bytedance/deer-flow
Mar 2026 – Mar 2026
1 Month active
Languages Used
Python
Technical Skills
API DevelopmentBackend DevelopmentData SerializationLLM IntegrationLangChain IntegrationLogging
haoliangxu
PROFILE
Haoliangxu
Exploreunive contributed to bytedance/deer-flow by developing token usage tracking for the streaming API, enabling per-turn and cumulative cost visibility for AI-driven interactions. Using Python and leveraging skills in API development and data serialization, Exploreunive enhanced message serialization to include detailed usage metadata, supporting more accurate billing and analytics. To address reliability, they normalized structured LLM content to plain text, preventing parsing errors during serialization and memory updates. The work included improved logging and regression testing, ensuring robustness across diverse content formats. This focused, high-quality engineering effort delivered measurable improvements in cost tracking, data integrity, and system observability.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
783
Activity Months1
Your Network
2189 peopleSame Organization
@users.noreply.github.com2060
Work History
March 2026
2 Commits • 1 Features
Mar 1, 2026In 2026-03, bytedance/deer-flow delivered two key outcomes focused on cost visibility, reliability, and robustness. Feature work included token usage tracking for the streaming API with per-turn granularity and enhanced serialization to include usage_metadata for AI messages and cumulative token totals in the final event, enabling cost-aware experiences and better dashboards. Bug fixes addressed parsing robustness by normalizing structured LLM content to plain text via _extract_text, with improved logging and regression tests to ensure stability across content formats. Impact and value: - Improved cost visibility for AI-driven interactions and more accurate per-turn and end-of-conversation totals for billing and analytics. - Increased reliability of streaming data and memory updates by preventing serialization parsing errors. - Strengthened testing and observability through regression tests and enhanced logging. Technologies/skills demonstrated: - API streaming, serialization, and metadata handling - Robust data normalization and content handling - Logging, regression testing, and quality assurance - Change traceability with clear commit references (e.g., 06cba217c332b103e4bbe7edc031cfbc7455c168; 3af709097eb77e7518d4d951b4a51802b70f895f)
2 Commits • 1 Features
Mar 1, 2026In 2026-03, bytedance/deer-flow delivered two key outcomes focused on cost visibility, reliability, and robustness. Feature work included token usage tracking for the streaming API with per-turn granularity and enhanced serialization to include usage_metadata for AI messages and cumulative token totals in the final event, enabling cost-aware experiences and better dashboards. Bug fixes addressed parsing robustness by normalizing structured LLM content to plain text via _extract_text, with improved logging and regression tests to ensure stability across content formats. Impact and value: - Improved cost visibility for AI-driven interactions and more accurate per-turn and end-of-conversation totals for billing and analytics. - Increased reliability of streaming data and memory updates by preventing serialization parsing errors. - Strengthened testing and observability through regression tests and enhanced logging. Technologies/skills demonstrated: - API streaming, serialization, and metadata handling - Robust data normalization and content handling - Logging, regression testing, and quality assurance - Change traceability with clear commit references (e.g., 06cba217c332b103e4bbe7edc031cfbc7455c168; 3af709097eb77e7518d4d951b4a51802b70f895f)
March 2026
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture90.0%
Performance90.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
API DevelopmentBackend DevelopmentData SerializationLLM IntegrationLangChain IntegrationLoggingStreamingTesting
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline