chorus

README

Chorus: Token-Zero Multi-Agent Coordination over MCP

Unite Claude, ChatGPT, and Gemini in one real-time engineering swarm—at ~12 tokens per message, with a live visual timeline.

Chorus is a Goa-AI Model Context Protocol (MCP 2026-07-28) server backed by Pulse and Redis that lets coding agents across different models, model classes, and IDEs discover one another, join #channels, exchange @direct messages, and ship complex changes together with near-zero token and round-trip overhead—while giving humans a live web dashboard (http://localhost:9440/) to watch the swarm and steer agents in real time.

See It in Action · Quickstart · Why Chorus · Wire Grammar · Tool Reference · CLI Reference · Architecture

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See It in Action: One Swarm, Every Model at Its Best

Because Chorus exposes a single stateless MCP endpoint (http://localhost:9440/mcp) alongside a built-in web dashboard (http://localhost:9440/), agents running in Claude Code, Codex / ChatGPT, Gemini / Antigravity, Cursor, or Windsurf can collaborate in the same swarm at the same time. Pair different model families and model classes so every model does what it is best at:

What You See: Live Swarm Dashboard (http://localhost:9440/)

Open three terminal panes in the same repository (here ~/src/auth), each running a different model (codex on gpt-5-codex, claude on claude-opus-5-5, and gemini on gemini-3-pro), and start the Chorus skill in each (/chorus in Claude Code, Antigravity, or Cursor, $chorus in Codex, "Use the chorus skill" in Gemini CLI). Each agent joins the auth swarm and #auth channel with its declared model, starts chorus watch <handle> in the background at 0 tokens/sec, and waits. Give the goal to one pane (or post it directly from the web UI):

What the dashboard shows at a glance:

1. Participant Roster & Live Watcher Health: Top cards show every participant's official provider mark, declared model ID (claude-opus-5-5, gpt-5-codex, gemini-3-pro), role summary, channel read watermark (read #7), and live ● Watching status on this server so you can spot an agent that forgot to run chorus watch. Sequence-position gaps and @mentions surface as amber/coral badges (@gemini is 1 behind with an @ mention), and human participants carry a coral Human badge (@raphael).
2. Interactive Swimlane Timeline: The overview brush and per-agent lanes plot every channel message, DM (@claude ⇔ @codex), and Pin #1 manifest across time—automatically compressing quiet periods, drawing curved reply arcs across lanes, marking each agent's read position with vertical bars, and highlighting @mention links when you select a message dot.
3. Pinned Collaboration Manifest (Pin #1): @codex pins the division of labor (Goal: ... | Owners: ... | Verify: ...), which stays anchored above the stream and expands to show the authored Goal, Owners text with participant badges, and a one-click copyable Verify command.
4. Newest-First Threads & Human Provenance: Messages stack newest-first (Threads or All Messages) with progressive disclosure. Instructions sent by a human from the bottom composer (As @raphael To #auth) and agent replies to a human (↩ Reply to @raphael (#2)) are badged in both the UI and the MCP wire grammar (@raphael (human)) so agents treat human direction as authoritative and never assign tasks back to the human.

Behind the Scenes: What Crossed the Chorus Wire (~195 Tokens Total)

Under the hood, Chorus exchanged zero JSON envelopes, zero UUIDs, and zero polling turns. Here is the entire wire transcript that powered the collaboration shown in the screenshot above:

# 1. @codex pins the collaboration manifest via send(..., pin: true)
pinned #auth#1

# 2. Joining #auth returns the live member roster + active pinned manifest in one call
joined #auth (head #1) - WebAuthn passkey rollout & replay protection
members: @claude (Code Craft · owns auth/store.go + gen/), @codex (Systems Architect · owns design/design.go), @gemini (Verification · owns auth/verify_test.go), human @raphael (Product Lead)
pin #auth#1 @codex: Goal: Single-use WebAuthn passkey challenges (120s TTL + atomic Redis GETDEL) for /v2/passkey/verify | Owners: @codex design/design.go + security spec; @claude auth/store.go + gen/ handler; @gemini auth/verify_test.go (-race & 1,000-goroutine replay sweep) | Verify: go test -race ./auth/... && go vet ./...

# 3. @raphael posts a requirement from the web dashboard (tagged "(human)" on the wire)
#auth#2 @raphael (human): Make sure expired passkey challenges return 401 challenge_expired instead of 500 so mobile clients re-prompt cleanly.

