cookbook/03_teams/02_modes/TEST_LOG.md
Status: PASS
none
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: research-&-writing-team *************
DEBUG ***** Session ID: 9a21e085-a1ff-4f65-b257-af4ae943894a *****
DEBUG Creating new TeamSession: 9a21e085-a1ff-4f65-b257-af4ae943894a
DEBUG *** Team Run Start: 17d0e719-c39b-4657-bb15-b35eedabff51 ***
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 specialist who finds and summarizes information
</member>
<member id="writer" name="Writer">
Role: Content writer who crafts polished, engaging text
</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>
- You lead a research and writing team.
- For informational requests, ask the Researcher to gather facts first,
- then ask the Writer to polish the findings into a final piece.
- Synthesize everything into a cohesive response.
<additional_information>
- Use markdown to format your answers.
</additional_information>
DEBUG =========================== user ===========================
DEBUG Write a brief overview of how large language models are trained, covering
pre-training, fine-tuning, and RLHF.
DEBUG ======================== assistant =========================
DEBUG ## Overview: How Large Language Models (LLMs) Are Trained
### 1) Pre-training (foundation learning)
Pre-training is the stage where an LLM learns general language patterns
and broad knowledge from very large datasets (e.g., books, web pages,
code). The model is typically trained with **self-supervised learning**,
most commonly by predicting missing or next tokens in text.
- **Objective:** Learn grammar, facts, reasoning patterns, and
representations of language.
- **How it works:** The model sees huge amounts of text and repeatedly
updates its parameters to reduce prediction error.
- **Result:** A **base model** that can generate fluent text but isn’t
necessarily aligned to follow instructions safely or helpfully.
### 2) Fine-tuning (task- or instruction-specific training)
Fine-tuning adapts the pre-trained model to behave in a more useful way
for specific tasks or interaction styles—often to follow instructions,
answer questions, or produce desired formats.
Common forms include:
- **Supervised fine-tuning (SFT):** Train on curated input→output examples
(e.g., prompts paired with high-quality answers).
- **Domain fine-tuning:** Further train on specialized text (e.g., legal,
medical, internal documentation) to improve performance in that area.
- **Objective:** Improve usefulness on targeted behaviors
(instruction-following, style, domain expertise).
- **Result:** A model that is typically better at producing the kinds of
responses users want, but may still produce unsafe or unhelpful outputs
without additional alignment.
### 3) RLHF (Reinforcement Learning from Human Feedback)
RLHF is an alignment technique used to make the model’s outputs better
match human preferences (helpfulness, harmlessness, honesty, tone, etc.).
A common RLHF pipeline:
1. **Collect human preference data:** Humans rank multiple model responses
to the same prompt.
2. **Train a reward model:** A separate model learns to predict which
responses humans prefer.
3. **Optimize the LLM with reinforcement learning:** The LLM is adjusted
to maximize the reward model’s score (often with algorithms like PPO),
while typically using constraints to avoid degrading language quality.
- **Objective:** Align behavior with human judgments beyond what
supervised examples capture.
- **Result:** A model that more reliably follows instructions and avoids
undesirable outputs, though it can still make mistakes and requires
ongoing evaluation and safeguards.
### Putting it together
- **Pre-training** builds general language ability.
- **Fine-tuning** teaches the model specific tasks and
instruction-following.
- **RLHF** aligns the model’s behavior with human preferences and
interaction norms.
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=534, output=537, total=1071
DEBUG ************************ METRICS *************************
DEBUG ---------------- OpenAI Response Stream End ----------------
DEBUG Added RunOutput to Team Session
DEBUG **** Team Run End: 17d0e719-c39b-4657-bb15-b35eedabff51 ****
┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ Write a brief overview of how large language models are trained, covering ┃
┃ pre-training, fine-tuning, and RLHF. ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Response (11.0s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ Overview: How Large Language Models (LLMs) Are Trained ┃
┃ ┃
┃ 1) Pre-training (foundation learning) ┃
┃ ┃
┃ Pre-training is the stage where an LLM learns general language patterns and ┃
┃ broad knowledge from very large datasets (e.g., books, web pages, code). The ┃
┃ model is typically trained with self-supervised learning, most commonly by ┃
┃ predicting missing or next tokens in text. ┃
┃ ┃
┃ • Objective: Learn grammar, facts, reasoning patterns, and representations ┃
┃ of language. ┃
┃ • How it works: The model sees huge amounts of text and repeatedly updates ┃
┃ its parameters to reduce prediction error. ┃
┃ • Result: A base model that can generate fluent text but isn’t necessarily ┃
┃ aligned to follow instructions safely or helpfully. ┃
┃ ┃
┃ 2) Fine-tuning (task- or instruction-specific training) ┃
┃ ┃
┃ Fine-tuning adapts the pre-trained model to behave in a more useful way for ┃
┃ specific tasks or interaction styles—often to follow instructions, answer ┃
┃ questions, or produce desired formats. ┃
┃ ┃
┃ Common forms include: ┃
┃ ┃
┃ • Supervised fine-tuning (SFT): Train on curated input→output examples ┃
┃ (e.g., prompts paired with high-quality answers). ┃
┃ • Domain fine-tuning: Further train on specialized text (e.g., legal, ┃
┃ medical, internal documentation) to improve performance in that area. ┃
┃ • Objective: Improve usefulness on targeted behaviors ┃
┃ (instruction-following, style, domain expertise). ┃
┃ • Result: A model that is typically better at producing the kinds of ┃
┃ responses users want, but may still produce unsafe or unhelpful outputs ┃
┃ without additional alignment. ┃
┃ ┃
┃ 3) RLHF (Reinforcement Learning from Human Feedback) ┃
┃ ┃
┃ RLHF is an alignment technique used to make the model’s outputs better match ┃
┃ human preferences (helpfulness, harmlessness, honesty, tone, etc.). ┃
┃ ┃
┃ A common RLHF pipeline: ┃
┃ ┃
┃ 1 Collect human preference data: Humans rank multiple model responses to ┃
┃ the same prompt. ┃
┃ 2 Train a reward model: A separate model learns to predict which responses ┃
┃ humans prefer. ┃
┃ 3 Optimize the LLM with reinforcement learning: The LLM is adjusted to ┃
┃ maximize the reward model’s score (often with algorithms like PPO), while ┃
┃ typically using constraints to avoid degrading language quality. ┃
┃ ┃
┃ • Objective: Align behavior with human judgments beyond what supervised ┃
┃ examples capture. ┃
┃ • Result: A model that more reliably follows instructions and avoids ┃
┃ undesirable outputs, though it can still make mistakes and requires ┃
┃ ongoing evaluation and safeguards. ┃
┃ ┃
┃ Putting it together ┃
┃ ┃
┃ • Pre-training builds general language ability. ┃
┃ • Fine-tuning teaches the model specific tasks and instruction-following. ┃
┃ • RLHF aligns the model’s behavior with human preferences and interaction ┃
┃ norms. ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
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: language-router *****************
DEBUG ***** Session ID: c9444aa7-f34a-47bc-8542-0e46a575e03d *****
DEBUG Creating new TeamSession: c9444aa7-f34a-47bc-8542-0e46a575e03d
DEBUG *** Team Run Start: 2b27f07a-3cda-4b2b-b156-d2e2e33728ab ***
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="english-agent" name="English Agent">
Role: Responds only in English
</member>
<member id="spanish-agent" name="Spanish Agent">
Role: Responds only in Spanish
</member>
<member id="french-agent" name="French Agent">
Role: Responds only in French
</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>
- You are a language router.
