Concise monthly summary for 2026-01 focused on business value, reliability, and technical achievements for eigent-ai/eigent. Highlights include WebSocket reliability improvements for local and restricted-regions deployments, Azure API integration and search agent stability enhancements, and model platform/provider updates enabling broader model support and smoother provider transitions.

December 2025 (Month: 2025-12) delivered security-forward feature work for eigent-ai/eigent, including authentication UX improvements, hardened external API interactions, stronger image upload validation, secure config/environment handling, and automated performance benchmarking. The efforts reduce risk, improve maintainability, and establish baseline performance metrics for ongoing optimization.

November 2025 monthly summary for eigent-ai/eigent focused on user-facing UX improvements, enhanced observability, and robust multi-provider model handling. Delivered concrete features with clear business value: improved task count context in project/group views, stronger end-to-end traceability for execution flows, and reliable model selection across cloud, custom API, and local providers. The work reduces support overhead, accelerates debugging, and increases confidence for deployments across environments.

Monthly Summary for eigent-ai/eigent — 2025-10. This period focused on delivering data hygiene improvements and runtime reliability to drive data privacy, storage efficiency, and stable deployments. Highlights include chat history local data cleanup, environment/terminal toolkit reliability upgrades, and build tooling improvements, with measurable impact on user workflows and developer velocity.

September 2025 monthly summary for datawhalechina/hello-agents. Focused on documentation quality and consistency improvements. Implemented four documentation fixes to improve accuracy, correct a broken formula, fix a README link, align PLANNER_PROMPT_TEMPLATE with code and docs for structured Python-list output, and remove duplicate log entries in the Plan-and-Solve agent docs. These changes enhance onboarding, reduce support overhead, and improve maintainability across the repository.