The context graph layer for coding agents. Watches sessions across Claude Code, Cursor, Codex, and more. Extracts the decisions, the reasoning, and the why. Makes it available to every agent, every session.
AI agents decide fast, but the reasoning is lost after every session. Decisions get re-debated. Patterns get re-discovered. The why behind every choice is scattered across agents, lost between sessions.
Everyone stores memory. Nobody extracts the reasoning. Lerim does.
Every coding session across Claude Code, Cursor, Codex, or any supported agent is automatically captured. No manual tagging, no workflow changes.
LLM pipelines turn raw conversations into structured knowledge: the decisions made, the reasoning behind them, and the pitfalls learned. Stored as plain markdown in your repo.
Any agent can query the context graph. Start a new session in a different tool and it builds on your project's full decision history. Not just what was done, but why.
Memories live as human-readable markdown in your repo. Git-friendly, portable. git diff what your agents have learned.
Claude Code, Codex, Cursor, OpenCode. What one agent learns, every agent recalls. One shared context graph across all your tools.
"Why did we choose Postgres?" Get evidence-cited answers drawn from your project's full decision history.
Syncs new sessions automatically. Extracts, deduplicates, and refines context continuously with zero manual effort.
Decisions and learnings are linked, not isolated. Agents follow connections to discover related context. Explore the graph visually in the dashboard.
Merges duplicates, consolidates related learnings, and archives stale entries. Context stays sharp, not cluttered.
Add Lerim as a skill to any supported agent. It runs in the background automatically -- syncing sessions, extracting learnings, consolidating overlap, and forgetting low-value noise with zero manual effort.
Your agent now knows how to use Lerim. It can recall relevant context before a task and write back new learnings after it finishes -- across every session and every tool.
Full control from the terminal. Set up once, start the service, and query your project's learning history directly.
Your agent learning records live as human-readable markdown files in your repo. Version-controlled, portable, and future-proof.
git diff what your agents learned.
memory/ decisions/ # architectural choices learnings/ # patterns & pitfalls summaries/ # session records archived/ # retired entries config.toml # project settings