Issues with Session Summaries when using it with Workflow

[arrafique]

Hi,
I’ve been encountering a couple of core functionality issues with the Agno Framework, and this one is among them.

      session_id = "test_session_004"
      user_id = "test_user_001"
      query = input("Enter:")
      agent = Agent(
          model= AIModel.get_model(), # GPT-OSS Model
          db=AgnoDB.get_storage(), # MongoDB
          description="You are an assistant. Respond in very brief sentences. Mostly in one line",
          user_id=user_id,
          session_id=session_id,
          num_history_runs=1,
          enable_session_summaries=True
      )
      workflow = Workflow(
          user_id=user_id,
          session_id=session_id,
          steps=[Step(agent=agent)],
          add_workflow_history_to_steps=True,
          db=AgnoDB.get_storage()
      )
      response = workflow.run(query)
      print(f"BOT: {response.content}")

DEBUG (1st Message)

DEBUG ****** Agent ID: 28c0ab95-1626-4f88-9ed8-964b0826ed48 ******              
DEBUG Creating new AgentSession: test_session_004                               
DEBUG ** Agent Run Start: 3c671a9c-191c-45e3-af98-1b177be31f57 ***              
DEBUG ------------------ OpenAI Response Start -------------------              
DEBUG ---------------- Model: openai/gpt-oss-120b ----------------              
DEBUG ========================== system ==========================              
DEBUG You are an assistant. Respond in very brief sentences. Mostly in one line 
DEBUG =========================== user ===========================              
DEBUG My name is Rafique                                                        
DEBUG ======================== assistant =========================              
DEBUG <reasoning>                                                               
      The user says "My name is Rafique". The system says respond in very brief 
      sentences, mostly one line. Probably acknowledge.                         
      </reasoning>                                                              
DEBUG Nice to meet you, Rafique.                                                
DEBUG ************************  METRICS  *************************              
DEBUG * Tokens:                      input=93, output=44, total=137             
DEBUG * Duration:                    1.0153s                                    
DEBUG * Tokens per second:           43.3351 tokens/s                           
DEBUG ************************  METRICS  *************************              
DEBUG ------------------- OpenAI Response End --------------------              
DEBUG Added RunOutput to Agent Session                                          
DEBUG ***************** Creating session summary *****************              
DEBUG ------------------ OpenAI Response Start -------------------              
DEBUG ---------------- Model: openai/gpt-oss-120b ----------------              
DEBUG ========================== system ==========================              
DEBUG Analyze the following conversation between a user and an assistant, and   
      extract the following details:                                            
      - Summary (str): Provide a concise summary of the session, focusing on    
      important information that would be helpful for future interactions.      
      - Topics (Optional[List[str]]): List the topics discussed in the session. 
      Keep the summary concise and to the point. Only include relevant          
      information.                                                              
      <conversation>User: My name is Rafique                                    
      Assistant: Nice to meet you, Rafique.                                     
      </conversation>                                                           
DEBUG =========================== user ===========================              
DEBUG Provide the summary of the conversation.                                  
DEBUG ======================== assistant =========================              
DEBUG <reasoning>                                                               
      We have a system prompt to "Analyze the following conversation between a  
      user and an assistant...". The user asks "Provide the summary of the      
      conversation." We must output JSON? The instruction says: "Provide a      
      concise summary of the session, focusing on important information... Keep 
      the summary concise and to the point. Only include relevant information." 
      It expects a structure:                                                   
      - Summary (str)                                                           
      - Topics (Optional[List[str]])                                            
                                                                                
      Probably JSON format. Many prior similar tasks require output like:       
      {                                                                         
        "Summary": "...",                                                       
        "Topics": [...]                                                         
      }                                                                         
      Thus we give summary: "User introduced themselves as Rafique, assistant   
      greeted them." Topics: maybe ["self-introduction", "greeting"]. Provide   
      concise.                                                                  
                                                                                
      Let's output JSON.                                                        
      </reasoning>                                                              
DEBUG {                                                                         
        "summary": "User introduced themselves as Rafique; the assistant        
      responded with a greeting.",                                              
        "topics": ["self-introduction", "greeting"]                             
      }                                                                         
DEBUG ************************  METRICS  *************************              
DEBUG * Tokens:                      input=175, output=190, total=365           
DEBUG * Duration:                    1.5015s                                    
DEBUG * Tokens per second:           126.5435 tokens/s                          
DEBUG ************************  METRICS  *************************              
DEBUG ------------------- OpenAI Response End --------------------              
DEBUG ***************** Session summary created ******************              
DEBUG Added RunOutput to Agent Session                                          
DEBUG *** Agent Run End: 3c671a9c-191c-45e3-af98-1b177be31f57 ****              
BOT: Nice to meet you, Rafique.

