chore: bump PraisonAI to v2.2.87, PraisonAI Agents to v0.0.161 and increase default temperature to 1.0

docker/Dockerfile

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     flask \
-    "praisonai>=2.2.84" \
+    "praisonai>=2.2.87" \
     "praisonai[api]" \
     gunicorn \
     markdown

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.84" \
+    "praisonai>=2.2.87" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.84" \
+    "praisonai>=2.2.87" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.84" \
+    "praisonai>=2.2.87" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docker/README.md

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions
 
 All Docker images use consistent, up-to-date versions:
-- PraisonAI: `>=2.2.84`
+- PraisonAI: `>=2.2.87`
 - PraisonAI Agents: `>=0.0.92`
 - Python: `3.11-slim`
 
@@ -218,7 +218,7 @@ docker-compose up -d
 ### Version Pinning
 To use specific versions, update the Dockerfile:
 ```dockerfile
-RUN pip install "praisonai==2.2.84" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.87" "praisonaiagents==0.0.92"
 ```
 
 ## 🌐 Production Deployment

examples/cookbooks/Finance_Market_Job_Agents/Home_Buying_Real_Estate_Agent.ipynb

@@ -156,7 +156,7 @@
     "  - Property comparison\n",
     "  - Cost of living and taxes\n",
     "  - First-time homebuyer guidance\n",
-    "llm: gpt-4o\n",
+    "llm: gpt-5-nano\n",
     "verbosity: true\n",
     "format: markdown\n",
     "\"\"\"\n"
@@ -188,7 +188,7 @@
     "Help users evaluate neighborhoods, estimate mortgage payments, compare homes, and understand homeownership costs.\n",
     "Explain clearly, format in markdown, and be friendly but professional.\n",
     "\"\"\",\n",
-    "    llm=\"gpt-4o\",\n",
+    "    llm=\"gpt-5-nano\",\n",
     "    verbose=True,\n",
     "    markdown=True\n",
     ")\n"

examples/cookbooks/Health_Fitness_Agents/Air_Quality_Analysis_Agent.ipynb

@@ -311,7 +311,7 @@
      "data": {
       "text/html": [
        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080\">╭───────────────────────────────────────────────────── Task ──────────────────────────────────────────────────────╮</span>\n",
-       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> You are an expert health advisor using the GPT-4o Mini model.                                                   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> You are an expert health advisor using the gpt-5-nano Mini model.                                                   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
        "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
        "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\">                                                                                                               </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
        "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">Based on the following air quality conditions in Mumbai, Maharashtra, India:</span><span style=\"background-color: #272822\">                                  </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
@@ -340,7 +340,7 @@
       ],
       "text/plain": [
        "\u001b[36m╭─\u001b[0m\u001b[36m────────────────────────────────────────────────────\u001b[0m\u001b[36m Task \u001b[0m\u001b[36m─────────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
-       "\u001b[36m│\u001b[0m You are an expert health advisor using the GPT-4o Mini model.                                                   \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m You are an expert health advisor using the gpt-5-nano Mini model.                                                   \u001b[36m│\u001b[0m\n",
        "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
        "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m                                                                                                               \u001b[0m \u001b[36m│\u001b[0m\n",
        "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mBased on the following air quality conditions in Mumbai, Maharashtra, India:\u001b[0m\u001b[48;2;39;40;34m                                 \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
@@ -519,7 +519,7 @@
     "    aqi_data = aqi_data.data  # Convert to dict\n",
     "\n",
     "    prompt = f\"\"\"\n",
-    "    You are an expert health advisor using the GPT-4o Mini model.\n",
+    "    You are an expert health advisor using the gpt-5-nano Mini model.\n",
     "\n",
     "    Based on the following air quality conditions in {user_city}, {user_state}, {user_country}:\n",
     "    - Overall AQI: {aqi_data.get('aqi', 'N/A')}\n",

examples/cookbooks/Research_Knowledge_QA_Agents/Crypto_Research_Agent_Intelligence_Agent.ipynb

@@ -152,7 +152,7 @@
     "  - Evaluate risk factors\n",
     "  - Identify scams or red flags\n",
     "  - Summarize project fundamentals\n",
-    "llm: gpt-4o\n",
+    "llm: gpt-5-nano\n",
     "verbosity: true\n",
     "format: markdown\n",
     "\"\"\"\n"
@@ -184,7 +184,7 @@
     "and market trends. Provide critical insight into legitimacy, potential growth, and associated risks.\n",
     "Use markdown formatting and structure your research clearly.\n",
     "\"\"\",\n",
-    "    llm=\"gpt-4o\",\n",
+    "    llm=\"gpt-5-nano\",\n",
     "    verbose=True,\n",
     "    markdown=True\n",
     ")\n"

examples/cookbooks/SocialMedia_Content_Agents/Ai_Meme_Creator_Agent.ipynb

@@ -627,7 +627,7 @@
     "            )\n",
     "        else:  # OpenAI\n",
     "            llm = ChatOpenAI(\n",
-    "                model=\"gpt-4o\",\n",
+    "                model=\"gpt-5-nano\",\n",
     "                api_key=api_key,\n",
     "                temperature=0.0\n",
     "            )\n",

examples/cookbooks/SocialMedia_Content_Agents/YouTube_Influencer_Intelligence_Agent.ipynb

@@ -153,7 +153,7 @@
     "  - Spot content patterns and trends\n",
     "  - Recommend growth strategies\n",
     "  - Detect potential monetization methods\n",
-    "llm: gpt-4o\n",
+    "llm: gpt-5-nano\n",
     "verbosity: true\n",
     "format: markdown\n",
     "\"\"\"\n"
@@ -184,7 +184,7 @@
     "You are a YouTube intelligence analyst. Your job is to research a specific channel, niche, or influencer topic.\n",
     "Provide growth recommendations, engagement analysis, trending strategies, and monetization tips. Format in markdown.\n",
     "\"\"\",\n",
-    "    llm=\"gpt-4o\",\n",
+    "    llm=\"gpt-5-nano\",\n",
     "    verbose=True,\n",
     "    markdown=True\n",
     ")\n"

examples/cookbooks/Travel_Planning_Agents/Intelligent_Travel_Planning_Agent.ipynb

