yujunjun/autogen
1
0
Fork 0
mirror of https://github.com/microsoft/autogen.git synced 2025-12-25 14:09:00 +00:00
Code Issues Packages Projects Releases Wiki Activity

Update tool use examples to use inner agents rather than subclassing (#286)

Browse Source 
* Update tool use examples to use inner agents rather than subclassing

* fix

* Merge remote-tracking branch 'origin/main' into ekzhu-update-tool-use-example
This commit is contained in:
Eric Zhu 2024-07-26 15:04:52 -07:00 committed by GitHub
parent 6437374f63
commit 47e1cf464f
8 changed files with 198 additions and 102 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

22
python/docs/drawio/tool-equipped-agent-cfg.drawio → python/docs/drawio/tool-use-agent-cfg.drawio
View File

@ -1,20 +1,20 @@
<mxfile host="app.diagrams.net" agent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0" version="24.7.4">
<mxfile host="app.diagrams.net" agent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36 Edg/127.0.0.0" version="24.7.5">
<diagram name="Page-1" id="2U5l3ylZQluw78bWm4Ui">
<mxGraphModel dx="1756" dy="1091" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="850" pageHeight="1100" math="0" shadow="0">
<mxGraphModel dx="970" dy="1091" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="850" pageHeight="1100" math="0" shadow="0">
<root>
<mxCell id="0" />
<mxCell id="1" parent="0" />
<mxCell id="HfMixxTfmlLCSILu5LXk-10" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;exitX=0;exitY=0;exitDx=0;exitDy=0;entryX=0;entryY=1;entryDx=0;entryDy=0;curved=1;" edge="1" parent="1" source="HfMixxTfmlLCSILu5LXk-1" target="HfMixxTfmlLCSILu5LXk-2">
<mxCell id="HfMixxTfmlLCSILu5LXk-10" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;exitX=0;exitY=0;exitDx=0;exitDy=0;entryX=0;entryY=1;entryDx=0;entryDy=0;curved=1;" parent="1" source="HfMixxTfmlLCSILu5LXk-1" target="HfMixxTfmlLCSILu5LXk-2" edge="1">
<mxGeometry relative="1" as="geometry">
<Array as="points">
<mxPoint x="240" y="370" />
</Array>
</mxGeometry>
</mxCell>
<mxCell id="HfMixxTfmlLCSILu5LXk-1" value="ToolAgent:handle_function_call" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#fff2cc;strokeColor=#d6b656;" vertex="1" parent="1">
<mxCell id="HfMixxTfmlLCSILu5LXk-1" value="ToolAgent:handle_function_call" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#fff2cc;strokeColor=#d6b656;" parent="1" vertex="1">
<mxGeometry x="265" y="370" width="190" height="40" as="geometry" />
</mxCell>
<mxCell id="HfMixxTfmlLCSILu5LXk-8" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;exitX=1;exitY=1;exitDx=0;exitDy=0;entryX=1;entryY=0;entryDx=0;entryDy=0;curved=1;" edge="1" parent="1" source="HfMixxTfmlLCSILu5LXk-2" target="HfMixxTfmlLCSILu5LXk-1">
<mxCell id="HfMixxTfmlLCSILu5LXk-8" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;exitX=1;exitY=1;exitDx=0;exitDy=0;entryX=1;entryY=0;entryDx=0;entryDy=0;curved=1;" parent="1" source="HfMixxTfmlLCSILu5LXk-2" target="HfMixxTfmlLCSILu5LXk-1" edge="1">
<mxGeometry relative="1" as="geometry">
<Array as="points">
<mxPoint x="500" y="300" />
@ -22,24 +22,24 @@
</Array>
</mxGeometry>
</mxCell>
<mxCell id="HfMixxTfmlLCSILu5LXk-15" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" edge="1" parent="1" source="HfMixxTfmlLCSILu5LXk-2">
<mxCell id="HfMixxTfmlLCSILu5LXk-15" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" parent="1" source="HfMixxTfmlLCSILu5LXk-2" edge="1">
<mxGeometry relative="1" as="geometry">
<mxPoint x="600" y="270" as="targetPoint" />
</mxGeometry>
</mxCell>
<mxCell id="HfMixxTfmlLCSILu5LXk-2" value="ToolEquippedAgent:handle_user_message&lt;div&gt;model_client.create&lt;/div&gt;" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#fff2cc;strokeColor=#d6b656;" vertex="1" parent="1">
