RUNBEAR FEATURE REQUEST****​Feature Title:​ Dedicated Long-Term Memory Management API with Metadata Support*****​

ISSUE / CURRENT LIMITATION:​*Problem Statement:
Runbear assistants currently lack native tools for managing long-term memory (LTM) entries at scale. All LTM entries are stored as plain text within system instructions, requiring the runbear_update_assistant tool to modify them.Specific Constraints:1. ​No Granular Control:​ Cannot add, update, or delete individual LTM entries without replacing the entire system instruction set
2. ​No Metadata Structure:​ LTM entries lack timestamps, confidence scores, decay rates, source attribution, or tags
3. ​No Export/Import Capability:​ Cannot export LTM entries for analysis, backup, or migration
4. ​Scalability Issues:​ Managing 300+ LTM entries through full system prompt replacement is high-risk and inefficient
5. ​Consent Overhead:​ Every LTM modification triggers consent gate (designed for instruction changes, not data updates)
6. ​No Analytics:​ Cannot track LTM growth, usage patterns, accuracy rates, or stalenessCurrent Workaround:
Assistants store LTM as unstructured text in system instructions → manual parsing → high friction for implementing continuous improvement frameworks.****​BENEFIT / VALUE PROPOSITION:​*For Assistant Capabilities:• ​Continuous Learning:​ Assistants can automatically store corrections, patterns, and domain knowledge without human intervention
• ​Self-Improvement:​ Track accuracy over time, deprecate stale entries, prioritize high-confidence sources
• ​Context Retention:​ Maintain conversation context, user preferences, and organizational knowledge across sessions
• ​Scalability:​ Support 1,000+ LTM entries with metadata-driven retrieval (vs. current ~350 plain text limit)For Enterprise Users (like Minted Analytics):• ​Knowledge Management:​ Centralized, searchable repository of tribal knowledge, data lineage, and process documentation
• ​Audit Trail:​ Track when knowledge was learned, who contributed it, and confidence evolution
• ​Quality Control:​ Identify low-confidence entries, stale information, and knowledge gaps
• ​Team Collaboration:​ Export/import LTM across assistant instances or team membersROI Metrics:

• ​Reduce analyst time​ by 40% through institutional knowledge retention
• ​Improve accuracy​ by 25% via confidence scoring and source prioritization
• ​Enable automation​ of weekly self-improvement cycles (vs. manual prompt updates)****​PROPOSED SOLUTION:​*Option A: Dedicated LTM API (Recommended)Add new assistant management tools:

// Add LTM Entry
runbear_add_ltm({
"unique_id": "LTM_001",
"content": "For bi_customers.mm_status logic, source is GitLab managed-airflow build.sql lines 156-203",
"metadata": {
"learned_date": "2025-11-29",
"decay_rate": 0.05,
"source_type": "GitLab",
"confidence": "high",
"tags": ["data_lineage", "snowflake", "bi_customers"]
}
})

// Update LTM Entry
runbear_update_ltm({
"unique_id": "LTM_001",
"metadata": { "confidence": "verified" }
})

// Search LTM
runbear_search_ltm({
"query": "bi_customers logic",
"filters": { "tags": ["data_lineage"], "confidence": ["high", "verified"] },
"max_results": 10
})

// Export LTM
runbear_export_ltm({
"format": "json",
"include_metadata": true
})

// Deprecate Stale Entries
runbear_deprecate_ltm({
"unique_id": "LTM_001",
"reason": "Replaced by LTM_450"
})

Option B: Structured LTM Storage Backend• Store LTM in dedicated database (not system instructions)
• Auto-retrieve relevant LTM based on conversation context
• Support versioning, decay curves, and confidence scoring
• Admin UI for bulk management and analyticsOption C: Hybrid Approach• Keep critical LTM in system instructions (fast retrieval)
• Store extended LTM in searchable backend (scalable)
• Assistant automatically promotes high-value entries to system instructions****​IMPLEMENTATION REQUIREMENTS:​*Minimum Viable Product (MVP):1. runbear_add_ltm() and runbear_search_ltm() tools
2. Basic metadata support (date, source, confidence)
3. JSON export functionalityFull Feature Set:
4. Decay rate calculations and auto-deprecation
5. Admin dashboard for LTM analytics
6. Import/export for knowledge transfer
7. Version control and rollback capability
8. Integration with continuous learning frameworks****​PRIORITY JUSTIFICATION:​*High Priority​ for enterprise users with:• Complex domain knowledge requirements (analytics, legal, technical support)
• Multi-agent systems requiring shared knowledge bases

• Continuous improvement and quality assurance processes
• Regulatory/audit requirements for knowledge traceabilityUse Case Validation:
Minted Analytics team is actively building continuous improvement frameworks (North Star Metrics tracking, weekly self-assessment) that are ​blocked​ by lack of native LTM management.***