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Sidekick is a terminal assistant you can run as sk, with chat, voice input, and tools for coding and other tasks. For a local-model setup, it uses Ollama on your computer; it can also be configured to use hosted model providers with your own API key. That distinction matters: Sidekick is local-first, but not every configuration is offline or local.
What Sidekick does
Sidekick is installed as the Python package sidekick-agent; its command-line interface is sk. The project README documents a fullscreen terminal interface, a plain-text REPL, single-shot commands, voice interaction, and an MCP server that communicates over standard input and output. Its tools and supporting features are intended to make it more than a chat window: the README also lists memory, full-text search, todos, shell history, skills, background jobs, scheduling, and audit logs. These are project-documented capabilities, not independently tested results.
The README currently describes 19 tools. That is a project feature count, not a performance measure, and may change as the software evolves. Irfan Wani’s overview describes Sidekick as “the opposite bet: a local-first terminal companion you talk to — chat, voice, and 19 tools — running on your own hardware.” The local-first framing reflects the option to use a model on your own machine, rather than a guarantee that all configurations keep all data or network activity local.
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Set it up with a local model
The documented quickstart uses uv and includes the optional voice dependencies. You need Python 3.12 or newer. A local model is not bundled: Sidekick’s README directs users to install Ollama and pull a model separately.
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Install Sidekick and its voice extras:
uv tool install sidekick-agent[voice]. The project also lists pipx, pip, the Arch User Repository, and conda-forge as installation routes. -
Install and run Ollama, then pull a model that your computer can handle. The README gives Qwen 2.5 Coder 7B and Llama 3.2 3B as examples; these are options, not benchmarked recommendations or promises of a particular speed or quality.
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Initialize Sidekick with
sk init, then start the interactive terminal interface withsk.PC Slower Than It Used to Be?
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Choosing a model involves trade-offs among available system RAM and GPU VRAM, model quality, and response speed. The project README includes an example system with a Ryzen 7 4800H, 16 threads, and a GTX 1650 with 4GB of VRAM. That is an illustration, not a minimum requirement or a recommended purchase; the project materials do not provide controlled performance comparisons.
Choose an interface for the task
Interactive chat
Run sk for the fullscreen terminal UI, or use the documented plain-text REPL when you want a simpler text interaction. These are the natural starting points for back-and-forth coding questions.
Single-shot runs
The README documents sk run for individual tasks, with JSON output, planning, and background options. Use this route when you want a command-oriented invocation rather than an ongoing chat session. Check the installed version’s command help for the exact flags supported by that release.
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Voice
The voice extra enables the Talk/microphone feature as part of setup. The README describes CPU-based faster-whisper transcription and says temporary recordings are deleted after each take. It also documents selecting an audio device. Without the voice extra, the microphone feature is omitted initially and installs on first use, according to the README. A built-in or existing microphone may be enough; an external microphone is optional.
MCP server
Sidekick also documents an MCP server over stdio, allowing compatible clients to connect through that protocol. This is a separate integration surface from the terminal chat interface.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Understand the privacy and safety boundaries
With Ollama and a local model, inference can run on your machine. Alternatively, the project supports hosted model providers configured with a user-supplied key; that option sends requests to the selected provider, so do not treat it as equivalent to a local-only setup. The README describes local storage and credential handling, but those implementation statements are project documentation, not an independent security audit or proof of absolute privacy.
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The project describes several controls on its own agent tools:
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File reads are described as automatic; writes, deletes, and general shell actions require approval.
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Certain destructive shell patterns are refused.
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Agent-initiated web fetches are deny-by-default, and the project’s URL-fetching tools block local and private addresses.
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Shell commands that invoke
curlare outside those egress controls. The controls therefore do not sandbox all network access from the machine.
Approval prompts and fetch restrictions can reduce risk, but an agent that can read files may access data available to it, and shell network calls are a documented exception. Review proposed commands and avoid exposing sensitive files or credentials to an agent unless you understand the consequences.
When Sidekick is a fit
Sidekick is worth considering if you want a terminal-based assistant and are comfortable setting up a Python package and a model runtime. A local setup depends on choosing a model that fits your hardware; a hosted provider is an alternative if you accept that its requests leave your computer. Voice is an optional interaction mode, and the project’s tool approvals may suit users who want to review consequential actions.
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It is not a turnkey promise of private, unrestricted automation: model behavior, speed, and quality depend on the model and system, while the documented controls have explicit boundaries. Consult the Sidekick README for the project’s current installation commands, supported interfaces, and safety details. The original overview is available on DEV Community.
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