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Clone this repository:\n   ```bash\n   git clone https://github.com/monotykamary/pi-lilac-provider.git\n   cd pi-lilac-provider\n   ```\n\n2. Set your Lilac API key:\n   ```bash\n   # Recommended: add to auth.json\n   # See Authentication section below\n\n   # Or set as environment variable\n   export LILAC_API_KEY=your-api-key-here\n   ```\n\n3. Run pi with the extension:\n   ```bash\n   pi -e /path/to/pi-lilac-provider\n   ```\n\n## Available Models\n\n| Model | Context | Vision | Reasoning | Input $/M | Cache Read $/M | Output $/M |\n|-------|---------|--------|-----------|-----------|-----------------|------------|\n| Gemma 4 | 262K | ✅ | ✅ | $0.11 | — | $0.35 |\n| GLM 5.2 | 524K | ❌ | ✅ | $0.90 | $0.17 | $3.00 |\n| Kimi K2.6 | 262K | ✅ | ✅ | $0.70 | $0.20 | $3.50 |\n| MiniMax M3 | 1.0M | ✅ | ✅ | $0.28 | $0.05 | $1.10 |\n\n*Costs are per million tokens. Prices subject to change — check [getlilac.com](https://getlilac.com/) for current pricing.*\n\n**Notes:**\n- **Gemma 4** has reasoning **off by default** — pi enables it when you set a thinking level (Shift+Tab)\n- **Kimi K2.6** and **GLM 5.1** have reasoning **on by default**\n- **Cache read** pricing applies to repeated input tokens served from cache on supported models\n- **Gemma 4** does not support cache read pricing\n\n## Usage\n\nAfter loading the extension, use the `/model` command in pi to select your preferred model:\n\n```\n/model lilac moonshotai/kimi-k2.6\n```\n\nOr start pi directly with a Lilac model:\n\n```bash\npi --provider lilac --model moonshotai/kimi-k2.6\n```\n\n### Thinking Mode\n\nAll Lilac models toggle reasoning via `chat_template_kwargs`, but the key each model's chat template honors differs per family. The provider uses pi's `chat-template` thinkingFormat with per-model `chatTemplateKwargs` (configured in `patch.json`) so the right key reaches each template:\n\n| Model | Reasoning key | Default |\n|-------|---------------|---------|\n| Kimi K2.6 | `thinking` (bool) | on |\n| GLM 5.1 | `enable_thinking` (bool) | on |\n| GLM 5.2 | `enable_thinking` (bool) + `reasoning_effort` (`max`\\|`high`) | on (`max`) |\n| Gemma 4 | `enable_thinking` (bool) | off |\n| MiniMax M2.7 | `thinking` + `enable_thinking` (bool) | on |\n| MiniMax M3 | `thinking_mode` (`disabled`\\|`adaptive`\\|`enabled`) | adaptive (server) |\n\nKimi K2.6, GLM 5.1, Gemma 4, and MiniMax M2.7 use the forward-compatible form that sends **both** `thinking` and `enable_thinking`, so whichever key the template honors is set. GLM 5.2 additionally maps pi's thinking levels to `reasoning_effort` (`high` = lower-latency, `max` = deepest). MiniMax M3 uses the `thinking_mode` enum, exposed as three pi thinking levels: `off` → `disabled` (never think), `minimal` → `adaptive` (the model decides), `high` → `enabled` (always think). Pi starts at `off` (`disabled`); cycle to `minimal` for M3's adaptive \"model decides\" mode. (The selector/footer show pi's level names — `minimal`/`high` — not the `thinking_mode` values; pi has no per-model level-relabel hook.)\n\n**Preserved thinking (full-history reasoning).** By default these templates trim older assistant reasoning between turns (each vendor's default), which degrades multi-turn recall. Three models opt into full-history preservation via a template flag sent alongside the reasoning key:\n\n| Model | Flag | Effect |\n|-------|------|--------|\n| Kimi K2.6 | `preserve_thinking: true` | keeps every assistant turn's reasoning (default: only the last) |\n| GLM 5.1 | `clear_thinking: false` | keeps reasoning for all turns (default: clears before the last user message) |\n| GLM 5.2 | `clear_thinking: false` | keeps reasoning for all turns (default: clears before the last user message) |\n\nKimi K2.6 and GLM 5.2 are E2E-verified on the sibling neuralwatt provider via a 3-turn, two-20-digit-number recall test (Kimi 0/6 → 6/6, GLM 5.2 1/4 → 4/4); GLM 5.1 uses the same `clear_thinking` mechanism (confirmed in its HuggingFace chat template). Gemma 4 and MiniMax M2.7/M3 expose no family-wide preserve flag, so their older assistant reasoning is trimmed per the template default.