[migration][gpt-4o-mini -> gpt-5.4]

Author: kyegomez

README.md

@@ -144,7 +144,7 @@ from swarms import Agent
 
 # Initialize a new agent
 agent = Agent(
-    model_name="gpt-4o-mini", # Specify the LLM
+    model_name="gpt-5.4", # Specify the LLM
     max_loops="auto",              # Set the number of interactions
     interactive=True,         # Enable interactive mode for real-time feedback
 )
@@ -164,14 +164,14 @@ from swarms import Agent, SequentialWorkflow
 researcher = Agent(
     agent_name="Researcher",
     system_prompt="Your job is to research the provided topic and provide a detailed summary.",
-    model_name="gpt-4o-mini",
+    model_name="gpt-5.4",
 )
 
 # Agent 2: The Writer
 writer = Agent(
     agent_name="Writer",
     system_prompt="Your job is to take the research summary and write a beautiful, engaging blog post about it.",
-    model_name="gpt-4o-mini",
+    model_name="gpt-5.4",
 )
 
 # Create a sequential workflow where the researcher's output feeds into the writer's input
@@ -200,7 +200,7 @@ swarm = AutoSwarmBuilder(
     verbose=True,
     max_loops=1,
     return_agents=True,
-    model_name="gpt-4o-mini",
+    model_name="gpt-5.4",
 )
 
 # Let the builder automatically create agents and workflows
@@ -257,14 +257,14 @@ from swarms import Agent, SequentialWorkflow
 researcher = Agent(
     agent_name="Researcher",
     system_prompt="Your job is to research the provided topic and provide a detailed summary.",
-    model_name="gpt-4o-mini",
+    model_name="gpt-5.4",
 )
 
 # Agent 2: The Writer
 writer = Agent(
     agent_name="Writer",
     system_prompt="Your job is to take the research summary and write a beautiful, engaging blog post about it.",
-    model_name="gpt-4o-mini",
+    model_name="gpt-5.4",
 )
 
 # Create a sequential workflow where the researcher's output feeds into the writer's input
@@ -289,21 +289,21 @@ from swarms import Agent, ConcurrentWorkflow
 market_analyst = Agent(
     agent_name="Market-Analyst",
     system_prompt="Analyze market trends and provide insights on the given topic.",
-    model_name="gpt-4o-mini",
+    model_name="gpt-5.4",
     max_loops=1,
 )
 
 financial_analyst = Agent(
     agent_name="Financial-Analyst", 
     system_prompt="Provide financial analysis and recommendations on the given topic.",
-    model_name="gpt-4o-mini",
+    model_name="gpt-5.4",
     max_loops=1,
 )
 
 risk_analyst = Agent(
     agent_name="Risk-Analyst",
     system_prompt="Assess risks and provide risk management strategies for the given topic.",
-    model_name="gpt-4o-mini", 
+    model_name="gpt-5.4", 
     max_loops=1,
 )
 
@@ -331,9 +331,9 @@ Inspired by `einsum`, `AgentRearrange` lets you define complex, non-linear relat
 from swarms import Agent, AgentRearrange
 
 # Define agents
-researcher = Agent(agent_name="researcher", model_name="gpt-4o-mini")
-writer = Agent(agent_name="writer", model_name="gpt-4o-mini")
-editor = Agent(agent_name="editor", model_name="gpt-4o-mini")
+researcher = Agent(agent_name="researcher", model_name="gpt-5.4")
+writer = Agent(agent_name="writer", model_name="gpt-5.4")
+editor = Agent(agent_name="editor", model_name="gpt-5.4")
 
 # Define a flow: researcher sends work to both writer and editor simultaneously
 # This is a one-to-many relationship
@@ -362,8 +362,8 @@ print(outputs)
 from swarms import Agent, GraphWorkflow, Node, Edge, NodeType
 
 # Define agents and a simple python function as nodes
-code_generator = Agent(agent_name="CodeGenerator", system_prompt="Write Python code for the given task.", model_name="gpt-4o-mini")
-code_tester = Agent(agent_name="CodeTester", system_prompt="Test the given Python code and find bugs.", model_name="gpt-4o-mini")
+code_generator = Agent(agent_name="CodeGenerator", system_prompt="Write Python code for the given task.", model_name="gpt-5.4")
+code_tester = Agent(agent_name="CodeTester", system_prompt="Test the given Python code and find bugs.", model_name="gpt-5.4")
 
 # Create nodes for the graph
 node1 = Node(id="generator", agent=code_generator)
@@ -396,9 +396,9 @@ from swarms import Agent
 from swarms.structs.swarm_router import SwarmRouter, SwarmType
 
 # Define a few generic agents
-writer = Agent(agent_name="Writer", system_prompt="You are a creative writer.", model_name="gpt-4o-mini")
-editor = Agent(agent_name="Editor", system_prompt="You are an expert editor for stories.", model_name="gpt-4o-mini")
-reviewer = Agent(agent_name="Reviewer", system_prompt="You are a final reviewer who gives a score.", model_name="gpt-4o-mini")
+writer = Agent(agent_name="Writer", system_prompt="You are a creative writer.", model_name="gpt-5.4")
+editor = Agent(agent_name="Editor", system_prompt="You are an expert editor for stories.", model_name="gpt-5.4")
+reviewer = Agent(agent_name="Reviewer", system_prompt="You are a final reviewer who gives a score.", model_name="gpt-5.4")
 
 # The agents and task will be the same for all examples
 agents = [writer, editor, reviewer]
@@ -426,7 +426,7 @@ print("Running a Mixture of Agents Workflow...")
 aggregator = Agent(
     agent_name="Aggregator",
     system_prompt="Combine the story, edits, and review into a final document.",
-    model_name="gpt-4o-mini"
+    model_name="gpt-5.4"
 )
 moa_router = SwarmRouter(
     swarm_type=SwarmType.MixtureOfAgents,
@@ -450,15 +450,15 @@ The `MixtureOfAgents` architecture processes tasks by feeding them to multiple "
 from swarms import Agent, MixtureOfAgents
 
 # Define expert agents
-financial_analyst = Agent(agent_name="FinancialAnalyst", system_prompt="Analyze financial data.", model_name="gpt-4o-mini")
-market_analyst = Agent(agent_name="MarketAnalyst", system_prompt="Analyze market trends.", model_name="gpt-4o-mini")
-risk_analyst = Agent(agent_name="RiskAnalyst", system_prompt="Analyze investment risks.", model_name="gpt-4o-mini")
+financial_analyst = Agent(agent_name="FinancialAnalyst", system_prompt="Analyze financial data.", model_name="gpt-5.4")
+market_analyst = Agent(agent_name="MarketAnalyst", system_prompt="Analyze market trends.", model_name="gpt-5.4")
+risk_analyst = Agent(agent_name="RiskAnalyst", system_prompt="Analyze investment risks.", model_name="gpt-5.4")
 
 # Define the aggregator agent
 aggregator = Agent(
     agent_name="InvestmentAdvisor",
     system_prompt="Synthesize the financial, market, and risk analyses to provide a final investment recommendation.",
-    model_name="gpt-4o-mini"
+    model_name="gpt-5.4"
 )
 
 # Create the MoA swarm
@@ -482,8 +482,8 @@ print(recommendation)
 from swarms import Agent, GroupChat
 
 # Define agents for a debate
-tech_optimist = Agent(agent_name="TechOptimist", system_prompt="Argue for the benefits of AI in society.", model_name="gpt-4o-mini")
-tech_critic = Agent(agent_name="TechCritic", system_prompt="Argue against the unchecked advancement of AI.", model_name="gpt-4o-mini")
+tech_optimist = Agent(agent_name="TechOptimist", system_prompt="Argue for the benefits of AI in society.", model_name="gpt-5.4")
+tech_critic = Agent(agent_name="TechCritic", system_prompt="Argue against the unchecked advancement of AI.", model_name="gpt-5.4")
 
 # Create the group chat
 chat = GroupChat(
@@ -514,25 +514,25 @@ from swarms import Agent, HierarchicalSwarm
 content_strategist = Agent(
     agent_name="Content-Strategist",
     system_prompt="You are a senior content strategist. Develop comprehensive content strategies, editorial calendars, and content roadmaps.",
-    model_name="gpt-4o-mini"
+    model_name="gpt-5.4"
 )
 
 creative_director = Agent(
     agent_name="Creative-Director", 
     system_prompt="You are a creative director. Develop compelling advertising concepts, visual directions, and campaign creativity.",
-    model_name="gpt-4o-mini"
+    model_name="gpt-5.4"
 )
 
 seo_specialist = Agent(
     agent_name="SEO-Specialist",
     system_prompt="You are an SEO expert. Conduct keyword research, optimize content, and develop organic growth strategies.",
-    model_name="gpt-4o-mini"
+    model_name="gpt-5.4"
 )
 
 brand_strategist = Agent(
     agent_name="Brand-Strategist",
     system_prompt="You are a brand strategist. Develop brand positioning, identity systems, and market differentiation strategies.",
-    model_name="gpt-4o-mini"
+    model_name="gpt-5.4"
 )
 
