mirror of
https://github.com/NousResearch/hermes-agent.git
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feat(agent): add lmstudio integration
This commit is contained in:
Rugved Somwanshi
kshitij
parent
7d4648461a
commit
214ca943ac
26 changed files with 1137 additions and 40 deletions
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@ -768,6 +768,7 @@ class ProviderEntry(NamedTuple):
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CANONICAL_PROVIDERS: list[ProviderEntry] = [
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ProviderEntry("nous", "Nous Portal", "Nous Portal (Nous Research subscription)"),
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ProviderEntry("openrouter", "OpenRouter", "OpenRouter (100+ models, pay-per-use)"),
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ProviderEntry("lmstudio", "LM Studio", "LM Studio (local desktop app with built-in model server)"),
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ProviderEntry("ai-gateway", "Vercel AI Gateway", "Vercel AI Gateway (200+ models, $5 free credit, no markup)"),
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ProviderEntry("anthropic", "Anthropic", "Anthropic (Claude models — API key or Claude Code)"),
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ProviderEntry("openai-codex", "OpenAI Codex", "OpenAI Codex"),
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@ -870,6 +871,9 @@ _PROVIDER_ALIASES = {
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"nvidia-nim": "nvidia",
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"build-nvidia": "nvidia",
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"nemotron": "nvidia",
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"lmstudio": "lmstudio",
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"lm-studio": "lmstudio",
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"lm_studio": "lmstudio",
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"ollama": "custom", # bare "ollama" = local; use "ollama-cloud" for cloud
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"ollama_cloud": "ollama-cloud",
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}
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@ -2195,6 +2199,225 @@ def _is_github_models_base_url(base_url: Optional[str]) -> bool:
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)
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def probe_lmstudio_models(
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api_key: Optional[str] = None,
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base_url: Optional[str] = None,
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timeout: float = 5.0,
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) -> Optional[list[str]]:
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"""Probe LM Studio's model listing.
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Returns chat-capable model keys on success, including the valid empty-list
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case when the server is reachable but has no non-embedding models.
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Returns ``None`` on network errors, malformed responses, or empty/invalid
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base URLs.
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Raises ``AuthError`` on HTTP 401/403 so callers can surface token issues
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separately from reachability problems.
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"""
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server_root = (base_url or "").strip().rstrip("/")
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if server_root.endswith("/v1"):
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server_root = server_root[:-3].rstrip("/")
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if not server_root:
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return None
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headers = {"User-Agent": _HERMES_USER_AGENT}
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token = str(api_key or "").strip()
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if token:
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headers["Authorization"] = f"Bearer {token}"
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request = urllib.request.Request(server_root + "/api/v1/models", headers=headers)
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try:
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with urllib.request.urlopen(request, timeout=timeout) as resp:
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payload = json.loads(resp.read().decode())
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except urllib.error.HTTPError as exc:
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if exc.code in (401, 403):
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from hermes_cli.auth import AuthError
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raise AuthError(
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f"LM Studio rejected the request with HTTP {exc.code}.",
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provider="lmstudio",
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code="auth_rejected",
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) from exc
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import logging
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logging.getLogger(__name__).debug(
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"LM Studio probe at %s failed with HTTP %s", server_root, exc.code,
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)
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return None
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except Exception as exc:
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import logging
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logging.getLogger(__name__).debug(
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"LM Studio probe at %s failed: %s", server_root, exc,
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)
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return None
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raw_models = payload.get("models") if isinstance(payload, dict) else None
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if not isinstance(raw_models, list):
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import logging
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logging.getLogger(__name__).debug(
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"LM Studio probe at %s returned malformed payload (no `models` list)",
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server_root,
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)
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return None
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keys: list[str] = []
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for raw in raw_models:
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if not isinstance(raw, dict):
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continue
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if str(raw.get("type") or "").strip().lower() == "embedding":
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continue
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key = str(raw.get("key") or raw.get("id") or "").strip()
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if key and key not in keys:
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keys.append(key)
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return keys
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def fetch_lmstudio_models(
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api_key: Optional[str] = None,
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base_url: Optional[str] = None,
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timeout: float = 5.0,
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) -> list[str]:
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"""Fetch LM Studio chat-capable model keys from native ``/api/v1/models``.
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Returns a list of model keys (e.g. ``publisher/model-name``) with embedding
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models filtered out. Returns an empty list on network errors, malformed
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responses, or empty/invalid base URLs.
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Raises ``AuthError`` on HTTP 401/403 so callers can distinguish a missing
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or wrong ``LM_API_KEY`` from an unreachable server — the most common
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LM Studio support case once auth-enabled mode is turned on.
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"""
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models = probe_lmstudio_models(api_key=api_key, base_url=base_url, timeout=timeout)
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return models or []
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def ensure_lmstudio_model_loaded(
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model: str,
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base_url: Optional[str],
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api_key: Optional[str],
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target_context_length: int,
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timeout: float = 120.0,
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) -> Optional[int]:
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"""Ensure LM Studio has ``model`` loaded with at least ``target_context_length``.
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No-op when an instance is already loaded with sufficient context. Otherwise
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POSTs ``/api/v1/models/load`` to (re)load with the target context, capped
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at the model's ``max_context_length``. Returns the resolved loaded context
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length, or ``None`` when the probe / load failed.
