mcp server
Lore
Cross-agent memory over MCP: hybrid recall, knowledge graph, private/shared visibility, redaction.
Description as published by the maintainer. Source
- version 1.4.2
- active
- memory and context
active — Most recent push to the repository was 2026-08-02. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| GitHub stars | 7 | Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. | cumulative, all time | GitHub | |
| Last commit | 2026-08-02 | Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. | point in time | GitHub | |
| Open issues | 0 | Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. | as of fetch | GitHub | |
| Latest published version | 1.4.2 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-06-27 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| License | MIT | Licence GitHub detected in the repository. Detection can be wrong; the LICENSE file is authoritative. | as of fetch | GitHub | |
| First listed in the MCP Registry | 2026-06-27 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | active | The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. | as of fetch | GitHub |
Where to get it
Related, by what their authors tagged them
-
Nautilus Compass
— last commit 2026-08-05, shares cross-agent
Drift-aware cross-agent memory · MCP/A2A · LongMemEval-S 56.6% · 1/15 Zep cost
-
egonex-ai-understand-anything-understand
— last commit 2026-07-30, shares knowledge-graph, memory
Analyze a codebase to produce an interactive knowledge graph for understanding architecture, components, and relationsh…
-
egonex-ai-understand-anything-understand-chat
— last commit 2026-07-30, shares knowledge-graph, memory
Use when you need to ask questions about a codebase or understand code using a knowledge graph
-
egonex-ai-understand-anything-understand-dashboard
— last commit 2026-07-30, shares knowledge-graph, memory
Launch the interactive web dashboard to visualize a codebase's knowledge graph
-
egonex-ai-understand-anything-understand-diff
— last commit 2026-07-30, shares knowledge-graph, memory
Use when you need to analyze git diffs or pull requests to understand what changed, affected components, and risks
-
egonex-ai-understand-anything-understand-domain
— last commit 2026-07-30, shares knowledge-graph, memory
Extract business domain knowledge from a codebase and generate an interactive domain flow graph. Works standalone (ligh…
-
egonex-ai-understand-anything-understand-explain
— last commit 2026-07-30, shares knowledge-graph, memory
Use when you need a deep-dive explanation of a specific file, function, or module in the codebase
-
egonex-ai-understand-anything-understand-figma
— last commit 2026-07-30, shares knowledge-graph, memory
Analyze a Figma file via the Figma REST API and generate an interactive design knowledge graph (pages, screens, compone…
-
egonex-ai-understand-anything-understand-knowledge
— last commit 2026-07-30, shares knowledge-graph, memory
Analyze a Karpathy-pattern LLM wiki knowledge base and generate an interactive knowledge graph with entity extraction,…
-
egonex-ai-understand-anything-understand-onboard
— last commit 2026-07-30, shares knowledge-graph, memory
Use when you need to generate an onboarding guide for new team members joining a project
These share tags the maintainers applied themselves, such as cross-agent, knowledge-graph, memory. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.
This is not a recommendation and not a test result. It is a map of what the authors said their work is about.
Also from agentkitai
How the author describes it
Topics the maintainer set on GitHub: ai-agents, cross-agent, knowledge-graph, mcp, memory, python, typescript.
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