Release v3.8.4

api.md

@@ -77,7 +77,7 @@ Methods:
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run_autonomous</a>(prompt: str, max_iterations: Optional[int] = None, timeout_seconds: Optional[float] = None)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">session_id</a>() -> Optional[str]</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">skill_manager</a>()</code>
-* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">start</a>(prompt: str, **kwargs)</code>
+* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">start</a>(prompt: str = None, **kwargs)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">store_memory</a>(content: str, memory_type: str = 'short_term', **kwargs)</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">switch_model</a>(new_model: str) -> None</code>
 * <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">thinking_budget</a>()</code>
@@ -117,7 +117,7 @@ Methods:
 * <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_output_to_file</a>(task, task_output)</code>
 * <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_session_state</a>(session_id: str, include_memory: bool = True) -> None</code>
 * <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">set_state</a>(key: str, value: Any) -> None</code>
-* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">start</a>(content = None, return_dict = False, **kwargs)</code>
+* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">start</a>(content = None, return_dict = False, output = None, **kwargs)</code>
 * <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">todo_list</a>()</code>
 * <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_plan_step_status</a>(step_id: str, status: str) -> bool</code>
 * <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_state</a>(updates: Dict) -> None</code>
@@ -194,7 +194,7 @@ Methods:
 * <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_output_to_file</a>(task, task_output)</code>
 * <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_session_state</a>(session_id: str, include_memory: bool = True) -> None</code>
 * <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">set_state</a>(key: str, value: Any) -> None</code>
-* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">start</a>(content = None, return_dict = False, **kwargs)</code>
+* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">start</a>(content = None, return_dict = False, output = None, **kwargs)</code>
 * <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">todo_list</a>()</code>
 * <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_plan_step_status</a>(step_id: str, status: str) -> bool</code>
 * <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_state</a>(updates: Dict) -> None</code>
@@ -546,9 +546,9 @@ Methods:
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_error</a>(message: str, console = None)</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_generating</a>(content: str = '', start_time: Optional[float] = None)</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_instruction</a>(message: str, console = None, agent_name: str = None, agent_role: str = None, agent_tools: List[str] = None)</code>
-* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_interaction</a>(message, response, markdown = True, generation_time = None, console = None, agent_name = None, agent_role = None, agent_tools = None, task_name = None, task_description = None, task_id = None)</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_interaction</a>(message, response, markdown = True, generation_time = None, console = None, agent_name = None, agent_role = None, agent_tools = None, task_name = None, task_description = None, task_id = None, metrics = None)</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_self_reflection</a>(message: str, console = None)</code>
-* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_tool_call</a>(message: str, console = None, tool_name: str = None, tool_input: dict = None, tool_output: str = None)</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_tool_call</a>(message: str, console = None, tool_name: str = None, tool_input: dict = None, tool_output: str = None, elapsed_time: float = None, success: bool = True)</code>
 * <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">register_display_callback</a>(display_type: str, callback_fn, is_async: bool = False)</code>
 
 # Other

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>=3.8.3" \
+    "praisonai>=3.8.4" \
     "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>=3.8.3" \
+    "praisonai>=3.8.4" \
     "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>=3.8.3" \
+    "praisonai>=3.8.4" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

src/praisonai-agents/01_basic_token_tracking.py

@@ -13,7 +13,7 @@ def main():
         role="Research Specialist", 
         goal="Find information about a topic",
         backstory="You are an expert researcher who finds comprehensive information.",
-        verbose=True,
+        output="verbose",
         llm="gpt-5-nano",  # Use a cost-effective model for examples
         metrics=True  # 🎯 SIMPLIFIED: Just add this parameter!
     )
@@ -29,7 +29,7 @@ def main():
     agents = Agents(
         agents=[research_agent],
         tasks=[research_task],
-        verbose=True
+        output="verbose"
     )
     
     print("🚀 Running agent task...")

src/praisonai-agents/03_agent_with_tools_monitoring.py

@@ -11,7 +11,7 @@ Shows how to monitor agent performance when using external tools.
 """
 
 from praisonaiagents import Agent
-from praisonaiagents.tools import duckduckgo, wikipedia
+from praisonaiagents.tools import duckduckgo, wikipedia_tools
 from praisonaiagents.telemetry import (
     monitor_function, track_api_call, get_api_stats,
     get_slowest_functions, performance_monitor
@@ -49,7 +49,7 @@ def main():
         Use the available tools to search for information and provide comprehensive answers.
         Be thorough in your research and cite your sources.""",
         llm="gpt-5-nano",
-        tools=[duckduckgo, wikipedia]  # Add search tools
+        tools=[duckduckgo, wikipedia_tools]  # Add search tools
     )
     
     # Test queries to demonstrate tool performance monitoring
@@ -97,7 +97,9 @@ def main():
     # Slowest functions analysis
     print("\n🐌 Slowest Functions:")
     slowest = get_slowest_functions()
-    for func_name, avg_time in slowest:
+    for item in slowest[:5]:
+        func_name = item.get('function', 'unknown')
+        avg_time = item.get('average_time', 0)
         print(f"  {func_name}: {avg_time:.3f}s average")
     
     # Real-time performance data

src/praisonai-agents/08_custom_tools_monitoring.py

@@ -263,7 +263,7 @@ def main():
     # Slowest operations
     print("\n🐌 Slowest Tool Operations:")
     slowest = get_slowest_functions()
-    tool_slowest = [(name, time) for name, time in slowest if 'tool' in name.lower()]
+    tool_slowest = [(item.get('function', 'unknown'), item.get('average_time', 0)) for item in slowest if 'tool' in item.get('function', '').lower()]
     
     for tool_name, avg_time in tool_slowest[:3]:
         print(f"  {tool_name}: {avg_time:.3f}s")

src/praisonai-agents/10_comprehensive_dashboard.py

@@ -302,13 +302,17 @@ def display_dashboard(dashboard_data, benchmark_results):
     perf_data = dashboard_data['performance_data']
     
     print("  Fastest Functions:")
-    fastest_functions = sorted(perf_data['slowest_functions'], key=lambda x: x[1])[:3]
-    for name, time in fastest_functions:
-        print(f"    {name}: {time:.3f}s")
+    fastest_functions = sorted(perf_data['slowest_functions'], key=lambda x: x.get('average_time', float('inf')))[:3]
+    for item in fastest_functions:
+        name = item.get('function', 'unknown')
+        time_val = item.get('average_time', 0)
+        print(f"    {name}: {time_val:.3f}s")
     
     print("  Slowest Functions:")
-    for name, time in perf_data['slowest_functions'][:3]:
-        print(f"    {name}: {time:.3f}s")
+    for item in perf_data['slowest_functions'][:3]:
+        name = item.get('function', 'unknown')
+        time_val = item.get('average_time', 0)
+        print(f"    {name}: {time_val:.3f}s")
     
     # Benchmark Results
     print(f"\n⚡ BENCHMARK RESULTS")

src/praisonai-agents/agentic_parallelisation.py

@@ -21,7 +21,7 @@ article_agent = Agent(
     goal="Fetch the user's favorite morning article",
     tools=[fetch_favorite_article],
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",
-    verbose=True
+    output="verbose"
 )
 
 news_agent = Agent(
@@ -30,7 +30,7 @@ news_agent = Agent(
     goal="Search for trending news in Kenya",
     tools=[search_trending_kenya],
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",
-    verbose=True
+    output="verbose"
 )
 
 twitter_agent = Agent(
@@ -39,7 +39,7 @@ twitter_agent = Agent(
     goal="Fetch the latest Twitter feed",
     tools=[fetch_twitter_feed],
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",
-    verbose=True
+    output="verbose"
 )
 
 aggregator = Agent(
@@ -47,7 +47,7 @@ aggregator = Agent(
     role="Result Aggregator",
     goal="Aggregate and summarize all results",
     llm="gemini/gemini-2.5-flash-lite-preview-06-17",
-    verbose=True
+    output="verbose"
 )
 
 # Tasks for each agent
@@ -92,7 +92,7 @@ async def main():
         agents=[article_agent, news_agent, twitter_agent, aggregator],
         tasks=[article_task, news_task, twitter_task, aggregate_task],
         process="workflow",
-        verbose=True
+        output="verbose"
     )
     results = await workflow.astart(dict_output=True)
 

src/praisonai-agents/any-llm-agents-async.py

@@ -5,8 +5,8 @@ async def main():
     agent = Agent(
         instructions="You are a helpful assistant",
         llm="gemini/gemini-1.5-flash-8b",
-        self_reflect=True,
-        verbose=True
+        reflection=True,
+        output="verbose"
     )
 
     # Use achat instead of start/chat for async operation

src/praisonai-agents/async-tavily-agents-gemini.py

@@ -79,7 +79,7 @@ async def run_parallel_searches():
         backstory="Expert in web search and information retrieval. You have access to the tavily_search tool to search the web.",
         tools=[tavily_search],
         llm=llm_config,
-        verbose=True
+        output="verbose"
     )
     
