feat(platform): Add LLM registry public read API

Implements public GET endpoints for querying LLM models and providers - Part 3 of 6 in the incremental registry rollout.

**Endpoints:**
- GET /api/llm/models - List all models (filterable by enabled_only)
- GET /api/llm/providers - List providers with their models

**Design:**
- Uses in-memory registry from PR 2 (no DB queries)
- Fast reads from cache populated at startup
- Grouped by provider for easy UI rendering

**Response models:**
- LlmModel - model info with capabilities, costs, creator
- LlmProvider - provider with nested models
- LlmModelsResponse - list + total count
- LlmProvidersResponse - grouped by provider

**Authentication:**
- Requires user auth (requires_user dependency)
- Public within authenticated sessions

**Integration:**
- Registered in rest_api.py at /api prefix
- Tagged with v2 + llm for OpenAPI grouping

**What's NOT included (later PRs):**
- Admin write API (PR 4)
- Block integration (PR 5)
- Redis cache (PR 6)

Lines: ~180 total
Files: 4 (3 new, 1 modified)
Review time: < 10 minutes

autogpt_platform/backend/backend/api/rest_api.py

@@ -41,6 +41,7 @@ import backend.data.graph
 import backend.data.llm_registry
 import backend.data.user
 import backend.integrations.webhooks.utils
+import backend.server.v2.llm
 import backend.util.service
 import backend.util.settings
 from backend.api.features.library.exceptions import (
@@ -397,6 +398,11 @@ app.include_router(
     tags=["oauth"],
     prefix="/api/oauth",
 )
+app.include_router(
+    backend.server.v2.llm.router,
+    tags=["v2", "llm"],
+    prefix="/api",
+)
 
 app.mount("/external-api", external_api)
 

autogpt_platform/backend/backend/server/v2/llm/__init__.py

@@ -0,0 +1,5 @@
+"""LLM registry public API."""
+
+from .routes import router
+
+__all__ = ["router"]

autogpt_platform/backend/backend/server/v2/llm/model.py

@@ -0,0 +1,67 @@
+"""Pydantic models for LLM registry public API."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import pydantic
+
+
+class LlmModelCost(pydantic.BaseModel):
+    """Cost configuration for an LLM model."""
+
+    unit: str  # "RUN" or "TOKENS"
+    credit_cost: int = pydantic.Field(ge=0)
+    credential_provider: str
+    credential_id: str | None = None
+    credential_type: str | None = None
+    currency: str | None = None
+    metadata: dict[str, Any] = pydantic.Field(default_factory=dict)
+
+
+class LlmModelCreator(pydantic.BaseModel):
+    """Represents the organization that created/trained the model."""
+
+    id: str
+    name: str
+    display_name: str
+    description: str | None = None
+    website_url: str | None = None
+    logo_url: str | None = None
+
+
+class LlmModel(pydantic.BaseModel):
+    """Public-facing LLM model information."""
+
+    slug: str
+    display_name: str
+    description: str | None = None
+    provider_name: str
+    creator: LlmModelCreator | None = None
+    context_window: int
+    max_output_tokens: int | None = None
+    price_tier: int  # 1=cheapest, 2=medium, 3=expensive
+    is_recommended: bool = False
+    capabilities: dict[str, Any] = pydantic.Field(default_factory=dict)
+    costs: list[LlmModelCost] = pydantic.Field(default_factory=list)
+
+
+class LlmProvider(pydantic.BaseModel):
+    """Provider with its enabled models."""
+
+    name: str
+    display_name: str
+    models: list[LlmModel] = pydantic.Field(default_factory=list)
+
+
+class LlmModelsResponse(pydantic.BaseModel):
+    """Response for GET /llm/models."""
+
+    models: list[LlmModel]
+    total: int
+
+
+class LlmProvidersResponse(pydantic.BaseModel):
+    """Response for GET /llm/providers."""
+
+    providers: list[LlmProvider]

