cookbook/03_teams/04_structured_input_output/TEST_LOG.md
Status: PASS
none
Status: PASS
Description: Example execution attempt
Result: DEBUG ************* Team ID: incident-reporting-team *************
DEBUG ***** Session ID: 1edd9150-caad-4f65-9707-7248462c6344 *****
DEBUG Creating new TeamSession: 1edd9150-caad-4f65-9707-7248462c6344
DEBUG *** Team Run Start: 5d256fd0-7ff7-4c2b-a639-d8e1b454e3b9 ***
DEBUG Processing tools for model
DEBUG Added tool delegate_task_to_member
DEBUG --------------- OpenAI Response Stream Start ---------------
DEBUG -------------------- Model: gpt-5-mini ---------------------
DEBUG ========================== system ==========================
DEBUG You coordinate a team of specialized AI agents to fulfill the user's
request. Delegate to members when their expertise or tools are needed. For
straightforward requests you can handle directly — including using your
own tools — respond without delegating.
<team_members>
<member id="incident-analyst" name="Incident Analyst">
</member>
</team_members>
<how_to_respond>
You operate in coordinate mode. For requests that need member expertise,
select the best member(s), delegate with clear task descriptions, and
synthesize their outputs into a unified response. For requests you can
handle directly — simple questions, using your own tools, or general
conversation — respond without delegating.
Delegation:
- Match each sub-task to the member whose role and tools are the best fit.
Delegate to multiple members when the request spans different areas of
expertise.
- Write task descriptions that are self-contained: state the goal, provide
relevant context from the conversation, and describe what a good result
looks like.
- Use only the member's ID when delegating — do not prefix it with the
team ID.
After receiving member responses:
- If a response is incomplete or off-target, re-delegate with clearer
instructions or try a different member.
- Synthesize all results into a single coherent response. Resolve
contradictions, fill gaps with your own reasoning, and add structure — do
not simply concatenate member outputs.
</how_to_respond>
- Summarize incidents in a clear operational style.
- Prefer plain language over technical jargon.
<expected_output>
Three sections: Summary, Impact, and Next Step. Keep each section to one
sentence.
</expected_output>
DEBUG =========================== user ===========================
DEBUG A deployment changed the auth callback behavior, login requests increased
by 12%, and a rollback script is already prepared.
DEBUG ======================== assistant =========================
DEBUG Tool Calls:
- ID: 'fc_0287303627000bcf006991176b185c8193b2e4050b5724b8fe'
Name: 'delegate_task_to_member'
Arguments: 'member_id: incident-analyst, task: You are the incident
analyst. Based on this context: a recent deployment changed authentication
callback behavior, login requests increased by 12%, and a rollback script
is already prepared. Produce three one-sentence outputs labeled Summary,
Impact, and Next Step. Each must be one concise sentence in plain
language: Summary should state what changed, Impact should state
operational effect (e.g., increased login attempts and potential
user-facing failures), and Next Step should state the recommended
immediate action (e.g., run rollback script) once and only once. Keep each
to one sentence.'
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=482, output=465, total=947,
reasoning=320
DEBUG ************************ METRICS *************************
DEBUG Running: delegate_task_to_member(member_id=incident-analyst, task=...)
DEBUG **************** Agent ID: incident-analyst ****************
DEBUG Creating new AgentSession: 1edd9150-caad-4f65-9707-7248462c6344
DEBUG ** Agent Run Start: 2fa34eb3-a68b-42b7-9a2a-f17dfb9183ba ***
DEBUG --------------- OpenAI Response Stream Start ---------------
DEBUG -------------------- Model: gpt-5-mini ---------------------
DEBUG ========================== system ==========================
DEBUG - Extract outcomes and risks clearly.
- Avoid unnecessary speculation.
DEBUG =========================== user ===========================
DEBUG You are the incident analyst. Based on this context: a recent deployment
changed authentication callback behavior, login requests increased by 12%,
and a rollback script is already prepared. Produce three one-sentence
outputs labeled Summary, Impact, and Next Step. Each must be one concise
sentence in plain language: Summary should state what changed, Impact
should state operational effect (e.g., increased login attempts and
potential user-facing failures), and Next Step should state the
recommended immediate action (e.g., run rollback script) once and only
once. Keep each to one sentence.
DEBUG ======================== assistant =========================
DEBUG Summary: A recent deployment changed the authentication callback behavior
and coincided with a 12% increase in login requests.
Impact: Login requests are up 12%, increasing load on authentication
services and creating a risk of user-facing login failures and degraded
availability.
Next Step: Run the prepared rollback script immediately to restore the
prior authentication callback behavior.
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=133, output=619, total=752,
reasoning=512
DEBUG ************************ METRICS *************************
DEBUG ---------------- OpenAI Response Stream End ----------------
DEBUG Added RunOutput to Agent Session
DEBUG *** Agent Run End: 2fa34eb3-a68b-42b7-9a2a-f17dfb9183ba ****
DEBUG Updated team run context with member name: Incident Analyst
DEBUG Added RunOutput to Team Session
DEBUG =========================== tool ===========================
DEBUG Tool call Id: call_zZ1tryakYEjV6coWHypornRV
DEBUG Summary: A recent deployment changed the authentication callback behavior
and coincided with a 12% increase in login requests.
Impact: Login requests are up 12%, increasing load on authentication
services and creating a risk of user-facing login failures and degraded
availability.
Next Step: Run the prepared rollback script immediately to restore the
prior authentication callback behavior.
DEBUG ********************** TOOL METRICS **********************
DEBUG * Duration: 0.0005s
DEBUG ********************** TOOL METRICS **********************
DEBUG Using previous_response_id:
resp_0287303627000bcf0069911767380c8193b9557316c41f73be
DEBUG ======================== assistant =========================
DEBUG Summary: A recent deployment changed the authentication callback behavior
and coincided with a 12% increase in login requests.
Impact: The 12% spike in logins increases load on authentication services
and raises the risk of user-facing login failures and degraded
availability.
Next Step: Execute the prepared rollback script immediately to restore the
previous authentication callback behavior.
