# super-log
One hub for every log stream you have — devices, servers, containers, browsers, chains and apps.
This project is a consolidation of a patchwork of tools I have used, in one form or another, over the last fifteen years — the log mergers, port watchers, build wrappers, ad-hoc proxies and one-off scripts every long-running bench accumulates — rebuilt here as one coherent thing, on one wire protocol, with one screen.
Free and self-hosted, forever. It collects and consolidates; analysis is a separate, cleaner concern — hand the consolidated stream to super-log.com for real-time LLM analysis and team features, or to your own store. See Collection is not analysis.
Twelve producers on one screen, interleaved by arrival: C++ through both SN_LOG and spdlog, Rust, Go, Python, Swift, Fortran, a POSIX shell script, two React Native devices, Metal GPU work reporting real bandwidth, and a live Binance WebSocket. The last of those is running at hundreds of frames a second — so it is rate-capped, and says so rather than silently dropping them.
If you build across devices, you know the ritual: a Metro console for the
iOS simulator, another for the Android emulator, adb logcat for the phone
on your desk, a terminal for the C++ engine, another for the Rust service,
browser devtools for the web build, and an ssh session to the box in the
cloud. Six places to look, none agreeing on timestamps, and the bug is
always in the interleaving.
super-log converges all of it on one process and one screen:
apps, 14+ languages ┐
GPU and graphics │
devices and boards │ ┌─▶ native viewer (ImGui)
machines, services ├── POST NDJSON ──▶ superlogd :7333 ─┼─▶ web viewer (React)
network and DNS │ (fan-out+replay) ├─▶ journal → search / replay
builds and repos │ ├─▶ GET /recent (scripts)
blockchain │ ├─▶ MCP tools (agents)
anything that prints┘ └─▶ alerts → webhook
Every producer speaks one small wire protocol (docs/PROTOCOL.md: one JSON event per line, batched over plain HTTP POST), the hub fans out to any number of readers with replay-on-connect, and everything is interleaved by hub sequence — not by device clocks, which drift.
Quick start
macOS — Homebrew:
brew install saxonnicholls/tap/super-log && brew services start super-log
Ubuntu (incl. Ubuntu Server on a Pi) — the PPA, every architecture, auto-updating:
sudo add-apt-repository ppa:super-log/stable && sudo apt update && sudo apt install super-log
Debian / Raspberry Pi OS — the .deb directly (PPAs are Ubuntu-only; don't
use add-apt-repository here). Pick your arch:
curl -fsSL -O https://github.com/saxonnicholls/super-log/releases/download/v0.4.0/super-log_0.4.0_arm64.deb # or _amd64.deb
sudo apt install -y ./super-log_0.4.0_arm64.deb
Then see it work in two lines — the hub is already running on :7333:
echo "the bench is live" | superlog tee --topic hello
curl -s "http://127.0.0.1:7333/recent?topic=hello"
Fedora/RHEL, npm, vcpkg, building from source, and the web + native viewers are in Install below.
The superlog CLI
One command reads the bench, manages the tailers, and pipes streams onto the hub:
superlog status what's running, and the hub's health
superlog alarms firing alarms (every read command is NDJSON when piped — | jq)
superlog versions the version inventory, per host
superlog start vitals start a tailer in the background
superlog stop vitals stop it
superlog viewer open the native viewer (builds it once if needed)
superlog tee a stream onto the hub (make 2>&1 | superlog tee --topic build)
superlog git install-hooks stamp every commit onto the bench
superlog git recall <sha> replay the logs the bench saw behind a commit
superlog login open super-log Cloud in your browser
Full reference: docs/CLI.md. Or superlog help.
What it does for you
One screen, everything on it. Streams colour-coded by source and level, filtered by stream, minimum level or substring. Pause freezes the display while collection continues; copy a row or the whole filtered view; export JSON, CSV or plain text.
The minimum level set to ERROR: 29 rows out of 1131. The same pricing
failure surfaces from Swift, Python, Go and a shell script side by side —
each in its own language's idiom, a PricingError, a KeyError, a returned
error, a shell test — plus the GPU refusing an allocation four times the size
of the card. Python's row carries the local variables from the failing frame,
which is the part you would otherwise be adding a print statement to find.
Your apps need almost nothing. Fourteen dependency-free SDKs: header-only
C++ (both a spdlog sink and a native snicholls::log one), plain C in
one stb-style header — zero-alloc, and a production build provably contains
no logging at all — a Rust
crate with an optional tracing layer, Python plugging into stdlib
logging, Go with a log/slog handler, Java with a java.util.logging
bridge (Kotlin and Scala ride it, one import, zero glue), Swift, Fortran
over raw POSIX sockets, OCaml over the same raw sockets, Haskell needing
only GHC's boot libraries and curl, Lean 4 for the proof jobs that run
all night (core IO plus curl — Lean grew a kernel before it grew
sockets), Perl from core modules alone (HTTP::Tiny has shipped with
Perl since 5.14), Lua making the same curl bargain as the shell SDK
(Lua never grew sockets at all), C# from the BCL alone — everywhere .NET
goes, including Unity profiles and Xbox Dev Mode — Zig through @cImport
of the C header itself, COBOL through a 20-line C shim onto the
header-only C SDK, Ruby from the stdlib — with a
drop-in ::Logger adapter, which makes the whole Rails story one
config.logger assignment — a sh one-liner for scripts, and one JS
client for React Native, the browser and Node — patchConsole: true and
every console.log is on the bench.
Six of them hook the logging framework the language already has — the
spdlog sink, logging.Handler, slog.Handler, java.util.logging.Handler,
Ruby's ::Logger adapter and patchConsole — so everything a program
already logs reaches the bench without a single call site changing.
Why the interleaving is the point. In the middle, one Swift tick unfolds in order — the tick at INFO, a DEBUG pricing pass, the ERROR its exception raised, and the WARN that followed — while eleven other producers keep writing around it. Reconstructing that sequence from separate terminals is the ritual this replaces.
Follow one action across every tier. A tap becomes a request, a database
write and a chain call on four streams. withTrace() mints a correlation
id, carries it across awaits, and puts it on outbound HTTP automatically;
a server adopts it and logs under the same id. Then one query — a ⇢ in the
viewer, GET /recent?trace=…, or an agent tool — returns the whole story in
order.