 # Create the hierarchical swarm with a director

docs/developer_guides/web_scraper.md

@@ -35,7 +35,7 @@ from swarms_tools import scrape_and_format_sync
 
 agent = Agent(
     agent_name="Web Scraper Agent",
-    model_name="gpt-4o-mini",
+    model_name="gpt-5.4",
     tools=[scrape_and_format_sync],
     dynamic_context_window=True,
     dynamic_temperature_enabled=True,
@@ -60,7 +60,7 @@ from swarms import Agent
 
 agent = Agent(
     agent_name="Web Scraper Agent",
-    model_name="gpt-4o-mini",
+    model_name="gpt-5.4",
     tools=[scrape_and_format_sync],
     dynamic_context_window=True,
     dynamic_temperature_enabled=True,

docs/examples/agent_stream.md

@@ -35,7 +35,7 @@ from swarms import Agent
 # Enable real-time streaming
 agent = Agent(
     agent_name="StoryAgent",
-    model_name="gpt-4o-mini",
+    model_name="gpt-5.4",
     streaming_on=True,  # 🔥 This enables real streaming!
     max_loops=1,
     print_on=True,  # By default, it's False for raw streaming!

docs/examples/cli_multi_agent_quickstart.md

@@ -163,7 +163,7 @@ Execute a single agent task:
 swarms agent \
     --name "Research-Agent" \
     --task "Summarize recent AI developments" \
-    --model "gpt-4o-mini" \
+    --model "gpt-5.4" \
     --max-loops 1
 ```
 

docs/examples/debate_quickstart.md

@@ -46,7 +46,7 @@ Create a DebateWithJudge system using preset agents (the simplest approach):
 debate = DebateWithJudge(
     preset_agents=True,      # Use built-in optimized agents
     max_loops=3,             # 3 rounds of debate
-    model_name="gpt-4o-mini",
+    model_name="gpt-5.4",
     verbose=True
 )
 ```
@@ -85,7 +85,7 @@ from swarms import DebateWithJudge
 debate_system = DebateWithJudge(
     preset_agents=True,
     max_loops=3,
-    model_name="gpt-4o-mini",
+    model_name="gpt-5.4",
     output_type="str-all-except-first",
     verbose=True,
 )
@@ -126,7 +126,7 @@ pro_agent = Agent(
         "You present arguments focusing on economic growth, technological competitiveness, "
         "and the risks of over-regulation stifling progress."
     ),
-    model_name="gpt-4o-mini",
+    model_name="gpt-5.4",
     max_loops=1,
 )
 
@@ -138,7 +138,7 @@ con_agent = Agent(
         "You present arguments focusing on public safety, ethical considerations, "
         "and the need for government oversight of powerful technologies."
     ),
-    model_name="gpt-4o-mini",
+    model_name="gpt-5.4",
     max_loops=1,
 )
 
@@ -150,7 +150,7 @@ judge_agent = Agent(
         "You synthesize the strongest arguments from both sides and provide "
         "balanced, actionable policy recommendations."
     ),
-    model_name="gpt-4o-mini",
+    model_name="gpt-5.4",
     max_loops=1,
 )
 
@@ -198,7 +198,7 @@ for topic, result in zip(topics, results):
 |-----------|---------|-------------|
 | `preset_agents` | `False` | Use built-in optimized agents |
 | `max_loops` | `3` | Number of debate rounds |
-| `model_name` | `"gpt-4o-mini"` | Model for preset agents |
+| `model_name` | `"gpt-5.4"` | Model for preset agents |
 | `output_type` | `"str-all-except-first"` | Output format |
 | `verbose` | `True` | Enable detailed logging |
 

docs/examples/exa_search.md

@@ -39,7 +39,7 @@ from swarms_tools import exa_search
 
 agent = Agent(
     name="Exa Search Agent",
-    llm="gpt-4o-mini",
+    llm="gpt-5.4",
     tools=[exa_search],
 )
 

docs/examples/x402_payment_integration.md

@@ -64,7 +64,7 @@ app = FastAPI(title="Research Agent API")
 research_agent = Agent(
     agent_name="Research-Agent",
     system_prompt="You are an expert research analyst. Conduct thorough research on the given topic and provide comprehensive, well-structured insights with citations.",
-    model_name="gpt-4o-mini",
+    model_name="gpt-5.4",
     max_loops=1,
     tools=[exa_search],
 )