     # Create summary agent

src/praisonai-agents/benchmarks/EXECUTION_BENCHMARK_RESULTS.md

@@ -1,6 +1,6 @@
 # PraisonAI Agents - Real Execution Benchmark
 
-**Generated:** 2026-01-13 05:32:09
+**Generated:** 2026-01-13 06:07:28
 **Model:** gpt-4o-mini
 **Iterations:** 3
 **Prompt:** "What is 2+2? Reply with just the number."
@@ -9,18 +9,18 @@
 
 | Framework | Method | Avg Time | Relative |
 |-----------|--------|----------|----------|
-| **PraisonAI** | `agent.start()` | **0.61s** | **1.00x (fastest)** |
-| PraisonAI (LiteLLM) | `agent.start()` | 0.69s | 1.13x |
-| CrewAI | `crew.kickoff()` | 0.85s | 1.39x |
-| Agno | `agent.run()` | 1.06s | 1.72x |
+| **PraisonAI (LiteLLM)** | `agent.start()` | **0.55s** | **1.00x (fastest)** |
+| PraisonAI | `agent.start()` | 0.64s | 1.15x |
+| CrewAI | `crew.kickoff()` | 0.95s | 1.71x |
+| Agno | `agent.run()` | 1.05s | 1.90x |
 
 ## Package Versions
 
 | Package | Version |
 |---------|--------|
 | PraisonAI | 0.11.7 |
-| Agno | 2.3.14 |
-| CrewAI | 1.6.1 |
+| Agno | 2.3.25 |
+| CrewAI | 1.8.0 |
 
 ## How to Reproduce
 

src/praisonai-agents/benchmarks/REAL_BENCHMARK_RESULTS.md

@@ -1,6 +1,6 @@
 # PraisonAI Agents - Real-World Execution Benchmark
 
-**Generated:** 2025-12-18 14:33:49
+**Generated:** 2026-01-13 06:05:09
 **Model:** gpt-4o-mini
 **Iterations:** 3 per test
 
@@ -8,60 +8,60 @@
 
 | Test | PraisonAI (avg) | Agno (avg) | Difference |
 |------|-----------------|------------|------------|
-| Single Agent | 2.04s | 3.18s | PraisonAI 1.56x faster |
-| 2 Agents | 2.49s | 20.49s | PraisonAI 8.21x faster |
-| 3 Agents | 6.52s | 13.97s | PraisonAI 2.14x faster |
+| Single Agent | 2.09s | 3.25s | PraisonAI 1.55x faster |
+| 2 Agents | 2.50s | 9.73s | PraisonAI 3.89x faster |
+| 3 Agents | 6.25s | 13.56s | PraisonAI 2.17x faster |
 
 ## Detailed Results
 
 ### praisonai_single
-- Average: 2.04s
-- Min: 1.95s
-- Max: 2.19s
-- Std Dev: 0.13s
-- Times: ['2.19s', '1.95s', '2.00s']
+- Average: 2.09s
+- Min: 1.64s
+- Max: 2.84s
+- Std Dev: 0.66s
+- Times: ['2.84s', '1.79s', '1.64s']
 
 ### agno_single
-- Average: 3.18s
-- Min: 3.00s
-- Max: 3.51s
-- Std Dev: 0.28s
-- Times: ['3.51s', '3.00s', '3.04s']
+- Average: 3.25s
+- Min: 3.14s
+- Max: 3.35s
+- Std Dev: 0.10s
+- Times: ['3.35s', '3.25s', '3.14s']
 
 ### praisonai_two_agents
-- Average: 2.49s
-- Min: 2.22s
-- Max: 2.65s
-- Std Dev: 0.24s
-- Times: ['2.65s', '2.22s', '2.62s']
+- Average: 2.50s
+- Min: 2.38s
+- Max: 2.75s
+- Std Dev: 0.21s
+- Times: ['2.38s', '2.75s', '2.38s']
 
 ### agno_two_agents
-- Average: 20.49s
-- Min: 9.96s
-- Max: 35.05s
-- Std Dev: 13.02s
-- Times: ['16.46s', '9.96s', '35.05s']
+- Average: 9.73s
+- Min: 9.34s
+- Max: 10.53s
+- Std Dev: 0.69s
+- Times: ['10.53s', '9.34s', '9.34s']
 
 ### praisonai_three_agents
-- Average: 6.52s
-- Min: 5.62s
-- Max: 7.39s
-- Std Dev: 0.88s
-- Times: ['5.62s', '6.54s', '7.39s']
+- Average: 6.25s
+- Min: 5.86s
+- Max: 6.80s
+- Std Dev: 0.49s
+- Times: ['6.80s', '6.09s', '5.86s']
 
 ### agno_three_agents
-- Average: 13.97s
-- Min: 11.69s
-- Max: 15.32s
-- Std Dev: 1.99s
-- Times: ['14.91s', '11.69s', '15.32s']
+- Average: 13.56s
+- Min: 12.26s
+- Max: 14.90s
+- Std Dev: 1.32s
+- Times: ['14.90s', '13.51s', '12.26s']
 
