memini
The memini server gives AI coding agents persistent, local-first project memory and mistake-prevention guardrails across sessions. It allows agents to recall context, check files for risks before editing, and record various types of memories to avoid repeating mistakes.
recall_project_context— Load the most important project memories (failed attempts, fragile files, decisions, deployment rules) at the start of a task, so the agent begins with relevant context instead of starting from scratch.check_before_editing— Check whether a file has recorded risks, failed attempts, or fragile-file warnings before editing it, enabling pre-edit guardrails especially for configuration and deployment files.remember_decision— Record an important design or implementation decision and its rationale, preventing future sessions from unknowingly reversing it.remember_failed_attempt— Record an approach that was tried and failed (what was tried, why it failed, and the actual fix if known), so no future session repeats the same mistake.remember_fragile_file— Mark a file as fragile or risky with awarnorblockseverity, so future sessions receive a guardrail warning or are denied editing access before touching it.end_session_summary— At the end of a work session, record what changed, what worked, and what failed, creating a session summary and proposing durable memories for future reference.
memini
Never the same mistake twice. Mistake-prevention guardrails and persistent project memory for AI coding agents.
AI coding agents are stateless: every session starts with amnesia. The agent that broke your build editing vercel.json on Monday will happily try the exact same edit on Thursday. memini gives each repo a persistent memory of failed attempts, fragile files, decisions, and deployment rules — and force-feeds the relevant warning to the agent at the moment it's about to repeat history.
Not a notebook the agent may choose to read. A guardrail it can't skip.

How it works
Memories live in your repo — a
.memini/folder with a local SQLite index and human-readable, PR-reviewable markdown views. Local-first: nothing leaves your machine.Hooks enforce guardrails — when the agent tries to edit a file with recorded risks, the edit is intercepted before it happens and the recorded lesson is injected:
[WARNING] Editing vercel.json broke the build (recorded 2026-07-03)— Tried changing buildCommand; deploy failed. Actual fix: move checkout server-side and setVITE_STRIPE_USE_SERVER=true.warnseverity: the agent is warned once per session, then may proceed.blockseverity: the edit is always denied until a human archives the memory.
Session start injects a digest of the most important memories (severity-first, token-budgeted).
MCP tools let the agent record what it learns:
remember_failed_attempt,remember_fragile_file,remember_decision,end_session_summary, plusrecall_project_contextandcheck_before_editing.Git-aware staleness — memories hash the files they reference;
pm staleflags memories whose evidence has changed, and stale memories stop firing guardrails until re-verified.
Related MCP server: claude-engram
Quickstart (90 seconds)
cd your-repo
npx -y memini init # creates .memini/ + installs Claude Code hooks
# record your first guardrail
npx -y memini remember failed_attempt \
"Editing vercel.json broke the build" \
-b "Tried changing buildCommand; deploy failed. Fix: move checkout server-side." \
--file vercel.json --severity warnThat's it. Next time any Claude Code session in this repo tries to edit vercel.json, it gets the warning first.
Cursor, Windsurf, and other MCP clients:
claude mcp add memini -- npx -y memini mcp # Claude Code MCP
npx -y memini install-mcp --write cursor # Cursor: MCP + rule + enforced preToolUse hook
npx -y memini install-copilot # GitHub Copilot: enforced preToolUse hook (.github/hooks)
npx -y memini install-mcp # print generic MCP configEnforcement is a chain of gates, and memini covers several:
Before the edit — the edit to a guardrailed file is blocked before it happens.
block→ denied,warn→ the user is prompted with the recorded history. Supported on:Claude Code —
pm initinstalls itCursor (1.7+) —
pm install-mcp --write cursorGitHub Copilot — CLI, cloud coding agent, and VS Code agent mode (preview) —
pm install-copilot
Before the commit — every tool — a git pre-commit guardrail blocks a commit that touches a
block-severity file, no matter which IDE or agent made the edit (Windsurf, Cline, a human…). Installed bypm init(orpm install-hooks --git). Fails open; overridable withgit commit --no-verify.Advisory — any MCP client — the
check_before_editing/recall_project_contexttools, plus an always-applied Cursor rule steering the agent to use them.
