Agentic Systems with LangGraph, SQLite WAL & Local LLMs

As Large Language Model applications evolve beyond simple prompt-response wrappers, cyclic agent state machines have become essential. In SupportFlow AI, I implemented an agentic customer support system designed around state graphs, local open-weight inference, and persistent session memory.

Why Cyclic State Graphs?

Traditional linear chains (DAGs) fail when an agent encounters unexpected input or missing context. LangGraph allows building state machines with conditional loops:

[ User Query ] ──► [ Intent Classifier Node ]
                            │
               ┌────────────┴────────────┐
               ▼                         ▼
      [ Knowledge RAG Node ]    [ Escalation Node ]
               │                         │
               ▼                         │
      [ Quality Check Gate ] ◄───────────┘
         │            │
      (Valid)     (Clarify)
         │            │
         ▼            └──────► [ Clarification Node ] ──┐
     [ Output ]                                         │
         ▲                                              │
         └──────────────────────────────────────────────┘

If the generated answer does not satisfy confidence thresholds during the verification step, the graph conditionally loops back to formulate clarification questions rather than delivering hallucinated responses.

Local Inference with Ollama & SQLite WAL

To ensure complete privacy and zero per-token cost during development:

• Local LLMs: Powered by Ollama running Mistral and Llama 3 models locally.
• SQLite WAL Persistence: Every state transition is written to an SQLite database with Write-Ahead Logging (WAL) enabled, allowing multi-threaded read concurrency while agents serialize conversation states.

Key Learnings

1. State boundaries must be strictly typed: Using Pydantic models for graph state prevents runtime type degradation as messages traverse multiple nodes.
2. Deterministic routing reduces latency: Simple regex and keyword classifiers should triage basic intents before invoking heavier LLM nodes.
3. Local LLMs are ready for agent routing: Smaller 7B/8B models excel at constrained JSON classification when prompted with few-shot examples.

Check out the code on GitHub.