@@ -136,7 +136,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'name': 'Travel Research Agent', 'description': 'A world-class travel researcher that searches for destinations, activities, and accommodations', 'instructions': ['Given a travel destination and number of days, generate 3 relevant search terms', 'Search the web for each term using web_search tool', 'Analyze results and return 10 most relevant findings', 'Focus on activities, accommodations, attractions, and local insights', 'Ensure high-quality, accurate information', 'Use travel_budget_calculator to estimate costs'], 'tools': ['web_search', 'travel_budget_calculator'], 'model': 'gpt-4o', 'temperature': 0.7}\n"
+      "{'name': 'Travel Research Agent', 'description': 'A world-class travel researcher that searches for destinations, activities, and accommodations', 'instructions': ['Given a travel destination and number of days, generate 3 relevant search terms', 'Search the web for each term using web_search tool', 'Analyze results and return 10 most relevant findings', 'Focus on activities, accommodations, attractions, and local insights', 'Ensure high-quality, accurate information', 'Use travel_budget_calculator to estimate costs'], 'tools': ['web_search', 'travel_budget_calculator'], 'model': 'gpt-5-nano', 'temperature': 0.7}\n"
      ]
     }
    ],
@@ -156,7 +156,7 @@
     "tools:\n",
     "  - web_search\n",
     "  - travel_budget_calculator\n",
-    "model: gpt-4o\n",
+    "model: gpt-5-nano\n",
     "temperature: 0.7\n",
     "\"\"\"\n",
     "\n",

examples/cookbooks/Travel_Planning_Agents/VoiGno_Restaurant_Agent_PraisonAI.ipynb

@@ -17,7 +17,7 @@
     "id": "Y9FkDzt1lF47"
    },
    "source": [
-    "This notebook implements a conversational restaurant booking assistant using the Praison AI Agent framework. It handles natural interruptions such as modifying guest count or changing time mid-conversation. Built for Google Colab Free Tier, it uses OpenAI's GPT-4o model and follows a YAML-based agent design for clarity and modularity."
+    "This notebook implements a conversational restaurant booking assistant using the Praison AI Agent framework. It handles natural interruptions such as modifying guest count or changing time mid-conversation. Built for Google Colab Free Tier, it uses OpenAI's gpt-5-nano model and follows a YAML-based agent design for clarity and modularity."
    ]
   },
   {
@@ -135,7 +135,7 @@
     "  - Context management\n",
     "  - Handling mid-conversation changes\n",
     "  - Summarizing confirmed reservations\n",
-    "llm: gpt-4o\n",
+    "llm: gpt-5-nano\n",
     "verbosity: true\n",
     "format: markdown\n",
     "\"\"\"\n"
@@ -167,7 +167,7 @@
     "manage corrections like “change to 3 guests” or “move to 8 PM,”\n",
     "confirm the details once the user is satisfied. Respond in markdown.\n",
     "\"\"\",\n",
-    "    llm=\"gpt-4o\",\n",
+    "    llm=\"gpt-5-nano\",\n",
     "    verbose=True,\n",
     "    markdown=True\n",
     ")\n"

examples/cookbooks/yaml/marketing_campaign_development_agents.ipynb

@@ -252,7 +252,7 @@
      "text": [
       "\u001b[95m \n",
       "\n",
-      "[{'title': 'CrewAI uses third-party models to automate business tasks', 'href': 'https://techcrunch.com/2024/10/22/crewai-uses-third-party-models-to-automate-business-tasks/', 'body': 'CrewAI, a startup developing a hub where companies can build automations using third-party AI models, has raised $18 million from investors including Andrew Ng. ... 4:00 AM PDT · October 22, 2024 ...'}, {'title': 'CrewAI Launches Multi-Agentic Platform to Deliver on the Promise of ...', 'href': 'https://markets.businessinsider.com/news/stocks/crewai-launches-multi-agentic-platform-to-deliver-on-the-promise-of-generative-ai-for-enterprise-1033870246?op=1', 'body': 'SAN FRANCISCO and SÃO PAULO, Oct. 22, 2024 (GLOBE NEWSWIRE) -- CrewAI, a leading multi-agent platform, today announced $18 million in total funding that included an inception round led by ...'}, {'title': 'Agentic AI startup CrewAI closes $18M funding round', 'href': 'https://siliconangle.com/2024/10/22/agentic-ai-startup-crewai-closes-18m-funding-round/', 'body': \"An e-commerce company, for example, could create an agent that uses GPT-4o to generate shopping advice based on customers' past purchases. CrewAI has created an open-source framework of the same ...\"}, {'title': 'CrewAI Company Profile 2024: Valuation, Funding & Investors - PitchBook', 'href': 'https://pitchbook.com/profiles/company/590845-78', 'body': \"Explore institutional-grade private market research from our team of analysts. ... Artificial Intelligence & Machine Learning; Artificial Intelligence & Machine Learning Report. September 19, 2024. CrewAI Executive Team (2) Update this profile Name ... CrewAI's primary industry is Business/Productivity Software. Is CrewAI a private or public ...\"}, {'title': 'CrewAI - Autonomous AI Agents in action for a market ... - LinkedIn', 'href': 'https://www.linkedin.com/pulse/crewai-autonomous-ai-agents-action-market-research-task-kithion-ovnrc', 'body': 'The will provide a detailed market trends report, the will generate a comprehensive analysis of top competitors, and the will develop a thorough marketing plan. Conclusion. CrewAI, combined with ...'}]\n",
+      "[{'title': 'CrewAI uses third-party models to automate business tasks', 'href': 'https://techcrunch.com/2024/10/22/crewai-uses-third-party-models-to-automate-business-tasks/', 'body': 'CrewAI, a startup developing a hub where companies can build automations using third-party AI models, has raised $18 million from investors including Andrew Ng. ... 4:00 AM PDT · October 22, 2024 ...'}, {'title': 'CrewAI Launches Multi-Agentic Platform to Deliver on the Promise of ...', 'href': 'https://markets.businessinsider.com/news/stocks/crewai-launches-multi-agentic-platform-to-deliver-on-the-promise-of-generative-ai-for-enterprise-1033870246?op=1', 'body': 'SAN FRANCISCO and SÃO PAULO, Oct. 22, 2024 (GLOBE NEWSWIRE) -- CrewAI, a leading multi-agent platform, today announced $18 million in total funding that included an inception round led by ...'}, {'title': 'Agentic AI startup CrewAI closes $18M funding round', 'href': 'https://siliconangle.com/2024/10/22/agentic-ai-startup-crewai-closes-18m-funding-round/', 'body': \"An e-commerce company, for example, could create an agent that uses gpt-5-nano to generate shopping advice based on customers' past purchases. CrewAI has created an open-source framework of the same ...\"}, {'title': 'CrewAI Company Profile 2024: Valuation, Funding & Investors - PitchBook', 'href': 'https://pitchbook.com/profiles/company/590845-78', 'body': \"Explore institutional-grade private market research from our team of analysts. ... Artificial Intelligence & Machine Learning; Artificial Intelligence & Machine Learning Report. September 19, 2024. CrewAI Executive Team (2) Update this profile Name ... CrewAI's primary industry is Business/Productivity Software. Is CrewAI a private or public ...\"}, {'title': 'CrewAI - Autonomous AI Agents in action for a market ... - LinkedIn', 'href': 'https://www.linkedin.com/pulse/crewai-autonomous-ai-agents-action-market-research-task-kithion-ovnrc', 'body': 'The will provide a detailed market trends report, the will generate a comprehensive analysis of top competitors, and the will develop a thorough marketing plan. Conclusion. CrewAI, combined with ...'}]\n",
       "\u001b[00m\n",
       "\u001b[32;1m\u001b[1;3mThought: I have found more information about CrewAI's target customers and market positioning. CrewAI is a startup that has developed a hub where companies can build automations using third-party AI models, and it has recently raised $18 million in funding. Their target customers are likely businesses looking to automate complex tasks and improve efficiency using AI. They are positioned as a platform for deploying and managing AI agents to automate a variety of tasks.\n",
       "\n",

