Dstl8 CLI

Your fast on-ramp and TUI

The dstl8 CLI is the on-ramp to Dstl8. The flow is: authenticate → add sources → install MCP. After that, your AI agent can investigate incidents, query logs, and analyze patterns through Dstl8's MCP tools.

The CLI also ships an interactive TUI for browsing workspaces, sources, incidents, heatmaps, and logs from a terminal.

Setup

Install the CLI

Pick the option that matches your environment. Each installs the same dstl8 binary.

Method	Command
Homebrew (macOS / Linux)	brew install control-theory/dstl8/dstl8
Shell installer	curl -fsSL https://install.dstl8.ai/script/dstl8-cli | sh
npm	npm install -g dstl8 (or npx dstl8)
Nix	nix profile install github:control-theory/dstl8
Manual	Download from GitHub Releases

Verify with dstl8 version.

Fast track: dstl8 setup

One command to get fully onboarded:

dstl8 setup

This walks through four steps — each optional, skip any with :skip and re-run dstl8 setup anytime to finish later:

1. Account — Creates your account via GitHub or browser signup (skipped if you're already logged in)

2. AI agent integration — Installs the Dstl8 MCP server into detected AI coding agents (Claude Code, Codex, Cursor, and others)

3. Data sources — Auto-detects sources on your machine (Kubernetes contexts, AWS credentials, Vercel/Supabase/GitHub configs) and sets them up

4. Explore — Opens the interactive dashboard or web dashboard

That's the entire authenticate → add sources → install MCP flow in one guided pass. Prefer to run each piece yourself, or need to script it? The three steps below are the same flow, done manually.

1. Authenticate

Pick the option that matches your situation — you only need one.

If you don't have a Dstl8 account yet:

dstl8 signup

Opens your browser to create a Dstl8 account and organization. Once you're done, the CLI saves a long-lived API token locally, and a Default workspace is created for you to start with.

If you already have an account — for example, you were invited to an existing org, or you're setting up a new device:

dstl8 login

Opens your browser to authenticate. If you were invited to an org, accept the invite in the browser first, then run dstl8 login to attach the CLI.

Either way, confirm with dstl8 profiles — the active profile is marked ►.

2. Add sources

Adding a source is what gets logs flowing into Dstl8. dstl8 sources add is an interactive wizard for every source type. It auto-detects local config (~/.aws/credentials, ~/.kube/config, vercel.json, supabase/config.toml, .git/config) and pre-fills sensible defaults.

dstl8 sources add kubernetes
dstl8 sources add cloudwatch
dstl8 sources add vercel
dstl8 sources add supabase
dstl8 sources add otlp
dstl8 sources add cloudflare

For pull-based sources (kubernetes, cloudwatch), the CLI verifies the connection before exiting. For webhook-based sources (vercel, supabase, otlp, github), the wizard prints a webhook URL and any auto-generated tokens — paste those into the upstream provider to complete the setup.

For scripted setups, pass --yes with the relevant flags to skip prompts:

dstl8 sources add cloudwatch --yes \
  --name prod-cw \
  --aws-region us-east-1

Confirm logs are flowing. Pull-based sources should ingest immediately. Webhook sources only start flowing after you paste the webhook URL into the upstream provider, so do that first.

dstl8 sources                      # source listed and ingesting
dstl8 logs fetch -n 5              # recent logs

3. Install MCP

This is the step that connects Dstl8 to your AI agent. dstl8 install auto-detects MCP-compatible clients on your machine and configures them:

dstl8 install                      # interactive picker
dstl8 install --all                # install to every detected client
dstl8 install claude-code          # install to a specific client
dstl8 install status               # see what's installed where

Supported clients:

Client	Install command
Claude Code	dstl8 install claude-code
Claude Desktop	dstl8 install claude-desktop, or download dstl8.mcpb from the latest release and double-click
Codex	dstl8 install codex
LM Studio	dstl8 install lm-studio
Cursor	dstl8 install --include-experimental cursor
Windsurf	dstl8 install --include-experimental windsurf

Restart your AI client after installing so it picks up the new MCP server. Your AI agent can now query Dstl8.

