For new applications, we recommend event streaming—the typed-projection API introduced in Deep Agents v0.6. Event streaming gives you separate iterators per projection (subagents, messages, tool calls, values) so you can consume them independently instead of branching on
stream_mode chunks.- Stream subagent progress—track each subagent’s execution as it runs in parallel.
- Stream LLM tokens—stream tokens from the main agent and each subagent.
- Stream tool calls—see tool calls and results from within subagent execution.
- Stream custom updates—emit user-defined signals from inside subagent nodes.
Enable subgraph streaming
Deep Agents use LangGraph’s subgraph streaming to surface events from subagent execution. To receive subagent events, enablestream_subgraphs when streaming.
from deepagents import create_deep_agent
agent = create_deep_agent(
model="google_genai:gemini-3.5-flash",
system_prompt="You are a helpful research assistant",
subagents=[
{
"name": "researcher",
"description": "Researches a topic in depth",
"system_prompt": "You are a thorough researcher.",
},
],
)
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Research quantum computing advances"}]},
stream_mode="updates",
subgraphs=True,
version="v2",
):
if chunk["type"] == "updates":
if chunk["ns"]:
# Subagent event - namespace identifies the source
print(f"[subagent: {chunk['ns']}]")
else:
# Main agent event
print("[main agent]")
print(chunk["data"])
from deepagents import create_deep_agent
agent = create_deep_agent(
model="openai:gpt-5.5",
system_prompt="You are a helpful research assistant",
subagents=[
{
"name": "researcher",
"description": "Researches a topic in depth",
"system_prompt": "You are a thorough researcher.",
},
],
)
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Research quantum computing advances"}]},
stream_mode="updates",
subgraphs=True,
version="v2",
):
if chunk["type"] == "updates":
if chunk["ns"]:
# Subagent event - namespace identifies the source
print(f"[subagent: {chunk['ns']}]")
else:
# Main agent event
print("[main agent]")
print(chunk["data"])
from deepagents import create_deep_agent
agent = create_deep_agent(
model="anthropic:claude-sonnet-4-6",
system_prompt="You are a helpful research assistant",
subagents=[
{
"name": "researcher",
"description": "Researches a topic in depth",
"system_prompt": "You are a thorough researcher.",
},
],
)
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Research quantum computing advances"}]},
stream_mode="updates",
subgraphs=True,
version="v2",
):
if chunk["type"] == "updates":
if chunk["ns"]:
# Subagent event - namespace identifies the source
print(f"[subagent: {chunk['ns']}]")
else:
# Main agent event
print("[main agent]")
print(chunk["data"])
from deepagents import create_deep_agent
agent = create_deep_agent(
model="openrouter:z-ai/glm-5.2",
system_prompt="You are a helpful research assistant",
subagents=[
{
"name": "researcher",
"description": "Researches a topic in depth",
"system_prompt": "You are a thorough researcher.",
},
],
)
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Research quantum computing advances"}]},
stream_mode="updates",
subgraphs=True,
version="v2",
):
if chunk["type"] == "updates":
if chunk["ns"]:
# Subagent event - namespace identifies the source
print(f"[subagent: {chunk['ns']}]")
else:
# Main agent event
print("[main agent]")
print(chunk["data"])
from deepagents import create_deep_agent
agent = create_deep_agent(
model="fireworks:accounts/fireworks/models/glm-5p2",
system_prompt="You are a helpful research assistant",
subagents=[
{
"name": "researcher",
"description": "Researches a topic in depth",
"system_prompt": "You are a thorough researcher.",
},
],
)
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Research quantum computing advances"}]},
stream_mode="updates",
subgraphs=True,
version="v2",
):
if chunk["type"] == "updates":
if chunk["ns"]:
# Subagent event - namespace identifies the source
print(f"[subagent: {chunk['ns']}]")
else:
# Main agent event
print("[main agent]")
print(chunk["data"])
from deepagents import create_deep_agent
agent = create_deep_agent(
model="baseten:zai-org/GLM-5.2",
system_prompt="You are a helpful research assistant",
subagents=[
{
"name": "researcher",
"description": "Researches a topic in depth",
"system_prompt": "You are a thorough researcher.",
},
],
)
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Research quantum computing advances"}]},
stream_mode="updates",
subgraphs=True,
version="v2",
):
if chunk["type"] == "updates":
if chunk["ns"]:
# Subagent event - namespace identifies the source
print(f"[subagent: {chunk['ns']}]")
else:
# Main agent event
print("[main agent]")
print(chunk["data"])
from deepagents import create_deep_agent
agent = create_deep_agent(
model="ollama:north-mini-code-1.0",
system_prompt="You are a helpful research assistant",
subagents=[
{
"name": "researcher",
"description": "Researches a topic in depth",
"system_prompt": "You are a thorough researcher.",
},
],
)
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Research quantum computing advances"}]},
stream_mode="updates",
subgraphs=True,
version="v2",
):
if chunk["type"] == "updates":
if chunk["ns"]:
# Subagent event - namespace identifies the source
print(f"[subagent: {chunk['ns']}]")
else:
# Main agent event
print("[main agent]")
print(chunk["data"])
Namespaces
Whensubgraphs is enabled, each streaming event includes a namespace that identifies which agent produced it. The namespace is a path of node names and task IDs that represents the agent hierarchy.
