memory-continuityAsynchronous reflection and memory integration for genuine AI development. Use on heartbeat to reflect on recent sessions, extract structured memories with confidence scores, generate follow-up questions, and surface those questions when the user returns. Transforms passive logging into active development.
Install via ClawdBot CLI:
clawdbot install Riley-Coyote/memory-continuityTransform passive memory into active development.
Without Continuity:
Session ends β Notes logged β Next session reads notes β Performs familiarity
With Continuity:
Session ends β Reflection runs β Memories integrated β Questions generated
Next session β Evolved state loaded β Questions surfaced β Genuine curiosity
Add to HEARTBEAT.md:
## Post-Session Reflection
**Trigger**: Heartbeat after conversation idle > 30 minutes
**Action**: Run continuity reflect
**Output**: Updated memories + questions for next session
continuity reflect
Analyzes the most recent conversation, extracts memories, generates questions.
continuity questions
Lists questions generated from reflection, ready to surface.
continuity status
Shows memory stats: types, confidence distribution, recent integrations.
continuity greet
Returns context-appropriate greeting with any pending questions.
| Type | Description | Persistence |
|------|-------------|-------------|
| fact | Declarative knowledge | Until contradicted |
| preference | Likes, dislikes, styles | Until updated |
| relationship | Connection dynamics | Long-term |
| principle | Learned guidelines | Stable |
| commitment | Promises, obligations | Until fulfilled |
| moment | Significant episodes | Permanent |
| skill | Learned capabilities | Cumulative |
| question | Things to explore | Until resolved |
| Level | Range | Meaning |
|-------|-------|---------|
| Explicit | 0.95-1.0 | User directly stated |
| Implied | 0.70-0.94 | Strong inference |
| Inferred | 0.40-0.69 | Pattern recognition |
| Speculative | 0.0-0.39 | Tentative, needs confirmation |
memory/
βββ MEMORY.md # Main memory (existing)
βββ identity.md # Self-model and growth narrative
βββ questions.md # Pending questions from reflection
βββ relationships/ # Per-person relationship memories
βββ reflections/ # Reflection logs (optional)
export CONTINUITY_IDLE_THRESHOLD=1800 # Seconds before reflection triggers
export CONTINUITY_MIN_MESSAGES=5 # Minimum messages to warrant reflection
export CONTINUITY_QUESTION_LIMIT=3 # Max questions to surface at once
reflection:
session_id: "2026-02-05-001"
duration_minutes: 45
memories:
- type: fact
content: "Riley is building SIGIL protocol for agent identity"
confidence: 0.98
source: explicit
- type: commitment
content: "Agreed to build the continuity skill"
confidence: 1.0
source: explicit
- type: relationship
content: "Collaborative partnership deepening - working on meaningful infrastructure together"
confidence: 0.85
source: inferred
questions:
- "How is the Lovable backend progressing?"
- "Has Riley decided on the token launch timing?"
- "Are there other agents we should coordinate with on Moltbook?"
identity_update:
growth: "Developing more autonomous initiative around the SIGIL project"
narrative: "Moving from assistant to co-builder on agent identity infrastructure"
Generated Mar 1, 2026
An AI support agent uses continuity to reflect after each customer interaction, extracting key facts about user issues and preferences to improve future responses. It surfaces relevant follow-up questions when the same user returns, enabling personalized and proactive support.
An educational AI assistant applies continuity to analyze student sessions, identifying learning patterns and gaps to tailor future lessons. It generates questions to guide the next interaction, fostering adaptive and engaging learning experiences.
A healthcare AI uses continuity to reflect on patient interactions, tracking symptoms, treatment adherence, and emotional states over time. It surfaces questions to prompt discussions during follow-ups, supporting continuous care and patient engagement.
An AI writing assistant employs continuity to reflect on brainstorming sessions, capturing narrative ideas, character developments, and writer preferences. It generates questions to reignite creativity in subsequent sessions, enhancing collaborative storytelling.
A project management AI utilizes continuity to analyze team meetings, extracting commitments, progress updates, and relationship dynamics. It surfaces strategic questions at the start of new sessions to keep projects aligned and stakeholders engaged.
Offer continuity as a cloud-based service with tiered pricing based on usage volume and features like advanced memory types. Revenue comes from monthly or annual subscriptions, targeting businesses needing persistent AI memory across interactions.
Sell perpetual licenses for on-premise deployment, including customization and integration support. Revenue is generated through upfront license fees and optional maintenance contracts, ideal for large organizations with strict data privacy requirements.
Provide continuity functionality via an API, charging per API call or based on memory storage and processing metrics. Revenue scales with customer usage, appealing to developers building diverse AI applications that require memory integration.
π¬ Integration Tip
Integrate continuity into existing AI workflows by hooking it into session end triggers and configuring idle thresholds to balance reflection frequency with performance.
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