refactor: move test files to src/praisonai-agents and bump version to 2.2.57

Merge pull request #897 from MervinPraison/claude/issue-896-20250714_104106
feat: enhance callbacks with agent and task context (fixes #896)
2026-11-09 11:51:37 -10:00 · 2025-07-14 18:40:18 +01:00 · 2025-07-14 18:27:25 +01:00 · 2025-07-14 17:21:11 +00:00 · 2025-07-14 18:01:38 +01:00 · 2025-07-14 17:57:58 +01:00
32 changed files with 1049 additions and 74 deletions
@@ -75,17 +75,29 @@ jobs:
                issue_number: issueNumber
              });
              
-              const existingComment = comments.data.find(comment => 
+              // Look for Claude's completed review comments
+              const claudeReviewComments = comments.data.filter(comment => 
+                (comment.user.login === 'claude[bot]' || comment.body.includes('✅') || comment.body.includes('## Summary')) &&
+                !comment.body.includes('⏳') && 
+                !comment.body.includes('🔄') &&
+                !comment.body.includes('Loading...')
+              );
+              
+              // Check if our review request comment already exists
+              const existingReviewRequest = comments.data.find(comment => 
                comment.body.includes(reviewText)
              );
              
-              const shouldPost = !existingComment;
+              // Post if Claude has completed a review AND we haven't posted the review request yet
+              const shouldPost = claudeReviewComments.length > 0 && !existingReviewRequest;
              core.setOutput('should-post', shouldPost);
+              console.log(`Claude review completed: ${claudeReviewComments.length > 0}`);
+              console.log(`Review request exists: ${!!existingReviewRequest}`);
              console.log(`Should post comment: ${shouldPost}`);
              
            } catch (error) {
              console.log('Error checking comments:', error);
-              core.setOutput('should-post', true); // If we can't check, proceed with posting
+              core.setOutput('should-post', false); // If we can't check, don't post
            }

      - name: Wait 30 seconds before posting comment
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    flask \
-    "praisonai>=2.2.56" \
+    "praisonai>=2.2.57" \
    "praisonai[api]" \
    gunicorn \
    markdown
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=2.2.56" \
+    "praisonai>=2.2.57" \
    "praisonai[chat]" \
    "embedchain[github,youtube]"

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=2.2.56" \
+    "praisonai>=2.2.57" \
    "praisonai[ui]" \
    "praisonai[chat]" \
    "praisonai[realtime]" \
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=2.2.56" \
+    "praisonai>=2.2.57" \
    "praisonai[ui]" \
    "praisonai[crewai]"

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions

 All Docker images use consistent, up-to-date versions:
- PraisonAI: `>=2.2.56`
+- PraisonAI: `>=2.2.57`
 - 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==2.2.56" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.57" "praisonaiagents==0.0.92"
 ```

 ## 🌐 Production Deployment
@@ -1100,6 +1100,10 @@ Your Goal: {self.goal}"""
                        execute_tool_fn=self.execute_tool,
                        agent_name=self.name,
                        agent_role=self.role,
+                        agent_tools=[t.__name__ for t in self.tools] if self.tools else None,
+                        task_name=task_name,
+                        task_description=task_description,
+                        task_id=task_id,
                        reasoning_steps=reasoning_steps
                    )
                else:
@@ -1116,6 +1120,10 @@ Your Goal: {self.goal}"""
                        execute_tool_fn=self.execute_tool,
                        agent_name=self.name,
                        agent_role=self.role,
+                        agent_tools=[t.__name__ for t in self.tools] if self.tools else None,
+                        task_name=task_name,
+                        task_description=task_description,
+                        task_id=task_id,
                        reasoning_steps=reasoning_steps
                    )
            else:
@@ -1162,14 +1170,26 @@ Your Goal: {self.goal}"""
                message=prompt,
                response=response,
                markdown=self.markdown,
-                generation_time=generation_time
+                generation_time=generation_time,
+                agent_name=self.name,
+                agent_role=self.role,
+                agent_tools=[t.__name__ for t in self.tools] if self.tools else None,
+                task_name=None,  # Not available in this context
+                task_description=None,  # Not available in this context 
+                task_id=None  # Not available in this context
            )
        # Only display interaction if not using custom LLM (to avoid double output) and verbose is True
        if self.verbose and not self._using_custom_llm:
            display_interaction(prompt, response, markdown=self.markdown, 
-                              generation_time=generation_time, console=self.console)
+                              generation_time=generation_time, console=self.console,
+                              agent_name=self.name,
+                              agent_role=self.role,
+                              agent_tools=[t.__name__ for t in self.tools] if self.tools else None,
+                              task_name=None,  # Not available in this context
+                              task_description=None,  # Not available in this context
+                              task_id=None)  # Not available in this context

-    def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None, reasoning_steps=False, stream=True):
+    def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None, reasoning_steps=False, stream=True, task_name=None, task_description=None, task_id=None):
        # Log all parameter values when in debug mode
        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
            param_info = {
@@ -1260,6 +1280,9 @@ Your Goal: {self.goal}"""
                    agent_name=self.name,
                    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)],
+                    task_name=task_name,
+                    task_description=task_description,
+                    task_id=task_id,
                    execute_tool_fn=self.execute_tool,  # Pass tool execution function
                    reasoning_steps=reasoning_steps,
                    stream=stream  # Pass the stream parameter from chat method
@@ -1582,6 +1605,9 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                        agent_name=self.name,
                        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)],
+                        task_name=task_name,
+                        task_description=task_description,
+                        task_id=task_id,
                        execute_tool_fn=self.execute_tool_async,
                        reasoning_steps=reasoning_steps
                    )
@@ -144,8 +144,17 @@ class ImageAgent(Agent):
        config = self.image_config.dict(exclude_none=True)
        config.update(kwargs)
        
-        # Use llm parameter as the model
-        config['model'] = self.llm
+        # Get the model name robustly from the parent Agent's property
+        model_info = self.llm_model
+        model_name = model_info.model if hasattr(model_info, 'model') else str(model_info)
+        
+        # Use the model name in config
+        config['model'] = model_name
+
+        # Check if we're using a Gemini model and remove unsupported parameters
+        if 'gemini' in model_name.lower():
+            # Gemini models don't support response_format parameter
+            config.pop('response_format', None)

        with Progress(
            SpinnerColumn(),
@@ -154,7 +163,7 @@ class ImageAgent(Agent):
        ) as progress:
            try:
                # Add a task for image generation
-                task = progress.add_task(f"[cyan]Generating image with {self.llm}...", total=None)
+                task = progress.add_task(f"[cyan]Generating image with {model_name}...", total=None)
                
                # Use litellm's image generation
                response = self.litellm(
@@ -689,7 +689,10 @@ Context:
                _get_multimodal_message(task_prompt, task.images),
                tools=task.tools,
                output_json=task.output_json,
-                output_pydantic=task.output_pydantic
+                output_pydantic=task.output_pydantic,
+                task_name=task.name,
+                task_description=task.description,
+                task_id=task_id
            )
        else:
            agent_output = executor_agent.chat(
@@ -698,6 +701,9 @@ Context:
                output_json=task.output_json,
                output_pydantic=task.output_pydantic,
                stream=self.stream,
+                task_name=task.name,
+                task_description=task.description,
+                task_id=task_id
            )

        if agent_output:
@@ -620,6 +620,9 @@ class LLM:
        agent_name: Optional[str] = None,
        agent_role: Optional[str] = None,
        agent_tools: Optional[List[str]] = None,
+        task_name: Optional[str] = None,
+        task_description: Optional[str] = None,
+        task_id: Optional[str] = None,
        execute_tool_fn: Optional[Callable] = None,
        stream: bool = True,
        **kwargs
@@ -760,8 +763,14 @@ class LLM:
                                original_prompt,
                                f"Reasoning:\n{reasoning_content}\n\nAnswer:\n{response_text}",
                                markdown=markdown,
-                                generation_time=generation_time_val,
-                                console=console
+                                generation_time=time.time() - current_time,
+                                console=console,
+                                agent_name=agent_name,
+                                agent_role=agent_role,
+                                agent_tools=agent_tools,
+                                task_name=task_name,
+                                task_description=task_description,
+                                task_id=task_id
                            )
                            interaction_displayed = True
                        elif verbose and not interaction_displayed:
@@ -769,8 +778,14 @@ class LLM:
                                original_prompt,
                                response_text,
                                markdown=markdown,
-                                generation_time=generation_time_val,
-                                console=console
+                                generation_time=time.time() - current_time,
+                                console=console,
+                                agent_name=agent_name,
+                                agent_role=agent_role,
+                                agent_tools=agent_tools,
+                                task_name=task_name,
+                                task_description=task_description,
+                                task_id=task_id
                            )
                            interaction_displayed = True
                    
@@ -883,7 +898,13 @@ class LLM:
                                    response_text,
                                    markdown=markdown,
                                    generation_time=time.time() - current_time,
-                                    console=console
+                                    console=console,
