Which integrations are available for this server?

Provides access to Cloudflare Agent Memory for storing facts, recalling semantically with synthesized answers, ingesting conversations, generating summaries, and managing namespaces. Adds Cloudflare memory as a Hermes provider with background prefetch/turn sync, memory management tools, session-end ingest, and provider status integration. Can be used as a memory backend in LangChain to store facts, recall answers, ingest conversations, and retrieve summaries. Can be used as a memory backend in LangGraph applications to store facts, recall answers, ingest conversations, and retrieve summaries. Lets OpenAI Codex agents persist and recall cross-session memories using Cloudflare Agent Memory.

How do I use cf-memory?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@cf-memory remember that the user prefers Python over TypeScript for new projects" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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cf-memory

by hansakoch

Overview Schema Related Servers Score Discussions

Python

Remote

CF Memory Plugin

Cloudflare Agent Memory for every AI coding agent and LLM framework.

Gives your agent persistent, cross-session memory powered by Cloudflare Agent Memory — a managed service that handles recall, fact extraction, and profile summaries. No vector DB to run, no embeddings to manage, no Worker to deploy.

Who this is for

AI coding agents (Claude Code, Codex, Cursor, Hermes, OpenClaw, TRAE, OpenCode, pi) that need to remember context across sessions
LLM frameworks (LangChain, LangGraph) building agents with persistent memory
MCP clients (any tool supporting Model Context Protocol)
Agent-to-agent systems using the A2A protocol
Anyone who wants a simple, hosted memory backend for their AI agent

What it does

Capability	Description
Remember	Store facts, instructions, events — CF classifies them automatically
Recall	Semantic search with synthesized answers (not just raw matches)
Ingest	Feed conversation turns — CF extracts facts/events/instructions/tasks
Summary	Markdown profile of everything stored, auto-generated
Namespaces	Isolate memory per app, user, or environment

Quick start (any agent)

pip install git+https://github.com/hansakoch/cf-memory-plugin.git

# Set credentials
export MCP_CLOUDFLARE_API_KEY="your-cf-api-token"
export CF_ACCOUNT_ID="your-account-id"

# Test it works
cf-memory test

Agent integrations

MCP clients (universal)

Works with any MCP-compatible client: Claude Desktop, Cursor, Windsurf, Continue, Zed, and more.

{
  "mcpServers": {
    "cf-memory": {
      "command": "cf-memory",
      "args": ["serve"],
      "env": {
        "MCP_CLOUDFLARE_API_KEY": "your-token",
        "CF_ACCOUNT_ID": "your-account-id"
      }
    }
  }
}

Tools exposed: remember, recall, list_memories, get_memory, delete_memory, ingest, summary, list_namespaces, create_namespace, delete_namespace

Hermes

Auto-discovered via pip entry point. No files to copy.

# Install
pip install git+https://github.com/hansakoch/cf-memory-plugin.git

# Activate
hermes config set memory.provider cloudflare-memory

# Verify
hermes memory status

# Management
hermes cloudflare-memory status
hermes cloudflare-memory test
hermes cloudflare-memory namespaces
hermes cloudflare-memory card

What Hermes gets:

prefetch() — 0ms (cached + background recall)
sync_turn() — 0ms (daemon thread ingest)
6 agent tools: cf_remember, cf_recall, cf_list, cf_get, cf_summary, cf_delete
on_session_end — auto-ingests full session for fact extraction
System prompt injection with provider status

Claude Code

Add to .claude/mcp.json in your project:

{
  "mcpServers": {
    "cf-memory": {
      "command": "cf-memory",
      "args": ["serve"],
      "env": {
        "MCP_CLOUDFLARE_API_KEY": "your-token",
        "CF_ACCOUNT_ID": "your-account-id"
      }
    }
  }
}

Or add globally: claude mcp add cf-memory -- cf-memory serve

Codex (OpenAI)

Add to ~/.codex/config.toml:

[mcp_servers.cf-memory]
command = "cf-memory"
args = ["serve"]
env = { MCP_CLOUDFLARE_API_KEY = "your-token", CF_ACCOUNT_ID = "your-account-id" }

Cursor

Add to .cursor/mcp.json in your project:

{
  "mcpServers": {
    "cf-memory": {
      "command": "cf-memory",
      "args": ["serve"],
      "env": {
        "MCP_CLOUDFLARE_API_KEY": "your-token",
        "CF_ACCOUNT_ID": "your-account-id"
      }
    }
  }
}

OpenClaw

Add to your OpenClaw config:

{
  "mcpServers": {
    "cf-memory": {
      "command": "cf-memory",
      "args": ["serve"],
      "env": {
        "MCP_CLOUDFLARE_API_KEY": "your-token",
        "CF_ACCOUNT_ID": "your-account-id"
      }
    }
  }
}

TRAE / TRAE CN / TraeCode CLI 2.0

Add MCP server in TRAE settings or .trae/mcp.json:

{
  "mcpServers": {
    "cf-memory": {
      "command": "cf-memory",
      "args": ["serve"],
      "env": {
        "MCP_CLOUDFLARE_API_KEY": "your-token",
        "CF_ACCOUNT_ID": "your-account-id"
      }
    }
  }
}

OpenCode

Add to ~/.opencode/config.json:

{
  "mcp": {
    "cf-memory": {
      "command": "cf-memory",
      "args": ["serve"],
      "env": {
        "MCP_CLOUDFLARE_API_KEY": "your-token",
        "CF_ACCOUNT_ID": "your-account-id"
      }
    }
  }
}

pi

Add MCP server to pi config:

{
  "mcpServers": {
    "cf-memory": {
      "command": "cf-memory",
      "args": ["serve"],
      "env": {
        "MCP_CLOUDFLARE_API_KEY": "your-token",
        "CF_ACCOUNT_ID = "your-account-id"
      }
    }
  }
}

Agent Plugins 1.0

Install as a plugin:

pip install git+https://github.com/hansakoch/cf-memory-plugin.git

The package registers via hermes_agent.memory_providers entry point. Any Agent Plugins 1.0 compatible host discovers it automatically.

LangChain / LangGraph

import asyncio
from cloudflare_memory import CloudflareMemoryClient

# Use as a memory backend in your LangChain/LangGraph agent
client = CloudflareMemoryClient(
    account_id="your-account-id",
    api_token="your-token",
    namespace="my-agent",
    profile="user-123",
)

# Store a fact
entry = asyncio.run(client.remember("User prefers Python over JavaScript."))

# Recall
result = asyncio.run(client.recall("What programming language does the user prefer?"))
print(result.answer)  # "Python"

# Ingest a conversation
asyncio.run(client.ingest([
    {"role": "user", "content": "I'm building a RAG pipeline."},
    {"role": "assistant", "content": "Great! Let me help with that."},
]))

# Get summary
summary = asyncio.run(client.get_summary())

A2A (Agent-to-Agent)

Start the A2A server for peer agents to discover and call:

cf-memory a2a --port 9120

Agent card at http://localhost:9120/.well-known/agent.json

Skills: remember, recall, ingest, list, get, summary

Python (standalone)

import asyncio
from cloudflare_memory import CloudflareMemoryClient

async def main():
    async with CloudflareMemoryClient(
        account_id="your-account-id",
        api_token="your-token",
        namespace="my-app",
        profile="default",
    ) as client:
        # Remember
        entry = await client.remember("User is based in London.")
        print(f"[{entry.type}] {entry.summary}")

        # Recall
        result = await client.recall("Where is the user based?")
        print(result.answer)

        # Ingest conversation (async — memories appear 3-8s later)
        await client.ingest([
            {"role": "user", "content": "I prefer dark mode."},
            {"role": "assistant", "content": "Noted!"},
        ])

        # Summary
        print(await client.get_summary())

asyncio.run(main())

Configuration

Environment variables

Variable	Required	Description
`MCP_CLOUDFLARE_API_KEY`	Yes	Cloudflare API token with Agent Memory permission
`CF_ACCOUNT_ID`	No	Cloudflare Account ID (defaults to Iceberg Media)
`CF_MEMORY_NAMESPACE`	No	Namespace name (default: `hermes`)
`CF_MEMORY_PROFILE`	No	Profile name (default: `default`)

Getting a Cloudflare API token

Go to Cloudflare Dashboard → API Tokens
Create a token with Agent Memory permission
You need a paid Workers subscription and beta access to Agent Memory

Limits (official)

Feature	Limit
Messages per ingest()	500
Message content	32 KB UTF-8
Recall query	1 KB UTF-8
Session ID	64 chars
Profile name	100 chars
Namespace name	32 chars

Performance

Designed to never add latency to your agent's turns:

Operation	Latency	Blocking?
`prefetch()`	0ms	No — cached + background
`sync_turn()`	0ms	No — daemon thread
`remember`	1.3–3.8s	User-initiated
`recall`	~5s	User-initiated
`list`	~0.4s	User-initiated
`summary`	~0.8s	User-initiated

CLI reference

# Standalone
cf-memory test                          # Connectivity check
cf-memory serve [--transport stdio|sse] # MCP server
cf-memory a2a [--port 9120]             # A2A agent server
cf-memory card                          # Print agent card JSON

# Hermes plugin
hermes cloudflare-memory status         # Provider status
hermes cloudflare-memory test           # Full connectivity test
hermes cloudflare-memory namespaces     # List namespaces
hermes cloudflare-memory create-ns NAME # Create namespace
hermes cloudflare-memory delete-ns NAME # Delete namespace
hermes cloudflare-memory card           # Print agent card

Development

git clone https://github.com/hansakoch/cf-memory-plugin.git
cd cloudflare-memory
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
pytest tests/ -v

License

MIT

This server cannot be installed

license - not tested

Not graded

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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