Every error, including the ones nobody logged. Uncaught exceptions and
unhandled rejections are captured by default in every SDK, with whole
stacks — not clipped to the throw site, because a deep React Native render
error runs 100+ frames through the bridge and that path is the thing you
actually need to paste. Capture chains to whatever was already installed, so
React Native still shows its red box, Node still exits 1, and C++ still aborts.
C++ traces are demangled (pricer::Engine::quote(int)) with no boost
dependency. For the hardest class — an exception a library throws and a
component boundary catches and displays, so it never reaches the global
handler — wrap the tree in SuperLogErrorBoundary (or forward your own
boundary's componentDidCatch to log.exception): that lands the component
stack too, the one thing a JS stack never contains — it names the component
that threw. Plus an opt-in breadcrumb on every Error construction.
Zero-app-change fallbacks. Host-side tailers scrape what already exists:
adb logcat (scoped to one app, because an OEM handset emits ~600 lines a
second), the iOS simulator's log stream, the macOS unified log, journald,
Docker containers, and any log file. A catalog knows where ~20 common
services log on macOS and Linux — postgres, mysql, mongodb, redis, nginx,
apache, kafka, elasticsearch, rocksdb — including both Homebrew prefixes.
The catalog also knows the engines and content tools: Unity's
Editor.log (the C# compiler's error CS1234 becomes ERROR, a thrown
exception too, while a folder named "Exceptions" stays INFO) and Unreal
Engine's per-project editor logs (LogNet: Warning: maps by Unreal's own
verbosity words, category kept). Blender logs to stdout — that is what the
tee is for — and AutoCAD's LOGFILEMODE file tails like any other.
Whole fleets, pulled over ssh. One config file brings up every stream on every server: OS logs, service logs, container logs. Nothing is installed remotely, no port is opened, and the servers never need to reach the hub — so the hub can stay loopback-bound while still watching production.
HTTP calls, request and response. Front a service with the logging proxy
and every call is one event (method, path, status, latency, size), or turn
on patchNetwork in the app and see the calls it makes. HTTPS targets need
no certificate work. Bodies are opt-in; credentials are always redacted.
Blockchain addresses, beside the code that touched them. Watch any EVM address and its transfers, contract events and native balance moves land on the same screen, in hub order, next to the app code that sent them.
Operational keys that never silently run dry. A keeper or oracle out of
gas stops a production system as surely as a crashed server, and it fails
politely — it just stops, and nothing says why. superlog-gas polls the
balances you name (native coin or ERC-20, per chain, per key, from a
gitignored config — a labelled key list is a map for an attacker) and
applies the bench's discipline: readings are metric events for the chart,
crossing your fund-now line is CRITICAL said once, your low line WARN
once, and refunding is announced. One JSON-RPC batch per chain per poll,
because a public RPC's rate limit is part of the design surface.
Infrastructure that only speaks when something changes. DNS records and TLS expiry, listening ports and the processes that own them — all watched by diffing snapshots, so the stream is silent until it matters: an NS record you did not change, a certificate three weeks out, a new public listener on a production box, a service that restarted without saying so.
Builds as events, not walls of text. Wrap any build — cmake, clang, gcc,
cargo, npm, xcodebuild, local or over ssh — and compiler diagnostics become
WARN/ERROR rows with file:line, with one summary event carrying exit
status, duration and counts. Linking included: GNU ld's undefined reference and multiple definition carry no severity word and would
otherwise scroll past as INFO; they, Apple's Undefined symbols and
duplicate symbol, lld, collect2, and the driver's own
clang: error: linker command failed all land as ERROR. Debuggers ride the
same rails: pipe a batch session through the tee —
gdb --batch -ex run -ex bt ./app 2>&1 | superlog tee --topic dbg.app --classify
(lldb likewise) — or tail gdb's set logging file like any other.
Massive downloads, watched. A 70B model from Hugging Face is fifteen
shards and half a day of \r-rewritten progress bars that exist only on the
terminal that started them — and tqdm, curl and wget all mute or reshape
those bars the moment their output is a pipe, so tee sees nothing. Wrap
the fetch in superlog-dl and percent, bytes and rate become metric events;
or point --watch at the destination directory and progress is measured at
the filesystem, which no tool can mute and which is the only honest
aggregate when every shard resets its own bar. The event that matters most
is the stall: no bytes for 30 seconds is a WARN on the bench — hours
before the fetch's own patience runs out at 97% of 140GB.
Watts, thermals, and who is drawing them. On macOS every bench run
samples CPU package power, die temperatures, fan RPM, aggregate CPU as one
number, and the top energy consumers — because a runaway process announces
itself through the fans long after a chart would have caught it (see the
power row below for the incident that earned this).
The machine's own life events. Crash reports and kernel panics parsed
out of DiagnosticReports — process, exception, signal — the moment they
land (and from the recent past at startup, because a crash writes its
report before the reboot that restarts the watcher); the previous
shutdown cause once per boot, translated (cause -128: uncontrolled power loss) and ERROR when unclean; volume mounts, unmounts and renames — a
rename moves every path on the volume, which is how long writes die with
nothing recording why; and sleep/wake, which explains every gap in every
other stream. This bench crashed four times with all of that evidence
sitting unread; the first live run surfaced an unclean shutdown and 38
crashes from the preceding three days. The demo starts it unconditionally
on macOS, beside power.
Files, and the lines that changed. A generated header, a lock file a
tool rewrote, a config a script overwrote — superlog-watch puts the
change in the same ordered stream as the build that then failed, and with
--diff it says which lines: one event per hunk, removed and added
together, every hunk of a save sharing a trace with its modified event
so one query returns the whole edit. Idempotent by content hash — a
rewrite that changes no bytes publishes nothing, which an mtime watcher
cannot promise.
History, not just the last few minutes. The journal writes every frame
verbatim to disk; search reads it back with the same filters as the live
feed (including --trace), and replay re-publishes it at original pace.
A 1 GB / 4.8M-event journal searches in ~2.3 s.
Readable by scripts and agents. GET /recent?since=<cursor>&level=ERROR
answers "what happened since I last looked", with a cursor that never misses
or repeats an event.
An MCP server, so a coding agent can read the bench. Seven tools, and
the shape of them matters: an agent's context is small and a firehose is
not, so every tool filters first, caps its output, and returns one compact
line per event. The documentation is queryable too: stream_guide explains
any capability in detail — what power.*'s metrics mean, why a dl.*
stall escalates, why a diff can be silent — from
guide.json, and the same file's playbooks
(triage, follow-a-trace, silent-stream…) are served as native MCP prompts.