+                                    agent_name=agent_name,
+                                    agent_role=agent_role,
+                                    agent_tools=agent_tools,
+                                    task_name=task_name,
+                                    task_description=task_description,
+                                    task_id=task_id
                                )
                                interaction_displayed = True
                    
@@ -963,16 +984,14 @@ class LLM:
                            iteration_count += 1
                            continue

-                        # For Ollama, add explicit prompt if we need a final answer
-                        if self._is_ollama_provider() and iteration_count > 0:
-                            # Add an explicit prompt for Ollama to generate the final answer
-                            messages.append({
-                                "role": "user", 
-                                "content": self.OLLAMA_FINAL_ANSWER_PROMPT
-                            })
+                        # Check if the LLM provided a final answer alongside the tool calls
+                        # If response_text contains substantive content, treat it as the final answer
+                        if response_text and response_text.strip() and len(response_text.strip()) > 10:
+                            # LLM provided a final answer after tool execution, don't continue
+                            final_response_text = response_text.strip()
+                            break
                        
-                        # After tool execution, continue the loop to check if more tools are needed
-                        # instead of immediately trying to get a final response
+                        # Otherwise, continue the loop to check if more tools are needed
                        iteration_count += 1
                        continue
                    else:
@@ -1010,7 +1029,13 @@ class LLM:
                        f"Reasoning:\n{stored_reasoning_content}\n\nAnswer:\n{response_text}",
                        markdown=markdown,
                        generation_time=time.time() - start_time,
-                        console=console
+                        console=console,
+                        agent_name=agent_name,
+                        agent_role=agent_role,
+                        agent_tools=agent_tools,
+                        task_name=task_name,
+                        task_description=task_description,
+                        task_id=task_id
                    )
                else:
                    display_interaction(
@@ -1018,7 +1043,13 @@ class LLM:
                        response_text,
                        markdown=markdown,
                        generation_time=time.time() - start_time,
-                        console=console
+                        console=console,
+                        agent_name=agent_name,
+                        agent_role=agent_role,
+                        agent_tools=agent_tools,
+                        task_name=task_name,
+                        task_description=task_description,
+                        task_id=task_id
                    )
                interaction_displayed = True
            
@@ -1043,7 +1074,9 @@ class LLM:
                    )
                if verbose and not interaction_displayed:
                    display_interaction(original_prompt, response_text, markdown=markdown,
-                                     generation_time=time.time() - start_time, console=console)
+                                     generation_time=time.time() - start_time, console=console,
+                                     agent_name=agent_name, agent_role=agent_role, agent_tools=agent_tools,
+                                     task_name=task_name, task_description=task_description, task_id=task_id)
                    interaction_displayed = True
                return response_text

@@ -1060,7 +1093,9 @@ class LLM:

                if verbose and not interaction_displayed:
                    display_interaction(original_prompt, response_text, markdown=markdown,
-                                     generation_time=time.time() - start_time, console=console)
+                                     generation_time=time.time() - start_time, console=console,
+                                     agent_name=agent_name, agent_role=agent_role, agent_tools=agent_tools,
+                                     task_name=task_name, task_description=task_description, task_id=task_id)
                    interaction_displayed = True
                # Return reasoning content if reasoning_steps is True
                if reasoning_steps and stored_reasoning_content:
@@ -1106,7 +1141,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                            f"{reasoning_content}\n\nReflection result:\n{reflection_text}",
                            markdown=markdown,
                            generation_time=time.time() - start_time,
-                            console=console
+                            console=console,
+                            agent_name=agent_name,
+                            agent_role=agent_role,
+                            agent_tools=agent_tools,
+                            task_name=task_name,
+                            task_description=task_description,
+                            task_id=task_id
                        )
                    elif verbose:
                        display_interaction(
@@ -1114,7 +1155,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                            reflection_text,
                            markdown=markdown,
                            generation_time=time.time() - start_time,
-                            console=console
+                            console=console,
+                            agent_name=agent_name,
+                            agent_role=agent_role,
+                            agent_tools=agent_tools,
+                            task_name=task_name,
+                            task_description=task_description,
+                            task_id=task_id
                        )
                else:
                    # Existing streaming approach
@@ -1165,14 +1212,18 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                    if satisfactory and reflection_count >= min_reflect - 1:
                        if verbose and not interaction_displayed:
                            display_interaction(prompt, response_text, markdown=markdown,
-                                             generation_time=time.time() - start_time, console=console)
+                                             generation_time=time.time() - start_time, console=console,
+                                             agent_name=agent_name, agent_role=agent_role, agent_tools=agent_tools,
+                                             task_name=task_name, task_description=task_description, task_id=task_id)
                            interaction_displayed = True
                        return response_text

                    if reflection_count >= max_reflect - 1:
                        if verbose and not interaction_displayed:
                            display_interaction(prompt, response_text, markdown=markdown,
-                                             generation_time=time.time() - start_time, console=console)
+                                             generation_time=time.time() - start_time, console=console,
+                                             agent_name=agent_name, agent_role=agent_role, agent_tools=agent_tools,
+                                             task_name=task_name, task_description=task_description, task_id=task_id)
                            interaction_displayed = True
                        return response_text

@@ -1193,6 +1244,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                    messages=messages,
                                    temperature=temperature,
                                    stream=True,
+                                    tools=formatted_tools,
                                    output_json=output_json,
                                    output_pydantic=output_pydantic,
                                    **kwargs
@@ -1209,6 +1261,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                messages=messages,
                                temperature=temperature,
                                stream=True,
+                                tools=formatted_tools,
                                output_json=output_json,
                                output_pydantic=output_pydantic,
                                **kwargs
@@ -1225,7 +1278,9 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                    if reflection_count >= max_reflect:
                        if verbose and not interaction_displayed:
                            display_interaction(prompt, response_text, markdown=markdown,
-                                             generation_time=time.time() - start_time, console=console)
+                                             generation_time=time.time() - start_time, console=console,
+                                             agent_name=agent_name, agent_role=agent_role, agent_tools=agent_tools,
+                                             task_name=task_name, task_description=task_description, task_id=task_id)
                            interaction_displayed = True
                        return response_text
                    continue
@@ -1273,6 +1328,9 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
        agent_name: Optional[str] = None,
        agent_role: Optional[str] = None,
        agent_tools: Optional[List[str]] = None,
+        task_name: Optional[str] = None,
+        task_description: Optional[str] = None,
+        task_id: Optional[str] = None,
        execute_tool_fn: Optional[Callable] = None,
        stream: bool = True,
        **kwargs
@@ -1380,7 +1438,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                            f"Reasoning:\n{reasoning_content}\n\nAnswer:\n{response_text}",
                            markdown=markdown,
                            generation_time=time.time() - start_time,
-                            console=console
+                            console=console,
+                            agent_name=agent_name,
+                            agent_role=agent_role,
+                            agent_tools=agent_tools,
+                            task_name=task_name,
+                            task_description=task_description,
+                            task_id=task_id
                        )
                        interaction_displayed = True
                    elif verbose and not interaction_displayed:
@@ -1389,7 +1453,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                            response_text,
                            markdown=markdown,
                            generation_time=time.time() - start_time,
-                            console=console
+                            console=console,
+                            agent_name=agent_name,
+                            agent_role=agent_role,
+                            agent_tools=agent_tools,
+                            task_name=task_name,
+                            task_description=task_description,
+                            task_id=task_id
                        )
                        interaction_displayed = True
                else:
@@ -1473,7 +1543,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                response_text,