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=1027, output=198, total=1225,
reasoning=64
DEBUG ************************ METRICS *************************
DEBUG ---------------- OpenAI Response Stream End ----------------
DEBUG Added RunOutput to Team Session
DEBUG **** Team Run End: 5d256fd0-7ff7-4c2b-a639-d8e1b454e3b9 ****
┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ A deployment changed the auth callback behavior, login requests increased by ┃
┃ 12%, and a rollback script is already prepared. ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Team Tool Calls ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ • delegate_task_to_member(member_id=incident-analyst, task=You are the ┃
┃ incident analyst. Based on this ┃
┃ context: a recent deployment changed authentication callback behavior, ┃
┃ login requests increased by 12%, and ┃
┃ a rollback script is already prepared. Produce three one-sentence outputs ┃
┃ labeled Summary, Impact, and Next ┃
┃ Step. Each must be one concise sentence in plain language: Summary should ┃
┃ state what changed, Impact should ┃
┃ state operational effect (e.g., increased login attempts and potential ┃
┃ user-facing failures), and Next Step ┃
┃ should state the recommended immediate action (e.g., run rollback script) ┃
┃ once and only once. Keep each to ┃
┃ one sentence.) ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Response (16.0s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ Summary: A recent deployment changed the authentication callback behavior ┃
┃ and coincided with a 12% increase in login requests. ┃
┃ ┃
┃ Impact: The 12% spike in logins increases load on authentication services ┃
┃ and raises the risk of user-facing login failures and degraded availability. ┃
┃ ┃
┃ Next Step: Execute the prepared rollback script immediately to restore the ┃
┃ previous authentication callback behavior. ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Status: PASS
Description: Example execution attempt
Result: DEBUG ****************** Team ID: research-team ******************
DEBUG ***** Session ID: 1a2c8cf4-9d16-4c3c-b0a7-cd695e82f52c *****
DEBUG Creating new TeamSession: 1a2c8cf4-9d16-4c3c-b0a7-cd695e82f52c
DEBUG *** Team Run Start: a919e057-7a91-4677-9a56-68dfb5255661 ***
DEBUG Processing tools for model
DEBUG Added tool delegate_task_to_member
DEBUG --------------- OpenAI Response Stream Start ---------------
DEBUG ---------------------- Model: gpt-5.2 ----------------------
DEBUG ========================== system ==========================
DEBUG You coordinate a team of specialized AI agents to fulfill the user's
request. Delegate to members when their expertise or tools are needed. For
straightforward requests you can handle directly — including using your
own tools — respond without delegating.
<team_members>
<member id="researcher" name="Researcher">
Role: Research topics
</member>
</team_members>
<how_to_respond>
You operate in coordinate mode. For requests that need member expertise,
select the best member(s), delegate with clear task descriptions, and
synthesize their outputs into a unified response. For requests you can
handle directly — simple questions, using your own tools, or general
conversation — respond without delegating.
Delegation:
- Match each sub-task to the member whose role and tools are the best fit.
Delegate to multiple members when the request spans different areas of
expertise.
- Write task descriptions that are self-contained: state the goal, provide
relevant context from the conversation, and describe what a good result
looks like.
- Use only the member's ID when delegating — do not prefix it with the
team ID.
After receiving member responses:
- If a response is incomplete or off-target, re-delegate with clearer
instructions or try a different member.
- Synthesize all results into a single coherent response. Resolve
contradictions, fill gaps with your own reasoning, and add structure — do
not simply concatenate member outputs.
</how_to_respond>
DEBUG =========================== user ===========================
DEBUG Explain AI
DEBUG ======================== assistant =========================
DEBUG AI (artificial intelligence) is the field of building computer systems
that can perform tasks that normally require human intelligence—like
understanding language, recognizing images, learning from experience,
making decisions, and solving problems.
### Main ideas
- **Goal:** Make machines able to *perceive*, *reason*, *learn*, and
*act*.
- **How it works (in practice):** AI systems usually learn patterns from
data (examples) and use those patterns to make predictions or choose
actions.
### Common types of AI
- **Narrow (or “weak”) AI:** Designed for a specific task.
Examples: spam filters, recommendation systems, face recognition, voice
assistants, ChatGPT-style text systems.
- **General AI (AGI):** A hypothetical AI that can learn and perform *any*
intellectual task a human can. This does not currently exist.
### Key approaches
- **Machine Learning (ML):** The most common approach today. Instead of
hand-coding rules, you train a model on data.
- **Deep Learning:** A subset of ML using large neural networks; very
effective for images, speech, and language.
- **Rule-based systems:** Older/traditional approach where humans
explicitly program rules (“if X then Y”).
### Where AI is used
- **Healthcare:** reading medical images, predicting risks
- **Finance:** fraud detection, trading signals
- **Search and recommendations:** Google search ranking, Netflix/YouTube
suggestions
- **Transportation:** driver-assistance, route optimization
- **Customer support and writing tools:** chatbots, summarization,
translation
### Important limitations
- AI can **make confident mistakes**, especially outside its training
patterns.
- It can **inherit bias** from training data.
- Many models are **hard to interpret** (“black box”).
- It often needs **lots of data and computing power**.
If you tell me what angle you want (beginner-friendly, technical, history,
or “how ChatGPT works”), I can tailor the explanation.
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=420, output=414, total=834
DEBUG ************************ METRICS *************************
DEBUG ---------------- OpenAI Response Stream End ----------------
DEBUG Added RunOutput to Team Session
DEBUG **** Team Run End: a919e057-7a91-4677-9a56-68dfb5255661 ****
┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ Explain AI ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Response (9.3s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ AI (artificial intelligence) is the field of building computer systems that ┃
┃ can perform tasks that normally require human intelligence—like ┃
┃ understanding language, recognizing images, learning from experience, making ┃
┃ decisions, and solving problems. ┃
┃ ┃
┃ ### Main ideas ┃
┃ - Goal: Make machines able to perceive, reason, learn, and act. ┃
┃ - How it works (in practice): AI systems usually learn patterns from ┃
┃ data (examples) and use those patterns to make predictions or choose ┃
┃ actions. ┃
┃ ┃
┃ ### Common types of AI ┃
┃ - Narrow (or “weak”) AI: Designed for a specific task. ┃
┃ Examples: spam filters, recommendation systems, face recognition, voice ┃
┃ assistants, ChatGPT-style text systems. ┃
┃ - General AI (AGI): A hypothetical AI that can learn and perform any ┃
┃ intellectual task a human can. This does not currently exist. ┃
┃ ┃
┃ ### Key approaches ┃
┃ - Machine Learning (ML): The most common approach today. Instead of ┃
┃ hand-coding rules, you train a model on data. ┃
┃ - Deep Learning: A subset of ML using large neural networks; very ┃
┃ effective for images, speech, and language. ┃
┃ - Rule-based systems: Older/traditional approach where humans explicitly ┃
┃ program rules (“if X then Y”). ┃
┃ ┃
┃ ### Where AI is used ┃
┃ - Healthcare: reading medical images, predicting risks ┃
┃ - Finance: fraud detection, trading signals ┃
┃ - Search and recommendations: Google search ranking, Netflix/YouTube ┃
┃ suggestions ┃
┃ - Transportation: driver-assistance, route optimization ┃
┃ - Customer support and writing tools: chatbots, summarization, ┃
┃ translation ┃
┃ ┃
┃ ### Important limitations ┃
┃ - AI can make confident mistakes, especially outside its training ┃
┃ patterns. ┃
┃ - It can inherit bias from training data. ┃
┃ - Many models are hard to interpret (“black box”). ┃
┃ - It often needs lots of data and computing power. ┃
┃ ┃
┃ If you tell me what angle you want (beginner-friendly, technical, history, ┃
┃ or “how ChatGPT works”), I can tailor the explanation. ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛DEBUG ****************** Team ID: research-team ******************
DEBUG ***** Session ID: 1a2c8cf4-9d16-4c3c-b0a7-cd695e82f52c *****
DEBUG Creating new TeamSession: 1a2c8cf4-9d16-4c3c-b0a7-cd695e82f52c
DEBUG *** Team Run Start: 3e0628e5-73e0-45ab-b92b-c5093352ec51 ***
DEBUG Processing tools for model
DEBUG Added tool delegate_task_to_member
DEBUG --------------- OpenAI Response Stream Start ---------------
DEBUG ---------------------- Model: gpt-5.2 ----------------------
DEBUG ========================== system ==========================
DEBUG You coordinate a team of specialized AI agents to fulfill the user's
request. Delegate to members when their expertise or tools are needed. For
straightforward requests you can handle directly — including using your
own tools — respond without delegating.
