stash

Stash

Overview

Stash is an open-source persistent memory layer for AI agents, written in Go. It launched in late April 2026 and crossed 700 GitHub stars within six weeks — modest compared to the viral repos, but the kind of steady organic growth that signals genuine developer adoption rather than marketing hype. The project solves a problem every developer building AI-powered applications runs into immediately: LLMs have no memory. Every conversation starts from zero.

The creator, Mohamed Al-Ashaal, is a backend engineer who’s been building developer tools and open-source projects for years. The project is licensed under Apache 2.0, which matters for anyone evaluating it for production use. No weird custom licenses, no “open core with the good parts locked away” — it’s straightforward permissive licensing.

The core problem Stash addresses is architectural. When you build an AI agent — whether it’s a coding assistant, a customer support bot, or an internal knowledge worker — you need it to remember context across sessions. Without persistent memory, your agent repeats mistakes, forgets user preferences, and can’t build on previous interactions. Most developers hack this together with vector databases and prompt stuffing, which works for demos but breaks down at scale. Stash takes a more principled approach: a 9-stage consolidation pipeline that 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, so costs stay manageable.

Why it matters

The AI agent ecosystem is exploding, but the infrastructure layer is still immature. Developers are building agents with LangChain, CrewAI, Mastra, and custom frameworks, but the memory problem remains the weakest link in most architectures. A June 2026 survey by LangChain found that 68% of agent developers cite “maintaining context across sessions” as their top technical challenge, ahead of tool orchestration and prompt engineering.

Stash fills this gap with a clean, self-hosted solution that integrates with the tools developers already use. It exposes an MCP (Model Context Protocol) server, which means any MCP-compatible client — Claude Desktop, Cursor, Windsurf, Cline, Continue, OpenAI Agents — can connect to it with a single URL. No SDKs to install, no vendor-specific APIs to learn. The MCP standard is gaining traction fast, and Stash was one of the earlier memory-layer projects to adopt it natively.

There’s also a practical cost argument. Managed memory services charge per-query or per-token, which gets expensive fast when your agent is doing real work. Stash runs on your own Postgres instance — you pay for compute, not per-memory-access. For teams running agents at any meaningful scale, the economics favor self-hosted.

Key Features

9-Stage Consolidation Pipeline. This is Stash’s most distinctive technical decision. Raw observations (episodes) go through a pipeline that extracts facts, identifies relationships between them, detects causal links, finds patterns, flags contradictions, tracks goals, logs failure patterns, and verifies hypotheses. Each stage runs incrementally — only processing new data since the last consolidation. This is fundamentally different from naive “store everything in a vector database and hope retrieval works” approaches.

Postgres + pgvector Backend. Stash uses PostgreSQL with the pgvector extension for both structured storage and vector similarity search. If you already run Postgres (and most fullstack developers do), adding Stash doesn’t introduce a new database to manage. The schema is clean, migrations are automatic, and you can inspect the data directly with standard SQL.

MCP Server Over SSE. The built-in MCP server exposes all memory operations as tools at http://localhost:8080/sse. Any MCP-compatible client connects with a single URL — no custom integrations needed. This works with Claude Desktop, Cursor, Windsurf, Cline, Continue, OpenAI Agents, and any other MCP client. The protocol is becoming the standard for agent-tool communication.

OpenAI-Compatible API Support. Stash works with any OpenAI-compatible provider — OpenAI directly, OpenRouter, Atlas Cloud, local Ollama, or any other compatible gateway. You configure it with a base URL and API key, and it handles both embeddings (for vectorization) and chat completions (for the consolidation reasoning). No vendor lock-in on the AI provider side.

Single Binary Deployment. Docker Compose gets you running in one command — Postgres, pgvector, migrations, MCP server, and background consolidation all included. For teams that want simpler infrastructure, the Go binary runs standalone. No Node.js runtime, no Python dependencies, no JVM. This matters for production deployments where operational simplicity translates directly to reliability.

Incremental Processing. The consolidation pipeline only processes data that’s new since the last run. If your agent generates 10,000 episodes in a day, Stash doesn’t re-process all of them every cycle — it processes the delta. This keeps compute costs proportional to new information rather than total memory size, which is the difference between a system that works in production and one that works in a demo.

Self-Hosted with Optional Cloud. The open-source version is fully featured for self-hosting. There’s also a hosted version at usestash.io (currently free in beta) that’s written from scratch for multi-tenancy and scalability. The cloud version shares no code with the open-source repo — it’s a separate product that targets teams who want managed infrastructure.