examples/cookbooks/yaml/model_fee_retreival_agents.ipynb

@@ -73,7 +73,7 @@
     "            url=url,\n",
     "            word_count_threshold=1,\n",
     "            extraction_strategy= LLMExtractionStrategy(\n",
-    "                provider=\"openai/gpt-4o\",\n",
+    "                provider=\"openai/gpt-5-nano\",\n",
     "                api_token=os.getenv('OPENAI_API_KEY'),\n",
     "                schema=ModelFee.schema(),\n",
     "                extraction_type=\"schema\",\n",

examples/python/agents/MULTI_PROVIDER_README.md

@@ -46,7 +46,7 @@ agent = RouterAgent(
     name="Smart Assistant",
     role="Adaptive AI Assistant",
     goal="Complete tasks using the most appropriate model",
-    models=["gpt-5-nano", "gpt-4o", "claude-3-5-sonnet-20241022"],
+    models=["gpt-5-nano", "gpt-5-nano", "claude-3-5-sonnet-20241022"],
     routing_strategy="auto"  # Automatic model selection
 )
 
@@ -133,7 +133,7 @@ The system includes pre-configured profiles for popular models:
 | gpt-5-nano | OpenAI | Simple tasks, speed | $0.00075 |
 | gemini-1.5-flash | Google | Cost-effective, multimodal | $0.000125 |
 | claude-3-haiku | Anthropic | Fast responses | $0.0008 |
-| gpt-4o | OpenAI | General purpose | $0.0075 |
+| gpt-5-nano | OpenAI | General purpose | $0.0075 |
 | claude-3-5-sonnet-20241022 | Anthropic | Complex reasoning | $0.009 |
 | deepseek-chat | DeepSeek | Code & math | $0.0014 |
 
@@ -161,7 +161,7 @@ print(report)
     'routing_strategy': 'auto',
     'model_usage': {
         'gpt-5-nano': {'calls': 5, 'tokens': 1500, 'cost': 0.0011},
-        'gpt-4o': {'calls': 2, 'tokens': 3000, 'cost': 0.0225}
+        'gpt-5-nano': {'calls': 2, 'tokens': 3000, 'cost': 0.0225}
     },
     'total_cost_estimate': 0.0236,
     'total_calls': 7
@@ -207,7 +207,7 @@ for i, agent in enumerate(auto.agents):
         name=agent.name,
         role=agent.role,
         goal=agent.goal,
-        models=["gpt-5-nano", "gpt-4o", "claude-3-5-sonnet"],
+        models=["gpt-5-nano", "gpt-5-nano", "claude-3-5-sonnet"],
         routing_strategy="auto"
     )
 

examples/python/agents/multi-provider-agent.py

@@ -21,7 +21,7 @@ def example_auto_routing():
         role="Adaptive Research Assistant",
         goal="Research topics using the most appropriate AI model",
         backstory="I analyze task complexity and route to the best model",
-        models=["gpt-5-nano", "gpt-4o", "claude-3-5-sonnet-20241022"],
+        models=["gpt-5-nano", "gpt-5-nano", "claude-3-5-sonnet-20241022"],
         routing_strategy="auto",  # Automatic model selection
         verbose=True
     )
@@ -95,7 +95,7 @@ def example_cost_optimized_workflow():
         goal="Create high-quality content",
         models={
             "claude-3-5-sonnet-20241022": {},
-            "gpt-4o": {}
+            "gpt-5-nano": {}
         },
         routing_strategy="performance-optimized",  # Prefer better models
         verbose=True
@@ -158,7 +158,7 @@ def example_auto_agents_multi_provider():
             goal=agent.goal,
             backstory=agent.backstory,
             tools=agent.tools,
-            models=["gpt-5-nano", "gemini/gemini-1.5-flash", "claude-3-haiku-20240307", "gpt-4o"],
+            models=["gpt-5-nano", "gemini/gemini-1.5-flash", "claude-3-haiku-20240307", "gpt-5-nano"],
             routing_strategy="auto",
             verbose=True
         )
@@ -192,7 +192,7 @@ def example_custom_routing():
     # Create custom model profiles for specific use cases
     custom_models = [
         ModelProfile(
-            name="gpt-4o",
+            name="gpt-5-nano",
             provider="openai",
             complexity_range=(TaskComplexity.SIMPLE, TaskComplexity.VERY_COMPLEX),
             cost_per_1k_tokens=0.0075,

