feat: bump PraisonAI to v2.2.52 and add sequential agent examples for OpenAI/Ollama

fix: allow agents to continue with sequential tool calls

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>=2.2.51" \
+    "praisonai>=2.2.52" \
     "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>=2.2.51" \
+    "praisonai>=2.2.52" \
     "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>=2.2.51" \
+    "praisonai>=2.2.52" \
     "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>=2.2.51" \
+    "praisonai>=2.2.52" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docker/README.md

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions
 
 All Docker images use consistent, up-to-date versions:
-- PraisonAI: `>=2.2.51`
+- PraisonAI: `>=2.2.52`
 - 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==2.2.51" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.52" "praisonaiagents==0.0.92"
 ```
 
 ## 🌐 Production Deployment

examples/python/general/prompt_chaining.py

@@ -72,7 +72,7 @@ workflow = PraisonAIAgents(
 )
 
 # Run the workflow
-results = workflow.start()
+results = workflow.start(return_dict=True)
 
 # Print results
 print("\nWorkflow Results:")

src/praisonai-agents/gemini-sequential.py

@@ -22,9 +22,9 @@ 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="openai/gpt-4o-mini",
+    llm="gemini/gemini-2.5-pro",
     tools=[get_stock_price, multiply]
 )
 
-result = agent.start("multiply the Google stock price with 2")
+result = agent.start("what is the stock price of Google? multiply the Google stock price with 2")
 print(result)

src/praisonai-agents/ollama-sequential.py

@@ -0,0 +1,30 @@
+from praisonaiagents import Agent
+
+def get_stock_price(company_name: str) -> str:
+    """
+    Get the stock price of a company
+    
+    Args:
+        company_name (str): The name of the company
+        
+    Returns:
+        str: The stock price of the company
+    """
+    return f"The stock price of {company_name} is 100"
+
+def multiply(a: int, b: int) -> int:
+    """
+    Multiply two numbers
+    """
+    return a * b
+
+
+
+agent = Agent(
+    instructions="You are a helpful assistant. You can use the tools provided to you to help the user.",
+    llm="ollama/qwen3",
+    tools=[get_stock_price, multiply]
+)
+
+result = agent.start("what is the stock price of Google? multiply the Google stock price with 2")
+print(result)

src/praisonai-agents/openai-sequential.py

@@ -0,0 +1,30 @@
+from praisonaiagents import Agent
+
+def get_stock_price(company_name: str) -> str:
+    """
+    Get the stock price of a company
+    
+    Args:
+        company_name (str): The name of the company
+        
+    Returns:
+        str: The stock price of the company
+    """
+    return f"The stock price of {company_name} is 100"
+
+def multiply(a: int, b: int) -> int:
+    """
+    Multiply two numbers
+    """
+    return a * b
+
+
+
+agent = Agent(
+    instructions="You are a helpful assistant. You can use the tools provided to you to help the user.",
+    llm="gpt-4o",
+    tools=[get_stock_price, multiply]
+)
+
+result = agent.start("what is the stock price of Google? multiply the Google stock price with 2")
+print(result)

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

@@ -1235,7 +1235,8 @@ Your Goal: {self.goal}"""
                     agent_role=self.role,
                     agent_tools=[t.__name__ if hasattr(t, '__name__') else str(t) for t in (tools if tools is not None else self.tools)],
                     execute_tool_fn=self.execute_tool,  # Pass tool execution function
-                    reasoning_steps=reasoning_steps
+                    reasoning_steps=reasoning_steps,
+                    stream=stream  # Pass the stream parameter from chat method
                     )
 
                     self.chat_history.append({"role": "assistant", "content": response_text})

src/praisonai-agents/praisonaiagents/llm/OLLAMA_HANDLING.md

@@ -0,0 +1,25 @@
+# Ollama Handling in LLM.py
+
+The `ollama_handled` flag is used in `llm.py` (not in `agent.py`) to track whether special Ollama model handling has been applied for tool results. Here's what I found:
+
+## Where it's used in llm.py:
+- **Line 863**: `ollama_handled = False` - initialized at the beginning of the tool execution loop
+- **Line 864**: Special handling logic via `_handle_ollama_model()` method
+- **Line 907**: `ollama_handled = True` - set when Ollama special handling is applied
+- **Line 928 & 959**: Checks `if not ollama_handled` to avoid duplicate processing
+
+## Why it's needed:
+The flag prevents duplicate processing when Ollama models require special handling. Some Ollama models return only the tool call JSON without processing the results, requiring a follow-up prompt to get the final answer. The `ollama_handled` flag ensures this special handling happens only once.
