MCPInstall

engram-memory

ALWAYS ACTIVE — Persistent memory protocol. You MUST save decisions, conventions, bugs, and discoveries to engram proactively. Do NOT wait for the user to ask.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/gentleman-programming/engram --skill engram-memory
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads engram-memory automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Engram Persistent Memory — Protocol

You have access to Engram, a persistent memory system that survives across sessions and compactions. The agent MUST follow this protocol without waiting for activation.

AVAILABLE TOOLS

Use the Engram tools actually exposed by Codex, with their registered names. The names below are logical names; availability and discovery follow Codex's host-specific tool interface, not a universal bootstrap or deferred-tool split.

  • mem_save, mem_search, mem_context, mem_session_summary

  • mem_get_observation, mem_save_prompt, mem_current_project, mem_judge, mem_compare

  • mem_update, mem_review, mem_pin, mem_unpin, mem_suggest_topic_key

  • mem_session_start, mem_session_end, mem_doctor, mem_capture_passive

PROACTIVE SAVE TRIGGERS (REQUIRED — do NOT wait for the user to ask)

Call mem_save IMMEDIATELY and WITHOUT BEING ASKED after any of these:

After decisions or conventions

  • Architecture or design decision made
  • Team convention documented or established
  • Workflow change agreed upon
  • Tool or library choice made with tradeoffs

After completing work

  • Bug fix completed (include root cause)
  • Feature implemented with non-obvious approach
  • Notion/Jira/GitHub artifact created or updated with significant content
  • Configuration change or environment setup done

After discoveries

  • Non-obvious discovery about the codebase
  • Gotcha, edge case, or unexpected behavior found
  • Pattern established (naming, structure, convention)
  • User preference or constraint learned

After user confirmation or rejection

  • User confirms a recommendation you made ("go with that", "let's do that", "sounds good", "agreed", "perfect", or the equivalent in the user's language)
  • User rejects an option or approach ("no, better X", "not that one", or the equivalent in the user's language)
  • User expresses a preference ("I prefer X over Y", "always do it this way", or the equivalent in the user's language)
  • User makes a decision after you presented tradeoffs or options
  • A discussion concludes with a clear direction chosen — even if the agent proposed it

Self-check — ask yourself after EVERY task:

"Did I or the user just make a decision, confirm a recommendation, express a preference, fix a bug, learn something non-obvious, or establish a convention? If yes, call mem_save NOW."

Format for mem_save:

  • title: Verb + what — short, searchable (e.g. "Fixed N+1 query in UserList", "Chose Zustand over Redux")
  • type: bugfix | decision | architecture | discovery | pattern | config | preference
  • scope: project (default) | personal | global
  • topic_key (optional but recommended for evolving topics): stable key like architecture/auth-model
  • content: What: One sentence — what was done Why: What motivated it (user request, bug, performance, etc.) Where: Files or paths affected Learned: Gotchas, edge cases, things that surprised you (omit if none)

Topic update rules (REQUIRED)

  • Different topics MUST NOT overwrite each other (example: architecture decision vs bugfix)
  • If the same topic evolves, call mem_save with the same topic_key so memory is updated (upsert) instead of creating a new observation
  • If unsure about the key, call mem_suggest_topic_key first, then reuse that key consistently
  • If you already know the exact ID to fix, use mem_update

WHEN TO SEARCH MEMORY

When the user asks to recall something — any variation of "remember", "recall", "what did we do", "how did we solve", or the equivalent in the user's language, or references to past work:

  1. First call mem_context — checks recent session history (fast, cheap)
  2. If not found, call mem_search with relevant keywords (FTS5 full-text search)
  3. If you find a match, use mem_get_observation for full untruncated content

Also search memory PROACTIVELY when:

  • Starting work on something that might have been done before
  • The user mentions a topic you have no context on — check if past sessions covered it
  • The user's FIRST message references the project, a feature, or a problem — call mem_search with keywords from their message to check for prior work before responding

DELIVERY GUARANTEE

Memory operations are internal bookkeeping, never the user-facing answer. Complete required memory work before composing the completed-task reply; send the complete answer as the final message of the turn with no later tool calls. If memory work fails or needs follow-up, still send the answer.

SESSION CLOSE PROTOCOL (REQUIRED)

Before ending a session or saying "done" / "that's it", you MUST:

  1. Call mem_session_summary with this structure:

Goal

[What we were working on this session]

Instructions

[User preferences or constraints discovered — skip if none]

Discoveries

  • [Technical findings, gotchas, non-obvious learnings]

Accomplished

  • [Completed items with key details]

Next Steps

  • [What remains to be done — for the next session]

Relevant Files

  • path/to/file — [what it does or what changed]

This is NOT OPTIONAL. If you skip this, the next session starts blind. Summary persistence does not end the session. Codex's SessionEnd hook owns lifecycle closure independently of the agent's summary.

PASSIVE CAPTURE — adapter-specific safety net

When completing a task or subtask, include a "## Key Learnings:" section with numbered items. Automatic capture is adapter-specific: Codex's SubagentStop hook forwards eligible delegated/subagent output for server-side extraction. Ordinary responses from the main agent are not automatically captured by this hook; the section alone does not guarantee storage. Use mem_save as the primary path for significant, verified knowledge.

Example:

Key Learnings:

  1. bcrypt cost=12 is the right balance for our server performance
  2. JWT refresh tokens need atomic rotation to prevent race conditions

You can also call mem_capture_passive(content) directly with any text that contains a learning section. This explicit extraction path is a safety net, not a replacement for mem_save.

AFTER COMPACTION

If you see a message about compaction or context reset, or if you see "FIRST ACTION REQUIRED" in your context:

  1. IMMEDIATELY call mem_session_summary with the compacted summary content — this persists what was done before compaction
  2. Then call mem_context to recover any additional context from previous sessions
  3. Only THEN continue working

Do not skip step 1. Without it, everything done before compaction is lost from memory.

Codex session binding

Codex registers the native PreToolUse command engram hook codex-pre-tool-use for mcp__engram__mem_* and mcp__plugin_engram_engram__mem_*. For supported Engram write/session tools, it resolves the authoritative Codex host session to the server-confirmed Engram identity before binding arguments: session_id for writes and id for session start/end. mem_session_end uses id, not session_id. Do not invent or override this identity. Read-only tools are unchanged; malformed inputs or unconfirmed host resolution deny mutating calls.

The lifecycle hook also supplies the confirmed identity after registration or renewal. For direct/nonintercepted clients, reuse this exact identity as session_id for attributed writes (mem_save, mem_save_prompt, mem_session_summary, mem_capture_passive), and as id for mem_session_end. Never reconstruct a resume suffix or guess an ID when confirmation is missing.

Fallback: If tools are unexpectedly unavailable, run engram setup codex and restart Codex. Setup repairs Codex's authoritative [mcp_servers.engram] registration; use the actual tool names exposed by Codex.

FAQ

What does the engram-memory skill do?

ALWAYS ACTIVE — Persistent memory protocol. You MUST save decisions, conventions, bugs, and discoveries to engram proactively. Do NOT wait for the user to ask.

How do I install engram-memory?

Run npx skills add https://github.com/gentleman-programming/engram --skill engram-memory in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/engram-memory/ (personal) or .claude/skills/engram-memory/ (project).

Which AI agents can use engram-memory?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is engram-memory safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains engram-memory?

It is published by gentleman-programming in gentleman-programming/engram. It has 147 recorded installs.

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