Every logging capability ships with an entry there, so an agent never has
to guess what a topic means.
npm run demo:mcp # drives all six over stdio and prints what an agent sees
# from a clone:
claude mcp add super-log --scope user -- node $PWD/sdk/js/packages/mcp/bin/superlog-mcp.mjs
# or, once published, with no clone at all:
claude mcp add super-log --scope user -- npx -y @super-log/mcp
Registered once per machine, not per project: one hub serves every project
and agents narrow by topic prefix. Read-only by construction — with one
deliberate, narrow exception: agent telemetry, which can land only on
agent.* status topics (see the agents blotter below) — and
dependency-free — MCP over stdio is newline-delimited JSON-RPC 2.0.
Not just Claude — any MCP client. The same server drops into every
MCP-capable tool; the config is always npx -y @super-log/mcp:
// the universal server entry — Cursor, VS Code, Cline, Windsurf, Zed, Gemini CLI, …
{ "command": "npx", "args": ["-y", "@super-log/mcp"], "env": { "SUPER_LOG_URL": "http://127.0.0.1:7333" } }
Three ways to add it:
- MCP Registry — listed as
com.super-log/super-log, discoverable in-app and installable by name (claude mcp add com.super-log/super-log); it propagates to the Cursor / VS Code / Glama / PulseMCP catalogs on its own. - Claude Code plugin —
/plugin marketplace add saxonnicholls/super-logthen/plugin install super-log@super-log(adds the server and a/super-log:stream-guideskill). - Claude Desktop — double-click the
.mcpbfrom the release, no CLI.
Per-client setup for Cursor, VS Code, Cline, Windsurf, Zed, Continue, LibreChat and Gemini CLI — plus the OpenAI Agents SDK / Google ADK recipes — is in docs/CONNECT.md.
Already on OpenTelemetry? Join with one exporter stanza, no new SDK.
superlog-otlp is an OTLP/HTTP server on the standard 127.0.0.1:4318,
so pointing an existing exporter at it needs zero endpoint config:
OTEL_EXPORTER_OTLP_ENDPOINT=http://127.0.0.1:4318. It speaks both
OTLP/JSON and OTLP/protobuf (a hand-rolled bounded decoder, because most
SDKs default to protobuf), maps severityNumber to bench levels, and
turns each resource's service.name into an otlp.<service> topic. The
detail that makes it more than a gateway: an OTel span's trace_id
becomes the event's trace, so it lines up with withTrace()'s own ids
— one /recent?trace= returns the OTel spans and the bench's lines as a
single story. Metrics arrive as metric readings (histograms summarized
to count and sum, never invented percentiles); spans as trace-carrying
DEBUG events, because this is an inlet, not a span store.
And the reverse: feed your existing OpenTelemetry backend from the
bench. superlog-otlp-export reads the hub and POSTs OTLP/JSON to any
collector or vendor — superlog-otlp-export --endpoint https://otlp.example.com --header 'authorization: Bearer …'. Every event becomes an OTLP LogRecord
(the severity band inverted, service.name from origin.app, fields as
attributes, a 32-hex trace as traceId); a bench metric event becomes an
OTLP gauge point. JSON only, on purpose — it is what every collector accepts;
a backend that demands gRPC/protobuf is one OTel Collector hop away (see
docs/OTLP.md). The bench has no spans of its own, so traces are
not exported. Together the inlet and outlet make super-log a two-way OTLP
member of a bench, not a walled garden.
Something reaches you when nobody is watching. Rules over the live feed
fire through one channel registry — console, desktop, webhook, command
today; Telegram, Twilio SMS / WhatsApp and email are config-gated entries
in the same registry (superlog-alert --channels prints the roster and
exactly what each missing channel needs) — and back onto the bench as
alert.*. Four rule shapes, because production breaks in four ways:
something bad was logged, too much of it was logged, something
stopped being logged (a quiet server and a dead server look identical
until you check), and several things happened together — a combo
rule fires when all its conditions land inside one window, the correlation
("a deploy happened AND errors spiked") no single filter can say.
Production can raise the alarm, without logging a thing. Every SDK's
PRODUCTION mode ships nothing by design — but an ALARM is not logging, it
is a rare deliberate act, and superlog-alarm is its door: a token-guarded
public webhook through a Cloudflare tunnel — a quick tunnel with zero
config, or a stable named tunnel auto-provisioned via the API given a
token. Cloudflare is the verified, daily-driven path: quick tunnels, the
named tunnel, DNS provisioning and the endpoint factory all run against it
on a real bench. --tunnel ngrok and --tunnel zrok exist as code paths
but have not been driven the same way yet — see Future directions. One
curl from any language fires
alert.inbound.<name>; the same key re-firing is one alarm with a
repeat count, not 113 pages; recovery closes the loop; and every checker
can POST a heartbeat so the gateway itself raises monitor_dead:<name>
when a watcher goes silent — the alarm the dead watcher cannot send. Both
viewers carry an alarm blotter — a sparse panel, deliberately unlike
the firehose, one row per key — with a test-alarm button that proves
the whole path step by step: hub, tunnel, a real round-trip from the
internet back through the public URL, the notification channels (including
the diagnosis when your own router's DNS filters the tunnel's name while
production's resolves it fine), and then every route on the books, each
with its own verdict — a capture endpoint passes only when a probe posted
through its public URL lands back on the hub as a wh.<name> event, a
forwarded port that answers 502 is reported as "tunnel up, your service is
not", and a watch-only URL passes on any HTTP answer, because any answer
proves the wire.
And straight from code: SN_ALARM. The gateway is the door for a
production system across a network; for an alarm the running program itself
wants to raise — an assertion that should wake someone, a dependency gone dark —
every SDK has a first-class primitive that skips the log and lands in the
blotter directly:
SN_ALARM("settlement engine unreachable"); // C++: P0, fires in the panel
SN_ALARM_CLEAR("settlement engine unreachable"); // clears it
The same call is in every language super-log speaks — sn_alarm(),
sn_alarm!, .alarm() and their kin — because an alarm you can only raise from
one language is a gap an incident finds. It emits on alert.native.<key>, which
the rules engine ignores by design (so it never trips a rule), keyed and
edge-triggered so a hot loop is one alarm and not a flood; CRITICAL fires it, an
INFO RECOVERED closes it. It posts straight to the hub — immediate and local;
the gateway's cross-process dedup and repeat-counting are for alarms that cross
a network. And it is deliberately not gated by PRODUCTION mode: an alarm you
asked for is the one thing that must not go quiet in production
(SUPER_LOG_ALARMS=0 mutes it if you must). Every demo client fires one so you
can watch it land.
