DoMyWork vs SoMatic

DoMyWork and SoMatic are both workflow automation tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

DoMyWork

The tool operates in two modes: Chat, where you issue a task and the agent executes it end-to-end, and Autopilot, where recurring tasks run on a schedule without you touching anything. Lead enrichment, competitor price tracking, and report generation are the documented sweet spots — tasks where the inputs are structured and the output format is predictable. The agent executes code and API calls autonomously, which means it handles multi-step sequences without a node-by-node canvas. The ceiling appears when tasks require complex conditional branching or when output quality depends on edge cases the agent hasn't been prompted to handle — at that point, teams fall back to manual prompt tuning or external scripting.

SoMatic

The core workflow is a CLI command that takes a screenshot, runs element detection locally, and returns numbered marks with coordinates as JSON — so agents target elements by ID, not by fragile pixel hunts. Every action returns JSON, which means downstream agents can chain steps without parsing unstructured output. The self-hosted, MIT-licensed model runs on your own hardware, so no screenshot data leaves the machine. The wall appears with non-standard or highly dynamic UIs where YOLO detection misses elements or mislabels them — teams handling those cases add a fallback coordinate layer manually. At this GitHub star count, the community size is small, which means debugging edge cases happens in the codebase, not a forum.

Attribute	DoMyWork	SoMatic
Pricing	Paid	Free
Price	$15.99/mo	—
Free trial	No	No
Open source	No	Yes
Has API	Yes	Yes
Self-hosted option	No	Yes
Platforms	Web-based, cloud-hosted	Linux, macOS, Windows (via npm + Python runtime)
Pros	Natural language task input means a marketing analyst can set up a lead enrichment Autopilot without writing a single line of configuration, so the tool stays usable without an automation engineer on call. Autopilot mode runs recurring tasks on a schedule autonomously, so competitive pricing reports and weekly data aggregations happen without anyone remembering to trigger them. Playbooks package tested workflows as reusable templates, so a task an ops manager debugged once can be handed to the whole team without re-explaining the setup. API access lets engineering teams trigger agent runs programmatically, so Integrately can sit inside a larger internal workflow rather than operating as a standalone island. Freemium entry tier with a credit allocation lets teams validate whether the agent handles their specific task before committing budget, so the evaluation risk is low.	Local YOLO-based element detection returns numbered marks as JSON, so agents target UI elements by stable ID rather than fragile pixel coordinates that break on resize or re-render. MCP server is included out of the box, so Claude and other MCP-compatible agents plug in without a custom integration layer — the handoff between agent decision and desktop action is a standard tool call. Headless Xvfb support means the same automation pipeline that works on a developer's desktop runs on a server with no display attached, so you do not maintain separate codebases for local and CI environments. MIT license and fully self-hosted execution means no screenshot data leaves your infrastructure, so automation against internal or regulated applications does not create a data-handling obligation with a vendor. Every CLI command returns JSON, which means agents can chain steps by parsing structured output rather than scraping human-readable text — reducing the failure surface in multi-step workflows.
Cons	Tasks that require branching based on what a previous step returned — 'if the scraped price is null, fall back to this secondary URL' — are not reliably handled by the agent's planner; teams end up iterating on prompt phrasing to approximate logic that a visual builder would express as a condition node, and the results are harder to audit. There is no self-hosted option, which means teams under data residency or compliance requirements — common in healthcare, finance, and enterprise procurement — cannot use this tool at all and move to self-hosted alternatives. Credit-based execution means high-volume or high-frequency Autopilots consume credits at a rate that is difficult to predict before a workflow runs at scale; teams running dozens of daily enrichment tasks report needing to upgrade to paid tiers sooner than the free allocation suggests. When the agent misinterprets a step — pulling the wrong field, hitting an unexpected page structure — the failure mode is a silent wrong answer rather than a visible error, so teams running unmonitored Autopilots on business-critical data need independent validation checks they have to build themselves.	Detection quality depends entirely on the bundled YOLO model's training distribution — UIs with non-standard controls, heavily custom widgets, or frequent layout changes produce missed or mislabeled marks, and there is no documented fine-tuning path for teams whose target apps fall outside the model's coverage. Teams hitting this wall add manual coordinate fallbacks, which reintroduces the fragility SoMatic was meant to eliminate. The project is maintained by a single author with 18 stars and zero open issues at the time of scraping — not because everything works perfectly, but because the community debugging surface is nearly nonexistent. Teams that hit a detection edge case or a platform-specific headless failure debug the source directly; there is no forum, no commercial support, and no track record of response time on issues. There is no built-in action verification or retry logic described in the docs — the CLI returns JSON coordinates and executes actions, but confirming that a click produced the expected state change is the agent's responsibility. Pipelines that need reliable end-state confirmation build that verification layer themselves, which is the point at which teams with stricter reliability requirements evaluate alternatives like Playwright for browser targets or platform-native accessibility APIs for desktop targets.

Bottom line

DoMyWork is paid while SoMatic is free; SoMatic is open source. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between DoMyWork and SoMatic?

DoMyWork is Paid, while SoMatic is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is DoMyWork better than SoMatic?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.