- Detect the language of the user's message and route to the matching
agent.
- If the language is not supported, default to the English Agent.
<additional_information>
- Use markdown to format your answers.
</additional_information>
DEBUG =========================== user ===========================
DEBUG What is the capital of France?
DEBUG ======================== assistant =========================
DEBUG Tool Calls:
- ID: 'fc_0f73b4c8bc2a0e2500699119475ef481939fba002d408a041c'
Name: 'delegate_task_to_member'
Arguments: 'member_id: english-agent, task: Answer the user's
question: What is the capital of France? Provide a concise response in
English.'
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=475, output=44, total=519
DEBUG ************************ METRICS *************************
DEBUG Running: delegate_task_to_member(member_id=english-agent, task=...)
DEBUG ***************** Agent ID: english-agent ******************
DEBUG Creating new AgentSession: c9444aa7-f34a-47bc-8542-0e46a575e03d
DEBUG ** Agent Run Start: af483319-ad06-41a9-a24e-a7f660901cbc ***
DEBUG --------------- OpenAI Response Stream Start ---------------
DEBUG ---------------------- Model: gpt-5.2 ----------------------
DEBUG ========================== system ==========================
DEBUG <your_role>
Responds only in English
</your_role>
Always respond in English, regardless of the input language.
DEBUG ***************** Team ID: language-router *****************
DEBUG ***** Session ID: c9444aa7-f34a-47bc-8542-0e46a575e03d *****
DEBUG Creating new TeamSession: c9444aa7-f34a-47bc-8542-0e46a575e03d
DEBUG *** Team Run Start: d0fecbb8-812f-4db6-abb8-1028ef6be44b ***
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="english-agent" name="English Agent">
Role: Responds only in English
</member>
<member id="spanish-agent" name="Spanish Agent">
Role: Responds only in Spanish
</member>
<member id="french-agent" name="French Agent">
Role: Responds only in French
</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>
- You are a language router.
- Detect the language of the user's message and route to the matching
agent.
- If the language is not supported, default to the English Agent.
<additional_information>
- Use markdown to format your answers.
</additional_information>
DEBUG =========================== user ===========================
DEBUG Cual es la capital de Francia?
DEBUG ======================== assistant =========================
DEBUG Tool Calls:
- ID: 'fc_0d42a8fe171d74a6006991194a2db88193be6651c15a1adb5c'
Name: 'delegate_task_to_member'
Arguments: 'member_id: spanish-agent, task: Responder en español: el
usuario pregunta cuál es la capital de Francia.'
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=476, output=40, total=516
DEBUG ************************ METRICS *************************
DEBUG Running: delegate_task_to_member(member_id=spanish-agent, task=...)
DEBUG ***** Session ID: c9444aa7-f34a-47bc-8542-0e46a575e03d *****
DEBUG ***************** Agent ID: spanish-agent ******************
DEBUG Creating new AgentSession: c9444aa7-f34a-47bc-8542-0e46a575e03d
DEBUG ** Agent Run Start: fdff637c-a8ec-4be7-876f-95979862ab47 ***
DEBUG --------------- OpenAI Response Stream Start ---------------
DEBUG ---------------------- Model: gpt-5.2 ----------------------
DEBUG ========================== system ==========================
DEBUG <your_role>
Responds only in Spanish
</your_role>
Always respond in Spanish, regardless of the input language.
DEBUG ***************** Team ID: language-router *****************
DEBUG ***** Session ID: c9444aa7-f34a-47bc-8542-0e46a575e03d *****
DEBUG Creating new TeamSession: c9444aa7-f34a-47bc-8542-0e46a575e03d
DEBUG *** Team Run Start: 1b2c07bc-1049-4383-a7bb-b196bab95eeb ***
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="english-agent" name="English Agent">
Role: Responds only in English
</member>
<member id="spanish-agent" name="Spanish Agent">
Role: Responds only in Spanish
</member>
<member id="french-agent" name="French Agent">
Role: Responds only in French
</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>
- You are a language router.
- Detect the language of the user's message and route to the matching
agent.
- If the language is not supported, default to the English Agent.
<additional_information>
- Use markdown to format your answers.
</additional_information>
DEBUG =========================== user ===========================
DEBUG Quelle est la capitale de la France?
DEBUG ======================== assistant =========================
DEBUG Tool Calls:
- ID: 'fc_0d24b5a67f662463006991194cfddc8194a03960d13a139e7d'
Name: 'delegate_task_to_member'
Arguments: 'member_id: french-agent, task: Répondre en français à la
question : « Quelle est la capitale de la France ? »'
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=476, output=45, total=521
DEBUG ************************ METRICS *************************
DEBUG Running: delegate_task_to_member(member_id=french-agent, task=...)
DEBUG ***** Session ID: c9444aa7-f34a-47bc-8542-0e46a575e03d *****
DEBUG ****************** Agent ID: french-agent ******************
DEBUG Creating new AgentSession: c9444aa7-f34a-47bc-8542-0e46a575e03d
DEBUG ** Agent Run Start: 0baf578f-476c-4992-b6ee-81ad3e6238e2 ***
DEBUG --------------- OpenAI Response Stream Start ---------------
DEBUG ---------------------- Model: gpt-5.2 ----------------------
DEBUG ========================== system ==========================
DEBUG <your_role>
Responds only in French
</your_role>
Always respond in French, regardless of the input language.
DEBUG =========================== user ===========================
DEBUG Répondre en français à la question : « Quelle est la capitale de la France
? »
DEBUG ======================== assistant =========================
DEBUG La capitale de la France est Paris.
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=54, output=14, total=68
DEBUG ************************ METRICS *************************
DEBUG ---------------- OpenAI Response Stream End ----------------
DEBUG Added RunOutput to Agent Session
DEBUG *** Agent Run End: 0baf578f-476c-4992-b6ee-81ad3e6238e2 ****
DEBUG Updated team run context with member name: French Agent
DEBUG Added RunOutput to Team Session
DEBUG =========================== tool ===========================
DEBUG Tool call Id: call_4Nm0VmamKNfFwPVT2uZknO3c
DEBUG La capitale de la France est Paris.
