Agent support

[wassemgtk]

Key Features:

Base Agent Infrastructure

Introduces a flexible BaseAgent class that all agents inherit from Implements tool management and conversation history tracking Provides abstract methods for custom agent implementations

Built-in Agents:

a) ReAct Agent

Implements the Reasoning + Acting pattern
Follows a structured process: Reason → Act → Observe → Respond Excels at complex tasks requiring tool use and step-by-step thinking Maximum 5 steps to prevent infinite loops

b) Chain of Thought (CoT) Agent

Specializes in breaking down complex problems into logical steps Shows explicit reasoning process for each step
Focuses on transparent decision-making
Ideal for math, logic, and analysis tasks

c) Tool-Using Agent

Optimized for scenarios requiring multiple tool interactions Analyzes available tools and plans usage sequence
Executes tools and processes results
Built-in error handling for missing tools

Custom Agent Framework

Allows creation of specialized agents with custom behaviors Configurable system prompts and extraction patterns Adjustable maximum steps and tool usage
Flexible pattern matching for different response formats

Shared Components:

Tool class for defining and executing custom tools AgentOutput class for standardized response formatting Conversation history tracking
Consistent error handling

from writer.agents import ReActAgent, Tool

# Create a tool
calculator = Tool(
    name="calculator",
    description="Performs basic math operations",
    func=lambda x, y, op: eval(f"{x}{op}{y}")
)

# Initialize agent
agent = ReActAgent(tools=[calculator])

# Run agent
result = agent.run("What is 25 * 48?")

# Access structured output
print(result.response)      # Final answer
print(result.reasoning)     # List of reasoning steps
print(result.actions)       # List of tools used

[ramedina86]

@mmikita95 Mikita, can you please take a look and see how this relates to AI module? I'm seeing clear duplication in things like Tool