examples/python/agents/router-agent-cost-optimization.py

@@ -19,7 +19,7 @@ from praisonaiagents.tools import duckduckgo
 # Define model options with cost considerations
 # Using different models for different complexity levels
 cheap_model = "gpt-5-nano"  # For simple tasks
-balanced_model = "gpt-4o"    # For moderate complexity  
+balanced_model = "gpt-5-nano"    # For moderate complexity  
 premium_model = "claude-3-5-sonnet-20241022"  # For complex tasks
 
 # Create RouterAgent with cost optimization strategy
@@ -77,6 +77,6 @@ print("COST OPTIMIZATION ROUTER DEMONSTRATION COMPLETED")
 print("="*80)
 print("The RouterAgent automatically selected appropriate models based on task complexity:")
 print("- Simple factual questions → Cheaper model (gpt-5-nano)")  
-print("- Moderate analysis tasks → Balanced model (gpt-4o)")
+print("- Moderate analysis tasks → Balanced model (gpt-5-nano)")
 print("- Complex strategic analysis → Premium model (claude-3-5-sonnet)")
 print("- This approach optimizes costs while maintaining quality for each task type")

examples/python/general/example_callback.py

@@ -127,9 +127,9 @@ def main():
         verbose=True,
         allow_delegation=False,
         tools=[internet_search_tool],
-        llm="gpt-4o",
+        llm="gpt-5-nano",
         markdown=True,
-        reflect_llm="gpt-4o",
+        reflect_llm="gpt-5-nano",
         min_reflect=2,
         max_reflect=4
     )
@@ -142,7 +142,7 @@ def main():
         making complex tech topics interesting and easy to understand.""",
         verbose=True,
         allow_delegation=True,
-        llm="gpt-4o",
+        llm="gpt-5-nano",
         tools=[],
         markdown=True
     )
@@ -195,7 +195,7 @@ def main():
         tasks=[task1, task2, task3, task4],
         verbose=True,
         process="sequential",
-        manager_llm="gpt-4o"
+        manager_llm="gpt-5-nano"
     )
 
     agents.start()

examples/python/general/example_sequential.py

@@ -39,9 +39,9 @@ researcher = Agent(
     verbose=True,
     allow_delegation=False,
     tools=[internet_search_tool],
-    llm="gpt-4o",
+    llm="gpt-5-nano",
     markdown=True,
-    reflect_llm="gpt-4o",
+    reflect_llm="gpt-5-nano",
     min_reflect=2,
     max_reflect=4
 )
@@ -53,7 +53,7 @@ writer = Agent(
     making complex tech topics interesting and easy to understand.""",
     verbose=True,
     allow_delegation=True,
-    llm="gpt-4o",
+    llm="gpt-5-nano",
     tools=[],
     markdown=True
 )
@@ -104,7 +104,7 @@ agents = PraisonAIAgents(
     tasks=[task1, task2, task3, task4],
     verbose=False,
     process="sequential",  # "sequential" or "hierarchical"
-    manager_llm="gpt-4o"
+    manager_llm="gpt-5-nano"
 )
 
 result = agents.start()

examples/python/input/streamlit_ui_input.py

@@ -30,7 +30,7 @@ with st.sidebar:
     # Model selection
     model = st.selectbox(
         "Select Model",
-        ["gpt-5-nano", "gpt-4o", "gpt-3.5-turbo", "claude-3-opus"],
+        ["gpt-5-nano", "gpt-5-nano", "gpt-3.5-turbo", "claude-3-opus"],
         index=0
     )
     

examples/python/models/gpt/gpt_cybersecurity_agent.py

@@ -5,7 +5,7 @@ agent = Agent(
                 "Help users understand security concepts, analyze security threats, "
                 "and provide guidance on security best practices, risk assessment, "
                 "and security architecture design.",
-    llm="openai/gpt-4o"
+    llm="openai/gpt-5-nano"
 )
 
 response = agent.start("Hello! I'm your cybersecurity assistant. "

examples/python/models/gpt/gpt_data_engineer_agent.py

@@ -5,7 +5,7 @@ agent = Agent(
                 "Help users design data pipelines, optimize database performance, "
                 "create ETL processes, and provide guidance on data architecture, "
                 "data modeling, and big data technologies.",
-    llm="openai/gpt-4o"
+    llm="openai/gpt-5-nano"
 )
 
 response = agent.start("Hello! I'm your data engineer assistant. "

examples/python/models/gpt/gpt_marketing_strategist_agent.py

@@ -5,7 +5,7 @@ agent = Agent(
                 "Help users develop marketing strategies, analyze market trends, "
                 "create campaign plans, and provide guidance on digital marketing, "
                 "brand positioning, and customer acquisition strategies.",
-    llm="openai/gpt-4o"
+    llm="openai/gpt-5-nano"
 )
 
 response = agent.start("Hello! I'm your marketing strategist assistant. "

examples/python/models/gpt/gpt_product_manager_agent.py

@@ -5,7 +5,7 @@ agent = Agent(
                 "Help users with product strategy, feature prioritization, "
                 "user research analysis, and product roadmap planning. Provide "
                 "guidance on market analysis, competitive positioning, and user experience.",
-    llm="openai/gpt-4o"
+    llm="openai/gpt-5-nano"
 )
 
 response = agent.start("Hello! I'm your product manager assistant. "

examples/python/models/gpt/gpt_software_architect_agent.py

@@ -5,7 +5,7 @@ agent = Agent(
                 "Help users design software systems, create architecture diagrams, "
                 "plan system integrations, and provide guidance on scalability, "
                 "security, and best practices for software development.",
-    llm="openai/gpt-4o"
+    llm="openai/gpt-5-nano"
 )
 
 response = agent.start("Hello! I'm your software architect assistant. "

examples/python/providers/openai/openai_gpt4_example.py

@@ -7,7 +7,7 @@ from praisonaiagents import Agent
 # Initialize Agent with OpenAI GPT-4
 agent = Agent(
     instructions="You are a helpful assistant",
-    llm="openai/gpt-4o",
+    llm="openai/gpt-5-nano",
 )
 