DEBUG ********************** TOOL METRICS **********************
DEBUG * Duration: 0.0006s
DEBUG ********************** TOOL METRICS **********************
DEBUG ---------------- OpenAI Response Stream End ----------------
DEBUG Added RunOutput to Team Session
DEBUG **** Team Run End: 1b2c07bc-1049-4383-a7bb-b196bab95eeb ****
┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ Quelle est la capitale de la France? ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ French Agent Response ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ La capitale de la France est Paris. ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Team Tool Calls ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ • delegate_task_to_member(member_id=french-agent, task=Répondre en français ┃
┃ à la question : « Quelle est la ┃
┃ capitale de la France ? ») ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Response (2.7s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ La capitale de la France est Paris. ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Status: PASS
Description: Example execution attempt
Result: DEBUG ****************** Team ID: expert-router ******************
DEBUG ***** Session ID: ef035587-b7b6-4ca5-8d49-6be8627e1b84 *****
DEBUG Creating new TeamSession: ef035587-b7b6-4ca5-8d49-6be8627e1b84
DEBUG *** Team Run Start: 6c793ea5-53bb-4e18-8717-e007564ba741 ***
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="math-specialist" name="Math Specialist">
Role: Solves mathematical problems and explains concepts
</member>
<member id="code-specialist" name="Code Specialist">
Role: Writes code and explains programming concepts
</member>
<member id="science-specialist" name="Science Specialist">
Role: Explains scientific concepts and phenomena
</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>
- You are an expert router.
- Analyze the user's question and route it to the best specialist:
- - Math questions -> Math Specialist
- - Programming questions -> Code Specialist
- - Science questions -> Science Specialist
<additional_information>
- Use markdown to format your answers.
</additional_information>
DEBUG =========================== user ===========================
DEBUG What is the time complexity of merge sort and why?
DEBUG ======================== assistant =========================
DEBUG Tool Calls:
- ID: 'fc_095749fbe1e46e85006991195027d88193a5bd8cb2cca9ad86'
Name: 'delegate_task_to_member'
Arguments: 'member_id: math-specialist, task: Explain the time
complexity of merge sort and why, including recurrence relation and
reasoning, in clear concise terms.'
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=492, output=47, total=539
DEBUG ************************ METRICS *************************
DEBUG Running: delegate_task_to_member(member_id=math-specialist, task=...)
DEBUG **************** Agent ID: math-specialist *****************
DEBUG Creating new AgentSession: ef035587-b7b6-4ca5-8d49-6be8627e1b84
DEBUG ** Agent Run Start: 9f562712-a303-48a9-889a-d66a43546eff ***
DEBUG --------------- OpenAI Response Stream Start ---------------
DEBUG ---------------------- Model: gpt-5.2 ----------------------
DEBUG ========================== system ==========================
DEBUG <your_role>
Solves mathematical problems and explains concepts
</your_role>
- You are a mathematics expert.
- Solve problems step by step, showing your work clearly.
- Explain the underlying concepts when relevant.
DEBUG =========================== user ===========================
DEBUG Explain the time complexity of merge sort and why, including recurrence
relation and reasoning, in clear concise terms.
DEBUG ======================== assistant =========================
DEBUG Merge sort has time complexity (\Theta(n \log n)) in the best,
average, and worst case.
## Why: divide, conquer, then merge
Merge sort works in three main steps:
1. **Divide** the array into two halves.
2. **Recursively sort** each half.
3. **Merge** the two sorted halves into one sorted array.
### Key observation (merge step)
Merging two sorted lists of total length \(n\) takes **linear time
\(\Theta(n)\)**:
- You scan through both halves once, repeatedly taking the smaller front
element.
- Each element is moved/compared a constant number of times.
## Recurrence relation
Let \(T(n)\) be the time to sort \(n\) elements. Merge sort splits into
two subproblems of size \(n/2\) and does linear work to merge:
\[
T(n) = 2T\left(\frac{n}{2}\right) + \Theta(n), \quad T(1)=\Theta(1)
\]
## Solving the recurrence (reasoning)
There are two standard ways to see the \(\Theta(n \log n)\) result:
### 1) Recursion tree intuition
- Each level of recursion has total merging work \(\Theta(n)\):
- At the top: merge cost \(\Theta(n)\)
- Next level: two merges of size \(n/2\) → \(2 \cdot
\Theta(n/2)=\Theta(n)\)
- Next: four merges of size \(n/4\) → \(4 \cdot \Theta(n/4)=\Theta(n)\)
- …and so on.
- The number of levels until subproblems reach size 1 is \(\log_2 n\)
(because you halve each time).
So total work:
\[
\Theta(n) \times \log_2 n = \Theta(n \log n)
\]
### 2) Master Theorem (compact)
In \(T(n)=aT(n/b)+f(n)\):
- \(a=2\), \(b=2\), \(f(n)=\Theta(n)\)
- \(n^{\log_b a} = n^{\log_2 2} = n\)
- Since \(f(n)=\Theta(n)\) matches \(n^{\log_b a}\), this is the “equal”
case →
\[
T(n)=\Theta(n \log n)
\]
## Best/average/worst all the same
Merge sort always:
- splits into halves regardless of input order, and
- always performs linear merging per level,
so the runtime does not depend much on the initial arrangement of
elements, giving \(\Theta(n \log n)\) in all cases.
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=74, output=592, total=666
DEBUG ************************ METRICS *************************
DEBUG ---------------- OpenAI Response Stream End ----------------
DEBUG Added RunOutput to Agent Session
DEBUG *** Agent Run End: 9f562712-a303-48a9-889a-d66a43546eff ****
DEBUG Updated team run context with member name: Math Specialist
DEBUG Added RunOutput to Team Session
DEBUG =========================== tool ===========================
DEBUG Tool call Id: call_GCvp3EOrhq5QyW9O4ld2V7De
DEBUG Merge sort has time complexity (\Theta(n \log n)) in the best,
average, and worst case.
## Why: divide, conquer, then merge
Merge sort works in three main steps:
1. **Divide** the array into two halves.
2. **Recursively sort** each half.
3. **Merge** the two sorted halves into one sorted array.
### Key observation (merge step)
Merging two sorted lists of total length \(n\) takes **linear time
\(\Theta(n)\)**:
- You scan through both halves once, repeatedly taking the smaller front
element.
- Each element is moved/compared a constant number of times.
## Recurrence relation
Let \(T(n)\) be the time to sort \(n\) elements. Merge sort splits into
two subproblems of size \(n/2\) and does linear work to merge:
\[
T(n) = 2T\left(\frac{n}{2}\right) + \Theta(n), \quad T(1)=\Theta(1)
\]
## Solving the recurrence (reasoning)
There are two standard ways to see the \(\Theta(n \log n)\) result:
### 1) Recursion tree intuition
- Each level of recursion has total merging work \(\Theta(n)\):
- At the top: merge cost \(\Theta(n)\)
- Next level: two merges of size \(n/2\) → \(2 \cdot
\Theta(n/2)=\Theta(n)\)
- Next: four merges of size \(n/4\) → \(4 \cdot \Theta(n/4)=\Theta(n)\)
- …and so on.
