Release v4.6.44

Merge pull request #1704 from MervinPraison/claude/issue-1703-20260520-1507
fix: Agent.achat hanging - unified dispatch missing response handling
2026-11-10 11:41:32 -10:00 · 2026-05-20 20:45:21 +01:00 · 2026-05-20 20:43:31 +01:00 · 2026-05-20 19:41:18 +00:00 · 2026-05-20 19:04:14 +00:00 · 2026-05-20 16:21:45 +00:00
12 changed files with 291 additions and 64 deletions
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=4.6.43" \
+    "praisonai>=4.6.44" \
    "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>=4.6.43" \
+    "praisonai>=4.6.44" \
    "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>=4.6.43" \
+    "praisonai>=4.6.44" \
    "praisonai[ui]" \
    "praisonai[crewai]"

@@ -4616,60 +4616,57 @@ Answer:"""
                error=f"Agent guardrail validation error: {str(e)}"
            )

-    def _apply_guardrail_with_retry(self, response_text, prompt, temperature=1.0, tools=None, task_name=None, task_description=None, task_id=None):
-        """Apply guardrail validation with retry logic.
-        
-        Args:
-            response_text: The response to validate
-            prompt: Original prompt for regeneration if needed
-            temperature: Temperature for regeneration
-            tools: Tools for regeneration
-            
-        Returns:
-            str: The validated response text or None if validation fails after retries
-        """
+    def _validate_with_guardrail(self, response_text):
+        """Validate response with guardrail. Returns (success, result, error)."""
        if not self._guardrail_fn:
-            return response_text
+            return True, response_text, None
            
        from ..main import TaskOutput
        
+        task_output = TaskOutput(
+            description="Agent response output",
+            raw=response_text,
+            agent=self.name
+        )
+        
+        guardrail_result = self._process_guardrail(task_output)
+        
+        if guardrail_result.success:
+            # Return the potentially modified result
+            if guardrail_result.result and hasattr(guardrail_result.result, 'raw'):
+                return True, guardrail_result.result.raw, None
+            elif guardrail_result.result:
+                return True, str(guardrail_result.result), None
+            else:
+                return True, response_text, None
+        else:
+            return False, None, guardrail_result.error
+
+    def _apply_guardrail_with_retry(self, response_text, prompt, temperature=1.0, tools=None, task_name=None, task_description=None, task_id=None):
+        """Apply guardrail validation with retry logic (sync version)."""
        retry_count = 0
        current_response = response_text
        
        while retry_count <= self.max_guardrail_retries:
-            # Create TaskOutput object
-            task_output = TaskOutput(
-                description="Agent response output",
-                raw=current_response,
-                agent=self.name
-            )
+            success, result, error = self._validate_with_guardrail(current_response)
            
-            # Process guardrail
-            guardrail_result = self._process_guardrail(task_output)
-            
-            if guardrail_result.success:
+            if success:
                logging.info(f"Agent {self.name}: Guardrail validation passed")
-                # Return the potentially modified result
-                if guardrail_result.result and hasattr(guardrail_result.result, 'raw'):
-                    return guardrail_result.result.raw
-                elif guardrail_result.result:
-                    return str(guardrail_result.result)
-                else:
-                    return current_response
+                return result
            
            # Guardrail failed
            if retry_count >= self.max_guardrail_retries:
                raise Exception(
                    f"Agent {self.name} response failed guardrail validation after {self.max_guardrail_retries} retries. "
-                    f"Last error: {guardrail_result.error}"
+                    f"Last error: {error}"
                )
            
            retry_count += 1
-            logging.warning(f"Agent {self.name}: Guardrail validation failed (retry {retry_count}/{self.max_guardrail_retries}): {guardrail_result.error}")
+            logging.warning(f"Agent {self.name}: Guardrail validation failed (retry {retry_count}/{self.max_guardrail_retries}): {error}")
            
