LangChain & LangGraph Engineering
Production LangChain and LangGraph applications, including stateful agent workflows, state-machine rescue, self-correcting pipelines, and full observability.
What happens next
- 1. Context We review the situation and constraints.
- 2. Fit We recommend an appropriate next step.
- 3. Scope If relevant, we discuss scope.
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
| Standard | What It Protects |
|---|---|
| Explicit chain construction | Each step remains debuggable and testable |
| Structured output parsing | Invalid responses stay out of downstream workflow paths |
| Trace coverage across chain execution | Latency, token usage, and component behavior are visible |
| State persistence with checkpointing | Long-running workflows can recover across process restarts |
| Prompt versioning and evaluation datasets | Changes can be reviewed against expected behavior |
| Input and output guardrails | Sensitive data and unsafe outputs are checked before workflow state advances |
When to Use This
| If Your Situation Is | Then We Recommend |
|---|---|
| Stateful agent workflow with checkpoints, retries, and HITL gates | LangGraph with Redis or Postgres checkpointing: this page |
| LangGraph workflow exists but suffers from state drift, routing loops, checkpoint failures, or missing trace discipline | Stabilization Sprint when the failure path is known; Production AI Readiness Review when diagnosis is still needed. LangChain & LangGraph Engineering for the follow-on build path. |
| Workflows spanning hours or 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 Paths
| If You Need To | Read |
|---|---|
| Study a code-analysis agent pattern | GitHub Code Analysis Agent with LangChain |
| Serve LangChain or LangGraph systems | FastAPI for LLM Systems: Production Template for LangChain and LangGraph Agents |
| Build around messy external APIs | Build an ETL Agent with LangChain for Messy APIs |
| Add LangSmith visibility | LLM Observability with LangSmith |
Engineering evidence
Codebase Analysis Agent: 30 Seconds to First Answer
ActiveWizards (Internal)
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
ActiveWizards (Internal)
Multi-agent system for repeatable competitive analysis across pricing, features, and positioning with structured Pydantic-validated output.
Related articles
Why AI Adoption Fails Without Workflow Redesign
Why AI adoption stalls after the pilot: unchanged handoffs, weak approval design, missing exception routing, and no operating model for reviewers, owners, and rollback.
AI EngineeringLangGraph 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.
Discuss your LangChain & LangGraph Engineering path
Tell us about your system, the decision ahead, and the constraints. We will review the context and recommend the next step.
Direct contact with a principal engineer.