Block’s new Apache 2.0 agent workspace Berd works across models and harnesses, stores conversation history locally

Block, the technology company founded by former Twitter CEO Jack Dorsey that owns Square, Cash App and the music streaming service Tidal, is open-sourcing Berd, a desktop application it originally built to give its own employees a single environment for working with AI agents across different models, tools and projects. Berd is a locally installed graphical desktop application rather than a browser-based workspace. It is available now on GitHub under a permissive Apache 2.0 license - meaning anyone can use, modify and redistribute it, including commercially - with free downloadable builds for macOS, Windows and Linux. The repository reached version 0.6.2 on Aug. 18, its seventh public release, and lists 91 contributors. “Berd is desktop-first because much of its value comes from working directly with projects, local files, tools, repositories, and agents running on or connected to the user’s computer,” Brad Axen, Head of AI Capabilities at Block, told VentureBeat in emailed responses. A daily work surface for AI agents Block positions Berd as a “daily AI work surface”: a single place where users can start chats, attach files or folders, choose agents and models, work inside persistent projects, configure AI providers, manage skills and extensions, review session history and build automations. The design goal is not merely convenience. According to Berd’s product specification, users should always be able to tell which project, files, agent, model, provider and session state are actively shaping a conversation. That emphasis on visible operational state is what Block says separates Berd from a generic chatbot wrapper. Configuration - providers, extensions, skills, automations, projects - is treated as part of the workflow rather than buried in an administrative layer, and the interface is designed to expose failures, unavailable providers, and loading and streaming states plainly rather than smoothing them over with assistant-style friendliness. Berd grew out of a practical problem inside Block. Employees were already working with capable agents - Block’s own Goose, Anthropic’s Claude Code and OpenAI’s Codex - but the experience around them had become fragmented. “We had capable agents through goose, Claude Code, and Codex, but working across them meant navigating different interfaces, configuration systems, and ways of managing context,” Lucinda Bell, Technology Communications at Block, said in an email. Berd, she said, gives Block teams “one consistent desktop application across models and harnesses.” For enterprises, that layer may matter as much as another incremental gain in model capability. Persistent projects mean users can return to an established collection of files, instructions and agent configurations instead of reconstructing context for each task. Block also explicitly designed Berd to make agentic work accessible beyond engineering, letting people start with a conversation and add tools, context and structure as the work requires. Block is not positioning Berd as a mobile product. Axen said the company’s open source collaboration platform Buzz already provides a mobile experience and is Block’s preferred environment for work on the go. Block thinks agents should look different when they are different Berd takes an unusual approach to a problem most enterprise AI products leave implicit: how users tell one configured agent apart from another. Instead of treating every agent as another blank chat window, Berd gives agents roles, instructions, skills, tools and recognizable visual identities. Block created collections of animated characters - its flagship designs are called “Gloopies” - and Berd’s public site showcases predefined personas such as Berdy, Pushback, Choosey, Copycat, Tinker and Wildcard, each built around a different style of work: Pushback plays devil’s advocate on drafts, Choosey helps narrow decisions, Copycat learns to write in the user’s style. The visual layer is not meant to substitute personality for substance. Block’s own formulation, from a company blog post on Berd’s design, is functional: “The avatars make the agent recognizable. Its role, skills, and tools make it useful.” That distinction could grow more important as workers juggle several specialized agents at once. A recognizable identity serves as visible shorthand for an otherwise invisible package of instructions, tools and permissions - agents appear on the workspace as distinct animated characters rather than identical entries in a chat list, so users can tell them apart before opening a conversation. The product specification guards against the characters turning Berd into a novelty. It describes the intended personality as “focused, capable, companionable,” while explicitly rejecting toy-like presentation, oversized decorative UI and interfaces that hide operational state behind friendliness. An orchestration layer, not another model or agent runtime