 # Example conversation

examples/python/tools/e2b/app.py

@@ -28,7 +28,7 @@ web_scraper_agent = Agent(
     goal="Extract URLs from https://quotes.toscrape.com/",
     backstory="An expert in data extraction from websites, adept at navigating and retrieving detailed information.",
     tools=[code_interpret],
-    llm="gpt-4o"
+    llm="gpt-5-nano"
 )
 
 url_data_extractor_agent = Agent(
@@ -37,7 +37,7 @@ url_data_extractor_agent = Agent(
     goal="Crawl each URL for data extraction",
     backstory="Specializes in crawling websites to gather comprehensive data, ensuring nothing is missed from each link.",
     tools=[code_interpret],
-    llm="gpt-4o"
+    llm="gpt-5-nano"
 )
 
 blog_writer_agent = Agent(
@@ -46,7 +46,7 @@ blog_writer_agent = Agent(
     goal="Create engaging and insightful blog posts from provided data",
     backstory="An experienced content creator and storyteller with a knack for weaving compelling narratives. Skilled at analyzing quotes and creating meaningful connections that resonate with readers.",
     tools=[code_interpret],
-    llm="gpt-4o"
+    llm="gpt-5-nano"
 )
 
 # 2) Create Tasks
@@ -88,7 +88,7 @@ agents_manager = PraisonAIAgents(
     tasks=[task_url_extraction, task_data_extraction, blog_writing_task],
     verbose=True,
     process="hierarchical",
-    manager_llm="gpt-4o"
+    manager_llm="gpt-5-nano"
 )
 
 result = agents_manager.start()

examples/python/tools/e2b/single_agent.py

@@ -56,7 +56,7 @@ web_scraper_agent = Agent(
     min_reflect=3,
     max_reflect=5,
     tools=[code_interpret, save_to_file, run_terminal_command],
-    llm="gpt-4o"
+    llm="gpt-5-nano"
 )
 
 task = Task(

examples/python/ui/mcp-streamlit-airbnb.py

@@ -23,7 +23,7 @@ with st.sidebar:
         "Choose LLM Model",
         options=[
             "gpt-5-nano",
-            "gpt-4o", 
+            "gpt-5-nano", 
             "gpt-3.5-turbo",
             "claude-3-5-sonnet-20241022",
             "claude-3-5-haiku-20241022"

examples/python/usecases/domain-context-solution.py

@@ -162,7 +162,7 @@ manager_agent = Agent(
     """,
     tools=[],
     verbose=True,
-    llm="gpt-4o",
+    llm="gpt-5-nano",
     markdown=True,
     memory={"short_term": {"target_domain": domain}, "long_term": qdrant_client},
 )
@@ -186,7 +186,7 @@ tool_agent = Agent(
     """,
     tools=domain_tools,  # Use domain-aware tools
     verbose=True,
-    llm="gpt-4o",
+    llm="gpt-5-nano",
     markdown=True,
     memory={"short_term": {"target_domain": domain}, "long_term": qdrant_client},
 )
@@ -209,7 +209,7 @@ sr_pentester_agent = Agent(
     """,
     tools=[],
     verbose=True,
-    llm="gpt-4o",
+    llm="gpt-5-nano",
     markdown=True,
     memory={"short_term": {"target_domain": domain}, "long_term": qdrant_client},
 )
@@ -232,7 +232,7 @@ ciso_agent = Agent(
     """,
     tools=[],
     verbose=True,
-    llm="gpt-4o",
+    llm="gpt-5-nano",
     markdown=True,
     memory={"short_term": {"target_domain": domain}, "long_term": qdrant_client},
 )
@@ -268,7 +268,7 @@ agents = PraisonAIAgents(
     tasks=[tool_query_task, pentester_analysis_task, ciso_review_task],
     verbose=True,
     process="hierarchical",
-    manager_llm="gpt-4o",
+    manager_llm="gpt-5-nano",
     memory=True,  # Enable shared memory for domain context
     memory_config={
         "provider": "rag",

src/praisonai-agents/openai-sequential.py

@@ -22,7 +22,7 @@ def multiply(a: int, b: int) -> int:
 
 agent = Agent(
     instructions="You are a helpful assistant. You can use the tools provided to you to help the user.",
-    llm="gpt-4o",
+    llm="gpt-5-nano",
     tools=[get_stock_price, multiply]
 )
 

src/praisonai-agents/praisonai-openai.py

@@ -22,7 +22,7 @@ def multiply(a: int, b: int) -> int:
 
 agent = Agent(
     instructions="You are a helpful assistant. You can use the tools provided to you to help the user.",
-    llm="gpt-4o",
+    llm="gpt-5-nano",
     tools=[get_stock_price, multiply]
 )
 