The TUI

dstl8 tui opens an interactive terminal UI for browsing your data without leaving the shell. Most investigation work runs through your AI client once MCP is set up, but the TUI is useful for quick visual checks and one-off navigation.

The TUI splits into org-level views (Overview, Workspaces, Incidents, Events, Sources) and workspace views (Dashboard, Incidents, Heatmap, Log Viewer, Events, and — for workspaces with a Kubernetes source — Topology). Use ← → to switch tabs, ↑ ↓ or j k to navigate, Enter to drill in, and Esc to go back. Ctrl+C quits.

Sources

The org-level Sources tab lists every source connected to your organization. Press Enter to drill into streams, then Enter again on a stream to see its logs. Press a on any source or stream to assign it to a workspace.

Workspace dashboard

Each workspace has a Dashboard with a Möbius AI summary at the top, a list of active incidents, and the sources feeding the workspace. Press Enter on the Möbius summary for a full analysis.

Incidents

The Incidents tab gives you a sortable, filterable table — Open, Resolved, Closed, or All. Press s to change the sort, f to cycle filters, and Enter on a row to open the incident detail view.

The detail view renders Möbius's full analysis: summary, description, evidence, recommended actions, alongside the impacted resources and event timeline.

Heatmap

The Heatmap plots sentiment over time by stream, namespace, or service. Anomalies surface as red dots on the affected row. Press w to change the time window, g to change the grouping, and Enter on a row to view the filtered logs for that group.

Log Viewer

The Log Viewer combines a stacked severity bar chart (1-minute buckets) with a scrollable log table. Press Enter on a log line to open a detail modal with full metadata. c copies the body to your clipboard.

For the complete list of keybindings across every view, see the README.

Other CLI commands

A handful of commands are useful outside the TUI, especially in scripts and CI.

Piping logs in with tap

dstl8 tap pipes any logs into Dstl8 without setting up a dedicated source — it auto-detects the format, converts to OTLP, and streams to your org:

kubectl logs -f deploy/api | dstl8 tap
heroku logs --tail         | dstl8 tap
cat app.log                | dstl8 tap
dstl8 tap --listen                       # local OTLP endpoint for SDKs/collectors

See the full Tap guide for supported formats, the --listen OTLP server mode, and per-vendor examples.

Sending and querying events

dstl8 events sends and queries platform events — deploy markers, config changes, CI results, and notes on the timeline:

dstl8 events send --type deploy --title "Deployed checkout v1.42" --env prod
dstl8 events list --start -7d --type deploy,ci
dstl8 events search "rollback"

See the Events guide for the full flag reference and CI/CD integration.

Exploring cluster topology

dstl8 topology (alias: topo) explores the Kubernetes topology captured by cluster agents — the resource tree and blast-radius impact for any component:

dstl8 topology clusters                                 # clusters with topology data
dstl8 topology view --kinds deployment,service          # the cluster resource tree
dstl8 topology impact deployment/checkout/payment-api   # what depends on it, what it depends on

See the Topology guide for the web UI and TUI views and the full flag reference.

Tailing and fetching logs

dstl8 logs tail streams logs to stdout in real time:

dstl8 logs tail                                    # all logs
dstl8 logs tail --source Prod -s error             # one source, errors only
dstl8 logs tail --stream-name api --search "timeout"

dstl8 logs fetch pulls a time range and exits — useful for scripts and verifying ingestion:

dstl8 logs fetch --start 24h -n 500 --json
dstl8 logs fetch --start 7d --end 24h --source Prod -s error

Time arguments accept relative (30m, 1h, 24h, 7d) or absolute (2024-01-15, 2024-01-15T09:30:00) values. --json produces NDJSON for piping.

Profiles and workspaces

dstl8 profiles                     # list configured profiles
dstl8 switch <profile>             # change the active profile
dstl8 workspaces                   # list workspaces
dstl8 workspaces create <name> --description "Production monitoring"
dstl8 sources assign <source> <workspace>

Configuration lives in ~/.config/dstl8/. Use --profile <name> on any command to run it against a specific profile without switching.

More

The full command reference, every flag, and release notes live in the dstl8-cli README and on the releases page.