Apache Maka vs ShogunAI: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Apache Maka and ShogunAI — 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.
ShogunAI
ShogunAI
A local-first macOS memory and execution assistant that remembers your workday on-device and finishes work inside the tools you already use.
Key features
- On-Device Memory Layer: Captures mail, meetings, documents and screen context locally and indexes them into an encrypted store on your Mac, with no cloud copy by default.
- Contextual Recall with Sources: Answers plain-language questions across Mail, chat, docs and calendar from a single search, attaching the source and timestamp to every hit so answers can be checked.
- Execution Layer with Three Autonomy Levels: Reversible work runs automatically, drafts wait for review, and anything leaving your Mac stops for explicit approval — with every action logged as what ran, on what evidence, and what left the device.
- Inline Draft at the Caret: Press Option and ShogunAI reads the field around your cursor plus the memory behind it, then writes the continuation directly in the app you are already typing in as a local write you send yourself.
- Meeting Minutes, Not Recordings: Transcribes a meeting as it starts and on completion writes a summary, the decisions made and the commitments it heard, filing next actions into your work state with one tap; audio is never written to disk.
- Two-Way Live Translation: Set the language you speak and the language they speak — their speech reaches you in yours and yours reaches them in theirs, with only text retained afterwards.
- Daily Brief: Assembles what moved overnight, what is still open and what you promised someone before the day starts, rather than on request.
- Shared Memory Across Models and Agents: The same structured state of people, projects, commitments and open loops reaches Claude, Cursor, ChatGPT and anything driven over MCP, CLI or REST, so no session starts cold.
Best for
- Eliminating Cold Starts: Stop re-pasting last week's decisions and open threads at the beginning of every model session — every assistant starts from the same live memory of your work.
- Closing Open Loops: Surface the follow-up that is due today, draft the reply with the correct file attached, and hold it for approval before it reaches the recipient.
- Meeting Follow-Through: Turn a call into decisions, commitments and filed next actions automatically instead of re-listening to a recording.
- Answering 'What Did We Decide?': Recall a specific decision from a Notion brief or Gmail thread weeks later, with the source and time attached so it can be verified.
- Privacy-Constrained Work: Run an assistant over sensitive client or company context on machines where a cloud-indexed copy of the workday is not acceptable.
- Cross-Language Collaboration: Hold live meetings with counterparts in another language and keep only the translated text afterwards.
- Consultant and Founder Context Switching: Keep separate projects, people and commitments straight across many concurrent engagements without manual note discipline.