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Nocturne Memory MCP Server

Long-term memory MCP server for agents, with URI-addressed memories, namespaces, search, aliases, glossary triggers, a visual dashboard, and reviewable rollback snapshots backed by SQLite or PostgreSQL.

by Dataojitori · submitted by oktofeesh1·added 2026-06-06·
Review first review before installing

Open the source and read safety notes before installing.

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Source URLs
https://raw.githubusercontent.com/Dataojitori/nocturne_memory/main/README_EN.md, https://github.com/Dataojitori/nocturne_memory
Brand
Nocturne Memory
Brand domain
misaligned.top
Brand asset source
brandfetch
Safety notes
Nocturne Memory can create, update, delete, alias, trigger, and search persistent memory nodes., Stdio startup can launch a local admin dashboard, initialize the database, and build the frontend if dependencies are present., Network-facing SSE or HTTP mode refuses to start without an API token when bound to a reachable host, but localhost-only deployments should still set one., Write tools can change long-term agent behavior; review snapshots and rollback queues before accepting broad edits or cleanup operations., Keep `public_readonly_mcp` enabled only for demos or intentionally read-only deployments.
Privacy notes
Memories can contain personal history, health details, credentials accidentally pasted into chats, private project context, relationship content, strategies, drafts, and agent identity instructions., Database files, PostgreSQL URLs, API tokens, config files, snapshots, dashboard views, MCP transcripts, and search results can expose the full memory graph., The README includes public demo guidance, but private memories should be stored only in a controlled self-hosted instance., Redact memory URIs, node content, boot URIs, glossary triggers, search terms, database paths, API tokens, and snapshot diffs before sharing logs or screenshots.
Author
Dataojitori
Submitted by
oktofeesh1
Claim status
unclaimed
Last verified
2026-06-06

Decision playbook

Review trust signals before you adopt

Signals are present but mixed. Use the checklist below to confirm the source and operational safety for your environment.

Compare context
Selected

0

Current score

63

Baseline

Delta

No baseline selected

No major trust-signal divergence detected in the current selection.

Source and provenance checks

Needs review

Confirm ownership and provenance before trusting install instructions.

  • Source link availableRequired

    Open the canonical repository and verify ownership.

    Done
  • Source provenance statusRequired

    Marked as source-backed.

    Done
  • Metadata reviewed

    No reviewed flag detected in metadata.

    Pending

Safety and privacy checks

Complete

Validate risk disclosures before installation or API wiring.

  • Safety notes presentRequired

    Review the listed safety guidance before running commands.

    Done
  • Privacy notes presentRequired

    Review data handling notes before connecting accounts or secrets.

    Done
  • Trust level risk gateRequired

    Trust level does not block evaluation.

    Done

Package and install checks

Needs review

Check package metadata and artifact integrity signals.

  • Install payload available

    Install or copy payload is available for review.

    Done
  • Package verification flag

    No package verification flag provided.

    Pending
  • Checksum metadata

    No checksum provided for downloaded artifact.

    Pending

Compare-driven decision checks

Needs review

Use compare context to validate trade-offs before adoption.

  • Compare tray has multiple entries

    Add at least one more entry to compare trust differences.

    Pending
  • Baseline comparison available

    No baseline peer selected yet.

    Pending
  • Diverging trust signals identified

    No major trust-signal divergence found.

    Pending

Setup at a glance

CLI install

Copy-ready — paste the snippet to get started.

20 minutes

Adoption plan

Balanced adoption plan

Current risk score 24/100. Use staged verification before broader rollout.

Risk 24

Pre-adoption checks

Validate source and review signals before any execution.

  • Confirm source provenanceRequired

    Source URL/provenance metadata is present.

    Done
  • Confirm metadata review state

    No review metadata found; increase manual validation.

    Pending
  • Verify install payload

    Install/config payload exists and can be inspected.

    Done

Security checks

Confirm safety, privacy, and package integrity signals.

  • Review safety notesRequired

    Safety notes are present.

    Done
  • Review privacy notesRequired

    Privacy notes are present.

    Done
  • Verify package integrity metadata

    No package verification/checksum metadata.

    Pending

Rollout

Adopt in controlled steps based on the selected plan.