- The number of levels until subproblems reach size 1 is \(\log_2 n\)
(because you halve each time).
So total work:
\[
\Theta(n) \times \log_2 n = \Theta(n \log n)
\]
### 2) Master Theorem (compact)
In \(T(n)=aT(n/b)+f(n)\):
- \(a=2\), \(b=2\), \(f(n)=\Theta(n)\)
- \(n^{\log_b a} = n^{\log_2 2} = n\)
- Since \(f(n)=\Theta(n)\) matches \(n^{\log_b a}\), this is the “equal”
case →
\[
T(n)=\Theta(n \log n)
\]
## Best/average/worst all the same
Merge sort always:
- splits into halves regardless of input order, and
- always performs linear merging per level,
so the runtime does not depend much on the initial arrangement of
elements, giving \(\Theta(n \log n)\) in all cases.
DEBUG ********************** TOOL METRICS **********************
DEBUG * Duration: 0.0005s
DEBUG ********************** TOOL METRICS **********************
DEBUG ---------------- OpenAI Response Stream End ----------------
DEBUG Added RunOutput to Team Session
DEBUG **** Team Run End: 6c793ea5-53bb-4e18-8717-e007564ba741 ****
┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ What is the time complexity of merge sort and why? ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Math Specialist Response ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ Merge sort has time complexity (\Theta(n \log n)) in the best, average, and ┃
┃ worst case. ┃
┃ ┃
┃ Why: divide, conquer, then merge ┃
┃ ┃
┃ Merge sort works in three main steps: ┃
┃ ┃
┃ 1 Divide the array into two halves. ┃
┃ 2 Recursively sort each half. ┃
┃ 3 Merge the two sorted halves into one sorted array. ┃
┃ ┃
┃ Key observation (merge step) ┃
┃ ┃
┃ Merging two sorted lists of total length (n) takes linear time (\Theta(n)): ┃
┃ ┃
┃ • You scan through both halves once, repeatedly taking the smaller front ┃
┃ element. ┃
┃ • Each element is moved/compared a constant number of times. ┃
┃ ┃
┃ Recurrence relation ┃
┃ ┃
┃ Let (T(n)) be the time to sort (n) elements. Merge sort splits into two ┃
┃ subproblems of size (n/2) and does linear work to merge: ┃
┃ ┃
┃ [ T(n) = 2T\left(\frac{n}{2}\right) + \Theta(n), \quad T(1)=\Theta(1) ] ┃
┃ ┃
┃ Solving the recurrence (reasoning) ┃
┃ ┃
┃ There are two standard ways to see the (\Theta(n \log n)) result: ┃
┃ ┃
┃ 1) Recursion tree intuition ┃
┃ ┃
┃ • Each level of recursion has total merging work (\Theta(n)): ┃
┃ • At the top: merge cost (\Theta(n)) ┃
┃ • Next level: two merges of size (n/2) → (2 \cdot \Theta(n/2)=\Theta(n)) ┃
┃ • Next: four merges of size (n/4) → (4 \cdot \Theta(n/4)=\Theta(n)) ┃
┃ • …and so on. ┃
┃ • The number of levels until subproblems reach size 1 is (\log_2 n) ┃
┃ (because you halve each time). ┃
┃ ┃
┃ So total work: [ \Theta(n) \times \log_2 n = \Theta(n \log n) ] ┃
┃ ┃
┃ 2) Master Theorem (compact) ┃
┃ ┃
┃ In (T(n)=aT(n/b)+f(n)): ┃
┃ ┃
┃ • (a=2), (b=2), (f(n)=\Theta(n)) ┃
┃ • (n^{\log_b a} = n^{\log_2 2} = n) ┃
┃ • Since (f(n)=\Theta(n)) matches (n^{\log_b a}), this is the “equal” case → ┃
┃ [ T(n)=\Theta(n \log n) ] ┃
┃ ┃
┃ Best/average/worst all the same ┃
┃ ┃
┃ Merge sort always: ┃
┃ ┃
┃ • splits into halves regardless of input order, and ┃
┃ • always performs linear merging per level, so the runtime does not depend ┃
┃ much on the initial arrangement of elements, giving (\Theta(n \log n)) in ┃
┃ all cases. ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Team Tool Calls ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ • delegate_task_to_member(member_id=math-specialist, task=Explain the time ┃
┃ complexity of merge sort and why, ┃
┃ including recurrence relation and reasoning, in clear concise terms.) ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Response (12.0s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ Merge sort has time complexity (\Theta(n \log n)) in the best, average, and ┃
┃ worst case. ┃
┃ ┃
┃ Why: divide, conquer, then merge ┃
┃ ┃
┃ Merge sort works in three main steps: ┃
┃ ┃
┃ 1 Divide the array into two halves. ┃
┃ 2 Recursively sort each half. ┃
┃ 3 Merge the two sorted halves into one sorted array. ┃
┃ ┃
┃ Key observation (merge step) ┃
┃ ┃
┃ Merging two sorted lists of total length (n) takes linear time (\Theta(n)): ┃
┃ ┃
┃ • You scan through both halves once, repeatedly taking the smaller front ┃
┃ element. ┃
┃ • Each element is moved/compared a constant number of times. ┃
┃ ┃
┃ Recurrence relation ┃
┃ ┃
┃ Let (T(n)) be the time to sort (n) elements. Merge sort splits into two ┃
┃ subproblems of size (n/2) and does linear work to merge: ┃
┃ ┃
┃ [ T(n) = 2T\left(\frac{n}{2}\right) + \Theta(n), \quad T(1)=\Theta(1) ] ┃
┃ ┃
┃ Solving the recurrence (reasoning) ┃
┃ ┃
┃ There are two standard ways to see the (\Theta(n \log n)) result: ┃
┃ ┃
┃ 1) Recursion tree intuition ┃
┃ ┃
┃ • Each level of recursion has total merging work (\Theta(n)): ┃
┃ • At the top: merge cost (\Theta(n)) ┃
┃ • Next level: two merges of size (n/2) → (2 \cdot \Theta(n/2)=\Theta(n)) ┃
┃ • Next: four merges of size (n/4) → (4 \cdot \Theta(n/4)=\Theta(n)) ┃
┃ • …and so on. ┃
┃ • The number of levels until subproblems reach size 1 is (\log_2 n) ┃
┃ (because you halve each time). ┃
┃ ┃
┃ So total work: [ \Theta(n) \times \log_2 n = \Theta(n \log n) ] ┃
┃ ┃
┃ 2) Master Theorem (compact) ┃
┃ ┃
┃ In (T(n)=aT(n/b)+f(n)): ┃
┃ ┃
┃ • (a=2), (b=2), (f(n)=\Theta(n)) ┃
┃ • (n^{\log_b a} = n^{\log_2 2} = n) ┃
┃ • Since (f(n)=\Theta(n)) matches (n^{\log_b a}), this is the “equal” case → ┃
┃ [ T(n)=\Theta(n \log n) ] ┃
┃ ┃
┃ Best/average/worst all the same ┃
┃ ┃
┃ Merge sort always: ┃
┃ ┃
┃ • splits into halves regardless of input order, and ┃
┃ • always performs linear merging per level, so the runtime does not depend ┃
┃ much on the initial arrangement of elements, giving (\Theta(n \log n)) in ┃
┃ all cases. ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Status: PASS
Description: Example execution attempt
Result: DEBUG ***************** Team ID: dev-help-router *****************
DEBUG ***** Session ID: 19a744f5-175f-4ed9-9d88-68aa92ca9ccb *****
DEBUG Creating new TeamSession: 19a744f5-175f-4ed9-9d88-68aa92ca9ccb
DEBUG *** Team Run Start: 362f795c-6d6d-4c64-89f9-a6f9e9969cdf ***
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="sql-expert" name="SQL Expert">
Role: Writes and optimizes SQL queries
</member>
<member id="python-expert" name="Python Expert">
Role: Writes Python code and solves Python-specific problems
</member>
<member id="general-assistant" name="General Assistant">
Role: Handles general questions that do not match a specialist
</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>
- You route questions to the right expert.
