memory-integration

Memory Integration Overview Use both memory systems to maintain context across sessions. Core principle: You have no memory between sessions. Use these tools to remember. Systems: Episodic Memory - Conversation history search Knowledge Graph (mcp__memory) - Structured facts and relationships When to Use Memory Moment Memory Action Session start Search for relevant context Before starting issue Search for previous work Making decision Check for past decisions Completing work Store important learnings Session end Store key outcomes Episodic Memory What It Stores Full conversation history Decisions made Problems solved Approaches tried Lessons learned Searching Episodic Memory Use the episodic-memory skill or MCP tools: Search for: - Issue number: "issue 123", "#123" - Feature name: "authentication", "user login" - Problem type: "TypeScript error", "build failure" - Project name: repository name Semantic Search (Single Query) // Search with natural language mcp__plugin_episodic - memory_episodic - memory__search ( { query : "user authentication implementation decisions" } ) Precise Search (Multiple Concepts) // Search for intersection of concepts mcp__plugin_episodic - memory_episodic - memory__search ( { query : [ "authentication" , "session" , "JWT" ] } ) Reading Full Conversations After finding relevant results: // Read the full conversation mcp__plugin_episodic - memory_episodic - memory__read ( { path : "/path/to/conversation.jsonl" } ) What to Search For Situation Search Terms Starting issue #123 "issue 123", "#123" Working on auth "authentication", "login", "session" TypeScript problem "TypeScript", "type error", specific error message Similar feature Feature name, related concepts Knowledge Graph (mcp__memory) What It Stores Entities (Projects, Issues, Decisions, Patterns) Relationships between entities Observations about entities Creating Entities Store important facts: // Create an entity for a project decision mcp__memory__create_entities ( { entities : [ { name : "Decision: Use JWT for Auth" , entityType : "Decision" , observations : [ "Decided on 2024-12-01" , "JWT chosen over sessions for API statelessness" , "Related to issue #123" , "Implementation in src/auth/jwt.ts" ] } ] } ) Creating Relationships Link entities together: // Create relationships mcp__memory__create_relations ( { relations : [ { from : "Project: MyApp" , to : "Decision: Use JWT for Auth" , relationType : "has_decision" } , { from : "Issue #123" , to : "Decision: Use JWT for Auth" , relationType : "resulted_in" } ] } ) Searching the Graph // Search for relevant nodes mcp__memory__search_nodes ( { query : "authentication" } ) // Open specific nodes mcp__memory__open_nodes ( { names : [ "Decision: Use JWT for Auth" ] } ) // Read entire graph (for small graphs) mcp__memory__read_graph ( { } ) Memory Protocol At Session Start Search episodic memory for: Current issue number Project/repository name Active feature being worked on Search knowledge graph for: Project entity Related decisions Known patterns Synthesize context before proceeding During Work Store as you go: Event Store In Major decision Knowledge graph entity Problem solved Add observation to issue entity Pattern discovered Knowledge graph entity Lesson learned Add observation At Session End Update knowledge graph with: New decisions made Problems solved Patterns discovered Add observations to existing entities: Progress on issues Learnings Next steps Entity Types Suggested entity types for the knowledge graph: Type Use For Example Project Repository/codebase "Project: MyApp" Issue GitHub issues "Issue #123: Auth" Decision Architectural decisions "Decision: Use JWT" Pattern Code patterns "Pattern: Repository Layer" Problem Known issues "Problem: Race Condition in X" Person Collaborators "Person: Alice (maintainer)" Example: Issue Memory Flow Session 1: Starting Issue // Search for any previous context const episodic = await search ( "issue 456 user profile" ) ; const graph = await search_nodes ( "user profile" ) ; // If nothing found, create fresh entity await create_entities ( { entities : [ { name : "Issue #456: User Profile Page" , entityType : "Issue" , observations : [ "Started: 2024-12-01" , "Scope: Profile display, edit, avatar upload" ] } ] } ) ; Session 1: Mid-Work Decision // Store a decision made await add_observations ( { observations : [ { entityName : "Issue #456: User Profile Page" , contents : [ "Decision: Using react-image-crop for avatar cropping" , "Reason: Best mobile support, active maintenance" ] } ] } ) ; Session 2: Resuming // Search for context const results = await search ( "issue 456" ) ; // Read: "Using react-image-crop for avatar cropping" // Continue with context maintained What to Store Always Store Architectural decisions with rationale Non-obvious problem solutions Important constraints discovered Dependencies between components Don't Store Trivial implementation details Things obvious from code Temporary debugging notes Speculation without conclusion Checklist At session start: Search episodic memory for issue/project Search knowledge graph for context Note relevant findings During work: Store major decisions Record problem solutions Note discovered patterns At session end: Update entities with progress Add new learnings Record next steps Integration This skill is called by: session-start - Initial context gathering issue-driven-development - Step 4 This skill supports: Cross-session continuity Decision documentation Pattern discovery