Crew44

What is Crew44?

Crew44 is a local-first workspace for orchestrating multiple AI agents as a coordinated team. Instead of using a single coding agent for every task, it lets you assign specialist roles such as Partner, Engineer, Product Lead, and Designer, each with its own model, skills, and memory.

The product is built for tasks that benefit from parallel work, handoffs, and persistence over time. Crew44 keeps everything on the user’s machine, supports multiple agent runtimes, and includes a mechanism for proposing memory, skill, and routing updates based on past runs.

Key Features

• Multi-agent workspace: Run several specialist agents in one workspace so planning, coding, review, and design work can happen in parallel with shared context.
• Role-based agent setup: The default crew includes a Partner, Engineer, Product Lead, and Designer, each assigned a specific job rather than one generalist agent doing everything.
• Handoff-based workflow: Agents can pass work with short briefs, which helps preserve context without forcing one agent to re-derive the full task each time.
• Persistent memory and skills: Skills and per-project memory are stored as plain files on disk and carry forward across runs, so the crew can retain project-specific knowledge.
• Auto-optimization with review control: A background Partner can mine past runs and suggest new memories, skills, and routing changes, but the user must accept, edit, snooze, or dismiss them before they are applied.
• Model-per-role routing: Different roles can be bound to different models, and models can be swapped mid-task depending on whether the job needs planning, code generation, or review.
• Local-first operation: The product is designed to keep work on the user’s machine rather than relying on an account-based cloud workflow.
• Works with multiple runtimes: Supported runtimes listed on the page include Claude Code, Codex, Gemini CLI, Cursor Agent, OpenClaw, OpenCode, Hermes, Kimi, Qwen Code, and Qoder.

How to Use Crew44

Download Crew44 and open it as a local workspace for your AI agents. Add a task, assign or let the crew choose roles, and let the agents collaborate through handoffs while each one works with its own model and context.

As runs accumulate, Crew44 can surface memory, skill, and routing suggestions from the history of work. You review those suggestions manually, then accept changes you want to keep in the crew’s files.

Use Cases

• Refactoring a backend feature: Use an Engineer to implement the change, a Product Lead to check the spec or acceptance criteria, and a Partner to coordinate the task and preserve decisions.
• Reviewing and improving recurring work: When the same issue appears across multiple runs, Crew44 can surface patterns and propose a reusable skill or memory entry.
• Splitting planning and coding across models: Assign a deeper model to planning, a faster model to implementation, and a separate model to review so each stage uses a suitable runtime.
• Maintaining project-specific knowledge: Keep codebase conventions, environment notes, and repeated decisions available across sessions instead of re-explaining them every time.
• Working without an account-based cloud setup: Use the product locally when you want the workspace and its files to stay on your machine.

FAQ

Does Crew44 require an account? The page says no account is required.

Does Crew44 keep data on the machine? Yes. The page describes it as local-first and says everything stays on your machine.

Can Crew44 use different AI models? Yes. It supports assigning different models to different roles and swapping models during a task.

Does Crew44 automatically change your project files? No. Suggestions for memories, skills, and routing changes require user review before anything is written to disk.

What runtimes does it support? The page lists support for several runtimes, including Claude Code, Codex, Gemini CLI, Cursor Agent, OpenClaw, OpenCode, Hermes, Kimi, Qwen Code, and Qoder.

Alternatives

• Single-agent coding assistants: These are simpler tools where one agent handles planning, implementation, and review in a single loop. They are a fit when you do not need specialist roles or long-lived team memory.
• General-purpose agent runners or orchestrators: These tools may also coordinate multiple steps, but they are not necessarily organized around persistent specialist roles with explicit handoffs and per-role models.
• Local developer automation workflows: A scriptable, file-based workflow can offer similar control over where data lives and how tasks run, but it usually requires more manual setup than Crew44’s agent workspace.
• Cloud-based AI coding platforms: These can be easier to access across devices, but they generally place more emphasis on hosted workflows than on keeping the whole crew and its files on the user’s machine.