            # Regenerate response for retry
            try:
-                retry_prompt = f"{prompt}\n\nNote: Previous response failed validation due to: {guardrail_result.error}. Please provide an improved response."
+                retry_prompt = f"{prompt}\n\nNote: Previous response failed validation due to: {error}. Please provide an improved response."
                response = self._chat_completion([{"role": "user", "content": retry_prompt}], temperature, tools, task_name=task_name, task_description=task_description, task_id=task_id)
                if response and response.choices:
                    content = response.choices[0].message.content
@@ -4678,10 +4675,46 @@ Answer:"""
                    raise Exception("Failed to generate retry response")
            except Exception as e:
                logging.error(f"Agent {self.name}: Error during guardrail retry: {e}")
-                # If we can't regenerate, fail the guardrail
+                raise Exception(f"Agent {self.name} guardrail retry failed: {e}")
+        
+        return current_response
+
+    async def _aapply_guardrail_with_retry(self, response_text, prompt, temperature=1.0, tools=None, task_name=None, task_description=None, task_id=None):
+        """Apply guardrail validation with retry logic (async version)."""
+        retry_count = 0
+        current_response = response_text
+        
+        while retry_count <= self.max_guardrail_retries:
+            success, result, error = self._validate_with_guardrail(current_response)
+            
+            if success:
+                logging.info(f"Agent {self.name}: Guardrail validation passed")
+                return result
+            
+            # Guardrail failed
+            if retry_count >= self.max_guardrail_retries:
                raise Exception(
-                    f"Agent {self.name} guardrail retry failed: {e}"
+                    f"Agent {self.name} response failed guardrail validation after {self.max_guardrail_retries} retries. "
+                    f"Last error: {error}"
                )
+            
+            retry_count += 1
+            logging.warning(f"Agent {self.name}: Guardrail validation failed (retry {retry_count}/{self.max_guardrail_retries}): {error}")
+            
+            # Regenerate response for retry (async version)
+            try:
+                retry_prompt = f"{prompt}\n\nNote: Previous response failed validation due to: {error}. Please provide an improved response."
+                response = await self._execute_unified_achat_completion([{"role": "user", "content": retry_prompt}], temperature, tools, task_name=task_name, task_description=task_description, task_id=task_id)
+                if response and hasattr(response, 'choices') and response.choices:
+                    content = response.choices[0].message.content
+                    current_response = content.strip() if content else ""
+                elif isinstance(response, str):
+                    current_response = response.strip()
+                else:
+                    raise Exception("Failed to generate retry response")
+            except Exception as e:
+                logging.error(f"Agent {self.name}: Error during guardrail retry: {e}")
+                raise Exception(f"Agent {self.name} guardrail retry failed: {e}")
        
