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fix(core,docs): window-derived output caps and compaction threshold, slower retry ladder, retry-all-but-auth (#235)

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Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
Yaowei Zheng
2026-08-07 19:14:26 +08:00
committed by GitHub
parent 169c7d5006
commit 864261eade
20 changed files with 846 additions and 251 deletions
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packages/docs/content/models.en.md
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@@ -21,8 +21,8 @@ Each Project's available models are recorded in the hidden `.project_config.toml
| --- | --- |
| `provider` | Config group name; paired with `model_id` it forms the unique key |
| `model_id` | Upstream request id |
| `context_window` | Context window |
| `max_tokens` | Optional per-model output cap (max output tokens per request). When set it overrides the Agent's `model.max_tokens`; unset inherits it. Lower it for small-context models: the per-Agent default (32000) cannot fit into e.g. a 32k window together with any prompt. Omitting the field on a Web full-table save clears it |
| `context_window` | Context window (tokens). Load-bearing, not just display: each request's effective output cap and the compaction threshold are derived from it, so requests never ask for more output than the window still fits. Unset (or implausibly small, under 4096): the output clamp turns off and compaction derives from an assumed 128000 — set the real value for models with smaller windows |
| `max_tokens` | Optional per-model output cap (max output tokens per request). When set it overrides the Agent's `model.max_tokens`; unset inherits it. The cap is a ceiling, not the literal wire value: each request sends `min(max_tokens, context_window − estimated input − safety margin)`, so small-window models work without hand-tuning it. Omitting the field on a Web full-table save clears it |
| `client_type` | Protocol hint (e.g. `openai`); inferred by AgentHub from the model id when omitted |
| `display_name` | Display name |
| `vision` | Whether image input is supported, default true |
@@ -77,6 +77,13 @@ The preset catalog also carries OpenRouter's free tier: `:free` model variants (
Some models in the preset catalog: deepseek-v4-pro / deepseek-v4-flash, gemini-3.1-pro-preview, claude-opus-4-8 / claude-sonnet-4-6, gpt-5.5, glm-5.2, kimi-k2.6, qwen3.8-max (not exhaustive).
## Local / self-hosted OpenAI-compatible endpoints (e.g. vLLM)
A local inference server is just a `custom` entry: `client_type = "openai"`, `base_url` pointing at the server (e.g. `http://127.0.0.1:8000/v1`), and the served model name as `model_id`. Two settings make it run smoothly:
- **Enable tool calling server-side.** For vLLM, start the server with `--enable-auto-tool-choice` and the `--tool-call-parser` matching your model (e.g. `hermes` for Qwen, `llama3_json` for Llama 3.x); without them tool calls arrive as plain text and the agent loop cannot execute anything.
- **Set the entry's `context_window` to the server's real window** — for vLLM, the `--max-model-len` value (e.g. `32768`). The per-request output cap and the compaction threshold both derive from this window automatically: requests clamp `max_tokens` to what the window still fits, and compaction fires before the window overflows, so no hand-tuned `max_tokens` is needed. Left unset, the per-request output clamp is off and compaction assumes a 128000 window, so a smaller real window will reject requests.
## Thinking levels
Five levels: `none | low | medium | high | xhigh`, configured per Agent as `model.thinking_level` in `system_config.yaml`, default medium. The Web pickers offer `low` and above only (many models cannot disable thinking; `none` stays a valid stored value and still displays). The chat draft view offers a quick picker next to the model selector: a picked level is written back to the selected Agent's setting immediately (the switched-to level becomes that Agent's new default and applies from the next session). Inside an active session the thinking level is a **per-turn parameter**: the composer's picker lists only the levels and starts out showing the Agent config's level — while the user hasn't picked one it auto-follows the config (sends omit the level, so config edits keep taking effect); once picked, the level sticks for that session and rides on every subsequent send (it applies to that session's subsequent Tasks only and never writes back to the Agent config). See [Configuration](/configuration).