<team_members>
<member id="researcher" name="Researcher">
Role: Research topics
</member>
</team_members>
<how_to_respond>
You operate in coordinate mode. For requests that need member expertise,
select the best member(s), delegate with clear task descriptions, and
synthesize their outputs into a unified response. For requests you can
handle directly — simple questions, using your own tools, or general
conversation — respond without delegating.
Delegation:
- Match each sub-task to the member whose role and tools are the best fit.
Delegate to multiple members when the request spans different areas of
expertise.
- Write task descriptions that are self-contained: state the goal, provide
relevant context from the conversation, and describe what a good result
looks like.
- Use only the member's ID when delegating — do not prefix it with the
team ID.
After receiving member responses:
- If a response is incomplete or off-target, re-delegate with clearer
instructions or try a different member.
- Synthesize all results into a single coherent response. Resolve
contradictions, fill gaps with your own reasoning, and add structure — do
not simply concatenate member outputs.
</how_to_respond>
DEBUG =========================== user ===========================
DEBUG What is machine learning?
Keep it brief.
DEBUG ======================== assistant =========================
DEBUG Machine learning is a branch of AI where computers learn patterns from
data to make predictions or decisions, improving with experience rather
than being explicitly programmed for each task.
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=427, output=35, total=462
DEBUG ************************ METRICS *************************
DEBUG ---------------- OpenAI Response Stream End ----------------
DEBUG Added RunOutput to Team Session
DEBUG **** Team Run End: 3e0628e5-73e0-45ab-b92b-c5093352ec51 ****
┏━ Response (1.8s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ Machine learning is a branch of AI where computers learn patterns from data ┃
┃ to make predictions or decisions, improving with experience rather than ┃
┃ being explicitly programmed for each task. ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛DEBUG ****************** Team ID: research-team ******************
DEBUG ***** Session ID: 1a2c8cf4-9d16-4c3c-b0a7-cd695e82f52c *****
DEBUG Creating new TeamSession: 1a2c8cf4-9d16-4c3c-b0a7-cd695e82f52c
DEBUG *** Team Run Start: a8707b4a-c653-4c06-9349-9d0e47c8d0c0 ***
DEBUG Processing tools for model
DEBUG Added tool delegate_task_to_member
DEBUG --------------- OpenAI Response Stream Start ---------------
DEBUG ---------------------- Model: gpt-5.2 ----------------------
DEBUG ========================== system ==========================
DEBUG You coordinate a team of specialized AI agents to fulfill the user's
request. Delegate to members when their expertise or tools are needed. For
straightforward requests you can handle directly — including using your
own tools — respond without delegating.
<team_members>
<member id="researcher" name="Researcher">
Role: Research topics
</member>
</team_members>
<how_to_respond>
You operate in coordinate mode. For requests that need member expertise,
select the best member(s), delegate with clear task descriptions, and
synthesize their outputs into a unified response. For requests you can
handle directly — simple questions, using your own tools, or general
conversation — respond without delegating.
Delegation:
- Match each sub-task to the member whose role and tools are the best fit.
Delegate to multiple members when the request spans different areas of
expertise.
- Write task descriptions that are self-contained: state the goal, provide
relevant context from the conversation, and describe what a good result
looks like.
- Use only the member's ID when delegating — do not prefix it with the
team ID.
After receiving member responses:
- If a response is incomplete or off-target, re-delegate with clearer
instructions or try a different member.
- Synthesize all results into a single coherent response. Resolve
contradictions, fill gaps with your own reasoning, and add structure — do
not simply concatenate member outputs.
</how_to_respond>
DEBUG =========================== user ===========================
DEBUG [{'role': 'user', 'content': 'What is deep learning?'}]
DEBUG ======================== assistant =========================
DEBUG Deep learning is a branch of machine learning that trains artificial
neural networks with many layers (“deep” networks) to learn patterns
from data.
- **How it works:** Instead of hand-crafting features (rules like “edges”
or “keywords”), a deep network learns a hierarchy of representations
automatically—for example, in images it may learn edges → textures → parts
→ objects.
- **What it’s good at:** Problems with lots of data and complex patterns,
such as **image recognition**, **speech recognition**, **machine
translation**, **text generation**, and **medical imaging**.
- **Why it matters:** With enough data and compute (often GPUs/TPUs), deep
learning models can achieve very high accuracy by optimizing their
parameters using methods like **backpropagation** and **gradient
descent**.
Common deep learning models include **convolutional neural networks
(CNNs)** for images, **recurrent networks (RNNs)** and **transformers**
for sequences like text and audio.
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=436, output=205, total=641
DEBUG ************************ METRICS *************************
DEBUG ---------------- OpenAI Response Stream End ----------------
DEBUG Added RunOutput to Team Session
DEBUG **** Team Run End: a8707b4a-c653-4c06-9349-9d0e47c8d0c0 ****
┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ What is deep learning? ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Response (4.9s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ Deep learning is a branch of machine learning that trains artificial ┃
┃ neural networks with many layers (“deep” networks) to learn patterns from ┃
┃ data. ┃
┃ ┃
┃ - How it works: Instead of hand-crafting features (rules like “edges” or ┃
┃ “keywords”), a deep network learns a hierarchy of representations ┃
┃ automatically—for example, in images it may learn edges → textures → parts → ┃
┃ objects. ┃
┃ - What it’s good at: Problems with lots of data and complex patterns, ┃
┃ such as image recognition, speech recognition, machine ┃
┃ translation, text generation, and medical imaging. ┃
┃ - Why it matters: With enough data and compute (often GPUs/TPUs), deep ┃
┃ learning models can achieve very high accuracy by optimizing their ┃
┃ parameters using methods like backpropagation and gradient descent. ┃
┃ ┃
┃ Common deep learning models include convolutional neural networks (CNNs) ┃
┃ for images, recurrent networks (RNNs) and transformers for sequences ┃
┃ like text and audio. ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Status: FAIL
Description: Example execution attempt
Result: Timeout after 30s
Status: FAIL
Description: Example execution attempt
Result: DEBUG *************** Team ID: stock-research-team ***************
DEBUG ***** Session ID: 07cc0364-71c6-4cb4-8849-af24a3b0fe69 *****
DEBUG Creating new TeamSession: 07cc0364-71c6-4cb4-8849-af24a3b0fe69
DEBUG Model supports native structured outputs but it is not enabled. Using JSON
mode instead.