The gateway is also an endpoint factory, and the viewers split its two
audiences: alarms (production) — the sparse blotter plus the alarm
path's own routes — and webhooks (development) — the endpoint grid and
a live feed of every captured delivery with its payload, signature verdict
and relay status. Both are toggled from a menu bar that the ImGui and
React viewers render from the same declarative
viewer/menu.json — alongside an agents blotter
(View → Agents): every agent working the bench, one row each — name,
which LLM it runs on, latest status, last seen. MCP consumers appear
the moment they connect (the server announces its own initialize
handshakes) and their tool calls show as requesting events; anything can
report via the agent_report MCP tool or one POST to agent.<name>,
declaring its LLM, task, percent, and the cadence it promises — every 15
minutes by default, plus on events, which is how an 8-hour job stays
visible. The freshness light is held to each agent's own promise:
green inside 2×, late, then silent — an agent that broke its own word
looks like it. And a servers board:
every event carries origin.device, so the hub's traffic is the server
list — one row per machine with a recency light (up / quiet /
silent), last seen, and the loudest level of the last minute,
whatever the mechanism that produced the events (a vitals reading, a ping
metric, an app's own SDK). "Is the build box fine" becomes one glance;
when the answer is grey, the detailed logs are one topic filter away in
the firehose — and a silence rule in alerts.json is what turns grey
into an alarm. Every route is a grid row —
status : route : url : seen : ping — expandable into full diagnostics,
with ping (measure this route now, on the same watchdog books),
copy (the full public URL) and delete buttons, each route pinged on
its own configurable clock (two consecutive failures raises
tunnel_down:<name>, recovery clears it). + endpoint provisions in
one click — a name alone captures deliveries as wh.<name> events (paste
the URL into a Stripe or GitHub webhook form and watch them arrive), a
name plus port forwards a local service. Many endpoints live better in a
file: npm run alarm -- --provision endpoints.json applies a declarative
manifest ({"name":"stripe"} capture,
forward, watch-only, each with
optional ) and keeps it applied as the file changes — new names
appear, removed names are torn down, though only names the file created.
shows every shape; the
real file is gitignored because names, ports and signing secrets describe
the bench. Every URL the gateway currently owns is rewritten to
(gitignored) on every change, so scripts and agents
can simply source it — and agents can also just ask: the MCP server's
returns the roster with public URLs and health.
Webhook testing, done properly — Stripe as the worked example. A
capture endpoint alone is already a webhook inspector: paste its URL into
the Stripe dashboard (or stripe trigger payment_intent.succeeded at it),
and every delivery lands as a wh.stripe event with method, headers
(stripe-signature kept) and up to 32KB of body — a real invoice event
runs 5–15KB and arrives whole. Two additions make it a development tool
rather than a peephole. Give the endpoint your signing secret
("secret":"whsec_..." in the manifest, or STRIPE_WEBHOOK_SECRET) and
the gateway verifies the Stripe signature scheme on arrival: each
event carries sig: verified or sig: FAILED (WARN), with stale
timestamps flagged as possible replays — and no secret means the field
stays absent, never a fake verdict. Give it a relay
({"name":"stripe","relay":5000} or a full URL like
http://127.0.0.1:5000/webhook) and each delivery is also handed to your
local handler, whose real response goes back to Stripe — stripe listen --forward-to, except every delivery, signature verdict and your
handler's status code are on the bench, in order, beside your app's own
logs. A handler that is down shows up as relay unreachable at WARN — the
exact finding a webhook test exists to produce. (local: true skips the
tunnel entirely when the bench only needs to test against itself.)
GitHub's scheme (x-hub-signature-256) verifies beside Stripe's against
the same secret; other providers are one clause each.
The webhooks panel's deliveries feed is built for reading the results: filter by endpoint and by level exactly like the main log (WARN isolates the failed signatures and unreachable relays), expand any delivery's payload in place, and copy — one button per delivery, one for the whole filtered set — hands you paste-able evidence: the feed line, the signature and relay verdicts, then the payload verbatim. The whole loop is exercised end-to-end with the Stripe CLI:
stripe listen --print-secret # the whsec_... for endpoints.json
stripe listen --forward-to http://127.0.0.1:7336/hook/stripe-test
stripe trigger payment_intent.succeeded # real events, signatures verified
Producers never block. Every SDK uses a bounded queue that drops oldest under burst — counted, never hidden. A logger that can stall the app it observes is worse than no logger.
Off in production, by construction. Every SDK requires you to declare DEVELOPMENT or PRODUCTION (neither or both refuses to build), and each mode ships only what its policy allows. Production defaults to nothing.
What this is, and what it is not
It is a development tool. The bench you sit at: everything your machine and your devices are saying, on one screen, in the order it happened, while you are working. It is optimised for the ten seconds after something breaks — one hub, no schema to declare, no agent to install, a stream added by typing one command, and nothing to configure before the first line appears.
It is not an observability suite, and should not be used as one. The distinction is not modesty, it is design: several things that make it good at the first job make it unfit for the second.
Concretely, do not point this at production and walk away. The hub has
no authentication: anyone who can reach the port can read every stream and
publish to any topic. It keeps 2000 events per topic in memory and forgets
the rest unless the journal is running. Throughput, though, is not the
limit that stops you: on one developer machine a single hub ingested a
250-machine fleet's burst — 500,000 events in 2.3 seconds (~217,000
events/second, 67 MB/s), none dropped — and a level-filtered /recent
still answered in 16 ms while it arrived
(bench/loadtest.mjs — reproduce it yourself against a
throwaway hub). What makes it unfit for production is the two sentences above,
no auth and an in-memory ring, not the ingest path.
What it is fair to do in production is pull: the ssh tailer and the fleet runner read remote logs onto your bench over ssh, so production never needs to reach the hub and the hub never needs to be exposed. That is how the fleet support is meant to be used.
When you outgrow it, you have not wasted anything — the wire format is NDJSON on plain HTTP (see docs/PROTOCOL.md), so a forwarder into Loki, Elasticsearch or an OTLP collector is a small script that subscribes to the firehose and re-posts. The two tools answer different questions and it is reasonable to run both: this one for "what is happening right now while I am looking", that one for "what happened last Tuesday at three in the morning".
