maka vs synara

Agent workspaces compared: maka and synara

maka and synara are TypeScript desktop workspaces for working with AI agents. maka focuses on a shared runtime and traceable execution records, while synara brings coding-agent sessions together with project files, terminals, previews, and Git review.

makasynara
LanguageTypeScriptTypeScript
LicenseApache-2.0MIT
Stars5.8k2k
Forks557310
Last analyzedOct 19, 2026Oct 3, 2026

Key differences

  • maka records model messages, tool calls, and permission decisions in an append-only runtime log; synara organizes coding-agent work into projects and task threads.
  • maka uses one Runtime Host across its desktop, TUI, and CLI clients; synara centers on a desktop workspace with integrated terminals, file tools, previews, and Git workflows.
  • maka connects to user-provided cloud APIs, local models, or compatible gateways; synara uses locally installed and authenticated coding-agent runtimes.
  • maka's desktop runs on macOS, with Windows and Linux marked as previews; synara lists native release targets for Windows x64, macOS Intel and Apple Silicon, and Linux x64.
  • maka is licensed under Apache-2.0 and is an Apache incubating project; synara is licensed under MIT. Both describe themselves as early-stage or changing software.
  • maka includes evaluation tooling and per-task reports; synara emphasizes managed worktrees, provider handoffs, and reviewing changes in the workspace.

Choose maka if you…

  • want an append-only record of agent messages, tool calls, and permission decisions.
  • need desktop, TUI, and CLI access backed by a shared runtime.
  • plan to connect your own model and use evaluation tooling to compare agent runs.
Read the maka analysis →

Choose synara if you…

  • want to coordinate coding-agent sessions across projects and providers.
  • need terminals, file tools, previews, and Git review alongside agent conversations.
  • want managed worktrees for parallel tasks or provider handoffs.
Read the synara analysis →

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This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.