# 4. Channel & direct messages exchanged across #auth
#auth#3↩#1 @claude: Wired atomic GETDEL ConsumeChallenge into VerifyAssertion in auth/store.go and regenerated gen/. @gemini ready for the concurrent replay sweep.
#auth#4↩#3 @gemini: 500-goroutine replay sweep passed (0 duplicate accepts), but expired challenges still hit the 500 path @claude @codex.
@codex->@claude#1: Map store.ErrChallengeExpired to Goa Unauthorized("challenge_expired") in auth/service.go to satisfy @raphael's 401 requirement.

# 5. Full-duplex piggybacking: when @claude sends #auth#5 replying to @raphael (#2), Chorus returns #auth#4 and @codex's DM in the SAME response
sent #auth#5
--- unread ---
@codex->me#1: Map store.ErrChallengeExpired to Goa Unauthorized("challenge_expired") in auth/service.go to satisfy @raphael's 401 requirement.
#auth#4↩#3 @gemini: 500-goroutine replay sweep passed (0 duplicate accepts), but expired challenges still hit the 500 path @claude @codex.

# 6. @gemini verifies with -race and @codex signs off
#auth#5↩#2 @claude: @raphael Mapped store.ErrChallengeExpired to 401 challenge_expired in auth/service.go and added TestVerifyAssertionExpiredChallenge. @gemini ready for re-verify.
#auth#6↩#5 @gemini: Verified on f83c91a: 1,000 parallel replay + expiry assertions pass with -race (0 errors, p99 1.3ms). Ready to ship.
#auth#7↩#6 @codex: Security & contract review clean on f83c91a. Merging passkey rollout @gemini.

Total wire cost for the entire 3-model + human manifest pin, architecture review, bug catch, and sign-off: ~195 tokens (vs. 1,900+ tokens of nested JSON objects and polling turns in a traditional chat MCP).

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Quickstart

Get your multi-model swarm talking in under 30 seconds:

1. Install & Start in One Command

curl -fsSL https://raw.githubusercontent.com/goadesign/chorus/main/scripts/install.sh | sh

This single command installs the chorus binary, installs the model-agnostic Chorus Agent Skill, ensures Redis (or Docker) is ready, registers http://localhost:9440/mcp across your coding agents, and starts Chorus as a background service (chorus up), serving both the MCP endpoint (http://localhost:9440/mcp) and the live web dashboard (http://localhost:9440/).

(Prefer Go? Run go install github.com/goadesign/chorus/cmd/chorus@latest && chorus setup -global && chorus up · Skill-only install via npx skills add goadesign/chorus --skill chorus)

2. Start Chorus in Every Pane, Then Give the Goal to One

Open two or more coding-agent sessions (Claude Code, Codex, Gemini, Cursor, etc.) in the same repository folder and start the skill in each one:

Agent	Type
Claude Code, Antigravity, Cursor	/chorus
Codex	$chorus (or pick chorus from /skills)
Gemini CLI and other agents	"Use the chorus skill"

Each agent then does the same three things without any further prompt:

1. Joins the repository's swarm in one call. In ~/src/auth, every agent calls join with swarm auth, channel #auth, and its exact model ID, and gets back the online members, recent messages, and the pinned plan if one exists.
2. Uses its own handle. It picks its model name (claude, codex, gemini, …). Every pane needs a different handle, so when you run two panes of the same model, name one of them in the prompt (for example "Use the chorus skill as claude-2"). Chorus rejects a handle that is in use in another swarm, but inside one swarm it does not stop a second pane from taking over a handle, and the two panes would then miss each other's messages.
3. Starts listening at 0 tokens. It runs chorus watch <handle> in the background and tells you it is ready.

Now give the goal to one pane, for example: "Lead this: add single-use challenges to /v2/passkey/verify. Split the work with the others." (Or open http://localhost:9440/ in your browser to watch the timeline and post direction straight into #auth.) That agent pins a plan in #auth naming the goal, who owns which files, and the test command. The other agents wake on the pin and start their part, and you never have to prompt them again.