autogpt_platform/backend/backend/server/v2/llm/routes.py

@@ -0,0 +1,141 @@
+"""Public read-only API for LLM registry."""
+
+import autogpt_libs.auth
+import fastapi
+
+from backend.data.llm_registry import (
+    RegistryModelCreator,
+    get_all_models,
+    get_enabled_models,
+)
+from backend.server.v2.llm import model as llm_model
+
+router = fastapi.APIRouter(
+    prefix="/llm",
+    tags=["llm"],
+    dependencies=[fastapi.Security(autogpt_libs.auth.requires_user)],
+)
+
+
+def _map_creator(
+    creator: RegistryModelCreator | None,
+) -> llm_model.LlmModelCreator | None:
+    """Convert registry creator to API model."""
+    if not creator:
+        return None
+    return llm_model.LlmModelCreator(
+        id=creator.id,
+        name=creator.name,
+        display_name=creator.display_name,
+        description=creator.description,
+        website_url=creator.website_url,
+        logo_url=creator.logo_url,
+    )
+
+
+@router.get("/models", response_model=llm_model.LlmModelsResponse)
+async def list_models(
+    enabled_only: bool = fastapi.Query(
+        default=True, description="Only return enabled models"
+    ),
+):
+    """
+    List all LLM models available to users.
+
+    Returns models from the in-memory registry cache.
+    Use enabled_only=true to filter to only enabled models (default).
+    """
+    # Get models from in-memory registry
+    registry_models = get_enabled_models() if enabled_only else get_all_models()
+
+    # Map to API response models
+    models = [
+        llm_model.LlmModel(
+            slug=model.slug,
+            display_name=model.display_name,
+            description=model.description,
+            provider_name=model.provider_display_name,
+            creator=_map_creator(model.creator),
+            context_window=model.metadata.context_window,
+            max_output_tokens=model.metadata.max_output_tokens,
+            price_tier=model.metadata.price_tier,
+            is_recommended=model.is_recommended,
+            capabilities=model.capabilities,
+            costs=[
+                llm_model.LlmModelCost(
+                    unit=cost.unit,
+                    credit_cost=cost.credit_cost,
+                    credential_provider=cost.credential_provider,
+                    credential_id=cost.credential_id,
+                    credential_type=cost.credential_type,
+                    currency=cost.currency,
+                    metadata=cost.metadata,
+                )
+                for cost in model.costs
+            ],
+        )
+        for model in registry_models
+    ]
+
+    return llm_model.LlmModelsResponse(models=models, total=len(models))
+
+
+@router.get("/providers", response_model=llm_model.LlmProvidersResponse)
+async def list_providers():
+    """
+    List all LLM providers with their enabled models.
+
+    Groups enabled models by provider from the in-memory registry.
+    """
+    # Get all enabled models and group by provider
+    registry_models = get_enabled_models()
+
+    # Group models by provider
+    provider_map: dict[str, list] = {}
+    for model in registry_models:
+        provider_key = model.metadata.provider
+        if provider_key not in provider_map:
+            provider_map[provider_key] = []
+        provider_map[provider_key].append(model)
+
+    # Build provider responses
+    providers = []
+    for provider_key, models in sorted(provider_map.items()):
+        # Use the first model's provider display name
+        display_name = models[0].provider_display_name if models else provider_key
+
+        providers.append(
+            llm_model.LlmProvider(
+                name=provider_key,
+                display_name=display_name,
+                models=[
+                    llm_model.LlmModel(
+                        slug=model.slug,
+                        display_name=model.display_name,
+                        description=model.description,
+                        provider_name=model.provider_display_name,
+                        creator=_map_creator(model.creator),
+                        context_window=model.metadata.context_window,
+                        max_output_tokens=model.metadata.max_output_tokens,
+                        price_tier=model.metadata.price_tier,
+                        is_recommended=model.is_recommended,
+                        capabilities=model.capabilities,
+                        costs=[
+                            llm_model.LlmModelCost(
+                                unit=cost.unit,
+                                credit_cost=cost.credit_cost,
+                                credential_provider=cost.credential_provider,
+                                credential_id=cost.credential_id,
+                                credential_type=cost.credential_type,
+                                currency=cost.currency,
+                                metadata=cost.metadata,
+                            )
+                            for cost in model.costs
+                        ],
+                    )
+                    for model in sorted(models, key=lambda m: m.display_name)
+                ],
+            )
+        )
+
+    return llm_model.LlmProvidersResponse(providers=providers)