Documentation Index
Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
Use this file to discover all available pages before exploring further.
Building a Single Agent
This guide walks you through creating your first AI agent with PraisonAI.
Prerequisites
pip install praisonaiagents
export OPENAI_API_KEY="your-api-key"
Basic Agent
from praisonaiagents import Agent
# Create a simple agent
agent = Agent(
name="Assistant",
instructions="You are a helpful assistant."
)
# Run the agent
result = agent.start("Hello! What can you help me with?")
print(result)
Agent with Custom Role
from praisonaiagents import Agent
agent = Agent(
name="Researcher",
role="Senior Research Analyst",
goal="Provide accurate and comprehensive research",
backstory="You are an experienced researcher with expertise in AI and technology.",
instructions="Always cite sources and provide balanced perspectives."
)
result = agent.start("What are the latest trends in AI?")
print(result)
from praisonaiagents import Agent
from praisonaiagents import internet_search
agent = Agent(
name="Web Researcher",
instructions="Search the web to find accurate information.",
tools=[internet_search]
)
result = agent.start("Find the latest news about OpenAI")
print(result)
Agent with Memory
from praisonaiagents import Agent, Memory
# Create memory instance
memory = Memory()
agent = Agent(
name="Assistant",
instructions="You are a helpful assistant with memory.",
memory=memory
)
# First conversation
agent.start("My name is Alice")
# Agent remembers the name
result = agent.start("What's my name?")
print(result) # "Your name is Alice"
Agent Configuration Options
| Parameter | Type | Description |
|---|
name | str | Agent name |
role | str | Agent’s role description |
goal | str | What the agent aims to achieve |
backstory | str | Background context for the agent |
instructions | str | System instructions |
model | str | LLM model to use (default: gpt-4o-mini) |
tools | list | List of tools available to the agent |
memory | Memory | Memory instance for persistence |
verbose | bool | Enable verbose logging |
Using Different Models
from praisonaiagents import Agent
# OpenAI
agent = Agent(name="GPT Agent", llm="gpt-4o")
# Anthropic
agent = Agent(name="Claude Agent", llm="claude-3-5-sonnet-20241022")
# Ollama (local)
agent = Agent(name="Local Agent", llm="ollama/llama3.2")
# Google
agent = Agent(name="Gemini Agent", llm="gemini/gemini-2.0-flash")
Chat Mode
from praisonaiagents import Agent
agent = Agent(
name="ChatBot",
instructions="You are a friendly chatbot."
)
# Multi-turn conversation
messages = [
{"role": "user", "content": "Hi!"},
{"role": "assistant", "content": "Hello! How can I help?"},
{"role": "user", "content": "Tell me a joke"}
]
result = agent.chat(messages)
print(result)
Next Steps