  • Run in isolated sandbox firstRequired

    Use a constrained sandbox and observe behavior across multiple tasks.

    Pending
  • Roll out graduallyRequired

    Roll out to a small cohort before wider usage.

    Pending
  • Set monitoring and fallback

    Define rollback path and monitor errors after adoption.

    Pending

Evidence readiness

Evidence readiness matrix · balanced

Missing required evidence: Metadata review. Risk score 31.

Risk 31

Source provenance

Present

Source repository/provenance is listed.

Required in this preset

Metadata review

Missing

Review metadata is missing.

Required in this preset

Safety notes

Present

Safety notes are present.

Required in this preset

Privacy notes

Present

Privacy notes are present.

Optional in this preset

Package integrity

Missing

Package integrity metadata is missing.

Optional in this preset

Install payload

Present

Install payload is available.

Required in this preset

Required gaps: Metadata review

Decision timeline

Decision timeline · balanced

Blocking gaps: Check metadata review status. Risk 28.

Risk 28

triage

Confirm source provenanceRequired

Source/provenance metadata is available.

Done

triage

Check metadata review statusRequired

Review metadata is missing.

Pending

verify

Review safety notesRequired

Safety notes are available.

Done

verify

Review privacy notes

Privacy notes are available.

Done

verify

Validate package integrity metadata

Package integrity metadata is missing.

Pending

rollout

Verify install payload and commandsRequired

Install payload is available.

Done

Blockers: Check metadata review status

Prerequisite readiness

Prerequisite readiness

5 prerequisites to line up before setup. Have accounts and credentials ready first.

0/5 ready
Account & credentials1Install & runtime2Network & hosting220 minutes

Safety & privacy surface

Safety & privacy surface

5 safety and 4 privacy notes across 5 risk areas. Review closely: credentials & tokens, permissions & scopes.

5 areas
  • SafetyData retentionNocturne Memory can create, update, delete, alias, trigger, and search persistent memory nodes.
  • SafetyPermissions & scopesStdio startup can launch a local admin dashboard, initialize the database, and build the frontend if dependencies are present.
  • SafetyCredentials & tokensNetwork-facing SSE or HTTP mode refuses to start without an API token when bound to a reachable host, but localhost-only deployments should still set one.
  • SafetyLocal filesWrite tools can change long-term agent behavior; review snapshots and rollback queues before accepting broad edits or cleanup operations.
  • SafetyGeneralKeep `public_readonly_mcp` enabled only for demos or intentionally read-only deployments.
  • PrivacyCredentials & tokensMemories can contain personal history, health details, credentials accidentally pasted into chats, private project context, relationship content, strategies, drafts, and agent identity instructions.
  • PrivacyCredentials & tokensDatabase files, PostgreSQL URLs, API tokens, config files, snapshots, dashboard views, MCP transcripts, and search results can expose the full memory graph.
  • PrivacyData retentionThe README includes public demo guidance, but private memories should be stored only in a controlled self-hosted instance.
  • PrivacyCredentials & tokensRedact memory URIs, node content, boot URIs, glossary triggers, search terms, database paths, API tokens, and snapshot diffs before sharing logs or screenshots.

Disclosure: MIT-licensed open-source memory server. The project uses strong AI-persona framing in its README; this entry describes the source-backed MCP memory behavior without endorsing storing sensitive personal data unreviewed.

Safety notes

  • Nocturne Memory can create, update, delete, alias, trigger, and search persistent memory nodes.
  • Stdio startup can launch a local admin dashboard, initialize the database, and build the frontend if dependencies are present.
  • Network-facing SSE or HTTP mode refuses to start without an API token when bound to a reachable host, but localhost-only deployments should still set one.
  • Write tools can change long-term agent behavior; review snapshots and rollback queues before accepting broad edits or cleanup operations.
  • Keep `public_readonly_mcp` enabled only for demos or intentionally read-only deployments.

Privacy notes

  • Memories can contain personal history, health details, credentials accidentally pasted into chats, private project context, relationship content, strategies, drafts, and agent identity instructions.
  • Database files, PostgreSQL URLs, API tokens, config files, snapshots, dashboard views, MCP transcripts, and search results can expose the full memory graph.
  • The README includes public demo guidance, but private memories should be stored only in a controlled self-hosted instance.
  • Redact memory URIs, node content, boot URIs, glossary triggers, search terms, database paths, API tokens, and snapshot diffs before sharing logs or screenshots.

