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LangGraph Development Services

Checkpointed, auditable AI agents on LangGraph, for engineering leaders whose prototype agent stopped being trustworthy the moment real traffic hit it.

AlphaCorp AI's LangGraph development services design, build, and deploy agents on LangGraph, the MIT-licensed orchestration runtime that models an agent as a directed graph of nodes, edges, and shared state, with built-in checkpointing, human-in-the-loop pauses, and step-level tracing. We take a workflow that currently lives in a fragile prompt loop and turn it into an explicit graph your team can inspect, resume after a crash, gate with human approval, and evaluate decision by decision. We build on LangGraph 1.0, generally available since October 22, 2025, in Python or LangGraph.js, and deploy to your own cloud, LangSmith, Amazon Bedrock, or Microsoft Foundry. The service fits mid-to-large teams in healthcare, financial services, SaaS, and logistics that need an agent to hold up under an audit.

Track record

Creators of RustyRAG

Realtime RAG, built in Rust · Sub-200ms end-to-end

10+ years delivering AI solutionsIgnas Vaitukaitis · Founder & CEO

Read RustyRAG’s source before you sign.

Shipped for

VersarWashington, DC
GynisusNew York
CampusReelNew York
LuniqGermany
HospitalityFlowSingapore

LangGraph development services by the numbers

Agents reached production in 2025. Most of the ones that fail do so for reasons a graph runtime is built to remove.

57%of organizations had AI agents in production by late 2025, up from 51% a year earlierLangChain, 2025 ↗

>40%of agentic AI projects will be canceled by the end of 2027, on cost, unclear value, and weak risk controlsGartner, 2025 ↗

CVSS 9.3severity of CVE-2025-68664, a serialization-injection flaw in langchain-core reachable through prompt injectionNVD, 2025 ↗

Overview

What our LangGraph development services build

AlphaCorp AI builds the six layers a LangGraph agent needs before it can run unattended: the graph, its state, its pause points, its sub-agents, its retrieval, and its observability. Each one maps to a concrete deliverable.

Graph architecture and conditional routing

We model your workflow as nodes, LLM calls and deterministic code, joined by edges that branch or loop on state. Routing decisions live in code your team can read and test instead of being buried in a system prompt.

Durable state and checkpointing

We wire LangGraph's persistence layer so an interrupted run resumes from the last saved step. Picking between the exit, async, and sync durability modes is a real design choice, and we choose per node based on what a lost step would cost you.

Human-in-the-loop approval gates

We use LangGraph's native interrupt APIs to pause the graph so a person can inspect, edit, or approve state before it continues. Replit uses this pattern to expose package installs and file creation to the user; we apply it to payments, record changes, and outbound messages.

Multi-agent networks

We build supervisor and hierarchical patterns of the kind LinkedIn and Uber run on LangGraph, as part of our broader AI agent development practice. Each sub-agent is its own graph with its own state schema.

Retrieval nodes backed by RustyRAG

When a node needs grounded context, we plug in a retrieval pipeline from our RAG development work, RustyRAG included, so the agent cites your documents instead of guessing.

Tracing, evaluation, and deployment

We instrument every LLM call and tool invocation in LangSmith, write evaluators that score intermediate trajectory decisions rather than only final outputs, and ship the graph through the MLOps and DevOps pipeline that keeps it running.

Bar chart of the top barriers to putting AI agents in production in 2025, from LangChain's State of Agent Engineering survey of 1,340 practitioners run November 18 to December 2, 2025. Quality and accuracy leads at 33% of all respondents, security follows at 24.9% among larger enterprises, and latency is lowest at 20% of all respondents. — Quality and accuracy was the leading production barrier for 33% of the 1,340 practitioners surveyed, ahead of latency at 20%. LangChain State of Agent Engineering, 2025 ↗

03Stack

The Stack We Ship On

We pick the best tool for each job, not the trendiest. This is what runs behind the agents, retrieval pipelines and automation we put into production.

Languages

PythonRustTypeScript

Foundation Models

AnthropicOpenAIGeminiLlamaMistralHugging Face

Fast Inference

GroqCerebrasOpenRouterReplicateOllamavLLM

Agents & Orchestration

LangGraphLangChainLlamaIndexCrewAIn8n

Vector & Memory

MilvusPineconepgvectorChromaWeaviateRedis

Voice, Image & Fine-Tuning

ElevenLabsLiveKitVapiComfyUIPyTorch / LoRAModal

Cloud & Delivery

AWSAzureGoogle CloudDockerKubernetesVercel

Evals & Observability

LangSmithLangfuseWeights & BiasesGrafana

Process

How a LangGraph development services engagement runs

An engagement runs in five sequential steps, and the first one decides whether LangGraph is the right tool at all.

