prism-mem: Automatic Knowledge Extraction for AI Coding Agents

Rahul Reddy Talatala Posted on May 31 prism-mem: Automatic Knowledge Extraction for AI Coding Agents # ai # agents # coding # programming AI coding agents are stateless between sessions. Every time you start a new session, the agent knows nothing about what you built yesterday, why you made certain decisions, or what you explicitly decided to stop doing. You write a CLAUDE.md by hand, it goes stale after a few sessions, and you spend the first few minutes of every session re-explaining context the agent should already know. In a 3-session demo on a real project, prism-mem compressed 411,463 bytes of raw Claude Code transcripts into 11,707 characters of structured, queryable knowledge -- a 35x reduction -- with zero manual updates. The agent that started session 4 had accurate context from everything that happened in sessions 1, 2, and 3, including a database migration it never witnessed. prism-mem does this by reading what actually happened instead of asking you to write it down. 🤔 The Problem With Manual Context Files Here is what typically happens on a real project: Session 1: You write a clean CLAUDE.md. Tech stack, decisions, conventions. It is accurate. Session 3: You migrated from SQLite to PostgreSQL. You forgot to update CLAUDE.md. Session 5: The agent confidently uses SQLite APIs because that is what the context file says. Manual context files are snapshots. They reflect what you thought was important enough to write down at the moment you wrote them. They do not capture the dozens of small decisions that happen during a session: the library you tried and abandoned, the architecture pattern you switched away from, the refactor that changed the data model. prism-mem reads session transcripts and git diffs after every commit, extracts structured knowledge as (subject, predicate, object) triples, links them into a graph, automatically detects when old facts are contradicted by new ones, and regenerates your context files. No manual input at any step. 📖 Where