LiteCode shrinks coding agents to 8k

This is a practical architecture note more than a product launch, and that’s the point: it shows how to make AI coding tools usable on cheap local or free-tier models without pretending context limits don’t exist.

• –The planner/executor/orchestrator split is the main design win because it keeps each LLM call narrowly scoped and makes parallelism a first-class optimization.
• –Enforcing budgets in code, not prompts, is the right lesson; if the agent can’t fit the file, it needs deterministic fallback logic, not optimism.
• –The biggest operational risk is project-map drift and file renames, so the validation layer matters as much as the model calls.
• –The memory and line-index details make this feel like an engineering system rather than a demo, which is what makes it credible for local LLM workflows.
• –The open question is scale: once projects get larger, the context map itself becomes the bottleneck, so folder-level summaries may need smarter compression or retrieval.