meridian-governance-cli
Scriptable command-line client for Meridian Governance Router, over meridian-client. Installs as the mdn command — one short command, same shape as mta or claude.
The PyPI package is meridian-governance-cli; the command it installs is mdn. Not to be confused with the unrelated meridian-cli package also on PyPI.
Closer in spirit to the aws or stripe CLIs than to an app's own CLI:
subcommands over an API, scriptable, mostly no interactive loop required —
with one exception. chat absorbed
Local-AI's reference
implementation upstream (meridian-agent-v1.0.py, decided 2026-10-18) so
customers get one governed resolve -> dispatch -> audit loop from Meridian
itself, interactively or scripted, rather than writing that sequence
themselves against the SDK.
Install
curl -fsSL https://dev-meridian.mytrustworthy.ai/install.sh | bash
Detects Python 3.9+, installs via pipx if present, otherwise into an
isolated venv under ~/.local/share/mdn with a shim on ~/.local/bin. One
command, nothing else to figure out. It also drops a small manifest at
~/.meridian/install-manifest.json recording which path it took, so
uninstalling doesn't require remembering:
curl -fsSL https://dev-meridian.mytrustworthy.ai/uninstall.sh | bash
Reads that manifest to remove the right thing (pipx uninstall or the
venv it created), offers to revoke your API key on the server first
(mdn logout --revoke), and separately offers to delete
~/.meridian (saved credentials, chat session logs) — three
confirmations, nothing deleted without asking.
Or plain pip, if you'd rather manage the venv yourself (no manifest, so
uninstall.sh won't know about it — just pip uninstall when you're done):
pip install meridian-governance-cli
Get credentials
mdn signup # new org — prompts for org name, email, password
# or
mdn login # existing account — authenticates, mints a new key
mdn whoami # confirm what's saved
Credentials live in ~/.meridian/config.json (mode 600 — api_key is in
there in plaintext, same tradeoff mta-cli makes for its own token). Override
the location with MERIDIAN_CLI_HOME if you want more than one profile.
Commands
mdn resolve my_agent [--project-id ID] [--json]
mdn explain ROUTING_LOG_ID
mdn audit ROUTING_LOG_ID --input-tokens N --output-tokens N
mdn projects create --name NAME [--quality Q] [--cost C] [--risk R]
mdn projects list
mdn agents create PROJECT_ID AGENT_NAME [--pinned-model M] [--quality-override Q] [--allowed-hubs h1,h2]
mdn agents list PROJECT_ID
mdn hubs list
mdn hubs discover
mdn keys list [--json]
mdn keys create [--name NAME]
mdn keys revoke KEY_ID
mdn catalog
mdn logout [--revoke]
mdn chat [--agent NAME] [--code-agent NAME] [--project-id ID] [--max-tokens N]
mdn chat --one-shot "message" [--json] [--max-tokens N]
mdn chat --daemon [--host HOST] [--port PORT] [--refresh-interval SECS]
Every data-returning command without a dedicated human-readable format
(explain, audit, catalog, projects list, agents list) prints raw
JSON — pipe into jq.
chat
Per-turn flow: resolve(agent) -> catalog/hubs join -> dispatch to the
chosen hub's OpenAI-compatible endpoint -> audit. Routing, scoring and
governance all live in Meridian; chat just drives the loop. OpenAI-compat
hubs only for now (vLLM, Ollama, OpenAI-shaped endpoints) — bedrock/
anthropic/etc. need a protocol translator, not yet added.
- No args: interactive REPL.
/helpinside it for slash commands (/catalog,/hubs,/refresh,/history,/export,/window, …). - Three REPL modes, switched with
/chat,/code,/manage(defaultchat):chat— plain conversation,resolve(--agent)(default agent namechat).code— same loop, different binding:resolve(--code-agent)(default agent namecode) plus a one-time system preamble. Its own history, separate fromchatmode's. This is not a tool-using coding agent — Meridian's dispatch layer never sendstoolsto the hub, so this is "a different agent/model to resolve against," nothing more. A real tool-execution loop would be new dispatch-layer work, not a REPL mode.manage— no LLM calls at all:/projects,/agents,/keys(list/create,/keys revoke ID) over the account you're logged into. Same functionsmdn projects/mdn agents/mdn keyscall outside the REPL (meridian_cli/manage.py) — one implementation, not two that can drift./project [ID]works in any mode: with no argument, shows the active project and lists your org's projects; with an ID, switches to it (validated against your org's actual projects first). Affects the nextresolve()call inchat/codemode immediately.
--one-shot "message": single turn, prints the response, exits.--jsonemits structured output (response,model,hub,routing_log_id,latency_ms,usage) instead of plain text.--daemon: long-lived local OpenAI-compatible endpoint (POST /v1/chat/completions,GET /v1/models,GET /health) — point an existing OpenAI-SDK-based app athttp://127.0.0.1:8700and it works unchanged, with Meridian's governance and audit trail in the middle. The F9 "broker" shape from Local-AI's feedback. Stateless across requests, same as the real OpenAI API: your app holds history and sends the fullmessagesarray each call.--max-tokens: reasoning models can need a much larger budget than the default (8192) just to reach visible content — confirmed live against a real deployment where the same five-word prompt burned anywhere from ~2400 to the full 8192-token budget purely on reasoning, before any answer. If a response comes back as a token-budget notice instead of text, raise this — unless the prompt matches the pattern below, where raising it won't help.- Exact-count / constraint-satisfaction prompts can make reasoning never
converge, at any budget. Isolated live against
qwen3.6:35b:"Say hello in exactly five words."burned the entire reasoning budget at 8192, then again at 16384 — raising--max-tokensdidn't make it converge faster, it just filled whatever ceiling it was given. The same model answers the unconstrained version ("Say hello.") normally in 186 reasoning tokens, and a substantive non-counting prompt normally in ~700. So this isn't the model's reasoning budget being too small — it's a specific model failure mode where exact-count constraints seem to trap it in verifying the count. Not a Meridian or CLI bug; a model characteristic to know about before raising--max-tokensin response to a stuck "exactly N words/items" prompt — raising it will not fix that case.
hubs discover
Probes localhost for a local Ollama (:11434/api/tags) or vLLM
(:8000/v1/models), proposes registering what it finds (models included),
and asks for confirmation before actually registering anything — it never
writes to your org's catalog without that confirmation. If nothing
responds on localhost, it falls back to prompting for a provider + URL
manually, probes that instead, and asks for confirmation the same way.
Same wizard from mdn hubs discover and /discover inside mdn chat's
manage mode (meridian_cli/manage.py + chat.py's cmd_manage_discover)
— one implementation either way.
Deliberately only probes localhost, never a LAN subnet: unlike a
single-household tool that can hardcode its own known boxes' IPs,
Meridian is multi-tenant — there's no fixed list of "this customer's
boxes" to guess at. localhost is the one address that means the same
thing for every caller; anything else goes through the manual-entry
fallback instead of a scan.
Also why this is a client-side probe at all, not a call to the backend's
own /discover endpoint: on a SaaS instance, "localhost" from the
server's perspective means the server's own container, not your
laptop. Discovery and model-listing both happen in the CLI process so
they can actually reach localhost on the machine mdn is running on;
the resulting model list is registered directly via register_models,
never by asking the server to re-discover something it structurally
cannot reach.
What this deliberately doesn't do
hubs discoveronly knows Ollama and vLLM, and only probeslocalhost. No cloud-hub registration commands (bedrock/anthropic/openrouter/etc.) — those need credentials entered deliberately, not something to probe for. Console-only for now.
Metadata
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