                                markdown=markdown,
                                generation_time=time.time() - start_time,
-                                console=console
+                                console=console,
+                                agent_name=agent_name,
+                                agent_role=agent_role,
+                                agent_tools=agent_tools,
+                                task_name=task_name,
+                                task_description=task_description,
+                                task_id=task_id
                            )
                            interaction_displayed = True

@@ -1567,7 +1643,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                f"Reasoning:\n{reasoning_content}\n\nAnswer:\n{response_text}",
                                markdown=markdown,
                                generation_time=time.time() - start_time,
-                                console=console
+                                console=console,
+                                agent_name=agent_name,
+                                agent_role=agent_role,
+                                agent_tools=agent_tools,
+                                task_name=task_name,
+                                task_description=task_description,
+                                task_id=task_id
                            )
                            interaction_displayed = True
                        elif verbose and not interaction_displayed:
@@ -1576,7 +1658,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                response_text,
                                markdown=markdown,
                                generation_time=time.time() - start_time,
-                                console=console
+                                console=console,
+                                agent_name=agent_name,
+                                agent_role=agent_role,
+                                agent_tools=agent_tools,
+                                task_name=task_name,
+                                task_description=task_description,
+                                task_id=task_id
                            )
                            interaction_displayed = True
                    else:
@@ -1626,6 +1714,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                    if reasoning_steps and reasoning_content:
                        stored_reasoning_content = reasoning_content
                    
+                    # Check if the LLM provided a final answer alongside the tool calls
+                    # If response_text contains substantive content, treat it as the final answer
+                    if response_text and response_text.strip() and len(response_text.strip()) > 10:
+                        # LLM provided a final answer after tool execution, don't continue
+                        final_response_text = response_text.strip()
+                        break
+                    
                    # Continue the loop to check if more tools are needed
                    iteration_count += 1
                    continue
@@ -1642,7 +1737,9 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                self.chat_history.append({"role": "assistant", "content": response_text})
                if verbose and not interaction_displayed:
                    display_interaction(original_prompt, response_text, markdown=markdown,
-                                     generation_time=time.time() - start_time, console=console)
+                                     generation_time=time.time() - start_time, console=console,
+                                     agent_name=agent_name, agent_role=agent_role, agent_tools=agent_tools,
+                                     task_name=task_name, task_description=task_description, task_id=task_id)
                    interaction_displayed = True
                return response_text

@@ -1658,11 +1755,19 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                            f"Reasoning:\n{stored_reasoning_content}\n\nAnswer:\n{display_text}",
                            markdown=markdown,
                            generation_time=time.time() - start_time,
-                            console=console
+                            console=console,
+                            agent_name=agent_name,
+                            agent_role=agent_role,
+                            agent_tools=agent_tools,
+                            task_name=task_name,
+                            task_description=task_description,
+                            task_id=task_id
                        )
                    else:
                        display_interaction(original_prompt, display_text, markdown=markdown,
-                                         generation_time=time.time() - start_time, console=console)
+                                         generation_time=time.time() - start_time, console=console,
+                                         agent_name=agent_name, agent_role=agent_role, agent_tools=agent_tools,
+                                         task_name=task_name, task_description=task_description, task_id=task_id)
                    interaction_displayed = True
                
                # Return reasoning content if reasoning_steps is True and we have it
@@ -1707,7 +1812,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                        f"{reasoning_content}\n\nReflection result:\n{reflection_text}",
                        markdown=markdown,
                        generation_time=time.time() - start_time,
-                        console=console
+                        console=console,
+                        agent_name=agent_name,
+                        agent_role=agent_role,
+                        agent_tools=agent_tools,
+                        task_name=task_name,
+                        task_description=task_description,
+                        task_id=task_id
                    )
                elif verbose:
                    display_interaction(
@@ -1715,7 +1826,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                        reflection_text,
                        markdown=markdown,
                        generation_time=time.time() - start_time,
-                        console=console
+                        console=console,
+                        agent_name=agent_name,
+                        agent_role=agent_role,
+                        agent_tools=agent_tools,
+                        task_name=task_name,
+                        task_description=task_description,
+                        task_id=task_id
                    )
            else:
                # Existing streaming approach
@@ -1767,14 +1884,18 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                    if satisfactory and reflection_count >= min_reflect - 1:
                        if verbose and not interaction_displayed:
                            display_interaction(prompt, response_text, markdown=markdown,
-                                             generation_time=time.time() - start_time, console=console)
+                                             generation_time=time.time() - start_time, console=console,
+                                             agent_name=agent_name, agent_role=agent_role, agent_tools=agent_tools,
+                                             task_name=task_name, task_description=task_description, task_id=task_id)
                            interaction_displayed = True
                        return response_text

                    if reflection_count >= max_reflect - 1:
                        if verbose and not interaction_displayed:
                            display_interaction(prompt, response_text, markdown=markdown,
-                                             generation_time=time.time() - start_time, console=console)
+                                             generation_time=time.time() - start_time, console=console,
+                                             agent_name=agent_name, agent_role=agent_role, agent_tools=agent_tools,
+                                             task_name=task_name, task_description=task_description, task_id=task_id)
                            interaction_displayed = True
                        return response_text

@@ -2039,6 +2160,12 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
        verbose: bool = True,
        markdown: bool = True,
        console: Optional[Console] = None,
+        agent_name: Optional[str] = None,
+        agent_role: Optional[str] = None,
+        agent_tools: Optional[List[str]] = None,
+        task_name: Optional[str] = None,
+        task_description: Optional[str] = None,
+        task_id: Optional[str] = None,
        **kwargs
    ) -> str:
        """Simple function to get model response without tool calls or complex features"""
@@ -2107,7 +2234,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                    response_text,
                    markdown=markdown,
                    generation_time=time.time() - start_time,
-                    console=console or self.console
+                    console=console or self.console,
+                    agent_name=agent_name,
+                    agent_role=agent_role,
+                    agent_tools=agent_tools,
+                    task_name=task_name,
+                    task_description=task_description,
+                    task_id=task_id
                )
            
            return response_text.strip() if response_text else ""
@@ -2126,6 +2259,12 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
        verbose: bool = True,
        markdown: bool = True,
        console: Optional[Console] = None,
+        agent_name: Optional[str] = None,
+        agent_role: Optional[str] = None,
+        agent_tools: Optional[List[str]] = None,
+        task_name: Optional[str] = None,
+        task_description: Optional[str] = None,
+        task_id: Optional[str] = None,
        **kwargs
    ) -> str:
        """Async version of response function"""
@@ -2195,7 +2334,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                    response_text,
                    markdown=markdown,
                    generation_time=time.time() - start_time,
-                    console=console or self.console
+                    console=console or self.console,
+                    agent_name=agent_name,
+                    agent_role=agent_role,
+                    agent_tools=agent_tools,
+                    task_name=task_name,
+                    task_description=task_description,
+                    task_id=task_id
                )
            
            return response_text.strip() if response_text else ""
@@ -73,7 +73,18 @@ def execute_sync_callback(display_type: str, **kwargs):
    """
    if display_type in sync_display_callbacks:
        callback = sync_display_callbacks[display_type]
-        callback(**kwargs)
+        import inspect
+        sig = inspect.signature(callback)
+        
+        # Filter kwargs to what the callback accepts to maintain backward compatibility
+        if any(p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()):
+            # Callback accepts **kwargs, so pass all arguments
+            supported_kwargs = kwargs
+        else:
+            # Only pass arguments that the callback signature supports
+            supported_kwargs = {k: v for k, v in kwargs.items() if k in sig.parameters}
+        
+        callback(**supported_kwargs)

 async def execute_callback(display_type: str, **kwargs):
    """Execute both sync and async callbacks for a given display type.
