This repository contains practical implementations of core agentic AI design patterns using LangGraph. These patterns represent the shift from simple prompting to iterative, stateful AI workflows.
- Concept: An agent generates a response, critiques it, and then refines the output based on that critique.
- Workflow:
Generator -> Critic -> Generator (refined) - Example: A code generation assistant that iterates on a Python function to improve efficiency and readability.
- Graph:
- Concept: An agent uses external tools in a Thought-Action-Observation loop to gather data and solve complex problems.
- Workflow:
Agent -> Tools -> Agent (final answer) - Example: A research assistant that uses mock weather and calculation tools to answer specific queries.
- Graph:
3. Planning
- Concept: A "Planner" agent decomposes a high-level task into actionable sub-tasks, which are then sequentially executed.
- Workflow:
Planner -> Executor (loop) -> End - Example: A marketing strategist that breaks down a goal into specific, executable steps.
- Graph:
- Concept: Multiple specialized agents (e.g., a "Writer" and an "Editor") work together in a coordinated workflow.
- Workflow:
Writer -> Editor -> End (or loop back) - Example: A content creation team producing a blog post through iterative draft-and-review cycles.
- Graph:
- Concept: A "Router" agent analyzes incoming requests and directs them to the most appropriate specialized agent.
- Workflow:
Router -> Technical/Billing/General -> End - Example: A customer support system that routes inquiries to Technical, Billing, or General Inquiry agents.
- Graph:
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Environment Setup: Create a
.envfile in the root directory and add your OpenAI API key:OPENAI_API_KEY=your_api_key_here
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Install Dependencies (using uv):
uv sync
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Run Examples: Each pattern can be executed directly as a script:
uv run patterns/01_reflection/example.py
- Python 3.13+
- LangGraph
- LangChain OpenAI
- Grandalf (for ASCII graph visualization)