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The agent that grows with you
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Python
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Fetches from https://models.dev/api.json — a community-maintained databaseof 4000+ models across 109+ providers. Provides:
- **Provider metadata**: name, base URL, env vars, documentation link- **Model metadata**: context window, max output, cost/M tokens, capabilities (reasoning, tools, vision, PDF, audio), modalities, knowledge cutoff, open-weights flag, family grouping, deprecation status
Data resolution order (like TypeScript OpenCode): 1. Bundled snapshot (ships with the package — offline-first) 2. Disk cache (~/.hermes/models_dev_cache.json) 3. Network fetch (https://models.dev/api.json) 4. Background refresh every 60 minutes
Other modules should import the dataclasses and query functions from hererather than parsing the raw JSON themselves."""
import jsonimport loggingimport timefrom dataclasses import dataclassfrom pathlib import Pathfrom typing import Any, Dict, List, Optional, Tuple
from utils import atomic_json_write
import requests
logger = logging.getLogger(__name__)
MODELS_DEV_URL = "https://models.dev/api.json"_MODELS_DEV_CACHE_TTL = 3600 # 1 hour in-memory
# In-memory cache_models_dev_cache: Dict[str, Any] = {}_models_dev_cache_time: float = 0
# ---------------------------------------------------------------------------# Dataclasses — rich metadata for providers and models# ---------------------------------------------------------------------------
@dataclassclass ModelInfo: """Full metadata for a single model from models.dev."""
id: str name: str family: str provider_id: str # models.dev provider ID (e.g. "anthropic")
# Capabilities reasoning: bool = False tool_call: bool = False attachment: bool = False # supports image/file attachments (vision) temperature: bool = False structured_output: bool = False open_weights: bool = False
# Modalities input_modalities: Tuple[str, ...] = () # ("text", "image", "pdf", ...) output_modalities: Tuple[str, ...] = ()
# Limits context_window: int = 0 max_output: int = 0 max_input: Optional[int] = None
# Cost (per million tokens, USD) cost_input: float = 0.0 cost_output: float = 0.0 cost_cache_read: Optional[float] = None cost_cache_write: Optional[float] = None
# Metadata knowledge_cutoff: str = "" release_date: str = "" status: str = "" # "alpha", "beta", "deprecated", or "" interleaved: Any = False # True or {"field": "reasoning_content"}
def has_cost_data(self) -> bool: return self.cost_input > 0 or self.cost_output > 0
def supports_vision(self) -> bool: return self.attachment or "image" in self.input_modalities
def supports_pdf(self) -> bool: return "pdf" in self.input_modalities
def supports_audio_input(self) -> bool: return "audio" in self.input_modalities
def format_cost(self) -> str: """Human-readable cost string, e.g. '$3.00/M in, $15.00/M out'.""" if not self.has_cost_data(): return "unknown" parts = [f"${self.cost_input:.2f}/M in", f"${self.cost_output:.2f}/M out"] if self.cost_cache_read is not None: parts.append(f"cache read ${self.cost_cache_read:.2f}/M") return ", ".join(parts)
def format_capabilities(self) -> str: """Human-readable capabilities, e.g. 'reasoning, tools, vision, PDF'.""" caps = [] if self.reasoning: caps.append("reasoning") if self.tool_call: caps.append("tools") if self.supports_vision(): caps.append("vision") if self.supports_pdf(): caps.append("PDF") if self.supports_audio_input(): caps.append("audio") if self.structured_output: caps.append("structured output") if self.open_weights: caps.append("open weights") return ", ".join(caps) if caps else "basic"
@dataclassclass ProviderInfo: """Full metadata for a provider from models.dev."""