 ## Package Versions
 
 | Package | Version |
 |---------|--------|
-| PraisonAI | 0.1.5 |
-| Agno | 2.3.14 |
+| PraisonAI | 0.11.7 |
+| Agno | 2.3.25 |
 
 ## How to Reproduce
 

src/praisonai-agents/gemini_self_reflection.py

@@ -14,7 +14,7 @@ llm_config = {
 agent = Agent(
     instructions="You are a helpful assistant. Be concise and clear.",
     llm=llm_config,
-    verbose=True,
+    output="verbose",
     self_reflect=True,
     max_reflect=2,
     min_reflect=1

src/praisonai-agents/guardrail_agent_example.py

@@ -7,8 +7,7 @@ def validate_content(data):
 
 agent = Agent(
     instructions="You are a writer",
-    guardrail=validate_content,
-    max_guardrail_retries=1
+    guardrails=validate_content
 )
 
 agent.start("Write a welcome message with 5 words")

src/praisonai-agents/llm-gemini-advanced.py

@@ -32,7 +32,7 @@ agent = Agent(
     instructions="You are a helpful Assistant specialized in scientific explanations. "
                 "Provide clear, accurate, and engaging responses.",
     llm=llm_config,                  # Pass the detailed configuration
-    verbose=True,                    # Enable detailed output
+    output="verbose",                    # Enable detailed output
     markdown=True,                   # Format responses in markdown
     self_reflect=True,              # Enable self-reflection
     max_reflect=3,                  # Maximum reflection iterations

src/praisonai-agents/mongodb_knowledge_basic.py

@@ -55,10 +55,8 @@ def main():
         retrieve information from a comprehensive MongoDB knowledge base. You excel 
         at finding relevant information, synthesizing knowledge from multiple sources, 
         and providing accurate, context-aware responses.""",
-        knowledge_config=mongodb_knowledge_config,
         knowledge=[os.path.join(os.path.dirname(__file__), "llms.md")],
         memory=True,
-        verbose=True,
         llm="gpt-5-nano"
     )
     
@@ -72,7 +70,6 @@ def main():
         knowledge base. You ensure information is accurate, well-structured, 
         and properly categorized.""",
         memory=True,
-        verbose=True,
         llm="gpt-5-nano"
     )
     
@@ -128,7 +125,7 @@ def main():
         agents=[research_agent, knowledge_agent],
         tasks=knowledge_tasks,
         memory=True,
-        verbose=True
+        output="verbose"
     )
     
     # Execute the knowledge management pipeline

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

@@ -1540,6 +1540,7 @@ Now provide your final answer using this result."""
             max_iterations = 10  # Prevent infinite loops
             iteration_count = 0
             final_response_text = ""
+            response_text = ""  # Initialize to prevent UnboundLocalError on API errors
             stored_reasoning_content = None  # Store reasoning content from tool execution
             accumulated_tool_results = []  # Store all tool results across iterations
 

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

@@ -691,7 +691,7 @@ def force_shutdown_telemetry():
 def enable_telemetry():
     """Programmatically enable telemetry (if not disabled by environment)."""
     global _telemetry_instance
-    if not _TELEMETRY_DISABLED:
+    if not _is_telemetry_disabled():
         if _telemetry_instance:
             _telemetry_instance.enabled = True
         else:

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.11.13"
+version = "0.11.14"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 readme = "README.md"
 requires-python = ">=3.10"

src/praisonai-agents/uv.lock

@@ -2422,7 +2422,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.11.13"
+version = "0.11.14"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },

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/v3.8.3.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.8.3.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.8.4.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.8.4.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

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==3.8.3 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==3.8.4 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

src/praisonai/praisonai/version.py

@@ -1 +1 @@
-__version__ = "3.8.3"
+__version__ = "3.8.4"

src/praisonai/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.11.13",
+    "praisonaiagents>=0.11.14",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",

src/praisonai/uv.lock

@@ -4276,7 +4276,7 @@ requires-dist = [
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.1.0" },
     { name = "praisonai-tools", marker = "extra == 'autogen-v4'", specifier = ">=0.1.0" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.1.0" },
-    { name = "praisonaiagents", specifier = ">=0.11.13" },
+    { name = "praisonaiagents", specifier = ">=0.11.14" },
     { name = "pyautogen", marker = "extra == 'autogen'", specifier = "==0.2.29" },
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     { name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
@@ -4324,7 +4324,7 @@ wheels = [
 
 [[package]]
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-version = "0.11.13"
+version = "0.11.14"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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@@ -4333,9 +4333,9 @@ dependencies = [
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     { name = "rich" },
 ]
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 [[package]]