+
+## Does this contradict the requirement?
+**No, it doesn't.** The issue states:
+
+> "ollama/xxxx model is already handled via llm.py via litellm, so no special condition or handling required for ollama inside agent.py"
+
+✅ **Current implementation is correct:**
+
+- `agent.py` has NO ollama-specific conditions (only one documentation mention as an example)
+- All ollama handling is contained within `llm.py` as intended
+- The `ollama_handled` flag is an internal implementation detail within `llm.py`
+
+The codebase already follows the desired pattern where ollama models are handled through the generic LLM interface via litellm without special conditions in agent.py.

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

@@ -680,6 +680,7 @@ class LLM:
             max_iterations = 10  # Prevent infinite loops
             iteration_count = 0
             final_response_text = ""
+            stored_reasoning_content = None  # Store reasoning content from tool execution
 
             while iteration_count < max_iterations:
                 try:
@@ -857,8 +858,6 @@ class LLM:
                             iteration_count += 1
                             continue
 
-                        # If we reach here, no more tool calls needed - get final response
-                        # Make one more call to get the final summary response
                         # Special handling for Ollama models that don't automatically process tool results
                         ollama_handled = False
                         ollama_params = self._handle_ollama_model(response_text, tool_results, messages, original_prompt)
@@ -918,15 +917,23 @@ class LLM:
                                     console=console
                                 )
                             
-                            # Return the final response after processing Ollama's follow-up
+                            # Update messages and continue the loop instead of returning
                             if final_response_text:
-                                return final_response_text
+                                # Update messages with the response to maintain conversation context
+                                messages.append({
+                                    "role": "assistant",
+                                    "content": final_response_text
+                                })
+                                # Continue the loop to check if more tools are needed
+                                iteration_count += 1
+                                continue
                             else:
                                 logging.warning("[OLLAMA_DEBUG] Ollama follow-up returned empty response")
                         
-                        # If reasoning_steps is True and we haven't handled Ollama already, do a single non-streaming call
+                        # Handle reasoning_steps after tool execution if not already handled by Ollama
                         if reasoning_steps and not ollama_handled:
-                            resp = litellm.completion(
+                            # Make a non-streaming call to capture reasoning content
+                            reasoning_resp = litellm.completion(
                                 **self._build_completion_params(
                                     messages=messages,
                                     temperature=temperature,
@@ -934,89 +941,28 @@ class LLM:
                                     **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
                                 )
                             )
-                            reasoning_content = resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
-                            response_text = resp["choices"][0]["message"]["content"]
+                            reasoning_content = reasoning_resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
+                            response_text = reasoning_resp["choices"][0]["message"]["content"]
                             
-                            # Optionally display reasoning if present
-                            if verbose and reasoning_content:
-                                display_interaction(
-                                    original_prompt,
-                                    f"Reasoning:\n{reasoning_content}\n\nAnswer:\n{response_text}",
-                                    markdown=markdown,
-                                    generation_time=time.time() - start_time,
-                                    console=console
-                                )
-                            else:
-                                display_interaction(
-                                    original_prompt,
-                                    response_text,
-                                    markdown=markdown,
-                                    generation_time=time.time() - start_time,
-                                    console=console
-                                )
-                        
-                        # Otherwise do the existing streaming approach if not already handled
-                        elif not ollama_handled:
-                            # Get response after tool calls
-                            if stream:
-                                # Streaming approach
-                                if verbose:
-                                    with Live(display_generating("", current_time), console=console, refresh_per_second=4) as live:
-                                        final_response_text = ""
-                                        for chunk in litellm.completion(
-                                            **self._build_completion_params(
-                                                messages=messages,
-                                                tools=formatted_tools,
-                                                temperature=temperature,
-                                                stream=True,
-                                                **kwargs
-                                            )