Prerequisites

  • Python 3.10 or newer.
  • Node.js if the dashboard frontend needs to be built on first startup.
  • MCP client that supports stdio, SSE, or streamable HTTP depending on deployment mode.
  • SQLite or PostgreSQL storage selected and backed up before storing important memories.
  • API token configured before exposing the web, SSE, or HTTP server beyond localhost.

Schema details

Install type
cli
Troubleshooting
No
Source repository stats
Scope
Source repo
Collection metadata
Estimated setup
20 minutes
Difficulty
intermediate
Tool listing metadata
Disclosure
MIT-licensed open-source memory server. The project uses strong AI-persona framing in its README; this entry describes the source-backed MCP memory behavior without endorsing storing sensitive personal data unreviewed.
Full copyable content
{
  "mcpServers": {
    "nocturne_memory": {
      "command": "python",
      "args": ["<repo>/backend/mcp_server.py"]
    }
  }
}

About this resource

Content

Nocturne Memory MCP Server is a self-hosted long-term memory server for agents. It stores URI-addressed memory nodes, supports aliases and glossary triggers, offers search and system views, and includes review and rollback mechanics for changes made to the memory graph.

Use it when a team wants Claude or another MCP client to share durable memory across sessions while keeping the memory database, dashboard, and write-review process under local control.

Source Review

These sources were reviewed on 2026-06-06. Prefer the live repository, English README, license, MCP tool reference, MCP server implementation, configuration, auth, SSE runner, snapshot store, and Python requirements for current setup and behavior.

Features

  • Read a memory by URI with read_memory.
  • Create child memories with priority, disclosure text, and optional titles.
  • Update memory content through append or exact patch modes.
  • Delete a memory path without necessarily deleting the underlying node body.
  • Add alias paths that point to existing memories.
  • Manage glossary triggers for cross-linking memory recall.
  • Search memories by keyword, domain, and result limit.
  • Read system views such as boot memory, recent memories, glossary, indexes, and diagnostics.
  • Use namespace isolation for separate agents or personas.
  • Review changes and roll back memory edits through snapshot tracking.
  • Run over stdio locally or expose SSE/streamable HTTP through the combined server when properly authenticated.

Installation

Clone the repository, install backend dependencies, and configure an MCP client to run the Python MCP server:

git clone https://github.com/Dataojitori/nocturne_memory.git
cd nocturne_memory
pip install -r backend/requirements.txt

Example stdio MCP configuration:

{
  "mcpServers": {
    "nocturne_memory": {
      "command": "python",
      "args": ["<repo>/backend/mcp_server.py"]
    }
  }
}

For network-facing deployments, configure api_token, keep the host local unless the server is intentionally exposed, and use the SSE or streamable HTTP runner only behind reviewed access controls.

Use Cases

  • Give Claude durable project memory across coding sessions.
  • Store boot memories that orient an agent at the start of a session.
  • Keep separate memory namespaces for different agents, clients, or workspaces.
  • Search remembered facts without embedding the entire memory database in a prompt.
  • Track memory changes before accepting or rolling them back.
  • Use glossary triggers to cross-link concepts that should be recalled together.
  • Run a visual dashboard for reviewing and editing the memory graph.

Safety and Privacy

Long-term memory changes future agent behavior. Read existing memories before editing them, keep write operations small, and review snapshots before accepting large restructures, cleanup, or deletion. Require an API token before exposing Nocturne over SSE or HTTP, and keep local deployments bound to localhost unless there is a clear access-control plan.

Treat the memory database as sensitive. Memory nodes may contain personal history, private project strategy, credentials pasted by mistake, emotional support context, relationship content, health details, drafts, and agent instructions. Protect database files, PostgreSQL URLs, config files, API tokens, snapshots, dashboard access, MCP transcripts, search results, and boot URIs.

Duplicate Check

No Dataojitori/nocturne_memory, Nocturne Memory MCP, rollbackable memory MCP, agent memory graph, or matching source URL entry was found in content/mcp or README.md. Existing memory entries cover different memory servers and do not cover Nocturne's URI-addressed graph, dashboard, namespace, glossary, and rollback workflow.

