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AI coding tools forget everything. LLM Kanban remembers.

A persistent Kanban-style board that gives LLMs external memory, time tracking, and structured workflows — solving the context window and session persistence problems that limit AI coding tools.

BACKLOGIN PROGTESTINGDONEAICONTEXT PERSISTS

External memory for AI development.

External Memory

Context that persists across sessions

LLM Kanban maintains a persistent state file that survives session resets, context window limits, and model switches. The AI picks up exactly where it left off — with full history of decisions, progress, and blockers.

Task Workflow

Backlog → In Progress → Testing → Done

Cards move through structured columns just like human Kanban. The LLM picks up a card, moves it to In Progress, implements the tasks, documents progress, and advances it to Testing — all tracked and auditable.

Collaboration Models

LLM-driven and Human-driven modes

In LLM-driven mode, AI autonomously implements and advances cards while humans review in Testing. In Human-driven mode, engineers direct work while AI handles implementation details. Both models use the same board, same workflow, same visibility.

Time Tracking & Analytics

Know exactly where AI time goes

Every card tracks estimated vs. actual time, implementation notes, and completion metadata. Teams get real data on AI productivity — not estimates. This compounds into institutional knowledge about which tasks AI handles best.

How LLM Kanban works in practice.

01

Create the board

Define columns (Backlog, In Progress, Testing, Done), create cards with descriptions and acceptance criteria, and set the LLM's operating instructions.

02

LLM picks up work

The AI reads the board state, selects the highest-priority card, moves it to In Progress, and begins implementation — documenting every decision in the card's progress log.

03

Human reviews in Testing

Completed work moves to Testing where engineers review, provide feedback, and either advance to Done or return to In Progress with notes. The AI reads the feedback and iterates.

04

Context compounds over time

As the board accumulates history — completed cards, design decisions, known patterns — the AI's effectiveness grows. New sessions start with full institutional context, not a blank slate.

Give your AI tools a memory.

60 minutes. We'll show you how LLM Kanban integrates into your existing workflow and where persistent AI context creates the most leverage for your team.

Book a Strategy Session

Free. See the framework in action on a real project.