GhostWriter by MyHandler vs TencentDB Agent Memory: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of GhostWriter by MyHandler and TencentDB Agent Memory — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
GhostWriter by MyHandler
MyHandler.ai
Windows writing assistant that reads the screen around your cursor on a hotkey double-tap and types a context-aware draft in your voice.
Key features
- Hotkey Double-Tap Drafting: No prompt or dictation is needed — a double-tap with the cursor in any text field triggers capture and drafting directly in place.
- Screen Context Capture: Reads the thread, form or partial sentence surrounding the cursor locally on the machine, using that on-screen context as the entire input.
- Writing Intent Classification: Determines whether the moment calls for a reply, a continuation, a filled-in answer or a fresh compose before writing a single word.
- Sender and Identity Mapping: Maps each message in a thread to its sender and works out which handle is the user's, so the draft answers the other party rather than the user's own words.
- Calendar Cross-Check: When a draft proposes a time, it is validated against the next two weeks of a connected calendar before the text appears.
- Selection Rewriting: Selecting existing text before the hotkey rewrites that selection instead of composing something new.
- Nothing Sent Automatically: The generated text appears at the cursor for the user to read, edit or delete; sending always remains a manual step.
- Local Vault with Zero-Retention Cloud: Capture runs against an encrypted vault on the user's own PC and the assembled context is processed by a zero-data-retention cloud model.
Best for
- Email Backlog: Clearing a queue of owed replies by drafting each one from the thread already on screen instead of retyping the same answer.
- Chat and Slack Replies: Answering a message in a team chat where the draft is grounded in who asked whom for what in the visible thread.
- Web Form Completion: Filling a blank answer box under a question on a web form or application without switching to a separate chat window.
- Sentence Continuation: Picking up a half-written paragraph exactly where it stops, without the assistant restating what was already typed.
- Meeting Scheduling Replies: Responding to a request for a time with a proposal that has already been checked against the user's calendar.
- Tone-Sensitive Rewrites: Selecting a blunt or rough draft and having it rewritten in the user's own voice before sending.
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.