Apache Maka vs Checksum: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Apache Maka and Checksum — 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.
C
Checksum
Checksum
Checksum runs AI agents that generate, execute, and self-heal Playwright end-to-end, CI, and API tests so teams get full coverage without maintenance.
Key features
- End-to-End Agent: Creates production-ready Playwright tests from your app and automatically heals broken tests as the UI and flows evolve.
- CI Agent: Generates 50-200 tests for each pull request scoped to the exact code that changed, and executes them so the PR is already verified by review time.
- API Agent: Covers thousands of endpoints in days with tests that chain across 40+ steps and verify state changes and downstream effects, not just response codes.
- Autonomous Test Healing: Broken tests are repaired by the agents instead of engineers, cutting reported maintenance time by roughly 90%.
- Production Error Monitoring: Watches live errors and converts each real bug into a regression test so the same failure cannot ship twice.
- You Own Every Test: Output is standard Playwright committed to your repo through a normal pull request, so the suite moves with you if you ever leave.
- Results as a Service: Human engineers give a final verification pass on delivered tests, so you receive working suites rather than raw AI output.
- Workflow-Based Pricing: Billing is tied only to the number of maintained workflows — unlimited test runs, healings, and users at every tier.
Best for
- Bootstrapping a First Test Suite: Teams with little or no automated coverage reach 100-150 working E2E tests within the first week.
- Guarding AI-Generated Code: Engineering orgs shipping large volumes of agent-written code get every PR independently exercised before merge.
- Replacing Manual Release Testing: QA teams retire manual regression passes — one customer reported saving 90 hours of manual testing per month.
- Scaling API Coverage: Backend teams cover thousands of endpoints in days instead of spending months hand-writing integration tests.
- Eliminating Flaky Test Maintenance: Engineers stop spending sprint capacity repairing selectors and broken assertions after UI changes.
- Increasing Deploy Frequency: Teams held back by painful release testing gain enough confidence to deploy far more often.