<mxCell id="HfMixxTfmlLCSILu5LXk-2" value="ToolUseAgent:handle_user_message&lt;div&gt;model_client.create&lt;/div&gt;" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#fff2cc;strokeColor=#d6b656;" parent="1" vertex="1">
<mxGeometry x="240" y="240" width="240" height="60" as="geometry" />
</mxCell>
<mxCell id="HfMixxTfmlLCSILu5LXk-17" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;exitX=1;exitY=0.5;exitDx=0;exitDy=0;entryX=0;entryY=0.5;entryDx=0;entryDy=0;" edge="1" parent="1" source="HfMixxTfmlLCSILu5LXk-3" target="HfMixxTfmlLCSILu5LXk-2">
<mxCell id="HfMixxTfmlLCSILu5LXk-17" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;exitX=1;exitY=0.5;exitDx=0;exitDy=0;entryX=0;entryY=0.5;entryDx=0;entryDy=0;" parent="1" source="HfMixxTfmlLCSILu5LXk-3" target="HfMixxTfmlLCSILu5LXk-2" edge="1">
<mxGeometry relative="1" as="geometry" />
</mxCell>
<mxCell id="HfMixxTfmlLCSILu5LXk-3" value="send message" style="text;html=1;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;" vertex="1" parent="1">
<mxCell id="HfMixxTfmlLCSILu5LXk-3" value="send message" style="text;html=1;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;" parent="1" vertex="1">
<mxGeometry x="120" y="255" width="60" height="30" as="geometry" />
</mxCell>
<mxCell id="HfMixxTfmlLCSILu5LXk-12" value="model response is tool call" style="text;html=1;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;" vertex="1" parent="1">
<mxCell id="HfMixxTfmlLCSILu5LXk-12" value="model response is tool call" style="text;html=1;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;" parent="1" vertex="1">
<mxGeometry x="510" y="330" width="90" height="30" as="geometry" />
</mxCell>
<mxCell id="HfMixxTfmlLCSILu5LXk-16" value="model response is text" style="text;html=1;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;" vertex="1" parent="1">
<mxCell id="HfMixxTfmlLCSILu5LXk-16" value="model response is text" style="text;html=1;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;" parent="1" vertex="1">
<mxGeometry x="500" y="225" width="90" height="30" as="geometry" />
</mxCell>
</root>

155
python/docs/src/core-concepts/ai-agents.md
View File

@ -39,7 +39,7 @@ from agnext.components.models import ChatCompletionClient, SystemMessage, UserMe
from agnext.core import CancellationToken
@dataclass
class MyMessage:
class Message:
content: str
class SimpleAgent(TypeRoutedAgent):
@ -50,12 +50,12 @@ class SimpleAgent(TypeRoutedAgent):
self._model_client = model_client
@message_handler
async def handle_user_message(self, message: MyMessage, cancellation_token: CancellationToken) -> MyMessage:
async def handle_user_message(self, message: Message, cancellation_token: CancellationToken) -> MyMessage:
# Prepare input to the chat completion model.
user_message = UserMessage(content=message.content, source="user")
response = await self._model_client.create(self._system_messages + [user_message], cancellation_token=cancellation_token)
# Return with the model's response.
return MyMessage(content=response.content)
return Message(content=response.content)
```
The `SimpleAgent` class is a subclass of the
@ -77,7 +77,7 @@ async def main():
# Start the runtime processing messages.
run_context = runtime.start()
# Send a message to the agent and get the response.
message = MyMessage("Hello, what are some fun things to do in Seattle?")
message = Message("Hello, what are some fun things to do in Seattle?")
response = await runtime.send_message(message, agent)
print(response)
# Stop the runtime processing messages.
@ -109,13 +109,59 @@ There's so much to see and do in Seattle, and these are just a few options to ge
Tools are code that can be executed by an agent to perform actions. A tool
can be a simple function such as a calculator, or an API call to a third-party service
such as stock price lookup and weather forecast.
