VoDaiLocz 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- Supersession: Automatically replacing outdated facts with new information to maintain "ground truth."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:
- Working Context (RAM): The immediate token window. Ephemeral, high-speed, and limited in capacity.
- Episodic Memory: Logged history of past interactions, enabling agents to query "what we discussed last time."
- Semantic Vector Store: Long-term storage for semantic concepts, documentation snippets, and project conventions, retrieved via similarity search.
- 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:
- Critical Path: Keep in active context if needed for the current turn.
- Supportive Memory: Fetch from vector store if the task requires historical context.
- Episodic Recall: Search past logs only when explicitly prompted or when a long-term pattern is requested.
- Avoid Bloat: Do not put long documents directly into the prompt; use
read_resourceor 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.