TencentDB Agent Memory vs Visiby: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of TencentDB Agent Memory and Visiby — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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TencentDB Agent Memory
Tencent Cloud
Team-level memory hub for AI agents — layered long-term memory + symbolic short-term memory that cuts tokens 61% and lifts task success 51%.
Key features
- Symbolic short-term memory: Offloads heavy tool logs and condenses task state into compact Mermaid symbol graphs, cutting in-context tokens dramatically.
- Layered long-term memory: L0 Conversation → L1 Atom → L2 Scenario → L3 Persona semantic pyramid instead of flat vector storage.
- Four reusable memory assets: Chat Memory, Skill, LLM-Wiki, and Code-Graph — governed, shared, and equipped across agents and frameworks.
- Drill-down traceability: Deterministic path from every high-level abstraction back to raw evidence via node id — no irreversible lossy summarization.
- Heterogeneous storage: Raw facts/logs in a database for full-text retrieval, top-layer personas and canvases as human-readable Markdown for inspection.
- Benchmarked gains: -61.38% tokens and +51.52% relative pass rate on WideSearch with OpenClaw; +59% on PersonaMem accuracy across long-horizon sessions.
- Zero-config with OpenClaw: Local SQLite + sqlite-vec backend by default; automatic conversation capture, memory extraction, and recall before each turn.
- Hermes Gateway integration: Works with the Nous Research Hermes agent gateway for hosted agent deployments.
Best for
- AI engineering teams running long-horizon coding agents (SWE-bench-style workloads) who need to cut input tokens and lift task success across a session.
- Product teams building personal assistants that must remember user preferences across weeks of conversation without shipping the whole chat history to the model.
- Agent framework authors who want a drop-in memory layer for OpenClaw or Hermes Gateway with symbolic + layered storage rather than a flat vector store.
- Enterprise teams building a shared memory hub so multiple agents (support, dev, analyst) reuse the same personas, SOPs, and Code-Graph facts.
- Research groups benchmarking agent memory approaches who need a reproducible open-source baseline with published PersonaMem and WideSearch numbers.
- Cost-sensitive operators of long-running agents who want a traceable, auditable memory system that avoids lossy summarization.
Visiby
FNA Technology
AI visibility platform that tracks how ChatGPT, Perplexity, Claude, Gemini and AI Overviews cite your brand, and ships fixes.
Key features
- AI Citation Tracking: Continuously samples roughly 50,000 prompts per week across ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews to record where and how a brand is cited.
- Per-Engine Visibility Scoring: Reports a composite AI Visibility score plus share of voice and prompts won or lost, broken out engine by engine so declines can be traced to a specific model.
- Prompts & Citations Explorer: Lets teams open any tracked prompt and read the actual model answer to see which competitor was named and why.
- Brand Entity Analysis: Maps the adjectives each engine associates with your brand versus competitors and suggests reframing plays to change that portrait.
- Competitor Intelligence: Tracks rival citation share on comparison and 'alternatives to' prompts, highlighting categories where a competitor dominates.
- Prioritized Action Plan: Converts findings into P0/P1 recommendations such as schema additions or comparison pages, each with a time estimate and projected score gain.
- Site Audit for AI Parseability: Audits pages for missing entity definitions, structured Q&A data and other signals that prevent models from citing the site correctly.
- White-Label Reporting and API: Higher tiers add white-label client reports, SSO/SAML and API access for agencies managing multiple brands.
Best for
- AI Search Monitoring: Marketing teams track whether ChatGPT and Perplexity recommend their product or a competitor on high-intent category prompts.
- Competitive Benchmarking: Brands quantify how much citation share a named rival is capturing on 'alternatives to' and 'best of' queries.
- Content Prioritization: Content teams decide which pages to write or refresh based on which prompts are currently missed rather than on keyword volume alone.
- Technical AEO Audits: SEO specialists find pages lacking FAQ schema or entity markers that keep answer engines from parsing them.
- Agency Client Reporting: Agencies run pooled prompt tracking across multiple client workspaces and deliver white-label AI visibility reports.
- Executive Reporting: Operators present a weekly digest showing search clicks alongside AI citation share to explain traffic shifts leadership sees.