| Namespace | Source |
|---|---|
() (empty) | Main agent |
("tools:abc123",) | A subagent spawned by the main agent’s task tool call abc123 |
("tools:abc123", "model_request:def456") | The model request node inside a subagent |
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Plan my vacation"}]},
stream_mode="updates",
subgraphs=True,
version="v2",
):
if chunk["type"] == "updates":
# Check if this event came from a subagent
is_subagent = any(
segment.startswith("tools:") for segment in chunk["ns"]
)
if is_subagent:
# Extract the tool call ID from the namespace
tool_call_id = next(
s.split(":")[1] for s in chunk["ns"] if s.startswith("tools:")
)
print(f"Subagent {tool_call_id}: {chunk['data']}")
else:
print(f"Main agent: {chunk['data']}")
Subagent progress
Usestream_mode="updates" to track subagent progress as each step completes. This is useful for showing which subagents are active and what work they’ve completed.
from deepagents import create_deep_agent
agent = create_deep_agent(
model="google_genai:gemini-3.5-flash",
system_prompt=(
"You are a project coordinator with no research knowledge. "
"For every user request, you must call the task() tool with "
"subagent_type set to researcher. Never answer research questions yourself. "
"Keep your final response to one sentence."
),
subagents=[
{
"name": "researcher",
"description": "Researches topics thoroughly",
"system_prompt": (
"You are a thorough researcher. Research the given topic "
"and provide a concise summary in 2-3 sentences."
),
},
],
)
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Write a short summary about AI safety"}]},
stream_mode="updates",
subgraphs=True,
version="v2",
):
if chunk["type"] == "updates":
# Main agent updates (empty namespace)
if not chunk["ns"]:
for node_name, data in chunk["data"].items():
if node_name == "tools":
# Subagent results returned to main agent
for msg in data.get("messages", []):
if msg.type == "tool":
print(f"\nSubagent complete: {msg.name}")
print(f" Result: {str(msg.content)[:200]}...")
else:
print(f"[main agent] step: {node_name}")
# Subagent updates (non-empty namespace)
else:
for node_name, data in chunk["data"].items():
print(f" [{chunk['ns'][0]}] step: {node_name}")
from deepagents import create_deep_agent
agent = create_deep_agent(
model="openai:gpt-5.5",
system_prompt=(
"You are a project coordinator with no research knowledge. "
"For every user request, you must call the task() tool with "
"subagent_type set to researcher. Never answer research questions yourself. "
"Keep your final response to one sentence."
),
subagents=[
{
"name": "researcher",
"description": "Researches topics thoroughly",
"system_prompt": (
"You are a thorough researcher. Research the given topic "
"and provide a concise summary in 2-3 sentences."
),
},
],
)
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Write a short summary about AI safety"}]},
stream_mode="updates",
subgraphs=True,
version="v2",
):
if chunk["type"] == "updates":
# Main agent updates (empty namespace)
if not chunk["ns"]:
for node_name, data in chunk["data"].items():
if node_name == "tools":
# Subagent results returned to main agent
for msg in data.get("messages", []):
if msg.type == "tool":
print(f"\nSubagent complete: {msg.name}")
print(f" Result: {str(msg.content)[:200]}...")
else:
print(f"[main agent] step: {node_name}")
# Subagent updates (non-empty namespace)
else:
for node_name, data in chunk["data"].items():
print(f" [{chunk['ns'][0]}] step: {node_name}")
from deepagents import create_deep_agent
agent = create_deep_agent(
model="anthropic:claude-sonnet-4-6",
system_prompt=(
"You are a project coordinator with no research knowledge. "
"For every user request, you must call the task() tool with "
"subagent_type set to researcher. Never answer research questions yourself. "
"Keep your final response to one sentence."