        return current_response
    
@@ -2079,7 +2079,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                    
                    # Apply guardrail validation for custom LLM response
                    try:
-                        validated_response = self._apply_guardrail_with_retry(response_text, prompt, temperature, tools, task_name, task_description, task_id)
+                        validated_response = await self._aapply_guardrail_with_retry(response_text, prompt, temperature, tools, task_name, task_description, task_id)
                        # Execute callback after validation
                        self._execute_callback_and_display(normalized_content, validated_response, time.time() - start_time, task_name, task_description, task_id)
                        return await self._atrigger_after_agent_hook(prompt, validated_response, start_time)
@@ -2187,22 +2187,216 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                            task_id=task_id,
                            response_format=response_format
                        )
-                        return await self._finalize_unified_achat_response(
-                            response=response,
-                            original_prompt=original_prompt,
-                            temperature=temperature,
-                            tools=tools,
-                            output_json=output_json,
-                            output_pydantic=output_pydantic,
-                            reasoning_steps=reasoning_steps,
-                            stream=stream,
-                            messages=messages,
-                            chat_history_length=chat_history_length,
-                            start_time=start_time,
-                            task_name=task_name,
-                            task_description=task_description,
-                            task_id=task_id,
-                        )
+                        
+                        # Process response - mirror sync _chat_impl behavior (lines ~1544-1602)
+                        if not response:
+                            # Rollback chat history on response failure
+                            self._truncate_chat_history(chat_history_length)
+                            return None
+
+                        # Extract response content using the same method as sync
+                        response_text = self._extract_llm_response_content(response)
+                        if isinstance(response_text, str):
+                            response_text = response_text.strip()
+
+                        # Handle output_json or output_pydantic if specified
+                        if output_json or output_pydantic:
+                            # Add to chat history and return raw response
+                            # User message already added before LLM call via _build_messages
+                            self._append_to_chat_history({"role": "assistant", "content": response_text})
+                            # Persist assistant message to DB
+                            self._persist_message("assistant", response_text)
+                            # Apply guardrail validation even for JSON output
+                            try:
+                                validated_response = await self._aapply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
+                                # Execute callback after validation
+                                self._execute_callback_and_display(original_prompt, validated_response, time.time() - start_time, task_name, task_description, task_id)
+                                return await self._atrigger_after_agent_hook(original_prompt, validated_response, start_time)
+                            except Exception as e:
+                                logging.error(f"Agent {self.name}: Guardrail validation failed for JSON output: {e}")
+                                # Rollback chat history on guardrail failure
+                                self._truncate_chat_history(chat_history_length)
+                                return None
+
+                        # For regular responses (no self-reflection)
+                        if not self.self_reflect:
+                            # User message already added before LLM call via _build_messages
+                            self._append_to_chat_history({"role": "assistant", "content": response_text})
+                            # Persist assistant message to DB (non-reflect path)
+                            self._persist_message("assistant", response_text)
+                            if self.verbose:
+                                logging.debug(f"Agent {self.name} final response: {response_text}")
+                            # Return only reasoning content if reasoning_steps is True
+                            if reasoning_steps and hasattr(response, 'choices') and response.choices and hasattr(response.choices[0].message, 'reasoning_content') and response.choices[0].message.reasoning_content:
+                                # Apply guardrail to reasoning content
+                                try:
+                                    validated_reasoning = await self._aapply_guardrail_with_retry(response.choices[0].message.reasoning_content, original_prompt, temperature, tools, task_name, task_description, task_id)
+                                    # Execute callback after validation
+                                    self._execute_callback_and_display(original_prompt, validated_reasoning, time.time() - start_time, task_name, task_description, task_id)
+                                    return await self._atrigger_after_agent_hook(original_prompt, validated_reasoning, start_time)
+                                except Exception as e:
+                                    logging.error(f"Agent {self.name}: Guardrail validation failed for reasoning content: {e}")
+                                    # Rollback chat history on guardrail failure
+                                    self._truncate_chat_history(chat_history_length)
+                                    return None
+                            else:
+                                # Apply guardrail to regular response content
+                                try:
+                                    validated_response = await self._aapply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
+                                    # Execute callback after validation
+                                    self._execute_callback_and_display(original_prompt, validated_response, time.time() - start_time, task_name, task_description, task_id)
+                                    return await self._atrigger_after_agent_hook(original_prompt, validated_response, start_time)
+                                except Exception as e:
