Perseus-Computing-LLC/perseus
by Various
The memory & context layer for AI agents: load only the context they actually need. Resolves live workspace state into verified facts before the context window opens. 94% fewer pro
MCP
Perseus-Computing-LLC/perseus
Added 13 July 2026
Overview
Perseus is a memory and context layer for AI agents that loads only the context they actually need. It resolves live workspace state into verified facts before the context window opens, reducing prompt tokens by 94% with zero overhead. It provides 33 MCP tools and is local-first with an MIT license.
Best for
Best for
Developers building AI agents that need efficient context management
Use cases
- Reducing prompt token usage for AI agents
- Providing verified facts from live workspace state
- Integrating with MCP tools for context management
How to use
Install
pip install perseus-ctx # 1. install Tools exposed
perseus_servicesperseus_readperseus_listperseus_treeperseus_envperseus_dateperseus_waypointperseus_sessionperseus_focusperseus_healthperseus_driftperseus_memoryperseus_mimirperseus_mnemeperseus_skillsperseus_includeperseus_agoraperseus_inboxperseus_captureperseus_context_diff
Tested with
Claude Desktop, Claude Code, Cursor
Notes
Perseus is a memory and context layer for AI agents that loads only the context they actually need. It resolves live workspace state into verified facts before the context window opens, reducing prompt tokens by 94% with zero overhead. It provides 33 MCP tools and is local-first with an MIT license.
21 stars on GitHub. Last updated 2026-07-13. Licensed MIT.
Use cases
- Reducing prompt token usage for AI agents
- Providing verified facts from live workspace state
- Integrating with MCP tools for context management
Pros
- 94% reduction in prompt tokens
- Zero overhead (0 ms)
- Local-first and open source (MIT)
Cons
- Limited to Python environment
- Relatively new with only 21 stars
- Requires integration with existing AI agent workflows
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- 94% reduction in prompt tokens
- Zero overhead (0 ms)
- Local-first and open source (MIT)
Cons
- Limited to Python environment
- Relatively new with only 21 stars
- Requires integration with existing AI agent workflows
Pairs with
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