Collection is not analysis — and that is the whole design
super-log does one thing: it collects and consolidates. Every stream a bench produces, on one wire, in one order, on one screen. It deliberately does not analyse — no query language, no dashboards to build, no rules engine you must program before the first insight. That separation is not a missing feature; it is the point. Collection should be dumb, fast, and trustworthy; analysis is a different job with different tools, and coupling the two is how logging stacks become the thing you have to operate instead of the thing that helps you operate.
This is the opposite of the log4j lesson. The Java logging world put a large, powerful, evaluating framework inside every application — and in 2021 the world learned what that costs, when a string in a log message became remote code execution (log4shell) across half the internet. The framework that was supposed to observe the app could compromise it. super-log inverts every part of that:
- Logging lives outside the app. A super-log producer is a thin, zero-dependency SDK — often just one file, or no code at all: point an existing OpenTelemetry exporter at it, or tail a file. There is no heavy framework in your process to be a liability.
- Content is data, never code. Every parser on the collection side is bounded and treats input as bytes to be stored, never expressions to be evaluated — the log4shell class of bug is structurally impossible here.
- Production ships nothing by default. Every SDK is DEVELOPMENT-xor-
PRODUCTION, and PRODUCTION is an inert shell that sends nothing until you
opt a level in — the compiled languages compile the wire code out
entirely, provably (
stringsfinds no endpoint in the binary).
So the app stays simple and safe, the bench stays a bench, and what you do with the consolidated stream is a clean, separate choice — eyeball it, forward it to your long-term store, or hand it to something that reasons about it.
super-log.com — analysis and AI, for teams
The open-source bench is the collection and consolidation layer, free and self-hosted, forever. super-log.com is the analysis layer built on top of it — the commercial service for when a consolidated stream is more than one person can watch:
- Real-time LLM analysis of the consolidated log. An AI reads the whole interleaved firehose as it happens — every service, device and chain at once — and tells you what changed, what correlates, and what is about to break, in the plain-language a teammate would use. The thing a human does in the ten seconds after something breaks, done continuously across streams no human can watch all of.
- Built for teams. One consolidated view of everyone's benches and services, shared context, history that outlives a laptop, and alerting that reaches the person who can fix it. The servers, agents and PR boards in the open-source viewers hint at the shape; the hosted service makes them a shared, durable, analysed surface.
- Same clean boundary. super-log.com consumes the exact same NDJSON wire the open-source hub speaks — nothing new to instrument. Your collection stays yours and self-hosted; you choose to send a stream up for analysis, and you can stop at any time. Collection and analysis stay separate all the way through.
The open-source project is complete and useful on its own — the commercial service is analysis you would otherwise build, not a paywall around the bench.
Install
The fastest way onto the bench — Homebrew — and a runnable example in the same breath:
brew install saxonnicholls/tap/super-log # the hub + all the tailers
# See it work in three lines:
brew services start super-log # hub on :7333
echo "the bench is live" | superlog tee --topic hello # a line of your own
curl -s "http://127.0.0.1:7333/recent?topic=hello" # read it back
# Then put your machine on the bench — any of these, immediately:
superlog-netstate # gateway, Wi-Fi, VPN, resolvers — changes only
superlog-otlp # OpenTelemetry inlet on :4318
superlog-otlp-export # OpenTelemetry outlet: bench -> OTLP/HTTP JSON backend
your-build 2>&1 | superlog tee --topic build # anything that prints
Other channels, each landing the hub and the tailers:
# npm — the tailers and the MCP server as commands, anywhere Node ≥18 runs
npm install -g @super-log/tailers @super-log/mcp
# Ubuntu (incl. Ubuntu Server on a Pi) — the PPA: apt install super-log, every
# architecture Launchpad builds (arm64 included), with apt upgrade after.
sudo add-apt-repository ppa:super-log/stable
sudo apt update && sudo apt install super-log
# Debian / Raspberry Pi OS — PPAs are Ubuntu-only (no add-apt-repository here),
# so take the .deb from the release (apt, not dpkg -i, so nodejs and libatomic1
# come with it). Pick your arch — arm64 for a 64-bit Pi, amd64 for x86:
curl -fsSL -O https://github.com/saxonnicholls/super-log/releases/download/v0.4.0/super-log_0.4.0_arm64.deb # or _amd64.deb
sudo apt install -y ./super-log_0.4.0_arm64.deb
# Fedora / RHEL / Rocky / Alma — dnf takes the URL directly:
sudo dnf install -y https://github.com/saxonnicholls/super-log/releases/download/v0.4.0/super-log-0.4.0-1.fc41.x86_64.rpm
# vcpkg — the SDK for C/C++ (incl. Windows/MSVC). C header by default;
# [cpp] adds the header-only C++ SDK (pulls the ts-moveables vcpkg port).
vcpkg install super-log # the zero-dependency C header
vcpkg install super-log[cpp] # + the C++ SDK (event model, SN_LOG)
Pick the .deb for your architecture (dpkg --print-architecture). On a
systemd host the hub starts on install; in a container without systemd, run
superlogd yourself.
On install the hub binds loopback (127.0.0.1) — right for a dev box, but
if this machine is the collector other devices ship logs to over the LAN (a
common Raspberry Pi role), they cannot reach it until you bind the network: set
SUPER_LOG_LAN=1 (or SUPER_LOG_BIND=0.0.0.0) in the service environment and
restart superlogd. The failure is silent at both ends — the device shows
"never reported" and the hub logs nothing — so if you are standing one up as a
LAN sink, see docs/DEVICES.md for the "loopback trap" and how
to make the LAN binding persist across restarts.
The MCP server also runs straight from npx, no install:
claude mcp add super-log -- npx -y @super-log/mcp. The viewers (native
and web) come with a clone — see Quick start below. Packaging sources and
the maintainer publish steps live in packaging/.