+                                    logging.error(f"Agent {self.name}: Guardrail validation failed: {e}")
+                                    # Rollback chat history on guardrail failure
+                                    self._truncate_chat_history(chat_history_length)
+                                    return None
+                        
+                        # If self-reflection is enabled, implement reflection logic
+                        if self.self_reflect:
+                            # Implement async self-reflection similar to sync path
+                            reflection_prompt = f"""
+Reflect on your previous response: '{response_text}'.
+{self.reflect_prompt if self.reflect_prompt else "Identify any flaws, improvements, or actions."}
+Provide a "satisfactory" status ('yes' or 'no').
+Output MUST be JSON with 'reflection' and 'satisfactory'.
+                            """
+                            
+                            # Add reflection prompt to messages
+                            reflection_messages = messages + [
+                                {"role": "assistant", "content": response_text},
+                                {"role": "user", "content": reflection_prompt}
+                            ]
+                            
+                            reflection_count = 0
+                            
+                            while True:
+                                try:
+                                    # Check if OpenAI client is available for self-reflection
+                                    if self._openai_client is None:
+                                        # For custom LLMs, self-reflection with structured output is not supported
+                                        if self.verbose:
+                                            _get_display_functions()['display_self_reflection'](f"Agent {self.name}: Self-reflection with structured output is not supported for custom LLM providers. Skipping reflection.", console=self.console)
+                                        # Return the original response without reflection
+                                        self._append_to_chat_history({"role": "assistant", "content": response_text})
+                                        # Persist assistant message to DB
+                                        self._persist_message("assistant", response_text)
+                                        try:
+                                            validated_response = await self._aapply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
+                                            # Execute callback after validation
+                                            self._execute_callback_and_display(original_prompt, validated_response, time.time() - start_time, task_name, task_description, task_id)
+                                            return await self._atrigger_after_agent_hook(original_prompt, validated_response, start_time)
+                                        except Exception as e:
+                                            logging.error(f"Agent {self.name}: Guardrail validation failed: {e}")
+                                            # Rollback chat history on guardrail failure
+                                            self._truncate_chat_history(chat_history_length)
+                                            return None
+                                    
+                                    reflection_response = await self._openai_client.async_client.beta.chat.completions.parse(
+                                        model=self.reflect_llm if self.reflect_llm else self.llm,
+                                        messages=reflection_messages,
+                                        temperature=temperature,
+                                        response_format=_get_display_functions()['ReflectionOutput']
+                                    )
+                                    
+                                    reflection_output = reflection_response.choices[0].message.parsed
+                                    
+                                    if self.verbose:
+                                        _get_display_functions()['display_self_reflection'](f"Agent {self.name} self reflection (using {self.reflect_llm if self.reflect_llm else self.llm}): reflection='{reflection_output.reflection}' satisfactory='{reflection_output.satisfactory}'", console=self.console)
+                                    
+                                    # Only consider satisfactory after minimum reflections
+                                    if reflection_output.satisfactory == "yes" and reflection_count >= self.min_reflect - 1:
+                                        if self.verbose:
+                                            _get_display_functions()['display_self_reflection']("Agent marked the response as satisfactory after meeting minimum reflections", console=self.console)
+                                        # Add to chat history and return
+                                        self._append_to_chat_history({"role": "assistant", "content": response_text})
+                                        # Persist assistant message to DB
+                                        self._persist_message("assistant", response_text)
+                                        try:
+                                            validated_response = await self._aapply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
+                                            # Execute callback after validation
+                                            self._execute_callback_and_display(original_prompt, validated_response, time.time() - start_time, task_name, task_description, task_id)
+                                            return await self._atrigger_after_agent_hook(original_prompt, validated_response, start_time)
+                                        except Exception as e:
+                                            logging.error(f"Agent {self.name}: Guardrail validation failed after reflection: {e}")
+                                            # Rollback chat history on guardrail failure
+                                            self._truncate_chat_history(chat_history_length)
+                                            return None
+                                    
+                                    # Check if we've hit max reflections
+                                    if reflection_count >= self.max_reflect - 1:
+                                        if self.verbose:
+                                            _get_display_functions()['display_self_reflection']("Maximum reflection count reached, returning current response", console=self.console)
+                                        # Add to chat history and return
+                                        self._append_to_chat_history({"role": "assistant", "content": response_text})
