Memory Management System for Conversational AI Agents

PrototipeAI provides memory infrastructure for conversational and agentic AI systems, enabling agents to retain and retrieve context across conversations, sessions, users, and workflow executions. The platform includes a memory API for generation and retrieval, granular visibility into stored memories with classification, and fine-grained control over memory instructions for agents.

Memory management supports multiple scopes: per-session, per-user, per-entity, and episodic memory with defined lifecycle boundaries. In agentic workflows, the system provides granular control over memory sharing between agents, session-level memory isolation, and precise memory composition for context generation.

Memory Generation and Retrieval API

REST API endpoints enable agents to create, update, retrieve, and delete memory entries programmatically. API accepts structured memory objects with classification tags, relevance scores, and expiration policies. Retrieval supports semantic search, keyword matching, temporal filtering, and custom relevance ranking.

• POST /api/v1/memory: Create memory entry with content, classification, scope, and metadata
• GET /api/v1/memory: Retrieve memories by scope, entity, time range, or semantic query
• PATCH /api/v1/memory/:id: Update memory content, classification, or relevance score
• DELETE /api/v1/memory/:id: Remove memory with audit trail
• POST /api/v1/memory/search: Semantic or hybrid search across memory store
• Authentication via Bearer token with scope-based permissions

Granular Memory Visualization

Platform UI displays all agent memories with classification labels, scope identifiers, creation timestamps, relevance scores, and retrieval frequency. Users inspect individual memory entries, view retrieval history, and analyze which memories agents include in conversation context. Classification tags organize memories by type (user preference, conversation fact, entity attribute, temporal event).

• Memory browser with filtering by scope, classification, and date range
• Classification labels: user_preference, conversation_fact, entity_attribute, temporal_event, workflow_state
• Relevance scores showing importance for context inclusion
• Retrieval analytics: Frequency, recency, and context usage per memory
• Memory dependency graph showing relationships between entries
• Export to CSV or JSON for external analysis

Fine-Grained Memory Instruction Control

Agents receive specific instructions defining when to create memories, what information to extract, how to classify entries, and when to retrieve stored context. Platform allows customization of memory generation rules with if-then logic, pattern matching, and entity extraction. Users adjust memory instructions through UI or programmatically, with immediate effect on agent behavior.

• Conditional memory creation rules (e.g., "Create memory when user states preference")
• Information extraction patterns defining what to remember from conversations
• Classification logic automatically tagging memory entries by type
• Retrieval triggers specifying when to fetch memories (user mention, topic change)
• Memory prioritization rules determining context inclusion order
• Instruction versioning with A/B testing of memory strategies

Session-Based Memory Management

Memory system maintains session-scoped entries that persist only during active conversation and expire after session termination. Session memory enables agents to track conversation-specific context, temporary states, and intermediate results without polluting long-term memory. Configurable expiration policies (time-based, event-based, or manual).

• Session ID assignment for conversation grouping
• Automatic memory scoping to current session
• Session expiration after configurable timeout (default: 30 minutes inactivity)
• Session memory isolation between concurrent conversations
• Promotion of session memories to persistent storage (optional)
• Session summary generation at conversation conclusion

User-Scoped Persistent Memory

User-level memories persist across sessions and channels, enabling personalized agent behavior based on historical interactions. System maintains separate memory spaces per user with privacy controls, data retention policies, and GDPR-compliant deletion. User memories include preferences, historical facts, and relationship context.

• Unique memory namespace per user ID
• Cross-session and cross-channel memory access
• User preference tracking with version history
• Relationship context (agent knows user's role, history, permissions)
• Privacy controls for sensitive user data
• Bulk export and deletion for GDPR compliance

Episodic Memory with Lifecycle Management

Episodic memory structures organize memories into bounded time periods or narrative sequences with defined beginning, middle, and end. Agents track multi-step processes, project lifecycles, or recurring events with episodic boundaries. System automatically manages episode transitions, archival, and retrieval based on temporal or event-based triggers.

• Episode creation with start/end events or time boundaries
• Automatic episode segmentation based on conversation patterns
• Episode-scoped memory retention (memories expire with episode)
• Cross-episode memory linking for recurring patterns
• Episode archival with compressed storage for historical access
• Episode-aware retrieval prioritizing current episode context

Entity-Based Memory Organization

Memory entries can be scoped to any entity type beyond users and sessions: projects, organizations, documents, products, or custom domain objects. Entity-scoped memories enable agents to maintain context about business objects, aggregate knowledge from multiple users, and provide consistent information across interactions.

• Custom entity type definitions (project, organization, document, etc.)
• Entity-scoped memory namespaces with access control
• Multi-user contributions to shared entity memories
• Entity relationship tracking for connected memories
• Entity memory summarization aggregating information from multiple sources
• Entity-aware retrieval filtering by entity attributes

Granular Memory Sharing Between Agents

In multi-agent workflows, platform provides fine-grained control over which memories each agent can access. Configuration specifies sharing policies: no sharing (private), explicit sharing (whitelist), or broadcast (all agents). Memory sharing rules operate at individual memory entry level or scope level.

