Kilo Code + z.ai (GLM)

TAUSIK runs as a first-class MCP host inside Kilo Code (the VSCode addon and its CLI), driven by z.ai GLM models. This works because of two design choices (Decision #119):

• Kilo is a runtime host (axis-1) — it owns the bootstrap directory, the MCP config, and active-model detection. It is the only thing that changes when you switch IDE.
• z.ai GLM is a model family (axis-2) — pure data in model_profiles, not code. Switching or adding GLM models needs no code change.

z.ai's endpoint is Anthropic-compatible, so the session transcript looks exactly like Claude's — only the model field reads glm-*. Routing, verdicts, and cost all work unchanged.

---

1. Point your agent at z.ai

z.ai exposes an Anthropic-compatible endpoint. Set these for Claude Code or Kilo (Kilo respects the same Anthropic env vars):

export ANTHROPIC_BASE_URL="https://api.z.ai/api/anthropic"
export ANTHROPIC_AUTH_TOKEN="<your-z.ai-api-key>"   # NEVER commit this

Secret hygiene: the z.ai key is a credential. Keep it in your shell profile or the IDE's secret store — never in .tausik/config.json, .kilo/, or anything tracked by git.

The GLM Coding Plan ($10/mo tier) gives access to the GLM family (glm-4.5-air, glm-4.6, and the glm-5.x line).

2. Bootstrap TAUSIK for Kilo

python .tausik-lib/bootstrap/bootstrap.py --ide kilo

This writes the TAUSIK MCP server stanza to both known Kilo config paths (robust across Kilo versions — Decision #120):

• .kilo/kilo.jsonc (current kilo.ai docs)
• .kilocode/mcp.json (older Cline-lineage builds)

Both contain the same mcp entry:

{
  "mcp": {
    "tausik-project": {
      "type": "local",
      "command": ["<python>", "${workspaceFolder}/.kilo/mcp/project/server.py", "--project", "${workspaceFolder}"],
      "enabled": true
    }
  }
}

Paths are rename-proof: a server inside the project and --project use ${workspaceFolder} (Kilo expands it at launch), so renaming the project folder does not break the config. An external lib server keeps its absolute path. Existing servers and other keys are merged, not overwritten. Re-running is idempotent.

Restart Kilo after bootstrap so it loads the new MCP config.

If your Kilo build reads neither default path

Override the target(s) in .tausik/config.json:

{ "kilo": { "config_paths": ["kilo.jsonc"] } }

(paths are project-relative; the list fully replaces the defaults.)

3. Tell TAUSIK which GLM model is active

Kilo has no Claude-style JSONL transcript, so TAUSIK reads the active model from (in order):

1. the KILO_MODEL environment variable — e.g. export KILO_MODEL=glm-4.6
2. a model field in .kilo/kilo.json (or ~/.config/kilo/kilo.json)

With that set, task start shows GLM recommendations and correct under/over-powered verdicts. Without it, recommendations fall back to model_profiles.default_family (below) and then to Claude.

4. Switch / add GLM models — no code change

Defaults shipped in scripts/model_profiles.py:

Capability rank	GLM model
light (haiku)	glm-4.5-air
mid (sonnet)	glm-4.6
strong (opus)	glm-4.6
flagship (fable)	glm-4.6

Override or extend any of these — and pin GLM as the default family — in .tausik/config.json:

{
  "model_profiles": {
    "default_family": "glm",
    "families": {
      "glm": {
        "opus":  { "model": "glm-5.2", "display": "GLM-5.2" },
        "fable": { "model": "glm-5.2", "display": "GLM-5.2" }
      }
    }
  }
}

default_family: "glm" makes task start recommend GLM models even before any transcript/KILO_MODEL detection — ideal when you only ever run Kilo + z.ai.

How it fits together

Kilo Code (addon/CLI)  ──MCP──▶  tausik-project server  (.kilo/kilo.jsonc | .kilocode/mcp.json)
        │
        └── model: glm-4.6  ──▶  model_profiles (family=glm) ──▶ routing rank → glm model + verdict

The runtime is Kilo; the model is GLM. Neither knows about the other — that separation is what makes "TAUSIK in Kilo with any z.ai model" a config exercise, not a code one.