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agentic-ai-patterns

Memory-Augmented Agent

Persistent long-term memory across sessions

The agent loads memories from a JSON store at the start of each session, retrieves only the relevant ones, incorporates them into its response, then consolidates new learnings before exiting.

flowchart TD
    S([__start__]) --> L[load_memory]
    L --> RV[retrieve_relevant]
    RV --> RS[respond]
    RS --> C[consolidate]
    C --> E([__end__])

Memory-Augmented agents maintain continuity across conversations by persisting knowledge beyond a single session. At startup, the agent loads all memories from a JSON file and runs a relevance filter — using an LLM to select which memories are pertinent to the current question. This prevents context bloat from irrelevant history.

Three memory types reflect different kinds of knowledge: **episodic** memories (past interactions and their outcomes), **semantic** memories (facts learned about the domain), and **procedural** memories (learned strategies and preferences). Each type is tagged so the retrieval filter can reason about relevance by type.

After responding, the `consolidate` node extracts new learnings from the interaction and appends them to the persistent store with a timestamp. Over time, the agent becomes increasingly personalized and effective for its specific user and domain — building genuine long-term memory.