Keel — An AI assistant whose memory belongs to you. vs ReExplain: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Keel — An AI assistant whose memory belongs to you. and ReExplain — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Keel — An AI assistant whose memory belongs to you.
Keel Labs
Local-first desktop assistant for Mac/Windows that stores plain Markdown on your disk and lets you swap models while keeping your context.
Key features
- Local-First Storage: Stores all assistant memory and workspace data as plain Markdown files on the user's disk to ensure portability, offline access, and easy backup/versioning with Git.
- Model-Agnostic Integration: Supports swapping between model providers (e.g., Claude, GPT, OpenRouter, Ollama) allowing users to change inference backends without losing context or notes.
- Plain Markdown Workspace: Uses human-readable Markdown as the native data format, enabling easy editing, searching, and integration with existing text-based workflows and tools.
- Cross-Platform Desktop App: Provides a native desktop experience for both macOS and Windows users optimized for local performance and file-system based storage.
- Privacy-First Design: Keeps context and memory under user control by defaulting to local storage and enabling use of local or self-hosted model endpoints.
- Bring-Your-Own-Model (BYOM): Allows connecting to local or third-party model runtimes (e.g., Ollama or OpenRouter) so users can run models they trust or prefer.
- Open Source Repository: Project source and releases are available on GitHub, facilitating community contributions, audits, and self-hosted deployments.
- Local-first desktop application for macOS and Windows
- Stores workspace and memory as plain Markdown files on the user's disk
- Bring-your-own-model: support for swapping Claude, GPT, OpenRouter, Ollama
- Context persistence on local filesystem independent of model backend
- Open-source codebase hosted on GitHub (Keel-Labs/keel)
- Plain-markdown workspace for notes, context capture, and memory organization
Best for
- Personal Knowledge Base: Maintain an evolving, searchable personal assistant memory as plain Markdown files that you control and can version with Git.
- Privacy-Sensitive Assistance: Use local or self-hosted model runtimes to run queries and keep sensitive context on-device rather than sending it to hosted services.
- Experimenting with Models: Quickly switch between model providers (Claude, GPT, OpenRouter, Ollama) to compare outputs while keeping the same conversation context and notes.
- Developer Workflows: Keep notes, prompts, and agent memory in Markdown within a repository to integrate with codebases, CI, and version history.
- Offline or Local Inference: Connect to locally running model servers (e.g., via Ollama) to generate responses without relying on external APIs.
- Content Drafting and Iteration: Draft, refine, and store content in Markdown while swapping models to explore different creative or editorial styles.
- Personal knowledge base with locally stored assistant memory
- Using different LLM providers interchangeably while keeping the same local context
- Privacy-focused workflows where sensitive context stays on the user's disk
- Developer experimentation with multiple model backends and routing
- Note-taking and organization using Markdown-backed agent memory
ReExplain
ReExplain
Upload a PDF, re-explain the ideas in your own words, and let AI challenge your understanding with adaptive questions.
Key features
- Upload PDF Materials: Drop in a textbook chapter, paper, or study notes (up to 4 MB / 25 pages)
- Feynman-Style Sessions: Re-explain the material in your own words as an interactive exercise
- Adaptive Questioning: AI generates follow-up questions that target your specific weak spots
- Understanding Gap Detection: Surface concepts you thought you knew but cannot articulate
- GPT 5.6 Powered: Uses a current frontier model for question generation and evaluation
- Dark Mode: Comfortable reading experience for long study sessions
Best for
- Study a textbook chapter before an exam and verify comprehension actively
- Digest a research paper by re-explaining sections in plain language
- Prepare for oral exams or interviews where you must talk through concepts
- Turn passive re-reading into active recall for durable memory
- Identify blind spots in your understanding of technical material
- Onboard yourself to a new subject area using materials you already have