LangChain

LangChain Agent Development

Production LangChain agents with memory, tools, and real-world integrations that actually ship.

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Overview

Production LangChain agents with memory, tools, and real-world integrations that actually ship.

We build robust LangChain agents and chains for enterprises that need proven architecture, maintainable code, and reliable performance.

Who this is for

Built for these teams and businesses

Engineering teams adding AI to existing productsEnterprises with complex LLM pipelinesCompanies that need RAG + agent hybrid systems

Use cases

What we build for you

◆RAG pipelines with intelligent retrieval and re-ranking
◆Document processing and extraction agents
◆Conversational agents with persistent memory
◆API orchestration and data pipeline agents
◆Code review and generation agents

Problems we solve

— LangChain prototypes that don't make it to production
— Agents that hallucinate without proper grounding
— Memory and context management at scale
— Need engineers who know LangChain deeply

Tech stack

Platforms and tools we use

LangChainLangSmithOpenAI APIAnthropic Claude APIVector databases (Pinecone, Weaviate, pgvector)

What you get

Deliverables

◆ LangChain agent build

◆ LangSmith tracing setup

◆ RAG pipeline if applicable

◆ Production-ready deployment

Typical timeline: 2–8 weeks

Process

How an engagement works

Discovery call

We map your workflows, understand your tools, and identify the highest-ROI agent opportunity in your business.

Architecture design

We design the agent: inputs, outputs, integrations, error handling, and the logic that makes it reliable in production.

Build and integrate

We build the agent, connect it to your tools and data, and run it through real scenarios — not sanitized demos.

Test and harden

We stress-test edge cases, add monitoring, and make sure the agent degrades gracefully when something unexpected happens.

Deploy and hand off

We deploy to production, set up alerting, and train your team to manage and iterate on the agent going forward.

Why Lumeor

What to expect working with us

◆Production deployments only — we don't hand over prototypes and call them done
◆We set up monitoring and alerting on every agent we ship
◆Transparent about what AI can and can't do reliably
◆Model-agnostic — we pick the right tool for your use case, not what we're certified in
◆We document everything so your team can maintain and extend what we build
◆30-day stabilization period included in every engagement
◆We've shipped agents for businesses across every major US industry

FAQ

Common questions

How long does it take to build and deploy an AI agent?

Most simple agents (single workflow, clear inputs/outputs) are live in 1–2 weeks. More complex agents with multiple integrations or multi-agent architectures take 3–8 weeks. We'll give you an accurate timeline after our first call.

What happens if the agent breaks after you deliver it?

We include a 30-day stabilization period in every engagement. If something breaks, we fix it. We also set up monitoring and alerting so we catch issues before you do. Post-stabilization support retainers are also available.

Can you build agents that connect to my existing software?

Yes — that's the norm, not the exception. We integrate with CRMs, ERPs, ticketing systems, communication tools, databases, and custom APIs. If it has an API or webhook, we can connect an agent to it.

Which AI models and platforms do you use?

We're model-agnostic and platform-agnostic. We use OpenAI, Anthropic Claude, Google Gemini, and open-source models depending on the use case. For automation, we commonly use n8n, CrewAI, LangChain, and custom Python builds.

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