Agents

Create AI agents that understand instructions, use tools, and complete tasks

An Agent is an AI that follows your instructions, uses tools, and completes tasks through conversation.

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    instructions="You are a helpful research assistant. Answer questions accurately and concisely.",
)

agent.start("What are the main benefits of solar energy?")

The user asks a question; the agent runs a single turn and returns the answer.

Quick Start

1. Simple Agent

   from praisonaiagents import Agent
   
   agent = Agent(instructions="You are a helpful assistant.")
   response = agent.start("Explain machine learning in one paragraph.")
   print(response)
   
2. Agent with Tools

   from praisonaiagents import Agent
   from praisonaiagents.tools import duckduckgo
   
   agent = Agent(
       name="Researcher",
       instructions="You are a research agent. Use web search for current information.",
       tools=[duckduckgo],
   )
   agent.start("What are the latest developments in quantum computing?")
   
3. Agent with Features

   from praisonaiagents import Agent
   
   agent = Agent(
       name="SmartAssistant",
       instructions="You are a smart assistant that remembers and plans.",
       memory=True,
       planning=True,
       reflection=True,
   )
   agent.start("Help me plan a comprehensive study schedule for learning Python.")
   

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How It Works

Phase	What happens
1. Receive	Agent receives user input
2. Think	LLM decides whether to respond or call a tool
3. Act	If tool needed, executes it and feeds result back
4. Respond	Final answer returned to user

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Key Parameters

Parameter	Type	Description
name	str	Agent name (shown in logs and multi-agent setups)
instructions	str	System prompt — what the agent does and how
llm	str	Model name (e.g., "gpt-4o", "claude-3-5-sonnet")
tools	list	List of tools the agent can use
memory	bool | MemoryConfig	Enable conversation memory
planning	bool | PlanningConfig	Enable pre-execution planning
reflection	bool | ReflectionConfig	Enable self-review of responses
caching	bool | CachingConfig	Enable response caching
output	str | OutputConfig	Control response verbosity
execution	str | ExecutionConfig	Control iteration and budget limits

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Common Patterns

Pattern 1 — Simple Q&A agent

from praisonaiagents import Agent

agent = Agent(instructions="Answer questions clearly and accurately.")
response = agent.start("What is the difference between RAM and storage?")
print(response)

Pattern 2 — Specialized domain agent

from praisonaiagents import Agent

agent = Agent(
    name="LegalAnalyst",
    instructions="""You are a legal document analyst.
    Identify key clauses, risks, and obligations.
    Always recommend consulting a qualified attorney.""",
    llm="gpt-4o",
)
agent.start("Analyze this software license agreement for unusual terms.")

Pattern 3 — Fully-featured production agent

from praisonaiagents import Agent, MemoryConfig, ExecutionConfig

agent = Agent(
    name="ProductionAgent",
    instructions="You are a reliable production assistant.",
    memory=MemoryConfig(backend="redis", user_id="user_123"),
    execution=ExecutionConfig(max_iter=30, max_budget=0.25),
    caching=True,
)
response = agent.start("Summarize our Q3 performance metrics.")
print(response)

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Best Practices

Write clear instructions

The instructions parameter is your agent’s personality and purpose. Be specific: “You are a customer support agent for Acme Corp. Answer questions about orders, returns, and products. Escalate billing disputes to a human.” Clear instructions outperform vague ones every time.

Add features progressively

Start with just Agent(instructions="...") and add features (memory, planning, reflection) one at a time. Each feature has a cost — only add what your use case actually needs.

Use the right model

gpt-4o-mini handles simple Q&A and extraction cheaply and fast. Use gpt-4o or claude-3-5-sonnet for complex reasoning, code generation, and nuanced writing tasks.

Name agents in multi-agent setups

Always set name when using multiple agents. Names appear in logs, hooks callbacks, and help diagnose which agent produced which output in complex workflows.

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Related

Tools — add capabilities like web search and code execution

Memory — give agents persistent memory across sessions