),
subagents=[
{
"name": "researcher",
"description": "Researches topics thoroughly",
"system_prompt": (
"You are a thorough researcher. Research the given topic "
"and provide a concise summary in 2-3 sentences."
),
},
],
)
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Write a short summary about AI safety"}]},
stream_mode="updates",
subgraphs=True,
version="v2",
):
if chunk["type"] == "updates":
# Main agent updates (empty namespace)
if not chunk["ns"]:
for node_name, data in chunk["data"].items():
if node_name == "tools":
# Subagent results returned to main agent
for msg in data.get("messages", []):
if msg.type == "tool":
print(f"\nSubagent complete: {msg.name}")
print(f" Result: {str(msg.content)[:200]}...")
else:
print(f"[main agent] step: {node_name}")
# Subagent updates (non-empty namespace)
else:
for node_name, data in chunk["data"].items():
print(f" [{chunk['ns'][0]}] step: {node_name}")
from deepagents import create_deep_agent
agent = create_deep_agent(
model="openrouter:z-ai/glm-5.2",
system_prompt=(
"You are a project coordinator with no research knowledge. "
"For every user request, you must call the task() tool with "
"subagent_type set to researcher. Never answer research questions yourself. "
"Keep your final response to one sentence."
),
subagents=[
{
"name": "researcher",
"description": "Researches topics thoroughly",
"system_prompt": (
"You are a thorough researcher. Research the given topic "
"and provide a concise summary in 2-3 sentences."
),
},
],
)
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Write a short summary about AI safety"}]},
stream_mode="updates",
subgraphs=True,
version="v2",
):
if chunk["type"] == "updates":
# Main agent updates (empty namespace)
if not chunk["ns"]:
for node_name, data in chunk["data"].items():
if node_name == "tools":
# Subagent results returned to main agent
for msg in data.get("messages", []):
if msg.type == "tool":
print(f"\nSubagent complete: {msg.name}")
print(f" Result: {str(msg.content)[:200]}...")
else:
print(f"[main agent] step: {node_name}")
# Subagent updates (non-empty namespace)
else:
for node_name, data in chunk["data"].items():
print(f" [{chunk['ns'][0]}] step: {node_name}")
from deepagents import create_deep_agent
agent = create_deep_agent(
model="fireworks:accounts/fireworks/models/glm-5p2",
system_prompt=(
"You are a project coordinator with no research knowledge. "
"For every user request, you must call the task() tool with "
"subagent_type set to researcher. Never answer research questions yourself. "
"Keep your final response to one sentence."
),
subagents=[
{
"name": "researcher",
"description": "Researches topics thoroughly",
"system_prompt": (
"You are a thorough researcher. Research the given topic "
"and provide a concise summary in 2-3 sentences."
),
},
],
)
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Write a short summary about AI safety"}]},
stream_mode="updates",
subgraphs=True,
version="v2",
):
if chunk["type"] == "updates":
# Main agent updates (empty namespace)
if not chunk["ns"]:
for node_name, data in chunk["data"].items():
if node_name == "tools":
# Subagent results returned to main agent
for msg in data.get("messages", []):
if msg.type == "tool":
print(f"\nSubagent complete: {msg.name}")
print(f" Result: {str(msg.content)[:200]}...")
else:
print(f"[main agent] step: {node_name}")
# Subagent updates (non-empty namespace)
else:
for node_name, data in chunk["data"].items():
print(f" [{chunk['ns'][0]}] step: {node_name}")
from deepagents import create_deep_agent
agent = create_deep_agent(
model="baseten:zai-org/GLM-5.2",
system_prompt=(
"You are a project coordinator with no research knowledge. "
"For every user request, you must call the task() tool with "
"subagent_type set to researcher. Never answer research questions yourself. "
"Keep your final response to one sentence."
),
subagents=[
{
"name": "researcher",
"description": "Researches topics thoroughly",
"system_prompt": (
"You are a thorough researcher. Research the given topic "
"and provide a concise summary in 2-3 sentences."
