diff --git a/README.md b/README.md
index 86506c61..0fbada48 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@
- 
+ 
@@ -28,6 +28,7 @@ Personal memory helps "**understand user preferences**", task memory helps agent
## ๐ฐ Latest Updates
+- **[2025-10]** ๐ ReMe v0.1.10.2 released! Core enhancement: direct Python import support. You can now use ReMe without starting an HTTP or MCP service - simply `from reme_ai import ReMeApp` and call methods directly in your Python code.
- **[2025-10]** ๐ง Tool Memory support is now available! Enables data-driven tool selection and parameter optimization through historical performance tracking. Check out the [Tool Memory Guide](docs/tool_memory/tool_memory.md) and [benchmark results](docs/tool_memory/tool_bench.md).
- **[2025-09]** ๐ ReMe v0.1.9 has been officially released, adding support for asynchronous operations. It has also been
integrated into the memory service of agentscope-runtime.
@@ -166,6 +167,45 @@ response = requests.post("http://localhost:8002/retrieve_task_memory", json={
})
```
+
+Python import version
+
+```python
+import asyncio
+from reme_ai import ReMeApp
+
+async def main():
+ async with ReMeApp() as app:
+ # Experience Summarizer: Learn from execution trajectories
+ result = await app.async_execute(
+ name="summary_task_memory",
+ workspace_id="task_workspace",
+ trajectories=[
+ {
+ "messages": [
+ {"role": "user", "content": "Help me create a project plan"}
+ ],
+ "score": 1.0
+ }
+ ]
+ )
+ print(result)
+
+ # Retriever: Get relevant memories
+ result = await app.async_execute(
+ name="retrieve_task_memory",
+ workspace_id="task_workspace",
+ query="How to efficiently manage project progress?",
+ top_k=1
+ )
+ print(result)
+
+if __name__ == "__main__":
+ asyncio.run(main())
+```
+
+
+
curl version
@@ -255,6 +295,46 @@ response = requests.post("http://localhost:8002/retrieve_personal_memory", json=
})
```
+
+Python import version
+
+```python
+import asyncio
+from reme_ai import ReMeApp
+
+async def main():
+ async with ReMeApp() as app:
+ # Memory Integration: Learn from user interactions
+ result = await app.async_execute(
+ name="summary_personal_memory",
+ workspace_id="task_workspace",
+ trajectories=[
+ {
+ "messages": [
+ {"role": "user", "content": "I like to drink coffee while working in the morning"},
+ {"role": "assistant",
+ "content": "I understand, you prefer to start your workday with coffee to stay energized"}
+ ]
+ }
+ ]
+ )
+ print(result)
+
+ # Memory Retrieval: Get personal memory fragments
+ result = await app.async_execute(
+ name="retrieve_personal_memory",
+ workspace_id="task_workspace",
+ query="What are the user's work habits?",
+ top_k=5
+ )
+ print(result)
+
+if __name__ == "__main__":
+ asyncio.run(main())
+```
+
+
+
curl version
@@ -359,6 +439,55 @@ response = requests.post("http://localhost:8002/retrieve_tool_memory", json={
})
```
+
+Python import version
+
+```python
+import asyncio
+from reme_ai import ReMeApp
+
+async def main():
+ async with ReMeApp() as app:
+ # Record tool execution results
+ result = await app.async_execute(
+ name="add_tool_call_result",
+ workspace_id="tool_workspace",
+ tool_call_results=[
+ {
+ "create_time": "2025-10-21 10:30:00",
+ "tool_name": "web_search",
+ "input": {"query": "Python asyncio tutorial", "max_results": 10},
