mirror of
https://github.com/zhayujie/chatgpt-on-wechat.git
synced 2026-03-20 13:58:30 +08:00
022c13f3a4
- Use LLM to summarize discarded context into concise daily memory entries - Batch trim to half when exceeding max_turns/max_tokens, reducing flush frequency - Run summarization asynchronously in background thread, no blocking on replies - Add daily scheduled flush (23:55) as fallback for low-activity days - Sync trimmed messages back to agent to keep context state consistent
24 lines
745 B
Python
24 lines
745 B
Python
"""
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Memory module for AgentMesh
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Provides both long-term memory (vector/keyword search) and short-term
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conversation history persistence (SQLite).
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"""
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from agent.memory.manager import MemoryManager
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from agent.memory.config import MemoryConfig, get_default_memory_config, set_global_memory_config
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from agent.memory.embedding import create_embedding_provider
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from agent.memory.conversation_store import ConversationStore, get_conversation_store
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from agent.memory.summarizer import ensure_daily_memory_file
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__all__ = [
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'MemoryManager',
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'MemoryConfig',
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'get_default_memory_config',
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'set_global_memory_config',
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'create_embedding_provider',
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'ConversationStore',
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'get_conversation_store',
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'ensure_daily_memory_file',
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]
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