LangGraph development

When you need this

• You built a LangGraph prototype that works until a run is interrupted, after which nobody is sure which node executed, which did not, and whether the tool it called had a side effect.
• Your workflow has grown into one agent with twenty tools and a very long prompt, and you suspect it should be a supervisor routing to specialized workers.
• The graph has no checkpointer, or has one that was never tested for resume, and your product needs a human to approve something in the middle of a run.
• You are migrating from LangChain chains or a hand-rolled loop to LangGraph and want the state schema and the edges designed properly the first time.
• You need to run the graph behind an API with streaming, concurrency and a real database, and the notebook setup does not translate.

What you get

• LangGraph orchestration: a short graph design document plus the implemented graph with typed state and conditional edges
• Checkpointing and state persistence wired to your store, with resume behavior verified by tests
• State and retry handling per node, including bounded retries, fallback paths and interrupt points
• Tool-calling hardening: schema-validated tool inputs, timeouts and a tool-call ledger
• A supervisor or multi-agent layout where the workload justifies it, with a written rationale where it does not
• MCP integration and RAG integration where the graph needs external tools or grounded context
• Evaluation tests: trajectory and output tests that run in CI
• Docker deployment and documentation, including a runbook for interrupted and stuck runs

Outcomes

• A graph whose state is typed and documented, so a change to the schema is a deliberate migration rather than a silent break in production.
• Checkpointed runs that resume from the last completed node after a crash, a timeout or a human interrupt, without repeating side effects.
• Human-in-the-loop points placed exactly where a decision is irreversible, with the run paused and inspectable rather than guessed.
• Bounded retries and fallback paths per node, with the failure recorded in state instead of buried in a log line.
• Trajectory tests that assert which nodes ran and in what order, run in CI and used as the acceptance criteria for the sprint.

How it works

1. 1

   20-minute technical call

   We walk through the workflow as it exists, ideally with the graph drawn or the code open. I want to know what state flows through it, which nodes talk to the outside world, where a human has to intervene, and what a bad run looked like the last time it happened.

2. 2

   Written diagnosis

   You receive a written diagnosis of the graph: where state is lost or duplicated, which edges are ambiguous, whether the checkpointer is configured so resume actually works, where the graph does work a plain function could do, and what a run costs in tokens. It stands on its own if you decide to do the work in-house.

3. 3

   Fixed-scope proposal

   A fixed-scope proposal under the AI Agent Engineering Sprint, starting at $1,200 for 7–10 working days. It lists the nodes, the state schema, the interrupt points, the tests and the deployment target that define done.

4. 4

   Implementation and handover

   I implement the graph, its persistence and its tests in your repository, then hand over the code, the documentation and a runbook with a walkthrough. The bar I set is that your engineers can add a node or a tool without calling me.

Proof: what I built

• Jozveh-AI runs a LangGraph supervisor over eight agents: source material passes through retrieval and reranking on Neo4j GraphRAG, source-cited generation, then RAGAS, a claim-level LLM judge and grounding filters before an RTL document is produced. The pipeline is covered by 673 tests across 77 files.

  Read the Jozveh-AI case study

• In OmidGPT the agentic runtime handles resumable tool turns, a tool-call ledger and per-tool user consent, which is the same state and retry discipline I apply when a LangGraph node calls a tool with side effects.

  Read the OmidGPT case study