@@ -82,16 +93,38 @@ async def execute_callback(display_type: str, **kwargs):
        display_type (str): Type of display event
        **kwargs: Arguments to pass to the callback functions
    """
+    import inspect
+    
    # Execute synchronous callback if registered
    if display_type in sync_display_callbacks:
        callback = sync_display_callbacks[display_type]
+        sig = inspect.signature(callback)
+        
+        # Filter kwargs to what the callback accepts to maintain backward compatibility
+        if any(p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()):
+            # Callback accepts **kwargs, so pass all arguments
+            supported_kwargs = kwargs
+        else:
+            # Only pass arguments that the callback signature supports
+            supported_kwargs = {k: v for k, v in kwargs.items() if k in sig.parameters}
+        
        loop = asyncio.get_event_loop()
-        await loop.run_in_executor(None, lambda: callback(**kwargs))
+        await loop.run_in_executor(None, lambda: callback(**supported_kwargs))
    
    # Execute asynchronous callback if registered
    if display_type in async_display_callbacks:
        callback = async_display_callbacks[display_type]
-        await callback(**kwargs)
+        sig = inspect.signature(callback)
+        
+        # Filter kwargs to what the callback accepts to maintain backward compatibility
+        if any(p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()):
+            # Callback accepts **kwargs, so pass all arguments
+            supported_kwargs = kwargs
+        else:
+            # Only pass arguments that the callback signature supports
+            supported_kwargs = {k: v for k, v in kwargs.items() if k in sig.parameters}
+        
+        await callback(**supported_kwargs)

 def _clean_display_content(content: str, max_length: int = 20000) -> str:
    """Helper function to clean and truncate content for display."""
@@ -114,7 +147,7 @@ def _clean_display_content(content: str, max_length: int = 20000) -> str:
    
    return content.strip()

-def display_interaction(message, response, markdown=True, generation_time=None, console=None):
+def display_interaction(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):
    """Synchronous version of display_interaction."""
    if console is None:
        console = Console()
@@ -126,6 +159,35 @@ def display_interaction(message, response, markdown=True, generation_time=None,
    message = _clean_display_content(str(message))
    response = _clean_display_content(str(response))

+
+    # Execute synchronous callback if registered
+    if 'interaction' in sync_display_callbacks:
+        callback = sync_display_callbacks['interaction']
+        import inspect
+        sig = inspect.signature(callback)
+        
+        all_kwargs = {
+            'message': message,
+            'response': response,
+            'markdown': markdown,
+            'generation_time': generation_time,
+            'agent_name': agent_name,
+            'agent_role': agent_role,
+            'agent_tools': agent_tools,
+            'task_name': task_name,
+            'task_description': task_description,
+            'task_id': task_id
+        }
+        
+        # Filter kwargs to what the callback accepts to maintain backward compatibility
+        if any(p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()):
+            # Callback accepts **kwargs, so pass all arguments
+            supported_kwargs = all_kwargs
+        else:
+            # Only pass arguments that the callback signature supports
+            supported_kwargs = {k: v for k, v in all_kwargs.items() if k in sig.parameters}
+        
+        callback(**supported_kwargs)
    # Rest of the display logic...
    if generation_time:
        console.print(Text(f"Response generated in {generation_time:.1f}s", style="dim"))
@@ -204,7 +266,7 @@ def display_generating(content: str = "", start_time: Optional[float] = None):
    return Panel(Markdown(content), title=f"Generating...{elapsed_str}", border_style="green")

 # Async versions with 'a' prefix
-async def adisplay_interaction(message, response, markdown=True, generation_time=None, console=None):
+async def adisplay_interaction(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):
    """Async version of display_interaction."""
    if console is None:
        console = Console()
@@ -222,7 +284,13 @@ async def adisplay_interaction(message, response, markdown=True, generation_time
        message=message,
        response=response,
        markdown=markdown,
-        generation_time=generation_time
+        generation_time=generation_time,
+        agent_name=agent_name,
+        agent_role=agent_role,
+        agent_tools=agent_tools,
+        task_name=task_name,
+        task_description=task_description,
+        task_id=task_id
    )

    # Rest of the display logic...
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "praisonaiagents"
-version = "0.0.130"
+version = "0.0.131"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 requires-python = ">=3.10"
 authors = [
@@ -0,0 +1,82 @@
+#!/usr/bin/env python3
+"""Test script for callback enhancement - Issue #896"""
+
+import sys
+import os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src', 'praisonai-agents'))
+
+import asyncio
+from praisonaiagents import (
+    register_display_callback,
+    Agent, 
+    Task, 
+    PraisonAIAgents
+)
+
+def enhanced_callback(message=None, response=None, agent_name=None, agent_role=None, agent_tools=None, task_name=None, task_description=None, task_id=None, **kwargs):
+    """Enhanced callback that demonstrates the new context information"""
+    print("="*80)
+    print("ENHANCED CALLBACK TRIGGERED")
+    print("="*80)
+    print(f"Message: {message}")
+    print(f"Response: {response}")
+    print(f"Agent Name: {agent_name}")
+    print(f"Agent Role: {agent_role}")
+    print(f"Agent Tools: {agent_tools}")
+    print(f"Task Name: {task_name}")
+    print(f"Task Description: {task_description}")
+    print(f"Task ID: {task_id}")
+    print(f"Other kwargs: {kwargs}")
+    print("="*80)
+    print()
+
+# Register the enhanced callback
+register_display_callback('interaction', enhanced_callback, is_async=False)
+
+def test_callback_enhancement():
+    """Test function to verify the callback enhancement works"""
+    
+    # Create an agent with detailed information
+    agent = Agent(
+        name="TestAgent",
+        role="Assistant",
+        goal="Help with testing callbacks",
+        backstory="I am a helpful assistant for testing purposes",
+        llm="openai/gpt-4o-mini",
+        verbose=True  
+    )
+
+    # Create a task with detailed information
+    task = Task(
+        name="callback_test_task",
+        description="Test the enhanced callback functionality",
+        agent=agent,
+        expected_output="A simple response to test callbacks"
+    )
+
+    # Run the agents
+    try:
+        agents = PraisonAIAgents(
+            agents=[agent],
+            tasks=[task],
+            verbose=True
+        )
+        result = agents.start()
+        print(f"Test completed successfully! Result: {result}")
+        return True
+    except Exception as e:
+        print(f"Test failed with error: {e}")
+        return False
+
+if __name__ == "__main__":
+    print("Testing callback enhancement (Issue #896)...")
