Deep Researcher Agent
An autonomous agent that runs deep-learning experiments around the clock on a local machine, a remote server over SSH, or a Slurm cluster, tracking real job outcomes with constant-size memory and zero-cost monitoring.
Deep Researcher Agent autonomously runs a user's deep-learning experiments continuously, using a leader-worker architecture and constant-size memory rather than an ever-growing context, with monitoring that costs nothing while jobs run. It can drive experiments locally, over SSH, or on a Slurm cluster, where training is submitted with sbatch --parsable over a single transient SSH call so no process is left running on the login node, and job liveness is read from sacct and squeue with bounded checks that guarantee the monitor loop eventually terminates.
The agent records a job's true terminal state, such as failed, timed out, or cancelled, rather than always marking a finished job as completed, and reasons over that outcome when deciding what to try next. It can run against a Chinese LLM API instead of a Claude or Codex subscription by setting a one-word provider preset (deepseek, qwen, kimi, or glm) that fills in the matching OpenAI-compatible base URL and key environment variable, with the same path also usable for self-hosted or proxied endpoints.
It is written in Python and Apache-2.0 licensed.