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  1. Home
  2. ๐Ÿง  Knowledge & Memory
  3. GoodMemory
GoodMemory logo
Health: ActiveRecent health check succeeded.Last checked 9/9/2026, 2:05:11 PM

GoodMemory

User RatingsBe the first to rate and review this MCP server!
View Repository17 GitHub StarsTotal stargazers on GitHub for the source repository (17 stars).Visit Website

Local-first memory layer for AI agents with durable scoped storage, read-only recall, audit tools, and opt-in governed writeback.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent โ€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag โ€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON โ–พ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "hjqcan-goodmemory": {
      "command": "npx",
      "args": [
        "-y",
        "goodmemory"
      ]
    }
  }
}

๐Ÿ’ก Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives๐Ÿง  More in Knowledge & Memory

Overview

GoodMemory provides a durable memory API designed for AI chat apps, copilots, and agent hosts to store and recall scoped user or project data. It supports local-first storage using SQLite or Postgres and offers read-only recall, trace, statistics, and artifact inspection by default. Writeback operations are opt-in and governed with review, forget, and audit controls to maintain data integrity. It integrates with Codex and Claude Code environments and exposes hooks for customization and evaluation.

Use cases

โ€ขStore and recall user or project-specific memory across AI sessions
โ€ขAudit and trace memory usage and changes within AI agent workflows
โ€ขIntegrate durable memory with Codex or Claude Code agent hosts
โ€ขCustomize memory writeback rules and annotations for controlled updates
โ€ขExport or delete memory data for compliance or debugging

Key features

โ€ขDurable memory API with remember, recall, feedback, forget, and export
โ€ขLocal-first storage using SQLite by default, with optional Postgres support
โ€ขRead-only recall and inspection tools including trace and stats
โ€ขOpt-in governed writeback with review, forget, and audit controls
โ€ขIntegration hooks for Codex and Claude Code agent hosts
โ€ขTypeScript declarations and compiled artifacts for SDK and HTTP usage

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by GoodMemory.

Extracted Tool Capabilities
Durable memory API with remember, recall, feedback, forget, and export
Local-first storage using SQLite by default, with optional Postgres support
Read-only recall and inspection tools including trace and stats
Opt-in governed writeback with review, forget, and audit controls
Integration hooks for Codex and Claude Code agent hosts
TypeScript declarations and compiled artifacts for SDK and HTTP usage

Documentation Overview

GoodMemory

Language: English | ็ฎ€ไฝ“ไธญๆ–‡

GoodMemory is a memory layer for AI products and coding agents.

Release source: this is the immutable 0.7.5 stable release source. Registry commands require goodmemory@0.7.5 to be published. The release workflow verifies npm latest and artifact integrity before creating the GitHub Release.

It gives chat apps, copilots, and agent hosts a durable user/project memory loop: write selected facts, retrieve the right context, inject it into the next turn, audit what happened, and delete it when it is wrong.

GoodMemory is not an LLM, agent framework, vector database, or generic RAG system. It is the product memory layer between your app or installed agent host and the model runtime.

What You Get

  • Durable memory API: remember, recall, buildContext, feedback, forget, exportMemory, importMemory, and deleteAllMemory.
  • Installed agent memory for Codex and Claude Code through goodmemory setup, managed hooks, installed Codex pre-action, goodmemory status, read-only MCP, and opt-in writeback.
  • Public write customization with GoodMemoryConfig.remember, RememberProfile, rememberRules, RememberInput.annotations, and named extractor ids.
  • Package exports for goodmemory, goodmemory/ai-sdk, goodmemory/host, and goodmemory/http through compiled dist artifacts and TypeScript declarations.
  • Local-first storage: Bun gets durable SQLite by default; explicit Postgres, injected adapters, and embedding providers can be added when needed.
  • Evaluation and release evidence paths for deterministic tests, live evals, provider-backed evals, package smoke tests, and quality gates.