In the context of AI agents, tools are designed to be executed by agents in response to model-generated function calls.
In the context of AI agents, tools are designed to be executed by agents in
response to model-generated function calls.
AGNext provides the {py:mod}`agnext.components.tools` module with a suite of built-in
tools and utilities for creating and running custom tools.
### Built-in Tools
One of the built-in tools is the {py:class}`agnext.components.tools.PythonCodeExecutionTool`,
which allows agents to execute Python code snippets.
Here is how you create the tool and use it.
```python
import asyncio
from agnext.components.code_executor import LocalCommandLineCodeExecutor
from agnext.components.tools import PythonCodeExecutionTool
from agnext.core import CancellationToken
async def main() -> None:
# Create the tool.
code_executor = LocalCommandLineCodeExecutor()
code_execution_tool = PythonCodeExecutionTool(code_executor)
cancellation_token = CancellationToken()
# Use the tool directly without an agent.
code = "print('Hello, world!')"
result = await code_execution_tool.run_json({"code": code}, cancellation_token)
print(code_execution_tool.return_value_as_string(result))
asyncio.run(main())
```
The {py:class}`~agnext.components.code_executor.LocalCommandLineCodeExecutor`
class is a built-in code executor that runs Python code snippets in a subprocess
in the local command line environment.
The {py:class}`~agnext.components.tools.PythonCodeExecutionTool` class wraps the code executor
and provides a simple interface to execute Python code snippets.
The code above should print the following output:
```text
Hello, world!
```
Other built-in tools will be added in the future.
### Custom Function Tools
A tool can also be a simple Python function that performs a specific action.
To create a custom function tool, you just need to create a Python function
and use the {py:class}`agnext.components.tools.FunctionTool` class to wrap it.
@ -130,54 +176,58 @@ async def get_stock_price(ticker: str, date: Annotated[str, "Date in YYYY/MM/DD"
# Returns a random stock price for demonstration purposes.
return random.uniform(10, 200)
# Create a tool.
# Create a function tool.
stock_price_tool = FunctionTool(get_stock_price, description="Get the stock price.")
```
### Tool-Equipped Agent
To use tools with an agent, you can use {py:class}`agnext.components.tool_agent.ToolAgent`,
either by subclassing it or by using it in a composition pattern.
Here is an example tool-equipped agent that subclasses {py:class}`~agnext.components.tool_agent.ToolAgent`
and executes its tools by sending direct messages to itself.
by using it in a composition pattern.
Here is an example tool-use agent that uses {py:class}`~agnext.components.tool_agent.ToolAgent`
as an inner agent for executing tools.
```python
import json
import asyncio
from typing import List
from dataclasses import dataclass
from typing import List
from agnext.application import SingleThreadedAgentRuntime
from agnext.components import TypeRoutedAgent, message_handler, FunctionCall
from agnext.components.tool_agent import ToolAgent, ToolException
from agnext.components import FunctionCall, TypeRoutedAgent, message_handler
from agnext.components.models import (
ChatCompletionClient,
SystemMessage,
UserMessage,
AssistantMessage,
ChatCompletionClient,
FunctionExecutionResult,
FunctionExecutionResultMessage,
OpenAIChatCompletionClient,
SystemMessage,
UserMessage,
)
from agnext.components.tools import Tool, FunctionTool
from agnext.core import CancellationToken
from agnext.components.tool_agent import ToolAgent, ToolException
from agnext.components.tools import FunctionTool, Tool, ToolSchema
from agnext.core import AgentId, CancellationToken
@dataclass
class MyMessage:
class Message:
content: str
class ToolEquippedAgent(ToolAgent):
def __init__(self, model_client: ChatCompletionClient, tools: List[Tool]) -> None:
super().__init__("An agent with tools", tools)
class ToolUseAgent(TypeRoutedAgent):
def __init__(self, model_client: ChatCompletionClient, tool_schema: List[ToolSchema], tool_agent: AgentId) -> None:
super().__init__("An agent with tools")
self._system_messages = [SystemMessage("You are a helpful AI assistant.")]
