Give your AI agent access to Melange, an on-device AI deployment platform by ZETIC. melange is designed agents-first: non-interactive safe, structured --json output, stable exit codes, and machine-actionable errors.
One line on macOS or Linux — installs the CLI and the agent skill that drives it:
curl -fsSL https://raw.githubusercontent.com/zetic-ai/melange-cli/main/script/install.sh | shRestart your coding agent afterward so it can discover the skill. Re-run the same line any time to update both.
The installer downloads the release binary for your platform, verifies its
SHA-256 checksum, and installs the skill for universal agents and Claude Code.
When cosign is
on your PATH it also verifies the release-workflow signature; pass
| sh -s -- --require-signature to make that mandatory.
Installer options
Append flags after sh -s --, or set the matching environment variable:
| Flag | Environment variable | Effect |
|---|---|---|
--version v1.2.3 |
MELANGE_VERSION |
Install a specific release instead of the latest |
--install-dir DIR |
MELANGE_INSTALL_DIR |
Binary directory; defaults to /usr/local/bin, falling back to ~/.local/bin |
--cli-only |
MELANGE_SKIP_SKILL=1 |
Skip the agent skill |
--skill-only |
MELANGE_SKIP_CLI=1 |
Skip the CLI |
--agent "A B" |
MELANGE_SKILL_AGENTS |
Agents to install the skill for; defaults to universal claude-code |
--require-signature |
MELANGE_REQUIRE_SIGNATURE=1 |
Fail unless the release signature is verified |
curl -fsSL https://raw.githubusercontent.com/zetic-ai/melange-cli/main/script/install.sh \
| sh -s -- --version v1.2.3 --agent "universal claude-code codex"The skill is installed with npx skills
when a recent Node is available, and copied straight into the agent skill
directories otherwise.
Alternatives: Homebrew, npm, Go, and manual installation
These install the CLI only — add the agent skill separately with the
npx skills add command below.
Homebrew (macOS or Linux):
brew install zetic-ai/tap/melangenpm (macOS, Linux, or Windows — the only supported path on Windows):
npm install -g @zetic-ai/melange-cliWith a current Go toolchain:
go install github.com/zetic-ai/melange-cli/cmd/melange@latestPrebuilt binaries for macOS, Linux, and Windows on amd64/arm64, with checksums and SBOMs, are available on the releases page.
The agent skill, for driving melange from Claude Code, Codex, Cursor,
OpenCode, and other compatible coding agents:
# Install for universal agents and Claude Code
npx skills add zetic-ai/melange-cli --skill melange-cli \
--agent universal claude-code --global --yes
# Or choose one or more supported agents interactively
npx skills add zetic-ai/melange-cli --skill melange-cli --globalRestart your agent afterward so it can discover the skill.
By default melange auth login opens a browser for OAuth (recommended, zoa_/zor_ stored in OS keyring and auto-refreshed). For CI/headless use a Personal Access Token (ztp_) from Melange Settings → Personal Access Tokens:
export MELANGE_API_KEY="ztp_your_personal_access_token"
# or
melange auth login --with-token < token.txtOr store it interactively once:
melange auth login # browser OAuth; falls back to PAT paste if browser unavailablemelange auth statusA read-only path to confirm the install works. Nothing here creates anything or counts against a quota:
melange auth login # or export MELANGE_API_KEY
melange library list --search whisper # browse the public model library
melange library view zetic/whisper-tiny # one model in detail
melange repo list # your own repositoriesAdd --json (optionally with --jq) to any command for machine-readable output.