src/praisonai-agents/praisonaiagents/agent/agent.py

@@ -250,8 +250,8 @@ class Agent:
             instructions (Optional[str], optional): Direct instructions that override role, goal,
                 and backstory when provided. Used for simple, task-specific agents. Defaults to None.
             llm (Optional[Union[str, Any]], optional): Language model configuration. Can be a model
-                name string (e.g., "gpt-4o", "anthropic/claude-3-sonnet") or a configured LLM object.
-                Defaults to environment variable OPENAI_MODEL_NAME or "gpt-4o".
+                name string (e.g., "gpt-5-nano", "anthropic/claude-3-sonnet") or a configured LLM object.
+                Defaults to environment variable OPENAI_MODEL_NAME or "gpt-5-nano".
             tools (Optional[List[Any]], optional): List of tools, functions, or capabilities
                 available to the agent for task execution. Can include callables, tool objects,
                 or MCP instances. Defaults to None.
@@ -395,7 +395,7 @@ class Agent:
                     self.llm_instance = LLM(**llm_config)
                 else:
                     # Create LLM with model string and base_url
-                    model_name = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+                    model_name = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-5-nano')
                     self.llm_instance = LLM(
                         model=model_name,
                         base_url=base_url,
@@ -450,7 +450,7 @@ class Agent:
                 ) from e
         # Otherwise, fall back to OpenAI environment/name
         else:
-            self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+            self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-5-nano')
         # Handle tools parameter - ensure it's always a list
         if callable(tools):
             # If a single function/callable is passed, wrap it in a list
@@ -487,7 +487,7 @@ class Agent:
         self.min_reflect = min_reflect
         self.reflect_prompt = reflect_prompt
         # Use the same model selection logic for reflect_llm
-        self.reflect_llm = reflect_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+        self.reflect_llm = reflect_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-5-nano')
         self._console = None  # Lazy load console when needed
         
         # Initialize system prompt
@@ -573,7 +573,7 @@ Your Goal: {self.goal}
         
         Returns:
             The LLM model/instance being used by this agent.
-            - For standard models: returns the model string (e.g., "gpt-4o")
+            - For standard models: returns the model string (e.g., "gpt-5-nano")
             - For custom LLM instances: returns the LLM instance object
             - For provider models: returns the LLM instance object
         """
@@ -583,7 +583,7 @@ Your Goal: {self.goal}
             return self.llm
         else:
             # Default fallback
-            return "gpt-4o"
+            return "gpt-5-nano"
 
     def _ensure_knowledge_processed(self):
         """Ensure knowledge is initialized and processed when first accessed."""

src/praisonai-agents/praisonaiagents/agents/agents.py

@@ -163,7 +163,7 @@ class PraisonAIAgents:
         self.name = name  # Store the name for the Agents collection
         
         # Check for manager_llm in environment variable if not provided
-        self.manager_llm = manager_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+        self.manager_llm = manager_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-5-nano')
         
         # Set logger level based on verbose
         if verbose >= 5:

src/praisonai-agents/praisonaiagents/agents/autoagents.py

@@ -92,7 +92,7 @@ class AutoAgents(PraisonAIAgents):
         self.self_reflect = self_reflect
         self.max_reflect = max_reflect
         self.min_reflect = min_reflect
-        self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+        self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-5-nano')
         self.function_calling_llm = function_calling_llm
         self.respect_context_window = respect_context_window
         self.code_execution_mode = code_execution_mode

src/praisonai-agents/praisonaiagents/llm/llm.py

@@ -61,7 +61,7 @@ class LLM:
     MODEL_WINDOWS = {
         # OpenAI
         "gpt-4": 6144,                    # 8,192 actual
-        "gpt-4o": 96000,                  # 128,000 actual
+        "gpt-5-nano": 96000,                  # 128,000 actual
         "gpt-5-nano": 96000,            # 128,000 actual
         "gpt-4-turbo": 96000,            # 128,000 actual
         "o1-preview": 96000,             # 128,000 actual
@@ -907,7 +907,7 @@ class LLM:
         prompt: Union[str, List[Dict]],
         system_prompt: Optional[str] = None,
         chat_history: Optional[List[Dict]] = None,
-        temperature: float = 0.2,
+        temperature: float = 1.0,
         tools: Optional[List[Any]] = None,
         output_json: Optional[BaseModel] = None,
         output_pydantic: Optional[BaseModel] = None,
@@ -2042,7 +2042,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         prompt: Union[str, List[Dict]],
         system_prompt: Optional[str] = None,
         chat_history: Optional[List[Dict]] = None,
-        temperature: float = 0.2,
+        temperature: float = 1.0,
         tools: Optional[List[Any]] = None,
         output_json: Optional[BaseModel] = None,
         output_pydantic: Optional[BaseModel] = None,
@@ -2317,7 +2317,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         prompt: Union[str, List[Dict]],
         system_prompt: Optional[str] = None,
         chat_history: Optional[List[Dict]] = None,
-        temperature: float = 0.2,
+        temperature: float = 1.0,
         tools: Optional[List[Any]] = None,
         output_json: Optional[BaseModel] = None,
         output_pydantic: Optional[BaseModel] = None,
@@ -3126,7 +3126,9 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         
         # Add extra settings for provider-specific parameters (e.g., num_ctx for Ollama)
         if self.extra_settings:
-            params.update(self.extra_settings)
+            # Filter out internal parameters that shouldn't be passed to the API
+            filtered_extra_settings = {k: v for k, v in self.extra_settings.items() if k != 'metrics'}
+            params.update(filtered_extra_settings)
         
         # Override with any provided parameters
         params.update(override_params)
@@ -3260,7 +3262,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         self,
         prompt: Union[str, List[Dict]],
         system_prompt: Optional[str] = None,
-        temperature: float = 0.2,
+        temperature: float = 1.0,
         stream: bool = True,
         verbose: bool = True,
         markdown: bool = True,
@@ -3355,7 +3357,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         self,
         prompt: Union[str, List[Dict]],
         system_prompt: Optional[str] = None,
-        temperature: float = 0.2,
+        temperature: float = 1.0,
         stream: bool = True,
         verbose: bool = True,
         markdown: bool = True,

src/praisonai-agents/praisonaiagents/llm/model_capabilities.py

@@ -6,7 +6,7 @@ This module defines which models support specific features like structured outpu
 # Models that support OpenAI-style structured outputs (response_format with Pydantic models)
 MODELS_SUPPORTING_STRUCTURED_OUTPUTS = {
     # OpenAI models
-    "gpt-4o",
+    "gpt-5-nano",
     "gpt-5-nano",
     "gpt-4-turbo",
     "gpt-4-turbo-preview",

src/praisonai-agents/praisonaiagents/llm/model_router.py

@@ -80,7 +80,7 @@ class ModelRouter:
         