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"""
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server_root = (base_url or "").strip().rstrip("/")
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if server_root.endswith("/v1"):
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server_root = server_root[:-3].rstrip("/")
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if not server_root:
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return None
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headers = {"User-Agent": _HERMES_USER_AGENT}
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token = str(api_key or "").strip()
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if token:
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headers["Authorization"] = f"Bearer {token}"
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try:
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with urllib.request.urlopen(
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urllib.request.Request(server_root + "/api/v1/models", headers=headers),
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timeout=10,
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) as resp:
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payload = json.loads(resp.read().decode())
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except Exception:
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return None
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raw_models = payload.get("models") if isinstance(payload, dict) else None
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if not isinstance(raw_models, list):
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return None
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target_entry = None
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for raw in raw_models:
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if not isinstance(raw, dict):
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continue
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if raw.get("key") == model or raw.get("id") == model:
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target_entry = raw
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break
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if target_entry is None:
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return None
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max_ctx = target_entry.get("max_context_length")
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if isinstance(max_ctx, int) and max_ctx > 0:
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target_context_length = min(target_context_length, max_ctx)
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for inst in target_entry.get("loaded_instances") or []:
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cfg = inst.get("config") if isinstance(inst, dict) else None
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loaded_ctx = cfg.get("context_length") if isinstance(cfg, dict) else None
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if isinstance(loaded_ctx, int) and loaded_ctx >= target_context_length:
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return loaded_ctx
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body = json.dumps({
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"model": model,
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"context_length": target_context_length,
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}).encode()
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load_headers = dict(headers)
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load_headers["Content-Type"] = "application/json"
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try:
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with urllib.request.urlopen(
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urllib.request.Request(
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server_root + "/api/v1/models/load",
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data=body,
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headers=load_headers,
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method="POST",
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),
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timeout=timeout,
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) as resp:
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resp.read()
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except Exception:
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return None
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return target_context_length
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def lmstudio_model_reasoning_options(
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model: str,
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base_url: Optional[str],
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api_key: Optional[str] = None,
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timeout: float = 5.0,
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) -> list[str]:
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"""Return the reasoning ``allowed_options`` LM Studio publishes for ``model``.
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Pulls ``capabilities.reasoning.allowed_options`` from ``/api/v1/models``.
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Returns ``[]`` when the model is unknown, the endpoint is unreachable,
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or the model does not declare a reasoning capability.
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"""
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server_root = (base_url or "").strip().rstrip("/")
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if server_root.endswith("/v1"):
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server_root = server_root[:-3].rstrip("/")
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if not server_root:
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return []
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headers = {"User-Agent": _HERMES_USER_AGENT}
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token = str(api_key or "").strip()
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if token:
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headers["Authorization"] = f"Bearer {token}"
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try:
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with urllib.request.urlopen(
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urllib.request.Request(server_root + "/api/v1/models", headers=headers),
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timeout=timeout,
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) as resp:
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payload = json.loads(resp.read().decode())
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except Exception:
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return []
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raw_models = payload.get("models") if isinstance(payload, dict) else None
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if not isinstance(raw_models, list):
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return []
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for raw in raw_models:
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if not isinstance(raw, dict):
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continue
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if raw.get("key") != model and raw.get("id") != model:
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continue
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caps = raw.get("capabilities")
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reasoning = caps.get("reasoning") if isinstance(caps, dict) else None
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opts = reasoning.get("allowed_options") if isinstance(reasoning, dict) else None
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if isinstance(opts, list):
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return [str(o).strip().lower() for o in opts if isinstance(o, str)]
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return []
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return []
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def _fetch_github_models(api_key: Optional[str] = None, timeout: float = 5.0) -> Optional[list[str]]:
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catalog = fetch_github_model_catalog(api_key=api_key, timeout=timeout)
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if not catalog:
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@ -2790,6 +3013,40 @@ def validate_requested_model(
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"message": "Model names cannot contain spaces.",
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}
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if normalized == "lmstudio":
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from hermes_cli.auth import AuthError
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# Use probe_lmstudio_models so we can distinguish None (unreachable
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# / malformed response) from [] (reachable, but no chat-capable models
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# are loaded). fetch_lmstudio_models collapses both to [].
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try:
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models = probe_lmstudio_models(api_key=api_key, base_url=base_url)
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except AuthError as exc:
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return {
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"accepted": False, "persist": False, "recognized": False,
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"message": (
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f"{exc} Set `LM_API_KEY` (or update it) to match the server's bearer token."
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),
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}
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if models is None:
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return {
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"accepted": False, "persist": False, "recognized": False,
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"message": f"Could not reach LM Studio's `/api/v1/models` to validate `{requested}`.",
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}
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if not models:
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return {
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"accepted": False, "persist": False, "recognized": False,
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"message": (
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f"LM Studio is reachable but no chat-capable models are loaded. "
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f"Load `{requested}` in LM Studio (Developer tab → Load Model) and try again."
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),
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}
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if requested_for_lookup in set(models):
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return {"accepted": True, "persist": True, "recognized": True, "message": None}
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return {
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"accepted": False, "persist": False, "recognized": False,
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"message": f"Model `{requested}` was not found in LM Studio's model listing.",
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}
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if normalized == "custom":
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# Try probing with correct auth for the api_mode.
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if api_mode == "anthropic_messages":
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