gem-hunter
Language: Go
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.
Your AI has amnesia. We fixed it.
Every LLM starts every conversation from zero. Stash gives your agent persistent memory — it remembers, recalls, consolidates, and learns across sessions. No more explaining yourself from scratch.
Open source. Self-hosted. Works with any MCP-compatible agent.
Don't want to self-host? usestash.io is the hosted cloud version — sign in with Google, copy one MCP URL, and you're done. Free to start.
git clone https://github.com/alash3al/stash.git cd stash cp .env.example .env # edit with your API key + model docker compose up
That's it. Postgres + pgvector, migrations, MCP server with background consolidation — all in one command.
Next: Getting Started guide — connect your MCP client, run init / remember / recall, and verify everything works.
Fully local (no cloud API): Ollama setup guide — host Ollama + Docker Compose, private embeddings and reasoner.
Atlas Cloud is a full-modal AI inference platform that gives developers a single AI API to access video generation, image generation, and LLM APIs. Instead of managing multiple vendor integrations, you connect once and get unified access to 300+ curated models across all modalities.
Check out Atlas Cloud's coding plan promotion: https://www.atlascloud.ai/console/coding-plan
Stash already supports Atlas Cloud through its OpenAI-compatible API. Set your .env like this:
STASH_OPENAI_API_KEY=your-atlas-cloud-api-key STASH_OPENAI_BASE_URL=https://api.atlascloud.ai/v1 STASH_EMBEDDING_MODEL=text-embedding-3-small STASH_REASONER_MODEL=deepseek-ai/DeepSeek-V3-0324 STASH_VECTOR_DIM=1536
Atlas Cloud works well here because Stash only needs:
See Getting Started for a fuller configuration checklist.
After docker compose up, Stash exposes an MCP server over SSE at:
http://localhost:8080/sse
Point any MCP-compatible client at that URL. Example configs:
Cursor — ~/.cursor/mcp.json
{
"mcpServers": {
"stash": {
"url": "http://localhost:8080/sse"
}
}
}Claude Desktop — claude_desktop_config.json
{
"mcpServers": {
"stash": {
"url": "http://localhost:8080/sse"
}
}
}OpenCode — ~/.config/opencode/config.json
{
"mcp": {
"stash": {
"type": "remote",
"url": "http://localhost:8080/sse",
"enabled": true
}
}
}Windsurf — ~/.codeium/windsurf/mcp_config.json
{
"mcpServers": {
"stash": {
"url": "http://localhost:8080/sse"
}
}
}Works with any agent that supports MCP over SSE — Claude Desktop, Cursor, Windsurf, Cline, Continue, OpenAI Agents, Ollama, OpenRouter, Atlas Cloud-backed setups, and more.
Stash is a cognitive layer between your AI agent and the world. Episodes become facts. Facts become relationships. Relationships become patterns. Patterns become wisdom.
A 9-stage consolidation pipeline turns raw observations into structured knowledge — facts, relationships, causal links, patterns, contradictions, goal tracking, failure patterns, and hypothesis verification. Each stage only processes new data since the last run.
A hosted, multi-tenant version of Stash is available at usestash.io and is currently free while in beta.
The cloud version is written from scratch — it shares no code with this repository. It is designed from the ground up for scalability, multi-tenancy, and long-term sustainability as a product. Feature sets differ in both directions: some things available here aren't in the cloud, and vice versa.
Apache 2.0