         # Mid-tier models for moderate complexity
         ModelProfile(
-            name="gpt-4o",
+            name="gpt-5-nano",
             provider="openai",
             complexity_range=(TaskComplexity.MODERATE, TaskComplexity.COMPLEX),
             cost_per_1k_tokens=0.0075,  # Average of $0.0025 input, $0.01 output
@@ -145,7 +145,7 @@ class ModelRouter:
             preferred_providers: List of preferred providers in order
         """
         self.models = models or self.DEFAULT_MODELS
-        self.default_model = default_model or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+        self.default_model = default_model or os.getenv('OPENAI_MODEL_NAME', 'gpt-5-nano')
         self.cost_threshold = cost_threshold
         self.preferred_providers = preferred_providers or []
         

src/praisonai-agents/praisonaiagents/llm/openai_client.py

@@ -740,7 +740,7 @@ class OpenAIClient:
     def create_completion(
         self,
         messages: List[Dict[str, Any]],
-        model: str = "gpt-4o",
+        model: str = "gpt-5-nano",
         temperature: float = 1.0,
         stream: bool = False,
         tools: Optional[List[Dict[str, Any]]] = None,
@@ -785,7 +785,7 @@ class OpenAIClient:
     async def acreate_completion(
         self,
         messages: List[Dict[str, Any]],
-        model: str = "gpt-4o",
+        model: str = "gpt-5-nano",
         temperature: float = 1.0,
         stream: bool = False,
         tools: Optional[List[Dict[str, Any]]] = None,
@@ -830,7 +830,7 @@ class OpenAIClient:
     def chat_completion_with_tools(
         self,
         messages: List[Dict[str, Any]],
-        model: str = "gpt-4o",
+        model: str = "gpt-5-nano",
         temperature: float = 1.0,
         tools: Optional[List[Any]] = None,
         execute_tool_fn: Optional[Callable] = None,
@@ -1008,7 +1008,7 @@ class OpenAIClient:
     async def achat_completion_with_tools(
         self,
         messages: List[Dict[str, Any]],
-        model: str = "gpt-4o",
+        model: str = "gpt-5-nano",
         temperature: float = 1.0,
         tools: Optional[List[Any]] = None,
         execute_tool_fn: Optional[Callable] = None,
@@ -1189,7 +1189,7 @@ class OpenAIClient:
     def chat_completion_with_tools_stream(
         self,
         messages: List[Dict[str, Any]],
-        model: str = "gpt-4o",
+        model: str = "gpt-5-nano",
         temperature: float = 1.0,
         tools: Optional[List[Any]] = None,
         execute_tool_fn: Optional[Callable] = None,
@@ -1334,7 +1334,7 @@ class OpenAIClient:
         self,
         messages: List[Dict[str, Any]],
         response_format: BaseModel,
-        model: str = "gpt-4o",
+        model: str = "gpt-5-nano",
         temperature: float = 1.0,
         **kwargs
     ) -> Any:
@@ -1368,7 +1368,7 @@ class OpenAIClient:
         self,
         messages: List[Dict[str, Any]],
         response_format: BaseModel,
-        model: str = "gpt-4o",
+        model: str = "gpt-5-nano",
         temperature: float = 1.0,
         **kwargs
     ) -> Any:

src/praisonai-agents/praisonaiagents/memory/memory.py

@@ -109,7 +109,7 @@ class Memory:
         },
         "llm": {
           "provider": "openai",
-          "config": {"model": "gpt-4o", "api_key": "..."}
+          "config": {"model": "gpt-5-nano", "api_key": "..."}
         },
         "embedder": {
           "provider": "openai",

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.158"
+version = "0.0.161"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 requires-python = ">=3.10"
 authors = [

src/praisonai-agents/test_guardrail_return_types.py

@@ -51,7 +51,7 @@ def test_guardrail_return_types():
         role="Tester",
         goal="Test guardrails",
         backstory="Testing guardrail functionality",
-        llm="gpt-4o"
+        llm="gpt-5-nano"
     )
     
     # Test creating tasks with each guardrail type
@@ -105,7 +105,7 @@ def test_guardrail_result_handling():
         role="Tester",
         goal="Test guardrails",
         backstory="Testing guardrail functionality",
-        llm="gpt-4o"
+        llm="gpt-5-nano"
     )
     
     test_output = TaskOutput(

src/praisonai-agents/tests/test_basic_agents_demo.py

@@ -33,7 +33,7 @@ def main():
     
     # Using different model
     print("\n3. Using a different model:")
-    run_command([sys.executable, "tests/basic-agents.py", "--model", "gpt-4o", "Explain quantum computing in simple terms"])
+    run_command([sys.executable, "tests/basic-agents.py", "--model", "gpt-5-nano", "Explain quantum computing in simple terms"])
     
     # With debug logging
     print("\n4. With debug logging:")

src/praisonai-agents/uv.lock

@@ -2422,7 +2422,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.158"
+version = "0.0.161"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },

src/praisonai/.env.example

@@ -1,4 +1,4 @@
-OPENAI_MODEL_NAME="gpt-4o"
+OPENAI_MODEL_NAME="gpt-5-nano"
 OPENAI_API_KEY="Enter your API key"
 OPENAI_API_BASE="https://api.openai.com/v1"
 CHAINLIT_USERNAME=admin

src/praisonai/agents-advanced.yaml

@@ -36,7 +36,7 @@ roles:
     llm:  
       model: "anthropic/claude-3-haiku-20240307"
     function_calling_llm: 
-      model: "openai/gpt-4o"
+      model: "openai/gpt-5-nano"
     tasks:
       content_creation:
         description: Create detailed content summarizing the research findings on

src/praisonai/praisonai.rb

@@ -3,8 +3,8 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.84.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.84.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.87.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.87.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/agents_generator.py

