bytedance/deer-flow
Apr 2026 – May 2026
2 Months active
Languages Used
PythonTypeScriptMarkdownYAML
Technical Skills
API developmentReactbackend developmentfrontend developmentAPI integrationNode.js
YuJitang
PROFILE
Yujitang
Worked on the bytedance/deer-flow repository to deliver end-to-end token usage visualization and real-time tracking features for assistant responses. Developed backend adjustments in Python and Node.js to align model responses with token usage, while building React and TypeScript frontend components for clear UI display and configurable feature toggles. Enhanced the system’s middleware to support accurate, per-thread and cross-agent token accounting, addressing double-counting risks and improving cost visibility for multi-agent flows. Updated documentation and implemented robust testing to ensure maintainability and reliability. The work improved transparency, resource tracking, and debugging capabilities for teams relying on precise usage metrics.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
8Total
Bugs
1
Commits
8
Features
2
Lines of code
4,945
Activity Months2
Your Network
200 peopleWork History
May 2026
7 Commits • 1 Features
May 1, 2026May 2026 (2026-05) monthly summary for bytedance/deer-flow. Focused on delivering accurate, real-time token usage tracking across the system, enabling per-thread and cross-agent attribution, improved UI display, and automated reporting of usage in RunJournal. Implemented end-to-end improvements in backend middleware, frontend metadata, and run-total aggregation to improve cost visibility and accountability in multi-agent flows. Deduplication and accuracy improvements address double-counting risks and prepare the system for scalable token accounting across complex LLM tool usage.
7 Commits • 1 Features
May 1, 2026May 2026 (2026-05) monthly summary for bytedance/deer-flow. Focused on delivering accurate, real-time token usage tracking across the system, enabling per-thread and cross-agent attribution, improved UI display, and automated reporting of usage in RunJournal. Implemented end-to-end improvements in backend middleware, frontend metadata, and run-total aggregation to improve cost visibility and accountability in multi-agent flows. Deduplication and accuracy improvements address double-counting risks and prepare the system for scalable token accounting across complex LLM tool usage.
May 2026
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 — Monthly work summary for bytedance/deer-flow: Delivered end-to-end Token Usage Visualization for Assistant Responses, including backend adjustments to model responses, frontend visualization components, and a configurable enable/disable toggle. Fixed alignment issues between client model responses and token usage and addressed token usage review feedback. The feature enhances transparency of resource consumption, supports cost-aware usage, and improves debugging; delivered with documentation updates and tests to support long-term maintainability. Technologies demonstrated include backend API adjustments, frontend component development, feature flag/config architecture, and cross-team collaboration, with co-authored commits: 105db0098784ed3c44158420938052c51b1691f3.
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 — Monthly work summary for bytedance/deer-flow: Delivered end-to-end Token Usage Visualization for Assistant Responses, including backend adjustments to model responses, frontend visualization components, and a configurable enable/disable toggle. Fixed alignment issues between client model responses and token usage and addressed token usage review feedback. The feature enhances transparency of resource consumption, supports cost-aware usage, and improves debugging; delivered with documentation updates and tests to support long-term maintainability. Technologies demonstrated include backend API adjustments, frontend component development, feature flag/config architecture, and cross-team collaboration, with co-authored commits: 105db0098784ed3c44158420938052c51b1691f3.
Activity
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Quality Metrics
Correctness94.8%
Maintainability87.6%
Architecture90.0%
Performance87.6%
AI Usage52.6%
Skills & Technologies
Programming Languages
MarkdownPythonTypeScriptYAML
Technical Skills
API developmentAPI integrationNode.jsPythonReactTypeScriptasynchronous programmingbackend developmentconfiguration managementdocumentationfront end developmentfrontend developmentfull stack developmentmiddleware developmentreal-time data handling
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