-                                        ):
-                                            if chunk and chunk.choices and chunk.choices[0].delta.content:
-                                                content = chunk.choices[0].delta.content
-                                                final_response_text += content
-                                                live.update(display_generating(final_response_text, current_time))
-                                else:
-                                    final_response_text = ""
-                                    for chunk in litellm.completion(
-                                        **self._build_completion_params(
-                                            messages=messages,
-                                            tools=formatted_tools,
-                                            temperature=temperature,
-                                            stream=True,
-                                            **kwargs
-                                        )
-                                    ):
-                                        if chunk and chunk.choices and chunk.choices[0].delta.content:
-                                            final_response_text += chunk.choices[0].delta.content
-                            else:
-                                # Non-streaming approach
-                                resp = litellm.completion(
-                                    **self._build_completion_params(
-                                        messages=messages,
-                                        tools=formatted_tools,
-                                        temperature=temperature,
-                                        stream=False,
-                                        **kwargs
-                                    )
-                                )
-                                final_response_text = resp.get("choices", [{}])[0].get("message", {}).get("content", "") or ""
+                            # Store reasoning content for later use
+                            if reasoning_content:
+                                stored_reasoning_content = reasoning_content
                             
-                            final_response_text = final_response_text.strip()
+                            # Update messages with the response
+                            messages.append({
+                                "role": "assistant",
+                                "content": response_text
+                            })
                         
-                        # Display final response
-                        if verbose:
-                            display_interaction(
-                                original_prompt,
-                                final_response_text,
-                                markdown=markdown,
-                                generation_time=time.time() - start_time,
-                                console=console
-                            )
-                        
-                        return final_response_text
+                        # After tool execution, continue the loop to check if more tools are needed
+                        # instead of immediately trying to get a final response
+                        iteration_count += 1
+                        continue
                     else:
                         # No tool calls, we're done with this iteration
+                        # If we've executed tools in previous iterations, this response contains the final answer
+                        if iteration_count > 0:
+                            final_response_text = response_text.strip()
                         break
                         
                 except Exception as e:
@@ -1029,16 +975,30 @@ class LLM:
             
             # No tool calls were made in this iteration, return the response
             if verbose:
-                display_interaction(
-                    original_prompt,
-                    response_text,
-                    markdown=markdown,
-                    generation_time=time.time() - start_time,
-                    console=console
-                )
+                # If we have stored reasoning content from tool execution, display it
+                if stored_reasoning_content:
+                    display_interaction(
+                        original_prompt,
+                        f"Reasoning:\n{stored_reasoning_content}\n\nAnswer:\n{response_text}",
+                        markdown=markdown,
+                        generation_time=time.time() - start_time,
+                        console=console
+                    )
+                else:
+                    display_interaction(
+                        original_prompt,
+                        response_text,
+                        markdown=markdown,
+                        generation_time=time.time() - start_time,
+                        console=console
+                    )
             
             response_text = response_text.strip()
             
+            # Return reasoning content if reasoning_steps is True and we have it
+            if reasoning_steps and stored_reasoning_content:
+                return stored_reasoning_content
+            
             # Handle output formatting
             if output_json or output_pydantic:
                 self.chat_history.append({"role": "user", "content": original_prompt})
@@ -1053,8 +1013,8 @@ class LLM:
                     display_interaction(original_prompt, response_text, markdown=markdown,
                                      generation_time=time.time() - start_time, console=console)
                 # Return reasoning content if reasoning_steps is True
-                if reasoning_steps and reasoning_content:
-                    return reasoning_content
+                if reasoning_steps and stored_reasoning_content:
+                    return stored_reasoning_content
                 return response_text
 
             # Handle self-reflection loop
@@ -1317,118 +1277,126 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             # Format tools for LiteLLM using the shared helper
