Cross-Session Memory: Injecting Past Session Summaries into System Prompts | Alpha | PandaiTech

Cross-Session Memory: Injecting Past Session Summaries into System Prompts

Techniques to give your AI agents long-term memory by dynamically injecting summaries from previous sessions into the system prompt of a new session.

Learning Timeline
Key Insights

Token Saving Tips

Avoid including the entire raw conversation history. Use AI to generate a 'Condensed Summary' so you don't waste your Token Limit (Context Window) in new sessions.

Privacy & Security Warning

Ensure all data stored in your database is secure. Never inject sensitive information like passwords or Personally Identifiable Information (PII) directly into the System Prompt if your agent is accessible to the public.

Memory 'Freshness' Strategy

If a user interacts frequently, ensure you only fetch the 3-5 most recent summaries. This prevents the System Prompt from becoming bloated, which can lead to AI hallucinations or confusion.
Prompts

Prompt for Generating a Session Summary (Session Summarizer)

Target: ChatGPT / Claude
Summarize the key points, user preferences, and unresolved tasks from the conversation below. Format the output as a concise internal note for an AI assistant to remember in the next session. Conversation Transcript: [INSERT_TRANSCRIPT_HERE] Summary:

System Prompt Structure with Past Memory

Target: System Message / System Prompt
You are a helpful AI assistant. To provide a personalized experience, here is a summary of your previous interactions with this user: <session_memory> {{session_summary}} </session_memory> Use this information to maintain continuity, but prioritize the user's current requests.
Step by Step

How to Implement Cross-Session Memory in an AI Agent

  1. Open the Dashboard of your preferred AI Agent or LLM orchestration platform.
  2. Access the 'System Prompt' or 'System Message' section in the agent configuration.
  3. Create a dynamic placeholder within the System Prompt named {{session_summary}}.
  4. Configure a database integration (such as Supabase or Pinecone) to store conversation history.
  5. Set up a 'Post-Processing Step' to run after each session ends to generate a summary using AI.
  6. Save that summary into the database, mapped to the specific User ID or Session ID.
  7. Use an API Call or Logic Hook to fetch the latest summary from the database before a new session begins.
  8. Inject the retrieved summary data into the {{session_summary}} placeholder in the System Prompt.
  9. Click 'Save' or 'Deploy' to activate the long-term memory functionality.

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