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beyondagent/dataset/appworld/data
beyond*
step_experiences/*
build/*
*.egg-info/*

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cookbook/__init__.py
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doc/quick_start.md
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# Quick Start
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}")
```

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experiencemaker/__init__.py
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__version__ = "0.1.0"

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pyproject.toml
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[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"]
where = ["."]
include = ["experiencemaker*"]