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-Here is a simple user guide. - -### Step0: Configuration -- start es -- set env APIKEY / host -- python -m model_service -port 8000 -config simple/path - -### Step1: Own an agent - -Assume you have a runnable [agent code](./mxc_agent.py). -Here, we use a basic LLM combined with a simple react framework including three tools(code, web_search, terminate) as an example. - -```python -class MxcAgent(BaseModel): - llm: BaseLLM | None = Field(default=None) - max_steps: int = Field(default=10) - tools: List[BaseTool] = Field(default_factory=list) - - def think(self, query: str, **kwargs) -> bool: - ... - - def act(self, **kwargs): - ... - - def run(self, query: str, **kwargs) -> List[Message]: - messages: List[Message] = [] - - for i in range(self.max_steps): - should_act: bool = self.think(query, messages=messages, **kwargs) - if should_act: - self.act(messages=messages, **kwargs) - else: - break - return messages - - -query = "Analyze Xiaomi Corporation -agent = MxcAgent(llm=OpenAICompatibleBaseLLM(model_name="qwen3-32b", temperature=0.0001), - max_steps=10, - tools=[CodeTool(), DashscopeSearchTool(), TerminateTool()]) -messages = agent.run(query=query) -answer = messages[-1].content -print(answer) -``` - -### Step2: Implement AgentWrapper - -In order to utilize the **context generator** and **summarizer** capabilities of beyondagent, please inherit from **MxcAgent** and **BaseAgentWrapperMixin** to implement the AgentWrapper. - -Here, you need to customize two parts: -- how to integrate the content message(insight) generated by the `self.context_generator` into the context. -- implement the execute function to output the trajectory. - -Below is a simple example of integrating **trajectory-level insight** into the context. - -```python -from beyondagent.core.module.agent_wrapper.base_agent_wrapper import BaseAgentWrapperMixin - -class MxcAgentWrapper(MxcAgent, BaseAgentWrapperMixin): - def execute(self, query: str, **kwargs) -> Trajectory: - trajectory = Trajectory(steps=messages, query=query) - context_msg = self.context_generator.execute(trajectory=trajectory) - new_query = f""" -previous insight: -{context_msg.content} -Please consider the helpful parts from these in answering the question, to make the response more comprehensive and substantial. - -user query: -{query} - """.strip() - - messages = self.run(new_query, **kwargs) - return Trajectory(query=query, steps=messages, answer=messages[-1].content, done=True) - -``` - -### Step3: Run AgentRunner with insight - -Once you have completed the implementation of the AgentWrapper class, you will be able to utilize the capabilities of -beyondagent. - -Here is an example using **SimpleAgentRunner**. -We first executed two historical tasks, then summarized the experience and made it persistent. -Finally, we utilized the historical experience in a new task. - -[insights demo](./insight.json) - - -```python -from beyondagent.core.module.runner.simple_agent_runner import SimpleAgentRunner - - - -mxc_agent_wrapper = MxcAgentWrapper(llm=OpenAICompatibleBaseLLM(model_name="qwen3-32b", temperature=0.0001), - max_steps=10, - tools=[CodeTool(), DashscopeSearchTool(), TerminateTool()]) -agent_runner = SimpleAgentRunner(agent_wrapper=mxc_agent_wrapper, summarizer="default", context_generator="default") - -# historical tasks -agent_runner.rollout_trajectory(query="Analyze the company Tesla.") -agent_runner.rollout_trajectory(query="Analyze the company Apple.") - -# summary insights and store them -agent_runner.summary_and_store() - -# run agent with historical insights -trajectory = agent_runner.rollout_trajectory(query="Analyze the company Xiaomi Corporation.") -``` - - -### Step4: Evaluation(Optional) - -If we have a reward function that allows us to compare the performance before and after adding context, we can try this -part. - -Use `run_agent` to obtain the answer from the original agent (answer1), and use `run_agent_wrapper` to get the answer -with added insights and experience (answer2). - -Here, the reward function is used to compare and score the two answers. The `reward.reward_value` indicates the win rate -of answer2. - -```python -# task -query = "Analyze Xiaomi Corporation." - -# run agent -agent = MxcAgent(llm=OpenAICompatibleBaseLLM(model_name="qwen3-32b", temperature=0.0001), - max_steps=10, - tools=[CodeTool(), DashscopeSearchTool(), TerminateTool()]) -messages = agent.run(query=query) -answer1 = messages[-1].content - -# agent runner: Assume we already have some historical experience. -mxc_agent_wrapper = MxcAgentWrapper(llm=OpenAICompatibleBaseLLM(model_name="qwen3-32b", temperature=0.0001), - max_steps=10, - tools=[CodeTool(), DashscopeSearchTool(), TerminateTool()]) -agent_runner = SimpleAgentRunner(agent_wrapper=mxc_agent_wrapper, summarizer="default", context_generator="default") -trajectory = agent_runner.rollout_trajectory(query=query) -answer2 = trajectory.answer - -# pair-wise LLM evaluation -from beyondagent.core.module.reward_fn.simple_reward_fn import SimpleRewardFn -reward_fn = SimpleRewardFn(llm=OpenAICompatibleBaseLLM(model_name="qwen3-32b", temperature=0.0001)) -reward = reward_fn.execute(query=query, answer1=answer1, answer2=answer2, eval_times=5) -print(f"final reward={reward.reward_value}") -``` \ No newline at end of file diff --git a/experiencemaker/__init__.py b/experiencemaker/__init__.py index e69de29b..3dc1f76b 100644 --- a/experiencemaker/__init__.py +++ b/experiencemaker/__init__.py @@ -0,0 +1 @@ +__version__ = "0.1.0" diff --git a/pyproject.toml b/pyproject.toml index 8fde5105..2cd7f8a3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,30 +1,29 @@ +[build-system] +requires = ["setuptools"] +build-backend = "setuptools.build_meta" + [project] -name = "beyond-agent" -version = "0.1.1" -description = "Agent " +name = "ExperienceMaker" +version = "0.1.0" +description = "make experience from trajectory" +authors = [{ name = "experiencemaker group", email = "experiencemaker@alibaba-inc.com" }] readme = "README.md" -requires-python = ">=3.11" +requires-python = ">=3.12" dependencies = [ - "beast-logger", - "anthropic>=0.45.1", - "argparse>=1.4.0", - "httpx>=0.28.1", - "mcp[cli]>=1.2.1", - "python-dotenv>=1.0.1", - "firecrawl-py>=1.14.1", - "loguru", - "pillow", - "regex", - "fastapi", - "arxiv", - "tqdm", - "requests_oauthlib", -# "teamwork-mcp>=0.2.1", - "agentscope", -# "beyond @ file:///${PROJECT_ROOT}/experiencemaker/third_party/beyond", - "astor", + "dashscope>=1.19.1", + "elasticsearch>=8.14.0", + "fastapi>=0.115.13", + "loguru>=0.7.3", + "mcp>=1.9.4", + "numpy>=2.3.0", + "openai>=1.88.0", + "pydantic>=2.11.7", + "PyYAML>=6.0.2", + "Requests>=2.32.4", + "uvicorn>=0.34.3", + "setuptools>=75.0", ] - [tool.setuptools.packages.find] -include = ["beyondagent"] \ No newline at end of file +where = ["."] +include = ["experiencemaker*"]