Keeping memory useful over time
A memory tool is only as good as what's in it, and it rots if left alone. memini keeps it healthy:
Auto-capture — a Claude Code session-end hook nudges the agent to record durable lessons (a fix that worked, a fragile file, a failed approach) when a session actually changed files, so you don't have to remember to
pm remember. It stays silent on idle sessions.Usefulness tracking — every time a guardrail actually fires, memini counts it.
pm statsshows which memories are pulling their weight and which have never fired.pm review— flags near-duplicates, contradictory guardrails on the same file, and dormant guardrails that have never fired long after creation. All local heuristics, no LLM — you decide what to keep, merge, or archive.Git-aware staleness — memories hash the files they reference; when the code changes, the memory is flagged and stops firing until re-verified (
pm stale/pm verify).
CLI
Command | What it does |
| Set up |
| Record a memory |
| Search memories / preview the agent digest |
| Guardrail check (exit 1 if risks recorded) — usable in CI |
| Manage memories |
| What your memory is doing — guardrail fires, coverage, staleness |
| Surface quality issues: duplicates, contradictions, dormant guardrails |
| Detect and re-verify outdated memories |
| Run the MCP server (stdio) |
| Diagnose setup |
Memory types: decision, failed_attempt, fragile_file, architecture, deployment, client_preference, session_summary.
Scopes: sharing rules across repos
Some lessons are project-specific; some apply to every repo on your machine that belongs to the same org or client. memini has three scopes:
Scope | Where it lives | Use it for |
|
| this repo's failed fixes, fragile files, decisions |
|
| org/client conventions shared by every repo under that folder |
|
| personal rules that follow you everywhere |
cd ~/work/acme && pm init --workspace # one-time: workspace store covering ~/work/acme/*
# from inside any repo under ~/work/acme:
pm remember deployment "DB connections must use org OAuth, never PATs" \
--file "databricks.yml" --severity warn --scope workspace
pm promote <id> --workspace # lift a project lesson that turned out to be org-wideEvery repo under the workspace folder — including ones you create later — gets those guardrails automatically. Resolution walks up the directory tree, like .gitconfig or ESLint configs. Workspace/user file guardrails match by glob (vercel.json matches any repo's vercel.json; config/**/*.yml works too), and wider-scope memories only fire when human-verified — agents can propose memories to project scope only, so a prompt-injected agent can't plant rules that spread across repos. pm doctor shows which scopes are active.
Design principles
Enforced, not advisory. MCP memory tools are optional for the agent; hooks are not. The guardrail path works even if the agent never thinks to check its memory.
Human-readable, PR-able. Every memory renders to markdown under
.memini/that your team reviews like any other change.Git-linked evidence. Memories record the branch, commit, and file hashes they were born from, so claims are verifiable and staleness is detectable.
Local-first. SQLite + markdown in your repo. No accounts, no cloud, no telemetry. Secrets are auto-redacted from memory bodies before they're stored.
Cross-tool. Core is a CLI + files; Claude Code hooks and MCP are thin adapters.
Security
Local-first by design: no server, no account, no telemetry. Secrets are auto-redacted before storage, file references are contained to the repo, and injected memory text is size-capped and framed as data. See SECURITY.md for the full threat model — including the honest limitations (guardrails intercept edit tools, not arbitrary shell; warn is advisory, block is not).
Status
Early (v0.1). Team sync — shared memory across your whole team, with a review workflow — is on the roadmap. Feedback and issues welcome.
License
MIT
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
- Alicense-qualityCmaintenanceProvides AI coding assistants with persistent project memory to retain architectural decisions, code patterns, and domain knowledge across sessions. It stores data locally in a SQLite database, allowing agents to remember, recall, and manage project-specific context using full-text search.Last updated19Apache 2.0
- AlicenseBqualityBmaintenancePersistent memory and session intelligence for AI coding assistants. Auto-tracks mistakes, decisions, and context via hooks. Mines your full session history for patterns, predictions, and cross-session search.Last updated2116MIT
- AlicenseAqualityAmaintenanceLocal-first memory layer for AI coding agents — captures issues, attempts, fixes, and decisions, and warns at git commit before you repeat a mistake.Last updated15182MIT
- Alicense-qualityBmaintenanceProvides a persistent, local-first memory for coding agents over MCP, enabling automatic recall and recording of past work, failures, and decisions to reduce repetition and token usage.Last updatedMIT
Related MCP Connectors
Give your AI agent a persistent map of your project's structure, dependencies, and bugs.
Secure, user-owned long-term memory for AI agents over OAuth-protected remote MCP. Save, search, recall, update, and govern preferences, project context, decisions, and task state across ChatGPT, Claude, Copilot, IDEs, and CLIs.
Persistent memory for AI agents. Search, store, and recall across sessions.
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/lumayapartners/memini'
If you have feedback or need assistance with the MCP directory API, please join our Discord server