LMCache vs Screencap: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of LMCache and Screencap — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

LMCache

Free

LMCache is an open-source KV cache layer that speeds up LLM inference by storing and reusing KV caches across GPU, CPU, disk, and S3.

KV Cache Reuse: Stores KV caches of reusable text across the datacenter so prefixes are not recomputed across requests or serving engines.
Multi-Tier Storage: Persists caches across GPU, CPU, local disk, and S3 with acceleration techniques like zero CPU copy, NIXL, and GDS.
vLLM Integration: Combines with vLLM to deliver 3-10x reductions in delay and GPU cycles for multi-round QA and RAG workloads.
Pluggable KV Transformation: A flexible SERDE interface lets researchers add compression, token dropping, and custom serialization.
Vendor-Neutral Layer: Works as a KV cache layer across mainstream serving engines, inference frameworks, hardware vendors, and storage systems.
Faster Time-to-First-Token: Cuts TTFT and improves throughput for long-context, agentic, and knowledge-augmented workloads.

Retrieval-Augmented Generation: Reuse cached document prefixes to cut latency and GPU cost in RAG pipelines.
Multi-Turn Conversations: Avoid recomputing conversation-history KV caches across turns in chat applications.
Long-Context Agents: Accelerate agentic workloads that repeatedly process large shared context.
Enterprise-Scale Inference: Share KV caches across multiple serving instances to raise throughput in production clusters.
Cache Compression Research: Prototype custom KV compression and serialization through the pluggable SERDE interface.

Proteus Computer Use

Freemium

Local-first macOS screen recorder that captures, labels, and indexes team workflows so knowledge stays searchable and private.

Local-First Capture: Recordings live in ~/.screencap on your Mac and never leave unless you explicitly share them.
On-Device Task Segmentation: An on-device model breaks long recordings into labeled tasks like payroll runs, expense approvals, or CRM data entry.
Full-Text Search of Workflows: Every spoken word and on-screen moment is indexed so any past workflow can be surfaced months later by search.
Privacy-Enforced Recording: Password managers and banking apps are cut before a frame is written; email and chat are masked in real time.
MCP Context Snapshots: While recording, Screencap queries connected MCP servers to capture the exact Gusto/Attio/Linear/Notion record on screen inside the video.
Deliberate Sharing With PII Scrubbing: Every shared copy is scrubbed of names, secrets, and PII, and only the recordings you pick ever leave the machine.
Source-Available Codebase: The full capture engine, encryption, agent, and anonymizer are public on GitHub under PolyForm Noncommercial 1.0.0.

Team Onboarding: Assemble ordered collections of real workflow recordings so new teammates learn exactly how work is actually done.
Institutional Knowledge Capture: Preserve the tacit steps behind payroll runs, reconciliations, and quarterly reports as searchable video.
Ops Documentation: Replace stale wikis by pointing teammates at labeled task recordings that stay current with the real system.
Compliance-Sensitive Recording: Capture back-office work in banking, finance, and HR without leaking passwords, account balances, or PII.
Computer-Use Dataset Contribution: Optionally donate reviewed, scrubbed recordings to a public dataset for training open computer-use models.