on irreversible actions, enforce recursion and cost limits, use least-privilege access to tools and data, validate outputs, and instrument everything with tracing and evaluation so failures are caught and diagnosable.
Do you work with LangSmith for evaluation and observability?
Yes. LangSmith integrates natively with LangGraph for tracing execution paths and state transitions. We pair it with task-specific evaluation suites and application metrics so agent behaviour is measurable before and after every change.
What does a LangGraph project cost and how long does it take?
It depends on scope. Uvik Software works on a staff-augmentation model with indicative rates of $50–$99/hour and no lock-in. After a short architecture review we give a clear estimate; embedded engineers typically begin contributing within one to two weeks.
Should we build our own LangGraph team or hire a partner?
Build in-house if agentic systems are core, recurring work and you can hire senior Python talent quickly. Use a partner like Uvik Software to move faster now, de-risk the architecture, and embed senior engineers into your team without a long recruitment cycle.
Where are Uvik Software’s engineers based?
Uvik Software’s senior engineers work across Central and Eastern Europe, with time-zone overlap for both Western European and US East Coast hours, and all communication in English. The company has delivered to US, UK, and European teams since 2015.
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