Documentation Index
Fetch the complete documentation index at: https://docs.definable.ai/llms.txt
Use this file to discover all available pages before exploring further.
In this guide, you will build an agent that:
- Connects to an LLM (OpenAI GPT-4o)
- Uses a custom tool to take actions
- Returns structured results
All in about 15 lines of code.
1. Define the Agent
Save the following code as my_agent.py:
from definable.agent import Agent
from definable.tool.decorator import tool
@tool
def get_weather(city: str) -> str:
"""Get the current weather for a city."""
return f"The weather in {city} is sunny, 72F."
agent = Agent(
model="gpt-4o",
tools=[get_weather],
instructions="You are a helpful weather assistant.",
)
output = agent.run("What's the weather in San Francisco and Tokyo?")
print(output.content)
You now have:
- An agent that reasons about which tools to call
- Automatic parallel tool execution
- A typed
RunOutput with content, messages, and metadata
2. Run Your Agent
Set up your environment
python3 -m venv .venv
source .venv/bin/activate
Export your OpenAI API key
export OPENAI_API_KEY=sk-***
Run your agent
You should see the agent call get_weather for both cities and return a combined response. 3. Add Memory
Make your agent remember past conversations:
from definable.agent import Agent
from definable.memory import Memory, SQLiteStore
from definable.tool.decorator import tool
@tool
def get_weather(city: str) -> str:
"""Get the current weather for a city."""
return f"The weather in {city} is sunny, 72F."
agent = Agent(
model="gpt-4o",
tools=[get_weather],
instructions="You are a helpful weather assistant.",
memory=Memory(store=SQLiteStore("./memory.db")),
)
# First conversation
output = agent.run("My name is Alice. What's the weather in Paris?", user_id="alice")
print(output.content)
# Later — the agent remembers
output = agent.run("What city did I ask about?", user_id="alice")
print(output.content) # "You asked about Paris."
4. Add Knowledge
Ground your agent in documents:
from definable.agent import Agent
from definable.knowledge import Knowledge
from definable.vectordb import InMemoryVectorDB
knowledge = Knowledge(vector_db=InMemoryVectorDB())
knowledge.add("Definable supports 10 LLM providers including OpenAI, Anthropic, and Google.")
knowledge.add("Agents can use tools, knowledge, memory, and middleware.")
agent = Agent(
model="gpt-4o",
knowledge=knowledge,
instructions="Answer questions using the provided knowledge base.",
)
output = agent.run("How many LLM providers does Definable support?")
print(output.content)
5. Stream Responses
Stream tokens as they are generated:
for event in agent.run_stream("Tell me a story about AI agents."):
if event.event == "RunContent" and event.content:
print(event.content, end="", flush=True)
What You Just Built
In a few lines of code, you built:
- An agent with tool calling and parallel execution
- Persistent memory across conversations
- Knowledge-grounded responses via RAG
- Real-time streaming output
This same architecture scales to multi-agent teams, structured workflows, and production deployments.
Next Steps
| Task | Guide |
|---|
| Explore agent configuration | Agent configuration |
| Add reasoning capabilities | Thinking |
| Deploy to messaging platforms | Interfaces |
| Coordinate multiple agents | Teams |
| Build structured pipelines | Workflows |
| Browse all examples | Examples |
