---

name: "agent-memory" description: >- Use when implementing or managing persistent, hierarchical memory systems for AI agents. Covers cross-session state, fact supersession, and self-managed memory tools to enable long-term recall and adaptive agent behavior.

Agent Memory Framework

Overview

The agent-memory skill provides a standardized architectural approach to building intelligent memory systems for agents within the KILO-KIT ecosystem. It bridges the gap between ephemeral context windows and durable, long-term storage, enabling agents to maintain user preferences, project-specific conventions, and debugging history across multiple sessions.

When To Use

• When designing systems that must persist state across independent interaction sessions.
• When an agent needs to manage large volumes of user-specific facts that exceed the context window.
• When implementing self-managed memory tools (MemGPT/Letta patterns) to allow agents to control their own knowledge base.
• When building systems requiring automatic entity updates (Mem0 pattern) to resolve conflicting or stale information.

Core Concepts

• Memory Hierarchy: Differentiating between transient working context and persistent knowledge.
• Fact Extraction: Identifying core entities, preferences, and relationships from conversational flow.
• Supersession: Automatically replacing outdated facts with new information to maintain "ground truth."
• Temporal Validity: Tracking the lifespan and relevance of memory entries over time.
• Persistence: Ensuring data survives agent resets or session termination.

Memory Architecture

The framework defines four distinct tiers of memory:

1. Working Context (RAM): The immediate token window. Ephemeral, high-speed, and limited in capacity.
2. Episodic Memory: Logged history of past interactions, enabling agents to query "what we discussed last time."
3. Semantic Vector Store: Long-term storage for semantic concepts, documentation snippets, and project conventions, retrieved via similarity search.
4. Archival Storage (Disk): Cold storage for large documents or historical artifacts that are rarely needed but must be maintained.

Implementation Patterns

Fact Extraction & Supersession (Mem0 Pattern)

• Implement extraction loops that analyze messages for key-value pairs (e.g., user_preference: dark_mode).
• When a new fact conflicts with an old one, perform an "update" (supersession) rather than appending duplicates. This ensures the agent always acts on the most recent truth.

Temporal Validity Windows (Zep/Graphiti Pattern)

• Annotate memory items with timestamps and TTL (Time-To-Live).
• Implement background cleanup processes to purge or archive expired or invalidated information based on these windows.

Self-Managed Memory Tools (Letta/MemGPT Pattern)

• Equip agents with dedicated function calls:
  • memory_write(key, value, importance)
  • memory_read(query)
  • memory_archive(id)
• Empower the agent to decide when to offload items from the working context to memory storage.

CRUD Operations

• Create: Automatically extract new knowledge from successful interactions and persist to storage.
• Read: Perform semantic search across the vector store and episodic history to augment the current context.
• Update: Supersede stale facts with verified new information to prevent hallucinations or conflicting behavior.
• Delete/Archive: Offload deprecated or low-value information to archival storage to maintain high-signal density in memory.

Context Window Management

Use the following heuristic for resource allocation:

1. Critical Path: Keep in active context if needed for the current turn.
2. Supportive Memory: Fetch from vector store if the task requires historical context.
3. Episodic Recall: Search past logs only when explicitly prompted or when a long-term pattern is requested.
4. Avoid Bloat: Do not put long documents directly into the prompt; use read_resource or semantic retrieval instead.

Quality Gates

• Consistency Check: Run automated verification to detect conflicting memory entries.
• Persistence Test: Verify that user-defined preferences are available after agent restart.
• Signal-to-Noise Ratio: Periodically audit the memory store to remove redundant, outdated, or low-relevance facts.
• Latency Monitoring: Ensure that retrieving from memory does not introduce unacceptable delays in response time.

References

• Mem0 Documentation
• Letta / MemGPT Architecture
• Zep Memory Framework
• KILO-KIT Internal Documentation
• Graphiti Memory Patterns