Overview
The chat() method sends a message and returns the final text response. It’s a convenience wrapper around stream() that returns only the last AI text from messages-tuple events.
response = client.chat("What is 7 * 8?", thread_id="my-thread")
print(response) # "56"
Method Signature
def chat(
self,
message: str,
*,
thread_id: str | None = None,
**kwargs
) -> str
Parameters
User message text to send to the agent.
Thread ID for conversation context. Auto-generated if None.Without a checkpointer at initialization, thread_id is only used for file isolation (uploads/artifacts), not conversation history.
model_name
str
default:"client default"
Override the model for this specific call.
thinking_enabled
bool
default:"client default"
Override thinking mode for this call.
plan_mode
bool
default:"client default"
Override plan mode for this call.
subagent_enabled
bool
default:"client default"
Override subagent delegation for this call.
Maximum number of agent steps per turn.
Return Value
The last AI message text, or empty string if no response was generated.
Examples
Basic Usage
from src.client import DeerFlowClient
client = DeerFlowClient()
response = client.chat("hello")
print(response)
With Thread ID
response = client.chat(
"Analyze this data",
thread_id="analysis-session-1"
)
Override Model
response = client.chat(
"Complex reasoning task",
model_name="claude-3-opus",
thinking_enabled=True
)
Multi-Turn Conversation
from langgraph.checkpoint.memory import MemorySaver
# Initialize with checkpointer for conversation history
client = DeerFlowClient(checkpointer=MemorySaver())
thread_id = "conversation-1"
# First turn
response1 = client.chat("My name is Alice", thread_id=thread_id)
print(response1) # "Nice to meet you, Alice!"
# Second turn - context preserved
response2 = client.chat("What's my name?", thread_id=thread_id)
print(response2) # "Your name is Alice."
Behavior Notes
If the agent emits multiple text segments in one turn, intermediate segments are discarded. Use stream() directly to capture all events.
Example: Multiple AI Messages
If the agent produces:
- “Let me think about this…”
- “I’ll use a tool to help.”
- “The final answer is 42.”
The chat() method will return only: “The final answer is 42.”
To see all intermediate messages, use stream() instead:
for event in client.stream("What is the meaning of life?"):
if event.type == "messages-tuple" and event.data.get("type") == "ai":
print(event.data.get("content"))
Error Handling
try:
response = client.chat("Hello", thread_id="test")
except Exception as e:
print(f"Error: {e}")
- Configuration errors (missing API keys, invalid model names)
- Network errors (when using cloud models)
- Tool execution errors (file not found, permission denied)
Testing
From the test suite:
def test_returns_last_message(self, client):
"""chat() returns the last AI message text."""
ai1 = AIMessage(content="thinking...", id="ai-1")
ai2 = AIMessage(content="final answer", id="ai-2")
chunks = [
{"messages": [HumanMessage(content="q", id="h-1"), ai1]},
{"messages": [HumanMessage(content="q", id="h-1"), ai1, ai2]},
]
agent = _make_agent_mock(chunks)
with (
patch.object(client, "_ensure_agent"),
patch.object(client, "_agent", agent),
):
result = client.chat("q", thread_id="t6")
assert result == "final answer"
See Also