How to Use any-agent: Build AI Agents in Python Easily

At its core, any-agent is a Python AI agent framework that simplifies the design of LLM-driven agents. Unlike bulkier systems that require multiple integrations and YAML files, any-agent is Pythonic and modular out of the box. This means you can go from concept to prototype without battling complex setup.

The library supports:

• Multiple LLM providers like OpenAI, Anthropic Claude , Hugging Face, and Google Gemini agents.
• Tool-calling agents that can use built-in or custom plugins (like a web browser, calculator, or database).
• Memory-enabled agents with persistent context, letting your agent remember previous steps or inputs.
• Simple chaining and even multi-agent collaboration using built-in coordination modules.
• Seamless integration with LangGraph, CopilotKit, and other orchestration layers for advanced flows.

If you're looking for a Python-based AI agent builder that feels lightweight, flexible, and production-aware — any-agent is built for you.

Always use a virtual environment to avoid package conflicts:

python -m venv venv

Activate it:

Windows:

venv\Scripts\activate

macOS/Linux:

source venv/bin/activate

Why? Because isolation prevents breaking your global Python environment — and it makes deployments cleaner later.

A well-structured project helps scale from prototype to production. Start with the Webisoft-style setup, using just three Python files:

• main.py: Entry point of your agent
• actions.py: External tools (e.g., web scraper, math, API functions)
• prompts.py: Your system prompt using the ReAct loop

This simplicity makes it easy to manage and expand as you add new tools or logic.

Agents need internal structure to simulate thinking. That’s where ReAct (Reasoning + Action) comes in. Here's a tested prompt template:

system_prompt = """
You run in a loop of Thought, Action,
PAUSE, Action_Response. At the end of the loop, you
output an Answer. Use Thought to understand the question
you have been asked. Use Action to run one of the
actions available to you — then return PAUSE.
Action_Response will be the result of running those
actions. ... Answer: The response time for google.com is
0.3 seconds.
"""

With this loop structure, you create agentic workflow automation where the agent reflects, selects a tool, waits for results, and proceeds logically.

Let’s build a summarization agent using the any-agent library.

Step 1: Define the Agent

from any_agent import Agent

agent = Agent({
   name="Summarizer",
   goal="Summarize long-form content into concise bullet points"
})

Here, `goal` tells the agent what it should accomplish. This becomes part of its internal logic and prompt design.

Step 2: Add a Language Model

from any_agent.llms import OpenAI

agent.llm = OpenAI(api_key="your-api-key", model="gpt-3.5-turbo")

You can easily switch to `gpt-4o` if your use case requires better performance or nuanced output.

Run the Agent (Basic Prompt Execution)

result = agent.run("Summarize this article: https://example.com/ai-news")
print(result)

In under 10 lines of code, you’ve created a Python agent that calls OpenAI models and processes content.

This forms the basis for rapid AI prototyping in real-world settings.

Let your agent remember what was said in previous interactions.

from any_agent.memory import SimpleMemory

agent.memory = SimpleMemory()

Want database-backed memory?

agent.memory = SimpleMemory({
   max_messages=10,
   db_connection="postgresql://user:pass@localhost/your_db"
})

This makes your agent capable of streaming agent outputs, holding multi-step conversations, or accumulating historical context.

Extend your agent's capabilities using tool modules:

from any_agent.tools import WebBrowser, Calculator

agent.add_tool(WebBrowser())
agent.add_tool(Calculator())

Your agent now has Browse and computation abilities — key features in tool-calling agents.

Imagine an agent that checks server response times:

get_response_time: google.com

Returns:

Action_Response: 0.3

Multiple agents can work in tandem using the Coordinator class.

from any_agent.agents import Coordinator

coordinator = Coordinator([agent1, agent2])
coordinator.run("Collaborate to write a blog post on AI agents.")

Visualize and debug your agent flow using LangGraph:

langgraph dev --host localhost --port 8000

Visit:

http://localhost:8000

Performance and reliability matter — here’s how to enhance both:

• Turn on debug logs:
  agent.debug = True

• Wrap executions in try/except blocks to catch errors.
• Keep prompts short — avoid hitting LLM token limits.
• Use async tools when possible for faster response times.
• Avoid infinite loops — use conditionals or caps.

Agents often deal with external APIs or user input — secure them like production apps:

• Never hardcode secrets — use .env files.
• Validate and sanitize all tool inputs and outputs.
• Monitor agent behavior to detect rogue loops or spam.
• If using shell tools or file access, sandbox their execution.

• Always define clear goals for each agent.
• Separate logic from prompts and memory.
• Reuse components (tools, memory, prompt templates) across projects.
• Design agents to fail gracefully — fallback options matter.
• Document each agent’s use case, toolset, and LLM backend.

These principles will help you go from solo experiments to scalable agent deployment in team environments.