Workflow and framework fit review

We map the process the agent will own, identify every point where it needs to pause, branch, or recover, and say plainly if a simpler ReAct loop or a provider SDK would serve you better.

State schema and graph design

We define the shared state object, the nodes, the conditional edges, and the durability mode for each step, and review the design with your engineers before code is written.

Build and security hardening

We implement the graph in Python or LangGraph.js, pin langchain-core at or above 1.2.5, or 0.3.81 on the older line, and review every boundary where LLM or tool output enters state.

Evaluation against production traces

We run LLM-as-judge, code-based, and multi-turn evaluators in LangSmith against real trajectories, scoring routing decisions and tool calls, and iterate until the numbers hold.

Deployment and handover

We deploy to your target runtime, hand over the graph with documentation and evaluator suites, and train your team on operating it.

Vertical five-step process diagram of an AlphaCorp AI LangGraph development services engagement. Step 1, workflow and framework fit review: map the process, find every pause, branch and recovery point, and say plainly if a simpler ReAct loop or provider SDK fits better. Step 2, state schema and graph design: define the shared state object, nodes, conditional edges and durability mode per step, reviewed with your engineers before code is written. Step 3, build and security hardening: implement the graph in Python or LangGraph.js, pin langchain-core at 1.2.5 or later, and review every boundary where LLM or tool output enters state. Step 4, evaluation against production traces: run LLM-as-judge, code-based and multi-turn evaluators in LangSmith against real trajectories. Step 5, deployment and handover: deploy to your target runtime, hand over the graph with documentation and evaluator suit — The engagement runs in five sequential steps, and step one can end with the recommendation not to use LangGraph at all.

Benefits

Why invest in LangGraph development services

A LangGraph build pays for itself by removing the specific failure modes that get agent projects canceled: silent crashes, unreviewable decisions, and no way to prove what the agent did.

Runs resume instead of restarting

A crash mid-run no longer throws away twenty completed steps. LangGraph's durable execution resumes from the last checkpoint, which matters most for long-running back-office processes.

Every decision is traceable

Each LLM call, tool invocation, and intermediate reasoning step lands in a trace. When a stakeholder asks why the agent refunded a customer, you show them the path through the graph.

Humans approve the risky steps

Pause points are product features rather than prompt instructions the model may ignore. That is the difference between an agent your compliance team tolerates and one it signs off on.

Quality is measured before launch

Quality and accuracy was the top production barrier for 33% of practitioners in LangChain's 2025 survey, and the same survey found only 52.4% ran offline evaluations. We ship evaluator suites with every graph, so you are in the other half.

A stable foundation since 2025

LangGraph 1.0 shipped on October 22, 2025 with a backward-compatibility commitment, after more than a year in production at companies including JPMorgan, BlackRock, and Cisco. You are building on a stable API instead of a moving research project.

Why AlphaCorp AI

Why teams pick AlphaCorp AI for LangGraph development services

AlphaCorp AI is the engineering studio behind RustyRAG, an open-source sub-200ms RAG engine, and the engineers you meet on the first call are the ones who write your graph. The team works remote-first from Rio de Janeiro on US Eastern hours, in English, Portuguese, and Spanish. The framework carries its own track record: LangChain reports LangGraph running in production at Klarna, Uber, LinkedIn, Replit, and Elastic before the 1.0 release, which are vendor-published case studies, so weigh them accordingly.

We name the overkill case up front. A July 2026 practitioner paper on graph-based agents cautions that for basic tool-use tasks a plain ReAct loop or a provider SDK can outperform LangGraph. We agree. If your workflow has no pauses, no branching, and no need to recover mid-run, step one of our engagement ends with that recommendation and a much smaller scope.

Evaluation is part of the deliverable. Observability adoption hit 89% in LangChain's 2025 survey while online evaluation sat at 37.3%. Most teams can see their traces and still cannot say whether the agent is getting better. Trajectory evaluators ship with our graphs, and so does the baseline they were measured against.

We review the serialization boundary. Two of the three LangChain-ecosystem vulnerabilities disclosed in 2025 were serialization-injection flaws, and the third was indirect prompt injection through an email toolkit. Orchestration logic is the easy part. We spend real time on where untrusted output enters state.

