Hire a LangChain & LangGraph Engineer for Production AI

Who this is for

• You followed a LangChain quick-start and now the codebase is spaghetti — your team can't reason about it and refactor risk is high.
• You need to add tool-use, memory, streaming, or proper state management to an existing chatbot and your team hasn't shipped either at scale.
• You want to migrate a brittle LangChain app to LangGraph to get checkpointer-based crash recovery and a cleaner mental model.

What you get

• ✓A clean LangGraph or LangChain codebase with opinionated structure — chains, graphs, prompts, tools, evals each in their own place.
• ✓Production-grade retrievers: chunking, embeddings, hybrid search (BM25 + dense), reranking, and answer citation.
• ✓Custom tools wired to your APIs with proper schemas, error handling, retry logic, and MCP-native exposure.
• ✓Streaming end-to-end so your UI doesn't sit spinning on long generations.
• ✓LangSmith or LangFuse tracing with cost and latency dashboards.
• ✓Eval harness using LangSmith datasets or custom scripts — every prompt change is measurable.
• ✓Full code review and a 4-hour team workshop on the architecture.

How a LangGraph engagement runs

1. Week 1

   Codebase audit or greenfield scope

   If existing code: walkthrough + audit report with prioritized fixes. If greenfield: state schema and tool list.

2. Week 2

   Architecture + eval baseline

   Choose LangChain vs LangGraph, set up tracing, write the first 20 eval cases so every future change is measurable.

3. Weeks 3–4

   Build sprints

   Retrievers, tools, memory, streaming — in priority order. Weekly demo with traces and eval scores.

4. Week 5+

   Hardening + handoff

   Load-test, tune, document. Optional Retainer for ongoing prompt and eval work.

What I build with

Default to LangGraph over linear LangChain for production systems — cyclical graphs make agentic loops and error correction tractable, and the checkpointer gives you crash recovery for free. For RAG, hybrid retrieval (BM25 + dense vectors) beats pure vector search; in tests vector-only misses up to 24% of exact keyword matches like SKU numbers and client names. My RAG pipelines use pgvector + Cohere reranking with P95 latency under 180ms. MCP-native tool exposure from day one so you stop paying the N×M integration tax.

LangChain projects I've shipped

How pricing works

What drives the price up

• Greenfield vs audit + refactor of an existing codebase
• Number of retrievers, tools, and external integrations
• Whether you need a full eval harness or just baseline tracing
• Python vs TypeScript (both supported)

Standalone engagements run $8–25K depending on scope. Most LangGraph work lands inside an AI Product Sprint ($30–60K) or as part of an Agentic Product Retainer ($15–30K/mo). The standalone tier exists for teams who already know exactly what they need.

Related offers