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chore(version): bump version to 0.1.10.1

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- Updated __version__ in reme_ai/__init__.py
- Updated project version in pyproject.toml- Changed flowllm dependency to include reme extra
- Fixed typo in README.md query example
This commit is contained in:
jinli.yl 2025-10-22 20:24:55 +08:00
parent 04692b1473
commit 4697070068
4 changed files with 17 additions and 30 deletions
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2
README.md
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@ -277,7 +277,7 @@ curl -X POST http://localhost:8002/retrieve_personal_memory \
-H "Content-Type: application/json" \
-d '{
"workspace_id": "task_workspace",
"query": "What are the user's work habits?",
"query": "What are the users work habits?",
"top_k": 5
}'
```

39
docs/future_work.md
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@ -1,31 +1,18 @@
# Future Work
## Planned Features
- [ ] P0 ReMe documentation style migration: Recommend using the same doc and jupyter structure as Agentscope Runtime @jiaji
- [ ] P0 ReMe integration with agentscope Personal/Task/Tool @jinli
- [ ] P0 ReMe sample library examples [show case](https://github.com/agentscope-ai/agentscope-samples/tree/main/functionality/long_term_memory_mem0)
- [ ] P0 Decouple flowllm dependencies
- [ ] P0 ReMe support for import, improve code documentation
- [ ] P1 ReMe integration with asio tool_memory
- [ ] P2 ReMe integration with agentscope-Runtime tool_memory
### 1. Automatic Tool Exploration Mode
Add an automatic tool exploration mode that generates tool memory by:
- Automatically discovering and testing available tools
- [ ] P0 Task Memory Research Paper @zhoyin
- Learning tool usage patterns and best practices
- Building a comprehensive tool memory database from exploration results
### 2. Desktop Pet Personal Assistant
Build a desktop pet personal assistant with:
- Interactive desktop companion interface
- Personalized assistance capabilities
- Integration with ReMe's memory system
### 3. Task Memory Research Implementation
We are currently working on implementing features based on task memory research papers. Coming soon.
### 4. Mem-Agent Exploration
We are exploring mem-agent to implement agentic memory pathways:
- Investigating agent-driven memory management
- Developing autonomous memory retrieval and storage mechanisms
- Building more intelligent memory update strategies
- [ ] P1 Context interface definition
- [ ] P2 Database layer interface unification
- [ ] P2 Automatic Tool Exploration Mode
- [ ] P2 Mem-Agent Exploration
- [ ] P2 Desktop Pet Personal Assistant

4
pyproject.toml
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@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "reme_ai"
version = "0.1.10"
version = "0.1.10.1"
description = "Remember me"
authors = [
{ name = "jinli.yl", email = "jinli.yl@alibaba-inc.com" },
@ -24,7 +24,7 @@ classifiers = [
keywords = ["llm", "memory", "experience", "memoryscope", "ai", "mcp", "http"]
dependencies = [
"flowllm>=0.1.11.1",
"flowllm[reme]>=0.1.11.2",
]
[tool.setuptools.packages.find]

2
reme_ai/__init__.py
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@ -11,4 +11,4 @@ from . import retrieve
from . import summary
from . import vector_store
__version__ = "0.1.10"
__version__ = "0.1.10.1"