CleanSlop vs TencentDB Agent Memory: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of CleanSlop and TencentDB Agent Memory — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
CleanSlop
CleanSlop
Scans vibe-coded apps for launch-blocking issues like leaked keys and data exposure, and reports what to fix first, right from your coding agent.
Key features
- Agent-Native Scanning: Tell Claude Code, Codex, Cursor or other agents to scan with CleanSlop and fix findings where you build.
- System-Level Code Mapping: Maps routes, background jobs, data and external services so each finding follows a real call path.
- Prioritized Reports: Findings grouped as needs attention, worth a look and minor, worst first.
- Evidence for Every Finding: Shows where each issue lives in your code and why it was flagged.
- Plain-English Fixes: Explains what is wrong, why it matters and what to do about it.
- Copy for LLM: Hands any report to your AI tool so it can apply the fixes.
- GitHub App Integration: Scan only the repositories you select, read-only access.
- Expert Triage Calls: Book a 15-minute call with a CleanSlop engineer to walk through findings.
Best for
- A solo founder checks a Next.js and Supabase app built with an AI coding assistant before launching.
- A developer discovers an exposed AI API key bundled into browser code and moves it to a server route.
- A side-project builder verifies that one user's data cannot be read by another through database policies.
- A Claude Code user runs a scan from the terminal and has the agent fix the top findings.
- A team lead gets a short prioritized list instead of hundreds of linter warnings.
- An indie hacker books an expert triage call to confirm an ambiguous finding.
T
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.