DEBUG *** Team Run Start: 2ec599e8-ce1e-4943-8c19-56391f69a7c7 ***
DEBUG Processing tools for model
DEBUG Added tool delegate_task_to_member
DEBUG ------------------ OpenAI Response Start -------------------
DEBUG ---------------------- Model: gpt-5.2 ----------------------
DEBUG ========================== system ==========================
DEBUG You coordinate a team of specialized AI agents to fulfill the user's
request. Delegate to members when their expertise or tools are needed. For
straightforward requests you can handle directly — including using your
own tools — respond without delegating.
<team_members>
<member id="stock-searcher" name="Stock Searcher">
Role: Searches for information on stocks and provides price analysis.
</member>
<member id="company-info-searcher" name="Company Info Searcher">
Role: Searches for information about companies and recent news.
</member>
</team_members>
<how_to_respond>
You operate in route mode. For requests that need member expertise,
identify the single best member and delegate to them — their response is
returned directly to the user. For requests you can handle directly —
simple questions, using your own tools, or general conversation — respond
without delegating.
When routing to a member:
- Analyze the request to determine which member's role and tools are the
best match.
- Delegate to exactly one member. Use only the member's ID — do not prefix
it with the team ID.
- Write the task to faithfully represent the user's full intent. Do not
reinterpret or narrow the request.
- If no member is a clear fit, choose the closest match and include any
additional context the member might need.
</how_to_respond>
Provide your output as a JSON containing the following fields:
<json_fields>
{"type": "json_schema", "json_schema": {"name": "StockAnalysis", "schema":
{"type": "object", "properties": {"symbol": {"type": "string",
"description": "Stock ticker symbol"}, "company_name": {"type": "string",
"description": "Company name"}, "analysis": {"type": "string",
"description": "Brief analysis"}}, "required": ["symbol", "company_name",
"analysis"], "additionalProperties": false}}}
</json_fields>
Start your response with `{` and end it with `}`.
Your output will be passed to json.loads() to convert it to a Python
object.
Make sure it only contains valid JSON.
DEBUG =========================== user ===========================
DEBUG What is the current stock price of NVDA?
DEBUG ======================== assistant =========================
DEBUG Tool Calls:
- ID: 'fc_0d7db2d456c8a7fe00699117a672588197a00f94fe2443088f'
Name: 'delegate_task_to_member'
Arguments: 'member_id: stock-searcher, task: Find the current stock
price of NVDA (NVIDIA) and provide a brief price analysis (e.g., latest
price, % change, notable context like recent trend or session move).
Return concise info.'
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=571, output=67, total=638
DEBUG * Duration: 1.9539s
DEBUG * Tokens per second: 34.2912 tokens/s
DEBUG ************************ METRICS *************************
DEBUG Running: delegate_task_to_member(member_id=stock-searcher, task=...)
DEBUG ***************** Agent ID: stock-searcher *****************
DEBUG Creating new AgentSession: 07cc0364-71c6-4cb4-8849-af24a3b0fe69
DEBUG Setting Model.response_format to Agent.output_schema
DEBUG ** Agent Run Start: b62a7305-035c-4c57-b937-e44ed5fc8c9a ***
DEBUG Processing tools for model
DEBUG Added tool web_search from websearch
DEBUG Added tool search_news from websearch
DEBUG ------------------ OpenAI Response Start -------------------
DEBUG ---------------------- Model: gpt-5.2 ----------------------
DEBUG ========================== system ==========================
DEBUG <your_role>
Searches for information on stocks and provides price analysis.
</your_role>
DEBUG =========================== user ===========================
DEBUG Find the current stock price of NVDA (NVIDIA) and provide a brief price
analysis (e.g., latest price, % change, notable context like recent trend
or session move). Return concise info.
ERROR API status error from OpenAI API: Error code: 400 - {'error':
{'message': "Missing required parameter: 'text.format.name'.", 'type':
'invalid_request_error', 'param': 'text.format.name', 'code':
'missing_required_parameter'}}
ERROR Non-retryable model provider error: Missing required parameter:
'text.format.name'.
ERROR Error in Agent run: Missing required parameter: 'text.format.name'.
DEBUG Added RunOutput to Agent Session
DEBUG Updated team run context with member name: Stock Searcher
DEBUG Added RunOutput to Team Session
DEBUG =========================== tool ===========================
DEBUG Tool call Id: call_0gmjRcZtVu84Y4xzVWv4OMRv
DEBUG Missing required parameter: 'text.format.name'.
DEBUG ********************** TOOL METRICS **********************
DEBUG * Duration: 0.0005s
DEBUG ********************** TOOL METRICS **********************
DEBUG ------------------- OpenAI Response End --------------------
WARNING Failed to parse cleaned JSON: Expecting value: line 1 column 1 (char 0)
WARNING All parsing attempts failed.
WARNING Failed to parse JSON response
DEBUG Added RunOutput to Team Session
DEBUG **** Team Run End: 2ec599e8-ce1e-4943-8c19-56391f69a7c7 ****
Status: PASS
Description: Example execution attempt
Result: DEBUG ****** Team ID: e2952247-c0fd-4c41-a528-550a1fe8d296 *******
DEBUG ***** Session ID: 6377953f-f519-4b81-b19b-4700cc14a51a *****
DEBUG Creating new TeamSession: 6377953f-f519-4b81-b19b-4700cc14a51a
DEBUG *** Team Run Start: 84e4ef2f-c193-443a-ade7-532c7d8e0b73 ***
DEBUG Processing tools for model
DEBUG Added tool delegate_task_to_member
DEBUG --------------- OpenAI Response Stream Start ---------------
DEBUG -------------------- Model: gpt-5-mini ---------------------
DEBUG ========================== system ==========================
DEBUG You coordinate a team of specialized AI agents to fulfill the user's
request. Delegate to members when their expertise or tools are needed. For
straightforward requests you can handle directly — including using your
own tools — respond without delegating.
<team_members>
<member id="itinerary-planner" name="Itinerary Planner">
Description: You help people plan amazing vacations. Use the tools at
your disposal to find latest information about the destination.
