Your First Agent

This page builds a working agent backed by a real language model. You will write the graph, run it in Python, and see a response.

What you need

• 10xGraph installed: pip install 10xgraph
• A language model API key (this example uses Google Gemini)

Set your API key:

export GOOGLE_API_KEY=your-api-key

The graph

Create first_agent.py:

from tenxgraph.core.graph import Agent, StateGraph, ToolNode
from tenxgraph.core.state import AgentState
from tenxgraph.utils import END

# Create the agent node backed by a language model
agent = Agent(
    model="google/gemini-2.5-flash",
    system_prompt=[
        {
            "role": "system",
            "content": "You are a helpful assistant. Answer questions clearly and concisely.",
        }
    ],
)

# Build the graph
graph = StateGraph(AgentState)
graph.add_node("assistant", agent)
graph.set_entry_point("assistant")
graph.add_edge("assistant", END)

app = graph.compile()

Run it

Add the invocation code at the bottom of first_agent.py:

from tenxgraph.core.state import Message

result = app.invoke(
    {"messages": [Message.text_message("What is the capital of France?")]},
    config={"thread_id": "beginner-demo-1"},
)

print(result["messages"][-1].text())

Run the file:

python first_agent.py

Expected output (the exact words will vary):

The capital of France is Paris.

What happened

sequenceDiagram
  participant Script as first_agent.py
  participant App as compiled app
  participant Agent as Agent node
  participant LLM as Language model

  Script->>App: invoke(messages)
  App->>Agent: run with AgentState
  Agent->>LLM: send message
  LLM-->>Agent: assistant reply
  Agent-->>App: new Message
  App-->>Script: result messages

1. app.invoke adds your message to AgentState.context.
2. The graph routes to the assistant node.
3. Agent sends the conversation to the language model.
4. The model returns a reply, which becomes an assistant Message.
5. The graph reaches END and returns the updated state.

The thread_id in config groups this conversation. Every call with the same thread_id will eventually share history once you add a checkpointer.

Key imports

from tenxgraph.core.graph import Agent, StateGraph
from tenxgraph.core.state import AgentState, Message
from tenxgraph.utils import END

What you learned

• Agent wraps a language model as a graph node.
• StateGraph wires the node into a runnable app.
• app.invoke runs the graph and returns updated state.
• A thread_id in config identifies the conversation.

Next step

Give the agent something it can do — add a tool.