+    print("This test will demonstrate the new agent_name and task context in callbacks")
+    print()
+    
+    # Run the test
+    success = test_callback_enhancement()
+    
+    if success:
+        print("✅ Test passed! Callback enhancement is working correctly.")
+    else:
+        print("❌ Test failed! Check the error output above.")
@@ -34,7 +34,7 @@ agent = Agent(
    role="Assistant",
    goal="Help with tasks",
    backstory="I am a helpful assistant",
-    llm="openai/gpt-4o-mini",  # Using a simple model for testing
+    llm="gpt-4o-mini",  # Using a simple model for testing
    verbose=False
 )

@@ -0,0 +1,44 @@
+#!/usr/bin/env python3
+"""Simple test for callback enhancement - Issue #896"""
+
+import sys
+import os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src', 'praisonai-agents'))
+
+# Test just the callback registration and display functions
+from praisonaiagents import register_display_callback, display_interaction
+
+def test_callback(message=None, response=None, agent_name=None, agent_role=None, agent_tools=None, task_name=None, task_description=None, task_id=None, **kwargs):
+    """Test callback function"""
+    print("Callback triggered with:")
+    print(f"  Message: {message}")
+    print(f"  Response: {response}")
+    print(f"  Agent Name: {agent_name}")
+    print(f"  Agent Role: {agent_role}")
+    print(f"  Agent Tools: {agent_tools}")
+    print(f"  Task Name: {task_name}")
+    print(f"  Task Description: {task_description}")
+    print(f"  Task ID: {task_id}")
+    print(f"  Other kwargs: {kwargs}")
+    print()
+
+# Register the callback
+register_display_callback('interaction', test_callback, is_async=False)
+
+# Test the display_interaction function with new parameters
+print("Testing display_interaction with enhanced parameters...")
+
+display_interaction(
+    message="Test message",
+    response="Test response",
+    agent_name="TestAgent",
+    agent_role="Assistant",
+    agent_tools=["tool1", "tool2"],
+    task_name="test_task",
+    task_description="Test task description",
+    task_id="task_123",
+    markdown=True,
+    generation_time=1.5
+)
+
+print("✅ Simple callback test completed!")
@@ -0,0 +1,36 @@
+#!/usr/bin/env python3
+"""Simple test to verify the self-reflection fix works"""
+
+from praisonaiagents import Agent
+from praisonaiagents.tools import calculator
+
+def test_self_reflection_fix():
+    """Test that self-reflection works with tools after the fix"""
+    print("=== Testing Self-Reflection Fix ===")
+    
+    # Create an agent with self-reflection and a simple tool
+    agent = Agent(
+        role="Math Assistant",
+        goal="Solve math problems accurately",
+        backstory="You are a helpful math assistant",
+        self_reflect=True,
+        llm="gpt-4o-mini",  # Use a more widely available model
+        verbose=True,
+        tools=[calculator],
+        min_reflect=1,
+        max_reflect=2
+    )
+
+    # Test with a simple calculation that might trigger self-reflection
+    response = agent.start("What is 25 * 17? Show your work and double-check the answer.")
+    print(f"\nResponse: {response}")
+    
+    if response:
+        print("\n✅ SUCCESS: Self-reflection with tools is working!")
+        return True
+    else:
+        print("\n❌ FAILED: Self-reflection with tools is not working")
+        return False
+
+if __name__ == "__main__":
+    test_self_reflection_fix()
@@ -0,0 +1,60 @@
+#!/usr/bin/env python3
+"""Test to verify the self-reflection fix works with tools"""
+
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import calculator
+
+def test_self_reflection_fix_verification():
+    """Test that self-reflection now works with tools"""
+    print("=== Testing Self-Reflection Fix Verification ===")
+    
+    # Create an agent with self-reflection enabled and tools
+    agent = Agent(
+        name="MathAgent",
+        role="Math Assistant",
+        goal="Solve mathematical problems accurately",
+        backstory="You are a helpful math assistant who double-checks calculations",
+        tools=[calculator],
+        self_reflect=True,  # This should now work with tools
+        min_reflect=1,
+        max_reflect=2,
+        verbose=True
+    )
+
+    # Define a task that would benefit from self-reflection
+    task = Task(
+        description="Calculate 123 * 456 and verify the result is correct",
+        expected_output="The calculation result with verification",
+        agent=agent,
+        name="math_calculation"
+    )
+
+    # Create and run the agents
+    agents = PraisonAIAgents(
+        agents=[agent],
+        tasks=[task],
+        process="sequential"
+    )
+    
+    try:
+        result = agents.start()
+        
+        if result:
+            print(f"\n✅ SUCCESS: Self-reflection with tools is working!")
+            print(f"Result: {result}")
+            return True
+        else:
+            print(f"\n❌ FAILED: No result returned")
+            return False
+            
+    except Exception as e:
+        print(f"\n❌ ERROR: {str(e)}")
+        return False
+
+if __name__ == "__main__":
+    success = test_self_reflection_fix_verification()
+    
+    if success:
+        print("\n🎉 Fix verification successful! Self-reflection now works with tools.")
+    else:
+        print("\n💥 Fix verification failed. Self-reflection with tools is still not working.")
@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+"""
+Demonstration of Issue #896 Enhancement: Callback Needs More Contextual Information
+
+This script demonstrates how the enhanced callback system now provides agent_name, 
+agent_role, agent_tools, task_name, task_description, and task_id to callbacks.
+
+Usage:
+    python test_issue_896_demo.py
+
+Author: Generated by Claude Code
+"""
+
+import sys
+import os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src', 'praisonai-agents'))
+
+from praisonaiagents import register_display_callback, display_interaction
+
+def enhanced_callback(message=None, response=None, agent_name=None, agent_role=None, 
+                     agent_tools=None, task_name=None, task_description=None, 
+                     task_id=None, **kwargs):
+    """
+    Enhanced callback function that demonstrates the new contextual information.
+    
+    This callback now receives:
+    - agent_name: The name of the agent
+    - agent_role: The role of the agent  
+    - agent_tools: List of tools available to the agent
+    - task_name: The name of the task being executed
+    - task_description: The description of the task
+    - task_id: The unique identifier of the task
+    """
+    print("="*80)
+    print("🔔 ENHANCED CALLBACK TRIGGERED")
+    print("="*80)
+    print(f"📝 Message: {message}")
+    print(f"💬 Response: {response}")
+    print(f"🤖 Agent Name: {agent_name}")
+    print(f"👤 Agent Role: {agent_role}")
+    print(f"🛠️  Agent Tools: {agent_tools}")
+    print(f"📋 Task Name: {task_name}")
+    print(f"📄 Task Description: {task_description}")
+    print(f"🔢 Task ID: {task_id}")
+    print(f"⚙️  Other kwargs: {kwargs}")
+    print("="*80)
+    print()
+
+def legacy_callback(message=None, response=None, **kwargs):
+    """
+    Legacy callback function that demonstrates backward compatibility.