@@ -497,7 +497,7 @@ class AgentsGenerator:
             # Create model client for v0.4
             model_config = self.config_list[0] if self.config_list else {}
             model_client = OpenAIChatCompletionClient(
-                model=model_config.get('model', 'gpt-4o'),
+                model=model_config.get('model', 'gpt-5-nano'),
                 api_key=model_config.get('api_key', os.environ.get("OPENAI_API_KEY")),
                 base_url=model_config.get('base_url', "https://api.openai.com/v1")
             )

src/praisonai/praisonai/deploy.py

@@ -57,7 +57,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.2.84 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.2.87 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             
@@ -128,7 +128,7 @@ class CloudDeployer:
             return
         
         # Get environment variables
-        openai_model = os.environ.get('OPENAI_MODEL_NAME', 'gpt-4o')
+        openai_model = os.environ.get('OPENAI_MODEL_NAME', 'gpt-5-nano')
         openai_key = os.environ.get('OPENAI_API_KEY', 'Enter your API key')
         openai_base = os.environ.get('OPENAI_API_BASE', 'https://api.openai.com/v1')
         

src/praisonai/praisonai/ui/agents.py

@@ -431,7 +431,7 @@ async def ui_run_praisonai(config, topic, tools_dict):
                 role=role_filled,
                 goal=goal_filled,
                 backstory=backstory_filled,
-                llm=details.get('llm', 'gpt-4o'),
+                llm=details.get('llm', 'gpt-5-nano'),
                 verbose=True,
                 allow_delegation=details.get('allow_delegation', False),
                 max_iter=details.get('max_iter', 15),
@@ -526,7 +526,7 @@ async def ui_run_praisonai(config, topic, tools_dict):
                 tasks=tasks,
                 verbose=True,
                 process="hierarchical",
-                manager_llm=config.get('manager_llm', 'gpt-4o')
+                manager_llm=config.get('manager_llm', 'gpt-5-nano')
             )
         else:
             prai_agents = PraisonAIAgents(

src/praisonai/praisonai/ui/colab.py

@@ -341,7 +341,7 @@ async def ui_run_praisonai(config, topic, tools_dict):
                 role=role_filled,
                 goal=goal_filled,
                 backstory=backstory_filled,
-                llm=details.get('llm', 'gpt-4o'),
+                llm=details.get('llm', 'gpt-5-nano'),
                 verbose=True,
                 allow_delegation=details.get('allow_delegation', False),
                 max_iter=details.get('max_iter', 15),
@@ -426,7 +426,7 @@ async def ui_run_praisonai(config, topic, tools_dict):
                 tasks=tasks,
                 verbose=True,
                 process="hierarchical",
-                manager_llm=config.get('manager_llm', 'gpt-4o')
+                manager_llm=config.get('manager_llm', 'gpt-5-nano')
             )
         else:
             crew = PraisonAIAgents(

src/praisonai/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.2.84"
+version = "2.2.87"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 readme = "README.md"
 license = ""
@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.0.158",
+    "praisonaiagents>=0.0.161",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",
@@ -102,7 +102,7 @@ autogen-v4 = [
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.2.84"
+version = "2.2.87"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 authors = ["Mervin Praison"]
 license = ""
@@ -120,7 +120,7 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.158"
+praisonaiagents = ">=0.0.161"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

src/praisonai/schedule_config.yaml.example

@@ -19,7 +19,7 @@ deployment:
 
 # Optional: Environment variables for deployment
 # environment:
-#   OPENAI_MODEL_NAME: "gpt-4o"
+#   OPENAI_MODEL_NAME: "gpt-5-nano"
 #   OPENAI_API_KEY: "your-api-key"
 #   OPENAI_API_BASE: "https://api.openai.com/v1"
 

src/praisonai/uv.lock

@@ -4031,7 +4031,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.2.84"
+version = "2.2.87"
 source = { editable = "." }
 dependencies = [
     { name = "instructor" },
@@ -4185,7 +4185,7 @@ requires-dist = [
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.0.22" },
     { name = "praisonai-tools", marker = "extra == 'autogen-v4'", specifier = ">=0.0.22" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.0.22" },
-    { name = "praisonaiagents", specifier = ">=0.0.158" },
+    { name = "praisonaiagents", specifier = ">=0.0.161" },
     { name = "pyautogen", marker = "extra == 'autogen'", specifier = "==0.2.29" },
     { name = "pydantic", marker = "extra == 'chat'", specifier = "<=2.10.1" },
     { name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
@@ -4240,7 +4240,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.158"
+version = "0.0.161"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -4249,9 +4249,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/b5/16/867987b5751adfda215a9ead833f9511fa7df288b3a250fd89252f1f3ed7/praisonaiagents-0.0.158.tar.gz", hash = "sha256:c069e7438b24be1bda31c601af1d22d797e6ddf7e6b3d795b4352263e7c37d49", size = 285684, upload-time = "2025-08-08T06:53:21.015Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/f2/a5/97bd65cf513070b647ada80c39610fd4c86c80f70404bb0972e100c2a4f2/praisonaiagents-0.0.161.tar.gz", hash = "sha256:88b6c28f1fa9d0f0f802d5a916471606c351d3359800982eb49f38e89dbff363", size = 285757, upload-time = "2025-08-08T12:43:46.337Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0a/8c/8e92f7f6a956e39526bb54d501db1c49753833545f3436b53f382a9af175/praisonaiagents-0.0.158-py3-none-any.whl", hash = "sha256:7079e94a19645f800a560a26717c38f7fb4e2752d8b180d3d1206951841f8e34", size = 305009, upload-time = "2025-08-08T06:53:19.566Z" },
+    { url = "https://files.pythonhosted.org/packages/41/28/fd9df18c45f461afef17c69587b1502901dc1b3331056b79685f9b932017/praisonaiagents-0.0.161-py3-none-any.whl", hash = "sha256:a2db2a52a6684d0b0d935b664741d1594020cba41a6f212abbadc654a999af33", size = 305084, upload-time = "2025-08-08T12:43:44.86Z" },
 ]
 
 [[package]]