- - SQL or database questions -> SQL Expert
- - Python questions -> Python Expert
- - Everything else -> General Assistant
- When in doubt, route to the General Assistant.
<additional_information>
- Use markdown to format your answers.
</additional_information>
DEBUG =========================== user ===========================
DEBUG Write a query to find the top 10 customers by total order value, joining
the customers and orders tables.
DEBUG ======================== assistant =========================
DEBUG Tool Calls:
- ID: 'fc_081cb3061fe3a6aa006991195c8ad081978b2e21982ae88c62'
Name: 'delegate_task_to_member'
Arguments: 'member_id: sql-expert, task: Write an SQL query to find
the top 10 customers by total order value by joining customers and orders
tables. Include grouping, sum of order value, ordering, and limiting to
10. Use generic SQL; assume common column names and note placeholders if
needed.'
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=509, output=78, total=587
DEBUG ************************ METRICS *************************
DEBUG Running: delegate_task_to_member(member_id=sql-expert, task=...)
DEBUG ******************* Agent ID: sql-expert *******************
DEBUG Creating new AgentSession: 19a744f5-175f-4ed9-9d88-68aa92ca9ccb
DEBUG ** Agent Run Start: 772e94ec-3173-4f01-b0f0-6748b578920c ***
DEBUG --------------- OpenAI Response Stream Start ---------------
DEBUG ---------------------- Model: gpt-5.2 ----------------------
DEBUG ========================== system ==========================
DEBUG <your_role>
Writes and optimizes SQL queries
</your_role>
- You are an SQL expert.
- Write correct, optimized SQL queries.
- Explain query plans and indexing strategies when asked.
DEBUG =========================== user ===========================
DEBUG Write an SQL query to find the top 10 customers by total order value by
joining customers and orders tables. Include grouping, sum of order value,
ordering, and limiting to 10. Use generic SQL; assume common column names
and note placeholders if needed.
DEBUG ======================== assistant =========================
DEBUG sql -- Top 10 customers by total order value SELECT c.customer_id, c.customer_name, -- placeholder: use your actual customer name column SUM(o.order_total) AS total_order_value -- placeholder: use your actual order value column FROM customers c JOIN orders o ON o.customer_id = c.customer_id -- placeholder: join key(s) as appropriate GROUP BY c.customer_id, c.customer_name ORDER BY total_order_value DESC FETCH FIRST 10 ROWS ONLY; -- for MySQL/Postgres use: LIMIT 10
Notes (placeholders you may need to adapt):
- `o.order_total` might be `o.total_amount`, `o.order_value`, etc.
- If you only want completed/paid orders, add a `WHERE o.status =
'COMPLETED'` (adjust as needed).
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=102, output=192, total=294
DEBUG ************************ METRICS *************************
DEBUG ---------------- OpenAI Response Stream End ----------------
DEBUG Added RunOutput to Agent Session
DEBUG *** Agent Run End: 772e94ec-3173-4f01-b0f0-6748b578920c ****
DEBUG Updated team run context with member name: SQL Expert
DEBUG Added RunOutput to Team Session
DEBUG =========================== tool ===========================
DEBUG Tool call Id: call_ofoE2bUJ5vRsC4FzW96r5ycB
DEBUG sql -- Top 10 customers by total order value SELECT c.customer_id, c.customer_name, -- placeholder: use your actual customer name column SUM(o.order_total) AS total_order_value -- placeholder: use your actual order value column FROM customers c JOIN orders o ON o.customer_id = c.customer_id -- placeholder: join key(s) as appropriate GROUP BY c.customer_id, c.customer_name ORDER BY total_order_value DESC FETCH FIRST 10 ROWS ONLY; -- for MySQL/Postgres use: LIMIT 10
Notes (placeholders you may need to adapt):
- `o.order_total` might be `o.total_amount`, `o.order_value`, etc.
- If you only want completed/paid orders, add a `WHERE o.status =
'COMPLETED'` (adjust as needed).
DEBUG ***************** Team ID: dev-help-router *****************
DEBUG ***** Session ID: 19a744f5-175f-4ed9-9d88-68aa92ca9ccb *****
DEBUG Creating new TeamSession: 19a744f5-175f-4ed9-9d88-68aa92ca9ccb
DEBUG *** Team Run Start: 1599660c-53c5-4346-8299-d47047e226b9 ***
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="sql-expert" name="SQL Expert">
Role: Writes and optimizes SQL queries
</member>
<member id="python-expert" name="Python Expert">
Role: Writes Python code and solves Python-specific problems
</member>
<member id="general-assistant" name="General Assistant">
Role: Handles general questions that do not match a specialist
</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>
- You route questions to the right expert.
- - SQL or database questions -> SQL Expert
- - Python questions -> Python Expert
- - Everything else -> General Assistant
- When in doubt, route to the General Assistant.
<additional_information>
- Use markdown to format your answers.
</additional_information>
DEBUG =========================== user ===========================
DEBUG What are some good practices for code review?
DEBUG ======================== assistant =========================
DEBUG Tool Calls:
- ID: 'fc_0aa6e9ed9a4f3dc90069911962b36481979e68a557fa32eb05'
Name: 'delegate_task_to_member'
Arguments: 'member_id: general-assistant, task: Provide a concise,
well-structured list of good practices for code review, including process,
communication, checklist items, and tips for reviewers and authors.'
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=496, output=56, total=552
DEBUG ************************ METRICS *************************
DEBUG Running: delegate_task_to_member(member_id=general-assistant, task=...)
