Docent

Your private AI assistant. Chat with your PDFs and Office files, search the web, and connect MCP servers, all from the terminal. If no local agent is running, the CLI downloads the framework and models it needs and starts one itself.

It installs as both docent and smartloop; the two are the same command.

More at: docs.smartloop.ai

Install

macOS and Linux

curl -fsSL https://smartloop.ai/install | sh

Windows (PowerShell):

irm https://smartloop.ai/install.ps1 | iex

The binary goes to $CARGO_HOME/bin (~/.cargo/bin) when a Rust toolchain already owns that directory, since it is on your PATH anyway; otherwise to ~/.local/bin. If the chosen directory is not on your PATH, the installer appends an export line to the startup file for your shell — ~/.bashrc, ~/.zshrc, or fish_add_path in config.fish — so a new terminal picks it up. Re-running the installer will not add that line twice.

On Windows the binary goes to %USERPROFILE%\.smartloop\bin and the installer sets your user PATH through the registry. Restart the shell to pick it up.

Set SMARTLOOP_CLI_INSTALL_DIR to install elsewhere, or SMARTLOOP_CLI_VERSION to pin a specific release.

Prebuilt binaries are published for Linux (x86_64, aarch64; glibc, with the Vulkan loader bundled), macOS (Apple Silicon, signed and notarized) and Windows (x86_64, signed). The agent is built in, with GPU inference through Metal on macOS and Vulkan on Linux and Windows.

On Linux the release folder goes to ~/.local/share/smartloop/<version>/, and the bin directory gets symlinks to it.

From source

Requires Rust (2024 edition), CMake and a C/C++ toolchain, since the built-in agent compiles llama.cpp. Pick the GPU backend with a feature:

cargo install --path . --features metal    # macOS
cargo install --path . --features vulkan   # Linux, Windows (needs the Vulkan SDK)
cargo install --path .                     # CPU only

The framework crate comes from Smartloop's public registry at https://dl.smartloop.ai/crates/, declared in .cargo/config.toml; no account or token is needed.

Quick start

All you need is docent. Run it and you're chatting: no account and no separate agent setup.

docent

The first run downloads and starts the local agent for you (see First run). Everything below is optional:

• AI search: docent login signs you in and enables web (AI) search.
• Agent control: docent agent stop stops the local agent and docent agent start starts it again.

Usage

First run

Any command that talks to the agent starts it when none is running. It listens on a free port it picks itself and writes it to ~/.smartloop/server.port, where the CLI reads it. On first use that means:

1. Start the agent, which is built into the CLI (SLP framework 1.2.9), in the background with SLP_HOME=~/.smartloop, logging to ~/.smartloop/server.log. The agent keeps running after the CLI exits. An agent Studio desktop runs on the same home is used instead, when it is the same framework version.
2. Download the embedding model (bge-m3-Q4_K_M.gguf, ~417 MB) into the workspace's models/embeddings/ folder for document search.
3. Run the agent's bootstrap, which downloads the default base model, creates the default project and loads the model.

Each step is skipped when its files are already there. Progress shows as a checklist on stderr under the Docent banner that redraws in place, with the active download's bar in Smartloop pink:

[✓] Start agent                    port 50578
[✓] Embeddings (bge-m3)                417 MB
[•] Base model sl-mini
    ██████████████▋░░░░░░░░░░░░░░░   49%  377 MB/769 MB
[ ] Default project
[ ] Load model
[ ] Skills and connections

A step without a bar shows its elapsed time once it runs past a couple of seconds, and it ends with ✓ Setup complete in 1min 12s.

When stderr isn't a terminal, each step prints one line as it finishes instead. docent model enable shows the same checklist for its download.

To do this up front, or to stop (and later restart) the agent. Optional; the agent starts on its own when needed:

docent agent start
docent agent stop

Login (optional)

Signing in isn't required to use Docent. It unlocks AI search (web search) and the models marked (sign in).

docent login

Opens app.smartloop.ai in the browser to sign in, the same way the desktop app does: once you're in, the page hands the session to the local agent, which stores it and uses it for every platform call. The command waits (up to five minutes) and prints the account it signed in as. If the browser doesn't open, visit the URL it prints.

To sign in with a token instead, run docent login --token and paste it at the prompt (input is hidden), or use --token <token> or pipe it on stdin in scripts. docent logout clears the stored credentials.

Projects

List projects:

docent project list

Output is rendered as a table showing each project's ID, name, and whether it is a system project.

