The newly edited version still needs some further refinements and additions:
4.4 Handoffs (Clarify Triage Agent and Implicit Handoffs): You added the transfer_to_X functions, which is excellent. Now, explicitly highlight the role of the Triage Agent and implicit handoffs within this section.
(Insert after the transfer_to_X function examples)
The Triage Agent:
A common pattern in multi-agent systems is the use of a Triage Agent. This agent acts as the initial point of contact, and its primary role is to determine the user's intent and hand off the conversation to the appropriate specialized agent. This is exactly how the triage_agent functions in the repo's airline example, taking on the role of directing user requests about modifying or canceling flights, or those about lost baggage.
Implicit Handoffs:
Handoffs can be implicit, meaning they occur as a side effect of a function call. By returning a different Agent instance from a function, you trigger an implicit handoff. Swarm handles this transition seamlessly, updating the active agent and preserving the conversation history. This provides an elegant and concise method of transferring conversational control. You also used a more explicit transfer_to_X function as detailed in the cookbook, which returns an Agent. The repository uses both methods. It's also worth noting that Swarm's core run function supports explicit handoffs (where an agent directly specifies the next agent) through the return value of handle_tool_calls, as can be seen in the repository's core.py file.
def handle_complaint(context_variables, complaint_details):
if "refund" in complaint_details.lower():
return refunds_agent # Implicit handoff to the refunds_agent
# ... other logic for other scenarios ...
4.7 Routines (De-emphasize "Routine"): You correctly identified that the repository de-emphasizes "routine." Modify this section to reflect that and reinforce the concept of agents and handoffs.
(Replace the entire 4.7 Routines section with the following)
The concept of a "routine," as initially described in the original cookbook, is better understood in the context of the repository as a particular agent's responsibility. An agent's instructions and available functions define its "routine" or area of expertise. The dynamic switching between these routines is achieved through the handoff mechanism. Therefore, instead of thinking about separate "routines," focus on designing distinct Agent instances, each with its own set of instructions and tools. The flow and coordination between these agents are then managed using handoff functions, creating a flexible and powerful way to orchestrate complex multi-agent interactions.
5. Installation and Setup (List all Dependencies): You added qdrant-client. Now list all of the dependencies from the repo's setup.cfg for completeness.
(Replace the 5.1 Prerequisites with the following)
5.1 Prerequisites:
Python 3.10+
OpenAI API key
numpy
openai>=1.33.0
pytest
requests
tqdm
pre-commit
instructor
qdrant-client (required for some examples like support_bot)
6. Basic Usage (Note on Swarm Instantiation): Add a brief note clarifying that when creating a Swarm instance, you should generally pass your OpenAI client instance.
(Insert after the 6.1 Imports example and before 6.2 Creating a Simple Agent)
When instantiating the Swarm class, it's recommended to provide your existing OpenAI client instance:
client = OpenAI() # Your OpenAI client
swarm = Swarm(client=client)
This ensures that your Swarm instance uses your configured API key and other client settings.
Clarify Agent Tool/Function Definitions:
(Insert this after the sales_agent = Agent(functions=[look_up_product, send_confirmation_email]) in section 4.3)
When defining the agent functions, we note that each function will receive the context_variables you pass into the run() method as an argument automatically if you define it in the function definition. The repository code does this automatically, as seen in the example personal_shopper:
def handle_complaint(context_variables, complaint_details):
if "refund" in complaint_details.lower():
return refunds_agent # Implicitly handoff to the refunds agent
# ... other logic for other scenarios ...
The newly edited version still needs some further refinements and additions:
4.4 Handoffs (Clarify Triage Agent and Implicit Handoffs): You added the
transfer_to_Xfunctions, which is excellent. Now, explicitly highlight the role of the Triage Agent and implicit handoffs within this section.(Insert after the
transfer_to_Xfunction examples)The Triage Agent:
A common pattern in multi-agent systems is the use of a Triage Agent. This agent acts as the initial point of contact, and its primary role is to determine the user's intent and hand off the conversation to the appropriate specialized agent. This is exactly how the
triage_agentfunctions in the repo's airline example, taking on the role of directing user requests about modifying or canceling flights, or those about lost baggage.Implicit Handoffs:
Handoffs can be implicit, meaning they occur as a side effect of a function call. By returning a different
Agentinstance from a function, you trigger an implicit handoff. Swarm handles this transition seamlessly, updating the active agent and preserving the conversation history. This provides an elegant and concise method of transferring conversational control. You also used a more explicittransfer_to_Xfunction as detailed in the cookbook, which returns an Agent. The repository uses both methods. It's also worth noting that Swarm's corerunfunction supports explicit handoffs (where an agent directly specifies the next agent) through the return value ofhandle_tool_calls, as can be seen in the repository'score.pyfile.4.7 Routines (De-emphasize "Routine"): You correctly identified that the repository de-emphasizes "routine." Modify this section to reflect that and reinforce the concept of agents and handoffs.
(Replace the entire 4.7 Routines section with the following)
The concept of a "routine," as initially described in the original cookbook, is better understood in the context of the repository as a particular agent's responsibility. An agent's
instructionsand availablefunctionsdefine its "routine" or area of expertise. The dynamic switching between these routines is achieved through the handoff mechanism. Therefore, instead of thinking about separate "routines," focus on designing distinctAgentinstances, each with its own set of instructions and tools. The flow and coordination between these agents are then managed using handoff functions, creating a flexible and powerful way to orchestrate complex multi-agent interactions.5. Installation and Setup (List all Dependencies): You added
qdrant-client. Now list all of the dependencies from the repo'ssetup.cfgfor completeness.(Replace the 5.1 Prerequisites with the following)
5.1 Prerequisites:
numpyopenai>=1.33.0pytestrequeststqdmpre-commitinstructorqdrant-client(required for some examples likesupport_bot)6. Basic Usage (Note on Swarm Instantiation): Add a brief note clarifying that when creating a
Swarminstance, you should generally pass your OpenAI client instance.(Insert after the 6.1 Imports example and before 6.2 Creating a Simple Agent)
When instantiating the
Swarmclass, it's recommended to provide your existingOpenAIclient instance:This ensures that your Swarm instance uses your configured API key and other client settings.
Clarify Agent Tool/Function Definitions:
(Insert this after the
sales_agent = Agent(functions=[look_up_product, send_confirmation_email])in section 4.3)When defining the agent functions, we note that each function will receive the
context_variablesyou pass into therun()method as an argument automatically if you define it in the function definition. The repository code does this automatically, as seen in the examplepersonal_shopper: