alash3al

alash3al / stash

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Stash — persistent memory layer for AI agents. Episodes, facts, and working context stored in Postgres. MCP server included. Self-hosted, single binary, no cloud required.

alash3al.github.iostash ai ai-agents ai-memory memory
12
0
100% credibility
Found Apr 25, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Go
AI Summary

Stash is a self-hosted memory system for AI agents that stores experiences, recalls relevant information semantically, and automatically consolidates raw observations into structured knowledge like facts, goals, and patterns.

How It Works

1
📰 Discover Stash

You learn about Stash, the helper that gives your AI a lasting memory so it never forgets your chats again.

2
🚀 Set it up quickly

Grab the ready package, connect your AI thinking service once, and launch everything with a single easy start.

3
📁 Make a memory home

Create a special space just for your AI's thoughts and experiences, like setting up a personal diary.

4
💭 Capture moments

As you chat with your AI, save key highlights and observations so they stick around forever.

5
🔍 Recall the past

Next time you talk, your AI pulls up old memories perfectly matched to what you're discussing.

6
🧠 It starts learning

Stash quietly turns those notes into smart insights, tracks goals, spots patterns, and even learns from slip-ups.

🎉 Smarter AI companion

Your AI now remembers everything, grows wiser over time, and feels like a true long-term friend.

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Star Growth

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AI-Generated Review

What is stash?

Stash gives AI agents persistent memory across sessions, storing raw episodes, distilled facts, goals, failures, and context in Postgres. Unlike ephemeral chat histories or git stash (which isn't persistent), it runs an 8-stage consolidation pipeline to build relationships, causal links, patterns, and hypotheses from observations—query via semantic recall or MCP server. Built in Go as a self-hosted single binary with Docker Compose for instant Postgres + pgvector setup.

Why is it gaining traction?

It stands out as a stash github alternative to cloud memory services: no vendor lock-in, background consolidation handles deduping and decay automatically, and MCP protocol plugs into Cursor, Claude Desktop, Continue, or Ollama agents seamlessly. Devs love the zero-config docker-compose up for local testing, plus features like goal tracking and failure pattern detection that make agents smarter over time without manual tweaks.

Who should use this?

AI agent builders using MCP tools like Cline, Windsurf, or OpenAI Agents who need session-spanning recall without cloud costs. Perfect for indie devs prototyping autonomous agents, or teams self-hosting stash github docker for research—think persistent state for code agents in Cursor or local LLM workflows.

Verdict

Try it if you're building agents needing real memory—solid Docker setup and Apache 2.0 make it a low-risk experiment despite 12 stars and 1.0% credibility score signaling early maturity. Polish docs and add tests to hit escape velocity.

(198 words)

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