Mini Agents

Run multi-step workflows in a few lines with one Agent per role, coordinated by AgentTeam

Mini agents run multi-step workflows in a few lines — one Agent per role, coordinated by AgentTeam.

python

from praisonaiagents import Agent, AgentTeam

researcher = Agent(name="Researcher", instructions="Research topics briefly.")
writer = Agent(name="Writer", instructions="Summarise findings.")
team = AgentTeam(agents=[researcher, writer])
team.start("Research and summarise quantum computing.")

The user describes a goal; the team runs each agent in sequence and returns a combined result.

Diagram

How It Works

Diagram

Quick Start

Create multiple AI agents that can work together in just a few lines of code!

Code

Install Package
First, install the PraisonAI Agents package:
bash
```
pip install praisonaiagents duckduckgo_search
```
Set API Key
Set your OpenAI API key as an environment variable in your terminal:
bash
```
export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"
```

Create Your Agents

Single Agent

from praisonaiagents import Agent, AgentTeam
summarise_agent = Agent(instructions="Summarise Photosynthesis")
agents = AgentTeam(agents=[summarise_agent])
agents.start()

Multiple Agents

# Agent with Internet Search Tool
from praisonaiagents import Agent, AgentTeam
research_agent = Agent(instructions="Research about AI 2024", tools=[Tools.internet_search])
summarise_agent = Agent(instructions="Summarise research agent's findings")
agents = AgentTeam(agents=[research_agent, summarise_agent])
agents.start()

Run Your Agents
Execute your agents by running:
bash
```
python app.py
```

No Code

Install Package
First, install the PraisonAI Agents package:
bash
```
pip install praisonaiagents duckduckgo_search
```
Set API Key
Set your OpenAI API key as an environment variable in your terminal:
bash
```
export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"
```

Create Your Agents

Single Agent

# Create a new file `agents.yaml` with the following content:
agents:  # Canonical
  summarise_agent
    instructions: Summarise Photosynthesis

Multiple Agents

# Create a new file `agents.yaml` with the following content:
# Agent with Internet Search Tool
agents:  # Canonical
  research_agent
    instructions: Research about AI 2024
    tools:
      - internet_search
  summarise_agent
    instructions: Summarise research agent's findings

Run Your Agents
Execute your agents by running:
bash
```
python app.py
```

Understanding Mini AI Agents

What are Mini AI Agents?

Mini AI Agents are simplified yet powerful AI agents that can: – Work together to accomplish tasks – Use tools like internet search – Process and summarize information – Execute tasks sequentially

Key Components

Agent

Individual AI agents with specific roles and capabilities

python

Agent(instructions="Your agent's role description")

Tools

Built-in tools that agents can use

python

tools=[Tools.internet_search]

Agents Manager

Coordinates multiple agents

python

AgentTeam(agents=[agent1, agent2])

Sequential Flow

Agents work in sequence, passing results to each other

Available Tools

Internet Search

python

Tools.internet_search

Search the internet using DuckDuckGo

Custom Instructions

Basic

# Simple instructions
research_agent = Agent(
    instructions="Research about climate change"
)

Advanced

# Detailed instructions
research_agent = Agent(
    instructions="""
    You are a research agent focused on gathering information about:
    1. Latest AI developments in 2024
    2. Major breakthroughs in machine learning
    3. New AI applications in industry

    Provide detailed and accurate information from reliable sources.
    """
)

Best Practices

Agent Instructions

Write clear and specific instructions:

python

# Good
Agent(instructions="Research and analyze the latest AI developments in 2024")

# Too vague
Agent(instructions="Research AI")

Tool Usage

Provide tools only to agents that need them:

python

# Research agent needs search
research_agent = Agent(
    instructions="Research AI",
    tools=[Tools.internet_search]
)

# Summary agent doesn't need search
summary_agent = Agent(
    instructions="Summarize findings"
)

Common Patterns

Research and Analysis

python

# Research agent
researcher = Agent(
    instructions="Research latest developments in quantum computing",
    tools=[Tools.internet_search]
)

# Analysis agent
analyst = Agent(
    instructions="Analyze and explain the research findings in simple terms"
)

agents = AgentTeam(agents=[researcher, analyst])

Information Processing

python

# Data collector
collector = Agent(
    instructions="Collect information about renewable energy",
    tools=[Tools.internet_search]
)

# Summarizer
summarizer = Agent(
    instructions="Create a concise summary of the collected information"
)

# Report writer
writer = Agent(
    instructions="Write a detailed report based on the summary"
)

agents = AgentTeam(agents=[collector, summarizer, writer])