Iterative Problem Solver

The IterativeProblemSolver is a simple agentic loop that repeatedly attempts to solve a user's request using available tools until it either succeeds, fails, or reaches a maximum number of iterations.

How It Works

flowchart TD
    A[User Request] --> B[Initialize Agent]
    B --> C[Set up Chat Session]
    C --> D[Start Loop]
    D --> E[Execute Step]
    E --> F{Status?}
    F -- Continue --> G{Has Remaining<br>Iterations?}
    F -- Fail --> H
    G -- Yes --> D
    G -- No --> H[Generate Summary<br>Task Failed]
    F -- Complete --> I[Generate Summary<br>Task Complete]
    H --> J[Return Result]
    I --> J

At each step, the agent attempts to make progress on the user's request using the available tools. It continues until one of three conditions is met: 1. It successfully completes the task (responds with "DONE") 2. It fails to complete the task (responds with "FAIL") 3. It reaches the maximum number of allowed iterations

Basic Usage

from mojentic.agents import IterativeProblemSolver
from mojentic.llm import LLMBroker
from mojentic.llm.tools.date_resolver import ResolveDateTool

# Initialize an LLM broker
llm = LLMBroker(model="your-preferred-model")

# Define a user request
user_request = "What's the date next Tuesday?"

# Create the problem solver with necessary tools
solver = IterativeProblemSolver(
    llm=llm,
    available_tools=[ResolveDateTool()],
    max_iterations=3  # Maximum number of iterations
)

# Run the solver and get the final result
result = solver.solve(user_request)
print(result)