+ "output": "Found 10 relevant results...",
+ "token_cost": 150,
+ "success": True,
+ "time_cost": 2.3
+ }
+ ]
+ )
+ print(result)
+
+ # Generate usage guidelines from history
+ result = await app.async_execute(
+ name="summary_tool_memory",
+ workspace_id="tool_workspace",
+ tool_names="web_search"
+ )
+ print(result)
+
+ # Retrieve tool guidelines before use
+ result = await app.async_execute(
+ name="retrieve_tool_memory",
+ workspace_id="tool_workspace",
+ tool_names="web_search"
+ )
+ print(result)
+
+if __name__ == "__main__":
+ asyncio.run(main())
+```
+
+
+
curl version
@@ -489,6 +618,39 @@ response = requests.post("http://localhost:8002/retrieve_task_memory", json={
})
```
+
+Python import version
+
+```python
+import asyncio
+from reme_ai import ReMeApp
+
+async def main():
+ async with ReMeApp() as app:
+ # Load pre-built memories
+ result = await app.async_execute(
+ name="vector_store",
+ workspace_id="appworld",
+ action="load",
+ path="./docs/library/"
+ )
+ print(result)
+
+ # Query relevant memories
+ result = await app.async_execute(
+ name="retrieve_task_memory",
+ workspace_id="appworld",
+ query="How to navigate to settings and update user profile?",
+ top_k=1
+ )
+ print(result)
+
+if __name__ == "__main__":
+ asyncio.run(main())
+```
+
+
+
## ๐งช Experiments
### ๐ [Appworld Experiment](docs/cookbook/appworld/quickstart.md)
diff --git a/cookbook/simple_demo/import_usage_demo.py b/cookbook/simple_demo/import_usage_demo.py
new file mode 100644
index 00000000..b51325ca
--- /dev/null
+++ b/cookbook/simple_demo/import_usage_demo.py
@@ -0,0 +1,274 @@
+import asyncio
+
+from reme_ai import ReMeApp
+
+
+# ============================================
+# Task Memory Management Examples
+# ============================================
+
+async def summary_task_memory():
+ """
+ Experience Summarizer: Learn from execution trajectories
+
+ curl -X POST http://localhost:8002/summary_task_memory \
+ -H "Content-Type: application/json" \
+ -d '{
+ "workspace_id": "task_workspace",
+ "trajectories": [
+ {"messages": [{"role": "user", "content": "Help me create a project plan"}], "score": 1.0}
+ ]
+ }'
+ """
+ async with ReMeApp() as app:
+ result = await app.async_execute(
+ name="summary_task_memory",
+ workspace_id="task_workspace",
+ trajectories=[
+ {
+ "messages": [
+ {"role": "user", "content": "Help me create a project plan"}
+ ],
+ "score": 1.0
+ }
+ ]
+ )
+ print("Summary Task Memory Result:")
+ print(result)
+
+
+async def retrieve_task_memory():
+ """
+ Retriever: Get relevant memories
+
+ curl -X POST http://localhost:8002/retrieve_task_memory \
+ -H "Content-Type: application/json" \
+ -d '{
+ "workspace_id": "task_workspace",
+ "query": "How to efficiently manage project progress?",
+ "top_k": 1
+ }'
+ """
+ async with ReMeApp() as app:
+ result = await app.async_execute(
+ name="retrieve_task_memory",
+ workspace_id="task_workspace",
+ query="How to efficiently manage project progress?",
+ top_k=1
+ )
+ print("Retrieve Task Memory Result:")
+ print(result)
+
+
+# ============================================
+# Personal Memory Management Examples
+# ============================================
+
+async def summary_personal_memory():
+ """
+ Memory Integration: Learn from user interactions
+
+ curl -X POST http://localhost:8002/summary_personal_memory \