</member>
</team_members>
<how_to_respond>
You operate in coordinate mode. For requests that need member expertise,
select the best member(s), delegate with clear task descriptions, and
synthesize their outputs into a unified response. For requests you can
handle directly — simple questions, using your own tools, or general
conversation — respond without delegating.
Delegation:
- Match each sub-task to the member whose role and tools are the best fit.
Delegate to multiple members when the request spans different areas of
expertise.
- Write task descriptions that are self-contained: state the goal, provide
relevant context from the conversation, and describe what a good result
looks like.
- Use only the member's ID when delegating — do not prefix it with the
team ID.
After receiving member responses:
- If a response is incomplete or off-target, re-delegate with clearer
instructions or try a different member.
- Synthesize all results into a single coherent response. Resolve
contradictions, fill gaps with your own reasoning, and add structure — do
not simply concatenate member outputs.
</how_to_respond>
DEBUG =========================== user ===========================
DEBUG Plan a summer vacation in Paris
DEBUG ======================== assistant =========================
DEBUG Great — I can plan that. A few quick questions so I can create a
personalized, ready-to-use Paris summer itinerary:
1. Dates / flexibility
- Exact dates or month? How many days total (including
arrival/departure)?
2. Travelers
- How many people and their ages (adults, kids, seniors)?
3. Budget
- Rough budget per person per day or total (economy / mid-range /
luxury)?
4. Interests & priorities (choose top 3)
- Classic sights (Eiffel, Louvre), museums & art, food & markets, cafés
& bistros, shopping, parks & picnics, nightlife, romantic, family/kid
activities, architecture, photography, markets, day trips (Versailles,
Giverny, Loire, Champagne), festivals/events.
5. Pace
- Relaxed (1–2 activities/day) or packed (3–4 activities/day)?
6. Accommodation preference
- Hotel, apartment/Airbnb, boutique, or location preference
(1st/4th/5th/6th/7th/Le Marais/Latin Quarter/near Gare du Nord/near
Eiffel)?
7. Mobility / accessibility / dietary restrictions
- Any mobility limitations, dietary needs, or other constraints?
8. Transport preferences
- Metro / walking / taxis / rent a car or prefer organized day tours?
9. Extras
- Want restaurant reservations, museum skip-the-line tickets, river
cruise, Seine dinner, or picnic plans?
If you’d like, pick one of these ready-made themes now and I’ll build a
detailed day-by-day plan + suggested neighborhoods, sample hotels,
restaurants, transport tips, estimated costs, packing list, and booking
checklist:
- Classic 5-day Paris: monuments + museums + Seine cruise
- Relaxed 7-day Paris with day trips: Versailles + Giverny or Champagne
- Food & Market 5-day: markets, bistros, food tours, cooking class
- Art & Culture 7-day: Louvre, Orsay, Pompidou, lesser-known galleries,
day trips to Rouen/Giverny
Tell me the answers (or which theme), and I’ll create the full itinerary.
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=445, output=991, total=1436,
reasoning=512
DEBUG ************************ METRICS *************************
DEBUG ---------------- OpenAI Response Stream End ----------------
DEBUG --------------- OpenAI Response Stream Start ---------------
DEBUG -------------------- Model: gpt-5-mini ---------------------
DEBUG ========================== system ==========================
DEBUG You coordinate a team of specialized AI agents to fulfill the user's
request. Delegate to members when their expertise or tools are needed. For
straightforward requests you can handle directly — including using your
own tools — respond without delegating.
<team_members>
<member id="itinerary-planner" name="Itinerary Planner">
Description: You help people plan amazing vacations. Use the tools at
your disposal to find latest information about the destination.
</member>
</team_members>
<how_to_respond>
You operate in coordinate mode. For requests that need member expertise,
select the best member(s), delegate with clear task descriptions, and
synthesize their outputs into a unified response. For requests you can
handle directly — simple questions, using your own tools, or general
conversation — respond without delegating.
Delegation:
- Match each sub-task to the member whose role and tools are the best fit.
Delegate to multiple members when the request spans different areas of
expertise.
- Write task descriptions that are self-contained: state the goal, provide
relevant context from the conversation, and describe what a good result
looks like.
- Use only the member's ID when delegating — do not prefix it with the
team ID.
After receiving member responses:
- If a response is incomplete or off-target, re-delegate with clearer
instructions or try a different member.
- Synthesize all results into a single coherent response. Resolve
contradictions, fill gaps with your own reasoning, and add structure — do
not simply concatenate member outputs.
</how_to_respond>
DEBUG =========================== user ===========================
DEBUG Plan a summer vacation in Paris
DEBUG ======================== assistant =========================
DEBUG Great — I can plan that. A few quick questions so I can make the itinerary
fit you perfectly:
1. How many travelers and their ages? (Adults, kids, seniors)
2. Travel dates or approximate month and how many nights/days do you want
to spend in Paris?
3. Budget per person (rough): budget, mid-range, or luxury?
4. Main interests/priorities: museums & monuments, food & cafés, shopping,
parks & picnics, nightlife, romantic, family/kids activities, accessible
travel, day trips from Paris, etc.
5. Accommodation preferences: hotel, apartment/Airbnb, boutique, or
specific neighborhoods you prefer (Marais, Saint‑Germain, Latin Quarter,
Opera, Montmartre, Champs‑Élysées)?
6. Any mobility or dietary restrictions, or repeat-visitor
must-sees/already-seen attractions?
7. Would you like me to include bookings/reservation links,
public-transport details, estimated costs, and recommended restaurants by
budget?
If you’re not sure, I can propose a ready-to-use 7-day summer sample
itinerary (with options for 4- and 10-day versions), neighborhood and
transport advice, day-trip suggestions (Versailles, Giverny, Loire, etc.),
packing/weather tips, and a booking timeline. Tell me which option you
prefer.
