Persei

An autonomous coding agent built on LangGraph and deepagents. It plans, executes, and verifies real changes against a real project checkout, with human approval required for anything risky. It also runs Planning Chat, a separate conversational assistant for research and design discussion that remembers what it learns across sessions and can hand a finished plan straight to the build pipeline.

It learns from its own work. After a failed attempt it reflects (what failed, why, what to do differently) before trying again; every finished task leaves an episode; once a day those episodes are consolidated into each project’s memory; and months of past tasks stay searchable, so “have we hit this before?” has an answer. See Memory and reflection.

The backend is a FastAPI app (agent/server.py); the frontend is React/Vite. Together they cover submitting and watching build tasks, chatting with Planning Chat, and administering models, users, and repos.

The agent targets a fixed set of local projects (PROJECTS in agent/config.py / projects.json). Each task gets its own workspace — a git worktree of the live repo at /home/agent-workspaces/.tasks//, on its own branch agent/, filled from the project’s workspace (/home/agent-workspaces/) with dependencies hardlinked and build output copied (agent/workspaces.py). So tasks on the same project can run side by side: Settings → Tasks at once per project sets how many (default 10, at most 16; each task has its own 2 GB sandbox, so size it to the machine). They code in parallel and take turns only for checks, review and merge; a task that merges second is rebased onto the first and reviewed again. The limit is a set of Postgres advisory locks (agent/graph.py, project_slot) rather than anything in the process, because it is a property of the project: a second worker or an overlapping restart sees the same slots, and Postgres drops a claim when its connection closes.

Community

Questions, help with an install, or an idea for what it should do next: join the Telegram group. Bugs are best as a GitHub issue, and security problems go through a private advisory, never in public.

Screenshots

Watch a task work. Every tool call and its output as it happens, which model made it, the agent’s own summary, and the review gate’s READY or NEEDS_FIXES at the end. You can