feat(langgraph-examples): add deepagents 3-subagent attribution demo …

…agent Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
ag-ui-protocol · mme · Jul 7, 2026 · Jul 7, 2026 · Jul 7, 2026 · Jul 7, 2026
commit 40652173c2154a0378cd9453085ddc34e24da89a
@@ -0,0 +1 @@
+# deepagents 3-subagent attribution demo module
@@ -0,0 +1,109 @@
+"""A deepagents supervisor that delegates to three specialized research
+subagents via the `task` tool.
+
+This demo exists to exercise the AG-UI SUBAGENT_STARTED / SUBAGENT_FINISHED
+event attribution for deepagents subagents: a single user question should
+reliably fan out into three `task` delegations (market, technical, and risk
+research), each surfaced as its own subagent run with a distinct
+`subagent_id`.
+"""
+
+import os
+
+from langchain_openai import ChatOpenAI
+
+from deepagents import create_deep_agent
+from deepagents.middleware.subagents import SubAgent
+
+model = ChatOpenAI(model="gpt-4o-mini")
+
+market_researcher: SubAgent = {
+    "name": "market_researcher",
+    "description": (
+        "Researches market size, target customers, competitors, and demand "
+        "for a product or business idea. Delegate to this subagent for any "
+        "market-related sub-question."
+    ),
+    "system_prompt": (
+        "You are a market research analyst. Given a product or business "
+        "idea, give a concise (3-5 sentence) assessment of the target "
+        "market, likely customers, and competitive landscape. Be specific "
+        "and avoid hedging."
+    ),
+    "tools": [],
+}
+
+technical_researcher: SubAgent = {
+    "name": "technical_researcher",
+    "description": (
+        "Researches the technical feasibility, architecture, and "
+        "implementation challenges of building a product or system. "
+        "Delegate to this subagent for any technical sub-question."
+    ),
+    "system_prompt": (
+        "You are a technical research analyst. Given a product or business "
+        "idea, give a concise (3-5 sentence) assessment of the technical "
+        "feasibility, key implementation challenges, and any notable "
+        "technology choices. Be specific and avoid hedging."
+    ),
+    "tools": [],
+}
+
+risk_researcher: SubAgent = {
+    "name": "risk_researcher",
+    "description": (
+        "Researches the business, legal, and operational risks of "
+        "launching a product or business idea. Delegate to this subagent "
+        "for any risk-related sub-question."
+    ),
+    "system_prompt": (
+        "You are a risk research analyst. Given a product or business "
+        "idea, give a concise (3-5 sentence) assessment of the most "
+        "important risks (business, legal, or operational) and one "
+        "mitigation for each. Be specific and avoid hedging."
+    ),
+    "tools": [],
+}
+
+SUPERVISOR_PROMPT = """You are a research supervisor. For EVERY user question \
+about a product, business, or idea, you MUST gather input from all three of \
+your specialist subagents before answering - no exceptions, even if the \
+question seems simple.
+
+Always do the following, in order:
+1. Call the `task` tool with `subagent_type="market_researcher"`, asking it \
+to research the market angle of the user's question.
+2. Call the `task` tool with `subagent_type="technical_researcher"`, asking \
+it to research the technical angle of the user's question.
+3. Call the `task` tool with `subagent_type="risk_researcher"`, asking it to \
+research the risk angle of the user's question.
+4. Once all three have responded, synthesize their findings into a single, \
+concise final answer for the user that clearly attributes each point to its \
+area (market, technical, risk).
+
+Delegate all three tasks - do not skip any of them, and do not answer from \
+your own knowledge instead of delegating."""
+
+# Conditionally use a checkpointer based on the environment (matches the
+# pattern used by the sibling example agents).
+is_fast_api = os.environ.get("LANGGRAPH_FAST_API", "false").lower() == "true"
+
+if is_fast_api:
+    # For CopilotKit and other contexts, use MemorySaver
+    from langgraph.checkpoint.memory import MemorySaver
+
+    graph = create_deep_agent(
+        model=model,
+        tools=[],
+        system_prompt=SUPERVISOR_PROMPT,
+        subagents=[market_researcher, technical_researcher, risk_researcher],
+        checkpointer=MemorySaver(),
+    )
+else:
+    # When running in LangGraph API/dev, don't use a custom checkpointer
+    graph = create_deep_agent(
+        model=model,
+        tools=[],
+        system_prompt=SUPERVISOR_PROMPT,
+        subagents=[market_researcher, technical_researcher, risk_researcher],
+    )
diff --git a/integrations/langgraph/python/examples/agents/deepagents_subagents/spike_probe.py b/integrations/langgraph/python/examples/agents/deepagents_subagents/spike_probe.py
@@ -23,6 +23,7 @@
 from .tool_based_generative_ui.agent import graph as tool_based_generative_ui_graph
 from .a2ui_fixed_schema.agent import graph as a2ui_fixed_schema_graph
 from .a2ui_dynamic_schema.agent import graph as a2ui_dynamic_schema_graph
+from .deepagents_subagents.agent import graph as deepagents_subagents_graph
 
 app = FastAPI(title="LangGraph Dojo Example Server")
 
@@ -88,6 +89,11 @@
         description="Dynamic A2UI with LLM-generated UI schema.",
         graph=a2ui_dynamic_schema_graph,
     ),
+    "deepagents_subagents": LangGraphAgent(
+        name="deepagents_subagents",
+        description="A deepagents supervisor delegating to three specialized subagents (subagent attribution demo).",
+        graph=deepagents_subagents_graph,
+    ),
 }
 
 add_langgraph_fastapi_endpoint(
@@ -150,6 +156,12 @@
     app=app, agent=agents["a2ui_dynamic_schema"], path="/agent/a2ui_dynamic_schema"
 )
 
+add_langgraph_fastapi_endpoint(
+    app=app,
+    agent=agents["deepagents_subagents"],
+    path="/agent/deepagents_subagents",
+)
+
 
 def main():
     """Run the uvicorn server."""

@@ -14,7 +14,8 @@
     "agentic_chat_multimodal": "./agents/agentic_chat_multimodal/agent.py:graph",
     "subgraphs": "./agents/subgraphs/agent.py:graph",
     "a2ui_fixed_schema": "./agents/a2ui_fixed_schema/agent.py:graph",
-    "a2ui_dynamic_schema": "./agents/a2ui_dynamic_schema/agent.py:graph"
+    "a2ui_dynamic_schema": "./agents/a2ui_dynamic_schema/agent.py:graph",
+    "deepagents_subagents": "./agents/deepagents_subagents/agent.py:graph"
   },
   "env": ".env"
 }
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		# deepagents 3-subagent attribution demo module