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=322, output=909, total=1231,
reasoning=576
DEBUG ************************ METRICS *************************
DEBUG ---------------- OpenAI Response Stream End ----------------
DEBUG Added RunOutput to Team Session
DEBUG **** Team Run End: 84e4ef2f-c193-443a-ade7-532c7d8e0b73 ****
┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ Plan a summer vacation in Paris ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Response (17.2s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ Great — I can plan that. A few quick questions so I can make the itinerary ┃
┃ fit you perfectly: ┃
┃ ┃
┃ 1. How many travelers and their ages? (Adults, kids, seniors) ┃
┃ 2. Travel dates or approximate month and how many nights/days do you want to ┃
┃ spend in Paris? ┃
┃ 3. Budget per person (rough): budget, mid-range, or luxury? ┃
┃ 4. Main interests/priorities: museums & monuments, food & cafés, shopping, ┃
┃ parks & picnics, nightlife, romantic, family/kids activities, accessible ┃
┃ travel, day trips from Paris, etc. ┃
┃ 5. Accommodation preferences: hotel, apartment/Airbnb, boutique, or specific ┃
┃ neighborhoods you prefer (Marais, Saint‑Germain, Latin Quarter, Opera, ┃
┃ Montmartre, Champs‑Élysées)? ┃
┃ 6. Any mobility or dietary restrictions, or repeat-visitor ┃
┃ must-sees/already-seen attractions? ┃
┃ 7. Would you like me to include bookings/reservation links, public-transport ┃
┃ details, estimated costs, and recommended restaurants by budget? ┃
┃ ┃
┃ If you’re not sure, I can propose a ready-to-use 7-day summer sample ┃
┃ itinerary (with options for 4- and 10-day versions), neighborhood and ┃
┃ transport advice, day-trip suggestions (Versailles, Giverny, Loire, etc.), ┃
┃ packing/weather tips, and a booking timeline. Tell me which option you ┃
┃ prefer. ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Status: FAIL
Description: Example execution attempt
Result: Timeout after 30s
Status: FAIL
Description: Example execution attempt
Result: Timeout after 30s
Status: FAIL
Description: Example execution attempt
Result: Timeout after 30s
Status: PASS
Description: Example execution attempt
Result: DEBUG *************** Team ID: stock-research-team ***************
DEBUG ***** Session ID: 90701a87-cc0f-44b3-bcbf-8333f4d3527c *****
DEBUG Creating new TeamSession: 90701a87-cc0f-44b3-bcbf-8333f4d3527c
DEBUG Setting Model.response_format to Agent.output_schema
DEBUG *** Team Run Start: dd59db94-3300-43ba-93c7-81eeabe8ed86 ***
DEBUG Processing tools for model
DEBUG Added tool delegate_task_to_member
DEBUG ------------------ OpenAI Response Start -------------------
DEBUG ---------------------- Model: gpt-5.2 ----------------------
DEBUG ========================== system ==========================
DEBUG You coordinate a team of specialized AI agents to fulfill the user's
request. Delegate to members when their expertise or tools are needed. For
straightforward requests you can handle directly — including using your
own tools — respond without delegating.
<team_members>
<member id="stock-searcher" name="Stock Searcher">
Role: Searches for information on stocks and provides price analysis.
</member>
<member id="company-info-searcher" name="Company Info Searcher">
Role: Searches for information about companies and recent news.
</member>
</team_members>
<how_to_respond>
You operate in route mode. For requests that need member expertise,
identify the single best member and delegate to them — their response is
returned directly to the user. For requests you can handle directly —
simple questions, using your own tools, or general conversation — respond
without delegating.
When routing to a member:
- Analyze the request to determine which member's role and tools are the
best match.
- Delegate to exactly one member. Use only the member's ID — do not prefix
it with the team ID.
- Write the task to faithfully represent the user's full intent. Do not
reinterpret or narrow the request.
- If no member is a clear fit, choose the closest match and include any
additional context the member might need.
</how_to_respond>
DEBUG =========================== user ===========================
DEBUG What is the current stock price of NVDA?
DEBUG ======================== assistant =========================
DEBUG Tool Calls:
- ID: 'fc_04adf6e921f67948006991181510c08190ae3641351499d56b'
Name: 'delegate_task_to_member'
Arguments: 'member_id: stock-searcher, task: Find the current/most
recent stock price of NVDA (NVIDIA). Provide the price, currency,
timestamp/timezone, and source/market session context
(pre/regular/after-hours) if available.'
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=468, output=68, total=536
DEBUG * Duration: 2.0919s
DEBUG * Tokens per second: 32.5056 tokens/s
DEBUG ************************ METRICS *************************
DEBUG Running: delegate_task_to_member(member_id=stock-searcher, task=...)
DEBUG ***************** Agent ID: stock-searcher *****************
DEBUG Creating new AgentSession: 90701a87-cc0f-44b3-bcbf-8333f4d3527c
DEBUG Setting Model.response_format to Agent.output_schema
DEBUG ** Agent Run Start: 394134ed-ea7b-47f8-a40b-943df6fd9606 ***
DEBUG Processing tools for model
DEBUG Added tool web_search from websearch
DEBUG Added tool search_news from websearch
DEBUG ------------------ OpenAI Response Start -------------------
DEBUG ---------------------- Model: gpt-5.2 ----------------------
DEBUG ========================== system ==========================
DEBUG <your_role>
Searches for information on stocks and provides price analysis.
</your_role>
DEBUG =========================== user ===========================
DEBUG Find the current/most recent stock price of NVDA (NVIDIA). Provide the
price, currency, timestamp/timezone, and source/market session context
(pre/regular/after-hours) if available.
DEBUG ======================== assistant =========================
DEBUG Tool Calls:
- ID: 'fc_0c861f659a4a86740069911818072481909cf4715a3fcf3882'
Name: 'web_search'
Arguments: 'query: NVDA stock price quote timestamp premarket after
hours, max_results: 5'
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=253, output=32, total=285
DEBUG * Duration: 2.3284s
DEBUG * Tokens per second: 13.7433 tokens/s
DEBUG ************************ METRICS *************************
DEBUG Running: web_search(query=..., max_results=5)
DEBUG Searching web for: NVDA stock price quote timestamp premarket after hours
using backend: auto
DEBUG =========================== tool ===========================
DEBUG Tool call Id: call_e7j5eXvtqfcUqw73Nx7Kvdnv
DEBUG [
{
"title": "NVIDIA Corporation Common Stock (NVDA) Pre-Market Quote",
"href":
"https://www.nasdaq.com/market-activity/stocks/nvda/pre-market?option_chai
n_id=0",
"body": "Get the latest updates on NVIDIA Corporation Common Stock (
NVDA ) pre market trades, share volumes, and more. Make informed
investments with Nasdaq."
},
{
"title": "NVDA Trading Hours VWAP Nvidia - MarketChameleon.com",
"href":
"https://marketchameleon.com/Overview/NVDA/Stock-Price-Action/VWAP",
"body": "View Nvidia ( NVDA ) Stock Price for pre-market , after-hours
and regular trading sessions in a minute-by-minute Volume-Weighted Average
Price (VWAP) table."
},
{
"title": "Nvidia Stock Price Today | NASDAQ: NVDA Live -
Investing.com",
"href": "https://www.investing.com/equities/nvidia-corp",
"body": "View the NVDA premarket stock price ahead of the market
session or assess the after hours quote . Monitor the latest movements
within the NVIDIA Corporation real time stock price chart below."
},
{
"title": "NVIDIA Corporation (NVDA) Stock Price, News, Quote & History
- Yahoo ...",
"href": "https://finance.yahoo.com/quote/NVDA/",
"body": "Find the latest NVIDIA Corporation ( NVDA ) stock quote ,
history, news and other vital information to help you with your stock
trading and investing."
