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Run approval-gated automation and verifiable LLM workflows in a local-first setup.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "MIDAS" yet — see the docs or source repo.
Using MIDAS, design a local-first deployment workflow: inspect the shell commands to be run, list risks, wait for my approval before execution, and generate an auditable execution log.
A deployment workflow with approval steps, risk checks, and a traceable execution log plan.
Use MIDAS to build an LLM workflow for support tickets: read local data, draft responses, record inputs and outputs for each step, and require human confirmation before sending.
An auditable, traceable ticket-handling workflow with a human approval step.
Have MIDAS assess whether this automation task is safe: it reads local files, calls an LLM, and modifies config files. Provide potential risks, required approval points, and least-privilege recommendations.
A security assessment, approval recommendations, and a least-privilege control checklist.
Search the web locally and generate grounded answers with an Ollama model.
Run a fully local multi-agent AI system with predefined workflows via MCP.
Route coding tasks across local and remote LLMs with benchmarking and code search.
Delegate low-risk tasks to a cheaper model with main-agent review.
Build AI agent workflows and automate tasks using MCP-connected services.
Run a secure local personal agent with memory, channels, and tool use.