),
},
],
)
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Write a short summary about AI safety"}]},
stream_mode="updates",
subgraphs=True,
version="v2",
):
if chunk["type"] == "updates":
# Main agent updates (empty namespace)
if not chunk["ns"]:
for node_name, data in chunk["data"].items():
if node_name == "tools":
# Subagent results returned to main agent
for msg in data.get("messages", []):
if msg.type == "tool":
print(f"\nSubagent complete: {msg.name}")
print(f" Result: {str(msg.content)[:200]}...")
else:
print(f"[main agent] step: {node_name}")
# Subagent updates (non-empty namespace)
else:
for node_name, data in chunk["data"].items():
print(f" [{chunk['ns'][0]}] step: {node_name}")
from deepagents import create_deep_agent
agent = create_deep_agent(
model="ollama:north-mini-code-1.0",
system_prompt=(
"You are a project coordinator with no research knowledge. "
"For every user request, you must call the task() tool with "
"subagent_type set to researcher. Never answer research questions yourself. "
"Keep your final response to one sentence."
),
subagents=[
{
"name": "researcher",
"description": "Researches topics thoroughly",
"system_prompt": (
"You are a thorough researcher. Research the given topic "
"and provide a concise summary in 2-3 sentences."
),
},
],
)
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Write a short summary about AI safety"}]},
stream_mode="updates",
subgraphs=True,
version="v2",
):
if chunk["type"] == "updates":
# Main agent updates (empty namespace)
if not chunk["ns"]:
for node_name, data in chunk["data"].items():
if node_name == "tools":
# Subagent results returned to main agent
for msg in data.get("messages", []):
if msg.type == "tool":
print(f"\nSubagent complete: {msg.name}")
print(f" Result: {str(msg.content)[:200]}...")
else:
print(f"[main agent] step: {node_name}")
# Subagent updates (non-empty namespace)
else:
for node_name, data in chunk["data"].items():
print(f" [{chunk['ns'][0]}] step: {node_name}")
Output
[main agent] step: model_request
[tools:call_abc123] step: model_request
[tools:call_abc123] step: tools
[tools:call_abc123] step: model_request
Subagent complete: task
Result: ## AI Safety Report...
[main agent] step: model_request
LLM tokens
Usestream_mode="messages" to stream individual tokens from both the main agent and subagents. Each message event includes metadata that identifies the source agent.
current_source = ""
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Research quantum computing advances"}]},
stream_mode="messages",
subgraphs=True,
version="v2",
):
if chunk["type"] == "messages":
token, metadata = chunk["data"]
# Check if this event came from a subagent (namespace contains "tools:")
is_subagent = any(s.startswith("tools:") for s in chunk["ns"])
if is_subagent:
# Token from a subagent
subagent_ns = next(s for s in chunk["ns"] if s.startswith("tools:"))
if subagent_ns != current_source:
print(f"\n\n--- [subagent: {subagent_ns}] ---")
current_source = subagent_ns
if token.content:
print(token.content, end="", flush=True)
else:
# Token from the main agent
if "main" != current_source:
print("\n\n--- [main agent] ---")
current_source = "main"
if token.content:
print(token.content, end="", flush=True)
print()
Tool calls
When subagents use tools, you can stream tool call events to display what each subagent is doing. Tool call chunks appear in themessages stream mode.
from langchain.messages import AIMessageChunk, ToolMessage
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Research recent quantum computing advances"}]},
stream_mode="messages",
subgraphs=True,
version="v2",
):
if chunk["type"] == "messages":
token, metadata = chunk["data"]
# Identify source: "main" or the subagent namespace segment
is_subagent = any(s.startswith("tools:") for s in chunk["ns"])
source = next((s for s in chunk["ns"] if s.startswith("tools:")), "main") if is_subagent else "main"
# Tool call chunks (streaming tool invocations)
if isinstance(token, AIMessageChunk) and token.tool_call_chunks:
for tc in token.tool_call_chunks:
if tc.get("name"):
print(f"\n[{source}] Tool call: {tc['name']}")
# Args stream in chunks - write them incrementally
if tc.get("args"):
print(tc["args"], end="", flush=True)
# Tool results
if isinstance(token, ToolMessage):
print(f"\n[{source}] Tool result [{token.name}]: {str(token.content)[:150]}")
# Regular AI content (skip tool call messages)
if (
isinstance(token, AIMessageChunk)
and token.content
and not token.tool_call_chunks
):
print(token.content, end="", flush=True)
print()
Custom updates
Useget_stream_writer inside your subagent tools to emit custom progress events:
import time
from langchain.tools import tool
from langgraph.config import get_stream_writer
from deepagents import create_deep_agent
@tool
def analyze_data(topic: str) -> str:
"""Run a data analysis on a given topic.
