Pi Coding Agent with Qwen 35B Q2: Using Filesystem as External Memory and Enforcing Context Guards

A Reddit user shared their approach to agentic coding with local LLMs, built on Pi coding agent with Qwen 35B (Q2_K_XL quant via LM Studio). The core insight: treat the LLM as a logic processor, not a context database. The implementation enforces strict guards at the API boundary — the model cannot bypass them.

Key constraints enforced by the system

• Write/edit limit: Rejects any output over 100 lines. Model must write a skeleton first, then fill in one section at a time. If it tries to dump a full file, the call is blocked with instructions to split the work.
• Thinking block cap: If the model's reasoning exceeds 2000 chars, it receives a correction to write conclusions to disk and move on.
• Context monitor: At 65% context usage, the model is told to write its state to files. At 80%, everything stops — the model writes its 'brain' to disk while still coherent.
• Persistent output: If the model gives a long answer without writing a file, it's instructed to save findings to a step file. Nothing stays only in context.

External brain structure

The system uses .think/ and .plan/ directories as the model's external memory. Every step, decision, and finding is written to a file. When context compresses, the model reads its own notes back. The session purpose is saved separately to _purpose.md and re-injected after context compression, preserving the original goal.

Knowledge distillation

A /distill command crawls a codebase, builds an import graph, topologically sorts files, and has the model summarize them one per turn into a knowledge base. The manifest is split into pages of 50 files to avoid consuming the whole context. Users can drop files like svelte5-gotchas.md or astro-gotchas.md into a knowledge folder; an isolated LLM call selects which ones are relevant to the current task, and only the content gets injected into the main conversation.

Real-world result

The user asked the model to build a Three.js plane flying game. The first attempt tried to write 652 lines in one call — the guard rejected it. The model replanned, wrote a skeleton, then filled in features one edit at a time. The final result was a working game with 3D plane model, obstacles, HUD, minimap, and start/game over screens — all at Q2 quant.

The full setup runs at Q2_K_XL quantization as the floor; the user notes Q4 or Q8 should yield better results. The code is available on GitHub: github.com/Kodrack/Pi-forge.