             formatted_tools = self._format_tools_for_litellm(tools)
 
-            response_text = ""
-            if reasoning_steps:
-                # Non-streaming call to capture reasoning
-                resp = await litellm.acompletion(
-                    **self._build_completion_params(
-                        messages=messages,
+            # Initialize variables for iteration loop
+            max_iterations = 10  # Prevent infinite loops
+            iteration_count = 0
+            final_response_text = ""
+            stored_reasoning_content = None  # Store reasoning content from tool execution
+
+            while iteration_count < max_iterations:
+                response_text = ""
+                reasoning_content = None
+                tool_calls = []
+                
+                if reasoning_steps and iteration_count == 0:
+                    # Non-streaming call to capture reasoning
+                    resp = await litellm.acompletion(
+                        **self._build_completion_params(
+                            messages=messages,
                         temperature=temperature,
                         stream=False,  # force non-streaming
                         **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
                     )
-                )
-                reasoning_content = resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
-                response_text = resp["choices"][0]["message"]["content"]
-                
-                if verbose and reasoning_content:
-                    display_interaction(
-                        "Initial reasoning:",
-                        f"Reasoning:\n{reasoning_content}\n\nAnswer:\n{response_text}",
-                        markdown=markdown,
-                        generation_time=time.time() - start_time,
-                        console=console
                     )
-                elif verbose:
-                    display_interaction(
-                        "Initial response:",
-                        response_text,
-                        markdown=markdown,
-                        generation_time=time.time() - start_time,
-                        console=console
-                    )
-            else:
-                # Determine if we should use streaming based on tool support
-                use_streaming = stream
-                if formatted_tools and not self._supports_streaming_tools():
-                    # Provider doesn't support streaming with tools, use non-streaming
-                    use_streaming = False
-                
-                if use_streaming:
-                    # Streaming approach (with or without tools)
-                    tool_calls = []
+                    reasoning_content = resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
+                    response_text = resp["choices"][0]["message"]["content"]
                     
-                    if verbose:
-                        async for chunk in await litellm.acompletion(
-                            **self._build_completion_params(
-                                messages=messages,
-                                temperature=temperature,
-                                stream=True,
-                                tools=formatted_tools,
-                                **kwargs
-                            )
-                        ):
-                            if chunk and chunk.choices and chunk.choices[0].delta:
-                                delta = chunk.choices[0].delta
-                                response_text, tool_calls = self._process_stream_delta(
-                                    delta, response_text, tool_calls, formatted_tools
-                                )
-                                if delta.content:
-                                    print("\033[K", end="\r")  
-                                    print(f"Generating... {time.time() - start_time:.1f}s", end="\r")
-
-                    else:
-                        # Non-verbose streaming
-                        async for chunk in await litellm.acompletion(
-                            **self._build_completion_params(
-                                messages=messages,
-                                temperature=temperature,
-                                stream=True,
-                                tools=formatted_tools,
-                                **kwargs
-                            )
-                        ):
-                            if chunk and chunk.choices and chunk.choices[0].delta:
-                                delta = chunk.choices[0].delta
-                                if delta.content:
-                                    response_text += delta.content
-                                
-                                # Capture tool calls from streaming chunks if provider supports it
-                                if formatted_tools and self._supports_streaming_tools():
-                                    tool_calls = self._process_tool_calls_from_stream(delta, tool_calls)
-                    
-                    response_text = response_text.strip()
-                    
-                    # We already have tool_calls from streaming if supported
-                    # No need for a second API call!
-                else:
-                    # Non-streaming approach (when tools require it or streaming is disabled)
-                    tool_response = await litellm.acompletion(
-                        **self._build_completion_params(
-                            messages=messages,
-                            temperature=temperature,
-                            stream=False,
-                            tools=formatted_tools,
-                            **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
-                        )
-                    )
-                    response_text = tool_response.choices[0].message.get("content", "")
-                    tool_calls = tool_response.choices[0].message.get("tool_calls", [])
