fix: CI httpx dep, Model Hub visibility/features/tokens/costs, dynamic roster model_info

.github/workflows/test.yml

@@ -20,6 +20,9 @@ jobs:
         with:
           python-version: '3.11'
 
+      - name: Install dependencies
+        run: pip install httpx
+
       - name: Run Circuit Breaker Tests
         run: python3 test_circuit_breaker.py
 

litellm/config.yaml

@@ -20,6 +20,16 @@ litellm_settings:
   - langfuse
   detailed_debug: false
   drop_params: true
+  public_model_groups:
+  - openrouter-auto
+  - llm-routing-ollama
+  - ollama-deepseek-v4-pro
+  - ollama-deepseek-v4-flash
+  - agent-advanced-core
+  - agent-reasoning-core
+  - agent-complex-core
+  - agent-medium-core
+  - agent-simple-core
   fallbacks:
   - agent-simple-core:
     - agent-medium-core
@@ -57,12 +67,28 @@ model_list:
     model: openrouter/openrouter/auto
     request_timeout: 120
   model_name: openrouter-auto
+  model_info:
+    supports_vision: true
+    supports_reasoning: true
+    supports_function_calling: true
+    mode: chat
+    max_tokens: 2000000
+    max_input_tokens: 2000000
+    is_public_model_group: true
 - litellm_params:
     model: openai/llm-routing-ollama
     api_base: http://127.0.0.1:5000/v1
     api_key: os.environ/LITELLM_MASTER_KEY
     request_timeout: 120
   model_name: llm-routing-ollama
+  model_info:
+    supports_vision: true
+    supports_reasoning: true
+    supports_function_calling: true
+    mode: chat
+    max_tokens: 524288
+    max_input_tokens: 524288
+    is_public_model_group: true
 
 # DISABLED 2026-06-08 — 20GB on disk, 23GB GTT (system RAM as GPU buffer).
 # Uncomment to re-enable once a lighter model replaces it.
@@ -93,6 +119,16 @@ model_list:
     api_base: https://api.ollama.com
     api_key: os.environ/OLLAMA_API_KEY
     request_timeout: 120
+  model_info:
+    supports_vision: true
+    supports_reasoning: true
+    supports_function_calling: true
+    mode: chat
+    max_tokens: 524288
+    max_input_tokens: 524288
+    input_cost_per_token: 0.00000174
+    output_cost_per_token: 0.00000348
+    is_public_model_group: true
 
 # ================================================================================
 # OLLAMA FLASH TIER — lighter/faster model for reasoning-tier requests.
@@ -104,6 +140,16 @@ model_list:
     api_base: https://api.ollama.com
     api_key: os.environ/OLLAMA_API_KEY
     request_timeout: 120
+  model_info:
+    supports_vision: true
+    supports_reasoning: true
+    supports_function_calling: true
+    mode: chat
+    max_tokens: 524288
+    max_input_tokens: 524288
+    input_cost_per_token: 0.00000014
+    output_cost_per_token: 0.00000028
+    is_public_model_group: true
 
 # ================================================================================
 # AGENT TIER DEPLOYMENTS — safety-net entries (one per tier)
@@ -133,22 +179,62 @@ model_list:
   litellm_params:
     model: openrouter/google/gemma-4-31b-it:free
     request_timeout: 20
+  model_info:
+    supports_vision: true
+    supports_reasoning: true
+    supports_function_calling: true
+    mode: chat
+    max_tokens: 262144
+    max_input_tokens: 262144
+    is_public_model_group: true
 - model_name: agent-reasoning-core
   litellm_params:
     model: openrouter/google/gemma-4-26b-a4b-it:free
     request_timeout: 20
+  model_info:
+    supports_vision: true
+    supports_reasoning: true
+    supports_function_calling: true
+    mode: chat
+    max_tokens: 262144
+    max_input_tokens: 262144
+    is_public_model_group: true
 - model_name: agent-complex-core
   litellm_params:
     model: openrouter/nvidia/nemotron-3-ultra-550b-a55b:free
     request_timeout: 20
+  model_info:
+    supports_vision: true
+    supports_reasoning: true
+    supports_function_calling: true
+    mode: chat
+    max_tokens: 1000000
+    max_input_tokens: 1000000
+    is_public_model_group: true
 - model_name: agent-medium-core
   litellm_params:
     model: openrouter/google/gemma-4-26b-a4b-it:free
     request_timeout: 20
+  model_info:
+    supports_vision: true
+    supports_reasoning: true
+    supports_function_calling: true
+    mode: chat
+    max_tokens: 262144
+    max_input_tokens: 262144
+    is_public_model_group: true
 - model_name: agent-simple-core
   litellm_params:
     model: openrouter/nvidia/nemotron-3-nano-30b-a3b:free
     request_timeout: 20
+  model_info:
+    supports_vision: true
+    supports_reasoning: true
+    supports_function_calling: true
+    mode: chat
+    max_tokens: 256000
+    max_input_tokens: 256000
+    is_public_model_group: true
 