Create a project from a blank template:

docent project create --name my-project
docent project create --name my-project --description "Research notes"

A blank project starts with no skills; the service seeds it with the workspace defaults. Everything the project stores — skills, documents, its index — lives under the service's own project directory, so there is no working directory to choose.

Import a project from an archive produced by an earlier export:

docent project create --import my-project.zip
docent project create --import my-project.zip --name restored-project

--name is optional here — pass it to rename the imported project. The import gets a fresh project ID, and MCP OAuth credentials are stripped from the archive on the way in.

Delete a project:

docent project delete --id <project-id>

Check which endpoint the CLI uses, whether the local agent is running there, which model it has loaded, and the per-project agents it has started:

docent agent status

Endpoint: http://localhost:50578
Status:   healthy
Model:    sl-mini (Q4_K_M, 32768 ctx, 769 MB)
Process:  pid 8738, 250 MB
+--------------+------+-------+-------+------+--------+
| Project      | PID  | Port  | Alive | Idle | Memory |
+--------------+------+-------+-------+------+--------+
| general_chat | 2668 | 62110 | true  | 94s  | 122 MB |
+--------------+------+-------+-------+------+--------+

Endpoint is the URL the CLI talks to (SMARTLOOP_API_URL, or the default). The command exits with status 1 when the agent can't be reached there.

Start an interactive chat with the local agent:

docent

docent on its own is short for docent run. In a terminal this opens a full-screen app, laid out like Claude Code. While a reply runs, a live status line above the prompt shows the agent's current step (web search, document lookup, model selection) and for how long, with a line per running download. The finished reply keeps its sources and the model that answered:

> what is the capital of France?

■ Paris is the capital of France.

  References
  [1] https://en.wikipedia.org/wiki/Paris
  ⎿  sl-mini · 7 tokens · 0.4 tok/s · 16s

[-] Reading en.wikipedia.org… (4s)
╭──────────────────────────────────────────────────────────────────────╮
│ >                                                                    │
╰──────────────────────────────────────────────────────────────────────╯
  [enter] send  [esc] interrupt  [?] shortcuts

It chats in the server's current project, or the one given with --project. On exit it prints the session id, to resume with --session.

Key	Does
?	shortcuts
Shift+Enter, Alt+Enter, Ctrl+J	new line
Esc	interrupt the reply
wheel, PgUp/PgDn	scroll; drag to select and copy
drop a file	attach an image (PNG, JPEG) or document (PDF, Office, CSV, text)
Ctrl+O	models: enable, disable, download
Ctrl+S	web search on or off

Command	Does
/login, /logout	sign in in the browser (--token to paste one)
/models	enable, disable and download models
/mcp	MCP servers; /mcp add <url> connects one
/status	account, model, agent and versions
/usage	web searches left, tokens and cost saved
/upgrade	Pro plan: 1,000 web searches a month
/clear	clear the chat and start a new session
/help, /quit

Pass an initial prompt to send immediately:

docent run "what are some things to do in madrid spain?"

Options:

docent run --project <project-id>   # chat in this project
docent run --session <session-id>   # resume an existing session; a new one is created when omitted
docent run --plain                  # line-by-line chat instead of the full-screen app

When stdin or stdout isn't a terminal, or with --plain, run chats line by line instead: without --project it lists your projects and asks which one to chat in (press Enter for the current one), then keeps reading prompts from stdin until EOF, /quit, /exit, /q, or exit. The response streams token by token on stdout, and the agent's progress is printed on stderr as [step] message lines, so it doesn't interleave with the answer. When the answer draws on documents or web pages, the sources it used follow it on stdout as a numbered list:

References
[1] https://releases.rs/

Models

The orchestrator picks which model serves each turn from the models enabled for the project. List what's available and its state for the current project:

docent model list

Enable or disable a model:

docent model enable gemma4-e2b
docent model disable gemma4-e2b

enable downloads the weights first when they aren't on disk yet, showing progress, and only then switches the model on. Weights are shared across projects, so each model downloads once. Models marked (sign in) need docent login first. disable also unloads the model from memory. All three take --project <project-id> to act on a project other than the current one. The sl-mini orchestrator is always on and isn't listed.

Configuration

The CLI connects to the local agent on whatever port it picked (~/.smartloop/server.port). Point it elsewhere with SMARTLOOP_API_URL:

SMARTLOOP_API_URL=http://localhost:9000 docent project list

With SMARTLOOP_API_URL set, the CLI only connects: it never installs or starts an agent for that URL.

Variable	Default	Effect
SLP_HOME	~/.smartloop	Where the workspace, models and logs live
SLP_PORT	a free port	Pin the managed local agent to this port

License

Released under the MIT License.