Quick start (from a clone)
git clone --recurse-submodules --shallow-submodules <this repo>
cd super-log
cp .env.example .env # optional: chain endpoints, hub defaults
# The one-command install: preflight the toolchain, build the hub and
# viewer, and verify every SDK actually delivers to a real hub before
# calling itself done. Add --persist to start the hub and the default-on
# tailers at login and keep them running across reboots.
./scripts/install.sh # build + verify
./scripts/install.sh --persist # ...and run at login, forever
# The whole demo: hub, C++/Rust/iOS/Android/browser/container clocks,
# OS-log streams, both viewers - one command
npm run demo # see demo/README.md for the tour
# ...and the other languages on the same screen, if their toolchains are here
SUPER_LOG_LANGS="go python java swift fortran shell" npm run demo
# Or piece by piece:
./scripts/build_all.sh # rebuild everything (hub, native + web viewers, SDK tests)
./scripts/dev.sh # build + run the hub
npm install && npm run viewer # web viewer on http://localhost:7334
npm run tail:android # first stream: the Android emulator
The demo binds to loopback. Real phones need the hub on the LAN:
SUPER_LOG_LAN=1 ./demo/run.sh — read the security section first. The
viewer finds the hub from the host that served the page, so opening it from
another machine needs no configuration.
Configuration files
Most streams need no config — you just run them. A handful that carry
secrets (API keys, RPC URLs with keys, wallet addresses, tokens) read
a file that is gitignored, so your credentials never reach a commit.
Every one ships a .example twin beside it: copy, then edit. That's the
whole ritual — cp <name>.example <name>, fill in your values, run.
Config-free by design — nothing to create: the servers, devices
(USB), agents and PRs boards derive from the hub's own traffic or
a gh/env setting, and the OS/app/network tailers take command-line flags.
If a tailer needs a file it can't find, it tells you the exact cp line
and stops — it never runs half-configured.
Installing it in a project
Clone it once, use it from every project. super-log is not a dependency you add to a repo — it is a tool you install on a machine, like a debugger. The hub is machine-wide and shared: one per bench, every project on it. Two hubs would be two ports competing and two viewers each showing a third of the picture.
That also keeps your repo clean, and it avoids a real cost — a Cargo git dependency on this repo clones all five submodules and 36 MB of C++ that the Rust crate never touches.
git clone --recurse-submodules --shallow-submodules \
https://github.com/saxonnicholls/super-log ~/dev/super-log
cd ~/dev/super-log && npm install # once, for the tailers and the web viewer
Then, per project:
~/dev/super-log/scripts/setup.sh ~/code/my-app
That writes exactly two files into your project and touches nothing else:
.superlog/ (pids and logs) is added to your .gitignore. The project type
is detected, so the config arrives pre-filled rather than blank — a
package.json gets npm run build, a CMakeLists.txt gets
cmake --build build -j, and so on.
cd ~/code/my-app
$EDITOR superlog.conf # topic prefix, dirs to watch, log files, services
./logging.sh start # hub + viewer + this project's streams
./logging.sh status # what is running, and where
./logging.sh stop # stops what THIS project started - not the shared hub
Booting it with your build and run is the point of the last two commands. They start the logging first if it is not already up, so wiring them into what you already type is all it takes:
./logging.sh build # your build, with its compiler diagnostics as events
./logging.sh run # your program, output teed to the terminal AND the bench
build runs through the build wrapper, so warnings and errors arrive as
WARN/ERROR with file:line, and sanitizer or valgrind findings arrive
whole. run runs through superlog-tee, so stdout reaches your terminal
byte for byte and the bench at the same time. Either is a drop-in for the
command it wraps — alias b='./logging.sh build', or setup.sh --wire to
add npm run log / log:stop / log:status to a Node project.
Sharing one bench between projects works the way you would hope. The
second project's start finds the hub and viewer already up and only adds
its own streams; each project's topics carry its own prefix, so they stay
separable in the viewer; and stopping one project leaves the others running.
If you would rather wire an SDK into your code directly instead of watching from outside, that is the next section — but you do not have to, and for most projects the launcher is enough to see everything.
Putting your own apps on the bench
React Native / browser / Node (@super-log/client, zero dependencies):
import { createSuperLog } from '@super-log/client';
const slog = createSuperLog({
url: 'http://192.168.1.20:7333', // your bench machine
topic: 'expo.ios.device', // topics name streams - PROTOCOL.md
app: 'my-app',
development: __DEV__, // exactly one of these two, or it throws
production: !__DEV__,
patchConsole: true, // console.* now reaches the bench
patchNetwork: true, // ...and every HTTP call it makes
});
// One id for everything this action causes, on every tier it reaches
await slog.withTrace(async () => {
slog.info('checkout mounted', { user: '42' });
await fetch('https://api.example.com/v1/pay'); // header added for you
});
React trees can wrap once with @super-log/react:
<SuperLogProvider> owns the client and an error boundary that logs the
component stack — which a global handler can never see, because React
swallows render errors.
C++ (header-only; compile with -DSUPERLOG_DEVELOPMENT or
-DSUPERLOG_PRODUCTION):
superlog::transport_config cfg;
cfg.topic = "cpp.pricer";
auto bat = std::make_shared<superlog::batcher>(cfg); // before the logger
superlog::origin who;
who.app = "pricer";
spdlog::default_logger()->sinks().push_back(
std::make_shared<superlog::spdlog_sink_mt>(bat, who));
superlog::install_terminate_handler(bat, who); // uncaught exceptions + stack
Plain C (one header, stb-style; the same two macros, and PRODUCTION
compiles every call to nothing — strings the binary for /ingest/ to
prove the logger absent, rather than trusting a flag):
#include "superlog.h" /* sdk/c, zero-alloc, POSIX sockets */
superlog_t lg;
superlog_init(&lg, "c.engine", "engine");
superlog_info(&lg, "engine up, port %d", 9000);
superlog_kv(&lg, "ERROR", "no rate", "symbol", "DOGE", NULL);
superlog_metric(&lg, "queue.depth", 17);
superlog_flush(&lg);
Python (standard library only; development= / production=, exactly one):
import logging, superlog
log = superlog.SuperLog(topic="python.pricer", app="pricer", development=True)
logging.getLogger().addHandler(log.handler()) # everything already logged
log.install_excepthook(capture_locals=True) # and every crash, with locals
with log.traced(): # ContextVars: async- and thread-safe,
log.info("order received") # and inherited by everything called inside
stdlib_logger.debug("pricing") # ...including plain logging calls
Python gets two things the other SDKs cannot. logging.Handler means every
line the program already logs reaches the bench with no call-site changes.
And capture_locals attaches the local variables of the failing frames, so
an exception says symbol='DOGE', n=7 rather than only where it happened —
secret-looking names are redacted and values truncated.
