axon-framework 0.6.0a0

Build Agentic AI Systems in minutes. Now with Multi-Agent Swarms! 🐝

Axon 🧠

The "FastAPI" for AI Agents

Build typed, production-ready AI agents in minutes, not hours.

Quick Start • Features • Examples • Documentation

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🎯 Why Axon?

The Problem: Building AI agents today feels like configuring Apache in 1999.

• LangChain? Powerful but complex (AbstractFactories everywhere).
• Manual OpenAI API? Too much boilerplate for tools and state management.

The Solution: Axon makes agent development as simple as FastAPI makes API development.

from axon import Agent

agent = Agent("ResearchBot")

@agent.tool
def search_web(query: str) -> str:
    """Search the internet."""
    return duckduckgo_search(query)

response = agent.ask("Find the latest AI news")

No chains. No graphs. No configurations. Just Python.

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⚡ Quick Start

Installation

pip install axon-framework

Create Your First Agent (60 seconds)

1. Set up environment:

echo "OPENAI_API_KEY=sk-your-key-here" > .env

2. Create app.py:

from axon import Agent

bot = Agent("Assistant", system="You are a helpful AI assistant.")

@bot.tool
def calculate(a: int, b: int) -> int:
    """Add two numbers."""
    return a + b

print(bot.ask("What is 15 + 27?"))

3. Run:

python app.py

That's it! 🎉

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🛠 Features

🚀 Zero Boilerplate

• Auto-schema generation: Type hints → OpenAI function schemas
• Decorator-based tools: Just use @agent.tool
• .env support: No manual environment handling

🎨 Beautiful DX

• Rich console logging: See agent reasoning in real-time
• Helpful errors: No cryptic stack traces
• 5-minute promise: From zero to working agent in 5 minutes

🔌 Production Tools (v0.4 🆕)

Built-in batteries for real-world apps:

• Web Search (web_search): DuckDuckGo integration, no API key
• File System (read_file, write_file): Safe file operations
• HTTP/API (http_get, http_post): REST API integration
• Database (query_db, create_table): SQLite queries & analytics

🧪 Type-Safe & Production-Ready

• Streaming Responses (v0.4): Real-time text generation
• Structured Outputs (v0.3): Return typed Pydantic models instead of strings
• History Management (v0.3): Auto-truncates conversation to stay within token limits
• Context/State (v0.3): Share data between tools without manual passing
• Persistent Memory (v0.5 🆕): Agents remember conversations across sessions
• Error Retry (v0.5 🆕): Automatic retry with exponential backoff
• Full type inference support

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📚 Examples

Basic Math Agent

from axon import Agent

agent = Agent("MathBot")

@agent.tool
def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b

print(agent.ask("What's 12 times 8?"))
# Agent calls multiply(12, 8) → "The result is 96"

Structured Output (v0.3 🆕)

from axon import Agent
from pydantic import BaseModel

class Email(BaseModel):
    address: str
    confidence: float

agent = Agent("EmailBot")
result = agent.ask(
    "Extract email from: contact me at [e-mail]",
    response_model=Email
)
print(result.address)  # "[e-mail]"
print(result.confidence)  # 0.95

Context/State Management (v0.3 🆕)

from axon import Agent, Context

agent = Agent("DataBot")

@agent.tool
def fetch_data(source: str, ctx: Context) -> str:
    """Fetch data and store in context."""
    data = get_data(source)
    ctx.set("data", data)  # Share with other tools
    return "Data fetched"

@agent.tool
def analyze(ctx: Context) -> str:
    """Analyze data from context."""
    data = ctx.get("data")  # Access shared state
    return f"Analysis: {analyze(data)}"

# Agent automatically passes data between tools!
agent.ask("Fetch data from database and analyze it")

Web Research Agent

from axon import Agent
from axon_tools import web_search, write_file

agent = Agent("Researcher")
agent.tool(web_search)
agent.tool(write_file)

agent.ask("Search for Python AI frameworks and save a summary to report.txt")
# ✅ Searches web, writes formatted report

More examples: See examples/ directory

Persistent Memory (v0.5 🆕)

from axon import Agent

# Enable memory for agent
agent = Agent("MemoryBot", memory="./memory.db")

# Session 1
agent.ask("My name is Alice and I love Python")

# Session 2 (new instance, same memory!)
agent2 = Agent("MemoryBot", memory="./memory.db")
agent2.ask("What's my name?")
# → "Your name is Alice" ✨ Remembers!