This tool performs the actual analysis and emits progress updates.
You MUST call this tool for any analysis request.
"""
writer = get_stream_writer()
writer({"status": "starting", "topic": topic, "progress": 0})
time.sleep(0.5)
writer({"status": "analyzing", "progress": 50})
time.sleep(0.5)
writer({"status": "complete", "progress": 100})
return (
f'Analysis of "{topic}": Customer sentiment is 85% positive, '
"driven by product quality and support response times."
)
agent = create_deep_agent(
model="google_genai:gemini-3.5-flash",
system_prompt=(
"You are a coordinator. For any analysis request, you MUST delegate "
"to the analyst subagent using the task tool. Never try to answer directly. "
"After receiving the result, summarize it in one sentence."
),
subagents=[
{
"name": "analyst",
"description": "Performs data analysis with real-time progress tracking",
"system_prompt": (
"You are a data analyst. You MUST call the analyze_data tool "
"for every analysis request. Do not use any other tools. "
"After the analysis completes, report the result."
),
"tools": [analyze_data],
},
],
)
custom_event_count = 0
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Analyze customer satisfaction trends"}]},
stream_mode="custom",
subgraphs=True,
version="v2",
):
if chunk["type"] == "custom":
custom_event_count += 1
is_subagent = any(s.startswith("tools:") for s in chunk["ns"])
if is_subagent:
subagent_ns = next(s for s in chunk["ns"] if s.startswith("tools:"))
print(f"[{subagent_ns}]", chunk["data"])
else:
print("[main]", chunk["data"])
import time
from langchain.tools import tool
from langgraph.config import get_stream_writer
from deepagents import create_deep_agent
@tool
def analyze_data(topic: str) -> str:
"""Run a data analysis on a given topic.
This tool performs the actual analysis and emits progress updates.
You MUST call this tool for any analysis request.
"""
writer = get_stream_writer()
writer({"status": "starting", "topic": topic, "progress": 0})
time.sleep(0.5)
writer({"status": "analyzing", "progress": 50})
time.sleep(0.5)
writer({"status": "complete", "progress": 100})
return (
f'Analysis of "{topic}": Customer sentiment is 85% positive, '
"driven by product quality and support response times."
)
agent = create_deep_agent(
model="openai:gpt-5.5",
system_prompt=(
"You are a coordinator. For any analysis request, you MUST delegate "
"to the analyst subagent using the task tool. Never try to answer directly. "
"After receiving the result, summarize it in one sentence."
),
subagents=[
{
"name": "analyst",
"description": "Performs data analysis with real-time progress tracking",
"system_prompt": (
"You are a data analyst. You MUST call the analyze_data tool "
"for every analysis request. Do not use any other tools. "
"After the analysis completes, report the result."
),
"tools": [analyze_data],
},
],
)
custom_event_count = 0
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Analyze customer satisfaction trends"}]},
stream_mode="custom",
subgraphs=True,
version="v2",
):
if chunk["type"] == "custom":
custom_event_count += 1
is_subagent = any(s.startswith("tools:") for s in chunk["ns"])
if is_subagent:
subagent_ns = next(s for s in chunk["ns"] if s.startswith("tools:"))
print(f"[{subagent_ns}]", chunk["data"])
else:
print("[main]", chunk["data"])
import time
from langchain.tools import tool
from langgraph.config import get_stream_writer
from deepagents import create_deep_agent
@tool
def analyze_data(topic: str) -> str:
"""Run a data analysis on a given topic.
This tool performs the actual analysis and emits progress updates.
You MUST call this tool for any analysis request.
"""
writer = get_stream_writer()
writer({"status": "starting", "topic": topic, "progress": 0})
time.sleep(0.5)
writer({"status": "analyzing", "progress": 50})
time.sleep(0.5)
writer({"status": "complete", "progress": 100})
return (
f'Analysis of "{topic}": Customer sentiment is 85% positive, '
"driven by product quality and support response times."
