LangChain & LangGraph Engineering
Production LangChain and LangGraph applications with stateful agent workflows, self-correcting pipelines, and full observability. We build LLM-powered systems that run reliably at scale with deterministic control flow and structured outputs.
What happens after you submit specs
1. Context
We inspect the system, constraints, and where delivery or architecture risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory track, scoped build, or a clear signal that the work is not ready yet.
3. Next Step
If there is a fit, we define the shortest path to a useful engagement and a production-ready outcome.
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.
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 |
| 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 |
| 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: 8 Hours to 5 Minutes
Multi-agent system with parallel execution. Automated competitive analysis across pricing, features, and positioning with structured Pydantic-validated output.
Related articles
When Agent Orchestration Beats a Single-Agent Workflow
A practical architecture decision guide: when agent orchestration actually beats a single-agent workflow on quality, control, and operating economics.
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
Submit system context, constraints, and delivery pressure. A Principal Engineer reviews every submission and recommends the right next step.
1. Context
We review the system, constraints, and where risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory, sprint, or pause.
3. Next Step
If there is a fit, we define the shortest useful engagement.
No SDRs. A Principal Engineer reviews every submission.