wnbhr/being
by Various
Personality Runtime — persistent memory, identity, and relationships for AI agents that work across multiple LLM platforms via MCP
MCP
wnbhr/being
Added 7 June 2026
Overview
A runtime for AI agents that maintains persistent memory, identity, and relationship data across different LLM platforms. It uses the Model Context Protocol (MCP) to enable cross-platform agent continuity.
Best for
Best for
Developers building cross-platform AI agents that need consistent personality and memory
Use cases
- Building agents with long-term memory across sessions
- Creating agents that maintain consistent identity across multiple LLM providers
- Developing multi-platform agent systems with shared relationship data
How to use
Install
npm start Tools exposed
Node.js 22+SupabaseAnthropic API keyOpenAI API keyGoogle API key
Example client config
{\n "mcpServers": {\n "my-being": {\n "url": "https://being.ruddia.com/mcp/<being_id>",\n "headers": {\n "Authorization": "Bearer brt_your_token_here"\n }\n }\n }\n} Notes
A runtime for AI agents that maintains persistent memory, identity, and relationship data across different LLM platforms. It uses the Model Context Protocol (MCP) to enable cross-platform agent continuity.
0 stars on GitHub. Last updated 2026-05-28.
Use cases
- Building agents with long-term memory across sessions
- Creating agents that maintain consistent identity across multiple LLM providers
- Developing multi-platform agent systems with shared relationship data
Pros
- Persistent memory and identity across LLM providers via MCP
- Standardized integration through the Model Context Protocol
- Written in TypeScript for broad compatibility with JavaScript/TypeScript stacks
Cons
- Very early stage with zero GitHub stars, indicating minimal adoption or testing
- Requires MCP-compatible LLM platforms, which may limit usability
- Likely limited documentation and community support due to low popularity
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Persistent memory and identity across LLM providers via MCP
- Standardized integration through the Model Context Protocol
- Written in TypeScript for broad compatibility with JavaScript/TypeScript stacks
Cons
- Very early stage with zero GitHub stars, indicating minimal adoption or testing
- Requires MCP-compatible LLM platforms, which may limit usability
- Likely limited documentation and community support due to low popularity
Pairs with
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