mcp server
io.github.agenerationforwordz-tech/strata
Self-hosted AI memory server. Persistent memory on your hardware, not the cloud.
Description as published by the maintainer. Source
- version 2.2.0
- active
- memory and context
active — Most recent push to the repository was 2026-07-25. 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 | 6 | 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 | 2026-07-25 | 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 | 2.2.0 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-04-20 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| First listed in the MCP Registry | 2026-04-20 | 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
-
Gearboy MCP Server
— last commit 2026-08-07, shares raspberry-pi
MCP server for Gearboy Nintendo Game Boy / Game Boy Color emulator
-
Gearcoleco MCP Server
— last commit 2026-08-07, shares raspberry-pi
MCP server for Gearcoleco ColecoVision emulator
-
Geargrafx MCP Server
— last commit 2026-08-07, shares raspberry-pi
MCP server for Geargrafx PC Engine / TurboGrafx-16 emulator
-
Gearsystem MCP Server
— last commit 2026-08-07, shares raspberry-pi
MCP server for Gearsystem Sega Master System / Game Gear / SG-1000 emulator
-
io.github.Astrix-Labs/genesys-memory
— last commit 2026-07-19, shares ai-memory, pgvector, postgresql
Open-source causal memory for AI agents: persistent, explainable, MCP-native memory (13 tools).
-
memex
— last commit 2026-06-04, shares pgvector, postgresql
Persistent memory for AI agents — semantic + recency search, ONNX embeddings, Docker Compose.
-
io.github.devopam/mcpg
— last commit 2026-08-06, shares pgvector, postgresql
Production-grade PostgreSQL MCP server — 254 tools for query, tuning & ops, read-only by default.
-
Connapse
— last commit 2026-07-21, shares pgvector, self-hosted, semantic-search
Self-hosted knowledge backend for AI agents with hybrid search and MCP tools
-
io.github.alibaizhanov/mengram
— last commit 2026-07-30, shares ai-memory, pgvector, semantic-search
Long-term memory for AI agents: semantic facts, episodic events, and procedural workflows
-
RemembrallMCP
— last commit 2026-07-28, shares pgvector, semantic-search
Persistent knowledge memory for AI agents. Hybrid search, code graph, pgvector.
These share tags the maintainers applied themselves, such as raspberry-pi, ai-memory, pgvector, postgresql. 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.
How the author describes it
Topics the maintainer set on GitHub: ai-agents, ai-memory, local-first, mcp, mcp-server, pgvector, postgresql, raspberry-pi, self-hosted, semantic-search.
Bring your own setup
We take apart real AI setups every week and show what broke, what cost too much, and what the trace actually said. If you run agents on real work, that is where the useful conversation is.
Join ZBS AI Practice Lab