+                                        # Persist assistant message to DB
+                                        self._persist_message("assistant", response_text)
+                                        try:
+                                            validated_response = await self._aapply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
+                                            # Execute callback after validation
+                                            self._execute_callback_and_display(original_prompt, validated_response, time.time() - start_time, task_name, task_description, task_id)
+                                            return await self._atrigger_after_agent_hook(original_prompt, validated_response, start_time)
+                                        except Exception as e:
+                                            logging.error(f"Agent {self.name}: Guardrail validation failed after max reflections: {e}")
+                                            # Rollback chat history on guardrail failure
+                                            self._truncate_chat_history(chat_history_length)
+                                            return None
+                                    
+                                    # Regenerate response based on reflection
+                                    regenerate_messages = reflection_messages + [
+                                        {"role": "assistant", "content": f"Self Reflection: {reflection_output.reflection} Satisfactory?: {reflection_output.satisfactory}"},
+                                        {"role": "user", "content": "Now regenerate your response using the reflection you made"}
+                                    ]
+                                    
+                                    new_response = await self._execute_unified_achat_completion(
+                                        messages=regenerate_messages,
+                                        temperature=temperature,
+                                        tools=formatted_tools,
+                                        stream=stream,
+                                        reasoning_steps=reasoning_steps,
+                                        task_name=task_name,
+                                        task_description=task_description,
+                                        task_id=task_id,
+                                        response_format=response_format
+                                    )
+                                    
+                                    if new_response:
+                                        new_response_text = self._extract_llm_response_content(new_response)
+                                        if isinstance(new_response_text, str):
+                                            response_text = new_response_text.strip()
+                                        # Update reflection_messages to include the new response for next iteration
+                                        reflection_messages = regenerate_messages + [
+                                            {"role": "assistant", "content": response_text}
+                                        ]
+                                    
+                                    reflection_count += 1
+                                    
+                                except Exception as e:
+                                    if self.verbose:
+                                        _get_display_functions()['display_error'](f"Error in parsing self-reflection json {e}. Retrying", console=self.console)
+                                    logging.error("Reflection parsing failed.", exc_info=True)
+                                    reflection_count += 1
+                                    if reflection_count >= self.max_reflect:
+                                        # Return original response after max reflection attempts
+                                        self._append_to_chat_history({"role": "assistant", "content": response_text})
+                                        # Persist assistant message to DB
+                                        self._persist_message("assistant", response_text)
+                                        try:
+                                            validated_response = await self._aapply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
+                                            # Execute callback after validation
+                                            self._execute_callback_and_display(original_prompt, validated_response, time.time() - start_time, task_name, task_description, task_id)
+                                            return await self._atrigger_after_agent_hook(original_prompt, validated_response, start_time)
+                                        except Exception as guard_e:
+                                            logging.error(f"Agent {self.name}: Guardrail validation failed after reflection error: {guard_e}")
+                                            # Rollback chat history on guardrail failure
+                                            self._truncate_chat_history(chat_history_length)
+                                            return None
+                                    continue
+                        
+                        # This should never be reached due to the returns above
+                        # But adding as safety fallback
+                        logging.warning(f"Agent {self.name}: Unexpected code path reached in unified dispatch")
+                        return None
                    else:
                        # LEGACY: Check if OpenAI client is available
                        if self._openai_client is None:
@@ -2339,7 +2533,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                        
                        # Apply guardrail validation for OpenAI client response
                        try:
-                            validated_response = self._apply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
+                            validated_response = await self._aapply_guardrail_with_retry(response_text, original_prompt, temperature, tools, task_name, task_description, task_id)
                            # Execute callback after validation
                            self._execute_callback_and_display(original_prompt, validated_response, time.time() - start_time, task_name, task_description, task_id)
                            return await self._atrigger_after_agent_hook(original_prompt, validated_response, start_time)
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "praisonaiagents"
-version = "1.6.43"
+version = "1.6.44"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 readme = "README.md"
 requires-python = ">=3.10"
@@ -3125,7 +3125,7 @@ wheels = [

