LangGraph & Autonomous Multi-Agent Systems
Cyclic stateful AI workflows engineered with LangGraph & Python.
Move beyond simple sequential chains. We build stateful, multi-actor AI agent networks using LangGraph, CrewAI, and custom Python backends that handle complex, non-deterministic business logic with persistent memory.
Autonomous Research & Report Generation
Research agents that scour web data, summarize insights, format code, and compile multi-page executive PDFs.
Complex Customer Operations & Ticket Resolution
Multi-step support agents that query databases, verify customer identities, issue refunds, and update CRM records.
State Schema & Node Mapping
Defining typed state representations, node transition rules, and conditional edge routing conditions.
Tool Binding & Memory Persistence
Binding API tools, vector database retrievers (Pinecone/Weaviate), and SQLite/PostgreSQL checkpointing for conversational memory.
Human-In-The-Loop Interrupts
Designing breakpoint triggers where critical actions wait for human supervisor approval before mutating external production data.
Evaluation & Production Scaling
Benchmark evals with LangSmith, streaming response setup, and FastAPI container deployment.
- →Custom LangGraph StateGraph Python Application
- →FastAPI REST & Streaming Server Wrappers
- →LangSmith Evaluation & Audit Logging Setup
- →Dockerized Container Deployment (AWS/GCP/Render)
What makes LangGraph different from standard LangChain?
LangChain is designed for linear DAG workflows, while LangGraph enables cyclic graphs with persistent state, making complex agent loops, branching decisions, and human interventions seamless.
Ready to start?
Tell us what you're building and we'll tell you exactly how we'd approach it.
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