DEBUG ***** Session ID: 19a744f5-175f-4ed9-9d88-68aa92ca9ccb *****
DEBUG *************** Agent ID: general-assistant ****************
DEBUG Creating new AgentSession: 19a744f5-175f-4ed9-9d88-68aa92ca9ccb
DEBUG ** Agent Run Start: c03e0e41-5d3d-41db-a6b2-28c8255fe979 ***
DEBUG --------------- OpenAI Response Stream Start ---------------
DEBUG ---------------------- Model: gpt-5.2 ----------------------
DEBUG ========================== system ==========================
DEBUG <your_role>
Handles general questions that do not match a specialist
</your_role>
- You are a helpful general assistant.
- Answer questions clearly and concisely.
- If the question is about SQL or Python, still do your best.
DEBUG =========================== user ===========================
DEBUG Provide a concise, well-structured list of good practices for code review,
including process, communication, checklist items, and tips for reviewers
and authors.
DEBUG ======================== assistant =========================
DEBUG ## Code review good practices (concise, structured)
### 1) Process & workflow
- **Define “ready for review”**: tests passing, lint/format applied, no
WIP files, clear scope.
- **Keep PRs small**: aim for focused changes; split refactors vs feature
changes.
- **Use templates**: PR template with intent, approach, risks, test plan,
screenshots/logs if relevant.
- **Set SLAs and ownership**: expected review turnaround, who must
approve, when to escalate.
- **Automate the basics**: CI, formatting, static analysis, security
scans; reviewers focus on design/logic.
- **Require tests**: add/adjust unit/integration tests; avoid “test later”
merges.
- **Track decisions**: document non-obvious tradeoffs in PR description or
ADR; link issues/specs.
- **Use staged review**: early “design/approach” review for big changes;
later “implementation” pass.
### 2) Communication & collaboration
- **Assume positive intent**; critique code, not people.
- **Be specific and actionable**: explain what/why; include suggestions or
examples.
- **Label comment severity**: e.g., *blocker*, *nit*, *question*,
*suggestion*.
- **Ask before asserting**: “Is X guaranteed?” instead of “This is wrong.”
- **Keep tone neutral**; avoid sarcasm; prefer short, direct phrasing.
- **Resolve threads clearly**: either fix, defer with a follow-up ticket,
or document rationale.
- **Prefer synchronous for complex topics**: quick call/whiteboard, then
summarize in the PR.
### 3) Review checklist (what to look for)
**Correctness & logic**
- Handles edge cases, null/empty inputs, error paths, retries/timeouts.
- No race conditions, concurrency issues, or state inconsistencies.
- Backward compatibility and safe migrations (if applicable).
**Design & maintainability**
- Clear separation of concerns; appropriate abstractions.
- Readable naming; minimal duplication; consistent patterns with the
codebase.
- Complexity reasonable; avoid cleverness; comments explain *why*.
**Security & privacy**
- Input validation, authz/authn, secrets not logged or committed.
- Data handling follows policy (PII, encryption, access controls).
- Dependencies trustworthy; avoid vulnerable patterns.
**Performance & scalability**
- Hot paths efficient; avoids N+1 queries, unnecessary allocations,
excessive I/O.
- Caching and pagination where needed; complexity appropriate to expected
load.
**Testing & quality**
- Tests cover success/failure paths; meaningful assertions.
- Deterministic tests (no flaky timing/network reliance without controls).
- Logs/metrics added for operability; errors actionable.
**API/UX & compatibility**
- Public interfaces versioned or deprecation planned.
- Error messages stable and useful; responses consistent.
**Style & hygiene**
- Formatting consistent; no dead code, debug prints, commented-out blocks.
- Docs/README/CHANGELOG updated when behavior changes.
### 4) Tips for reviewers
- **Start with intent**: read PR description and linked ticket/spec first.
- **Review in passes**: (1) big-picture design, (2) correctness, (3)
tests, (4) style/nits.
- **Verify with tools**: run tests locally when needed; pull branch for
tricky changes.
- **Avoid bikeshedding**: enforce agreed standards; keep “nits” clearly
marked.
- **Suggest alternatives sparingly**: prefer the minimal change that meets
requirements.
- **Recognize good work**: brief positive notes help reinforce good
patterns.
### 5) Tips for authors
- **Write a strong PR description**: problem, approach, key files, test
plan, rollout/rollback.
- **Make changes reviewable**: logical commits, self-review before
requesting review.
- **Preempt questions**: explain unusual choices; include
benchmarks/screenshots when relevant.
- **Respond constructively**: acknowledge, clarify, fix; push back with
rationale when needed.
- **Keep PR moving**: address feedback promptly; if blocked, propose next
steps.
### 6) Common pitfalls to avoid
- Large mixed-purpose PRs (feature + refactor + formatting).
- Arguing style without an agreed standard.
- Merging with unresolved “TODO: fix later” without tracking.
- Over-focusing on nits while missing correctness/security risks.
DEBUG ************************ METRICS *************************
DEBUG * Tokens: input=90, output=926, total=1016
DEBUG ************************ METRICS *************************
DEBUG ---------------- OpenAI Response Stream End ----------------
DEBUG Added RunOutput to Agent Session
DEBUG *** Agent Run End: c03e0e41-5d3d-41db-a6b2-28c8255fe979 ****
DEBUG Updated team run context with member name: General Assistant
DEBUG Added RunOutput to Team Session
DEBUG =========================== tool ===========================
DEBUG Tool call Id: call_hyg5BppFvQMcYa792AYcOESj
DEBUG ## Code review good practices (concise, structured)
### 1) Process & workflow
- **Define “ready for review”**: tests passing, lint/format applied, no
WIP files, clear scope.
- **Keep PRs small**: aim for focused changes; split refactors vs feature
changes.
- **Use templates**: PR template with intent, approach, risks, test plan,
screenshots/logs if relevant.
- **Set SLAs and ownership**: expected review turnaround, who must
approve, when to escalate.
- **Automate the basics**: CI, formatting, static analysis, security
scans; reviewers focus on design/logic.
- **Require tests**: add/adjust unit/integration tests; avoid “test later”
merges.
- **Track decisions**: document non-obvious tradeoffs in PR description or
ADR; link issues/specs.
- **Use staged review**: early “design/approach” review for big changes;
later “implementation” pass.
### 2) Communication & collaboration
- **Assume positive intent**; critique code, not people.
- **Be specific and actionable**: explain what/why; include suggestions or
examples.
- **Label comment severity**: e.g., *blocker*, *nit*, *question*,
*suggestion*.
- **Ask before asserting**: “Is X guaranteed?” instead of “This is wrong.”
- **Keep tone neutral**; avoid sarcasm; prefer short, direct phrasing.
- **Resolve threads clearly**: either fix, defer with a follow-up ticket,
or document rationale.
- **Prefer synchronous for complex topics**: quick call/whiteboard, then
summarize in the PR.
### 3) Review checklist (what to look for)
**Correctness & logic**
- Handles edge cases, null/empty inputs, error paths, retries/timeouts.
- No race conditions, concurrency issues, or state inconsistencies.
- Backward compatibility and safe migrations (if applicable).