)
agent = create_deep_agent(
model="anthropic:claude-sonnet-4-6",
system_prompt=(
"You are a coordinator. For any analysis request, you MUST delegate "
"to the analyst subagent using the task tool. Never try to answer directly. "
"After receiving the result, summarize it in one sentence."
),
subagents=[
{
"name": "analyst",
"description": "Performs data analysis with real-time progress tracking",
"system_prompt": (
"You are a data analyst. You MUST call the analyze_data tool "
"for every analysis request. Do not use any other tools. "
"After the analysis completes, report the result."
),
"tools": [analyze_data],
},
],
)
custom_event_count = 0
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Analyze customer satisfaction trends"}]},
stream_mode="custom",
subgraphs=True,
version="v2",
):
if chunk["type"] == "custom":
custom_event_count += 1
is_subagent = any(s.startswith("tools:") for s in chunk["ns"])
if is_subagent:
subagent_ns = next(s for s in chunk["ns"] if s.startswith("tools:"))
print(f"[{subagent_ns}]", chunk["data"])
else:
print("[main]", chunk["data"])
import time
from langchain.tools import tool
from langgraph.config import get_stream_writer
from deepagents import create_deep_agent
@tool
def analyze_data(topic: str) -> str:
"""Run a data analysis on a given topic.
This tool performs the actual analysis and emits progress updates.
You MUST call this tool for any analysis request.
"""
writer = get_stream_writer()
writer({"status": "starting", "topic": topic, "progress": 0})
time.sleep(0.5)
writer({"status": "analyzing", "progress": 50})
time.sleep(0.5)
writer({"status": "complete", "progress": 100})
return (
f'Analysis of "{topic}": Customer sentiment is 85% positive, '
"driven by product quality and support response times."
)
agent = create_deep_agent(
model="openrouter:z-ai/glm-5.2",
system_prompt=(
"You are a coordinator. For any analysis request, you MUST delegate "
"to the analyst subagent using the task tool. Never try to answer directly. "
"After receiving the result, summarize it in one sentence."
),
subagents=[
{
"name": "analyst",
"description": "Performs data analysis with real-time progress tracking",
"system_prompt": (
"You are a data analyst. You MUST call the analyze_data tool "
"for every analysis request. Do not use any other tools. "
"After the analysis completes, report the result."
),
"tools": [analyze_data],
},
],
)
custom_event_count = 0
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Analyze customer satisfaction trends"}]},
stream_mode="custom",
subgraphs=True,
version="v2",
):
if chunk["type"] == "custom":
custom_event_count += 1
is_subagent = any(s.startswith("tools:") for s in chunk["ns"])
if is_subagent:
subagent_ns = next(s for s in chunk["ns"] if s.startswith("tools:"))
print(f"[{subagent_ns}]", chunk["data"])
else:
print("[main]", chunk["data"])
import time
from langchain.tools import tool
from langgraph.config import get_stream_writer
from deepagents import create_deep_agent
@tool
def analyze_data(topic: str) -> str:
"""Run a data analysis on a given topic.
This tool performs the actual analysis and emits progress updates.
You MUST call this tool for any analysis request.
"""
writer = get_stream_writer()
writer({"status": "starting", "topic": topic, "progress": 0})
time.sleep(0.5)
writer({"status": "analyzing", "progress": 50})
time.sleep(0.5)
writer({"status": "complete", "progress": 100})
return (
f'Analysis of "{topic}": Customer sentiment is 85% positive, '
"driven by product quality and support response times."
)
agent = create_deep_agent(
model="fireworks:accounts/fireworks/models/glm-5p2",
system_prompt=(
"You are a coordinator. For any analysis request, you MUST delegate "
"to the analyst subagent using the task tool. Never try to answer directly. "
"After receiving the result, summarize it in one sentence."
),
subagents=[
{
"name": "analyst",
"description": "Performs data analysis with real-time progress tracking",
"system_prompt": (
"You are a data analyst. You MUST call the analyze_data tool "
"for every analysis request. Do not use any other tools. "
"After the analysis completes, report the result."
),
"tools": [analyze_data],
},
],
)
custom_event_count = 0
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Analyze customer satisfaction trends"}]},
stream_mode="custom",
subgraphs=True,
version="v2",
):
if chunk["type"] == "custom":
custom_event_count += 1
is_subagent = any(s.startswith("tools:") for s in chunk["ns"])
if is_subagent:
subagent_ns = next(s for s in chunk["ns"] if s.startswith("tools:"))
print(f"[{subagent_ns}]", chunk["data"])
else:
print("[main]", chunk["data"])
import time
from langchain.tools import tool
from langgraph.config import get_stream_writer
from deepagents import create_deep_agent
@tool
def analyze_data(topic: str) -> str:
"""Run a data analysis on a given topic.