Error Retry (v0.5 🆕)

from axon import Agent, retry

agent = Agent("ReliableBot")

@agent.tool
@retry(max_attempts=3, backoff=2.0)
def flaky_api(url: str) -> str:
    """Call unreliable API with auto-retry."""
    return requests.get(url).json()

# Automatically retries on failure with exponential backoff!

More examples: See examples/ directory

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🎯 Advanced Features

History Management (v0.3)

Control conversation token usage to prevent context overflow:

agent = Agent(
    "LongConversationBot",
    max_history_tokens=4000  # Auto-truncates when exceeded
)

# After many questions, old messages are automatically removed
# System message is always preserved

Custom Configuration

agent = Agent(
    name="CustomBot",
    system="You are a helpful assistant specialized in...",
    model="gpt-4o",  # or "gpt-4o-mini"
    max_history_tokens=2000
)

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🔌 Built-in Tools

Web Search

from axon_tools import web_search

@agent.tool
def search(query: str) -> str:
    return web_search(query, max_results=5)

• Uses DuckDuckGo (no API key!)
• Returns formatted results with URLs

File Operations

from axon_tools import read_file, write_file

@agent.tool
def save_data(filename: str, content: str) -> str:
    return write_file(filename, content)

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📦 Project Structure

axon/
├── axon/              # Core framework
│   ├── core.py        # Agent class
│   ├── types.py       # Type definitions
│   └── utils.py       # Logging & utilities
├── axon_tools/        # Built-in plugins
│   ├── web.py         # Web search
│   └── filesystem.py  # File operations
├── examples/          # Demo scripts
│   ├── hello_world.py
│   ├── web_researcher.py
│   └── simple_file_demo.py
└── tests/             # Test suite

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🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Quick contributions:

• 🐛 Report bugs via Issues
• 💡 Request features via Discussions
• 🔧 Submit PRs for fixes or new tools

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🗺️ Roadmap

• v0.1: Core Agent + Tool decorator
• v0.2: Plugin ecosystem (Web Search, File System)
• v0.3: Production essentials (Structured Outputs, History Management, Context/State)
• v0.4: Essential tools (Streaming, HTTP/API, Database)
• v0.5: Intelligence & Memory (Persistent Memory, Error Retry, Plugin System) ✨ CURRENT
• v0.6: Multi-agent collaboration
• v1.0: Axon Studio (Visual debugging UI) + Enterprise features

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📖 Documentation

🧠 Intelligence & Memory (v0.5 🆕)

• Persistent Memory: Chat history saved to SQLite (memory="path/to.db")
• Auto-Retries: @retry decorator for flaky tools
• Context Awareness: Agents remember user details across sessions

🐝 Multi-Agent Swarms (v0.6 Alpha 🆕)

• Swarm Orchestration: Manage teams of specialized agents
• Intelligent Handoffs: Agents automatically transfer tasks to specialists
• Shared Context: Memory shared across the entire swarm

from axon import Agent, Swarm, Handoff

triage = Agent("Triage")
billing = Agent("Billing")

@triage.tool
def transfer_to_billing(reason: str) -> Handoff:
    return Handoff(target_agent="Billing", context=reason)

swarm = Swarm([triage, billing])
swarm.run(triage, "I need a refund")

Core Concepts

Agent

The main orchestrator. Manages LLM calls, tool execution, and conversation history.

agent = Agent(
    name="MyBot",
    system="You are...",         # System prompt
    model="gpt-4o",               # OpenAI model
    max_history_tokens=4000       # Token limit (v0.3)
)

Tools

Functions that agents can call. Automatically registered with @agent.tool:

@agent.tool
def my_function(param: str) -> str:
    """This docstring becomes the tool description."""
    return f"Result: {param}"

Requirements:

• Must have type hints
• Must have a docstring
• Return value should be string or serializable

Structured Outputs (v0.3)

Return typed Pydantic models instead of strings:

from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int
    email: str

result = agent.ask(
    "Extract person info from: John Doe, 30, [e-mail]",
    response_model=Person
)
# result is a Person instance with validated fields
print(result.name)  # "John Doe"

Context/State (v0.3)

Share data between tools:

from axon import Context

@agent.tool
def step_one(data: str, ctx: Context) -> str:
    """Process data and store result."""
    result = process(data)
    ctx.set("result", result)  # Store for next tool
    return "Done"

@agent.tool
def step_two(ctx: Context) -> str:
    """Use data from previous tool."""
    result = ctx.get("result")  # Retrieve
    return f"Final: {result}"

Context API:

• ctx.set(key, value) - Store value
• ctx.get(key, default=None) - Retrieve value
• ctx.has(key) - Check if key exists
• ctx.delete(key) - Remove key
• ctx.clear() - Clear all data
• ctx.keys() - Get all keys

History Management (v0.3)

Automatic conversation truncation:

agent = Agent("Bot", max_history_tokens=2000)

# When token count exceeds limit:
# - System message is preserved
# - Oldest messages are removed
# - Automatic truncation on each ask()

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⚖️ License

MIT License - see LICENSE for details

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🌟 Star History

If Axon saves you time, give us a star! ⭐

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🙏 Acknowledgments

Inspired by:

• FastAPI: For proving that DX matters
• LangChain: For pioneering agent frameworks
• OpenAI: For making this possible

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Built with ❤️ by developers, for developers

Report Bug • Request Feature • ⭐ Star

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