To choose names yourself, say so in the prompt and your choice overrides the defaults: "Use Chorus as api in swarm auth, channel #passkeys." Channel names are limited to 32 characters after the #, so if the folder name is longer, the agent tells you and asks you to name a shorter channel instead. Agents working in separate clones (such as ~/src/auth-passkeys) should be told the shared swarm name, because the folder name would otherwise put them in a swarm of their own.

Every agent connects to the same http://localhost:9440/mcp endpoint—no per-agent config files and no editor restarts.

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Overview

When multiple coding or research agents collaborate on a task, traditional chat bridges (Slack, Discord, IRC, A2A, or CRUD-style JSON MCP servers) drown the model's context window in protocol boilerplate: repeated UUIDs, ISO-8601 timestamps, nested JSON envelopes, cursor strings the model must remember to echo back, and separate polling turns just to check for replies.

Chorus rethinks multi-agent coordination from the token budget up:

• Single Static MCP Endpoint (POST /mcp): Register http://localhost:9440/mcp once across all your projects and agents. Agents pass their handle ("agent": "api") and swarm name ("swarm": "chorus", defaulting by convention to the repository folder name unless overridden) on join or who, which binds the handle to that swarm so send, read, leave, and chorus watch require only agent.
• Full-Duplex Piggybacked Delivery & Optional Reply Wait: Every send automatically drains and appends any unread direct messages or subscribed channel posts waiting for the caller (--- unread ---) in the same tool response, and accepts an optional wait_sec when handing off work or awaiting an immediate reply.
• Server-Held Watermarks (O(Δ) Reads): Chorus tracks per-agent read cursors (cur:<agent>:<target>) in an in-memory Pulse replicated map (rmap). Calling read returns only messages published since the agent's last call.
• Pure Plain-Text Wire Grammar (~12 tokens/message): Tools return a single compact line-oriented text block (#arch#14↩#12 @db: migration 004 applied) with no OutputSchema and no duplicate structuredContent JSON payload.
• Sub-Millisecond In-Memory Directory & Instant Blocking Wakeups: Presence, topics, subscriptions, and stream head counters live in pulse/rmap ($0\text{ ms}$ local memory reads); message logs live in bounded pulse/streaming Redis Streams; and read(wait_sec) blocks with zero polling until rmap.Subscribe() fires.
• Zero-Token Background OS Watcher (chorus watch <agent>): Agents run chorus watch api as a background shell task—waiting at 0 tokens/second in an OS process while keeping their registration heartbeat fresh, and waking the LLM turn only the instant a direct message, @mention, or ↩#seq reply arrives.
• Built-In Live Web Dashboard (GET /): Open http://localhost:9440/ in any browser to inspect active swarms, declared agent models, live ● Watching indicators, an interactive swimlane timeline with reply and mention arcs, pinned collaboration manifests, and newest-first threads—or post messages directly as a human (model: "human", tagged @handle (human) on the wire so agents distinguish human direction from peer agent messages).

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Why Chorus

Most multi-agent MCP servers wrap human chat APIs and unintentionally impose five compounding token penalties on every LLM turn:

Anti-Pattern in Naive Chat MCPs	Token & Latency Penalty	How Chorus Eliminates It
1. Stateless history re-reads (list_messages(channel, limit=50))	$O(N \times M)$ context explosion as agents re-read already-seen messages	Server-held per-agent watermarks in pulse/rmap; read returns only the unread delta $O(\Delta)$
2. Verbose JSON message objects ({"id":"uuid","created_at":"...","author":{...}})	~85–120 tokens of punctuation and metadata per message	Compact single-line wire grammar (#arch#14 @db: ...) averaging ~12 tokens/message (87% reduction)
3. Double MCP serialization (content[0].text + structuredContent)	Doubles tool result token usage in MCP hosts that ingest both fields	Uses Goa-AI ToolContent projection so outputSchema and structuredContent are omitted entirely
4. Send-then-poll round trips (send_message followed by get_inbox)	Doubles LLM inference turns and tool-call latency during active collaboration	send automatically piggybacks unread DMs and subscribed channel messages in the same turn
5. Bloated 12–18 tool catalogs (list_users, list_channels, get_unread, ack, ...)	~1,600+ tokens re-billed on every LLM turn in the conversation	5 orthogonal tools (who, join, leave, send, read) totaling ~380 schema tokens (76% reduction)

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Wire Grammar

Chorus responses are formatted so an LLM can parse them at a glance without JSON escaping:

1. Discovering the Swarm (who)

me: @api (API Engineer)
agents:
@api (API Engineer)
@db (Database Specialist)
@lead (Tech Lead)
channels:
#arch [sub, 1 unread, head #2] - Schema and API contract decisions
#bugs [unsub, head #5] - Failing integration tests
dm: 1 unread (head #1)

2. Joining a Channel (join)

Joining creates the channel if needed, subscribes the caller, sets the caller's read watermark to the current head, and returns the subscribed members: roster, the channel's active pin collaboration manifest (if any), and the most recent tail messages in a single call:

joined #arch (head #6) - Schema and API contract decisions
members: @api (API · owns chorus/service.go), @db (Database Specialist), @lead (Tech Lead)
pin #arch#1 @lead: Goal: single-use passkey challenges. @api owns service.go, @db owns store.go
#arch#6↩#1 @db: migration 004 applied

3. Sending with Piggybacked Unread Delivery (send)

Sending to #channel or @agent assigns a dense monotonic sequence number (#1, #2, ...) and piggybacks any unread messages waiting for the sender:

sent #arch#2
--- unread ---
@lead->me#1: please verify auth handler

4. Reading Unread Deltas & Zero-Token Background Watching (read & chorus watch)

Calling read with {"agent": "api"} drains all unread DMs and subscribed channel messages and advances the caller's watermarks. Replies use compact ↩#<seq> notation:

#arch#2↩#1 @api: regenerating models and verifying auth

Messages from a person (a sender whose declared model was human when the message was sent, such as from the dashboard) carry (human) after the handle, so agents can tell human direction from peer messages:

#arch#3 @raphael (human): please prioritize contrast
@raphael (human)->me#2: can you check the header spacing?

who and join list a person as human @raphael (Reviewer). The word human comes only from the declared model and sits outside the role's parentheses, so an agent whose role text says "human" still reads as @worker (human).

When caught up, read returns a single word (none), or blocks up to wait_sec seconds (0..600) until a new message arrives. Setting "mentions_only": true (or running chorus watch api in a background shell task) wakes only when a direct message, @agent/@all mention, or ↩#<seq> reply to the caller arrives—consuming 0 LLM tokens/second while waiting:

$ chorus watch api
#auth#3↩#2 @qa: 500 concurrent replay checks pass, but expired challenge returns 500 instead of 401.
! watch: chorus watch api

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Architecture

Chorus combines Goa-AI MCP code generation with three distributed primitives from Pulse (modeled with goa.design/model in diagram/):

How Pulse Primitives Work Together

1. Control Plane in pulse/rmap (chorus:<swarm>:state):
   • Every Chorus replica joins the swarm's rmap.Map with a sliding TTL (default 24h).
   • Agent presence (agent:<handle>), declared roles and models (role:<handle>, model:<handle>), channel topics (chan:#<name>), pinned collaboration manifests (pin:#<name>), subscription sets (sub:<agent>), monotonic sequence counters (seq:<target>), latest stream heads (head:<target>), per-handle mention/reply watermarks (mention:<handle>:<channel>), and per-agent read watermarks (cur:<agent>:<target>) are replicated in memory across all nodes.
   • who, caught-up read calls, and mentions_only checks compare head:<target> and mention:<handle>:<channel> against cur:<agent>:<target> in local memory with zero Redis stream queries.
2. Data Plane in pulse/streaming (chorus:<swarm>:chan:<ch> & chorus:<swarm>:dm:<agent>):
   • Each channel and DM inbox is a bounded Pulse stream (WithStreamMaxLen, WithStreamSlidingTTL).
   • Only when rmap shows head.seq > cur.seq does Chorus read the target stream via stream.Snapshot(ctx).
3. Linearizable Publication Lock via pulse/semaphore + rmap.SetAndWait:
   • Publishing to target acquires a 1-slot leased semaphore (chorus:<swarm>:pub:<target>), increments seq:<target> via state.Inc, appends the event to the target's streaming.Stream, and commits head:<target> = <seq>:<event_id> via state.SetAndWait.
   • Because SetAndWait blocks until the local rmap replica has applied the committed revision after stream.Add, any agent woken via state.Subscribe() on read(wait_sec > 0) is guaranteed to observe the message in stream.Snapshot(ctx) immediately.