OpenAI Build Week 2026

Pre-existing foundation. GoodMemory existed before OpenAI Build Week. The pre-event foundation already included the core memory API, local SQLite/Postgres storage, installed-host integration, and the local Inspector. The hackathon entry is the work added after the submission period opened on July 13, 2026, not the entire repository.

Added during Build Week. Dated commits completed and published v0.6.0, strengthened generalized retrieval and iterative-recall verification, added claim-source provenance coverage, hardened installed-host canaries and leakage audits, and expanded the controlled Codex coding-effect evaluation path. Review the pre-event-to-Build-Week diff and its dated commit history for the exact boundary.

How Codex and GPT-5.6 were used. Codex with GPT-5.6 was the primary implementation and verification environment: exploring the repository, implementing and reviewing changes, writing regression tests, reproducing installed-host behavior, and exercising the release and coding-effect evidence paths. GPT-5.6 also powers disclosed non-judge model calls in current evaluation profiles; public-claim paths either use deterministic scoring or keep the judge independent from the answer model.

Run and verify. Install the published 0.7.5 registry baseline and inspect its local memory surface:

Terminal
npm install -g goodmemory@0.7.5
goodmemory setup --host codex
goodmemory status codex --workspace-root .
goodmemory inspector serve

Verify the repository from source with bun install --frozen-lockfile, bun test, and bun run typecheck. See the Devpost submission and public demo video.

Claim boundary: the submission demonstrates durable cross-session memory, governed writeback, recall evidence, and inspection/deletion infrastructure. It does not claim that GoodMemory has already proven an improvement in Codex coding outcomes; that paired hidden-test evaluation remains an active, fail-closed evidence track.

Start Here: Codex Or Claude Code

Terminal
npm install -g goodmemory@0.7.5
goodmemory setup

No account or hosted service is required. GoodMemory stores memory locally in SQLite by default, wires lifecycle hooks plus read-only MCP inspection, and keeps durable writeback opt-in. Verify the installation with goodmemory status.

Using another MCP client or integrating an application? Choose an integration path.

Benchmark Results

GoodMemory separates current-production claims, versioned historical evidence, and internal research. A number may enter the current-claims table only after gate:public-benchmark-claim --strict validates a committed declaration for the current package version: complete coverage, executionFailures: 0, a no-memory baseline, deterministic scoring or an independent judge, verified dataset source and license, and a reproducible run (commit + command + package version). No end-to-end benchmark runner is allowlisted today, so promotion is unavailable for both current and historical rows. Stored-answer rescores and legacy presentation projections cannot open that boundary or supply README score and disclosure fragments as self-attestation.

GoodMemory 0.7.5 has no current or versioned historical benchmark claim. The retained v0.7.3 LoCoMo projection is not end-to-end runner evidence, so it is an internal diagnostic. The v0.6.0 LoCoMo, BEAM, and MemoryAgentBench measurements and ImplicitMemBench are also internal diagnostics under the same fail-closed boundary. LongMemEval is withdrawn pending a clean rerun: the historical rules-only path used answer annotations, and the later label-free path exposed raw answer_* session IDs to retrieval and the reader. ImplicitMemBench's retry-merged result remains internal evidence because it does not replace a monolithic fresh run. HaluMem, MemGym, and MINTEval remain release evidence rather than public benchmark claims.

No benchmark result is currently presented as measured on 0.7.5.

Versioned evidence

No end-to-end benchmark runner is currently allowlisted for versioned evidence.

No benchmark result currently qualifies as versioned historical evidence.

The retained v0.7.3 LoCoMo projection remains available in the repository for audit, but it is not shipped as a verified package artifact and cannot authorize a public or versioned claim. Old artifacts will not be retrofitted; a future producer and its verifier must be implemented together before promotion opens.