DEBUG (Last Message)

DEBUG ****** Agent ID: 451c5146-f659-431f-b5e0-3f094b2e02d3 ******              
DEBUG Creating new AgentSession: test_session_004                               
DEBUG ** Agent Run Start: 48df1b0c-d006-4578-a7e7-a07d7416648f ***              
DEBUG ------------------ OpenAI Response Start -------------------              
DEBUG ---------------- Model: openai/gpt-oss-120b ----------------              
DEBUG ========================== system ==========================              
DEBUG You are an assistant. Respond in very brief sentences. Mostly in one line 
DEBUG =========================== user ===========================              
DEBUG What's my name?                                                           
DEBUG ======================== assistant =========================              
DEBUG <reasoning>                                                               
      We can't answer because we don't know name; must be brief.                
      </reasoning>                                                              
DEBUG I don’t have that information.                                            
DEBUG ************************  METRICS  *************************              
DEBUG * Tokens:                      input=92, output=30, total=122             
DEBUG * Duration:                    0.8022s                                    
DEBUG * Tokens per second:           37.3979 tokens/s                           
DEBUG ************************  METRICS  *************************              
DEBUG ------------------- OpenAI Response End --------------------              
DEBUG Added RunOutput to Agent Session                                          
DEBUG ***************** Creating session summary *****************              
DEBUG ------------------ OpenAI Response Start -------------------              
DEBUG ---------------- Model: openai/gpt-oss-120b ----------------              
DEBUG ========================== system ==========================              
DEBUG Analyze the following conversation between a user and an assistant, and   
      extract the following details:                                            
      - Summary (str): Provide a concise summary of the session, focusing on    
      important information that would be helpful for future interactions.      
      - Topics (Optional[List[str]]): List the topics discussed in the session. 
      Keep the summary concise and to the point. Only include relevant          
      information.                                                              
      <conversation>User: What's my name?                                       
      Assistant: I don’t have that information.                                 
      </conversation>                                                           
DEBUG =========================== user ===========================              
DEBUG Provide the summary of the conversation.                                  
DEBUG ======================== assistant =========================              
DEBUG <reasoning>                                                               
      The user asks to provide summary of conversation. We need to output JSON  
      with fields Summary and Topics optional. The convo: User asked "What's my 
      name?" Assistant responded "I don’t have that information." So summary:   
      user asked for their name, assistant said doesn't have that info. Topics: 
      maybe "personal identification" or "name". Provide concise.               
      </reasoning>                                                              
DEBUG {                                                                         
        "summary": "The user asked for their name, and the assistant responded  
      that it does not have that information.",                                 
        "topics": ["personal identification", "name inquiry"]                   
      }                                                                         
DEBUG ************************  METRICS  *************************              
DEBUG * Tokens:                      input=172, output=119, total=291           
DEBUG * Duration:                    1.1865s                                    
DEBUG * Tokens per second:           100.2932 tokens/s                          
DEBUG ************************  METRICS  *************************              
DEBUG ------------------- OpenAI Response End --------------------              
DEBUG ***************** Session summary created ******************              
DEBUG Added RunOutput to Agent Session                                          
DEBUG *** Agent Run End: 48df1b0c-d006-4578-a7e7-a07d7416648f ****              
BOT: I don’t have that information.

SCREENSHOT:
Without Workflow:

With Workflow:

ISSUE Description:
The session summaries work fine when used with a standalone agent. However, when we try the same within a workflow, the summarization doesn’t get updated in the database — even though the LLM performs the summarization (verified through debug logs).

This is critical because, in a workflow, summarization serves as the backbone for maintaining continuity and deriving context. Without it, we cannot inject large chat histories to retain the initial entity context.

Note: This use case is different from user memory. I want the workflow itself to retain awareness of entity context.
Any ideas or workarounds?

PS: Documentations are good but not good enough. You should really update the documentation to cover the role of attributes and usage. For instance, I never knew that i have to enable add_workflow_history_to_steps to enable the history for the "Agents", i thought add_history_to_context is enough to take care of it as i don't need history for all the steps.