Agently vs Apache Maka: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Agently and Apache Maka — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Agently
Agently
Company brain across your entire stack that spawns specialized AI agents, orchestrated by Jarvis, to autonomously ship real work.
Key features
- Company Brain Across 100+ Tools: Live ingest of Slack, Notion, Linear, Stripe, HubSpot, GitHub, Gmail, Google Drive, Figma, PostHog, Asana, Jira and more via two-way OAuth MCP connectors.
- Jarvis Orchestrator: A meta-agent that spins up specialized agents, routes work between them, and runs a shared board so founders set direction while Jarvis ships.
- Specialized Agent Roster: Prebuilt Researcher, Revenue, Growth, Support, Ops, and Briefer agents that trigger on real signals from the stack.
- Signal-to-Action Loop: Detects at-risk renewals, failed charges, escalated tickets, and doc changes, then decides and executes the follow-up work with an audit trail.
- Shippable Pages Artifacts: Outputs land as real files — presentations, gated PDFs, sheets, HTML pages, and Notion-style docs — that teams can share, gate, or export.
- Live Command Center: A dashboard shows every task, agent, and shipped artifact in real time with per-tool activity history.
- 60-Second Onboarding: Connecting tools sends the brain live within a minute so agents can start acting on the stack right away.
Best for
- Weekly Briefs and Board Updates: Automatically draft the weekly status doc, launch tracker, and board deck from live signals across the stack.
- Revenue Ops on Autopilot: Catch failed Stripe charges, at-risk renewals, and pipeline changes, then draft recovery emails and update HubSpot deals.
- Customer Support Escalations: Watch Linear and Slack for escalated tickets and reply/update them with grounded context from Notion and Gmail.
- Growth and Distribution Audits: Assemble funnel diagnostics and distribution audits as gated PDFs, complete with metrics and recommendations.
- Executive One-Person Chief of Staff: Solo founders and small teams replace recurring meetings with agent-drafted briefs and shipped artifacts.
- Ops and Compliance Reporting: Continuously reconcile tool state and generate signed-off reports for leadership without a human in the loop.
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.