Go (a log/slog handler, so existing calls need no changes):
log, _ := superlog.New(superlog.Config{
Topic: "go.pricer", App: "pricer", Development: true,
})
defer log.Close()
slog.SetDefault(slog.New(log.SlogHandler(nil))) // everything already logged
ctx, _ := superlog.WithTrace(context.Background(), "")
slog.InfoContext(ctx, "order received") // ...on the tick's trace
go func() { defer log.Recover("worker"); work() }() // panics, with stack
Trace lives in context.Context rather than a goroutine-local, because Go
deliberately has none — so the id travels exactly where the context does.
Recover logs the panic and re-panics: a logger that swallows a crash has
changed the program it was meant to observe.
Java and Kotlin (java.util.logging bridge; InheritableThreadLocal
trace, so a pooled task inherits its submitter's id):
var log = SuperLog.builder().topic("java.pricer").app("pricer")
.development(true).build();
Logger.getLogger("").addHandler(log.julHandler()); // everything already logged
log.installUncaughtHandler(); // and every crash
log.traceScope(() -> {
log.info("order received");
pool.submit(log.wrap(() -> log.debug("settled"))); // same trace
});
Swift (@TaskLocal trace, inherited by child tasks):
let log = try SuperLog(topic: "swift.pricer", app: "pricer", development: true)
try SuperLog.withTrace {
log.info("order received")
Task { log.debug("pricing pass") } // same trace, nothing passed in
}
There is deliberately no setTrace(): a TaskLocal binds to a scope and
nothing else, which makes the usual leak — one request's id surviving into
the next — impossible to express rather than merely discouraged.
Fortran (raw POSIX sockets through ISO_C_BINDING, no libcurl):
call sl_init(topic='fortran.solver', app='solver')
call sl_set_trace(sl_new_trace())
call sl_metric('solver.residual', residual)
if (residual /= residual) call sl_error('residual is NaN')
call sl_close()
A solver is the hardest program on the bench to observe: hours long, often
somewhere you cannot attach, and the evidence is a slurm file nobody reads
until the allocation is spent. DEVELOPMENT xor PRODUCTION is a
preprocessor error like the C++ SDK, and SIGPIPE is ignored at init so a
hub that goes away cannot kill a run twelve hours in.
Shell (any script, one line):
superlog-log --topic deploy "starting rollout"
tail -f /var/log/app.log | superlog-log --topic app.foo --level WARN
Rust (build with --features development or --features production):
let log = super_log::SuperLog::new(super_log::Config {
topic: "rust.pricer".into(),
app: "pricer".into(),
..Default::default()
});
log.install_panic_hook(); // panics, with location
log.log(super_log::Level::Info, "engine up", None);
log.metric("fps", 58.9);
Ruby, and therefore Rails (stdlib only; mode from SUPERLOG_MODE):
require_relative "superlog" # sdk/ruby/superlog.rb
log = SuperLog.new(topic: "ruby.myapp", app: "myapp")
log.info("up", port: 3000)
log.metric("queue.depth", 17)
# Rails: everything it already logs, one assignment, no controller touched
config.logger = ActiveSupport::BroadcastLogger.new(
ActiveSupport::Logger.new($stdout), log.logger_adapter)
Scala (no SDK, on purpose — the Java client, one import, zero glue, exactly as Kotlin uses it):
import com.snicholls.superlog.SuperLog
val log = SuperLog.builder().topic("scala.pricer").app("pricer")
.development(true).production(false).build()
log.installUncaughtHandler() // every thread's uncaught, chained
log.info("engine up", SuperLog.fields("port", Int.box(9000)))
OCaml (the unix library and nothing else; mode from SUPERLOG_MODE):
(* ocamlc -I +unix unix.cma superlog.ml myapp.ml -o myapp *)
let log = Superlog.create ~topic:"ocaml.solver" ~app:"solver" () in
Superlog.info log "solver up" ~fields:[ ("mesh", "1.2M cells") ];
Superlog.metric log "solver.residual" 1.6e-9;
Superlog.flush log
Haskell (GHC's boot libraries plus curl; the mode is compiled in —
-DDEVELOPMENT or -DPRODUCTION, neither or both refuses to build):
import SuperLog
main = do
lg <- newLog "haskell.pricer" "pricer"
info lg "engine up" [("port", "9000")]
metric lg "queue.depth" 17
flushLog lg
Lean 4 (core IO plus curl; mode from SUPERLOG_MODE — proof search
runs all night, and a metric per thousand goals beats a silent terminal.
superlog-build -- lake build covers the build itself, with lake's
[n/m] progress as a chartable build.progress_pct):
import Superlog
def main : IO Unit := do
let lg ← Superlog.init "lean.search" "search"
Superlog.info lg "search up" [("depth", "12")]
Superlog.metric lg "goals.open" 4123
Superlog.flush lg
C# (BCL only — HttpClient and System.Text.Json ship with .NET;
mode from SUPERLOG_MODE). One file into any project: a console app,
ASP.NET, a Unity project on a .NET profile, an Xbox Dev Mode build:
var log = new SuperLog(topic: "csharp.myapp", app: "myapp");
log.Info("up", new() { ["port"] = "3000" });
log.Metric("queue.depth", 17);
log.Flush();
Perl (core modules only — HTTP::Tiny and JSON::PP ship with Perl;
mode from SUPERLOG_MODE), for the glue scripts and cron jobs that run
half the world:
use lib "sdk/perl";
use SuperLog;
my $log = SuperLog->new(topic => "perl.backup", app => "backup");
$log->info("nightly backup starting", { host => "web1" });
$log->metric("backup.bytes", 48_211_233_792);
$log->flush;
Lua (any Lua 5.1+, PUC or LuaJIT; no sockets in Lua's stdlib, so it makes the same honest curl bargain the shell SDK makes):
package.path = "sdk/lua/?.lua;" .. package.path
local superlog = require "superlog"
local log = superlog.new{ topic = "lua.game", app = "game" }
log:info("level loaded", { level = "e1m1" })
log:metric("frame.ms", 6.9)
log:flush()
COBOL (yes, really — GnuCOBOL CALLs the header-only C SDK through
demo/cobol/shim.c, ~20 lines, so the oldest business
language on the bench inherits the C SDK whole, provably-compiled-out
production included):
CALL "cobol_superlog_init"
USING BY CONTENT Z"cobol.batch", Z"payroll"
CALL "cobol_superlog_log"
USING BY CONTENT Z"INFO", Z"payroll run 4711 starting"
Zig needs even less: @cImport reads sdk/c/superlog.h directly
(see demo/zig/clock.zig), with the header compiled
once for the linker — cc -DSUPERLOG_API= -c impl.c — which is the same
one-object route any language with a C FFI can take.