The tradeoff we accept. LangGraph adds a layer between you and the model provider, and that layer has a dependency chain you must patch. We pin versions, monitor advisories, and hand you a runbook, and you should budget engineering time for it after launch. Read about how the studio works.

Security in our LangGraph development services

AlphaCorp AI treats every LangGraph agent as software that executes tool calls on untrusted input, and hardens it on that assumption. We do not claim certifications on your behalf; we build the practices below into the graph and document them for your auditors.

Patched serialization boundaries. We pin langchain-core to 1.2.5 or later, 0.3.81 on the 0.3 line, the versions that closed CVE-2025-68664 and CVE-2025-68665, and we review any code path where a model-produced dictionary could reach the serializer.

Untrusted content stays untrusted. Inbox content, web pages, and tool results enter state as data and never as instructions, the lesson from CVE-2025-46059's Gmail toolkit exploit path in July 2025.

Approval gates on side effects. Any node that moves money, edits records, or sends messages sits behind a human-in-the-loop interrupt until your evaluators earn it autonomy.

Your data stays in your runtime. We deploy inside your cloud account, whether that is self-hosted, Amazon Bedrock, or Microsoft Foundry's managed LangGraph runtime, so prompts and state never leave your perimeter unless you choose LangSmith cloud tracing. LangChain 1.0's middleware system also lets us inject PII redaction and conversation summarization into the agent loop, keeping sensitive fields out of traces.

FAQ

LangGraph development services FAQs

What are LangGraph development services?

LangGraph development services design, build, and operate stateful AI agents on LangGraph, the open-source runtime that represents an agent as a graph of nodes and edges over shared state. AlphaCorp AI's version covers workflow fit, state design, implementation, security hardening, evaluation, and deployment. LangChain positions LangGraph as the orchestration layer beneath its own agent framework, and since LangChain 1.0 the create_agent API runs on top of it.

How much do LangGraph development services cost?

Scope decides the price, and a working session with AlphaCorp AI prices it: node count, number of integrations, approval gates, and evaluation depth are the variables that move the number. The framework itself is free under the MIT license. Platform costs are separate: LangSmith offers a free Developer tier, a Plus tier at $39 per seat per month, and custom-priced Enterprise with self-hosted options, plus metered compute beyond included allowances.

How long does a LangGraph engagement take?

Duration follows the number of nodes, integrations, and human approval points in your workflow, and we commit to a timeline only after the fit review in step one. A single-graph agent with a handful of tools is a shorter build than a hierarchical multi-agent system with retrieval and several pause points. We stage delivery so a working graph reaches your staging environment before the full evaluation suite is finished.

How does LangGraph compare to CrewAI, AutoGen, or Bedrock Agents?

LangGraph models agents as an explicit graph with conditional routing, CrewAI as organizational roles, AutoGen as agents that converse to reach consensus, and Amazon Bedrock Agents as a native supervisor pattern inside AWS. Pick LangGraph when you need checkpointed state, mid-run pauses, and per-step audit trails. Peer-reviewed comparison is still young: REALM-Bench benchmarks LangGraph, CrewAI, AutoGen, and others across six LLM families, and the Open Agent Specification work proposes a framework-agnostic evaluation harness. AlphaCorp AI reads those results with you rather than defaulting to a favorite.

Can you integrate a LangGraph agent with our existing systems?

Yes. Nodes are ordinary functions, so your existing APIs, databases, and internal services become tool calls or deterministic steps in the graph. Retrieval over your documents comes from our RAG pipelines, and the graph deploys into your cloud. Microsoft Foundry can host compiled LangGraph graphs directly, handling scaling and identity, and AWS documents a multi-agent reference pattern with Bedrock.

Should we build in Python or LangGraph.js?

Choose by where your engineers already live. The Python package is the primary implementation; LangGraph.js shares the same graph primitives and runs on Node.js, Deno, Cloudflare Workers, Vercel Edge Functions, and in browsers. Note that LangChain 1.0 dropped Python 3.9 support in October 2025, so a Python build means 3.10 or later. Our Python development and TypeScript engineers cover both.

What happens after the LangGraph agent launches?

AlphaCorp AI hands over the graph, the evaluator suite, the trace configuration, and a patching runbook, then offers ongoing support for dependency updates and evaluation reviews. LangChain Academy's free courses help your team stay current as the API evolves. Your engineers own the code from day one.

Updated September 10, 2026