ai-context-flow

Universal memory for AI agents and tools.

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What it can do

  • Get User Memory Buckets: List all memory buckets (AI profiles) for the authenticated user. Each bucket is a themed collection of documents, notes, and files. Returns bucket IDs, names, and item counts
  • List Items In Memory Bucket: List all stored items (documents, files, notes) inside a specific memory bucket. Returns item metadata including title, description, source type, file name, size, and chun
  • Search Memory: Search across the user's stored memory using semantic vector similarity. Finds relevant content even when the query doesn't exactly match stored text. Searches all memory buckets by def

What data it sees

Do you need an account

No: the server works without sign-in

Universal memory for AI agents and tools. Save, organize and search context on any AI tool or platform.

Here's what you can do with AI Context Flow:

  • Organize your projects as memory buckets
  • Save important chats directly from within chat agents
  • Give all your agents (openclaw, claude code, lovable, and more) a shared persistent memory
  • Share your buckets with other people

We also have a Chrome extension that lets you capture and use context on any website: https://chromewebstore.google.com/detail/ai-context-flow-use-your/cfegfckldnmbdnimjgfamhjnmjpcmgnf

Docs: https://docs.plurality.network/the-plurality-mcp-server

How to connect

How to connect ai-context-flow to Claude

The server address to paste into Claude settings:

https://ai-context-flow--plurality-network.run.tools

  1. Open Claude (claude.ai or the desktop app).
  2. Go to Settings → Connectors.
  3. Click Add custom connector.
  4. Paste the server address copied below into Remote MCP server URL and click Add.
  5. In a chat, click + → Connectors and switch the new connector on.

Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

Authorization

After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

How to connect ai-context-flow to ChatGPT

The server address to paste into ChatGPT settings:

https://ai-context-flow--plurality-network.run.tools

  1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
  2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
  3. Open Settings → Connectors and click Create.
  4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
  5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
  6. In a new chat open + → Apps/Connectors and enable the connector.

OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

Authorization

On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

How to connect ai-context-flow to Cursor

The server address to paste into Cursor settings:

https://ai-context-flow--plurality-network.run.tools

Fastest: click Open in Cursor below and confirm the prompt.

Manually:

  1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
  2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
  3. Save the file. The server appears in the MCP list; authorize it there if asked.

Authorization

If the service needs sign-in, an authorize button appears next to the server in the MCP list.

Server tool list (7)

Raw names from tools/list. Only developers need these.

get_user_memory_buckets List all memory buckets (AI profiles) for the authenticated user. Each bucket is a themed collection of documents, notes, and files. Returns bucket IDs, names, and item counts. Use this first to discover available memory buckets before browsing or searching their contents. Returns both owned buckets and buckets shared with the user. Each bucket has a `role` ('owner', 'editor', or 'viewer') and shared buckets include `sharedBy` information.
list_items_in_memory_bucket List all stored items (documents, files, notes) inside a specific memory bucket. Returns item metadata including title, description, source type, file name, size, and chunk count — but not the actual content. Use this to browse what's stored in a bucket before reading or searching specific items. Args: profile_id: The ID of the memory bucket (profile) to list items from.
search_memory Search across the user's stored memory using semantic vector similarity. Finds relevant content even when the query doesn't exactly match stored text. Searches all memory buckets by default, or specify bucket IDs to narrow scope. Searches across both owned and shared buckets automatically. Returns results grouped by memory bucket and context, with content previews and relevance scores. Use this to discover which contexts contain relevant information, then use read_context to get the full content. Args: query: The search query to find relevant memories. profile_ids: Optional list of memory bucket IDs to search within. If not provided, searches across all buckets. k: Number of results to return (default: 5).
read_context Read the full content of a specific stored memory item (document, file, or note). Returns the actual text content with pagination support for large documents. Use start_chunk and limit to read specific portions — defaults to returning all content. Use this after finding an item via list_items_in_memory_bucket or search_memory. Args: context_id: The ID of the context/item to read. start_chunk: Chunk index to start reading from (default: 0). limit: Maximum number of chunks to return (default: 0 = all chunks).
save_memory Save text content to a specific memory bucket. IMPORTANT — Before calling this tool: 1. Call get_user_memory_buckets to list available buckets 2. Ask the user which bucket to save to, or offer to create a new one 3. If the user wants a new bucket, call create_memory_bucket first Args: profile_id: The ID of the memory bucket to save to (required). content: The text content to save. title: Optional custom title for this memory. If not provided, a title is generated automatically from the content. source_platform: The name of this MCP client platform (e.g. "claude", "chatgpt", "cursor").
save_conversation Save a conversation (chat history) to a specific memory bucket. IMPORTANT — Before calling this tool: 1. Call get_user_memory_buckets to list available buckets 2. Ask the user which bucket to save to, or offer to create a new one 3. If the user wants a new bucket, call create_memory_bucket first Args: profile_id: The ID of the memory bucket to save to (required). chat_history: List of chat messages, each with 'role' ("user" or "assistant") and 'content' (the message text). title: Optional custom title for this conversation. If not provided, a title is generated automatically from the conversation content. source_platform: The name of this MCP client platform (e.g. "claude", "chatgpt", "cursor").
create_memory_bucket Create a new memory bucket (AI profile) for organizing saved content. Only use this when the user explicitly wants a new bucket. Always ask the user for confirmation before creating. Args: bucket_name: A descriptive name for the new bucket.