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  1. Home
  2. 🧠 Knowledge & Memory
  3. Memanto
M
Health: ActiveRecent health check succeeded.Last checked 10/14/2026, 11:01:19 PM

Memanto

InstallableWe know how to install it, but have not yet seen it complete a handshake. User RatingsBe the first to rate and review this MCP server!
View Repository2.3k GitHub StarsTotal stargazers on GitHub for the source repository (2,321 stars).
memoryknowledgemcpagents

Persistent semantic memory for MCP-compatible agents, with recall, conflict handling, expiry, and export.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” 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 β€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "memanto": {
      "command": "uvx",
      "args": [
        "memanto"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives🧠 More in Knowledge & Memory

Overview

The Memanto MCP server gives MCP-compatible agents a shared, persistent memory estate for decisions, preferences, facts, and failures. It extracts durable knowledge from conversations, consolidates duplicates, reconciles conflicting beliefs, applies expiry policies, and briefs agents with relevant context. CLI commands support remembering, recalling, answering questions, exporting memory, and connecting supported agents. Reach for it when multiple agents need managed memory instead of isolated transcript or vector-store data.

Use cases

β€’Share decisions across multiple agents
β€’Recall deployment policies across sessions
β€’Review contradictions between agent memories
β€’Apply retention and expiry policies
β€’Export an agent memory estate as Markdown

Key features

β€’Persistent semantic memory for MCP-compatible agents
β€’Conversation-based memory extraction
β€’Duplicate consolidation and conflict reconciliation
β€’Policy-driven expiry and memory restoration
β€’Agent bootstrapping with relevant context
β€’Open Knowledge Format import and export

Capabilities & Tool Schemas

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

Extracted Tool Capabilities
Persistent semantic memory for MCP-compatible agents
Conversation-based memory extraction
Duplicate consolidation and conflict reconciliation
Policy-driven expiry and memory restoration
Agent bootstrapping with relevant context
Open Knowledge Format import and export

How Memanto works

What Memanto does

The Memanto MCP server provides managed semantic memory for MCP-compatible agents. It is designed to sit beside an agent fleet as a memory-focused companion, rather than acting as a passive transcript archive. Memanto extracts durable knowledge such as decisions, preferences, facts, and failures from agent conversations, then maintains that knowledge over time.

Its memory estate can be shared across agents so that one agent can recall information learned by another. The system distinguishes current knowledge from earlier beliefs, keeps provenance and timestamps, and supports confidence tracking. Agent namespaces provide separation between different agents, such as production operations and experimentation.

Memanto belongs in the Knowledge & Memory category for workflows where agents need continuity across sessions, users, or tools. It is especially relevant when duplicated memories, contradictory information, stale preferences, or repeated work make an unmanaged memory store difficult to use.

How it works

Memanto observes interaction streams and extracts selected durable knowledge instead of retaining every conversation as memory. Scheduled processing consolidates duplicates, joins related fragments, and increases confidence when observations repeat. When new information conflicts with an older belief, the newer knowledge can supersede it while preserving the historical record.

Memory lifecycle policies control decay and expiry. Expired memories remain available for recall with an expiration marker and can be restored; explicit deletion is separate. The memanto conflicts command supports reviewing contradictions, while memanto forget handles forgetting operations.

Before an agent acts, memanto agent bootstrap supplies a relevant slice of the estate. Agents can also use commands such as memanto recall for searches and memanto answer for grounded responses. Queries can be evaluated as of a date or against changes since a named release, where those options are needed for historical review.

Setup and configuration

Install the package with Python, npm, or the Go SDK:

  • pip install memanto
  • npm install @moorcheh-ai/memanto
  • go get github.com/moorcheh-ai/memanto/sdks/go

Running memanto starts the setup path for either an on-premises deployment or the cloud option. The on-premises mode uses Docker and Ollama and is described as requiring no account or API key. memanto config backend switches between configured backends.

Connect supported agents with memanto connect, followed by a client name such as claude-code, cursor, codex, windsurf, or cline. The repository also documents connections for Goose and Copilot, although those are not represented in the directory's supported-client list. memanto ui opens a local dashboard for browsing, searching, and auditing the estate.

Tools and capabilities

The Memanto MCP server includes capabilities for:

  • Remembering knowledge from text or conversations with memanto remember.
  • Recalling relevant information with ordinary queries, date-based views, or change-based views.
  • Producing grounded answers through memanto answer.
  • Consolidating memories through scheduled processing with memanto schedule enable.
  • Reviewing contradictions with memanto conflicts.
  • Applying configurable retention and expiry policies.
  • Exporting memory as Markdown through the Open Knowledge Format with memanto memory export --okf.
  • Importing or migrating memory from Mem0, Letta, Supermemory, Zep, Hindsight, or OKF bundles.

Limitations and notes

The README describes on-premises operation with Docker and Ollama, plus a cloud deployment, but does not specify detailed hardware, model, or hosting requirements. Client connection support is shown through the Memanto CLI rather than a complete transport or configuration reference in the supplied material.

The Memanto MCP server manages memory but does not eliminate the need to define retention policies and review conflicts. Expiry is policy-driven, and expired records remain visible in recall until deleted or restored. Open Knowledge Format export is intended to keep the estate portable as readable, diffable Markdown rather than a proprietary archive.

Memanto is released under the MIT License.

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

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Adoption & maintenance

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

GitHub stars
2.3k
Stargazers on the source repository.
Last commit
Today
Most recent push to the default branch.

Reviews

No reviews yet β€” be the first to share how this listing worked for you.

Frequently Asked Questions about Memanto

Memanto is an MCP server that connects MCP-compatible agents to a shared managed memory estate. Its main tools support remembering and recalling knowledge, answering grounded questions, bootstrapping agent context, handling conflicts, and exporting memory in Open Knowledge Format for cross-agent use.

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Technical Specs & Signals

Category🧠Knowledge & Memory
PricingFree
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimePython
AuthNo auth required
LicenseMIT
ClientsCursor, Cline / VS Code, Windsurf, Claude Code, OpenAI Codex CLI
Last updatedOct 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 stars2,321
GitHub Star CountTotal stargazers on GitHub representing community popularity (2,321 stars).
Last commitToday
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Oct 14, 2026
49Quality signal: Fair Β· 49/100How this signal is calculated β–Ύ
Server availabilityNot measured

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

Verified ownership10/20
Documentation & tools17/30
Adoption & activity10/15
Community engagement0/10

A guidance signal from public completeness & health data β€” 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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