Architecturally, Berd is neither a new foundation model nor a new agent runtime. The application is built with Tauri 2 and React 19 - frameworks for building lightweight desktop apps with web technologies - and communicates with Goose, the open source agent framework Block introduced in January 2025, through a standard called the Agent Client Protocol (ACP). Goose runs quietly alongside Berd as a bundled “sidecar” process and handles the underlying agent loop; Berd handles projects, sessions, context, agents and configuration on top. For readers unfamiliar with it, Goose is a model-agnostic agent framework and runtime, originally focused on software development workflows. It connects large language models to files, commands and outside systems - including through the Model Context Protocol (MCP), an open standard for wiring AI systems to external tools and data - so agents can take actions rather than merely generate text. Goose works with proprietary model providers as well as locally run open models. Block has since contributed Goose to the Agentic AI Foundation, the Linux Foundation body it helped establish with Anthropic, OpenAI and others in December 2025, giving the framework a vendor-neutral home alongside MCP. Axen drew the boundary explicitly: agent harnesses such as Goose, Claude Code and Codex manage the loop between a model, its context and its tools, while Berd provides the consistent desktop environment around those harnesses. In the public release, users configure whichever providers and harnesses they want rather than inheriting a Block-selected model stack. The project’s README adds an operational detail that matters for anyone evaluating the software: Berd does not simply pull whatever Goose build happens to be current. Its build process pins a specific Goose backend version through a lockfile, verifies the cached binary matches that pinned version, and packages it with the app. Developers can explicitly substitute another Goose binary for local testing. For organizations inspecting Berd, that creates a defined version boundary between the desktop application and its default agent backend rather than letting the backend silently drift. How Berd compares to Codex, Claude Code, Cursor and Goose That separation matters because the agent-tooling market is already converging on products that manage increasingly autonomous AI workers. OpenAI describes its Codex app as a “command center” for agents, with separate project threads and multiple agents operating in parallel. Claude Code, Anthropic’s agent for reading codebases, editing files and executing commands, now spans the terminal, IDEs and a desktop app. Cursor describes its latest release, Cursor 3, as a unified workspace for software development with agents, including handoffs between local and cloud agents and multi-repository workflows. And Goose itself remains available directly, as a free command-line tool and desktop app for technically comfortable users. Berd’s differentiation is therefore less about inventing the agent workspace than about making that workspace open source, multi-harness and potentially useful outside coding. Every other product in the table below is either tied to a single vendor’s models, priced as a subscription, or aimed squarely at developers - and in most cases all three. Product | Maker | Pricing | Model support | Distinguishing features | Berd | Block | Free and open source (Apache 2.0); users pay only their own model provider costs | Any provider the user configures, across multiple agent harnesses | Desktop workspace over agent harnesses rather than a harness itself; local-first data storage; visual agent identities; persistent projects; enterprise custom distributions; designed for non-engineers as well as developers | Codex app | OpenAI | Included with paid ChatGPT plans, from Plus at $20/month to Pro at $200/month | OpenAI models only | “Command center” for coding agents; multiple agents running in parallel across separate project threads; cloud and local execution | Claude Code | Anthropic | Included with paid Claude plans, from Pro at $20/month to Max at $100-$200/month; also usage-based via API | Anthropic Claude models only | Deep autonomous coding - reads codebases, edits files, runs commands; available in terminal, IDEs, desktop and web; usage caps have drawn developer criticism | Cursor | Anysphere | Pro at $20/month; Ultra at $200/month | Multiple frontier models | AI-native code editor; Cursor 3 adds a unified agent workspace with local-to-cloud agent handoffs and multi-repository workflows; developer-focused | Goose | Block / Agentic AI Foundation | Free and open source (Apache 2.0); bring your own model, including free local models | Any provider, or fully local models via tools like Ollama | The model-agnostic agent framework and runtime underneath Berd; CLI and desktop versions; can run entirely offline with no data leaving the machine | Portability is part of Block’s pitch. Axen said much of the underlying work in Berd is deliberately kept portable