Use Cases

• Coding assistants with project memory — Agents that remember your codebase conventions, past decisions, and team preferences across sessions. Connect Stash to Cursor or Claude Desktop and your assistant stops asking the same questions every session.

• Customer support bots — Agents that maintain context about customer history, previous issues, and resolution patterns. The relationship extraction in the consolidation pipeline maps connections between customer issues and solutions.

• Research assistants — Agents that accumulate knowledge across research sessions, building up a knowledge graph of sources, findings, and contradictions. The pattern detection stage identifies emerging themes across multiple research interactions.

• Multi-agent systems — Teams of agents that need shared memory. Stash’s Postgres backend supports concurrent access, so multiple agents can read and write to the same memory store with proper isolation.

• Personal AI assistants — Individual productivity agents that learn your preferences, routines, and communication patterns over time. The fact extraction stage converts casual observations into structured knowledge the agent can use.

Pros and Cons

Pros:

• The 9-stage consolidation pipeline is genuinely novel — most memory solutions stop at “embed and store.” Stash’s approach of extracting facts, relationships, and patterns produces structured knowledge, not just searchable text.
• MCP integration means zero vendor lock-in on the client side. Any MCP-compatible tool works immediately, and the MCP ecosystem is growing fast in 2026.
• Postgres-backed architecture means you can query, back up, and manage memory data with tools you already know. No proprietary database or obscure storage format.

Cons:

• 711 stars is still early-stage adoption. The project is two months old — expect API changes, incomplete documentation, and rough edges in edge cases. Not yet suitable for mission-critical production workloads without careful evaluation.
• The consolidation pipeline requires a capable LLM (for the reasoning stages), which means ongoing API costs. The README doesn’t provide clear cost estimates per consolidation cycle, making it hard to budget for production use.
• No built-in authentication or multi-tenancy in the open-source version. If you’re running Stash for multiple users or teams, you’ll need to add your own access control layer.

Getting Started

# Clone and start with Docker Compose
git clone https://github.com/alash3al/stash.git
cd stash
cp .env.example .env   # Edit with your API key + model

# Start everything: Postgres + pgvector, migrations, MCP server
docker compose up

Configure your MCP client to connect:

// Cursor — ~/.cursor/mcp.json
{
  "mcpServers": {
    "stash": {
      "url": "http://localhost:8080/sse"
    }
  }
}

For fully local operation (no cloud API), use the Ollama setup guide at docs/LOCAL_OLLAMA.md.

Test with the MCP tools: init (create a session), remember (store an episode), recall (search memory).

Alternatives

Mem0 — Another popular AI memory layer with a Python SDK and managed cloud offering. Mem0 focuses on simplicity with a REST API and supports multiple vector databases. It’s more mature (20K+ GitHub stars) and has better documentation, but lacks Stash’s structured consolidation pipeline. Choose Mem0 if you want a quick integration with minimal setup and don’t need the multi-stage knowledge extraction.

Zep — A long-term memory service for AI agents that emphasizes temporal knowledge graphs and fact tracking. Zep offers both open-source and cloud versions with a focus on chat applications. It’s more feature-complete for chat-specific use cases but heavier on infrastructure. Choose Zep if your primary use case is conversational AI with complex fact-tracking requirements.

Cognee — An open-source framework for building knowledge graphs from unstructured data, often used for agent memory. Cognee takes a different architectural approach — it builds explicit knowledge graphs rather than using a consolidation pipeline. Choose Cognee if you want graph-based reasoning over your agent’s memory and are comfortable with a more complex setup.

Verdict

Stash is the kind of project that solves a real, immediate problem for developers building AI agents. The 9-stage consolidation pipeline is the most interesting technical choice — it treats memory as a first-class cognitive system rather than a glorified key-value store. At 711 stars and two months old, it’s early, but the architecture is sound and the MCP integration makes it drop-in compatible with the tools developers already use. If you’re building agents with any of the major frameworks and you’ve been stitching together vector databases and prompt engineering for memory, Stash is worth a serious weekend evaluation. The Postgres + Go stack means it fits naturally into a fullstack developer’s existing infrastructure, and the Apache 2.0 license means no surprises down the road. Watch this one — the memory layer is going to be a critical infrastructure category as agents move from demos to production, and Stash has the right architectural instincts.