Context Compression
Implement Agentic Memory Routing (AMR) to reduce RAG token costs by 15x and eliminate context degradation.
1. Infinite Context, Zero Degradation
Traditional RAG pipelines suffer from 'lost in the middle' syndrome when dealing with 100k+ file monorepos. Injecting full files into the context window destroys reasoning accuracy and drastically inflates token costs.
2. The Open Source .mem Standard
Our compression pipeline is built on the open-source .mem format. Instead of unstructured text dumps, we mandate a rigid YAML/Markdown hybrid that structures organizational memory, architectural intent, and task states into a machine-readable protocol that any model can parse flawlessly.
3. Agentic Memory Routing (AMR)
While the format is open, our routing engine is proprietary. By clustering related .mem state files in high-dimensional vector space, our AMR pipeline retrieves only the historically relevant context needed for a specific task, dropping inference costs by up to 15x.
4. Dynamic AST Node Pruning
When injecting code into the context window, we don't dump 5,000-line files. Our orchestration layer dynamically parses the Abstract Syntax Tree (AST) and prunes irrelevant functions, classes, and comments, delivering hyper-compressed, high-signal snippets to the reasoning models.