id: str # models.dev provider ID name: str # display name env: Tuple[str, ...] # env var names for API key api: str # base URL doc: str = "" # documentation URL model_count: int = 0
# ---------------------------------------------------------------------------# Provider ID mapping: Hermes ↔ models.dev# ---------------------------------------------------------------------------
# Hermes provider names → models.dev provider IDsPROVIDER_TO_MODELS_DEV: Dict[str, str] = { "openrouter": "openrouter", "anthropic": "anthropic", "openai": "openai", "openai-codex": "openai", "zai": "zai", "kimi-coding": "kimi-for-coding", "kimi-coding-cn": "kimi-for-coding", "minimax": "minimax", "minimax-cn": "minimax-cn", "deepseek": "deepseek", "alibaba": "alibaba", "qwen-oauth": "alibaba", "copilot": "github-copilot", "ai-gateway": "vercel", "opencode-zen": "opencode", "opencode-go": "opencode-go", "kilocode": "kilo", "fireworks": "fireworks-ai", "huggingface": "huggingface", "gemini": "google", "google": "google", "xai": "xai", "xiaomi": "xiaomi", "nvidia": "nvidia", "groq": "groq", "mistral": "mistral", "togetherai": "togetherai", "perplexity": "perplexity", "cohere": "cohere", "ollama-cloud": "ollama-cloud",}
# Reverse mapping: models.dev → Hermes (built lazily)_MODELS_DEV_TO_PROVIDER: Optional[Dict[str, str]] = None
def _get_cache_path() -> Path: """Return path to disk cache file.""" from hermes_constants import get_hermes_home return get_hermes_home() / "models_dev_cache.json"
def _load_disk_cache() -> Dict[str, Any]: """Load models.dev data from disk cache.""" try: cache_path = _get_cache_path() if cache_path.exists(): with open(cache_path, encoding="utf-8") as f: return json.load(f) except Exception as e: logger.debug("Failed to load models.dev disk cache: %s", e) return {}
def _save_disk_cache(data: Dict[str, Any]) -> None: """Save models.dev data to disk cache atomically.""" try: cache_path = _get_cache_path() atomic_json_write(cache_path, data, indent=None, separators=(",", ":")) except Exception as e: logger.debug("Failed to save models.dev disk cache: %s", e)
def fetch_models_dev(force_refresh: bool = False) -> Dict[str, Any]: """Fetch models.dev registry. In-memory cache (1hr) + disk fallback.
Returns the full registry dict keyed by provider ID, or empty dict on failure. """ global _models_dev_cache, _models_dev_cache_time
# Check in-memory cache if ( not force_refresh and _models_dev_cache and (time.time() - _models_dev_cache_time) < _MODELS_DEV_CACHE_TTL ): return _models_dev_cache
# Try network fetch try: response = requests.get(MODELS_DEV_URL, timeout=15) response.raise_for_status() data = response.json() if isinstance(data, dict) and data: _models_dev_cache = data _models_dev_cache_time = time.time() _save_disk_cache(data) logger.debug( "Fetched models.dev registry: %d providers, %d total models", len(data), sum(len(p.get("models", {})) for p in data.values() if isinstance(p, dict)), ) return data except Exception as e: logger.debug("Failed to fetch models.dev: %s", e)
# Fall back to disk cache — use a short TTL (5 min) so we retry # the network fetch soon instead of serving stale data for a full hour. if not _models_dev_cache: _models_dev_cache = _load_disk_cache() if _models_dev_cache: _models_dev_cache_time = time.time() - _MODELS_DEV_CACHE_TTL + 300 logger.debug("Loaded models.dev from disk cache (%d providers)", len(_models_dev_cache))
return _models_dev_cache
def lookup_models_dev_context(provider: str, model: str) -> Optional[int]: """Look up context_length for a provider+model combo in models.dev.
Returns the context window in tokens, or None if not found. Handles case-insensitive matching and filters out context=0 entries. """ mdev_provider_id = PROVIDER_TO_MODELS_DEV.get(provider) if not mdev_provider_id: return None
data = fetch_models_dev() provider_data = data.get(mdev_provider_id) if not isinstance(provider_data, dict): return None
models = provider_data.get("models", {}) if not isinstance(models, dict): return None
# Exact match entry = models.get(model) if entry: ctx = _extract_context(entry) if ctx: return ctx
# Case-insensitive match model_lower = model.lower() for mid, mdata in models.items(): if mid.lower() == model_lower: ctx = _extract_context(mdata) if ctx: return ctx
return None
def _extract_context(entry: Dict[str, Any]) -> Optional[int]: """Extract context_length from a models.dev model entry.
Returns None for invalid/zero values (some audio/image models have context=0). """ if not isinstance(entry, dict): return None limit = entry.get("limit") if not isinstance(limit, dict): return None ctx = limit.get("context") if isinstance(ctx, (int, float)) and ctx > 0: return int(ctx) return None
# ---------------------------------------------------------------------------# Model capability metadata# ---------------------------------------------------------------------------
@dataclassclass ModelCapabilities: """Structured capability metadata for a model from models.dev."""
supports_tools: bool = True supports_vision: bool = False supports_reasoning: bool = False context_window: int = 200000 max_output_tokens: int = 8192 model_family: str = ""
def _get_provider_models(provider: str) -> Optional[Dict[str, Any]]: """Resolve a Hermes provider ID to its models dict from models.dev.
