Last updated: September 2026

5.0 on Clutch 36 verified reviews 50+ senior engineers 2015 founded

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.

Book a LangGraph architecture review See our process

5.0 Clutch rating across verified reviews.

2015 Founded as a Python-first engineering company.

7+ 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.

Stateful Graph-Based Agents

Agents built with explicit state, checkpoints, and recovery mechanisms so workflows can continue reliably after failures.

Multi-Agent Workflow Orchestration

Complex agent systems coordinated through cycles, branching, conditional logic, and multi-step execution paths.

Reliable Python & FastAPI Backends

Production-ready APIs that expose agent workflows with authentication, observability, and backend reliability built in.

Human-in-the-Loop Approval Gates

Review and approval checkpoints for high-stakes, sensitive, or irreversible agent actions before they reach users or systems.

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. 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, 7+ 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; 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.

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.

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.

High-stakes actions:

payments, emails to customers, ticket changes, database writes — anything you would want a human to approve.

Long-running processes:

research, document generation, or multi-stage operations that may run for minutes, hours, or across sessions.

Coordination:

several specialised agents or tools that must hand off work and share context.

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.

Stateful single agents

An agent with an explicit state schema, tool use, looped reasoning, and persistence so it can pause and resume.

Multi-agent systems

Supervisor / worker or peer architectures where specialised agents coordinate, hand off, and share state.

LLM workflows & pipelines

Graph-structured, multi-step processes (extraction, generation, review, routing) with branching and retries.

Human-in-the-loop approval

Interrupt points where a person inspects, edits, approves, or rejects a step before the graph continues.

FastAPI / Python backends

APIs, authentication, async execution, queues (Celery), and streaming that put the agent behind a reliable service.

Memory & persistence

Checkpointers and stores (e.g. Postgres) for short-term working memory and long-term memory across sessions.

Evaluation & observability

Tracing, test suites, and metrics (e.g. LangSmith) so behaviour is measurable, debuggable, and auditable.

Rescue & productionization

Stabilising a prototype or stalled agent: fixing state design, loops, cost, latency, and reliability for launch.

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

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.

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.

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

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

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.

State & graph design.

We model the state schema, define nodes and edges, and decide where the graph loops, branches, and pauses for humans.

Build & integrate.

Senior Python engineers implement the graph, tools, and a FastAPI backend, reusing LangChain components where they help.

Persistence & memory.

We add the checkpointer and stores for recovery, multi-session memory, and time-travel debugging.

Evaluation & hardening.

We wire in tracing and evaluation, then control loops, cost, latency, and failure modes against production targets.

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

Indicative rates run $50–$99/hour depending on seniority and specialisation, with no project-management markups or long-term lock-in — typically a 40–60% saving versus equivalent local hires. Matched senior profiles are typically ready within about 48 hours, embed in roughly two weeks, and every engagement includes a 30-day replacement option if an engineer is not the right fit. Exact pricing depends on scope; we will give you a clear estimate after the architecture review.

Case Studies

LangGraph Workflows Designed for Production Reliability

Explore agent systems with checkpointed state, human approvals, retrieval, and resumable execution built around real production constraints.

Agent Orchestration

Enterprise Assistant Orchestration With LangGraph and MCP at Scale

Uvik Software rebuilt Glean’s multi-step agent orchestration with checkpointed LangGraph flows, MCP tool exposure, permission-aware execution, and faster recovery from failed tool calls.

Client

Glean

Industry

Technology and Software, enterprise AI

Delivery model

AI & Data Pod

Approval Workflows

Human Approval Gates for Reliable Agentic Workflow Automation

Uvik Software added durable human approval gates to Tines workflows, preserving agent state, routing reviews by policy, and reducing approval delays without holding execution workers.

Client

Tines

Industry

Technology and Software, security and IT workflow automation

Delivery model

Embedded Python Squad

Claims Automation

Explainable Claims Automation for Faster Health Insurance Reimbursements

Uvik Software automated Alan’s claims document processing with extraction, confidence-based routing, and recorded decision explanations while keeping lower-confidence cases under review.

Client

Alan

Industry

Financial and Regulated Services, digital health insurance

Delivery model

AI & Data Pod

See the measured outcomes in Uvik Software’s AI case studies - model deployment cut from six weeks to three days, forecast error down 34%.

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.

Book a LangGraph architecture review

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

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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