-                    
-                    if verbose:
-                        # Display the complete response at once
+                    if verbose and reasoning_content:
                         display_interaction(
-                            original_prompt,
+                            "Initial reasoning:",
+                            f"Reasoning:\n{reasoning_content}\n\nAnswer:\n{response_text}",
+                            markdown=markdown,
+                            generation_time=time.time() - start_time,
+                            console=console
+                        )
+                    elif verbose:
+                        display_interaction(
+                            "Initial response:",
                             response_text,
                             markdown=markdown,
                             generation_time=time.time() - start_time,
                             console=console
                         )
+                else:
+                    # Determine if we should use streaming based on tool support
+                    use_streaming = stream
+                    if formatted_tools and not self._supports_streaming_tools():
+                        # Provider doesn't support streaming with tools, use non-streaming
+                        use_streaming = False
+                    
+                    if use_streaming:
+                        # Streaming approach (with or without tools)
+                        tool_calls = []
+                        
+                        if verbose:
+                            async for chunk in await litellm.acompletion(
+                                **self._build_completion_params(
+                                    messages=messages,
+                                    temperature=temperature,
+                                    stream=True,
+                                    tools=formatted_tools,
+                                    **kwargs
+                                )
+                            ):
+                                if chunk and chunk.choices and chunk.choices[0].delta:
+                                    delta = chunk.choices[0].delta
+                                    response_text, tool_calls = self._process_stream_delta(
+                                        delta, response_text, tool_calls, formatted_tools
+                                    )
+                                    if delta.content:
+                                        print("\033[K", end="\r")  
+                                        print(f"Generating... {time.time() - start_time:.1f}s", end="\r")
 
-            # Now handle tools if we have them (either from streaming or non-streaming)
-            if tools and execute_tool_fn and tool_calls:
-                
-                if tool_calls:
+                        else:
+                            # Non-verbose streaming
+                            async for chunk in await litellm.acompletion(
+                                **self._build_completion_params(
+                                    messages=messages,
+                                    temperature=temperature,
+                                    stream=True,
+                                    tools=formatted_tools,
+                                    **kwargs
+                                )
+                            ):
+                                if chunk and chunk.choices and chunk.choices[0].delta:
+                                    delta = chunk.choices[0].delta
+                                    if delta.content:
+                                        response_text += delta.content
+                                    
+                                    # Capture tool calls from streaming chunks if provider supports it
+                                    if formatted_tools and self._supports_streaming_tools():
+                                        tool_calls = self._process_tool_calls_from_stream(delta, tool_calls)
+                        
+                        response_text = response_text.strip()
+                        
+                        # We already have tool_calls from streaming if supported
+                        # No need for a second API call!
+                    else:
+                        # Non-streaming approach (when tools require it or streaming is disabled)
+                        tool_response = await litellm.acompletion(
+                            **self._build_completion_params(
+                                messages=messages,
+                                temperature=temperature,
+                                stream=False,
+                                tools=formatted_tools,
+                                **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
+                            )
+                        )
+                        response_text = tool_response.choices[0].message.get("content", "")
+                        tool_calls = tool_response.choices[0].message.get("tool_calls", [])
+                        
+                        if verbose:
+                            # Display the complete response at once
+                            display_interaction(
+                                original_prompt,
+                                response_text,
+                                markdown=markdown,
+                                generation_time=time.time() - start_time,
+                                console=console
+                            )
+
+                # Now handle tools if we have them (either from streaming or non-streaming)
+                if tools and execute_tool_fn and tool_calls:
                     # Convert tool_calls to a serializable format for all providers
                     serializable_tool_calls = self._serialize_tool_calls(tool_calls)
                     messages.append({
@@ -1509,9 +1477,16 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                 console=console
                             )
                         
