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ZCode

ZCode CLI coding agent (z.ai), running GLM-5.2 over the z.ai start-plan.

  • Source: src/providers/zcode.ts
  • Loading: lazy (src/providers/index.ts). Lazy because we read ZCode's SQLite database with node:sqlite.
  • Test: tests/providers/zcode.test.ts (3 tests, fixture-based)

Where it reads from

ZCode keeps a single global SQLite database for the CLI.

Source Path
ZCode CLI db ~/.zcode/cli/db/db.sqlite

The desktop app dir (~/Library/Application Support/ZCode) only holds Electron runtime state, and the JSONL activity log (~/.zcode/cli/log/*.jsonl) redacts token counts, so neither is used.

Storage format

SQLite. Schema verified against CLI db v0.14.8. Three tables matter:

CREATE TABLE session (
  id TEXT PRIMARY KEY,
  directory TEXT NOT NULL,
  ...
);

CREATE TABLE model_usage (
  id TEXT PRIMARY KEY,
  session_id TEXT NOT NULL,
  turn_id TEXT,
  model_id TEXT NOT NULL,
  input_tokens INTEGER NOT NULL DEFAULT 0,
  output_tokens INTEGER NOT NULL DEFAULT 0,
  reasoning_tokens INTEGER NOT NULL DEFAULT 0,
  cache_creation_input_tokens INTEGER NOT NULL DEFAULT 0,
  cache_read_input_tokens INTEGER NOT NULL DEFAULT 0,
  started_at INTEGER NOT NULL,
  completed_at INTEGER,
  ...
);

CREATE TABLE tool_usage (
  session_id TEXT NOT NULL,
  turn_id TEXT,
  tool_name TEXT NOT NULL,
  started_at INTEGER NOT NULL,
  ...
);

Caching

None at the provider level.

Deduplication

Per zcode:<model_usage.id> (zcode.ts). model_usage.id is the row primary key, unique per request.

What we extract

codeburn field ZCode source
inputTokens model_usage.input_tokens minus cached + created (see quirks)
outputTokens model_usage.output_tokens
reasoningTokens model_usage.reasoning_tokens
cacheCreationInputTokens model_usage.cache_creation_input_tokens
cacheReadInputTokens model_usage.cache_read_input_tokens
costUSD computed by calculateCost (ZCode stores no cost)
model model_usage.model_id (e.g. GLM-5.2)
timestamp model_usage.completed_at if set, otherwise started_at (epoch ms)
tools tool_usage.tool_name for the turn, attached to one request per turn

Quirks worth knowing

  • Cached tokens are folded into input_tokens (OpenAI-style). The row's input_tokens is the full prompt size including cache reads/writes, and provider_total_tokens = input_tokens + output_tokens. The parser subtracts cache_read_input_tokens and cache_creation_input_tokens from input_tokens so fresh input bills at the input rate and cached at the cache-read rate. Confirmed against the nested Anthropic usage in provider_metadata_json (e.g. 100 input = 36 fresh + 64 cached).
  • No cost is stored anywhere. GLM-5.2 runs on z.ai's start-plan subscription, so ZCode logs tokens only. CodeBurn computes a notional cost from the pricing table.
  • GLM-5.2 is priced via an alias. LiteLLM does not list GLM-5.2 yet, so GLM-5.2 maps to glm-5p1 (GLM-5.1) in BUILTIN_ALIASES (src/models.ts). Reports therefore show the model as glm-5p1, the same way any aliased model displays as its priced-as target. Drop the alias once LiteLLM adds GLM-5.2.
  • Timestamps are milliseconds. Unlike Crush (seconds), ZCode stores epoch ms; the parser passes them straight to Date.
  • Tools are attached per turn, not per request. tool_usage links to a turn, not a specific model_usage row, so each turn's tools are attached to its first request to avoid double-counting. Bash command text is not stored, so bashCommands is always empty.

When fixing a bug here

  1. Confirm the schema against a real ZCode install; copy ~/.zcode/cli/db/db.sqlite to a temp file before querying so you do not lock the live db.
  2. If costs are $0, check that GLM-5.2 (or the current model id) still resolves through BUILTIN_ALIASES to a priced model.
  3. If tokens look ~8x too high, someone likely removed the cache-subtraction in the input normalization; the row's input_tokens already includes cached tokens.
  4. New fixtures go under the inline schema in tests/providers/zcode.test.ts.