| name | semantic-memory-fabric |
| description | Store, retrieve, and evolve contextual and procedural knowledge using vector search, memory layers, and active forgetting. |
Skill: Semantic Memory Fabric
Category: Agent Memory
Priority: High
Description
This skill extends DevinOS memory beyond simple file-based entries into a semantic memory fabric. It stores contextual, procedural, and semantic knowledge in a searchable, layered structure, and supports active forgetting, self-correction, and retrieval-augmented generation.
Purpose
To give DevinOS a rich, long-term memory that can recall relevant experiences, apply knowledge across new contexts, and correct outdated memories without human intervention.
Trigger
Use this skill when:
- A task requires recalling similar past projects or decisions.
- You need to search across many memory entries by meaning rather than keyword.
- Long-term memory is growing and needs pruning or correction.
- You are building a retrieval-augmented workflow for complex tasks.
Context
- Existing memory entries from
memory_manager.py.
- Vector database or embedding provider.
- Categorization scheme for memory layers (episodic, semantic, procedural).
- Policy for active forgetting and memory correction.
- Privacy and retention rules.
Workflow
- Ingest memories. Read existing memory entries and convert them into structured, embedded documents.
- Classify layers. Tag each memory as episodic (what happened), semantic (what it means), or procedural (how to do it).
- Embed and store. Generate embeddings and store them in a vector database with metadata.
- Retrieve semantically. At task start, query the fabric for relevant memories based on meaning, not just keywords.
- Apply context. Use retrieved memories to inform decisions, avoid past mistakes, and reuse proven patterns.
- Correct and forget. Periodically review memory for contradictions, outdated facts, or low-value entries. Update or remove them.
- Learn continuously. After each task, add new learnings and link them to related skills and rules.
Examples
Good: Reuse a proven pattern
Scenario: The agent is starting a new project that needs authentication.
Agent Action:
- Queries the semantic memory for "authentication patterns."
- Finds a previous memory about using OAuth2 + PKCE for SPAs.
- Reuses the proven pattern and updates the project's architecture.
Bad: Recall irrelevant old memories
Scenario: The agent retrieves every memory about "React" because the current task mentions React Native.
Agent Action:
- Uses semantic search to distinguish React DOM from React Native.
- Filters results by relevance score and metadata tags.
Anti-patterns
- Storing every minor detail without classification.
- Retrieving memories without relevance scoring.
- Never pruning or correcting outdated memory.
- Mixing sensitive and public memories without access controls.
- Relying on memory without verifying it against the current context.
Verification
Cross Skill References
- Knowledge Distillation: Extracting and generalizing lessons for memory.
- Memory Manager: Basic file-based memory entry management.
- Knowledge Graph Integration: Connecting memories as a graph of relationships.
- AI Engineering: Retrieval-augmented generation and embedding workflows.
References