This tool performs the actual analysis and emits progress updates.
You MUST call this tool for any analysis request.
"""
writer = get_stream_writer()
writer({"status": "starting", "topic": topic, "progress": 0})
time.sleep(0.5)
writer({"status": "analyzing", "progress": 50})
time.sleep(0.5)
writer({"status": "complete", "progress": 100})
return (
f'Analysis of "{topic}": Customer sentiment is 85% positive, '
"driven by product quality and support response times."
)
agent = create_deep_agent(
model="baseten:zai-org/GLM-5.2",
system_prompt=(
"You are a coordinator. For any analysis request, you MUST delegate "
"to the analyst subagent using the task tool. Never try to answer directly. "
"After receiving the result, summarize it in one sentence."
),
subagents=[
{
"name": "analyst",
"description": "Performs data analysis with real-time progress tracking",
"system_prompt": (
"You are a data analyst. You MUST call the analyze_data tool "
"for every analysis request. Do not use any other tools. "
"After the analysis completes, report the result."
),
"tools": [analyze_data],
},
],
)
custom_event_count = 0
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Analyze customer satisfaction trends"}]},
stream_mode="custom",
subgraphs=True,
version="v2",
):
if chunk["type"] == "custom":
custom_event_count += 1
is_subagent = any(s.startswith("tools:") for s in chunk["ns"])
if is_subagent:
subagent_ns = next(s for s in chunk["ns"] if s.startswith("tools:"))
print(f"[{subagent_ns}]", chunk["data"])
else:
print("[main]", chunk["data"])
import time
from langchain.tools import tool
from langgraph.config import get_stream_writer
from deepagents import create_deep_agent
@tool
def analyze_data(topic: str) -> str:
"""Run a data analysis on a given topic.
This tool performs the actual analysis and emits progress updates.
You MUST call this tool for any analysis request.
"""
writer = get_stream_writer()
writer({"status": "starting", "topic": topic, "progress": 0})
time.sleep(0.5)
writer({"status": "analyzing", "progress": 50})
time.sleep(0.5)
writer({"status": "complete", "progress": 100})
return (
f'Analysis of "{topic}": Customer sentiment is 85% positive, '
"driven by product quality and support response times."
)
agent = create_deep_agent(
model="ollama:north-mini-code-1.0",
system_prompt=(
"You are a coordinator. For any analysis request, you MUST delegate "
"to the analyst subagent using the task tool. Never try to answer directly. "
"After receiving the result, summarize it in one sentence."
),
subagents=[
{
"name": "analyst",
"description": "Performs data analysis with real-time progress tracking",
"system_prompt": (
"You are a data analyst. You MUST call the analyze_data tool "
"for every analysis request. Do not use any other tools. "
"After the analysis completes, report the result."
),
"tools": [analyze_data],
},
],
)
custom_event_count = 0
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Analyze customer satisfaction trends"}]},
stream_mode="custom",
subgraphs=True,
version="v2",
):
if chunk["type"] == "custom":
custom_event_count += 1
is_subagent = any(s.startswith("tools:") for s in chunk["ns"])
if is_subagent:
subagent_ns = next(s for s in chunk["ns"] if s.startswith("tools:"))
print(f"[{subagent_ns}]", chunk["data"])
else:
print("[main]", chunk["data"])
Output
[tools:call_abc123] {'status': 'starting', 'topic': 'customer satisfaction trends', 'progress': 0}
[tools:call_abc123] {'status': 'analyzing', 'progress': 50}
[tools:call_abc123] {'status': 'complete', 'progress': 100}
Stream multiple modes
Combine multiple stream modes to get a complete picture of agent execution:# Skip internal middleware steps - only show meaningful node names
INTERESTING_NODES = {"model", "tools"}
last_source = ""
mid_line = False # True when we've written tokens without a trailing newline
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Analyze the impact of remote work on team productivity"}]},
stream_mode=["updates", "messages", "custom"],
subgraphs=True,
version="v2",
):
is_subagent = any(s.startswith("tools:") for s in chunk["ns"])
source = "subagent" if is_subagent else "main"
if chunk["type"] == "updates":
for node_name in chunk["data"]:
if node_name not in INTERESTING_NODES:
continue
if mid_line:
print()
mid_line = False
print(f"[{source}] step: {node_name}")
elif chunk["type"] == "messages":
token, metadata = chunk["data"]
if token.content:
# Print a header when the source changes
if source != last_source:
if mid_line:
print()
mid_line = False
print(f"\n[{source}] ", end="")
last_source = source
print(token.content, end="", flush=True)
mid_line = True
elif chunk["type"] == "custom":
if mid_line:
print()
mid_line = False
print(f"[{source}] custom event:", chunk["data"])
print()
Common patterns
Track subagent lifecycle
Monitor when subagents start, run, and complete:active_subagents = {}
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Research the latest AI safety developments"}]},
stream_mode="updates",
subgraphs=True,
version="v2",
):
if chunk["type"] == "updates":
for node_name, data in chunk["data"].items():
