Memory System Specification

Document ID: AGENT-003
File Path: docs/04-agent-framework/memory-system.md
Version: 1.0.0
Status: Draft
Owner: AI Platform Team
Last Updated: 2026-06-26

---

1. Purpose

The Memory System provides persistent and transient memory capabilities for AI agents executing within the Wovyr AI Platform.

Unlike traditional chat history, the Memory System enables agents to remember information across:

• Conversations
• Workflow executions
• Projects
• Organizations
• Teams
• Long-running tasks

The Memory System transforms AI agents from stateless assistants into continuously learning collaborators.

---

2. Objectives

The Memory System shall provide:

• Persistent memory
• Short-term memory
• Long-term memory
• Semantic retrieval
• Context compression
• Vector search
• Knowledge graph integration
• Versioned memories
• Tenant isolation
• Memory sharing
• Automatic summarization

---

3. Design Principles

1. Memory is independent of the LLM.
2. Every memory is versioned.
3. Memory retrieval is deterministic.
4. Memories are searchable.
5. Memories have configurable retention.
6. Memory access is permission-controlled.
7. Memory supports replay and auditing.

---

4. High-Level Architecture

                    Agent Runtime
                         │
                         ▼
                  Memory Manager
                         │
        ┌────────────────┼─────────────────┐
        ▼                ▼                 ▼
 Working Memory   Episodic Memory   Semantic Memory
        │                │                 │
        └────────────────┼─────────────────┘
                         ▼
                Retrieval Engine
                         │
        ┌────────────────┼─────────────────┐
        ▼                ▼                 ▼
   Vector Store    Knowledge Graph    Object Store

---

5. Memory Layers

User Prompt

↓

Working Memory

↓

Conversation Memory

↓

Workflow Memory

↓

Episodic Memory

↓

Semantic Memory

↓

Knowledge Base

↓

Archive

Each layer has different persistence and retrieval policies.

---

6. Memory Types

Memory Type	Description
Working	Temporary execution context
Conversation	Chat history
Workflow	Workflow execution state
Episodic	Historical events
Semantic	Facts and knowledge
Shared	Team-wide memory
Organizational	Tenant-level knowledge
Archived	Historical records

---

7. Working Memory

Working Memory exists only during a single execution.

Characteristics:

• In-memory only
• Fast access
• Automatically discarded
• Not searchable
• Not persisted

Typical contents:

• Current task
• Intermediate reasoning
• Temporary variables
• Tool outputs

---

8. Conversation Memory

Stores conversational history.

Example:

conversationId:
agentId:
userId:
messages:
summary:
createdAt:
updatedAt:

Supports long-running conversations.

---

9. Workflow Memory

Workflow Memory stores execution-specific knowledge.

Examples:

• Workflow variables
• Generated artifacts
• AI decisions
• Tool outputs
• User approvals
• Intermediate plans

Workflow Memory is persisted with the workflow.

---

10. Episodic Memory

Stores historical experiences.

Examples:

• Previous projects
• Earlier conversations
• Similar workflow executions
• Past failures
• Successful solutions

Agents use episodic memory to improve future decisions.

---

11. Semantic Memory

Semantic Memory stores facts rather than conversations.

Examples:

• Product documentation
• APIs
• Coding standards
• Company policies
• Technical knowledge
• Best practices

Semantic Memory is retrieved using embeddings.

---

12. Shared Memory

Shared Memory enables collaboration.

Example:

Developer Agent

↓

Shared Memory

↑

QA Agent

↑

Documentation Agent

Shared Memory is permission-controlled.

---

13. Memory Lifecycle

Created

↓

Indexed

↓

Embedded

↓

Stored

↓

Retrieved

↓

Updated

↓

Archived

---

14. Memory Record

memoryId:
tenantId:
agentId:
workflowId:
conversationId:
type:
title:
content:
embedding:
tags:
labels:
metadata:
createdAt:
updatedAt:
version:

---

15. Embeddings

Every semantic memory may generate an embedding.

Supported providers:

• OpenAI
• Gemini
• VoyageAI
• Cohere
• Ollama
• Local embedding models

Embeddings enable semantic retrieval.