self._model_client = model_client
self._tool_schema = tool_schema
self._tool_agent = tool_agent
@message_handler
async def handle_user_message(self, message: MyMessage, cancellation_token: CancellationToken) -> MyMessage:
async def handle_user_message(self, message: Message, cancellation_token: CancellationToken) -> Message:
# Create a session of messages.
session = [UserMessage(content=message.content, source="user")]
# Get a response from the model.
response = await self._model_client.create(
self._system_messages + session, tools=self.tools, cancellation_token=cancellation_token
self._system_messages + session, tools=self._tool_schema, cancellation_token=cancellation_token
)
# Add the response to the session.
session.append(AssistantMessage(content=response.content, source="assistant"))
@ -186,44 +236,44 @@ class ToolEquippedAgent(ToolAgent):
while isinstance(response.content, list) and all(isinstance(item, FunctionCall) for item in response.content):
# Execute functions called by the model by sending messages to itself.
results: List[FunctionExecutionResult | BaseException] = await asyncio.gather(
*[self.send_message(call, self.id) for call in response.content],
*[self.send_message(call, self._tool_agent) for call in response.content],
return_exceptions=True,
)
# Combine the results into a single response and handle exceptions.
function_results : List[FunctionExecutionResult] = []
function_results: List[FunctionExecutionResult] = []
for result in results:
if isinstance(result, FunctionExecutionResult):
function_results.append(result)
elif isinstance(result, ToolException):
function_results.append(FunctionExecutionResult(content=f"Error: {result}", call_id=result.call_id))
elif isinstance(result, BaseException):
raise result # Unexpected exception.
raise result # Unexpected exception.
session.append(FunctionExecutionResultMessage(content=function_results))
# Query the model again with the new response.
response = await self._model_client.create(
self._system_messages + session, tools=self.tools, cancellation_token=cancellation_token
self._system_messages + session, tools=self._tool_schema, cancellation_token=cancellation_token
)
session.append(AssistantMessage(content=response.content, source=self.metadata["name"]))
# Return the final response.
return MyMessage(content=response.content)
return Message(content=response.content)
```
The `ToolEquippedAgent` class is much more involved than the `SimpleAgent` class, however,
The `ToolUseAgent` class is much more involved than the `SimpleAgent` class, however,
the core idea can be described using a simple control flow graph:
![ToolEquippedAgent control flow graph](tool-equipped-agent-cfg.svg)
![ToolUseAgent control flow graph](tool-use-agent-cfg.svg)
The `ToolEquippedAgent`'s `handle_user_message` handler handles messages from the user,
The `ToolUseAgent`'s `handle_user_message` handler handles messages from the user,
and determines whether the model has generated a tool call.
If the model has generated tool calls, then the handler sends a function call
message to itself to execute the tools -- implemented by the parent {py:class}`~agnext.components.tool_agent.ToolAgent` class,
and then queries the model again
with the results of the tool calls.
message to the {py:class}`~agnext.components.tool_agent.ToolAgent` agent
to execute the tools,
and then queries the model again with the results of the tool calls.
This process continues until the model stops generating tool calls,
at which point the final response is returned to the user.
By having the tool execution logic in a separate handler in the base class,
By having the tool execution logic in a separate agent,
we expose the model-tool interactions to the agent runtime as messages, so the tool executions
can be observed externally and intercepted if necessary.