Three different identifiers appear in command arguments, and they are not
interchangeable — passing a repository or display name where a MODEL_KEY or
TARGET_ID belongs is the most common mistake:
| Identifier | What it is | How to get it |
|---|---|---|
ACCOUNT/REPO |
A Melange repository address | melange repo list |
MODEL_KEY |
One converted model version inside a repository | melange model list -R ACCOUNT/REPO |
TARGET_ID |
One downloadable converted artifact; opaque (tm_…/ltm_…) |
melange model targets MODEL_KEY -R ACCOUNT/REPO |
They chain in that order:
melange model list -R zetic/whisper-tiny
melange model targets MODEL_KEY -R zetic/whisper-tiny
melange model download MODEL_KEY -R zetic/whisper-tiny --target TARGET_ID --yesmelange model download is billable, so it requires --yes when it cannot ask
interactively.
With the agent skill installed, your agent can:
- Search the public model library and inspect available model versions.
- Compare real device benchmarks, targets, and report availability.
- Create and manage repositories, imports, uploads, and model versions.
- Generate exact deployment guides for Android, iOS, and Flutter.
- Check authentication, usage, quotas, and plan-specific availability.
Discover a public model and deploy
Find a computer vision model by Meta in the Melange model library,
review its real device benchmark results, and give me the iOS deployment code/guide.Upload your own model
I want to upload model.pt2 with sample.npy to Melange.
Upload the model, monitor conversion, and report the final status without retrying implicitly.Compare benchmarks
Compare Gemma4 with another llm available in Melange. Use only benchmark values returned by Melange.
Show throughput and peak memory for iPhone 16 and Galaxy S25 where available.The CLI ships a built-in MCP server:
melange mcp serves 18 tools over stdio (17 over HTTP — upload_model needs
the caller's local files) so MCP clients call Melange directly instead of
shelling out. The stdio server reuses the CLI's credentials (MELANGE_API_KEY
or melange auth login), resolved lazily on the first tool call. Install the
CLI first (see Install) so melange is on your PATH, then
register it with your client. The full tool catalog and per-transport details
are in llms.txt.
claude mcp add melange -- melange mcpVerify with claude mcp list — the entry should show ✔ Connected.
Add to claude_desktop_config.json (Settings → Developer → Edit Config;
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json,
Windows: %APPDATA%\Claude\claude_desktop_config.json), then restart
Claude Desktop:
{
"mcpServers": {
"melange": {
"command": "melange",
"args": ["mcp"]
}
}
}Add to .cursor/mcp.json in your project (or ~/.cursor/mcp.json for all
projects):
{
"mcpServers": {
"melange": {
"command": "melange",
"args": ["mcp"]
}
}
}For remote agent clients, serve the Streamable HTTP transport. The server
itself holds no credentials: every request must carry its own token as
Authorization: Bearer <token>, so one deployment serves many callers.
The server speaks plain HTTP; terminate TLS in front of it (load balancer,
reverse proxy, or ingress). The https:// client URLs below assume that.
melange mcp --transport http --listen 0.0.0.0:8080Claude Code (PAT ztp_... or OAuth zoa_...):
claude mcp add --transport http melange https://your-host:8080/ \
--header "Authorization: Bearer ztp_your_personal_access_token" # or zoa_... OAuthCursor (.cursor/mcp.json):
{
"mcpServers": {
"melange": {
"url": "https://your-host:8080/",
"headers": {
"Authorization": "Bearer ztp_your_personal_access_token" // or zoa_... OAuth
}
}
}
}Claude Desktop registers local stdio servers through
claude_desktop_config.json (above); remote servers are added as custom
connectors in claude.ai settings instead. See melange mcp --help for the
HTTP deployment flags (--validate-tokens, --allowed-origins,
--resource).
Re-run the installer to update the CLI and the skill together:
curl -fsSL https://raw.githubusercontent.com/zetic-ai/melange-cli/main/script/install.sh | shIf you installed the CLI another way, update it the same way you installed it —
brew upgrade zetic-ai/tap/melange or npm update -g @zetic-ai/melange-cli —
and update the skill with npx skills update melange-cli --global.
- Command reference (generated):
docs/reference/melange.md - LLM/agent surface reference:
llms.txt
Apache-2.0