+ -H "Content-Type: application/json" \
+ -d '{
+ "workspace_id": "task_workspace",
+ "trajectories": [
+ {"messages": [
+ {"role": "user", "content": "I like to drink coffee while working in the morning"},
+ {"role": "assistant", "content": "I understand, you prefer to start your workday with coffee to stay energized"}
+ ]}
+ ]
+ }'
+ """
+ async with ReMeApp() as app:
+ result = await app.async_execute(
+ name="summary_personal_memory",
+ workspace_id="task_workspace",
+ trajectories=[
+ {
+ "messages": [
+ {"role": "user", "content": "I like to drink coffee while working in the morning"},
+ {"role": "assistant",
+ "content": "I understand, you prefer to start your workday with coffee to stay energized"}
+ ]
+ }
+ ]
+ )
+ print("Summary Personal Memory Result:")
+ print(result)
+
+
+async def retrieve_personal_memory():
+ """
+ Memory Retrieval: Get personal memory fragments
+
+ curl -X POST http://localhost:8002/retrieve_personal_memory \
+ -H "Content-Type: application/json" \
+ -d '{
+ "workspace_id": "task_workspace",
+ "query": "What are the users work habits?",
+ "top_k": 5
+ }'
+ """
+ async with ReMeApp() as app:
+ result = await app.async_execute(
+ name="retrieve_personal_memory",
+ workspace_id="task_workspace",
+ query="What are the user's work habits?",
+ top_k=5
+ )
+ print("Retrieve Personal Memory Result:")
+ print(result)
+
+
+# ============================================
+# Tool Memory Management Examples
+# ============================================
+
+async def add_tool_call_result():
+ """
+ Record tool execution results
+
+ curl -X POST http://localhost:8002/add_tool_call_result \
+ -H "Content-Type: application/json" \
+ -d '{
+ "workspace_id": "tool_workspace",
+ "tool_call_results": [
+ {
+ "create_time": "2025-10-21 10:30:00",
+ "tool_name": "web_search",
+ "input": {"query": "Python asyncio tutorial", "max_results": 10},
+ "output": "Found 10 relevant results...",
+ "token_cost": 150,
+ "success": true,
+ "time_cost": 2.3
+ }
+ ]
+ }'
+ """
+ async with ReMeApp() as app:
+ result = await app.async_execute(
+ name="add_tool_call_result",
+ workspace_id="tool_workspace",
+ tool_call_results=[
+ {
+ "create_time": "2025-10-21 10:30:00",
+ "tool_name": "web_search",
+ "input": {"query": "Python asyncio tutorial", "max_results": 10},
+ "output": "Found 10 relevant results...",
+ "token_cost": 150,
+ "success": True,
+ "time_cost": 2.3
+ }
+ ]
+ )
+ print("Add Tool Call Result:")
+ print(result)
+
+
+async def summary_tool_memory():
+ """
+ Generate usage guidelines from history
+
+ curl -X POST http://localhost:8002/summary_tool_memory \
+ -H "Content-Type: application/json" \
+ -d '{
+ "workspace_id": "tool_workspace",
+ "tool_names": "web_search"
+ }'
+ """
+ async with ReMeApp() as app:
+ result = await app.async_execute(
+ name="summary_tool_memory",
+ workspace_id="tool_workspace",
+ tool_names="web_search"
+ )
+ print("Summary Tool Memory Result:")
+ print(result)
+
+
+async def retrieve_tool_memory():
+ """
+ Retrieve tool guidelines before use
+
+ curl -X POST http://localhost:8002/retrieve_tool_memory \
+ -H "Content-Type: application/json" \
+ -d '{
+ "workspace_id": "tool_workspace",
+ "tool_names": "web_search"
+ }'
+ """
+ async with ReMeApp() as app:
+ result = await app.async_execute(
+ name="retrieve_tool_memory",