**Design & maintainability**
- Clear separation of concerns; appropriate abstractions.
- Readable naming; minimal duplication; consistent patterns with the
codebase.
- Complexity reasonable; avoid cleverness; comments explain *why*.
**Security & privacy**
- Input validation, authz/authn, secrets not logged or committed.
- Data handling follows policy (PII, encryption, access controls).
- Dependencies trustworthy; avoid vulnerable patterns.
**Performance & scalability**
- Hot paths efficient; avoids N+1 queries, unnecessary allocations,
excessive I/O.
- Caching and pagination where needed; complexity appropriate to expected
load.
**Testing & quality**
- Tests cover success/failure paths; meaningful assertions.
- Deterministic tests (no flaky timing/network reliance without controls).
- Logs/metrics added for operability; errors actionable.
**API/UX & compatibility**
- Public interfaces versioned or deprecation planned.
- Error messages stable and useful; responses consistent.
**Style & hygiene**
- Formatting consistent; no dead code, debug prints, commented-out blocks.
- Docs/README/CHANGELOG updated when behavior changes.
### 4) Tips for reviewers
- **Start with intent**: read PR description and linked ticket/spec first.
- **Review in passes**: (1) big-picture design, (2) correctness, (3)
tests, (4) style/nits.
- **Verify with tools**: run tests locally when needed; pull branch for
tricky changes.
- **Avoid bikeshedding**: enforce agreed standards; keep “nits” clearly
marked.
- **Suggest alternatives sparingly**: prefer the minimal change that meets
requirements.
- **Recognize good work**: brief positive notes help reinforce good
patterns.
### 5) Tips for authors
- **Write a strong PR description**: problem, approach, key files, test
plan, rollout/rollback.
- **Make changes reviewable**: logical commits, self-review before
requesting review.
- **Preempt questions**: explain unusual choices; include
benchmarks/screenshots when relevant.
- **Respond constructively**: acknowledge, clarify, fix; push back with
rationale when needed.
- **Keep PR moving**: address feedback promptly; if blocked, propose next
steps.
### 6) Common pitfalls to avoid
- Large mixed-purpose PRs (feature + refactor + formatting).
- Arguing style without an agreed standard.
- Merging with unresolved “TODO: fix later” without tracking.
- Over-focusing on nits while missing correctness/security risks.
DEBUG ********************** TOOL METRICS **********************
DEBUG * Duration: 0.0006s
DEBUG ********************** TOOL METRICS **********************
DEBUG ---------------- OpenAI Response Stream End ----------------
DEBUG Added RunOutput to Team Session
DEBUG **** Team Run End: 1599660c-53c5-4346-8299-d47047e226b9 ****
┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ What are some good practices for code review? ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ General Assistant Response ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ Code review good practices (concise, structured) ┃
┃ ┃
┃ 1) Process & workflow ┃
┃ ┃
┃ • Define “ready for review”: tests passing, lint/format applied, no WIP ┃
┃ files, clear scope. ┃
┃ • Keep PRs small: aim for focused changes; split refactors vs feature ┃
┃ changes. ┃
┃ • Use templates: PR template with intent, approach, risks, test plan, ┃
┃ screenshots/logs if relevant. ┃
┃ • Set SLAs and ownership: expected review turnaround, who must approve, ┃
┃ when to escalate. ┃
┃ • Automate the basics: CI, formatting, static analysis, security scans; ┃
┃ reviewers focus on design/logic. ┃
┃ • Require tests: add/adjust unit/integration tests; avoid “test later” ┃
┃ merges. ┃
┃ • Track decisions: document non-obvious tradeoffs in PR description or ADR; ┃
┃ link issues/specs. ┃
┃ • Use staged review: early “design/approach” review for big changes; later ┃
┃ “implementation” pass. ┃
┃ ┃
┃ 2) Communication & collaboration ┃
┃ ┃
┃ • Assume positive intent; critique code, not people. ┃
┃ • Be specific and actionable: explain what/why; include suggestions or ┃
┃ examples. ┃
┃ • Label comment severity: e.g., blocker, nit, question, suggestion. ┃
┃ • Ask before asserting: “Is X guaranteed?” instead of “This is wrong.” ┃
┃ • Keep tone neutral; avoid sarcasm; prefer short, direct phrasing. ┃
┃ • Resolve threads clearly: either fix, defer with a follow-up ticket, or ┃
┃ document rationale. ┃
┃ • Prefer synchronous for complex topics: quick call/whiteboard, then ┃
┃ summarize in the PR. ┃
┃ ┃
┃ 3) Review checklist (what to look for) ┃
┃ ┃
┃ Correctness & logic ┃
┃ ┃
┃ • Handles edge cases, null/empty inputs, error paths, retries/timeouts. ┃
┃ • No race conditions, concurrency issues, or state inconsistencies. ┃
┃ • Backward compatibility and safe migrations (if applicable). ┃
┃ ┃
┃ Design & maintainability ┃
┃ ┃
┃ • Clear separation of concerns; appropriate abstractions. ┃
┃ • Readable naming; minimal duplication; consistent patterns with the ┃
┃ codebase. ┃
┃ • Complexity reasonable; avoid cleverness; comments explain why. ┃
┃ ┃
┃ Security & privacy ┃
┃ ┃
┃ • Input validation, authz/authn, secrets not logged or committed. ┃
┃ • Data handling follows policy (PII, encryption, access controls). ┃
┃ • Dependencies trustworthy; avoid vulnerable patterns. ┃
┃ ┃
┃ Performance & scalability ┃
┃ ┃
┃ • Hot paths efficient; avoids N+1 queries, unnecessary allocations, ┃
┃ excessive I/O. ┃
┃ • Caching and pagination where needed; complexity appropriate to expected ┃
┃ load. ┃
┃ ┃
┃ Testing & quality ┃
┃ ┃
┃ • Tests cover success/failure paths; meaningful assertions. ┃
┃ • Deterministic tests (no flaky timing/network reliance without controls). ┃
┃ • Logs/metrics added for operability; errors actionable. ┃
┃ ┃
┃ API/UX & compatibility ┃
┃ ┃
┃ • Public interfaces versioned or deprecation planned. ┃
┃ • Error messages stable and useful; responses consistent. ┃
┃ ┃
┃ Style & hygiene ┃
┃ ┃
┃ • Formatting consistent; no dead code, debug prints, commented-out blocks. ┃
┃ • Docs/README/CHANGELOG updated when behavior changes. ┃
┃ ┃
┃ 4) Tips for reviewers ┃
┃ ┃
┃ • Start with intent: read PR description and linked ticket/spec first. ┃
┃ • Review in passes: (1) big-picture design, (2) correctness, (3) tests, (4) ┃
┃ style/nits. ┃
