Prime Agent

Install it on macOS or Linux with a single script that downloads a versioned release, verifies its SHA-256 checksum and prepares the Python runtime. Change into the project you want worked on and run prime-agent. On first launch, /login picks a subscription or API-key provider - the agent brings no inference of its own.

From there it behaves less like a chat and more like a process you manage.

• •Programmatic Everything - A persistent Python REPL is the primary model tool, so file edits, shell commands, tool use and context management are written as code rather than selected from a fixed tool list
• •Built-In Subagents - rlm(...) spawns real child agents for parallel or background work and returns their results programmatically, and running agents can message and steer one another directly
• •A Harness That Improves Itself - /refine persists focused, reviewable lessons as supplemental prompts, memories, skill descriptions or subagent specs, with refinement history and snapshot rollback, and without rewriting the base prompt
• •Executable Skills - Skills are importable Python packages, and a built-in skill creator turns a recurring workflow into a project or personal skill
• •Daemon-Backed Continuity - Active sessions, REPL state, schedules and subagents survive terminal disconnects and can be reattached

Researchers running long evaluations, and engineers with tasks measured in hours rather than prompts - migrations, large refactors, work that needs to survive a closed laptop. If you already think in Python, the RLM model is the appeal: composing subagents and manipulating context as values is far more expressive than a fixed tool schema, and skills as importable packages means your workflows are real code you can test.