Paritok vs TencentDB Agent Memory: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Paritok and TencentDB Agent Memory — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
P
Paritok
Paritok
Non-destructive compression gateway that drops between coding agents and LLMs to cut input tokens by up to 85% without changing the agent.
Key features
- Drop-In Gateway: One environment variable (ANTHROPIC_BASE_URL) reroutes your agent through Paritok — no agent, prompt, or tool changes.
- Tool Schema Compression: 46-schema tool blocks (~29K tokens) drop to ~8K per turn by keeping relevant tools and stubbing the rest, frozen per conversation for cache stability.
- Code-Native 4B Model: A 4B compression model trained on 45K real agent trajectories keeps identifiers, paths, and errors while shrinking file reads and outputs to ~26% of original.
- read_original Recall: Every compressed segment is tagged; the agent asks read_original(ref) and gets the exact bytes locally without spending an extra turn.
- Stale History Summarization: Turns beyond a configurable recent window get summarized once when your context budget fills, so recent turns stay pristine and overflows drop to zero.
- Multi-Agent Compatibility: Works today with Claude Code, Cursor, Codex, OpenHands, and any OpenAI-compatible upstream — Anthropic and OpenAI both supported.
- Compounding Savings: Saved share grows across a session — 25% at 1 turn, 54% at 10 turns, 63% at 20 turns — against a 96,500-token baseline.
- Open Weights and Benchmark: SWE-bench Lite floor of 86.5% quality retained at 25.7% compression rate, with weights and training pipeline published.
Best for
- MCP-Heavy Workflows: Cut input bills for agents that ship 70+ MCP tool schemas on every turn.
- Long Coding Sessions: Run 3× longer coding-agent sessions before context saturation forces a hard compact.
- Bill Reduction: Estimate 54% off input tokens on a 5-developer team at 20-turn Claude Sonnet sessions (~$6,550/year).
- Self-Hosted Privacy: Route agent traffic through your own hardware with no data leaving your network — 8GB GPU is enough.
- Enterprise Cost Governance: Add compression at the gateway layer so all coding agents on the team benefit without engineering per-agent.
- Cursor/Codex/Claude Code Fleet: Standardize compression across a mixed toolchain of coding agents behind one gateway.
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.