feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
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"""
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LLM 服务模块
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提供统一的 LLM 调用接口，支持：
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- 多提供商支持（OpenAI, Claude, Gemini, DeepSeek 等）
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- Prompt Caching（减少 Token 消耗）
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- Memory Compression（对话历史压缩）
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- 流式输出
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- 智能重试
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"""
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from .service import LLMService
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from .types import (
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LLMConfig,
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LLMProvider,
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LLMMessage,
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LLMRequest,
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LLMResponse,
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LLMUsage,
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LLMError,
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)
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from .prompt_cache import (
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PromptCacheManager,
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CacheConfig,
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CacheStrategy,
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CacheStats,
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prompt_cache_manager,
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estimate_tokens,
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)
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from .memory_compressor import MemoryCompressor
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__all__ = [
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# Service
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"LLMService",
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# Types
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"LLMConfig",
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"LLMProvider",
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"LLMMessage",
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"LLMRequest",
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"LLMResponse",
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"LLMUsage",
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"LLMError",
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# Prompt Cache
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"PromptCacheManager",
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"CacheConfig",
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"CacheStrategy",
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"CacheStats",
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"prompt_cache_manager",
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"estimate_tokens",
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# Memory Compression
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"MemoryCompressor",
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]
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