Services
A LangChain developer and LangGraph engineer for state machines, hybrid RAG, tool use, streaming and the eval harness that stops regressions. For teams that know what to build and need it to hold up in production.
Standalone engagements and LangGraph consulting 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.
If existing code: walkthrough + audit report with prioritized fixes. If greenfield: state schema and tool list.
Choose LangChain vs LangGraph, set up tracing, write the first 20 eval cases so every future change is measurable.
Retrievers, tools, memory, streaming — in priority order. Weekly demo with traces and eval scores.
Load-test, tune, document. Optional Retainer for ongoing prompt and eval work.
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.
Real questions from scoping calls. If yours isn't here, ask it on a free 30-minute call.
LangChain works for single-prompt chains, RAG, simple tool-use. LangGraph is better the moment you need state that survives across steps, conditional branches, or human-in-the-loop. In 2026 I default to LangGraph for anything beyond a single-shot query — the mental model is cleaner and the checkpointer gives you crash recovery for free.
Yes — one of my most common engagements. Two phases: wrap the existing chain as a LangGraph node first (no behavior change, just lets us start adding state), then decompose incrementally. Keeps the system shippable throughout the migration.
LangSmith by default — cleanest integration with the LangChain stack. LangFuse if you need self-hosted tracing for compliance or if budget is tight. Either way, tracing goes in before we ship — not after the first bug report.
Both. Python for data-heavy pipelines, TS when your agent lives inside a Next.js or Node app. I'll match your existing stack.
I write 20–30 golden test cases in Week 2, hook them to LangSmith datasets (or GitHub Actions), and every prompt/model change runs them automatically. No eval harness = no way to know if you broke something.