Commit Graph

4 Commits

Author SHA1 Message Date
lintsinghua 147dfbaf5e feat(agent): enhance streaming with in-memory event manager and fallback polling
- Implement dual-mode streaming: prioritize in-memory EventManager for running tasks with thinking_token support
- Add fallback to database polling for completed tasks without thinking_token replay capability
- Introduce SSE event formatter utility for consistent event serialization across streaming modes
- Add 10ms micro-delay for thinking_token events to ensure proper TCP packet separation and frontend incremental rendering
- Refactor stream_agent_with_thinking endpoint to support both runtime and historical event streaming
- Update event filtering logic to handle both in-memory and database event sources
- Improve logging with debug markers for thinking_token tracking and stream mode selection
- Optimize polling intervals: 0.3s for running tasks, 2.0s for completed tasks
- Reduce idle timeout from 10 minutes to 1 minute for completed task streams
- Update frontend useAgentStream hook to handle unified event format from dual-mode streaming
- Enhance AgentAudit UI to properly display streamed events from both sources
2025-12-12 10:39:32 +08:00
lintsinghua 70776ee5fd feat: Introduce structured agent collaboration with `TaskHandoff` and `analysis_v2` agent, updating core agent logic, tools, and audit UI. 2025-12-11 23:29:04 +08:00
lintsinghua 8938a8a3c9 feat(agent): enhance agent functionality with LLM-driven decision-making and event handling
- Introduce LLM-driven decision-making across various agents, allowing for dynamic adjustments based on real-time analysis.
- Implement new event types for LLM thinking, decisions, actions, and observations to enrich the event streaming experience.
- Update agent task responses to include additional metrics for better tracking of task progress and outcomes.
- Refactor UI components to highlight LLM-related events and improve user interaction during audits.
- Enhance API endpoints to support new event structures and improve overall error handling.
2025-12-11 21:14:32 +08:00
lintsinghua 9bc114af1f feat(agent): implement Agent audit module with LangGraph integration
- Introduce new Agent audit functionality for autonomous code security analysis and vulnerability verification.
- Add API endpoints for managing Agent tasks and configurations.
- Implement UI components for Agent mode selection and embedding model configuration.
- Enhance the overall architecture with a focus on RAG (Retrieval-Augmented Generation) for improved code semantic search.
- Create a sandbox environment for secure execution of vulnerability tests.
- Update documentation to include details on the new Agent audit features and usage instructions.
2025-12-11 19:09:10 +08:00