Source citations

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How it compares

Nocturne Memory MCP Server side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.

Field

Long-term memory MCP server for agents, with URI-addressed memories, namespaces, search, aliases, glossary triggers, a visual dashboard, and reviewable rollback snapshots backed by SQLite or PostgreSQL.

Open dossier

Connect Claude to a Google Colab runtime. Colab MCP starts a local FastMCP server and websocket proxy, waits for an authorized Colab-origin browser connection, then exposes the session's notebook and runtime tools to your MCP client over stdio.

Open dossier

Django extension that exposes MCP endpoints and stdio transport for Django apps, with declarative model query tools, custom toolsets, DRF create/list/ update/delete tool publishing, serializer output, and MCP inspection.

Open dossier

MCP server example from FunASR that lets Claude transcribe local audio files with local speech recognition, automatic language handling, timestamps, and speaker labels when available.

Open dossier
Next steps
Trust
Review statusNot reviewedNot reviewedNot reviewedNot reviewed
Package trustPackage not verifiedPackage not verifiedPackage not verifiedPackage not verified
Source provenanceSource-backedSource-backedSource-backedSource-backed
Submitteroktofeesh1oktofeesh1oktofeesh1oktofeesh1
Install riskReview firstReview firstReview firstReview first
Notes Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓
BrandNocturne Memory logoNocturne MemoryColab MCP logoColab MCPDjango MCP Server logoDjango MCP ServerFunASR logoFunASR
Categorymcpmcpmcpmcp
SourceSource-backedSource-backedSource-backedSource-backed
AuthorDataojitoriGoogle ColabSmart GTSFunASR
Added2026-06-062026-06-062026-06-062026-06-06
Platforms
Harness
Source repo
Safety notesNocturne Memory can create, update, delete, alias, trigger, and search persistent memory nodes. Stdio startup can launch a local admin dashboard, initialize the database, and build the frontend if dependencies are present. Network-facing SSE or HTTP mode refuses to start without an API token when bound to a reachable host, but localhost-only deployments should still set one. Write tools can change long-term agent behavior; review snapshots and rollback queues before accepting broad edits or cleanup operations. Keep `public_readonly_mcp` enabled only for demos or intentionally read-only deployments.Colab MCP runs a local websocket proxy and waits for a Google Colab browser session to connect before proxying session tools. The server creates a bearer-style proxy token and accepts a single authorized Colab-origin websocket connection at a time. A connected Colab runtime can execute code and interact with notebooks, outputs, files, variables, packages, and mounted resources depending on the session and tools exposed by Colab. Use disposable notebooks or reviewed development sessions before allowing Claude to run code or inspect outputs. Avoid connecting notebooks that have access to production data, cloud credentials, private datasets, paid accelerators, or long-running jobs unless the workflow has explicit approval.Django MCP Server can expose Django model querysets, custom Python methods, DRF create/list/update/delete views, serializers, resources, and low-level FastMCP tools to an MCP client. Published DRF create, update, and delete tools can mutate application data if their serializers, views, and authentication rules permit it. The README notes that built-in DRF authentication classes, permission classes, filter backends, and pagination are disabled for published DRF tools in favor of MCP authentication; review this carefully before reusing production views. Query tools can evaluate QuerySets and return database records; restrict queryset scope and fields before exposing sensitive models. Require confirmation and application-level authorization before exposing write tools, email-sending methods, admin-like actions, or tools that touch customer, employee, financial, health, or regulated data.The MCP server exposes a `transcribe_audio` tool that reads the local file path supplied by the agent. Configure clients so Claude can only request audio files from approved directories; do not expose arbitrary private folders or shared drives. First use can download FunASR model weights and dependencies from upstream model hosts; review network policy, cache location, and disk usage before use in restricted environments. Long recordings and GPU transcription can consume significant CPU, GPU, memory, and disk cache resources. Require confirmation before transcribing meetings, calls, interviews, voice notes, customer audio, regulated recordings, or files containing other people.
Privacy notesMemories can contain personal history, health details, credentials accidentally pasted into chats, private project context, relationship content, strategies, drafts, and agent identity instructions. Database files, PostgreSQL URLs, API tokens, config files, snapshots, dashboard views, MCP transcripts, and search results can expose the full memory graph. The README includes public demo guidance, but private memories should be stored only in a controlled self-hosted instance. Redact memory URIs, node content, boot URIs, glossary triggers, search terms, database paths, API tokens, and snapshot diffs before sharing logs or screenshots.Notebook code, prompts, outputs, logs, variables, datasets, file paths, runtime metadata, package lists, browser session details, and generated websocket tokens can be visible to the MCP client and model provider. Colab notebooks may contain API keys, OAuth tokens, mounted Drive paths, secrets in environment variables, private model weights, customer data, or unpublished research. Local log files are created under a temporary Colab MCP log directory by default unless a log directory is specified. Review Google Colab, MCP client, model provider, and organization retention policies before sending notebook or runtime context to an assistant.Django sessions, request headers, model names, field names, primary keys, QuerySet results, serializer output, DRF request bodies, custom tool arguments, and tool responses can be exposed to the MCP client. Exposed models may contain user accounts, permissions, customer records, orders, messages, files, logs, internal notes, audit trails, or application-specific secrets. Remote streamable HTTP deployments can move application data outside the original Django UI and audit path if MCP auth, OAuth metadata, and retention are not configured correctly. Stdio usage can still expose data through local MCP client logs, transcripts, and tool traces. Keep MCP endpoint access, serializer fields, queryset filters, and tool docstrings intentionally narrow for each app.Audio recordings can contain voices, names, accents, speaker identity, background speech, locations, health details, financial details, customer data, credentials spoken aloud, or other sensitive personal information. The upstream MCP example performs local inference and does not require an API key, but MCP clients, model providers, logs, terminal output, transcripts, screenshots, and shared chats can still retain audio paths and transcription text. Generated transcripts, timestamps, and speaker labels may identify individuals or reveal confidential conversations. Model downloads and package installation can contact PyPI, ModelScope, Hugging Face, or other dependency hosts depending on the environment and model configuration.
Prerequisites
  • Python 3.10 or newer.
  • Node.js if the dashboard frontend needs to be built on first startup.
  • MCP client that supports stdio, SSE, or streamable HTTP depending on deployment mode.
  • SQLite or PostgreSQL storage selected and backed up before storing important memories.
  • uv or uvx available to run the server from the GitHub repository.
  • Local MCP client that supports `notifications/tools/list_changed`.
  • Google Colab account and browser session you are authorized to use.
  • Local machine access, because upstream documents that the MCP client must run locally.
  • Django 4 or 5 application with Python 3.10 or newer.
  • mcp_server added to INSTALLED_APPS and mcp_server.urls included in the Django URL configuration.
  • Review of which Django models, querysets, custom methods, DRF views, serializers, and request context should be exposed to MCP clients.
  • Authentication classes configured through DJANGO_MCP_AUTHENTICATION_CLASSES before exposing non-public data over streamable HTTP.
  • Python environment with FunASR installed from PyPI or a reviewed source checkout.
  • Local checkout or copy of `examples/mcp_server/funasr_mcp.py` from the FunASR repository.
  • Audio files in an approved location and format such as WAV, MP3, FLAC, M4A, or OGG.
  • Optional GPU, Apple silicon, or CPU device selection through `FUNASR_DEVICE`.
Install
Clone the repository, install `backend/requirements.txt`, and configure an MCP client to run `backend/mcp_server.py` with Python.
uvx git+https://github.com/googlecolab/colab-mcp
pip install django-mcp-server
pip install funasr
Config
Manual-only setup:
{
  "host": "127.0.0.1",
  "web_port": 8233,
  "api_token": "<long-random-token>",
  "public_readonly_mcp": false
}
{
  "mcpServers": {
    "colab-mcp": {
      "command": "uvx",
      "args": ["git+https://github.com/googlecolab/colab-mcp"],
      "timeout": 30000
    }
  }
}
{
  "mcpServers": {
    "django": {
      "command": "python",
      "args": [
        "manage.py",
        "stdio_server"
      ],
      "type": "stdio"
    }
  }
}
Manual-only setup:
pip install funasr
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