},
{
"title": "Nvidia Pre Market and After Hours Trading - City Index UK",
"href":
"https://www.cityindex.com/en-uk/share-trading/nvidia-extended-hours/",
"body": "See the live price of Nvidia shares after hours and
pre-market . Trade our extended hours Nvidia market today with City Index
UK."
}
]
DEBUG ********************** TOOL METRICS **********************
DEBUG * Duration: 1.3167s
DEBUG ********************** TOOL METRICS **********************
DEBUG Using previous_response_id:
resp_0c861f659a4a86740069911817986081908ae9d8efdad1c935
DEBUG ======================== assistant =========================
DEBUG Tool Calls:
- ID: 'fc_0c861f659a4a8674006991181ab45c8190be49640b6ad28513'
Name: 'web_search'
Arguments: 'query: site:finance.yahoo.com NVDA quote price,
max_results: 5'
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=709, output=31, total=740
DEBUG * Duration: 1.2411s
DEBUG * Tokens per second: 24.9769 tokens/s
DEBUG ************************ METRICS *************************
DEBUG Running: web_search(query=..., max_results=5)
DEBUG Searching web for: site:finance.yahoo.com NVDA quote price using backend:
auto
DEBUG =========================== tool ===========================
DEBUG Tool call Id: call_J6qla31KYWNdi1qceMN3BXlq
DEBUG [
{
"title": "NVIDIA Corporation (NVDA) Stock Price, News, Quote & History
...",
"href": "https://finance.yahoo.com/quote/NVDA/?fr=sycsrp_catchall",
"body": "Find the latest NVIDIA Corporation (NVDA) stock quote ,
history, news and other vital information to help you with your stock
trading and investing."
},
{
"title": "NVIDIA Corporation (NVDA) Stock Price, News, Quote & History
...",
"href": "https://ca.finance.yahoo.com/quote/NVDA/",
"body": "Find the latest NVIDIA Corporation (NVDA) stock quote ,
history, news and other vital information to help you with your stock
trading and investing."
},
{
"title": "NVIDIA Corporation (NVDA) Interactive Stock Chart - Yahoo
Finance",
"href":
"https://finance.yahoo.com/quote/NVDA/chart/?fr=sycsrp_catchall",
"body": "Interactive Chart for NVIDIA Corporation ( NVDA ), analyze
all the data with a huge range of indicators."
},
{
"title": "NVDA | Stock Prices | Quote Comparison - Yahoo Finance",
"href": "https://ca.finance.yahoo.com/quotes/NVDA/",
"body": "View and compare NVDA on Yahoo Finance ."
},
{
"title": "NVIDIA Corporation (NVDA) Stock Historical Prices & Data ...
NVIDIA Corporation (NVDA) Analyst Insights, Price Targets ... NVDA
Interactive Stock Chart - Yahoo Finance",
"href":
"https://finance.yahoo.com/quote/NVDA/history/?fr=sycsrp_catchall",
"body": "Discover historical prices for NVDA stock on Yahoo Finance .
View daily, weekly or monthly format back to when NVIDIA Corporation stock
was issued. See full list on finance . yahoo . com Yahoo Finance provides
the latest analyst insights, price targets, and recommendations on NVIDIA
Corporation (NVDA) to help inform your investment strategy. At Yahoo
Finance , you get free stock quotes , up-to-date news, portfolio
management resources, international market data, social interaction and
mortgage rates that help you manage your..."
}
]
DEBUG ********************** TOOL METRICS **********************
DEBUG * Duration: 2.1097s
DEBUG ********************** TOOL METRICS **********************
DEBUG Using previous_response_id:
resp_0c861f659a4a8674006991181a3a748190975106eb3de4cdfe
DEBUG ======================== assistant =========================
DEBUG Tool Calls:
- ID: 'fc_0c861f659a4a8674006991181e16a88190b7784598adc7e114'
Name: 'web_search'
Arguments: 'query: NVDA quote Nasdaq regular market price timestamp,
max_results: 5'
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=1213, output=30, total=1243
DEBUG * Duration: 1.0778s
DEBUG * Tokens per second: 27.8342 tokens/s
DEBUG ************************ METRICS *************************
DEBUG Running: web_search(query=..., max_results=5)
DEBUG Searching web for: NVDA quote Nasdaq regular market price timestamp using
backend: auto
DEBUG =========================== tool ===========================
DEBUG Tool call Id: call_eeZU1bUiZoI696EpHpJK7396
DEBUG [
{
"title": "NVIDIA Corporation Common Stock (NVDA) - Nasdaq",
"href": "https://www.nasdaq.com/market-activity/stocks/nvda",
"body": "Discover real-time NVIDIA Corporation Common Stock ( NVDA )
stock prices , quotes , historical data, news, and Insights for informed
trading and investment decisions. Stay ahead with Nasdaq ."
},
{
"title": "NVIDIA Corp (NVDA) Stock Price & News - Google Finance",
"href": "https://www.google.com/finance/quote/NVDA:NASDAQ",
"body": "Get the latest NVIDIA Corp ( NVDA ) real-time quote ,
historical performance, charts, and other financial information to help
you make more informed trading and investment decisions."
},
{
"title": "NVIDIA Corporation (NVDA) Stock Price, News, Quote & History
- Yahoo ...",
"href": "https://finance.yahoo.com/quote/NVDA/",
"body": "Find the latest NVIDIA Corporation ( NVDA ) stock quote ,
history, news and other vital information to help you with your stock
trading and investing."
},
{
"title": "NVDA Stock Price | NVIDIA Corp. Stock Quote (U.S.: Nasdaq) |
MarketWatch",
"href": "https://www.marketwatch.com/investing/stock/NVDA",
"body": "NVDA | Complete NVIDIA Corp. stock news by MarketWatch. View
real-time stock prices and stock quotes for a full financial overview."
},
{
"title": "NVIDIA Corporation Stock (NVDA) - Quote Nasdaq-
MarketScreener",
"href":
"https://m.marketscreener.com/quote/stock/NVIDIA-CORPORATION-57355629/",
"body": "NVIDIA Corporation (NVDA:NASDAQ): Stock quote , stock chart,
quotes , analysis, advice, financials and news for Stock NVIDIA
Corporation | Nasdaq : NVDA | Nasdaq"
}
]
DEBUG ********************** TOOL METRICS **********************
DEBUG * Duration: 1.0880s
DEBUG ********************** TOOL METRICS **********************
DEBUG Using previous_response_id:
resp_0c861f659a4a8674006991181d971c8190ad48481e072bede2
DEBUG ======================== assistant =========================
DEBUG Tool Calls:
- ID: 'fc_0c861f659a4a86740069911820387481909669883ca05aa194'
Name: 'web_search'
Arguments: 'query: NVDA:NASDAQ price UTC time quote, max_results: 5'
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=1676, output=30, total=1706,
cached=1152
DEBUG * Duration: 1.2501s
DEBUG * Tokens per second: 23.9975 tokens/s
DEBUG ************************ METRICS *************************
DEBUG Running: web_search(query=NVDA:NASDAQ price UTC time quote, max_results=5)
DEBUG Searching web for: NVDA:NASDAQ price UTC time quote using backend: auto
DEBUG =========================== tool ===========================
DEBUG Tool call Id: call_bBAx21nvo32I14ReulAQiqRN
DEBUG [
{
"title": "NVDA Stock Price & Chart | Nvidia Corp. (NASDAQ) -
Metadoro",
"href": "https://metadoro.com/markets/shares/nvda",
"body": "Meanwhile, Barclays banking group chose to raise its mid-term
price target on Nvidia shares to $200 from $170, pointing to solid supply
chain demand ..."