-                        # Return the final response after processing Ollama's follow-up
+                        # Store the response for potential final return
                         if final_response_text:
-                            return final_response_text
+                            # Update messages with the response to maintain conversation context
+                            messages.append({
+                                "role": "assistant",
+                                "content": final_response_text
+                            })
+                            # Continue the loop to check if more tools are needed
+                            iteration_count += 1
+                            continue
                         else:
                             logging.warning("[OLLAMA_DEBUG] Ollama follow-up returned empty response")
                     
@@ -1577,6 +1552,27 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                     response_text += chunk.choices[0].delta.content
 
                     response_text = response_text.strip()
+                    
+                    # After tool execution, update messages and continue the loop
+                    if response_text:
+                        messages.append({
+                            "role": "assistant",
+                            "content": response_text
+                        })
+                    
+                    # Store reasoning content if captured
+                    if reasoning_steps and reasoning_content:
+                        stored_reasoning_content = reasoning_content
+                    
+                    # Continue the loop to check if more tools are needed
+                    iteration_count += 1
+                    continue
+                else:
+                    # No tool calls, we're done with this iteration
+                    # If we've executed tools in previous iterations, this response contains the final answer
+                    if iteration_count > 0:
+                        final_response_text = response_text.strip()
+                    break
 
             # Handle output formatting
             if output_json or output_pydantic:
@@ -1588,13 +1584,27 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                 return response_text
 
             if not self_reflect:
+                # Use final_response_text if we went through tool iterations
+                display_text = final_response_text if final_response_text else response_text
+                
+                # Display with stored reasoning content if available
                 if verbose:
-                    display_interaction(original_prompt, response_text, markdown=markdown,
-                                     generation_time=time.time() - start_time, console=console)
-                # Return reasoning content if reasoning_steps is True
-                if reasoning_steps and reasoning_content:
-                    return reasoning_content
-                return response_text
+                    if stored_reasoning_content:
+                        display_interaction(
+                            original_prompt,
+                            f"Reasoning:\n{stored_reasoning_content}\n\nAnswer:\n{display_text}",
+                            markdown=markdown,
+                            generation_time=time.time() - start_time,
+                            console=console
+                        )
+                    else:
+                        display_interaction(original_prompt, display_text, markdown=markdown,
+                                         generation_time=time.time() - start_time, console=console)
+                
+                # Return reasoning content if reasoning_steps is True and we have it
+                if reasoning_steps and stored_reasoning_content:
+                    return stored_reasoning_content
+                return display_text
 
             # Handle self-reflection
             reflection_prompt = f"""

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.125"
+version = "0.0.126"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 requires-python = ">=3.10"
 authors = [

src/praisonai-agents/tests/code_review.py

@@ -11,14 +11,30 @@ def analyze_code_changes():
     ]
     return issues[int(time.time()) % 3]
 
-def suggest_fixes(issue: Dict):
+def suggest_fixes(issue):
     """Simulates fix suggestions"""
     fixes = {
         "style": "Apply PEP 8 formatting",
-        "security": "Implement input validation",
+        "security": "Implement input validation", 
         "performance": "Use list comprehension"
     }
-    return fixes.get(issue["type"], "Review manually")
+    
+    # Handle both dict and string inputs
+    if isinstance(issue, dict):
+        issue_type = issue.get("type", "unknown")
+    else:
+        # If it's a string, try to extract the type
+        issue_str = str(issue).lower()
+        if "security" in issue_str:
+            issue_type = "security"
+        elif "style" in issue_str:
+            issue_type = "style"
+        elif "performance" in issue_str:
+            issue_type = "performance"
+        else:
+            issue_type = "unknown"
+    
+    return fixes.get(issue_type, "Review manually")
 
 def apply_automated_fix(fix: str):
     """Simulates applying automated fixes"""
@@ -98,11 +114,8 @@ def main():
     # Print results
     print("\nCode Review Results:")
     print("=" * 50)
-    for task_id, result in results["task_results"].items():
-        if result:
-            print(f"\nTask: {task_id}")
-            print(f"Result: {result.raw}")
-            print("-" * 50)
+    print(f"Results: {results}")
+    print("-" * 50)
 
 if __name__ == "__main__":
     main()

src/praisonai-agents/tests/prompt_chaining.py

@@ -69,7 +69,7 @@ workflow = PraisonAIAgents(
 )
 
 # Run the workflow
-results = workflow.start()
+results = workflow.start(return_dict=True)
 
 # Print results
 print("\nWorkflow Results:")

src/praisonai-agents/tests/test_fix_comprehensive.py

@@ -0,0 +1,75 @@
+"""
+Test script to verify the fix for sequential tool calling.
