feat(chat): add input schema to discovery tools + validate unknown fields

- Add 'inputs' field to AgentInfo model for find_agent/find_library_agent
- Fetch input schemas from graphs during agent search
- Reject unknown input fields in run_agent with helpful error message

Closes OPEN-2980

autogpt_platform/backend/backend/api/features/chat/tools/agent_search.py

@@ -1,10 +1,11 @@
 """Shared agent search functionality for find_agent and find_library_agent tools."""
 
 import logging
-from typing import Literal
+from typing import Any, Literal
 
 from backend.api.features.library import db as library_db
 from backend.api.features.store import db as store_db
+from backend.data.graph import get_graph
 from backend.util.exceptions import DatabaseError, NotFoundError
 
 from .models import (
@@ -14,12 +15,39 @@ from .models import (
     NoResultsResponse,
     ToolResponseBase,
 )
+from .utils import fetch_graph_from_store_slug
 
 logger = logging.getLogger(__name__)
 
 SearchSource = Literal["marketplace", "library"]
 
 
+async def _fetch_input_schema_for_store_agent(
+    creator: str, slug: str
+) -> dict[str, Any] | None:
+    """Fetch input schema for a marketplace agent. Returns None on error."""
+    try:
+        graph, _ = await fetch_graph_from_store_slug(creator, slug)
+        if graph and graph.input_schema:
+            return graph.input_schema.get("properties", {})
+    except Exception as e:
+        logger.debug(f"Could not fetch input schema for {creator}/{slug}: {e}")
+    return None
+
+
+async def _fetch_input_schema_for_library_agent(
+    graph_id: str, graph_version: int, user_id: str
+) -> dict[str, Any] | None:
+    """Fetch input schema for a library agent. Returns None on error."""
+    try:
+        graph = await get_graph(graph_id, graph_version, user_id=user_id)
+        if graph and graph.input_schema:
+            return graph.input_schema.get("properties", {})
+    except Exception as e:
+        logger.debug(f"Could not fetch input schema for graph {graph_id}: {e}")
+    return None
+
+
 async def search_agents(
     query: str,
     source: SearchSource,
@@ -55,6 +83,10 @@ async def search_agents(
             logger.info(f"Searching marketplace for: {query}")
             results = await store_db.get_store_agents(search_query=query, page_size=5)
             for agent in results.agents:
+                # Fetch input schema for this agent
+                inputs = await _fetch_input_schema_for_store_agent(
+                    agent.creator, agent.slug
+                )
                 agents.append(
                     AgentInfo(
                         id=f"{agent.creator}/{agent.slug}",
@@ -67,6 +99,7 @@ async def search_agents(
                         rating=agent.rating,
                         runs=agent.runs,
                         is_featured=False,
+                        inputs=inputs,
                     )
                 )
         else:  # library
@@ -77,6 +110,10 @@ async def search_agents(
                 page_size=10,
             )
             for agent in results.agents:
+                # Fetch input schema for this agent
+                inputs = await _fetch_input_schema_for_library_agent(
+                    agent.graph_id, agent.graph_version, user_id  # type: ignore[arg-type]
+                )
                 agents.append(
                     AgentInfo(
                         id=agent.id,
@@ -90,6 +127,7 @@ async def search_agents(
                         has_external_trigger=agent.has_external_trigger,
                         new_output=agent.new_output,
                         graph_id=agent.graph_id,
+                        inputs=inputs,
                     )
                 )
         logger.info(f"Found {len(agents)} agents in {source}")

autogpt_platform/backend/backend/api/features/chat/tools/models.py

@@ -68,6 +68,10 @@ class AgentInfo(BaseModel):
     has_external_trigger: bool | None = None
     new_output: bool | None = None
     graph_id: str | None = None
+    inputs: dict[str, Any] | None = Field(
+        default=None,
+        description="Input schema for the agent (properties from input_schema)",
+    )
 
 
 class AgentsFoundResponse(ToolResponseBase):

autogpt_platform/backend/backend/api/features/chat/tools/run_agent.py

@@ -273,6 +273,27 @@ class RunAgentTool(BaseTool):
             input_properties = graph.input_schema.get("properties", {})
             required_fields = set(graph.input_schema.get("required", []))
             provided_inputs = set(params.inputs.keys())
+            valid_fields = set(input_properties.keys())
+
+            # Check for unknown fields - reject early with helpful message
+            unknown_fields = provided_inputs - valid_fields
+            if unknown_fields:
+                valid_list = ", ".join(sorted(valid_fields)) if valid_fields else "none"
+                return AgentDetailsResponse(
+                    message=(
+                        f"Unknown input field(s) provided: {', '.join(sorted(unknown_fields))}. "
+                        f"Valid fields for '{graph.name}': {valid_list}. "
+                        "Please check the field names and try again."
+                    ),
+                    session_id=session_id,
+                    agent=self._build_agent_details(
+                        graph,
+                        extract_credentials_from_schema(graph.credentials_input_schema),
+                    ),
+                    user_authenticated=True,
+                    graph_id=graph.id,
+                    graph_version=graph.version,
+                )
 