\n\nIn pi, reasoning models automatically use the appropriate thinking format. Use Shift+Tab to control thinking level.\n\n### Vision\n\nKimi K2.6 and Gemma 4 support image inputs. Pass images in messages and pi will handle the formatting automatically.\n\nGemma 4 also supports video by accepting a sequence of frames as images.\n\n## Authentication\n\nThe Lilac API key can be configured in multiple ways (resolved in this order):\n\n1. **`auth.json`** (recommended) — Add to `~/.pi/agent/auth.json`:\n   ```json\n   { \"lilac\": { \"type\": \"api_key\", \"key\": \"your-api-key\" } }\n   ```\n   The `key` field supports literal values, env var names, and shell commands (prefix with `!`). See [pi's auth file docs](https://github.com/badlogic/pi-mono) for details.\n2. **Runtime override** — Use the `--api-key` CLI flag\n3. **Environment variable** — Set `LILAC_API_KEY`\n\nGet your API key at [getlilac.com](https://getlilac.com/).\n\n## Environment Variables\n\n| Variable | Required | Description |\n|----------|----------|-------------|\n| `LILAC_API_KEY` | No | Your Lilac API key (fallback if not in auth.json) |\n\n## Configuration\n\nAdd to your pi configuration for automatic loading:\n\n```json\n{\n  \"extensions\": [\n    \"/path/to/pi-lilac-provider\"\n  ]\n}\n```\n\n### Compat Settings\n\nLilac's API is OpenAI-compatible with these specifics:\n\n- **`thinkingFormat: \"chat-template\"`** — All reasoning models. Lilac's vLLM backend toggles reasoning via `chat_template_kwargs`, but the honored key differs per model family. Per-model `chatTemplateKwargs` in `patch.json` send the right key(s): `thinking`+`enable_thinking` (bool) for Kimi K2.6, GLM 5.1, Gemma 4, and MiniMax M2.7; `enable_thinking` + `reasoning_effort` for GLM 5.2; `thinking_mode` (adaptive|enabled|disabled) for MiniMax M3. Kimi K2.6, GLM 5.1, and GLM 5.2 additionally send a preservation flag (`preserve_thinking: true` / `clear_thinking: false`) to retain full reasoning history across turns — see [Preserved thinking](#thinking-mode) above. Override these per-model via [Model Overrides](#model-overrides).\n- **`maxTokensField: \"max_completion_tokens\"`** — All models. Lilac supports `max_completion_tokens` (preferred for reasoning models as it includes reasoning tokens).\n- **`supportsDeveloperRole: true`** — All models. Lilac's vLLM backend maps the developer role to system.\n- **`supportsStore: false`** — All models. Lilac doesn't support the `store` parameter.\n\n### Known Caveats\n\n- **GLM 5.1 intermittent tool call loss**: vLLM's streaming parser intermittently emits `finish_reason: \"tool_calls\"` without any `delta.tool_calls` chunks — even with `tool_stream: true` (set via `zaiToolStream` in compat). Pi maps this to `stopReason: \"toolUse\"` with zero toolCall blocks, causing an \"abrupt stop\". The extension's `message_end` handler converts this to a retryable error that triggers pi's built-in auto-retry mechanism, so the agent automatically re-prompts and typically succeeds on the next attempt.