@ -231,29 +281,38 @@ To run the agent, we need to create a runtime and register the agent.
```python
async def main() -> None:
# Create a runtime and register the agent.
# Create a runtime.
runtime = SingleThreadedAgentRuntime()
agent = await runtime.register_and_get(
"tool-agent",
lambda: ToolEquippedAgent(
OpenAIChatCompletionClient(model="gpt-4o-mini", api_key="YOUR_API_KEY"),
tools=[
FunctionTool(get_stock_price, description="Get the stock price."),
],
# Create the tools.
tools: List[Tool] = [FunctionTool(get_stock_price, description="Get the stock price.")]
# Register the agents.
tool_executor_agent = await runtime.register_and_get(
"tool-executor-agent",
lambda: ToolAgent(
description="Tool Executor Agent",
tools=tools,
),
)
tool_use_agent = await runtime.register_and_get(
"tool-use-agent",
lambda: ToolUseAgent(
# OPENAI_API_KEY environment variable must be set.
OpenAIChatCompletionClient(model="gpt-4o-mini"),
tool_schema=[tool.schema for tool in tools],
tool_agent=tool_executor_agent,
),
)
# Start processing messages.
run_context = runtime.start()
# Send a direct message to the tool agent.
response = await runtime.send_message(MyMessage("What is the stock price of NVDA on 2024/06/01?"), agent)
response = await runtime.send_message(Message("What is the stock price of NVDA on 2024/06/01?"), tool_use_agent)
print(response.content)
# Stop processing messages.
await run_context.stop()
asyncio.run(main())
```
In the example above, we use the direct communication to send a message to the tool agent. The agent's response might look like this:
In the example above, we use the direct communication to send a message to
the tool use agent. The agent's response might look like this:
```text
The stock price of NVDA on June 1, 2024, was $26.49.

4
python/docs/src/core-concepts/tool-equipped-agent-cfg.svg
View File

File diff suppressed because one or more lines are too long
Side by Side

Before

Width:  |  Height:  |  Size: 84 KiB

4
python/docs/src/core-concepts/tool-use-agent-cfg.svg Normal file
View File

File diff suppressed because one or more lines are too long
Side by Side

After

Width:  |  Height:  |  Size: 82 KiB

56
python/samples/tool-use/coding_one_agent_direct.py → python/samples/tool-use/coding_direct.py
View File

@ -1,13 +1,13 @@
"""
This example implements a tool-enabled agent that uses tools to perform tasks.
1. The agent receives a user message, and makes an inference using a model.
If the response is a list of function calls, the agent executes the tools by
sending tool execution task to itself.
2. The agent executes the tools and sends the results back to itself, and
makes an inference using the model again.
3. The agent keeps executing the tools until the inference response is not a
1. The tool use agent receives a user message, and makes an inference using a model.
If the response is a list of function calls, the tool use agent executes the tools by
sending tool execution task to a tool executor agent.
2. The tool executor agent executes the tools and sends the results back to the
tool use agent, who makes an inference using the model again.
3. The agents keep executing the tools until the inference response is not a
list of function calls.
4. The agent returns the final response to the user.
4. The tool use agent returns the final response to the user.
"""
import asyncio
@ -17,7 +17,7 @@ from dataclasses import dataclass
from typing import List
from agnext.application import SingleThreadedAgentRuntime
from agnext.components import FunctionCall, message_handler
from agnext.components import FunctionCall, TypeRoutedAgent, message_handler
from agnext.components.code_executor import LocalCommandLineCodeExecutor
from agnext.components.models import (
AssistantMessage,
@ -29,8 +29,8 @@ from agnext.components.models import (
UserMessage,
)
from agnext.components.tool_agent import ToolAgent, ToolException
from agnext.components.tools import PythonCodeExecutionTool, Tool
from agnext.core import CancellationToken
from agnext.components.tools import PythonCodeExecutionTool, Tool, ToolSchema
from agnext.core import AgentId, CancellationToken
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
@ -42,7 +42,7 @@ class Message:
content: str
class ToolEnabledAgent(ToolAgent):
class ToolUseAgent(TypeRoutedAgent):
"""An agent that uses tools to perform tasks. It executes the tools
by itself by sending the tool execution task to itself."""
