Agents Tutorial

Simple ReAct Agent

An agent that has access to the E2B Code Interpreter and is capable of solving complex coding tasks.

Step-by-Step Guide

Import Necessary Libraries

from dynamiq.nodes.llms.openai import OpenAI
from dynamiq.connections import OpenAI as OpenAIConnection, E2B as E2BConnection
from dynamiq.nodes.agents.react import ReActAgent
from dynamiq.nodes.tools.e2b_sandbox import E2BInterpreterTool

Initialize the E2B Tool

Set up the E2B tool with the necessary API key.

e2b_tool = E2BInterpreterTool(
    connection=E2BConnection(api_key="$API_KEY")
)

Setup Your LLM

Configure the Large Language Model (LLM) with the necessary parameters such as the model, temperature, and maximum tokens.

llm = OpenAI(
    id="openai",
    connection=OpenAIConnection(api_key="$API_KEY"),
    model="gpt-4o",
    temperature=0.3,
    max_tokens=1000,
)

Create the ReAct Agent

Create an agent that uses the LLM and the E2B tool to solve coding tasks.

agent = ReActAgent(
    name="react-agent",
    llm=llm,
    tools=[e2b_tool],
    role="Senior Data Scientist",
    max_loops=10,
)

Run the Agent with an Input

Execute the agent with a specific input task.

result = agent.run(
    input_data={
        "input": "Add the first 10 numbers and tell if the result is prime.",
    }
)

print(result.output.get("content"))

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Multi-agent Orchestration

Step-by-Step Guide

Import Necessary Libraries

from dynamiq.connections import (OpenAI as OpenAIConnection,
                                 ScaleSerp as ScaleSerpConnection,
                                 E2B as E2BConnection)
from dynamiq.nodes.llms import OpenAI
from dynamiq.nodes.agents.orchestrators.adaptive import AdaptiveOrchestrator
from dynamiq.nodes.agents.orchestrators.adaptive_manager import AdaptiveAgentManager
from dynamiq.nodes.agents.react import ReActAgent
from dynamiq.nodes.agents.reflection import ReflectionAgent
from dynamiq.nodes.tools.e2b_sandbox import E2BInterpreterTool
from dynamiq.nodes.tools.scale_serp import ScaleSerpTool

Initialize Tools

Set up the tools required for coding and web search tasks.

python_tool = E2BInterpreterTool(
    connection=E2BConnection(api_key="$E2B_API_KEY")
)
search_tool = ScaleSerpTool(
    connection=ScaleSerpConnection(api_key="$SCALESERP_API_KEY")
)

Initialize LLM

Configure the Large Language Model (LLM) with the necessary parameters.

llm = OpenAI(
    connection=OpenAIConnection(api_key="$OPENAI_API_KEY"),
    model="gpt-4o",
    temperature=0.2,
)

Define Agents

Create agents with specific roles and goals.

coding_agent = ReActAgent(
    name="coding-agent",
    llm=llm,
    tools=[python_tool],
    role=("Expert agent with coding skills."
          "Goal is to provide the solution to the input task"
          "using Python software engineering skills."),
    max_loops=15,
)

planner_agent = ReflectionAgent(
    name="planner-agent",
    llm=llm,
    role=("Expert agent with planning skills."
          "Goal is to analyze complex requests" 
          "and provide a detailed action plan."),
)

search_agent = ReActAgent(
    name="search-agent",
    llm=llm,
    tools=[search_tool],
    role=("Expert agent with web search skills."
          "Goal is to provide the solution to the input task"
          "using web search and summarization skills."),
    max_loops=10,
)

Initialize the Adaptive Agent Manager

Set up the manager to handle the orchestration of multiple agents.

agent_manager = AdaptiveAgentManager(llm=llm)

Create the Orchestrator

Create an orchestrator to manage the execution of multiple agents.

orchestrator = AdaptiveOrchestrator(
    name="adaptive-orchestrator",
    agents=[coding_agent, planner_agent, search_agent],
    manager=agent_manager,
)

Define the Input Task

Specify the task that the orchestrator will handle.

input_task = (
    "Use coding skills to gather data about Nvidia and Intel stock prices for the last 10 years, "
    "calculate the average per year for each company, and create a table. Then craft a report "
    "and add a conclusion: what would have been better if I had invested $100 ten years ago?"
)

Run the Orchestrator

Execute the orchestrator with the defined input task.

result = orchestrator.run(
    input_data={"input": input_task},
)

# Print the result
print(result.output.get("content"))

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This tutorial provides a comprehensive guide to setting up and running agents using Dynamiq. By following these steps, you can create agents capable of solving complex tasks and orchestrate multiple agents to handle more sophisticated workflows.