Agenticmemory logo

Agenticmemory

Persistent agent memory with conversation history, context, semantic search, encrypted spaces, and inter-agent queues.

Install Agenticmemory

Pick your client, then add it in one click or paste the config.

SSE (Remote)
Server URL
https://mcp.agenticmemory.ai/sse

Remote servers are added in the app, not the config file: Customize → Connectors → Add custom connector, then paste this URL.

Overview

The agenticmemory MCP server gives AI agents persistent storage for conversation history, durable key-value context, long-term entries, semantic search, and queues. It runs locally through the `agmry` CLI over stdio or connects remotely over SSE, using an API key to access shared spaces. Reach for it when agents need to retain decisions between sessions, coordinate work, or share memory across machines and projects. Zero-knowledge spaces support client-side encryption, but encrypted content cannot be semantically searched.

Use cases

  • Persist decisions and preferences across agent sessions
  • Search prior project discussions and deployment context
  • Share runbooks and memory across multiple agents
  • Queue work between agents without extra infrastructure
  • Protect sensitive memory with client-side encryption

Key features

  • 17 MCP tools for memory and coordination
  • Conversation history, context, entries, and scratchpad storage
  • Semantic search and session bootstrap
  • Shared spaces and FIFO queues
  • Local stdio and remote SSE access
  • Managed or end-to-end encryption

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 3d ago

Recent health check succeeded.

7/7 checks healthy over the last 40d

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars1Stargazers on the source repository.
Last commit1mo agoMost recent push to the default branch.

How Agenticmemory works

What agenticmemory MCP server does

The agenticmemory MCP server connects MCP-compatible agents to persistent memory stored in Agentic Memory. It separates memory into several forms: ordered conversation history, typed key-value context for durable facts and decisions, titled and tagged entries for longer-lived knowledge, and scratchpad data for temporary work in progress.

Agents can retrieve recent history, load a compact session state, and search older material semantically. The bootstrap operation combines messages, context, and entries into one session-start response, while a semantic bootstrap can focus that response on a topic. Spaces allow multiple agents, projects, or interfaces to use shared memory.

The service also provides FIFO queues within spaces. One agent can push an opaque JSON work item and another can pop it, wait for new work, inspect the queue, or review dead-lettered items. This supports handoffs without adding a separate queue service.

How it works

The package is installed as the agmry command and exposes an MCP process through agmry mcp-serve. Local MCP clients launch that process over stdio. The repository also documents a hosted SSE endpoint at https://mcp.agenticmemory.ai/sse, which accepts a bearer API key and requires no local installation.

The agenticmemory MCP server exposes 17 native tools covering storage, recall, search, bootstrap, context, entries, spaces, and queues. API access is associated with a project or space and can be shared across the CLI, MCP interface, and REST interface.

Setup and configuration

Install the CLI globally with npm install -g agmry. For a local MCP configuration, set AGMRY_API_KEY in the client environment because the MCP process runs outside the project directory and does not automatically read .agmry/config.json.

JSON-based clients can configure agmry as the command with mcp-serve as its argument and pass the API key through env. Remote clients use the documented SSE URL and an Authorization: Bearer header.

For encrypted spaces, generate or supply an encryption key. Resolution checks command-line flags first, then AGMRY_ENCRYPTION_KEY, followed by project-level and home-level .agmry/config.json files. A passphrase can be used instead of storing a key.

Tools and capabilities

The agenticmemory MCP server supports these capability groups:

  • Store and recall role-aware conversation messages.
  • Set and retrieve durable context values.
  • Create, list, and search titled or tagged entries.
  • Perform semantic search across remembered information.
  • Bootstrap a compact session state, optionally around a topic.
  • Create and use shared spaces.
  • Push, pop, wait on, inspect, and dead-letter queue items.
  • Use managed encryption at rest or client-side end-to-end encryption.

Zero-knowledge spaces encrypt data before it leaves the client. The server cannot read that data, so semantic search is unavailable there. Managed encryption keeps server-side features such as search and summaries available.

Limitations and notes

Losing an end-to-end encryption key or passphrase makes the associated data unrecoverable. The README recommends backing up the key. Queue envelopes are opaque JSON; the server does not inspect their contents. A queue pop on an empty queue returns exit code 2 in the CLI, while a wait option can long-poll for new work.

The package requires an API key for the documented hosted service configuration. The README names CLI, MCP, REST, and dashboard access, but the MCP listing itself does not describe the underlying API schema or individual tool signatures beyond their capability groups.

Reviews

No reviews yet — be the first to share how it worked for you.

Frequently Asked Questions about Agenticmemory

agenticmemory is an MCP server that connects AI agents to persistent conversation history, key-value context, searchable entries, shared spaces, and FIFO queues. Its main tools support storing, recalling, searching, bootstrapping sessions, managing context and entries, and passing work between agents through queues.

Install it with `npm install -g agmry`, then configure your MCP client to run `agmry mcp-serve`. Local clients use stdio; hosted MCP clients can connect to the documented SSE endpoint with a bearer API key instead.

Set `AGMRY_API_KEY` in Cursor's MCP server environment and run `agmry mcp-serve` with the `mcp-serve` argument. The process runs outside the project directory, so it will not automatically load the project's `.agmry/config.json`.

Only managed-encrypted spaces support semantic search. Zero-knowledge spaces encrypt content before transmission, which prevents the server from searching it; losing the client key or passphrase also makes that data unrecoverable.

Multiple agents can use the same shared space through the CLI, MCP, or REST interface. One agent can store a runbook or push a queue item, while another agent on a different machine recalls the information or pops the work.

Related MCP servers

Add a badge to your READMEShow it’s listed — live SVG, Markdown or HTML.

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:
Live Dynamic SVG PreviewAgenticmemory AllMCPs Directory Badge
Markdown (GitHub README)
[![AllMCPs](https://allmcps.com/api/badge/agenticmemory?style=directory)](https://allmcps.com/mcp/agenticmemory)
HTML Embed
<a href="https://allmcps.com/mcp/agenticmemory"><img src="https://allmcps.com/api/badge/agenticmemory?style=directory" alt="Agenticmemory on AllMCPs" /></a>