\n- **GLM 5.1 chain-of-thought leakage**: On the current vLLM build, disabling reasoning on GLM 5.1 may still leak chain-of-thought into `content` terminated by a `</think>` marker. Post-process the response to discard text up to and including the first `</think>` when reasoning is disabled. See [vllm-project/vllm#31319](https://github.com/vllm-project/vllm/issues/31319).\n- **Gemma 4 reasoning parser**: vLLM's reasoning parser can fail to populate the `reasoning` field when special tokens are stripped before the parser runs. Clients that require a clean split should post-process `<|channel|>thought ... <|channel|>` markers. See [vllm-project/vllm#38855](https://github.com/vllm-project/vllm/issues/38855).\n- **Gemma 4 structured output**: Combining `enable_thinking: false` with `response_format: json_schema` can silently disable xgrammar-backed structured output. If you rely on structured output with Gemma 4, leave thinking enabled or validate output client-side. See [vllm-project/vllm#39130](https://github.com/vllm-project/vllm/issues/39130).\n\n### Patch Overrides\n\nThe `patch.json` file contains overrides that are applied on top of `models.json` data. This is useful for:\n- Correcting API-derived values (e.g., GLM 5.1's `maxTokens` — API returns context length, actual max output is 131K)\n- Marking models as reasoning-capable when the API features list doesn't include it\n- Adding compat settings that the API doesn't provide\n- Overriding pricing when official rates change\n\n### Model Overrides\n\n`modelOverrides` lets you override compat flags and other model properties per model id, **on top of** `patch.json` + `custom-models.json`, without editing the extension. Keyed by model id; `compat` (including nested `chatTemplateKwargs`), `thinkingLevelMap`, and `cost` are deep-merged **recursively** (toggle one flag without redeclaring the rest), scalars and arrays are replaced. Applied at session start, so edits take effect on the next `pi` session.\n\nCreate `~/.pi/agent/extensions/lilac.json` (auto-populated with defaults on first run):\n\n```jsonc\n{\n  // Only respond when the active model's discount is >= this percent. null = off.\n  // See \"Flex (Discount Gating)\" below. Set via /lilac-settings.\n  \"flexThreshold\": null,\n  \"modelOverrides\": {\n    // Disable full-history reasoning for kimi-k2.6 (e.g. to save tokens):\n    \"moonshotai/kimi-k2.6\": { \"compat\": { \"chatTemplateKwargs\": { \"preserve_thinking\": false } } },\n    // Toggle the GLM 5.2 clear_thinking flag without redeclaring the rest of compat:\n    \"zai-org/glm-5.2\": { \"compat\": { \"chatTemplateKwargs\": { \"clear_thinking\": true } } },\n    // Override a single thinking level without redeclaring the whole map:\n    \"zai-org/glm-5.1\": { \"thinkingLevelMap\": { \"high\": \"max\" } }\n  }\n}\n```\n\nThe full set of overridable fields matches the model schema (`compat`, `thinkingLevelMap`, `cost`, `contextWindow`, `maxTokens`, `reasoning`, `input`). See [Compat Settings](#compat-settings) for the catalog of compat flags and what `chatTemplateKwargs` values mean per family. An invalid JSON file is left untouched (defaults are used) so a typo isn't silently wiped — fix the file and restart pi.\n\n### Flex (Discount Gating)\n\nLilac's per-model discount fluctuates with idle-GPU supply. **Flex** lets you set a discount threshold so pi **only sends a prompt to the LLM when the active model's current discount is at or above it** — e.g. \"only respond when the discount is ≥ 75%\". Below the threshold, the prompt is blocked (dropped with a warning) until the next discount poll brings the discount back up. This is a spend-control feature: you only spend when supply is cheap.