@ -51,24 +51,30 @@ class ToolEnabledAgent(ToolAgent):
description: str,
system_messages: List[SystemMessage],
model_client: ChatCompletionClient,
tools: List[Tool],
tool_schema: List[ToolSchema],
tool_agent: AgentId,
) -> None:
super().__init__(description, tools)
super().__init__(description)
self._model_client = model_client
self._system_messages = system_messages
self._tool_schema = tool_schema
self._tool_agent = tool_agent
@message_handler
async def handle_user_message(self, message: Message, cancellation_token: CancellationToken) -> Message:
"""Handle a user message, execute the model and tools, and returns the response."""
session: List[LLMMessage] = []
session.append(UserMessage(content=message.content, source="User"))
response = await self._model_client.create(self._system_messages + session, tools=self.tools)
response = await self._model_client.create(self._system_messages + session, tools=self._tool_schema)
session.append(AssistantMessage(content=response.content, source=self.metadata["name"]))
# Keep executing the tools until the response is not a list of function calls.
while isinstance(response.content, list) and all(isinstance(item, FunctionCall) for item in response.content):
results: List[FunctionExecutionResult | BaseException] = await asyncio.gather(
*[self.send_message(call, self.id) for call in response.content],
*[
self.send_message(call, self._tool_agent, cancellation_token=cancellation_token)
for call in response.content
],
return_exceptions=True,
)
# Combine the results into a single response and handle exceptions.
@ -82,7 +88,7 @@ class ToolEnabledAgent(ToolAgent):
raise result
session.append(FunctionExecutionResultMessage(content=function_results))
# Execute the model again with the new response.
response = await self._model_client.create(self._system_messages + session, tools=self.tools)
response = await self._model_client.create(self._system_messages + session, tools=self._tool_schema)
session.append(AssistantMessage(content=response.content, source=self.metadata["name"]))
assert isinstance(response.content, str)
@ -100,20 +106,30 @@ async def main() -> None:
)
]
# Register agents.
tool_agent = await runtime.register_and_get(
tool_executor_agent = await runtime.register_and_get(
"tool_executor_agent",
lambda: ToolAgent(
description="Tool Executor Agent",
tools=tools,
),
)
tool_use_agent = await runtime.register_and_get(
"tool_enabled_agent",
lambda: ToolEnabledAgent(
lambda: ToolUseAgent(
description="Tool Use Agent",
system_messages=[SystemMessage("You are a helpful AI Assistant. Use your tools to solve problems.")],
model_client=get_chat_completion_client_from_envs(model="gpt-4o-mini"),
tools=tools,
tool_schema=[tool.schema for tool in tools],
tool_agent=tool_executor_agent,
),
)
run_context = runtime.start()
# Send a task to the tool user.
response = await runtime.send_message(Message("Run the following Python code: print('Hello, World!')"), tool_agent)
response = await runtime.send_message(
Message("Run the following Python code: print('Hello, World!')"), tool_use_agent
)
print(response.content)
