LangChain & LangGraph Engineering
Production LangChain and LangGraph applications, including stateful agent workflows, state-machine rescue, self-correcting pipelines, and full observability.
What you get back
- 1. Diagnosis What works, what is blocked, and why.
- 2. Recommendation Audit, advisory, sprint, or pause.
- 3. Scope Next action, boundaries, and timing.
Stateful LLM Applications in Production
We engineer LangChain and LangGraph systems that go beyond prototype — stateful workflows with explicit control flow, self-correcting execution loops, and LangSmith tracing from development through production.
We also take over existing LangGraph systems when the graph has outgrown prompt-level debugging. The work starts with state schema review, checkpoint behavior, trace inspection, retry boundaries, and legal transition paths between nodes.
What We Build
| Capability | What We Deliver |
|---|---|
| Stateful agent workflows | LangGraph graphs with typed state, conditional edges, and human-in-the-loop checkpoints for approval gates and intervention points |
| LangGraph state machine rescue | Review and repair existing graphs with state drift, routing loops, checkpoint failures, retry ambiguity, or missing LangSmith trace discipline |
| Self-correcting pipelines | retry loops with structured error classification, output validation via Pydantic, and automatic re-prompting on schema violations |
| RAG infrastructure | retrieval-augmented generation with hybrid search (dense + sparse), re-ranking, citation extraction, and chunk-level provenance tracking |
| API-serving LLM chains | LangServe deployments with streaming responses, request batching, and per-endpoint rate limiting |
Engineering Standards
- LCEL composition for all chain construction — explicit, debuggable, and testable at each step
- Pydantic output parsers enforcing structured responses with automatic retry on validation failure
- LangSmith tracing on every chain execution: latency, token usage, and cost attribution per component
- State persistence with checkpointing for long-running workflows that survive process restarts
- Prompt versioning and A/B evaluation with LangSmith datasets and automated scoring
- Input/output guardrails with content filtering and PII detection before and after LLM calls
When to Use This
| If Your Situation Is | Then We Recommend |
|---|---|
| Stateful agent workflow with checkpoints, retries, and HITL gates | LangGraph with Redis/Postgres checkpointing — this page |
| LangGraph workflow exists but suffers from state drift, routing loops, checkpoint failures, or missing trace discipline | Stabilization Sprint first; LangChain & LangGraph Engineering for the follow-on build path |
| Workflows spanning hours/days or requiring cross-service orchestration | Temporal Workflow Engineering — durable execution beyond LangGraph |
| Need trace-level debugging, cost attribution, and eval pipelines | AI Observability Engineering — LangSmith or OpenTelemetry |
| Multi-agent coordination with specialist delegation | CrewAI Engineering — hierarchical agent teams |
| RAG or retrieval is the core problem, not orchestration | RAG Engineering — retrieval before workflow complexity |
Depth of Practice
Our engineering team maintains an extensive LangGraph and LangChain tutorial library, from self-correcting agents to event-driven architectures, on the ActiveWizards blog. We operate LangGraph workflows processing structured document analysis, automated code review, and multi-step research tasks across regulated industries.
Related Reading
Deployments in this area
Codebase Analysis Agent: 30 Seconds to First Answer
Language-aware chunking with Tree-sitter, FAISS vector retrieval, and LLM reasoning. 30 seconds from upload to first contextual answer on any codebase.
Competitor Intelligence Agent: Structured Research Workflow
Multi-agent system for repeatable competitive analysis across pricing, features, and positioning with structured Pydantic-validated output.
Related articles
LangGraph vs Direct API Orchestration: When the Framework Earns Its Weight
A decision framework for choosing between LangGraph and direct API calls — based on orchestration complexity, not ecosystem momentum.
AI EngineeringLangChain Callback Architecture: Building Production Observability Without Third-Party Lock-In
How to build custom LangChain callback handlers with OpenTelemetry integration for vendor-independent observability — what to trace, how to structure it, and what it costs.
AI EngineeringLangGraph Interrupt Patterns Beyond the Basics: Conditional Approval, Batch Review, and Timeout Handling
Three advanced LangGraph interrupt patterns — conditional approval, batch review, and timeout handling — with production Python implementations.
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