┃ • Verify with tools: run tests locally when needed; pull branch for tricky ┃
┃ changes. ┃
┃ • Avoid bikeshedding: enforce agreed standards; keep “nits” clearly marked. ┃
┃ • Suggest alternatives sparingly: prefer the minimal change that meets ┃
┃ requirements. ┃
┃ • Recognize good work: brief positive notes help reinforce good patterns. ┃
┃ ┃
┃ 5) Tips for authors ┃
┃ ┃
┃ • Write a strong PR description: problem, approach, key files, test plan, ┃
┃ rollout/rollback. ┃
┃ • Make changes reviewable: logical commits, self-review before requesting ┃
┃ review. ┃
┃ • Preempt questions: explain unusual choices; include ┃
┃ benchmarks/screenshots when relevant. ┃
┃ • Respond constructively: acknowledge, clarify, fix; push back with ┃
┃ rationale when needed. ┃
┃ • Keep PR moving: address feedback promptly; if blocked, propose next ┃
┃ steps. ┃
┃ ┃
┃ 6) Common pitfalls to avoid ┃
┃ ┃
┃ • Large mixed-purpose PRs (feature + refactor + formatting). ┃
┃ • Arguing style without an agreed standard. ┃
┃ • Merging with unresolved “TODO: fix later” without tracking. ┃
┃ • Over-focusing on nits while missing correctness/security risks. ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Team Tool Calls ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ • delegate_task_to_member(member_id=general-assistant, task=Provide a ┃
┃ concise, well-structured list of good ┃
┃ practices for code review, including process, communication, checklist ┃
┃ items, and tips for reviewers and ┃
┃ authors.) ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Response (21.2s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ Code review good practices (concise, structured) ┃
┃ ┃
┃ 1) Process & workflow ┃
┃ ┃
┃ • Define “ready for review”: tests passing, lint/format applied, no WIP ┃
┃ files, clear scope. ┃
┃ • Keep PRs small: aim for focused changes; split refactors vs feature ┃
┃ changes. ┃
┃ • Use templates: PR template with intent, approach, risks, test plan, ┃
┃ screenshots/logs if relevant. ┃
┃ • Set SLAs and ownership: expected review turnaround, who must approve, ┃
┃ when to escalate. ┃
┃ • Automate the basics: CI, formatting, static analysis, security scans; ┃
┃ reviewers focus on design/logic. ┃
┃ • Require tests: add/adjust unit/integration tests; avoid “test later” ┃
┃ merges. ┃
┃ • Track decisions: document non-obvious tradeoffs in PR description or ADR; ┃
┃ link issues/specs. ┃
┃ • Use staged review: early “design/approach” review for big changes; later ┃
┃ “implementation” pass. ┃
┃ ┃
┃ 2) Communication & collaboration ┃
┃ ┃
┃ • Assume positive intent; critique code, not people. ┃
┃ • Be specific and actionable: explain what/why; include suggestions or ┃
┃ examples. ┃
┃ • Label comment severity: e.g., blocker, nit, question, suggestion. ┃
┃ • Ask before asserting: “Is X guaranteed?” instead of “This is wrong.” ┃
┃ • Keep tone neutral; avoid sarcasm; prefer short, direct phrasing. ┃
┃ • Resolve threads clearly: either fix, defer with a follow-up ticket, or ┃
┃ document rationale. ┃
┃ • Prefer synchronous for complex topics: quick call/whiteboard, then ┃
┃ summarize in the PR. ┃
┃ ┃
┃ 3) Review checklist (what to look for) ┃
┃ ┃
┃ Correctness & logic ┃
┃ ┃
┃ • Handles edge cases, null/empty inputs, error paths, retries/timeouts. ┃
┃ • No race conditions, concurrency issues, or state inconsistencies. ┃
┃ • Backward compatibility and safe migrations (if applicable). ┃
┃ ┃
┃ Design & maintainability ┃
┃ ┃
┃ • Clear separation of concerns; appropriate abstractions. ┃
┃ • Readable naming; minimal duplication; consistent patterns with the ┃
┃ codebase. ┃
┃ • Complexity reasonable; avoid cleverness; comments explain why. ┃
┃ ┃
┃ Security & privacy ┃
┃ ┃
┃ • Input validation, authz/authn, secrets not logged or committed. ┃
┃ • Data handling follows policy (PII, encryption, access controls). ┃
┃ • Dependencies trustworthy; avoid vulnerable patterns. ┃
┃ ┃
┃ Performance & scalability ┃
┃ ┃
┃ • Hot paths efficient; avoids N+1 queries, unnecessary allocations, ┃
┃ excessive I/O. ┃
┃ • Caching and pagination where needed; complexity appropriate to expected ┃
┃ load. ┃
┃ ┃
┃ Testing & quality ┃
┃ ┃
┃ • Tests cover success/failure paths; meaningful assertions. ┃
┃ • Deterministic tests (no flaky timing/network reliance without controls). ┃
┃ • Logs/metrics added for operability; errors actionable. ┃
┃ ┃
┃ API/UX & compatibility ┃
┃ ┃
┃ • Public interfaces versioned or deprecation planned. ┃
┃ • Error messages stable and useful; responses consistent. ┃
┃ ┃
┃ Style & hygiene ┃
┃ ┃
┃ • Formatting consistent; no dead code, debug prints, commented-out blocks. ┃
┃ • Docs/README/CHANGELOG updated when behavior changes. ┃
┃ ┃
┃ 4) Tips for reviewers ┃
┃ ┃
┃ • Start with intent: read PR description and linked ticket/spec first. ┃
┃ • Review in passes: (1) big-picture design, (2) correctness, (3) tests, (4) ┃
┃ style/nits. ┃
┃ • Verify with tools: run tests locally when needed; pull branch for tricky ┃
┃ changes. ┃
┃ • Avoid bikeshedding: enforce agreed standards; keep “nits” clearly marked. ┃
┃ • Suggest alternatives sparingly: prefer the minimal change that meets ┃
┃ requirements. ┃
┃ • Recognize good work: brief positive notes help reinforce good patterns. ┃
┃ ┃
┃ 5) Tips for authors ┃
┃ ┃
┃ • Write a strong PR description: problem, approach, key files, test plan, ┃
┃ rollout/rollback. ┃
┃ • Make changes reviewable: logical commits, self-review before requesting ┃
┃ review. ┃
┃ • Preempt questions: explain unusual choices; include ┃
┃ benchmarks/screenshots when relevant. ┃
┃ • Respond constructively: acknowledge, clarify, fix; push back with ┃
┃ rationale when needed. ┃
┃ • Keep PR moving: address feedback promptly; if blocked, propose next ┃
┃ steps. ┃
┃ ┃
┃ 6) Common pitfalls to avoid ┃
┃ ┃
┃ • Large mixed-purpose PRs (feature + refactor + formatting). ┃
┃ • Arguing style without an agreed standard. ┃
┃ • Merging with unresolved “TODO: fix later” without tracking. ┃
┃ • Over-focusing on nits while missing correctness/security risks. ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
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: 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: Timeout after 30s
Status: FAIL
Description: Example execution attempt
Result: Timeout after 30s