+    
+    This callback works exactly as before, proving that existing callbacks
+    continue to function without modification.
+    """
+    print("🔄 Legacy callback still works!")
+    print(f"Message: {message}")
+    print(f"Response: {response}")
+    print(f"Other kwargs: {kwargs}")
+    print()
+
+def demo_enhanced_callbacks():
+    """Demonstrate the enhanced callback functionality"""
+    
+    print("🚀 Demonstrating Enhanced Callbacks for Issue #896")
+    print("="*60)
+    print()
+    
+    # Register both enhanced and legacy callbacks
+    register_display_callback('interaction', enhanced_callback, is_async=False)
+    
+    print("1. Testing enhanced callback with full context:")
+    print("-" * 50)
+    
+    # Test with full context
+    display_interaction(
+        message="You need to do the following task: Say the number 1.\nExpected Output: 1.\nPlease provide only the final result of your work.",
+        response="1",
+        agent_name="MyAgent",
+        agent_role="Assistant", 
+        agent_tools=["calculator", "text_processor"],
+        task_name="simple_task",
+        task_description="Say the number 1",
+        task_id="task_001",
+        markdown=True,
+        generation_time=1.3638858795166016
+    )
+    
+    print("2. Testing backward compatibility (missing context):")
+    print("-" * 50)
+    
+    # Test with minimal context (backward compatibility)
+    display_interaction(
+        message="Test message without context",
+        response="Test response", 
+        markdown=True,
+        generation_time=0.5
+    )
+    
+    print("3. Testing with partial context:")
+    print("-" * 50)
+    
+    # Test with partial context
+    display_interaction(
+        message="Partial context test",
+        response="Response with partial context",
+        agent_name="PartialAgent",
+        task_name="partial_task",
+        markdown=True,
+        generation_time=0.8
+    )
+    
+    print("✅ All tests completed successfully!")
+    print()
+    print("🎉 The enhanced callback system is working correctly!")
+    print("   - Callbacks now receive agent and task context")
+    print("   - Backward compatibility is maintained")
+    print("   - All parameters are optional with sensible defaults")
+
+if __name__ == "__main__":
+    demo_enhanced_callbacks()
@@ -0,0 +1,35 @@
+#!/usr/bin/env python3
+"""Test LLM class directly to verify self-reflection fix"""
+
+from praisonaiagents.llm import LLM
+from praisonaiagents.tools import calculator
+
+def test_llm_direct():
+    """Test LLM class directly with self-reflection and tools"""
+    print("=== Testing LLM Direct with Self-Reflection and Tools ===")
+    
+    # Create LLM instance
+    llm = LLM(model="gpt-4o-mini")
+    
+    # Test with self-reflection and tools
+    response = llm.get_response(
+        prompt="Calculate 15 * 23 and verify your answer",
+        system_prompt="You are a helpful math assistant. Use tools when needed.",
+        tools=[calculator],
+        self_reflect=True,
+        min_reflect=1,
+        max_reflect=2,
+        verbose=True
+    )
+    
+    print(f"\nResponse: {response}")
+    
+    if response:
+        print("\n✅ SUCCESS: LLM self-reflection with tools is working!")
+        return True
+    else:
+        print("\n❌ FAILED: LLM self-reflection with tools is not working")
+        return False
+
+if __name__ == "__main__":
+    test_llm_direct()
@@ -0,0 +1,90 @@
+#!/usr/bin/env python3
+"""Test script to verify self-reflection works with tools after the fix"""
+
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import duckduckgo_search
+
+def test_self_reflection_with_tools():
+    """Test self-reflection with tools - should work after the fix"""
+    print("=== Testing Self-Reflection WITH Tools ===")
+    
+    # Create an agent with self-reflection and tools
+    agent = Agent(
+        role="Senior Research Analyst",
+        goal="Analyze and provide insights on given topics",
+        backstory="You are an expert analyst with strong critical thinking skills",
+        self_reflect=True,
+        llm="gemini/gemini-2.5-flash-lite-preview-06-17",
+        verbose=True,
+        tools=[duckduckgo_search]
+    )
+
+    # Create a task
+    task = Task(
+        description="Search for recent developments in AI and provide a brief analysis",
+        expected_output="A detailed analysis report",
+        agent=agent
+    )
+
+    # Create and start the agents
+    agents = PraisonAIAgents(
+        agents=[agent],
+        tasks=[task],
+        process="sequential"
+    )
+
+    # Start execution
+    result = agents.start()
+    print(f"Result with tools: {result}")
+    return result
+
+def test_self_reflection_without_tools():
+    """Test self-reflection without tools - should work (baseline)"""
+    print("\n=== Testing Self-Reflection WITHOUT Tools ===")
+    
+    # Create an agent with self-reflection but no tools
+    agent = Agent(
+        role="Senior Research Analyst",
+        goal="Analyze and provide insights on given topics",
+        backstory="You are an expert analyst with strong critical thinking skills",
+        self_reflect=True,
+        llm="gemini/gemini-2.5-flash-lite-preview-06-17",
+        verbose=True
+    )
+
+    # Create a task
+    task = Task(
+        description="Analyze recent developments in AI",
+        expected_output="A detailed analysis report",
+        agent=agent
+    )
+
+    # Create and start the agents
+    agents = PraisonAIAgents(
+        agents=[agent],
+        tasks=[task],
+        process="sequential"
+    )
+
+    # Start execution
+    result = agents.start()
+    print(f"Result without tools: {result}")
+    return result
+
+if __name__ == "__main__":
+    print("Testing self-reflection fix...")
+    
+    # Test without tools (should work)
+    result_without_tools = test_self_reflection_without_tools()
+    
+    # Test with tools (should work after fix)
+    result_with_tools = test_self_reflection_with_tools()
+    
+    print("\n=== Test Summary ===")
+    print(f"Without tools: {'SUCCESS' if result_without_tools else 'FAILED'}")
+    print(f"With tools: {'SUCCESS' if result_with_tools else 'FAILED'}")
+    
+    if result_with_tools:
+        print("\n✅ Fix verified: Self-reflection now works with tools!")
+    else:
+        print("\n❌ Fix failed: Self-reflection still not working with tools")
@@ -0,0 +1,36 @@
+#!/usr/bin/env python3
+"""Test LLM class directly to verify self-reflection fix"""
+
+from praisonaiagents.llm import LLM
+from praisonaiagents.tools import calculator
+
+def test_llm_direct():
+    """Test LLM class directly with self-reflection and tools"""
+    print("=== Testing LLM Direct with Self-Reflection and Tools ===")
+    
+    # Create LLM instance
+    llm = LLM(model="gpt-4o-mini")
+    
+    # Test with self-reflection and tools
+    try:
+        response = llm.get_response(
+            prompt="Calculate 15 * 23 and verify your answer",
+            system_prompt="You are a helpful math assistant. Use tools when needed.",
+            tools=[calculator],
+            self_reflect=True,
+            min_reflect=1,
+            max_reflect=2,
+            verbose=True
+        )
+        
+        print(f"\nResponse: {response}")
+        
+        assert response, "LLM self-reflection with tools failed to produce a response."
+        print("\n✅ SUCCESS: LLM self-reflection with tools is working!")