 [[package]]
 name = "praisonaiagents"
-version = "1.6.43"
+version = "1.6.44"
 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/v4.6.43.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v4.6.43.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v4.6.44.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v4.6.44.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==4.6.43 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==4.6.44 gunicorn markdown\n")
            file.write("EXPOSE 8080\n")
            file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
            
@@ -1 +1 @@
-__version__ = "4.6.43"
+__version__ = "4.6.44"
@@ -12,7 +12,7 @@ dependencies = [
    "rich>=13.7",
    "markdown>=3.5",
    "pyparsing>=3.0.0",
-    "praisonaiagents>=1.6.43",
+    "praisonaiagents>=1.6.44",
    "python-dotenv>=0.19.0",
    "litellm>=1.83.14,<2",
    "PyYAML>=6.0",
@@ -4983,7 +4983,7 @@ wheels = [

 [[package]]
 name = "praisonaiagents"
-version = "1.6.43"
+version = "1.6.44"
 source = { directory = "../praisonai-agents" }
 dependencies = [
    { name = "aiohttp" },
Author	SHA1	Message	Date
praison	13750a4ccd	Release v4.6.44	2026-05-20 20:45:21 +01:00
Mervin Praison	c9e1d1a202	Merge pull request #1704 from MervinPraison/claude/issue-1703-20260520-1507 fix: Agent.achat hanging - unified dispatch missing response handling	2026-05-20 20:43:31 +01:00
praisonai-triage-agent[bot]andMervin Praison	5515290329	refactor: extract shared guardrail validation logic to reduce code duplication - Extract _validate_with_guardrail() method containing common validation logic - Simplified _apply_guardrail_with_retry() and _aapply_guardrail_with_retry() to use shared validation - Reduced code duplication from ~126 lines to ~25 lines of shared core logic - Maintains identical functionality while improving maintainability 🤖 Generated with [Claude Code](https://claude.ai/code) Co-authored-by: Mervin Praison <[email protected]>	2026-05-20 19:41:18 +00:00
praisonai-triage-agent[bot]andMervin Praison	cb1cefd94b	fix: resolve async guardrail retry bug and improve response handling - Fixed critical bug in _aapply_guardrail_with_retry line 4732 - Changed await self._achat_completion to await self._execute_unified_achat_completion - _achat_completion expects response object, not message list - Added response type safety guards (hasattr checks, isinstance fallbacks) - Prevents AttributeError crashes during async guardrail retries - Maintains both sync and async guardrail methods for architectural clarity 🤖 Generated with [Claude Code](https://claude.ai/code) Co-authored-by: Mervin Praison <[email protected]>	2026-05-20 19:04:14 +00:00
praisonai-triage-agent[bot]andMervin Praison	a90666a19f	fix: resolve async guardrails and response.choices access issues in unified dispatch - Add proper hasattr guards for response.choices access to prevent crashes when dispatcher returns plain strings - Fix stale reflection context by properly updating reflection_messages with new responses - Create async _aapply_guardrail_with_retry method to prevent event loop blocking - Update unified async dispatch path to use async guardrails while preserving sync guardrails in sync paths - Resolves critical issues identified in Greptile review while maintaining backward compatibility Co-authored-by: Mervin Praison <[email protected]>	2026-05-20 16:21:45 +00:00
Mervin Praison	89df701c2a	Merge branch 'main' into claude/issue-1703-20260520-1507	2026-05-20 16:51:34 +01:00
praisonai-triage-agent[bot]andMervin Praison	480848c26d	fix: implement async self-reflection in unified dispatch to prevent infinite loop - Add complete self-reflection logic to unified achat dispatch path - Prevent infinite loop when self.self_reflect=True with unified dispatch - Mirror sync implementation with proper async/await patterns - Handle OpenAI client unavailability gracefully for custom LLMs - Add proper return statements to break while loop - Maintain reflection limits (min_reflect, max_reflect) - Include comprehensive error handling and retry logic Fixes infinite hanging issue for achat with self-reflection enabled. Co-authored-by: Mervin Praison <[email protected]>	2026-05-20 15:33:16 +00:00
praisonai-triage-agent[bot]andMervinPraison	a064a286f3	fix: Agent.achat hanging - unified dispatch missing response handling (fixes #1703 ) - Fix execute_tool_async issue in _execute_unified_achat_completion - Add proper response processing after unified dispatch call - Mirror sync _chat_impl behavior for content extraction, history update - Add guardrails, callbacks, and proper return to exit while loop - Prevents infinite loop by handling response and returning result Co-authored-by: MervinPraison <[email protected]>	2026-05-20 15:11:24 +00:00