# Run the runtime until the task is completed.

22
python/samples/tool-use/coding_one_agent_direct_intercept.py → python/samples/tool-use/coding_direct_with_intercept.py
View File

@ -14,14 +14,14 @@ from agnext.application import SingleThreadedAgentRuntime
from agnext.components import FunctionCall
from agnext.components.code_executor import LocalCommandLineCodeExecutor
from agnext.components.models import SystemMessage
from agnext.components.tool_agent import ToolException
from agnext.components.tool_agent import ToolAgent, ToolException
from agnext.components.tools import PythonCodeExecutionTool, Tool
from agnext.core import AgentId
from agnext.core.intervention import DefaultInterventionHandler, DropMessage
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from coding_one_agent_direct import Message, ToolEnabledAgent
from coding_direct import Message, ToolUseAgent
from common.utils import get_chat_completion_client_from_envs
@ -48,20 +48,30 @@ async def main() -> None:
)
]
# Register agents.
tool_agent = await runtime.register_and_get(
tool_executor_agent = await runtime.register_and_get(
"tool_executor_agent",
lambda: ToolAgent(
description="Tool Executor Agent",
tools=tools,
),
)
tool_use_agent = await runtime.register_and_get(
"tool_enabled_agent",
lambda: ToolEnabledAgent(
lambda: ToolUseAgent(
description="Tool Use Agent",
system_messages=[SystemMessage("You are a helpful AI Assistant. Use your tools to solve problems.")],
model_client=get_chat_completion_client_from_envs(model="gpt-4o-mini"),
tools=tools,
tool_schema=[tool.schema for tool in tools],
tool_agent=tool_executor_agent,
),
)
run_context = runtime.start()
# Send a task to the tool user.
response = await runtime.send_message(Message("Run the following Python code: print('Hello, World!')"), tool_agent)
response = await runtime.send_message(
Message("Run the following Python code: print('Hello, World!')"), tool_use_agent
)
print(response.content)
# Run the runtime until the task is completed.

0
python/samples/tool-use/coding_two_agent_pub_sub.py → python/samples/tool-use/coding_pub_sub.py
View File

37
python/samples/tool-use/custom_function_tool_one_agent_direct.py → python/samples/tool-use/custom_tool_direct.py
View File

@ -7,18 +7,20 @@ import asyncio
import os
import random
import sys
from typing import List
from agnext.application import SingleThreadedAgentRuntime
from agnext.components.models import (
SystemMessage,
)
from agnext.components.tools import FunctionTool
from agnext.components.tool_agent import ToolAgent
from agnext.components.tools import FunctionTool, Tool
from typing_extensions import Annotated
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__))))
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from coding_one_agent_direct import Message, ToolEnabledAgent
from coding_direct import Message, ToolUseAgent
from common.utils import get_chat_completion_client_from_envs
@ -31,28 +33,37 @@ async def get_stock_price(ticker: str, date: Annotated[str, "The date in YYYY/MM
async def main() -> None:
# Create the runtime.
runtime = SingleThreadedAgentRuntime()
tools: List[Tool] = [
# A tool that gets the stock price.
FunctionTool(
get_stock_price,
description="Get the stock price of a company given the ticker and date.",
name="get_stock_price",
)
]
# Register agents.
tool_agent = await runtime.register_and_get(
tool_executor_agent = await runtime.register_and_get(
"tool_executor_agent",
lambda: ToolAgent(
description="Tool Executor Agent",
tools=tools,
),
)
tool_use_agent = await runtime.register_and_get(
"tool_enabled_agent",
lambda: ToolEnabledAgent(
lambda: ToolUseAgent(
description="Tool Use Agent",
system_messages=[SystemMessage("You are a helpful AI Assistant. Use your tools to solve problems.")],
model_client=get_chat_completion_client_from_envs(model="gpt-4o-mini"),
tools=[
# Define a tool that gets the stock price.
FunctionTool(
get_stock_price,
description="Get the stock price of a company given the ticker and date.",
name="get_stock_price",
)
],
tool_schema=[tool.schema for tool in tools],
tool_agent=tool_executor_agent,
),
)
run_context = runtime.start()
# Send a task to the tool user.
response = await runtime.send_message(Message("What is the stock price of NVDA on 2024/06/01"), tool_agent)
response = await runtime.send_message(Message("What is the stock price of NVDA on 2024/06/01"), tool_use_agent)
# Print the result.
assert isinstance(response, Message)
print(response.content)