Apache Maka vs Ninjō AI: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Apache Maka and Ninjō AI — 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.
Ninjō AI
Ninjo
Infrastructure for AI sales agents on Instagram, WhatsApp and other DM channels, built and improved by talking to an LLM over MCP.
Key features
- MCP Server Control Surface: Exposes agent creation, testing, analysis and improvement as MCP tools, so Claude, Claude Code, Codex or ChatGPT becomes the interface instead of a dashboard.
- Cortex Playbook Library: Ships prompt templates, KPI rubrics and anti-patterns distilled from agents that ran in production, so a new agent inherits patterns that already converted rather than starting blank.
- Multi-Channel DM Deployment: Connects agents to Instagram, WhatsApp and other direct-message channels where the selling actually happens, without a separate build per channel.
- Versioned Changes with Rollback: Every edit to an agent is versioned and instantly reversible, so a bad prompt change during a live launch can be undone rather than debugged under pressure.
- Synthetic Conversation Testing: Runs an agent against generated conversations before it reaches a real inbox, surfacing broken qualification logic ahead of launch.
- Follow-Ups and Keyword Triggers: Fires scheduled follow-up sequences and keyword-based branches so stalled conversations get reopened automatically.
- Built-In CRM and Funnel Analytics: Ninjo Studio provides real-time conversation views, contact records and funnel reporting in one panel for when you want direct oversight.
- Payment Recovery Flows: Agents can chase declined payments conversation by conversation, a pattern the team credits for recovering 47 declined payments in a single four-day launch.
Best for
- Creator and Coach Launches: Running a short high-volume launch where an agent qualifies inbound DMs, handles objections and sends payment links at a pace a human team cannot match.
- Instagram Lead Qualification: Filtering hundreds of daily inbound Instagram messages down to the prospects worth a human sales call.
- WhatsApp Sales Follow-Up: Reopening conversations that went quiet with timed follow-up sequences instead of leaving them to decay.
- Agency Multi-Client Operations: Managing many client agents from a chat interface so a three or four person team can operate over a hundred agents.
- Declined Payment Recovery: Having an agent work through failed transactions individually to recover revenue that would otherwise be written off.
- Rapid Agent Iteration: Rewriting an agent's qualification logic mid-campaign and rolling back immediately if conversion drops.