+ workspace_id="tool_workspace",
+ tool_names="web_search"
+ )
+ print("Retrieve Tool Memory Result:")
+ print(result)
+
+
+# ============================================
+# Vector Store Management Example
+# ============================================
+
+async def load_vector_store():
+ """
+ Load pre-built memories
+
+ curl -X POST http://localhost:8002/vector_store \
+ -H "Content-Type: application/json" \
+ -d '{
+ "workspace_id": "appworld",
+ "action": "load",
+ "path": "./docs/library/"
+ }'
+ """
+ async with ReMeApp() as app:
+ result = await app.async_execute(
+ name="vector_store",
+ workspace_id="appworld",
+ action="load",
+ path="./docs/library/"
+ )
+ print("Load Vector Store Result:")
+ print(result)
+
+
+# ============================================
+# Main Execution
+# ============================================
+
+async def main():
+ """Run all examples"""
+ print("=" * 60)
+ print("Task Memory Examples")
+ print("=" * 60)
+ await summary_task_memory()
+ print("\n")
+ await retrieve_task_memory()
+
+ print("\n" + "=" * 60)
+ print("Personal Memory Examples")
+ print("=" * 60)
+ await summary_personal_memory()
+ print("\n")
+ await retrieve_personal_memory()
+
+ print("\n" + "=" * 60)
+ print("Tool Memory Examples")
+ print("=" * 60)
+ await add_tool_call_result()
+ print("\n")
+ await summary_tool_memory()
+ print("\n")
+ await retrieve_tool_memory()
+
+ print("\n" + "=" * 60)
+ print("Vector Store Examples")
+ print("=" * 60)
+ await load_vector_store()
+
+
+if __name__ == "__main__":
+ asyncio.run(main())
diff --git a/docs/future_work.md b/docs/future_work.md
index 951a95c1..807abfed 100644
--- a/docs/future_work.md
+++ b/docs/future_work.md
@@ -2,17 +2,17 @@
- [ ] 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
+- [ ] P0 ReMe sample library examples [show case](https://github.com/agentscope-ai/agentscope-samples/tree/main/functionality/long_term_memory_mem0) @jinli
+- [ ] P0 Decouple flowllm dependencies @jinli
+- [ ] P0 ReMe support for import, improve code documentation @jinli
+- [ ] P1 ReMe integration with asio tool_memory @jinli
+- [ ] P2 ReMe integration with agentscope-Runtime tool_memory @jinli
- [ ] P0 Task Memory Research Paper @zhoyin
-- [ ] P1 Context interface definition
+- [ ] P1 Context interface definition @jinli
-- [ ] P2 Database layer interface unification
-- [ ] P2 Automatic Tool Exploration Mode
-- [ ] P2 Mem-Agent Exploration
+- [ ] P2 Database layer interface unification @jinli
+- [ ] P2 Automatic Tool Exploration Mode @wangcan
+- [ ] P2 Mem-Agent Exploration @weikang
- [ ] P2 Desktop Pet Personal Assistant
diff --git a/pyproject.toml b/pyproject.toml
index 83a62a11..cdb2ed86 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "reme_ai"
-version = "0.1.10.1"
+version = "0.1.10.2"
description = "Remember me"
authors = [
{ name = "jinli.yl", email = "jinli.yl@alibaba-inc.com" },
@@ -41,3 +41,5 @@ reme_ai = [
[project.scripts]
reme = "reme_ai.app:main"
+
+# python -m build && twine upload dist/*
\ No newline at end of file
diff --git a/reme_ai/__init__.py b/reme_ai/__init__.py
index 464e80d3..68eab1d5 100644
--- a/reme_ai/__init__.py
+++ b/reme_ai/__init__.py
@@ -1,14 +1,7 @@
-import warnings
-
-from pydantic.warnings import PydanticDeprecatedSince20
-
-warnings.filterwarnings("ignore", category=DeprecationWarning, module="websockets")