\n\nSet it via the **Flex threshold** row in [`/lilac-settings`](#settings-ui) — cycle `off` / `50` / `75`:\n\n```\n/lilac-settings     # open the settings panel → Flex threshold row\n```\n\nThe threshold persists in `~/.pi/agent/extensions/lilac.json` as `flexThreshold` (a number `0`–`100`, or `null` for off) alongside `modelOverrides`, so it survives restarts. `/lilac-settings` updates it live — no restart needed. For a custom value (e.g. 60), edit `flexThreshold` in that file directly.\n\nBehavior notes:\n\n- **Gating point:** flex checks at prompt-submission time. When blocked, the prompt is dropped (you get a warning notification) and you re-submit once the discount improves. It does **not** queue the prompt.\n- **Scope:** only **interactive** (TUI-typed) prompts are gated. `rpc`/`print` (automation) and extension-injected messages are not gated, so flex never causes a silent failure in a pipeline or an extension loop. Flex only applies to the active **lilac** model; non-lilac models always pass.\n- **No data / no discount entry = 0%.** A lilac model with no discount (list price), or before the first discount poll has data, counts as 0% and is blocked when flex is on. This matches how discounts are priced elsewhere in the extension.\n- **Freshness:** when a prompt is blocked, the extension triggers an immediate `/status` refresh (throttled to once per ~5s) so you're not stuck on a stale low value from the 5-minute idle poll. The next submission sees the fresh discount. The footer status reflects the gate: `… · flex ≥75% ok` or `… · flex ≥75% blocked`.\n- **Discount lock-in:** per Lilac, a discount is locked in when a request starts. Flex gates on the best-known discount at submit time, which is what gets locked in for that turn.\n\n### Settings UI\n\n`/lilac-settings` opens an interactive settings panel (mirrors pi core's `/settings` — bordered `SettingsList`, Esc to go back) to configure Lilac without editing JSON by hand:\n\n- **Flex threshold** (`off` / `50` / `75`) — the spend-control gate from [Flex (Discount Gating)](#flex-discount-gating); custom values via `lilac.json`.\n- **Preserved thinking** (nested submenu, one row per model) — toggles `clear_thinking` (GLM-5.2 / GLM-5.1) / `preserve_thinking` (Kimi K2.6) in `modelOverrides` between **Preserve Thinking** (keep full reasoning history across turns; the default, `clear_thinking: false` / `preserve_thinking: true`) and **Clear Thinking** (let the template drop older reasoning; saves tokens, but can degrade multi-turn recall / cause overthinking).\n\nChanges write to `~/.pi/agent/extensions/lilac.json`, refresh the in-memory config, and re-register the provider, so they take effect immediately — no restart needed.\n\nWhen you switch to — or start pi on — a Lilac model that carries a preserved-thinking flag (e.g. GLM-5.2 / GLM-5.1, Kimi K2.6), an info notification reports the state and how to change it, e.g. `Preserved thinking ON for glm-5.2 (clear_thinking: false) — suited for coding, but not for prose. Open /lilac-settings to change.` (OFF reads `... reasoning trimmed each turn (lighter; better for prose) ...`). It's an ordinary info notification (not a warning), so it doesn't paint bright yellow.\n\n## Updating Models\n\nRun the update script to fetch the latest models from Lilac's API:\n\n```bash\nexport LILAC_API_KEY=your-api-key\nnode scripts/update-models.js\n```\n\nThis will:\n1. Fetch models from `https://api.getlilac.com/v1/models`\n2. Convert per-token pricing to per-million-tokens\n3. Preserve existing curated data (pricing, compat) for known models\n4. Apply overrides from `patch.json`\n5. Update `models.json` and the README model table\n\nA GitHub Actions workflow runs this daily and creates a PR if models have changed.\n\n## License\n\nMIT\n","readmeFilename":"README.md"}