• Per-agent memory access control lists
• Explicit memory sharing between specific workflow agents
• Broadcast memories visible to all workflow participants
• Read-only vs read-write memory permissions
• Conditional sharing based on workflow state or agent role
• Memory handoff protocols between sequential workflow steps

Workflow Execution Memory

Workflow-level memory tracks execution state, intermediate results, agent outputs, and decision points across workflow execution. Execution memory is scoped to single workflow run and includes automatic checkpointing for failure recovery. Each workflow step has isolated memory space while accessing shared workflow context.

• Workflow execution ID for memory scoping
• Automatic capture of agent outputs at each workflow step
• Execution state checkpointing for recovery after failures
• Step-isolated memory preventing contamination between agents
• Shared workflow context accessible to all steps
• Execution memory archival after workflow completion

Agent-Specific Memory Isolation

Each agent in workflow maintains private memory space containing its instructions, intermediate reasoning, tool call results, and internal state. Private memories are invisible to other agents unless explicitly shared. Platform provides granular logging of each agent's memory reads and writes for debugging and optimization.

• Private memory namespace per workflow agent
• Instruction memory containing agent's system prompt and configuration
• Reasoning trace memory for multi-step thought processes
• Tool result memory caching external API responses
• Internal state memory for agent-specific variables
• Memory access logs showing reads/writes per agent

Context Composition Control

Platform provides precise control over how memories are composed into agent context. Configuration defines memory retrieval order, maximum context size, relevance thresholds, and deduplication rules. Context composition strategies balance comprehensiveness against token limits and response latency.

• Retrieval ranking: Recency, relevance, manual priority, or hybrid scoring
• Context size limits with automatic truncation or summarization
• Relevance thresholds filtering low-value memories from context
• Deduplication eliminating redundant information
• Memory formatting templates for consistent context structure
• Dynamic context allocation adjusting to conversation complexity

Platform automatically classifies memories into categories enabling targeted retrieval and context composition:

Personalized Customer Support Agents

Support agents remember customer preferences, previous issues, product ownership, and communication style across sessions. User-scoped memory enables consistent personalized service regardless of conversation channel (web, WhatsApp, email).

Multi-Step Research Workflows

Research workflows with specialized agents (data gathering, analysis, report writing) use shared workflow memory to pass findings between steps while maintaining agent-specific reasoning traces. Episodic memory bounds research project lifecycle.

Long-Running Project Assistance

Project management agents track multi-week initiatives using episodic memory with project start and completion boundaries. Entity-scoped memory maintains project-specific context accessible to all team members working with the agent.

Knowledge Base Construction

Agents accumulate entity-scoped memories from multiple users to build shared knowledge bases about products, policies, or procedures. Memory classification enables semantic organization of accumulated knowledge.

Debugging Complex Agentic Systems

Granular memory visualization and agent-specific memory isolation enable developers to trace information flow through multi-agent workflows, identifying where context is lost or contaminated between agents.

Create memory entry:

POST https://api.prototipeai.com/v1/memory
Authorization: Bearer {api_token}
Content-Type: application/json

{
  "content": "User prefers email communication over phone calls",
  "classification": "user_preference",
  "scope": {
    "type": "user",
    "id": "user-123"
  },
  "relevance_score": 0.9,
  "metadata": {
    "source": "conversation-456",
    "timestamp": "2025-01-15T10:30:00Z"
  }
}

Response:
{
  "memory_id": "mem-789",
  "status": "created",
  "expires_at": null
}

Retrieve relevant memories:

POST https://api.prototipeai.com/v1/memory/search
Authorization: Bearer {api_token}
Content-Type: application/json

{
  "query": "communication preferences",
  "scope": {
    "type": "user",
    "id": "user-123"
  },
  "limit": 5,
  "min_relevance": 0.7
}

Response:
{
  "memories": [
    {
      "memory_id": "mem-789",
      "content": "User prefers email communication over phone calls",
      "classification": "user_preference",
      "relevance_score": 0.92,
      "created_at": "2025-01-15T10:30:00Z"
    }
  ],
  "total": 1
}

Example memory generation instructions for agent:

Memory Instructions:

CREATE MEMORY when:
- User states a preference (classification: user_preference)
- User shares factual information relevant to future conversations (conversation_fact)
- Workflow reaches a checkpoint or decision point (workflow_state)

EXTRACT information:
- User preferences: communication channel, notification frequency, language
- Entity attributes: product names, project IDs, document references
- Temporal events: deadlines, scheduled meetings, milestone dates

RETRIEVE MEMORIES when:
- User asks follow-up question referencing previous conversation
- Workflow requires context from earlier steps
- Entity mentioned in conversation (fetch entity-scoped memories)

PRIORITIZE retrieval:
1. Memories from current episode (if applicable)
2. User preferences relevant to current topic
3. Recent conversation facts (last 7 days)
4. Entity attributes for mentioned entities

INCLUDE in context:
- Maximum 10 most relevant memories
- Minimum relevance score: 0.7
- Deduplicate similar content
- Format as bullet points in system prompt