Apache Maka vs Everywhere: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Apache Maka and Everywhere — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Apache Maka
The Apache Software Foundation
Apache-licensed local-first agent workspace that runs tools in a sandbox and records every model message and tool call as a recoverable execution log.
Key features
- Append-Only Execution Record: Model messages, tool calls, tool results, permission decisions, and turn termination events are written down durably, so the transcript is evidence rather than a disposable chat buffer.
- Context Trimming Without Data Loss: Old tool output can be omitted from the next prompt to shorten context while the full saved history remains intact and inspectable.
- Single Runtime Host: Desktop, terminal, and evaluation all execute through one runtime, so behavior does not diverge between how you develop and how you benchmark.
- Sandboxed Tool Boundary: Built-in Read, Write, Edit, Bash, Glob, and Grep tools run under a sandbox; anything leaving that boundary requires approval, and Computer Use and catalog skills are opt-in.
- Crash Recovery and Resume: Runs can be aborted, failures are classified, and an interrupted turn can optionally be resumed rather than restarted from scratch.
- Session Branching and Search: The desktop workspace supports creating, archiving, searching, renaming, retrying, regenerating, and branching sessions from any turn.
- Bring Your Own Model: Connect a cloud API, a locally hosted model, or a compatible gateway, with streaming output, thinking, usage reporting, and clearer provider errors.
- Declarative Evaluation Harness: maka eval expands multi-arm experiments into task by repetition by subject cells with immutable per-cell attempts and a result kernel covering score, normalized usage, attributable cost, duration, and failure reason.
- Local-First Storage: Sessions, settings, artifacts, and run records stay on the machine by default, with local memory and optional web search when configured.
Best for
- Auditable Agent Runs: Keeping a defensible record of exactly what an agent did and which permissions were granted during a task.
- Long Coding Sessions: Working through a multi-turn refactor with branching and resume instead of losing state when a turn fails.
- Agent Benchmarking: Running reproducible multi-arm experiments comparing models, prompts, or external agent subjects on the same task set.
- Air-Gapped or Regulated Work: Running an agent workspace where sessions and artifacts must remain on local infrastructure.
- Cost and Usage Analysis: Attributing token usage, cost, and duration per experiment cell to decide which model configuration to ship.
- Terminal Workflows: Driving an agent from the current project directory or scripting a single non-interactive turn from CI or a shell.
- Open-Source Agent Research: Building on a permissively licensed runtime whose execution semantics and architecture are fully documented.
Everywhere
Sylinko (DearVa)
Context-aware desktop AI assistant that senses your screen, understands application context, and acts in-place across platforms.
Key features
- Context-Aware Invocation: Captures text and UI context directly from the screen so the assistant can answer or act without manual screenshots, copying, or app switching.
- Multimodel Provider Support: Connects to numerous LLM/MCP providers (OpenAI, Anthropic/Claude, Google Gemini, Ollama, DeepSeek, Moonshot, OpenRouter, SiliconCloud) allowing flexible model selection and fallbacks.
- Inline Overlay UI: Modern frosted-glass overlay summoned via keyboard shortcuts that renders Markdown, supports voice input, and displays contextual responses adjacent to the relevant UI element.
- Actionable Responses: Beyond explanations, the assistant can draft emails, translate text, summarize web pages, analyze error messages, and propose step-by-step fixes based on captured context.
- Cross-Platform Desktop Integration: Built with .NET and Avalonia to run on Windows, macOS, and Linux with deep desktop integration for consistent experience.
- Extensibility and Open Source: Distributed under Apache-2.0 with community discussions and contribution paths on GitHub; supports plugins and integration with external tools.
- Privacy and Self-Hosting Friendly: Local client that can be configured to use user-supplied API keys and model endpoints, enabling control over which services process data.
- Context-aware screen sensing that captures and understands visible content without manual screenshots
- Direct desktop integration with context-aware invocation beside content
- Supports multiple model providers and runtimes (OpenAI, Anthropic/Claude, Google Gemini, Ollama, OpenRouter, Moonshot/Kimi, DeepSeek, SiliconCloud, etc.)
- Retrieval-augmented workflows (RAG) and integration with external search / web summarization
- UI-automation capabilities to take actions in applications based on assistant instructions
- Modern UI (frosted-glass style), Markdown rendering and rich text output
- Voice input and keyboard shortcut invocation
- Cross-platform support via Avalonia for Windows, macOS, and Linux
- Extensible via chat/plugins and connectors to model service providers
- Open-source codebase (Apache-2.0) with public documentation and GitHub repository
Best for
- Error Troubleshooting: Summon Everywhere next to an application error message to get a diagnosis and step-by-step remediation without copying the text or leaving the app.
- Quick Research Summaries: Call the assistant while reading a long article or documentation page to produce concise multi-point summaries in-place.
- Instant Translation: Select foreign-language text on-screen and request immediate translation without switching to a separate translator app.
- Email and Writing Polishing: Highlight draft text in an email or editor and ask Everywhere to rephrase, professionalize, or shorten the content inline.
- Contextual Code Help: Invoke the assistant on snippets of code or logs shown on-screen to get explanations, bug-hunting tips, or suggested fixes informed by surrounding context.
- Desktop Task Automation: Use the assistant to perform contextual actions (e.g., copy structured data, open related resources, or run configured workflows) based on UI elements.
- Diagnosing and resolving on-screen error messages by invoking assistant next to the error
- Summarizing long web articles with three-point summaries without leaving the page
- Instant translation of selected foreign text in-place
- Drafting and polishing emails or other text directly from a desktop editor
- Using multiple LLM providers or local runtimes for redundancy, privacy, or cost control
- Automating UI tasks by having the assistant take actions in applications