---

16. Retrieval Pipeline

User Request

↓

Embedding

↓

Similarity Search

↓

Ranking

↓

Policy Filter

↓

Context Builder

↓

Prompt Assembly

Retrieval occurs before prompt generation.

---

17. Retrieval Strategies

Supported strategies:

• Vector similarity
• Keyword search
• Hybrid search
• Metadata filtering
• Graph traversal
• Time-based ranking
• Importance scoring

Strategies may be combined.

---

18. Memory Indexing

Each memory is indexed using:

• Embeddings
• Keywords
• Labels
• Tags
• Metadata
• Creation date
• Last access time

Indexes are updated automatically.

---

19. Context Compression

Large memory collections are compressed.

Compression methods:

• Summarization
• Hierarchical clustering
• Semantic deduplication
• Sliding window
• Token optimization

Compression reduces LLM token usage.

---

20. Context Assembly

Prompt construction order:

System Prompt

↓

Policies

↓

Workflow Context

↓

Conversation

↓

Retrieved Memory

↓

User Input

↓

Tool Results

The Context Manager controls final prompt size.

---

21. Memory Versioning

Every update creates a new version.

Memory

↓

Version 1

↓

Version 2

↓

Version 3

Historical versions remain accessible.

---

22. Retention Policies

Retention examples:

Memory	Retention
Working	Execution only
Conversation	Configurable
Workflow	Permanent
Episodic	Permanent
Semantic	Permanent
Archive	Configurable

---

23. Memory Security

Security features:

• Encryption at rest
• Encryption in transit
• RBAC
• ABAC
• Tenant isolation
• Secret masking
• Audit logging

Sensitive memories require elevated permissions.

---

24. Memory Sharing

Sharing scopes:

• Private
• Agent
• Workflow
• Project
• Organization
• Public

Permissions determine accessibility.

---

25. Rust Interfaces

pub trait MemoryProvider {
    fn store(
        &self,
        memory: MemoryRecord,
    ) -> Result<MemoryId>;

    fn retrieve(
        &self,
        query: MemoryQuery,
    ) -> Result<Vec<MemoryRecord>>;

    fn update(
        &self,
        memory: MemoryRecord,
    ) -> Result<()>;

    fn delete(
        &self,
        id: MemoryId,
    ) -> Result<()>;
}

---

26. Module Organization

engine-memory/
├── manager/
├── retrieval/
├── embeddings/
├── vector-store/
├── graph/
├── compression/
├── indexing/
├── policies/
├── providers/
├── cache/
├── metrics/
└── mod.rs

---

27. Testing Strategy

Unit Tests

• Embedding generation
• Retrieval accuracy
• Context compression
• Versioning
• Retention

Integration Tests

• Agent Runtime integration
• Workflow Memory
• Shared Memory
• Vector Store
• Knowledge Graph

Performance Tests

• Billion-memory datasets
• Large embeddings
• High concurrency
• Massive retrieval operations

---

28. Non-Functional Requirements

Requirement	Target
Retrieval latency	< 30 ms
Embedding generation	Provider dependent
Context assembly	< 20 ms
Availability	99.99%
Horizontal scaling	Unlimited

---

29. Dependencies

• docs/03-workflow-engine/agent-runtime.md
• docs/03-workflow-engine/persistence-layer.md
• docs/03-workflow-engine/event-bus.md

---

30. Related Documents

• docs/04-agent-framework/agent-definition.md
• docs/04-agent-framework/tool-framework.md
• docs/04-agent-framework/planning-engine.md
• docs/04-agent-framework/context-manager.md
• docs/04-agent-framework/provider-sdk.md

---

31. Future Enhancements

• Memory federation
• Cross-agent knowledge transfer
• Autonomous memory pruning
• AI-generated knowledge graphs
• Multi-modal memory
• Time-travel memory queries
• Federated vector databases
• Memory confidence scoring
• Continual learning integration

---

32. Revision History

Version	Date	Description
1.0.0	2026-06-26	Initial Memory System Specification