onPanda is designed for hackers, power users, curious minds, and engineers. Its UI is built for deep exploration and efficient data annotation.
- The core loop is simple: hover over a token → click an alternative or edit freely → continue generation. You can edit every part of model output exposed by onPanda, including reasoning and tool calls.
- Edit prompts directly, branch tool calls, and use a tree structure to record branch history. This makes onPanda useful for model inspection and prompt engineering.
- Support multiple modalities, including images, video, and audio; use tool calls and connect MCP servers to perform tasks in real environments.
- Connect popular harnesses such as Claude Code, Codex, and OpenCode to execute tasks. Explore and compare their tool sets, system prompts, skills, and memory mechanisms.
- onPanda includes a built-in browser-agent, an agent that runs in the user's browser without installation. It uses the browser as its harness and provides JavaScript execution, information retrieval, interface interaction, multimedia I/O, local file access, and persistent memory.
- onPanda stands for on-Policy Alignment Data Annotator.
- As an annotation tool, onPanda efficiently labels on-policy data. Its token-level correction format also provides fine-grained supervision with precise positions and naturally paired positive–negative samples.
- We believe these unique properties will make token-level correction a highly efficient and practical paradigm for future LLM alignment.
- Our paper and benchmark for token-level correction: https://on-panda.github.io/research/
Try it online (works on mobile): https://onpanda.diyer22.com/
GitHub repo for self-hosting: https://github.com/on-panda/on-panda
Detailed introduction on X: https://x.com/diyerxx/status/2101020850405462041