+        
+    except Exception as e:
+        print(f"\n❌ ERROR: {str(e)}")
+        raise AssertionError(f"Test failed with error: {str(e)}")
+
+if __name__ == "__main__":
+    test_llm_direct()
@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+"""Comprehensive test script to verify self-reflection works with tools after the fix"""
+
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import duckduckgo_search
+
+def test_self_reflection_with_tools():
+    """Test self-reflection with tools - should work after the fix"""
+    print("=== Testing Self-Reflection WITH Tools ===")
+    
+    # Create an agent with self-reflection and tools
+    agent = Agent(
+        role="Senior Research Analyst",
+        goal="Analyze and provide insights on given topics",
+        backstory="You are an expert analyst with strong critical thinking skills",
+        self_reflect=True,
+        llm="gemini/gemini-2.5-flash-lite-preview-06-17",
+        verbose=True,
+        tools=[duckduckgo_search]
+    )
+
+    # Create a task
+    task = Task(
+        description="Search for recent developments in AI and provide a brief analysis",
+        expected_output="A detailed analysis report",
+        agent=agent
+    )
+
+    # Create and start the agents
+    agents = PraisonAIAgents(
+        agents=[agent],
+        tasks=[task],
+        process="sequential"
+    )
+
+    try:
+        # Start execution
+        result = agents.start()
+        print(f"Result with tools: {result}")
+        
+        assert result, "Self-reflection with tools failed to produce a result."
+        print("\n✅ SUCCESS: Self-reflection with tools is working!")
+        return result
+        
+    except Exception as e:
+        print(f"\n❌ ERROR: {str(e)}")
+        raise AssertionError(f"Test with tools failed: {str(e)}")
+
+def test_self_reflection_without_tools():
+    """Test self-reflection without tools - should work (baseline)"""
+    print("\n=== Testing Self-Reflection WITHOUT Tools ===")
+    
+    # Create an agent with self-reflection but no tools
+    agent = Agent(
+        role="Senior Research Analyst",
+        goal="Analyze and provide insights on given topics",
+        backstory="You are an expert analyst with strong critical thinking skills",
+        self_reflect=True,
+        llm="gemini/gemini-2.5-flash-lite-preview-06-17",
+        verbose=True
+    )
+
+    # Create a task
+    task = Task(
+        description="Analyze recent developments in AI",
+        expected_output="A detailed analysis report",
+        agent=agent
+    )
+
+    # Create and start the agents
+    agents = PraisonAIAgents(
+        agents=[agent],
+        tasks=[task],
+        process="sequential"
+    )
+
+    try:
+        # Start execution
+        result = agents.start()
+        print(f"Result without tools: {result}")
+        
+        assert result, "Self-reflection without tools failed to produce a result."
+        print("\n✅ SUCCESS: Self-reflection without tools is working!")
+        return result
+        
+    except Exception as e:
+        print(f"\n❌ ERROR: {str(e)}")
+        raise AssertionError(f"Test without tools failed: {str(e)}")
+
+if __name__ == "__main__":
+    print("Testing self-reflection fix...")
+    
+    # Test without tools (should work)
+    try:
+        result_without_tools = test_self_reflection_without_tools()
+        without_tools_success = True
+    except Exception as e:
+        print(f"Test without tools failed: {e}")
+        without_tools_success = False
+    
+    # Test with tools (should work after fix)
+    try:
+        result_with_tools = test_self_reflection_with_tools()
+        with_tools_success = True
+    except Exception as e:
+        print(f"Test with tools failed: {e}")
+        with_tools_success = False
+    
+    print("\n=== Test Summary ===")
+    print(f"Without tools: {'SUCCESS' if without_tools_success else 'FAILED'}")
+    print(f"With tools: {'SUCCESS' if with_tools_success else 'FAILED'}")
+    
+    if with_tools_success:
+        print("\n✅ Fix verified: Self-reflection now works with tools!")
+    else:
+        print("\n❌ Fix failed: Self-reflection still not working with tools")
+        raise AssertionError("Self-reflection with tools test failed")
@@ -0,0 +1,37 @@
+#!/usr/bin/env python3
+"""Simple test to verify the self-reflection fix works"""
+
+from praisonaiagents import Agent
+from praisonaiagents.tools import calculator
+
+def test_self_reflection_fix():
+    """Test that self-reflection works with tools after the fix"""
+    print("=== Testing Self-Reflection Fix ===")
+    
+    # Create an agent with self-reflection and a simple tool
+    agent = Agent(
+        role="Math Assistant",
+        goal="Solve math problems accurately",
+        backstory="You are a helpful math assistant",
+        self_reflect=True,
+        llm="gpt-4o-mini",  # Use a more widely available model
+        verbose=True,
+        tools=[calculator],
+        min_reflect=1,
+        max_reflect=2
+    )
+
+    # Test with a simple calculation that might trigger self-reflection
+    try:
+        response = agent.start("What is 25 * 17? Show your work and double-check the answer.")
+        print(f"\nResponse: {response}")
+        
+        assert response, "Self-reflection with tools failed to produce a response."
+        print("\n✅ SUCCESS: Self-reflection with tools is working!")
+        
+    except Exception as e:
+        print(f"\n❌ ERROR: {str(e)}")
+        raise AssertionError(f"Test failed with error: {str(e)}")
+
+if __name__ == "__main__":
+    test_self_reflection_fix()
@@ -0,0 +1,51 @@
+#!/usr/bin/env python3
+"""Test to verify the self-reflection fix works with tools"""
+
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import calculator
+
+def test_self_reflection_fix_verification():
+    """Test that self-reflection now works with tools"""
+    print("=== Testing Self-Reflection Fix Verification ===")
+    
+    # Create an agent with self-reflection enabled and tools
+    agent = Agent(
+        name="MathAgent",
+        role="Math Assistant",
+        goal="Solve mathematical problems accurately",
+        backstory="You are a helpful math assistant who double-checks calculations",
+        tools=[calculator],
+        self_reflect=True,  # This should now work with tools
+        min_reflect=1,
+        max_reflect=2,
+        verbose=True
+    )
+
+    # Define a task that would benefit from self-reflection
+    task = Task(
+        description="Calculate 123 * 456 and verify the result is correct",
+        expected_output="The calculation result with verification",
+        agent=agent,
+        name="math_calculation"
+    )
+
+    # Create and run the agents
+    agents = PraisonAIAgents(
+        agents=[agent],
+        tasks=[task],
+        process="sequential"
+    )
+    
+    try:
+        result = agents.start()
+        
+        assert result, "Self-reflection with tools failed to produce a result."
+        print("\n✅ SUCCESS: Self-reflection with tools is working!")