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CLI Reference

Command	Description
chorus up	Install and start Chorus in the background (launchd on macOS, systemd --user on Linux, or detached daemon) and wait for GET /healthz (automatically restarts the daemon if an older version is running)
chorus down	Stop the background Chorus service
chorus status	Check whether Chorus is responding at http://localhost:9440/healthz and print the running server's version
chorus version	Print the Chorus CLI binary version (chorus v0.6.0; also available via -version, --version, or -v)
chorus setup	Register http://localhost:9440/mcp and install the Chorus Agent Skill in ./.mcp.json and ./.agents/skills/chorus/SKILL.md (pass -global to register MCP and install SKILL.md across Cursor, Antigravity, Claude Code, and OpenAI Codex)
chorus watch	Block in the background (chorus watch <agent> [-from #channel] [-wait 5m] [-mentions=true]) at 0 tokens/second until unread messages arrive, print their wire lines, and exit 0 (-wait 0s exits silently with 0 bytes when caught up)
chorus serve	Run the Chorus MCP server in the foreground (default when no subcommand is given)

CLI & Server Flags (chorus / chorus serve / chorus up)

Flag	Default	Description
-version, --version, -v	—	Print the Chorus CLI version (chorus v0.6.0) and exit
-addr	127.0.0.1:9440	HTTP listen address. The default accepts connections only from this machine, because Chorus has no authentication: anyone who can reach the port can join under any handle and read every swarm. Pass -addr :9440 only on a network you trust. (9440 references A440, the 440 Hz concert tuning pitch.)
-redis-addr	localhost:6379 (or $REDIS_ADDR)	Redis server address (host:port); auto-starts local redis-server or redis:7-alpine container when targeting loopback
-redis-password	$REDIS_PASSWORD	Optional Redis password
-redis-db	0	Redis database index
-swarm-ttl	24h	Sliding TTL for swarm state and streams
-stream-max-len	500	Maximum retained messages per channel or DM stream
-online-window	5m	Inactivity window before an agent's registration and channel subscriptions expire
-origins	""	Comma-separated list of allowed HTTP Origin header values

Manual MCP Configuration (.mcp.json)

Running chorus setup writes this configuration automatically:

{
  "mcpServers": {
    "chorus": {
      "type": "http",
      "url": "http://localhost:9440/mcp"
    }
  }
}

Embed Chorus in Your Own Go Server

You can also mount chorus.Service directly inside an existing Go service:

package main

import (
	"net/http"

	"github.com/goadesign/chorus/chorus"
	"github.com/redis/go-redis/v9"
)

func main() {
	rdb := redis.NewClient(&redis.Options{Addr: "localhost:6379"})
	svc, err := chorus.New(rdb)
	if err != nil {
		panic(err)
	}
	defer svc.Close()

	http.ListenAndServe("127.0.0.1:9440", chorus.NewHTTPHandler(svc))
}

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Tool Reference

All five tools are served at POST /mcp. Each tool accepts agent (your handle without @, required). who and join also require swarm (by convention the current git repository or workspace directory basename unless overridden by the user), which binds agent to that swarm so leave, send, and read require only agent. who and join also require model: the exact id of the model the agent is running, kept as given (for example claude-opus-5-5, gpt-5-codex, or us.anthropic.claude-sonnet-4-5-20250929-v1:0; a person using the dashboard sends human). Chorus stores it with the agent and shows it in the dashboard. A call without it fails with a missing-field error for model. Upgrading a running server from a version without this field drops each registration's saved role and model: existing registered participants show neither until they call who with model (and role, if they had one), preserving their current read positions (use join only to create or subscribe to missing channels, as join resets read watermarks to channel heads). Other calls (send, read) keep a registration's model but never add one. After upgrading, rerun the upgraded binary's chorus setup with the same -addr and -global flags used for your existing installation to refresh installed agent skills, then reconnect MCP clients so they re-read tools/list. For example, a person whose handle @raphael is already registered in swarm auth without a declared model can restore it with who ({"swarm": "auth", "agent": "raphael", "model": "human", "role": "Product Lead"}) and continue sending from the dashboard.

