Apache Maka vs Orca: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Apache Maka and Orca — 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.
O
Orca
Stably AI
Desktop AI orchestrator that runs Codex, Claude Code, OpenCode, and Pi side by side in parallel git worktrees, all tracked in one place.
Key features
- Parallel Agent Worktrees: Fan one prompt across up to five agents, each in its own isolated git worktree — compare results and merge the winner.
- Multi-Agent Support: Run Codex, Claude Code, OpenCode, and Pi side by side, all tracked in one unified interface.
- Mobile Companion App: Monitor and steer your agents from iOS or Android — get notified when an agent finishes and send follow-ups from anywhere.
- Ghostty-Class Terminals: WebGL-rendered terminal splits with infinite panes and scrollback that survives restarts.
- Design Mode Browser: Click any UI element in the embedded Chromium window to send its HTML, CSS, and a cropped screenshot straight into your agent's prompt.
- Cross-Platform Desktop: Native builds for macOS, Windows, and Linux so the orchestrator runs alongside your existing dev environment.
- Unified Prompt & History Tracking: Every prompt, tool call, and terminal action across all agents is captured in one place for easy review.
- Steer from Anywhere: Follow-up prompts from the mobile companion keep long-running agent runs moving even when you step away from the desk.
Best for
- Prompt Bake-Off: Send the same feature request to Codex, Claude Code, and OpenCode simultaneously and merge whichever branch wins.
- Long-Running Refactors: Kick off multi-hour agent runs in isolated worktrees and check in from your phone as they progress.
- Design-to-Code Handoff: Click a live UI element in Design Mode and hand its markup and a screenshot to the agent for pixel-accurate implementation.
- Parallel Bug Reproduction: Try multiple diagnostic approaches at once — each agent operates on its own worktree without collision.
- Terminal-Heavy Workflows: Use Ghostty-class terminal splits to keep build watchers, servers, and agent output visible side by side.
- Team Handoffs: Track every agent action in one place so the next engineer can pick up context without replaying a chat log.