+        print(f"Result: {result}")
+            
+    except Exception as e:
+        print(f"\n❌ ERROR: {str(e)}")
+        raise AssertionError(f"Test failed with error: {str(e)}")
+
+if __name__ == "__main__":
+    test_self_reflection_fix_verification()
@@ -2375,7 +2375,7 @@ wheels = [

 [[package]]
 name = "praisonaiagents"
-version = "0.0.130"
+version = "0.0.131"
 source = { editable = "." }
 dependencies = [
    { name = "aiohttp" },
@@ -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/v2.2.56.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.56.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.57.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.57.tar.gz | shasum -a 256`.split.first
    license "MIT"
  
    depends_on "[email protected]"
@@ -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==2.2.56 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.2.57 gunicorn markdown\n")
            file.write("EXPOSE 8080\n")
            file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
            
@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.2.56"
+version = "2.2.57"
 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>=0.0.130",
+    "praisonaiagents>=0.0.131",
    "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 = "2.2.56"
+version = "2.2.57"
 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 = ">=0.0.130"
+praisonaiagents = ">=0.0.131"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"
@@ -3932,7 +3932,7 @@ wheels = [

 [[package]]
 name = "praisonai"
-version = "2.2.56"
+version = "2.2.57"
 source = { editable = "." }
 dependencies = [
    { name = "instructor" },
@@ -4074,7 +4074,7 @@ 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 = ">=0.0.130" },
+    { name = "praisonaiagents", specifier = ">=0.0.131" },
    { 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" },
@@ -4132,7 +4132,7 @@ wheels = [

 [[package]]
 name = "praisonaiagents"
-version = "0.0.130"
+version = "0.0.131"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "aiohttp" },
@@ -4142,9 +4142,9 @@ dependencies = [
    { name = "pydantic" },
    { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/30/3a/e4f4bf6724051ada87c8f4a02615075b7c9b3963bdb37268d79f8a2bde08/praisonaiagents-0.0.130.tar.gz", hash = "sha256:0f893fdb65b68b489d7a9daf732a9c0c8eb2ce942d2c7b1c073d41e9496da239", size = 193079, upload-time = "2025-07-14T16:26:59.782Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/ac/a8/6c8b10f2254a5914a0c282e8691bea7f3fff60ea99d975db11e8d3382c9f/praisonaiagents-0.0.131.tar.gz", hash = "sha256:8b6ceef477f2c1d4e33af1242c52cf1956826066ef4d7ed3bc2dcd50ce1c68c8", size = 198480, upload-time = "2025-07-14T17:35:51.53Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/3b/8e/54f1858a001dbaf0a97b31406e06bfd47cbdf335b2ee509f591fcd4311de/praisonaiagents-0.0.130-py3-none-any.whl", hash = "sha256:c75983e55f9a0bd74905c28f7000acdd6d5516e519ce2aa5ef3e37048544ea6e", size = 214415, upload-time = "2025-07-14T16:26:58.109Z" },
+    { url = "https://files.pythonhosted.org/packages/bd/d7/c443377ac2eaf68154b4ae6f87e5bc681f80774f8af9fcb368454ea9a7d8/praisonaiagents-0.0.131-py3-none-any.whl", hash = "sha256:fac2cdfe78da0fea89a74d54aab2fa9e920d61ca8d08ccebeb58132fc3770dc8", size = 218389, upload-time = "2025-07-14T17:35:49.94Z" },
 ]

 [[package]]
Author	SHA1	Message	Date
Mervin Praison	000bdf3418	refactor: move test files to src/praisonai-agents and bump version to 2.2.57	2025-07-14 18:40:18 +01:00
Mervin Praison	812abec2eb	Merge pull request #897 from MervinPraison/claude/issue-896-20250714_104106 feat: enhance callbacks with agent and task context (fixes #896)	2025-07-14 18:27:25 +01:00
github-actions[bot]andMervin Praison	e58f6deb4b	fix: resolve critical runtime issues in callback enhancement - Fix undefined variables in llm.py response() and aresponse() methods by adding missing parameters to method signatures - Fix backward compatibility by adding signature checking to callbacks in display_interaction(), execute_callback(), and execute_sync_callback() - Ensure consistent parameter propagation in agent.py get_response calls - Legacy callbacks now work without TypeError, new callbacks get full context Co-authored-by: Mervin Praison <[email protected]>	2025-07-14 17:21:11 +00:00
Mervin Praison	f23680c121	Merge pull request #903 from MervinPraison/claude/issue-899-20250714_122107 fix: exclude response_format parameter for Gemini image models	2025-07-14 18:01:38 +01:00
Mervin Praison	e569400260	Merge pull request #902 from MervinPraison/claude/issue-901-20250714_140528 Issue #901: Changes from Claude	2025-07-14 17:57:58 +01:00
Mervin Praison	6249d14d3f	Merge pull request #895 from MervinPraison/claude/issue-893-20250714_084251 fix: resolve Ollama sequential tool calling redundant execution issue	2025-07-14 17:55:05 +01:00
github-actions[bot]andMervin Praison	fb4ecf1893	fix: Address reviewer feedback on self-reflection test files - Move test files from src/praisonai-agents/ to tests/ directory per repository guidelines - Replace print statements and return values with proper assert statements - Fix f-string issues without placeholders - Add comprehensive exception handling for test failures - Create improved test structure following repository patterns 🤖 Generated with [Claude Code](https://claude.ai/code) Co-authored-by: Mervin Praison <[email protected]>	2025-07-14 14:55:27 +00:00
github-actions[bot]andMervin Praison	5a4cbf8aeb	fix: improve robustness of Gemini model detection in ImageAgent - Use self.llm_model property instead of self.llm for more robust model name resolution - Handles all LLM configuration types (strings, dicts, custom instances, base_url configs) - Fixes potential AttributeError when self.llm is not set - Maintains backward compatibility for all existing configurations Co-authored-by: Mervin Praison <[email protected]>	2025-07-14 14:54:16 +00:00
github-actions[bot]andMervin Praison	885a73f697	fix: add intelligent final answer detection to async method Apply the same intelligent final answer detection logic that was added to the sync method (get_response) to the async method (get_response_async). This ensures consistent behavior between sync and async methods when detecting substantive final answers alongside tool calls. The fix prevents redundant tool execution loops in async contexts by checking if the LLM provides substantive content (>10 characters) after tool execution, breaking the loop when appropriate. Co-authored-by: Mervin Praison <[email protected]>	2025-07-14 14:48:03 +00:00
Mervin Praison	6d55cfa87e	Merge branch 'main' into claude/issue-896-20250714_104106	2025-07-14 15:46:44 +01:00
github-actions[bot]andMervin Praison	e858cebed9	fix: Enable self-reflection with tools in LLM class - Fixed issue where self-reflection was not working when tools were present - Added tools=formatted_tools parameter to completion calls during reflection regeneration - Self-reflection now maintains tool access throughout the reflection process - Backward compatibility maintained, no existing features removed - Resolves issue #901 🤖 Generated with [Claude Code](https://claude.ai/code) Co-authored-by: Mervin Praison <[email protected]>	2025-07-14 14:17:28 +00:00
github-actions[bot]andMervin Praison	4ffbc1b52a	fix: exclude response_format parameter for Gemini image models - Add detection logic to identify Gemini models (case-insensitive) - Exclude response_format parameter for Gemini models to prevent litellm.UnsupportedParamsError - Maintain backward compatibility for non-Gemini models - Fixes #899 Co-authored-by: Mervin Praison <[email protected]>	2025-07-14 12:24:35 +00:00
github-actions[bot]Mervin Praison Claude	2ef506fa8a	feat: enhance callbacks with agent and task context (fixes #896 ) - Add agent_name, agent_role, agent_tools, task_name, task_description, task_id to callbacks - Update display_interaction() and adisplay_interaction() to accept new parameters - Enhance LLM.get_response() to accept and pass task context - Modify Agent.chat() to accept task parameters - Update task execution in agents.py to pass task information - Maintain backward compatibility with existing callbacks - Add test scripts to verify functionality Co-authored-by: Mervin Praison <[email protected]> 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>	2025-07-14 10:53:46 +00:00
Mervin Praison	7ce783e59b	Merge branch 'main' into claude/issue-893-20250714_084251	2025-07-14 11:11:05 +01:00
github-actions[bot]andClaude	35250e429e	fix: clean up redundant conditionals and improve tool execution logic in llm.py - Remove redundant conditional expressions that were causing unnecessary repetition - Add intelligent final answer detection after tool execution - Prevent unnecessary tool execution loops when LLM provides substantive response 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>	2025-07-14 08:51:02 +00:00