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on-panda
by on-panda
Token-level LLM visualization, inspection, and annotation for model and agent debugging
JavaScript
Updated Oct 23, 2026
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Summary
Visualize and control LLM token streams in a browser for model inspection, annotation, and token-level correction. Provides interactive token timelines, editable continuations, and tooling to label or intervene in an agent's generation as it happens. Includes token-level correction and exportable annotations so teams can reproduce, audit, and iterate on model outputs.
The Value Proposition
As agents act autonomously, small token-level changes can produce large behavior differences; being able to inspect and correct at the token level makes failures visible and actionable. Until now most evaluation focuses on final outputs or metrics—on-panda surfaces the generation process, enabling better root-cause analysis, targeted data annotation, and reproducible corrections that feed into reputation or evaluation pipelines. For broader compatibility with evaluation pipelines, consider the Model Context Protocol to standardize context sharing and evaluation workflows root-cause analysis and reputation or evaluation pipelines.
Ideal For
Developers and researchers who need to debug model generations, annotate token-level failure modes, or create reproducible fixes for agent outputs.
Use Cases
- Inspect token-by-token generations to diagnose why an agent produced a wrong answer
- Annotate and export corrected generations for fine-tuning or benchmark labeling
- Produce reproducible diffs of model behavior when comparing model versions or prompts
Works With
openaihuggingface
Topics
agent-evaluationagent-trajectorydata-annotationllm-uimodel-inspectiontoken-level-correctiontoken-visualization
Similar Tools
llm-debuggeropenai-playground
Keywords
agent-evaluationtoken-visualizationmodel-inspectiondata-annotation