Raw capture — raw/stash.md

url: https://github.com/alash3al/stash date_fetched: 2026-07-05 backfilled: true

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.iois 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:

• an embeddings model for vectorization
• a chat-capable reasoning model for consolidation
• an OpenAI-compatible base URL and API key

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