stash, from Fergana Labs, is a shared workspace and memory layer for AI coding agents that captures agent interactions and session artifacts to preserve context across work cycles. It collects prompts, tool executions, transcripts and generated files, exposing them via MCP, a CLI and a virtual filesystem for programmatic access. The tool targets engineering teams using agentic assistants and aims to make prior session state available to subsequent agents and collaborators.

What tasks can you actually use it for?

Stash functions as a persistent repository for agent activity, designed to store the concrete traces of agent work such as prompts, tool calls and generated artifacts. That captured history supports reuse tasks like continuing interrupted agent sessions, sharing investigative threads between developers, and packaging useful interactions into shareable units called Skills. For teams that rely on assistants for coding, the tool acts as a single place to consult past agent decisions.

How reliable are the workspace search and recall?

The workspace offers both semantic and keyword search across stored pages, sessions and tables, and exposes memory through a mountable virtual filesystem so agents and users can use standard terminal tools to inspect results. Recall quality depends on what agents record, since Stash archives transcripts and reasoning traces rather than validating them. Users should treat retrieved entries as recorded agent outputs and verify them before acting on high-stakes information.

What file types and integrations does it accept?

Stash accepts common documentation formats and agent transcripts, including:

• Markdown and HTML pages
• Structured tables
• PDF documents and uploaded artifacts

Integration points include an MCP server, a CLI and a virtual filesystem shell. The tool can operate as a local memory store without an LLM, while some advanced features require an external API key for generated answers.

Does it fit into a developer team's workflow?

The tool targets engineering teams and supports collaborative editing so humans and agents can view and modify the same workspace in real time. It automatically streams agent activity into the workspace, reducing the need for manual documenting of sessions, and offers a Skills system to package reusable content. Self-hosting options use Docker and Postgres, which means teams must plan for basic operational setup to run the server reliably.

Practical choice when persistent agent memory matters

The tool is a sensible option for engineering teams that require a lasting, shared record of agent work and decision trails; its main constraint is operational overhead for teams without platform support. Treat stored transcripts as working notes and include a human review step for production decisions. For groups that enforce verification and have basic self-hosting capacity, the tool strengthens team continuity around agent-driven tasks.