Agentmetry
Free · Open source
SOC and DevSecOps teams
Every time your AI coding agent opens a file it has already read twice this session, you are paying for the same tokens again — and if it hit a bug last week, there is nothing stopping it from writing the same bug today.
Engram sits between your IDE and its file reads, maintaining a local SQLite summary of your codebase so agents pull compressed context instead of raw files. The vendor states an 89% measured token reduction. It installs via npm, runs locally with zero cloud dependency, and connects to Claude Code, Cursor, Cline, Continue, Aider, Codex, Windsurf, and Zed through a combination of OpenVSX extensions, an Anthropic plugin, and adapter scripts. The bug-prevention layer surfaces past mistakes from revert history before the agent touches that code path again. This is a passive interceptor, not an agent — it does not plan tasks or run autonomously.
Bottom line: The right call for any team watching token costs climb on a large, stable codebase — less useful when your repo changes so fast that the cached summaries are stale before the next session starts.
Full access to engram CLI, local SQLite storage, all IDE integrations, and git-revert mining
View full pricing on github.com →
Pricing may have changed since last verified. Check the official site for current plans.
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More options in Inference Engines & Infra. Compare pricing, platforms, and features side by side.
Free · Open source
SOC and DevSecOps teams
Freemium
Teams needing deterministic rules plus RAG explanations
AI coding tools re-read the same files repeatedly within a session and across sessions, with no memory of what broke last time. Engram addresses this by maintaining a persistent, compressed representation of your codebase in a local SQLite database. When an agent requests file context, Engram intercepts that read and returns a summary instead of the raw content, reducing the token payload the model receives. Revert history feeds a bug-prevention index so that when the agent approaches a file with a known failure pattern, that context surfaces before the agent writes code.
The cross-IDE sharing model is the distinguishing architectural choice. Because the context store is local and IDE-agnostic, the same codebase summary is available to Claude Code, Cursor, Cline, and the rest simultaneously. Teams migrating between tools, or running multiple tools on the same project, do not rebuild context from scratch for each one. The vendor describes this as a ‘context spine’ — a single source of compressed truth that all attached tools draw from.
Engram fits best in projects where the codebase is large, file reads are the dominant token cost, and the repo is stable enough that summaries remain accurate across sessions. It breaks down when the codebase changes rapidly: the SQLite summaries lag behind the actual state, and an agent working from stale context will miss recent refactors. The tool is fully passive — it does not run tasks, loop on tool calls, or take autonomous actions, so it cannot substitute for an agent orchestration layer.
Installation is via npm. IDE integration uses OpenVSX for VS Code-compatible editors and a dedicated plugin for the Anthropic marketplace. The Apache 2.0 license covers all components, and the vendor states there is no cloud component — all data stays local. The GitHub repository shows active issue and pull request traffic, with 131 stars and 12 forks at the time of curation.
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