Build the dashboard.
Pull metrics, calculate changes, explain anomalies, and package the result.
You get: spreadsheet, chart, and summary.
Open-source AI coworker
Hand it to Anton. Get the work done.
Describe the outcome in plain language. Anton plans the steps, runs the work in a secure scratchpad, and delivers the finished output: a report, spreadsheet, dashboard, organized folder, digest, or workflow.
A MindsDB product
Examples
Give Anton the outcome.
Not the instructions.
Anton is for the work that usually takes a dozen tabs, a spreadsheet, a few scripts, and too much copying and formatting.
Clear the noise.
Find low-signal emails, classify them, unsubscribe where appropriate, and summarize what matters.
You get: a cleaner inbox and a short briefing.
Fix the folder.
Sort messy files, rename them consistently, flag duplicates, and show the plan first.
You get: organized files with a review trail.
Investigate the question.
Search sources, extract evidence, calculate assumptions, and write up the conclusion.
You get: a cited memo or deck.
Explain what changed.
Query data, run analysis, build charts, and trace the cause behind a movement.
You get: reproducible analysis and visuals.
Turn it into a workflow.
Connect tools, monitor changes, and repeat the task on a schedule.
You get: a worker you can reuse.
LLM choice
Use the right model
for the task.
Anton is not locked to one provider. Use frontier models from Anthropic or OpenAI, route through a model gateway, run open-source models locally, or connect your own endpoint.
Great for long-context reasoning and careful analysis.
Strong general-purpose planning, coding, and synthesis.
Run local or self-hosted models when control matters.
Bring the model stack your team already trusts.
How Anton works
A place to think,
run, and improve.
Anton does real work in persistent execution spaces — not just chat messages.
Scratchpads make the work inspectable.
Anton plans, writes code, runs queries, parses files, creates charts, and stores intermediate results in a secure scratchpad. Scratchpads persist, so Anton can iterate instead of starting from zero.
The credentials vault keeps secrets out of prompts.
Connect Anton to data, apps, APIs, and tools without putting credentials in front of the LLM. Anton retrieves what it needs through the vault while secrets stay isolated.
Memory helps Anton get better at your work.
Anton builds multi-layer memory across sessions, projects, rules, and lessons. It learns your workflows, definitions, and preferences so you do not repeat context every time.
Proof
Not a black box.
Every serious task creates artifacts you can inspect, replay, and reuse.
Inspectable artifacts.
The plan, code, intermediate data, chart specs, outputs, and final result.
plan: collect sources -> normalize -> analyze
code: generated Python + SQL
state: tables and charts persist
output: dashboard.html + summary.mdReal tool access without exposure.
The model can ask for the action; the vault handles the secret.
LLM sees: "query the customer database"
vault provides: scoped connection
scratchpad gets: result set, not the secret
logs show: what was accessed and whyRun it your way
Open source.
Run locally, self-host, or use Minds Hub.
Start on your machine for control and inspectability. Use Minds Hub when you want managed hosting, persistence, shared infrastructure, and always-on workers.
Run Anton anywhere.
Open source, inspectable, extensible. Use the desktop app, CLI, your own model keys, and your local files and tools.
Run Anton on Minds Hub.
Use the hosted version when tasks need to keep running, teams need shared infrastructure, or you want managed model access and agent operations.
Ready when you are
Stop prompting.
Start delegating.
Anton turns plain-language requests into finished work you can inspect, reuse, and improve.