Returns the models dict or None if the provider is unknown or has no data. """ mdev_provider_id = PROVIDER_TO_MODELS_DEV.get(provider) if not mdev_provider_id: return None
data = fetch_models_dev() provider_data = data.get(mdev_provider_id) if not isinstance(provider_data, dict): return None
models = provider_data.get("models", {}) if not isinstance(models, dict): return None
return models
def _find_model_entry(models: Dict[str, Any], model: str) -> Optional[Dict[str, Any]]: """Find a model entry by exact match, then case-insensitive fallback.""" # Exact match entry = models.get(model) if isinstance(entry, dict): return entry
# Case-insensitive match model_lower = model.lower() for mid, mdata in models.items(): if mid.lower() == model_lower and isinstance(mdata, dict): return mdata
return None
def get_model_capabilities(provider: str, model: str) -> Optional[ModelCapabilities]: """Look up full capability metadata from models.dev cache.
Uses the existing fetch_models_dev() and PROVIDER_TO_MODELS_DEV mapping. Returns None if model not found.
Extracts from model entry fields: - reasoning (bool) → supports_reasoning - tool_call (bool) → supports_tools - attachment (bool) → supports_vision - limit.context (int) → context_window - limit.output (int) → max_output_tokens - family (str) → model_family """ models = _get_provider_models(provider) if models is None: return None
entry = _find_model_entry(models, model) if entry is None: return None
# Extract capability flags (default to False if missing) supports_tools = bool(entry.get("tool_call", False)) # Vision: check both the `attachment` flag and `modalities.input` for "image". # Some models (e.g. gemma-4) list image in input modalities but not attachment. input_mods = entry.get("modalities", {}) if isinstance(input_mods, dict): input_mods = input_mods.get("input", []) else: input_mods = [] supports_vision = bool(entry.get("attachment", False)) or "image" in input_mods supports_reasoning = bool(entry.get("reasoning", False))
# Extract limits limit = entry.get("limit", {}) if not isinstance(limit, dict): limit = {}
ctx = limit.get("context") context_window = int(ctx) if isinstance(ctx, (int, float)) and ctx > 0 else 200000
out = limit.get("output") max_output_tokens = int(out) if isinstance(out, (int, float)) and out > 0 else 8192
model_family = entry.get("family", "") or ""
return ModelCapabilities( supports_tools=supports_tools, supports_vision=supports_vision, supports_reasoning=supports_reasoning, context_window=context_window, max_output_tokens=max_output_tokens, model_family=model_family, )
def list_provider_models(provider: str) -> List[str]: """Return all model IDs for a provider from models.dev.
Returns an empty list if the provider is unknown or has no data. """ models = _get_provider_models(provider) if models is None: return [] return [ mid for mid in models.keys() if not _should_hide_from_provider_catalog(provider, mid) ]
# Patterns that indicate non-agentic or noise models (TTS, embedding,# dated preview snapshots, live/streaming-only, image-only).import re_NOISE_PATTERNS: re.Pattern = re.compile( r"-tts\b|embedding|live-|-(preview|exp)-\d{2,4}[-_]|" r"-image\b|-image-preview\b|-customtools\b", re.IGNORECASE,)
# Google's live Gemini catalogs currently include a mix of stale slugs and# Gemma models whose TPM quotas are too small for normal Hermes agent traffic.# Keep capability metadata available for direct/manual use, but hide these from# the Gemini model catalogs we surface in setup and model selection._GOOGLE_HIDDEN_MODELS = frozenset({ # Low-TPM Gemma models that trip Google input-token quota walls under # agent-style traffic despite advertising large context windows. "gemma-4-31b-it", "gemma-4-26b-it", "gemma-4-26b-a4b-it", "gemma-3-1b", "gemma-3-1b-it", "gemma-3-2b", "gemma-3-2b-it", "gemma-3-4b", "gemma-3-4b-it", "gemma-3-12b", "gemma-3-12b-it", "gemma-3-27b", "gemma-3-27b-it", # Stale/retired Google slugs that still surface through models.dev-backed # Gemini selection but 404 on the current Google endpoints. "gemini-1.5-flash", "gemini-1.5-pro", "gemini-1.5-flash-8b", "gemini-2.0-flash", "gemini-2.0-flash-lite",})
def _should_hide_from_provider_catalog(provider: str, model_id: str) -> bool: provider_lower = (provider or "").strip().lower() model_lower = (model_id or "").strip().lower() if provider_lower in {"gemini", "google"} and model_lower in _GOOGLE_HIDDEN_MODELS: return True return False
def list_agentic_models(provider: str) -> List[str]: """Return model IDs suitable for agentic use from models.dev.