Where both are available, a row reports two tracks. The strict track is deterministic or judge-free โ€” a hard lower bound no LLM judge can inflate. The second track re-judges the same stored answers (not regenerated) under a benchmark-source or industry-standard prompt. Numerical comparability is claimed only when the pinned evaluator model and remaining benchmark configuration also match. Every per-protocol detail is recorded in the linked declarations.

The LongMemEval declaration is now paused_boundary, not historical evidence. Its old numeric artifacts remain only to preserve the audit trail. They must not be quoted as GoodMemory results until an opaque-session-id, label-free full rerun replaces them; see the withdrawal declaration. The v0.6.0 BEAM, MemoryAgentBench, and LoCoMo measurements remain internal diagnostics. They do not pass the empty end-to-end runner allowlist, so they are not versioned historical evidence.

The ImplicitMemBench Full-300 stored-answer rescore uses the canonical zero-failure run-phase61-full300-rerun-20260706-codex-current answers, then re-scores the same stored answers with gpt-5.4 (sourceAnswersUnchanged: true). The judge is cross-version but the same GPT family as the gpt-5.5 answer model, not a cross-family judge. The recorded score is 0.691 (207.35/300) versus an upstream-chat baseline of 0.400 (120/300), with 530 judge-required row decisions across the baseline and GoodMemory arms; deterministic structured_first_action rows are carried forward rather than judged. The older same-model diagnostic score was 0.708 and is not the recorded result. The freshest clean answer-regeneration drift check after recent code changes scored 0.6895 with executionFailures: 0; it shows current checkout drift, not a replacement for the stored-answer comparability artifact. Its measured sources do not independently expose both package version and commit at explicit JSON paths, so the result is paused as an internal diagnostic and is not versioned historical evidence. Dataset CC BY 4.0, fetched at eval time, never vendored.

Internal diagnostics (not public claims)

LongMemEval's first current-recall assembly development slice is superseded: although its reader context hid raw session IDs, the memory-builder boundary still received gold-bearing session IDs and turn markers. A stricter v2 protocol now removes answers, question type, answer markers, and raw session identities before memory construction. Its development slice must be rerun on a clean commit before the still-sealed holdout can be opened; no LongMemEval claim is restored. ImplicitMemBench's explicit retry-merged check reaches 0.6923666667 with zero failures, but it is not a replacement monolithic fresh Full-300 run. Both therefore remain outside the current-claims table. The underlying reports live under gitignored reports/ and are reproducible from the recorded commands.

Use task-board/00-README.txt for execution order and docs/GoodMemory-Current-Status-and-Evidence.md for claim boundaries.

Choose Your Integration Path

GoodMemory has three primary product entry points. They are not the only APIs: lower-level surfaces such as goodmemory/host, custom stores, eval tooling, and runtime helpers support these paths. They are the README-level ways to decide how to start.

Read the full README โ†’View source on GitHub โ†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks โ€” not a rating.

GitHub stars
17
Stargazers on the source repository.
npm downloads
1.1k
Package downloads in the last 30 days.
Last commit
27d ago
Most recent push to the default branch.

Reviews

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Frequently Asked Questions about GoodMemory

No, GoodMemory stores memory locally by default and does not require any hosted service or user account.

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Technical Specs & Signals

Category๐Ÿง Knowledge & Memory
More technical detailsExpand โ–พ
TransportSTDIO
RuntimeNode.js
Last updatedAug 14, 2026
Views0
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars17
GitHub Star CountTotal stargazers on GitHub representing community popularity (17 stars).
Last commit27d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Aug 14, 2026
npm downloads1,133/mo
Monthly npm DownloadsAverage monthly package installs recorded from npm registry statistics.
57Quality signal: Good ยท 57/100How this signal is calculated โ–พ
Server availabilityNot measured

Not scored for repo-hosted servers โ€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools24/30
Adoption & activity9/15
Community engagement0/10

A guidance signal from public completeness & health data โ€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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No high-severity advisories surfaced by our automated scan.

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Scanned 14d ago via OSV.dev ยท goodmemory (npm)

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