neolata-memGraph-native memory engine for AI agents — hybrid vector+keyword search, biological decay, Zettelkasten linking, trust-gated conflict resolution, explainabil...
Install via ClawdBot CLI:
clawdbot install Jeremiaheth/neolata-memGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Accesses sensitive credential files or environment variables
process.env.OPENAISends data to undocumented external endpoint (potential exfiltration)
webhook → https://...Calls external URL not in known-safe list
https://github.com/Jeremiaheth/neolata-memUses known external API (expected, informational)
api.openai.comGenerated Mar 21, 2026
A customer support AI agent uses neolata-mem to remember past interactions with customers, their preferences, and unresolved issues across sessions. The biological decay feature ensures outdated information fades while conflict resolution handles contradictory customer statements.
An academic research AI uses the memory engine to store findings, citations, and connections between concepts with Zettelkasten linking. Hybrid search allows semantic retrieval of related papers while local JSON storage keeps sensitive research data private.
A medical assistant AI stores patient symptoms, treatment responses, and doctor notes across visits. The trust-gated conflict resolution helps reconcile contradictory observations from different providers while Supabase storage enables secure team access.
A financial planning AI remembers client risk tolerance, investment goals, and past advice. Memory decay automatically reduces emphasis on outdated financial situations while webhook integration can trigger compliance logging.
Multiple specialized AI agents working on a software project share memory about code decisions, bug fixes, and architecture choices. Cross-agent search allows different agents to access relevant memories while compression prevents information overload.
Offer the memory engine as open-source (Elastic-2.0 license) with free community support. Generate revenue through enterprise features like advanced analytics dashboards, priority support, and custom integration services for large organizations.
Provide a fully managed version with automated backups, scaling, and monitoring. Customers pay monthly subscriptions based on storage volume and query frequency while benefiting from the zero-dependency architecture's simplicity.
Offer professional services to help companies implement the memory engine in their AI workflows. Revenue comes from implementation projects, custom feature development, and training workshops for development teams.
💬 Integration Tip
Start with the zero-config local JSON storage to test functionality, then gradually add embeddings or Supabase integration once the basic memory operations work correctly.
Scored Apr 19, 2026
AI Analysis
The skill's default configuration is fully local, and remote data flows (embeddings, LLM, webhooks, Supabase) are opt-in and explicitly documented. The rule-based signals for credential access and external calls correspond to these documented, user-configured features. No hidden instructions or obfuscation are present in the provided definition.
Audited Apr 17, 2026 · audit v1.0
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