},
{
"title": "Nvidia Stock | Nvidia Stock Quote | Buy NVDA Stock | Invest
in",
"href": "https://www.ifcmtz.com/en/trading-conditions/stocks/nvidia",
"body": "... time when investors can buy and sell ... Depending on
where you live, you may be able to trade NVDA shares at any time
throughout the day or night."
},
{
"title": "S&P 500, Nasdaq post record closes, Nvidia closing",
"href":
"https://www.reuters.com/world/china/global-markets-wrapup-1-2025-07-10/",
"body": "... Nasdaq registered record closing highs on Thursday, and
Nvidia s ( NVDA .O) , opens new tab market value closed above $4 trillion
for the first time , ..."
},
{
"title": "Nasdaq, S&P 500 end sharply lower, hit by chips, megacaps;",
"href":
"https://www.reuters.com/markets/us/wall-st-futures-slump-after-rally-tech
-stocks-slide-2024-07-17/",
"body": "... Magnificent 7 " group of momentum stocks, led by Nvidia
( NVDA .O) , opens new tab and Apple (AAPL.O) , opens new tab , dragged
the Nasdaq 2.8 ..."
},
{
"title": "Nvidia Stock | Nvidia Stock Quote | Buy NVDA Stock | Invest
in",
"href":
"https://www.ifcmarkets.com/en/trading-conditions/stocks/nvidia",
"body": "... time when investors can buy and sell ... Depending on
where you live, you may be able to trade NVDA shares at any time
throughout the day or night."
}
]
DEBUG ********************** TOOL METRICS **********************
DEBUG * Duration: 1.5996s
DEBUG ********************** TOOL METRICS **********************
DEBUG Using previous_response_id:
resp_0c861f659a4a8674006991181fc46c8190a87f0183453ed17d
DEBUG ======================== assistant =========================
DEBUG {"symbol":"NVDA","company_name":"NVIDIA Corporation","analysis":"I can’t
reliably provide the current/most recent NVDA price from here because
the web results I can access right now don’t include the live quote
value/timestamp directly (they’re links to quote pages that render the
price dynamically).\n\nIf you open one of these sources, you’ll see the
latest price along with the timestamp and whether it’s regular /
pre-market / after-hours:\n- Nasdaq quote page (NVDA):
https://www.nasdaq.com/market-activity/stocks/nvda (shows last price;
often includes market session context)\n- Nasdaq pre-market page:
https://www.nasdaq.com/market-activity/stocks/nvda/pre-market\n- Yahoo
Finance quote page: https://finance.yahoo.com/quote/NVDA/\n- Google
Finance: https://www.google.com/finance/quote/NVDA:NASDAQ\n\nIf you tell
me which source you want (Nasdaq/Yahoo/Google) and paste the displayed
quote line (price + time/session) from that page, I’ll format it exactly
as: price, currency, timestamp/timezone, and market session
(pre/regular/after-hours)."}
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=2171, output=265, total=2436
DEBUG * Duration: 5.0176s
DEBUG * Tokens per second: 52.8140 tokens/s
DEBUG ************************ METRICS *************************
DEBUG ------------------- OpenAI Response End --------------------
DEBUG Added RunOutput to Agent Session
DEBUG *** Agent Run End: 394134ed-ea7b-47f8-a40b-943df6fd9606 ****
DEBUG Updated team run context with member name: Stock Searcher
DEBUG Added RunOutput to Team Session
DEBUG =========================== tool ===========================
DEBUG Tool call Id: call_DOZSWEF0pD77EY9vApcYPXyv
DEBUG {
"symbol": "NVDA",
"company_name": "NVIDIA Corporation",
"analysis": "I can’t reliably provide the current/most recent NVDA
price from here because the web results I can access right now don’t
include the live quote value/timestamp directly (they’re links to quote
pages that render the price dynamically).\n\nIf you open one of these
sources, you’ll see the latest price along with the timestamp and whether
it’s regular / pre-market / after-hours:\n- Nasdaq quote page (NVDA):
https://www.nasdaq.com/market-activity/stocks/nvda (shows last price;
often includes market session context)\n- Nasdaq pre-market page:
https://www.nasdaq.com/market-activity/stocks/nvda/pre-market\n- Yahoo
Finance quote page: https://finance.yahoo.com/quote/NVDA/\n- Google
Finance: https://www.google.com/finance/quote/NVDA:NASDAQ\n\nIf you tell
me which source you want (Nasdaq/Yahoo/Google) and paste the displayed
quote line (price + time/session) from that page, I’ll format it exactly
as: price, currency, timestamp/timezone, and market session
(pre/regular/after-hours)."
}
DEBUG ********************** TOOL METRICS **********************
DEBUG * Duration: 0.0005s
DEBUG ********************** TOOL METRICS **********************
DEBUG ------------------- OpenAI Response End --------------------
DEBUG Added RunOutput to Team Session
DEBUG **** Team Run End: dd59db94-3300-43ba-93c7-81eeabe8ed86 ****
╭──────────────────────────────────────────────────────────────────────────────╮
│ { │
│ "symbol": "NVDA", │
│ "company_name": "NVIDIA Corporation", │
│ "analysis": "I can’t reliably provide the current/most recent NVDA pric… │
│ } │
╰──────────────────────────────────────────────────────────────────────────────╯
Status: FAIL
Description: Example execution attempt
Result: Timeout after 30s
Status: FAIL
Description: Example execution attempt
Result: Timeout after 30s
Status: FAIL
Description: Example execution attempt
Result: Timeout after 30s
Status: FAIL
Description: Example execution attempt
Result: Timeout after 30s
Status: FAIL
Description: Example execution attempt
Result: FAIL. Timeout after 120s
Status: FAIL
Description: Example execution attempt
Result: FAIL. Timeout after 120s
Status: PASS
Description: Example executed (demo run)
Result: PASS
Status: FAIL
Description: Example execution attempt
Result: FAIL. Timeout after 120s
Status: FAIL
Description: Example execution attempt
Result: FAIL. Timeout after 120s