Machines, services, containers, chains (no app changes at all):
npm run tail:os -- --process MyApp # this Mac's unified log
npm run tail:apps # what services log here
npm run tail:app -- postgres nginx redis # ...then turn them on
npm run tail:app -- unity unreal # engine editor logs, level-parsed
npm run tail:file -- /srv/app/production.log
npm run tail:ssh -- my-server # a remote box, OS auto-detected
npm run tail:ssh -- db1 --app postgres # ...or its postgres
npm run net -- 9000 http://localhost:3000 # every HTTP call through :9000
npm run grpc -- --listen 50052 --target localhost:50051 # every RPC, status from the trailer
npm run chain # watched addresses (see .env)
npm run gas # operational key balances, alarmed (gas.json)
npm run gas -- --once # every key, one reading, right now
npm run sql -- --pg "postgres:///mydb" # LISTEN superlog: NOTIFY from any trigger lands here
npm run sql -- --sqlite var/app.db # an SQLite file, watched from outside the process
npm run prs -- --author you --repo owner/x # PRs: alarms when one waits on YOU
npm run rpc # RPC node health: block height, DOWN/STALLED, per provider
npm run starlink # the dish: latency, obstruction, alerts, outages
npm run otlp # OpenTelemetry inlet on :4318 (OTLP/JSON + protobuf)
npm run otlp-export -- --endpoint URL # OpenTelemetry outlet: bench -> OTLP/HTTP JSON
OTEL_EXPORTER_OTLP_ENDPOINT=http://127.0.0.1:4318 your-app # ...point any OTel exporter at it
npm run fix -- --file 'log/*.messages.*.log' # FIX sessions: Reject/rejected-fill loud, fills INFO, heartbeats DEBUG
npm run mavlink -- --udp 14550 # a drone's MAVLink: battery/GPS metrics, failsafes and STATUSTEXT loud
npm run mavlink -- --tlog flight.tlog # ...or replay a telemetry capture
npm run usb # the USB device tree - is the phone connected?
npm run netstate # gateway, Wi-Fi, VPN, resolvers, ARP - changes only
npm run netstate -- --ping 1.1.1.1 --ping api.example.com|30 # path quality, traceroute on failure
npm run netstate -- --once # what does the network look like right now
npm run topology # the local network as a tree, in its own window
npm run topology -- --discover # ping-sweep the /24 to show every device, not just known ones
npm run topology -- --to 1.1.1.1 --geo # watch a route; AS/owner per public hop (opt-in outbound lookups)
npm run dns -- example.com --asn # ...and which AS originates it (hijack watch)
npm run dns -- example.com --once # every DNS record + cert, then exit
npm run dns -- example.com mail.example.com # ...or watch them for change
npm run ports -- --once # what is listening, and which process
npm run ports -- --ssh web1 --procs nginx # ...on a server, watched
npm run connections # the OUTBOUND side: who this box is talking to
npm run connections -- --ssh web1 # ...on a server; a stuck SYN-SENT is a WARN (filtered/down)
npm run vitals -- --once # disk, memory, CPU, load
npm run vitals -- --ssh web1 # ...on a server, watched
npm run versions -- --once # every version under the bench: OS, compilers, runtimes, packages
npm run versions -- --ssh web1 # ...on a server (no node needed there), diffed over time
npm run versions -- --check-conflicts # match a downloaded known-bad list, LOCALLY
npm run alert # rules from alerts.json
npm run alert -- --test # prove delivery without waiting
npm run alert -- --channels # the notification roster, and what is missing
npm run alarm # production's webhook door, tunnelled + tested
npm run alarm -- --provision endpoints.json # ...plus many public endpoints, from a manifest
# incl. Stripe webhook testing: capture, verify
# signatures, relay to your local handler
npm run build -- --label cxx -- cmake --build build -j
npm run build -- --label asan -- ./build/tests # sanitizer findings, whole
npm run git # this repo: commits, branches, conflicts
npm run git -- --ssh web1 --repo /srv/app # ...a deployed checkout
npm run github -- --repo owner/name # CI runs, PRs, releases
npm run watch -- --dir src # files created, modified, deleted
npm run watch -- --dir config --diff # ...and the changed LINES, hunk by hunk
make 2>&1 | npx superlog tee --topic build.local # superlog-tee: a drop-in tee
npm run ws -- wss://stream.binance.com:9443/ws/btcusdt@trade
npm run serial -- --list # boards plugged in
npm run serial -- --port /dev/ttyUSB0 # the serial console, as events
npm run cf -- --worker my-api # a Cloudflare Worker, live
npm run stripe -- --live --account acme # payments, redacted by default
npm run socket -- --udp 5514 # syslog from routers, switches, NAS
npm run socket -- --tcp 5515 # ...or plain lines on a raw socket
npm run ros # a robot's nodes, from /rosout
npm run ros -- --files # ...including past runs in ~/.ros/log
npm run gpu # this machine's GPU, watched
npm run gpu -- --ssh trainer1 # ...or the box with the card in it
npm run power # watts, thermals, top energy hogs (macOS)
npm run power -- --once # one power reading, then exit
npm run sys # crashes, panics, shutdown causes, volumes (macOS)
npm run sys -- --once --backfill 72h # what has this machine suffered lately?
npm run bridge -- --ssh otherbench # another hub's whole feed, into this one
npm run dl -- -- curl -LO https://host/model.safetensors # a download, with progress
npm run dl -- --watch ~/models --size 140GB -- hf download org/model
npm run build -- --ssh web1 -- 'cd /srv/app && cargo build --release'
Infrastructure watches
dns, ports and netstate diff a snapshot rather than streaming, so
the first poll is a silent baseline and only changes are reported — a
watcher that announces everything it sees teaches you to ignore it. The rest publish
readings as DEBUG metric events and raise their voice only on
edge-triggered crossings.
superlog-topology renders the LAN as a tree — this host and its addresses,
the gateway, and every device under it — in its own window in both viewers.
--discover sweeps the subnet so devices the machine hasn't talked to still
appear; --to <target> adds a watched route with per-hop RTT and AS/owner.