bytedance/deer-flow · critical · ValueError

No chat models are configured. Please configure at least one

Error message

No chat models are configured. Please configure at least one model in config.yaml.

What it means

ValueError from _resolve_model_name in the lead agent: app_config.models is empty, so there is no default model to fall back to. The agent refuses to build rather than running modelless. This is a configuration error — config.yaml has zero entries under models.

Source

Thrown at backend/packages/harness/deerflow/agents/lead_agent/agent.py:133

        tools.append(memory_tool)
        existing_names.add(memory_tool.name)


def _get_runtime_config(config: RunnableConfig) -> dict:
    """Merge legacy configurable options with LangGraph runtime context."""
    cfg = dict(config.get("configurable", {}) or {})
    context = config.get("context", {}) or {}
    if isinstance(context, dict):
        cfg.update(context)
    return cfg


def _resolve_model_name(requested_model_name: str | None = None, *, app_config: AppConfig | None = None) -> str:
    """Resolve a runtime model name safely, falling back to default if invalid. Returns None if no models are configured."""
    app_config = app_config or get_app_config()
    default_model_name = app_config.models[0].name if app_config.models else None
    if default_model_name is None:
        raise ValueError("No chat models are configured. Please configure at least one model in config.yaml.")

    if requested_model_name and app_config.get_model_config(requested_model_name):
        return requested_model_name

    if requested_model_name and requested_model_name != default_model_name:
        logger.warning(f"Model '{requested_model_name}' not found in config; fallback to default model '{default_model_name}'.")
    return default_model_name


def _authorize_model_name(
    model_name: str,
    *,
    context: Mapping[str, Any],
    app_config: AppConfig,
) -> str:
    """Enforce ``model:use`` authorization on the resolved model name.

    When ``authorization.enabled`` is false this is a no-op (returns

View on GitHub (pinned to 1dd6ba1acb)

Solutions

1. Add at least one full model entry under models: in config.yaml and restart the Gateway.
2. Validate config with `make doctor` (or the config validation endpoint) to catch YAML structure errors.
3. If config is managed via API, re-fetch it and confirm models is a non-empty list.
4. For tests, inject an AppConfig stub with at least one model instead of relying on global config.

Example fix

# config.yaml
# before
models: []

# after
models:
  - name: gpt-4o
    provider: openai
    api_key: ${OPENAI_API_KEY}

from deerflow.config import get_app_config
cfg = get_app_config()
assert cfg.models, 'config.yaml has no models configured — agent creation will fail'

def has_model(cfg) -> bool:
    return bool(getattr(cfg, 'models', None)) and all(getattr(m, 'name', None) for m in cfg.models)

try:
    agent = create_agent(...)
except ValueError as e:
    if 'No chat models are configured' in str(e):
        raise SystemExit('Fix config.yaml: add at least one model entry') from e
    raise

When it happens

Trigger: Any agent/run creation when config.yaml (or the resolved AppConfig) contains no models: empty models list, failed config load falling through to defaults, or a config override wiping the list.

Common situations: Fresh clone where config.yaml was copied from example but models section left empty; CI/test environment without model config; typo in YAML indentation making models parse as a scalar; config API PATCH that replaced models with [].

Related errors

• No chat model could be resolved. Please configure at least o
• Failed to load configuration during gateway startup: {e}
• scheduler.multi_instance=true requires database.backend='pos
• scheduler.multi_instance=true requires run_events.backend='d
• scheduler.multi_instance=true requires run_ownership.heartbe

AI-assisted analysis of bytedance/deer-flow@1dd6ba1acb (2026-08-14). Data as JSON: /api/errors/14123b5f4a6cda87. Report an issue: GitHub.