-warnings.filterwarnings("ignore", category=DeprecationWarning, module="uvicorn")
-warnings.filterwarnings("ignore", category=PydanticDeprecatedSince20)
-
+from .app import ReMeApp
from . import agent
from . import retrieve
from . import summary
from . import vector_store
-__version__ = "0.1.10.1"
+__version__ = "0.1.10.2"
diff --git a/reme_ai/agent/tools/llm_mock_search_op.py b/reme_ai/agent/tools/llm_mock_search_op.py
index 7ba53415..a2219064 100644
--- a/reme_ai/agent/tools/llm_mock_search_op.py
+++ b/reme_ai/agent/tools/llm_mock_search_op.py
@@ -3,13 +3,12 @@ import json
import random
from typing import Dict, Any
-from loguru import logger
-
from flowllm.context import FlowContext, C
from flowllm.enumeration.role import Role
from flowllm.op.base_async_tool_op import BaseAsyncToolOp
from flowllm.schema.message import Message
from flowllm.schema.tool_call import ToolCall
+from loguru import logger
@C.register_op()
@@ -254,9 +253,9 @@ class LLMMockSearchOp(BaseAsyncToolOp):
async def async_main():
- from flowllm.app import FlowLLMApp
+ from reme_ai.app import ReMeApp
- async with FlowLLMApp(load_default_config=True):
+ async with ReMeApp():
# Test with different query types
test_queries = [
"What is the capital of France?", # Simple
diff --git a/reme_ai/agent/tools/use_mock_search_op.py b/reme_ai/agent/tools/use_mock_search_op.py
index 5c043d7a..67247f97 100644
--- a/reme_ai/agent/tools/use_mock_search_op.py
+++ b/reme_ai/agent/tools/use_mock_search_op.py
@@ -8,7 +8,6 @@ from flowllm.op.base_async_tool_op import BaseAsyncToolOp
from flowllm.schema.message import Message
from flowllm.schema.tool_call import ToolCall
from flowllm.utils.timer import Timer
-from flowllm.utils.token_utils import TokenCounter
from loguru import logger
from reme_ai.agent.tools.mock_search_tools import SearchToolA, SearchToolB, SearchToolC
@@ -99,7 +98,7 @@ class UseMockSearchOp(BaseAsyncToolOp):
selected_op_output = json.loads(selected_op.output)
content = selected_op_output["content"]
success = selected_op_output["success"]
- token_cost = TokenCounter().count(content)
+ token_cost = len(content) // 4 # Estimate using a method where every 4 characters constitute one token.
time_cost = timer.time_cost
@@ -118,9 +117,9 @@ class UseMockSearchOp(BaseAsyncToolOp):
async def async_main():
- from flowllm.app import FlowLLMApp
+ from reme_ai.app import ReMeApp
- async with FlowLLMApp(load_default_config=True):
+ async with ReMeApp():
test_queries = [
"What is the capital of France?",
"How does quantum computing work?",
diff --git a/reme_ai/app.py b/reme_ai/app.py
index 9945b730..7c5e18e8 100644
--- a/reme_ai/app.py
+++ b/reme_ai/app.py
@@ -1,15 +1,33 @@
+import asyncio
import sys
+from typing import List
-from flowllm.app import FlowLLMApp
+from flowllm import FlowLLMApp, C
+from flowllm.schema.flow_response import FlowResponse
+from loguru import logger
from reme_ai.config.config_parser import ConfigParser
+class ReMeApp(FlowLLMApp):
+
+ def __init__(self, args: List[str] = None):
+ super().__init__(args=args, parser=ConfigParser)
+ self.registered_flows = C.flow_dict.keys()
+ logger.info(f"registered_flows={self.registered_flows}")
+
+ async def async_execute(self, name: str, **kwargs) -> dict:
+ assert name in self.registered_flows, f"Invalid flow_name={name} !"
