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/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
AI agents in customer service can use the Continuity Framework to reflect on past interactions, extract key customer preferences and issues, and generate follow-up questions for proactive support. This enables personalized service and reduces repetitive queries by maintaining context across sessions.
In educational technology, AI tutors can apply the framework to analyze student sessions, store learning progress as structured memories, and surface tailored questions to guide future lessons. This fosters adaptive learning paths and improves engagement by building on past interactions.
Healthcare AI systems can utilize the framework to reflect on patient consultations, extract health-related facts and commitments, and generate follow-up questions for ongoing care. This supports continuity of care by integrating patient history into each new session for more informed decisions.
AI assistants in project management tools can leverage the framework to reflect on team meetings, capture project principles and commitments, and surface questions to track progress. This enhances collaboration by maintaining a coherent memory of project dynamics and goals across time.
Offer the Continuity Framework as a cloud-based service with tiered pricing based on usage volume and features like advanced memory types. Revenue comes from monthly subscriptions, targeting businesses that need persistent AI memory for customer-facing applications.
Sell perpetual licenses or annual contracts for on-premise deployment of the framework, customized for specific industries like healthcare or finance. Revenue is generated through upfront licensing fees and ongoing support and maintenance charges.
Provide the framework's capabilities via an API, allowing developers to integrate continuity features into their own AI applications. Revenue is based on API call volume, with pay-as-you-go or enterprise plans for high-throughput users.
π¬ Integration Tip
Integrate the framework into existing AI workflows by configuring heartbeat triggers and memory storage paths, ensuring seamless reflection and question surfacing without disrupting core operations.
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