-| Category | Method | Function | Key components |
|---|
-| Context Management | check_context | 📊 Check context size | ContextChecker — checks whether context exceeds thresholds and splits messages |
-compact_memory | 📦 Compact history into summary | Compactor — ReActAgent that generates structured context summaries |
-compact_tool_result | ✂️ Compact long tool outputs | ToolResultCompactor — truncates long tool outputs and stores them in tool_result/ while keeping file references in messages |
-pre_reasoning_hook | 🔄 Pre-reasoning hook | compact_tool_result + check_context + compact_memory + summary_memory (async) |
-| Long-term Memory | summary_memory | 📝 Persist important memory to files | Summarizer — ReActAgent + file tools (read / write / edit) |
-memory_search | 🔍 Semantic memory search | MemorySearch — hybrid retrieval with vectors + BM25 |
-| Session Memory | get_in_memory_memory | 💾 Create in-session memory instance | Returns ReMeInMemoryMemory with dialog_path configured for persistence |
-await_summary_tasks | ⏳ Wait for async summary tasks | Block until all background summary tasks complete |
-| - | start | 🚀 Start memory system | Initialize file storage, file watcher, and embedding cache; clean up expired tool result files |
-| - | close | 📕 Shutdown and cleanup | Clean up tool result files, stop file watcher, and persist embedding cache |
-
-
----
-
-### 🚀 Quick start
-
-#### Installation
-
-**Install from source:**
-
-```bash
-git clone https://github.com/agentscope-ai/ReMe.git
-cd ReMe
-pip install -e ".[light]"
-```
-
-**Update to the latest version:**
-
-```bash
-git pull
-pip install -e ".[light]"
-```
-
-#### Environment variables
-
-`ReMeLight` uses environment variables to configure the embedding model and storage backends:
-
-| Variable | Description | Example |
-|----------------------|-------------------------------|-----------------------------------------------------|
-| `LLM_API_KEY` | LLM API key | `sk-xxx` |
-| `LLM_BASE_URL` | LLM base URL | `https://dashscope.aliyuncs.com/compatible-mode/v1` |
-| `EMBEDDING_API_KEY` | Embedding API key (optional) | `sk-xxx` |
-| `EMBEDDING_BASE_URL` | Embedding base URL (optional) | `https://dashscope.aliyuncs.com/compatible-mode/v1` |
-
-#### Python usage
-
-```python
-import asyncio
-
-from reme.reme_light import ReMeLight
-
-
-async def main():
- # Initialize ReMeLight
- reme = ReMeLight(
- default_as_llm_config={"model_name": "qwen3.5-35b-a3b"},
- # default_embedding_model_config={"model_name": "text-embedding-v4"},
- default_file_store_config={"fts_enabled": True, "vector_enabled": False},
- enable_load_env=True,
- )
- await reme.start()
-
- messages = [...] # List of conversation messages
-
- # 1. Check context size (token counting, determine if compaction is needed)
- messages_to_compact, messages_to_keep, is_valid = await reme.check_context(
- messages=messages,
- memory_compact_threshold=90000, # Threshold to trigger compaction (tokens)
- memory_compact_reserve=10000, # Token count to reserve for recent messages
- )
-
- # 2. Compact conversation history into a structured summary
- summary = await reme.compact_memory(
- messages=messages,
- previous_summary="",
- max_input_length=128000, # Model context window (tokens)
- compact_ratio=0.7, # Trigger compaction when exceeding max_input_length * 0.7
- language="zh", # Summary language (e.g., "zh" / "")
- )
-
- # 3. Compact long tool outputs (prevent tool results from blowing up context)
- messages = await reme.compact_tool_result(messages)
-
- # 4. Pre-reasoning hook (auto compact tool results + check context + generate summaries)
- processed_messages, compressed_summary = await reme.pre_reasoning_hook(
- messages=messages,
- system_prompt="You are a helpful AI assistant.",
- compressed_summary="",
- max_input_length=128000,
- compact_ratio=0.7,
- memory_compact_reserve=10000,
- enable_tool_result_compact=True,
- tool_result_compact_keep_n=3,
- )
-
- # 5. Persist important memory to files (writes to memory/YYYY-MM-DD.md)
- summary_result = await reme.summary_memory(
- messages=messages,
- language="zh",
- )
-
- # 6. Semantic memory search (vector + BM25 hybrid retrieval)
- result = await reme.memory_search(query="Python version preference", max_results=5)
-
- # 7. Create in-session memory instance (manages context for one conversation)
- memory = reme.get_in_memory_memory() # Auto-configures dialog_path
- for msg in messages:
- await memory.add(msg)
- token_stats = await memory.estimate_tokens(max_input_length=128000)
- print(f"Current context usage: {token_stats['context_usage_ratio']:.1f}%")
- print(f"Message token count: {token_stats['messages_tokens']}")
- print(f"Estimated total tokens: {token_stats['estimated_tokens']}")
-
- # 8. Mark messages as compressed (auto-persists to dialog/YYYY-MM-DD.jsonl)
- # await memory.mark_messages_compressed(messages_to_compact)
-
- # Shutdown ReMeLight
- await reme.close()
-
-
-if __name__ == "__main__":
- asyncio.run(main())
-```
-
-> 📂 Full example: [test_reme_light.py](tests/light/test_reme_light.py)
-> 📋 Sample run log: [test_reme_light_log.txt](tests/light/test_reme_light_log.txt) (223,838 tokens → 1,105 tokens, 99.5%
-> compression)
-
-### Architecture of the file-based ReMeLight memory system
-
-#### Context data structure
-
-```mermaid
-flowchart TD
- A[Context] --> B[compact_summary]
- B --> C[dialog path guide + Goal/Constraints/Progress/KeyDecisions/NextSteps]
- A --> E[messages: full dialogue history]
- A --> F[File System Cache]
- F --> G[dialog/YYYY-MM-DD.jsonl]
- F --> H[tool_result/uuid.txt N-day TTL]
-```
-
----
-
-[MemoryManager](https://github.com/agentscope-ai/CoPaw/blob/main/src/copaw/agents/memory/reme_light_memory_manager.py)
-inherits `ReMeLight` and integrates its memory capabilities into the agent reasoning loop:
-
-```mermaid
-graph LR
- Agent[Agent] -->|Before each reasoning step| Hook[pre_reasoning_hook]
- Hook --> TC[compact_tool_result
-
+
+ 
+ 
- 
-