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langchain and langgraph agents

LangChain and LangGraph agents, built, evaluated, and deployed.

Gaper builds production agents on LangChain and LangGraph and deploys them into your stack. We design the graph, wire the tools and retrieval, gate the risky steps with evals, then hand over code, evals, and a runbook your team owns.

Book a free AI assessmentWhat is an AI agent?
Map the workflowBuild the supervised agentSandbox, verify, go live
gaper · agent runtime
$ gaper deploy agent --to production
✓ plan ……………… 4 steps
✓ retrieve …… 1,240 docs grounded
✓ tool ………… salesforce.update_record
✓ eval ………… 12/12 checks passed
● live · p95 1.2s · 0 errors
● in productionowned by your team
In one sentence

A LangChain and LangGraph agent is a stateful, multi-step AI workflow built with the LangChain framework and its LangGraph orchestration layer. It plans, calls tools, retrieves context, and routes between nodes to finish work inside your systems, with checkpoints, retries, and human approval gates where risk matters.

ProductionNot another demo
Model-agnostic
In your cloudYour auth, your data
You own itCode, evals, runbook
Why this matters

LangChain makes a demo agent easy. A LangGraph agent that holds up on real data, loops without runaway cost, and recovers from tool failures in production is the hard part, and it is the only part that matters.

Production filter
  • Does it touch real systems?
  • Can the outcome be measured?
  • Where does human approval stay?
  • Who owns it after launch?
Free AI assessment

Book a free assessment. We will identify one high-leverage workflow, make the build-vs-buy call, and scope the smallest production release.

Map your first production agent
How we work

From strategy to production, owned by your team.

  1. 01

    Map the workflow

    We start from the documents, SOPs, portals, inboxes, and spreadsheets your team already uses, then turn the repeatable path into an agent workflow map.

  2. 02

    Build the supervised agent

    We build on OpenAI, Claude, Gemini, or the right model for the job, with evals, guardrails, citations, and human approval gates where risk matters.

  3. 03

    Connect the stack

    The agent gets the data layer, APIs, MCP tools, auth, and write-backs it needs to finish work inside your systems, not beside them.

  4. 04

    Sandbox, verify, go live

    We launch in a sandbox, verify every run, then move into supervised production with traces, rollback, and an owner.

What we build

Agents wired into the systems you already run.

LangGraph orchestration

Stateful graphs with branching, loops, checkpoints, and supervisor patterns, so complex jobs get decomposed and finished instead of stalling mid-run.

Tool and API actions

Agents that act: update the CRM, file the ticket, reconcile the ledger, wired through your APIs and MCP with retries and clean failure handling.

Retrieval and RAG

Grounded answers over your documents and data with citations and freshness, built on LangChain retrievers and your vector store, not a toy index.

Evals and human gates

Automated evals on every release plus approval gates on signatures, submissions, and policy changes, so quality is measured before users see it.

Tracing and observability

Full run traces, token cost, and quality dashboards via LangSmith or your own stack, so you can see exactly what each node did and why.

Migration and refactor

We take brittle LangChain prototypes or notebook agents and rebuild them as a maintainable LangGraph system that survives production load.

Built into production, not bolted on

We deploy the LangGraph agent where your data lives: your cloud, your auth, your controls. It inherits your security posture instead of widening your attack surface, and runs on the model that fits the job.

  • Runs in your environment or ours
  • SSO, RBAC, and audit logging
  • Model-agnostic: OpenAI, Claude, or Gemini
Deploy target
OpenAIOpenAIClaudeClaudeGeminiGeminiSalesforceSalesforceSnowflakeSnowflakePostgresPostgres
SSORBACAudit logCloud

You own the graph, and the code

We hand over a clean LangChain and LangGraph codebase with the evals, traces, and runbook to operate it. No black box, no lock-in, no agent trapped in a vendor workflow outside your stack.

  • Documented graph and node logic
  • Eval suite and runbook included
  • Extend and redeploy without us
Handover state
handoff packageCode, runbook, evals, dashboard
owned by your team
Source repoRunbookEval suiteOwner training

Access your auth

Data your environment

Ops monitor or handoff

From prototype to production, the part most teams miss

Most LangChain agents stall as demos: no evals, runaway loops, no owner. We design for production from day one, with checkpoints, cost ceilings, retries, and a real owner at launch.

  • Evals that gate every release
  • Loop limits and cost ceilings
  • Fallback and escalation paths
Release gate
  1. 01Eval suiteknown + edge casespass
  2. 02Policy checkguardrails enforcedpass
  3. 03Human fallbacklow-confidence routedhold
  4. 04Releaseshipped to prodlive

p95 latency 1.2s

eval pass 12/12

rollback ready

Model and stack agnostic
OpenAIClaudeGeminiLangChainMCPPythonTypeScriptPinecone
FAQ

Questions buyers ask us.

What does Gaper build with LangChain and LangGraph?+
We build production AI agents: stateful LangGraph workflows that plan, call tools, retrieve context, and route between nodes to finish real work in your systems. We design the graph, wire retrieval and integrations, add evals and human gates, and deploy it into your stack.
Do you staff or place LangChain developers?+
No. Gaper is not a staffing or staff-augmentation firm. We do not hire out, place, or supply developers. We build and deploy the LangChain and LangGraph agent for you and hand over the code, evals, and runbook, which your team owns.
Do we own what you build, or is it locked to you?+
You own it. We deliver a documented LangChain and LangGraph codebase with the eval suite, traces, and a runbook. You can run, extend, and redeploy it without us. No black box and no vendor lock-in.
Why LangGraph instead of plain LangChain chains?+
Plain chains break on anything stateful or multi-step. LangGraph gives you explicit state, branching, loops, checkpoints, and supervisor patterns, which is what production agents need to recover from failures and stay within cost limits. We use the right layer for the job and stay model-agnostic.
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Ready to deploy your first agent?

Book a free 30-minute assessment. We'll map the highest-leverage workflow and scope the smallest thing worth shipping, live in as little as 24 hours.

Build, deploy, runYour cloudYou own the code