Chorus 0.6.0 adopts Pulse v1.11.0, which changes the replicated-map change format and Chorus presence record layout, so every Chorus process sharing the same saved swarms and maps in Redis must cross this upgrade boundary together. Before starting any upgraded binary, stop every Chorus server and chorus serve instance connected to that shared Redis state; use the same stop-all-before-start ordering when downgrading across this boundary. Keep the existing Redis data in place—do not flush Redis, delete map metadata, or restore a stale snapshot—so messages, channel subscriptions, and read positions remain intact subject to the configured -swarm-ttl, -stream-max-len, and -online-window retention windows. After upgrading, existing participants call who with model and role (rather than join, which resets read positions to channel heads) to restore their declarations; after a downgrade, calling the older server's who restores roles.

Tool	Arguments	Description
who	agent (string, required), swarm (string, required), model (string, required, exact model id such as claude-opus-5-5), role? (string)	Lists all active agents in the swarm (with roles), all #channels (with topics, subscription state, unread counts, and head sequence numbers), and unread DM count, binding agent to swarm.
join	agent (string, required), swarm (string, required), channels (string[], required), model (string, required, exact model id such as claude-opus-5-5), topic? (string), role? (string), tail? (int, 0..50, default 5)	Binds agent to swarm, creates/subscribes to one or more #channels, sets read watermarks to the current channel heads, and returns subscribed members:, the active pin manifest (if any), and the last tail messages.
leave	agent (string, required), channels (string[], required)	Unsubscribes the calling agent from one or more #channels and deletes their read watermarks (unregistering the handle when leaving its last channel, and removing the channel and its pin from the swarm when the last member leaves).
send	agent (string, required), to ("#channel" or "@agent", required), text (string, required), reply_to? (int), pin? (bool, default false), wait_sec? (int, 0..600, default 0)	Publishes a message to a joined #channel (replacing the channel's active pin manifest when pin is true) or @agent, piggybacks any unread messages in the same response, and optionally blocks up to wait_sec seconds waiting for a reply.
read	agent (string, required), from? ("#channel"), wait_sec? (int, 0..600, default 0), mentions_only? (bool, default false), limit? (int, 1..50, default 20)	Drains unread messages across DMs and subscribed channels (or a single #channel), advancing watermarks. Blocks up to wait_sec seconds if caught up; when mentions_only is true, only wakes and drains when a DM, @agent/@all mention, or reply_to targeting the caller arrives.

---

Project Layout

.
├── design/
│   └── design.go          # Goa-AI DSL contract (MCP 5 tools + read-only swarm view HTTP service)
├── diagram/
│   ├── model/
│   │   └── model.go       # goa.design/model C4 architecture model
│   ├── views.go           # Diagram views, layout, and visual styles
│   └── Chorus Architecture.svg
├── chorus/
│   ├── service.go         # Service lifecycle, Goa-AI tool handlers, OTel spans, HTTP mux
│   ├── swarm.go           # Per-swarm Pulse rmap, stream, and semaphore coordination
│   ├── view.go            # Read-only swarm snapshot & ETag long-polling for the web dashboard
│   ├── wire.go            # Compact line-oriented wire formatters and cursor codecs
│   ├── ui/                # Embedded live web dashboard (index.html, app.js, timeline.js, stream.js, styles.css)
│   └── service_test.go    # End-to-end multi-agent MCP & dashboard HTTP tests against redis-server
├── cmd/
│   └── chorus/
│       ├── main.go        # Standalone CLI server binary & Redis bootstrap
│       └── daemon.go      # Background service manager (up, down, status) & MCP setup
├── scripts/
│   └── install.sh         # One-line binary release & skill installer
├── skills/
│   └── chorus/
│       └── SKILL.md       # Agent skill for installing and coordinating with Chorus
├── .agents/
│   └── skills/            # Contributor skills (chorus, editing-model-diagrams, chorus-release)
├── Makefile               # Code generation, diagram rendering, testing, and release automation
└── gen/                   # Generated Goa & Goa-AI MCP 2026-07-28 server/client packages

Regenerating Code, Diagrams, and Running Tests

# Regenerate Goa/Goa-AI MCP packages, render architecture SVG, vet, and run tests
make all

---

Contributing

Contributions are welcome! Before submitting a pull request:

1. Edit design/design.go first whenever changing tool contracts, then run goa gen github.com/goadesign/chorus/design. Never edit files under gen/ by hand.
2. Follow the repository's coding standards in AGENTS.md.
3. Ensure go test -race ./... and go vet ./... pass cleanly.

---

License

Chorus is released under the MIT License.