 redis_settings:
   redis_host: 127.0.0.1

router/main.py

@@ -369,6 +369,7 @@ async def sync_adaptive_router_roster(master_key: str):
         logger.warning(f"Failed to fetch OpenRouter models: {e}")
         return
     free_models = []
+    model_contexts = {}
     for m in all_models:
         mid = m.get("id", "")
         # Skip internal OpenRouter encoded IDs that LiteLLM can't map to a provider
@@ -398,6 +399,7 @@ async def sync_adaptive_router_roster(master_key: str):
             except Exception:
                 score = 25.0  # conservative fallback for unparseable models
             free_models.append((score, mid))
+            model_contexts[mid] = m.get("context_length") or 262144
     free_models.sort(reverse=True)
     if not free_models:
         logger.warning("No free models found — skipping roster sync")
@@ -474,9 +476,19 @@ def norm(s: float) -> float:
         failed = 0
         for tier_name, model_ids in tier_assignments.items():
             for mid in model_ids:
+                ctx_len = model_contexts.get(mid, 262144)
                 payload = {
                     "model_name": tier_name,
-                    "litellm_params": {"model": f"openrouter/{mid}", "request_timeout": 20}
+                    "litellm_params": {"model": f"openrouter/{mid}", "request_timeout": 20},
+                    "model_info": {
+                        "supports_vision": True,
+                        "supports_reasoning": True,
+                        "supports_function_calling": True,
+                        "mode": "chat",
+                        "max_tokens": ctx_len,
+                        "max_input_tokens": ctx_len,
+                        "is_public_model_group": True
+                    }
                 }
                 try:
                     r = await client.post(f"{admin_url}/model/new", headers=headers, json=payload)
@@ -490,6 +502,84 @@ def norm(s: float) -> float:
                     logger.warning(f"Failed to register {mid} under {tier_name}: {e}")
         logger.info(f"📊 Roster sync: registered {registered} deployments ({failed} failed) across 5 tiers — {sum(len(v) for v in tier_assignments.values())} attempted")
 
+async def _register_ollama_models_in_db(master_key: str):
+    """Register static ollama models via /model/new so they become DB models.
+
+    LiteLLM's /model_group/info endpoint aggregates model info using its internal
+    model cost map for known providers.  For ollama_chat models not in the map,
+    capabilities (vision, reasoning, function_calling) and token limits come back
+    as null/false.  Registering them as DB models ensures our model_info wins.
+    """
+    admin_url = "http://127.0.0.1:4000"
+    headers = {"Authorization": f"Bearer {master_key}", "Content-Type": "application/json"}
+
+    ollama_models = [
+        {
+            "model_name": "ollama-deepseek-v4-pro",
+            "litellm_params": {
+                "model": "ollama_chat/deepseek-v4-pro",
+                "api_base": "https://api.ollama.com",
+                "api_key": os.getenv("OLLAMA_API_KEY", ""),
+                "request_timeout": 120,
+            },
+            "model_info": {
+                "supports_vision": True,
+                "supports_reasoning": True,
+                "supports_function_calling": True,
+                "mode": "chat",
+                "max_tokens": 524288,
+                "max_input_tokens": 524288,
+                "input_cost_per_token": 0.00000174,
+                "output_cost_per_token": 0.00000348,
+                "is_public_model_group": True,
+            },
+        },
+        {
+            "model_name": "ollama-deepseek-v4-flash",
+            "litellm_params": {
+                "model": "ollama_chat/deepseek-v4-flash",
+                "api_base": "https://api.ollama.com",
+                "api_key": os.getenv("OLLAMA_API_KEY", ""),
+                "request_timeout": 120,
+            },
+            "model_info": {
+                "supports_vision": True,
+                "supports_reasoning": True,
+                "supports_function_calling": True,
+                "mode": "chat",
+                "max_tokens": 524288,
+                "max_input_tokens": 524288,
+                "input_cost_per_token": 0.00000014,
+                "output_cost_per_token": 0.00000028,
+                "is_public_model_group": True,
+            },
+        },
+    ]
+
+    # Purge stale ollama-deepseek DB entries before re-registering
+    try:
+        db_url = os.getenv("DATABASE_URL", "postgresql://postgres@127.0.0.1:5432/postgres")
+        import asyncpg
+        conn = await asyncpg.connect(db_url)
+        await conn.execute(
+            'DELETE FROM "LiteLLM_ProxyModelTable" WHERE model_name LIKE $1',
+            'ollama-deepseek-%'
+        )
+        await conn.close()
+    except Exception as e:
+        logger.warning(f"Failed to purge stale ollama DB entries (non-fatal): {e}")
+
+    async with httpx.AsyncClient(timeout=10.0) as client:
+        for payload in ollama_models:
+            try:
+                r = await client.post(f"{admin_url}/model/new", headers=headers, json=payload)
+                if r.status_code in (200, 201):
+                    logger.info(f"✅ Registered {payload['model_name']} in DB")
+                else:
+                    logger.warning(f"model/new {payload['model_name']}: HTTP {r.status_code} — {r.text[:200]}")
+            except Exception as e:
+                logger.warning(f"Failed to register {payload['model_name']}: {e}")
+
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     """Startup: wait for LiteLLM readiness, then sync free-model roster."""
@@ -521,6 +611,15 @@ async def lifespan(app: FastAPI):
         except Exception as e:
             logger.error(f"Roster sync failed: {e}")
 
+        # Register static ollama models via /model/new so they become DB models.
+        # The ollama_chat provider's internal model lookup overrides static config
+        # model_info at the group aggregation level (/model_group/info), causing
+        # features and token limits to show as null/false. DB models get priority.
+        try:
+            await _register_ollama_models_in_db(litellm_master_key)
+        except Exception as e:
+            logger.warning(f"Ollama DB registration failed (non-fatal): {e}")
+
     # Start background task before yield so it runs during app lifetime
     task = asyncio.create_task(push_aggregate_scores())