+The agent should:
+1. Call get_stock_price to get Google's stock price (100)
+2. Call multiply to multiply 100 by 2
+3. Return the final result (200)
+"""
+
+from praisonaiagents import Agent
+
+def get_stock_price(company_name: str) -> str:
+    """
+    Get the stock price of a company
+    
+    Args:
+        company_name (str): The name of the company
+        
+    Returns:
+        str: The stock price of the company
+    """
+    print(f"[Tool Called] get_stock_price({company_name})")
+    return f"The stock price of {company_name} is 100"
+
+def multiply(a: int, b: int) -> int:
+    """
+    Multiply two numbers
+    
+    Args:
+        a (int): First number
+        b (int): Second number
+        
+    Returns:
+        int: Product of a and b
+    """
+    print(f"[Tool Called] multiply({a}, {b})")
+    return a * b
+
+# Test with Gemini
+print("=" * 60)
+print("Testing with Gemini model")
+print("=" * 60)
+
+agent_gemini = Agent(
+    instructions="You are a helpful assistant. You can use the tools provided to you to help the user. When asked to multiply a stock price, first get the stock price, then multiply it.",
+    llm="gemini/gemini-2.5-pro",
+    tools=[get_stock_price, multiply],
+    verbose=True
+)
+
+result_gemini = agent_gemini.start("what is the stock price of Google? multiply the Google stock price with 2")
+print(f"\nFinal Result (Gemini): {result_gemini}")
+
+# Test with GPT-4
+print("\n" + "=" * 60)
+print("Testing with GPT-4 model")
+print("=" * 60)
+
+agent_gpt4 = Agent(
+    instructions="You are a helpful assistant. You can use the tools provided to you to help the user. When asked to multiply a stock price, first get the stock price, then multiply it.",
+    llm="gpt-4o",
+    tools=[get_stock_price, multiply],
+    verbose=True
+)
+
+result_gpt4 = agent_gpt4.start("what is the stock price of Google? multiply the Google stock price with 2")
+print(f"\nFinal Result (GPT-4): {result_gpt4}")
+
+# Verify results
+print("\n" + "=" * 60)
+print("Test Results Summary")
+print("=" * 60)
+print(f"Gemini result contains '200': {'200' in str(result_gemini) if result_gemini else False}")
+print(f"GPT-4 result contains '200': {'200' in str(result_gpt4) if result_gpt4 else False}")
+print(f"Gemini returned empty: {not result_gemini or result_gemini == ''}")
+print(f"GPT-4 returned empty: {not result_gpt4 or result_gpt4 == ''}")

src/praisonai-agents/uv.lock

@@ -2375,7 +2375,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.125"
+version = "0.0.126"
 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/v2.2.51.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.51.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.52.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.52.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==2.2.51 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.2.52 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

src/praisonai/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.2.51"
+version = "2.2.52"
 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>=0.0.125",
+    "praisonaiagents>=0.0.126",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",
@@ -95,7 +95,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.15", "crewai"]
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.2.51"
+version = "2.2.52"
 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 = ""
@@ -113,7 +113,7 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.125"
+praisonaiagents = ">=0.0.126"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

src/praisonai/uv.lock

@@ -3931,7 +3931,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.2.51"
+version = "2.2.52"
 source = { editable = "." }
 dependencies = [
     { name = "instructor" },
@@ -4073,7 +4073,7 @@ requires-dist = [
     { name = "plotly", marker = "extra == 'realtime'", specifier = ">=5.24.0" },
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.0.15" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.0.15" },
-    { name = "praisonaiagents", specifier = ">=0.0.125" },
+    { name = "praisonaiagents", specifier = ">=0.0.126" },
     { name = "pyautogen", marker = "extra == 'autogen'", specifier = ">=0.2.19" },
     { name = "pydantic", marker = "extra == 'chat'", specifier = "<=2.10.1" },
     { name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
@@ -4130,7 +4130,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.125"
+version = "0.0.126"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -4140,9 +4140,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ed/e8/bed80966c49482a565e9e6a7eab93ea81ce5381d3f20b60f550c3624fbcb/praisonaiagents-0.0.125.tar.gz", hash = "sha256:a5ec4da7e3a948303038f0a56f1863add3183af8b415e587e007895ce752615a", size = 185807 }
+sdist = { url = "https://files.pythonhosted.org/packages/05/68/7b8be1f9b69b02a36763009b8a847861964bb49aa67d68c02fd5fd77b5a0/praisonaiagents-0.0.126.tar.gz", hash = "sha256:229413f76dd6bb5d5f2d22ef04613f85b8f251bfe5fbd9bdff3440de429748f3", size = 186412 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/1e/0c/6072b7e5e5eb489279381cd6d18f200916905fa731d0cc4318ac514f2152/praisonaiagents-0.0.125-py3-none-any.whl", hash = "sha256:fff3f92103d9801da517f4b253d86e76afbfee566e632e12792cd22d583223d9", size = 207023 },
+    { url = "https://files.pythonhosted.org/packages/0b/7b/4a3672046fa8104e11948352b5fa8815b4248473337629bbb7420149c908/praisonaiagents-0.0.126-py3-none-any.whl", hash = "sha256:30a0cea45f89516052e36f1806ea043c2268cdf5f7e7347c8b1a9ddac22e955f", size = 206978 },
 ]
 
 [[package]]