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Give AI agents cross-session memory through compressed histories and prior recall.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "engram" yet — see the docs or source repo.
Use engram to recall conclusions and debugging strategies from similar past errors, then prioritize reusing them in this investigation.
The agent references relevant prior experience first, then produces a more consistent troubleshooting plan or fix.
Use engram to load the compressed conclusions from earlier sessions on this topic, continue the existing approach, and avoid repeating failed attempts.
The agent continues analysis from prior conclusions, reducing repeated exploration and preserving task continuity.
At the end of this task, use engram to compress and save key conclusions, effective strategies, and cautions for future sessions.
Reusable memory entries are created so later agent tasks can retrieve and apply them.
Developers or researchers working on the same task across multiple sessions can use it to preserve earlier conclusions and strategies, reducing repeated setup. This helps the agent resume work faster in later runs.
When a team wants an agent to prioritize methods that worked before, it can use this tool to recall relevant priors. That can make outputs more consistent and reduce unproductive trial and error.
It gives agents cross-session memory through MCP. It does this by compressing run trajectories into reusable conclusions and strategies, then recalling relevant priors in later tasks.
The description emphasizes cross-session memory rather than temporary context inside a single session. It focuses on preserving reusable conclusions and strategies for later runs.
The provided material does not include installation steps, runtime requirements, or key setup details. For prerequisites, see the source repository.
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