Filters for tool_call=True and excludes noise (TTS, embedding, dated preview snapshots, live/streaming, image-only models). Returns an empty list on any failure. """ models = _get_provider_models(provider) if models is None: return []
result = [] for mid, entry in models.items(): if not isinstance(entry, dict): continue if _should_hide_from_provider_catalog(provider, mid): continue if not entry.get("tool_call", False): continue if _NOISE_PATTERNS.search(mid): continue result.append(mid) return result
# ---------------------------------------------------------------------------# Rich dataclass constructors — parse raw models.dev JSON into dataclasses# ---------------------------------------------------------------------------
def _parse_model_info(model_id: str, raw: Dict[str, Any], provider_id: str) -> ModelInfo: """Convert a raw models.dev model entry dict into a ModelInfo dataclass.""" limit = raw.get("limit") or {} if not isinstance(limit, dict): limit = {}
cost = raw.get("cost") or {} if not isinstance(cost, dict): cost = {}
modalities = raw.get("modalities") or {} if not isinstance(modalities, dict): modalities = {}
input_mods = modalities.get("input") or [] output_mods = modalities.get("output") or []
ctx = limit.get("context") ctx_int = int(ctx) if isinstance(ctx, (int, float)) and ctx > 0 else 0 out = limit.get("output") out_int = int(out) if isinstance(out, (int, float)) and out > 0 else 0 inp = limit.get("input") inp_int = int(inp) if isinstance(inp, (int, float)) and inp > 0 else None
return ModelInfo( id=model_id, name=raw.get("name", "") or model_id, family=raw.get("family", "") or "", provider_id=provider_id, reasoning=bool(raw.get("reasoning", False)), tool_call=bool(raw.get("tool_call", False)), attachment=bool(raw.get("attachment", False)), temperature=bool(raw.get("temperature", False)), structured_output=bool(raw.get("structured_output", False)), open_weights=bool(raw.get("open_weights", False)), input_modalities=tuple(input_mods) if isinstance(input_mods, list) else (), output_modalities=tuple(output_mods) if isinstance(output_mods, list) else (), context_window=ctx_int, max_output=out_int, max_input=inp_int, cost_input=float(cost.get("input", 0) or 0), cost_output=float(cost.get("output", 0) or 0), cost_cache_read=float(cost["cache_read"]) if "cache_read" in cost and cost["cache_read"] is not None else None, cost_cache_write=float(cost["cache_write"]) if "cache_write" in cost and cost["cache_write"] is not None else None, knowledge_cutoff=raw.get("knowledge", "") or "", release_date=raw.get("release_date", "") or "", status=raw.get("status", "") or "", interleaved=raw.get("interleaved", False), )
def _parse_provider_info(provider_id: str, raw: Dict[str, Any]) -> ProviderInfo: """Convert a raw models.dev provider entry dict into a ProviderInfo.""" env = raw.get("env") or [] models = raw.get("models") or {} return ProviderInfo( id=provider_id, name=raw.get("name", "") or provider_id, env=tuple(env) if isinstance(env, list) else (), api=raw.get("api", "") or "", doc=raw.get("doc", "") or "", model_count=len(models) if isinstance(models, dict) else 0, )
# ---------------------------------------------------------------------------# Provider-level queries# ---------------------------------------------------------------------------
def get_provider_info(provider_id: str) -> Optional[ProviderInfo]: """Get full provider metadata from models.dev.
Accepts either a Hermes provider ID (e.g. "kilocode") or a models.dev ID (e.g. "kilo"). Returns None if the provider is not in the catalog. """ # Resolve Hermes ID → models.dev ID mdev_id = PROVIDER_TO_MODELS_DEV.get(provider_id, provider_id)
data = fetch_models_dev() raw = data.get(mdev_id) if not isinstance(raw, dict): return None
return _parse_provider_info(mdev_id, raw)
# ---------------------------------------------------------------------------# Model-level queries (rich ModelInfo)# ---------------------------------------------------------------------------
def get_model_info( provider_id: str, model_id: str) -> Optional[ModelInfo]: """Get full model metadata from models.dev.
Accepts Hermes or models.dev provider ID. Tries exact match then case-insensitive fallback. Returns None if not found. """ mdev_id = PROVIDER_TO_MODELS_DEV.get(provider_id, provider_id)
data = fetch_models_dev() pdata = data.get(mdev_id) if not isinstance(pdata, dict): return None
models = pdata.get("models", {}) if not isinstance(models, dict): return None
# Exact match raw = models.get(model_id) if isinstance(raw, dict): return _parse_model_info(model_id, raw, mdev_id)
# Case-insensitive fallback model_lower = model_id.lower() for mid, mdata in models.items(): if mid.lower() == model_lower and isinstance(mdata, dict): return _parse_model_info(mid, mdata, mdev_id)
return None