A zero-dependency Python CLI tool designed to provide AI coding agents with persistent session memory. It solves the problem of context window degradation and the "lost in the middle" phenomenon by allowing agents to perform efficient, read-only recalls from local SQLite session stores. Instead of burning thousands of tokens on project exploration or re-orientation, auto-memory enables targeted retrieval of recent files and task history using minimal token overhead.
Key features and technical details:
- Zero dependencies using only Python standard libraries.
- Read-only access to Copilot CLI's local SQLite database to ensure safety.
- Progressive disclosure mechanism ranging from cheap scans (~50 tokens) to full session details.
- Schema-aware design with built-in validation for tool updates.
- Compatible with GitHub Copilot CLI, Claude Code, Cursor, and other instruction-file supporting agents.
Key features and technical details:
- Zero dependencies using only Python standard libraries.
- Read-only access to Copilot CLI's local SQLite database to ensure safety.
- Progressive disclosure mechanism ranging from cheap scans (~50 tokens) to full session details.
- Schema-aware design with built-in validation for tool updates.
- Compatible with GitHub Copilot CLI, Claude Code, Cursor, and other instruction-file supporting agents.
The /llms.txt file is a proposal to standardize a method for providing LLMs with concise, expert-level information about a website. It addresses the limitations of LLM context windows by offering a dedicated markdown file containing background information, guidance, and links to detailed documentation. The format is designed to be both human and machine readable, enabling fixed processing methods. The proposal includes generating markdown versions of existing HTML pages (appending .md to the URL). This initiative aims to improve LLM performance in various applications, from software documentation to complex legal analysis, and is already being implemented in projects like FastHTML and nbdev.
A post with pithy observations and clear conclusions from building complex LLM workflows, covering topics like prompt chaining, data structuring, model limitations, and fine-tuning strategies.
