SecurityRonin/alaya
by Various
A memory engine for conversational AI agents, inspired by neuroscience and Buddhist psychology
MCP
SecurityRonin/alaya
Added 1 June 2026
Overview
SecurityRonin/alaya is a memory engine for conversational AI agents, implemented in Rust. It is inspired by principles from neuroscience and Buddhist psychology to manage agent memory. The tool provides a framework for storing and recalling contextual information across interactions.
Best for
Best for
Developers building conversational agents that need sustained context over extended interactions
Use cases
- Building conversational agents with persistent long-term memory
- Creating AI systems that recall past user interactions
- Developing context-aware responses in dialogue agents
How to use
Install
pip install alaya-memory Tools exposed
node_categoryimport_claude_memimport_claude_codeALAYA_DBALAYA_LLM_API_KEYALAYA_LLM_API_URLALAYA_LLM_MODEL
Tested with
Claude Desktop, Claude Code, Cursor, Cline, ChatGPT
Notes
SecurityRonin/alaya is a memory engine for conversational AI agents, implemented in Rust. It is inspired by principles from neuroscience and Buddhist psychology to manage agent memory. The tool provides a framework for storing and recalling contextual information across interactions.
12 stars on GitHub. Last updated 2026-04-20. Licensed MIT.
Use cases
- Building conversational agents with persistent long-term memory
- Creating AI systems that recall past user interactions
- Developing context-aware responses in dialogue agents
Pros
- Performant due to Rust implementation
- Unique conceptual foundation for memory management
- Open source for customization and integration
Cons
- Low community adoption with only 12 GitHub stars
- Limited documentation and ecosystem support
- Unconventional philosophical basis may require deeper understanding
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Performant due to Rust implementation
- Unique conceptual foundation for memory management
- Open source for customization and integration
Cons
- Low community adoption with only 12 GitHub stars
- Limited documentation and ecosystem support
- Unconventional philosophical basis may require deeper understanding
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