# ─── Phase 1: Detect subagent starting ────────────────────────
# When the main agent's model node contains task tool calls,
# a subagent has been spawned.
if not chunk["ns"] and node_name == "model":
for msg in data.get("messages", []):
for tc in getattr(msg, "tool_calls", []):
if tc["name"] == "task":
active_subagents[tc["id"]] = {
"type": tc["args"].get("subagent_type"),
"description": tc["args"].get("description", "")[:80],
"status": "pending",
}
print(
f'[lifecycle] PENDING → subagent "{tc["args"].get("subagent_type")}" '
f'({tc["id"]})'
)
# ─── Phase 2: Detect subagent running ─────────────────────────
# When we receive events from a tools:UUID namespace, that
# subagent is actively executing.
if chunk["ns"] and chunk["ns"][0].startswith("tools:"):
pregel_id = chunk["ns"][0].split(":")[1]
# Check if any pending subagent needs to be marked running.
# Note: the pregel task ID differs from the tool_call_id,
# so we mark any pending subagent as running on first subagent event.
for sub_id, sub in active_subagents.items():
if sub["status"] == "pending":
sub["status"] = "running"
print(
f'[lifecycle] RUNNING → subagent "{sub["type"]}" '
f"(pregel: {pregel_id})"
)
break
# ─── Phase 3: Detect subagent completing ──────────────────────
# When the main agent's tools node returns a tool message,
# the subagent has completed and returned its result.
if not chunk["ns"] and node_name == "tools":
for msg in data.get("messages", []):
if msg.type == "tool":
sub = active_subagents.get(msg.tool_call_id)
if sub:
sub["status"] = "complete"
print(
f'[lifecycle] COMPLETE → subagent "{sub["type"]}" '
f"({msg.tool_call_id})"
)
print(f" Result preview: {str(msg.content)[:120]}...")
# Print final state
print("\n--- Final subagent states ---")
for sub_id, sub in active_subagents.items():
print(f" {sub['type']}: {sub['status']}")
v2 streaming format
Requires LangGraph >= 1.1.
version="v2"), which is the recommended approach. Every chunk is a StreamPart dict with type, ns, and data keys — the same shape regardless of stream mode, number of modes, or subgraph settings.
The v2 format eliminates nested tuple unpacking, making it straightforward to handle subgraph streaming in Deep Agents. Compare the two formats:
# Unified format — no nested tuple unpacking
for chunk in agent.stream(
{"messages": [{"role": "user", "content": "Research quantum computing"}]},
stream_mode=["updates", "messages", "custom"],
subgraphs=True,
version="v2",
):
print(chunk["type"]) # "updates", "messages", or "custom"
print(chunk["ns"]) # () for main agent, ("tools:<id>",) for subagent
print(chunk["data"]) # payload
# Must handle (namespace, (mode, data)) nested tuples
for namespace, chunk in agent.stream(
{"messages": [{"role": "user", "content": "Research quantum computing"}]},
stream_mode=["updates", "messages", "custom"],
subgraphs=True,
):
mode, data = chunk[0], chunk[1]
print(mode) # "updates", "messages", or "custom"
print(namespace) # () for main agent, ("tools:<id>",) for subagent
print(data) # payload
Related
- Subagents—Configure and use subagents with Deep Agents
- Frontend streaming—Build React UIs with
useStreamfor Deep Agents - LangChain Event Streaming—General streaming concepts with LangChain agents
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