+ result: FlowResponse = await self.async_execute_flow(name=name, **kwargs)
+ return result.model_dump()
+
+ def execute(self, name: str, **kwargs) -> dict:
+ return asyncio.run(self.async_execute(name=name, **kwargs))
+
+
def main():
- with FlowLLMApp(args=sys.argv[1:], parser=ConfigParser) as app:
+ with ReMeApp(args=sys.argv[1:]) as app:
app.run_service()
if __name__ == "__main__":
main()
-
-# python -m build && twine upload dist/*
diff --git a/reme_ai/config/default.yaml b/reme_ai/config/default.yaml
index 992b0117..1314bcfb 100644
--- a/reme_ai/config/default.yaml
+++ b/reme_ai/config/default.yaml
@@ -185,12 +185,6 @@ llm:
params:
temperature: 0.6
- wk1:
- backend: openai_compatible
- model_name: qwen3-30b-a3b-instruct-2507
- params:
- temperature: 0.6
-
qwen3_30b_instruct:
backend: openai_compatible
model_name: qwen3-30b-a3b-instruct-2507
@@ -199,6 +193,30 @@ llm:
backend: openai_compatible
model_name: qwen3-30b-a3b-thinking-2507
+ qwen3_235b_instruct:
+ backend: openai_compatible
+ model_name: qwen3-235b-a22b-instruct-2507
+
+ qwen3_235b_thinking:
+ backend: openai_compatible
+ model_name: qwen3-235b-a22b-thinking-2507
+
+ qwen3_80b_instruct:
+ backend: openai_compatible
+ model_name: qwen3-next-80b-a3b-instruct
+
+ qwen3_80b_thinking:
+ backend: openai_compatible
+ model_name: qwen3-next-80b-a3b-thinking
+
+ qwen3_max_instruct:
+ backend: openai_compatible
+ model_name: qwen3-max
+
+ qwen25_max_instruct:
+ backend: openai_compatible
+ model_name: qwen-max-2025-01-25
+
embedding_model:
default:
backend: openai_compatible
diff --git a/reme_ai/service/agentscope_runtime_memory_service.py b/reme_ai/service/agentscope_runtime_memory_service.py
index e4c21713..c9c3f3d5 100644
--- a/reme_ai/service/agentscope_runtime_memory_service.py
+++ b/reme_ai/service/agentscope_runtime_memory_service.py
@@ -1,16 +1,15 @@
from abc import abstractmethod, ABC
from typing import Optional, Dict, Any
-from flowllm import FlowLLMApp
from pydantic import Field
-from reme_ai.config.config_parser import ConfigParser
+from reme_ai.app import ReMeApp
class AgentscopeRuntimeMemoryService(ABC):
def __init__(self):
- self.app = FlowLLMApp(parser=ConfigParser, load_default_config=True)
+ self.app = ReMeApp()
self.session_id_dict: dict = {}
def add_session_memory_id(self, session_id: str, memory_id):
diff --git a/reme_ai/summary/tool/parse_tool_call_result_op.py b/reme_ai/summary/tool/parse_tool_call_result_op.py
index 75137ee5..e40c2812 100644
--- a/reme_ai/summary/tool/parse_tool_call_result_op.py
+++ b/reme_ai/summary/tool/parse_tool_call_result_op.py
@@ -134,10 +134,11 @@ class ParseToolCallResultOp(BaseAsyncOp):
async def main():
"""Simple test for ParseToolCallResultOp"""
- from flowllm.app import FlowLLMApp
from datetime import datetime
-
- async with FlowLLMApp(load_default_config=True):
+
+ from reme_ai.app import ReMeApp
+
+ async with ReMeApp():
op = ParseToolCallResultOp()
# Create simple test data
diff --git a/reme_ai/summary/tool/summary_tool_memory_op.py b/reme_ai/summary/tool/summary_tool_memory_op.py
index 70976bc5..893337ea 100644
--- a/reme_ai/summary/tool/summary_tool_memory_op.py
+++ b/reme_ai/summary/tool/summary_tool_memory_op.py
@@ -195,13 +195,14 @@ class SummaryToolMemoryOp(BaseAsyncOp):
async def main():
- from flowllm.app import FlowLLMApp
from reme_ai.summary.tool.parse_tool_call_result_op import ParseToolCallResultOp
from reme_ai.vector_store.update_vector_store_op import UpdateVectorStoreOp
from datetime import datetime, timedelta
import random
- async with FlowLLMApp(load_default_config=True):
+ from reme_ai.app import ReMeApp
+
+ async with ReMeApp():
workspace_id = "test_workspace_complex"
tool_name = "web_search_tool"