             # If agent has inputs but none were provided AND use_defaults is not set,
             # always show what's available first so user can decide

autogpt_platform/backend/backend/api/features/chat/tools/utils.py

@@ -8,7 +8,7 @@ from backend.api.features.library import model as library_model
 from backend.api.features.store import db as store_db
 from backend.data import graph as graph_db
 from backend.data.graph import GraphModel
-from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
+from backend.data.model import Credentials, CredentialsFieldInfo, CredentialsMetaInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import NotFoundError
 
@@ -266,13 +266,14 @@ async def match_user_credentials_to_graph(
         credential_requirements,
         _node_fields,
     ) in aggregated_creds.items():
-        # Find first matching credential by provider and type
+        # Find first matching credential by provider, type, and scopes
         matching_cred = next(
             (
                 cred
                 for cred in available_creds
                 if cred.provider in credential_requirements.provider
                 and cred.type in credential_requirements.supported_types
+                and _credential_has_required_scopes(cred, credential_requirements)
             ),
             None,
         )
@@ -296,10 +297,17 @@ async def match_user_credentials_to_graph(
                     f"{credential_field_name} (validation failed: {e})"
                 )
         else:
+            # Build a helpful error message including scope requirements
+            error_parts = [
+                f"provider in {list(credential_requirements.provider)}",
+                f"type in {list(credential_requirements.supported_types)}",
+            ]
+            if credential_requirements.required_scopes:
+                error_parts.append(
+                    f"scopes including {list(credential_requirements.required_scopes)}"
+                )
             missing_creds.append(
-                f"{credential_field_name} "
-                f"(requires provider in {list(credential_requirements.provider)}, "
-                f"type in {list(credential_requirements.supported_types)})"
+                f"{credential_field_name} (requires {', '.join(error_parts)})"
             )
 
     logger.info(
@@ -309,6 +317,28 @@ async def match_user_credentials_to_graph(
     return graph_credentials_inputs, missing_creds
 
 
+def _credential_has_required_scopes(
+    credential: Credentials,
+    requirements: CredentialsFieldInfo,
+) -> bool:
+    """
+    Check if a credential has all the scopes required by the block.
+
+    For OAuth2 credentials, verifies that the credential's scopes are a superset
+    of the required scopes. For other credential types, returns True (no scope check).
+    """
+    # Only OAuth2 credentials have scopes to check
+    if credential.type != "oauth2":
+        return True
+
+    # If no scopes are required, any credential matches
+    if not requirements.required_scopes:
+        return True
+
+    # Check that credential scopes are a superset of required scopes
+    return set(credential.scopes).issuperset(requirements.required_scopes)
+
+
 async def check_user_has_required_credentials(
     user_id: str,
     required_credentials: list[CredentialsMetaInput],

autogpt_platform/backend/backend/blocks/llm.py

@@ -115,7 +115,6 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     CLAUDE_4_5_OPUS = "claude-opus-4-5-20251101"
     CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
     CLAUDE_4_5_HAIKU = "claude-haiku-4-5-20251001"
-    CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219"
     CLAUDE_3_HAIKU = "claude-3-haiku-20240307"
     # AI/ML API models
     AIML_API_QWEN2_5_72B = "Qwen/Qwen2.5-72B-Instruct-Turbo"
@@ -280,9 +279,6 @@ MODEL_METADATA = {
     LlmModel.CLAUDE_4_5_HAIKU: ModelMetadata(
         "anthropic", 200000, 64000, "Claude Haiku 4.5", "Anthropic", "Anthropic", 2
     ),  # claude-haiku-4-5-20251001
-    LlmModel.CLAUDE_3_7_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000, "Claude 3.7 Sonnet", "Anthropic", "Anthropic", 2
-    ),  # claude-3-7-sonnet-20250219
     LlmModel.CLAUDE_3_HAIKU: ModelMetadata(
         "anthropic", 200000, 4096, "Claude 3 Haiku", "Anthropic", "Anthropic", 1
     ),  # claude-3-haiku-20240307

autogpt_platform/backend/backend/blocks/stagehand/blocks.py

@@ -83,7 +83,7 @@ class StagehandRecommendedLlmModel(str, Enum):
     GPT41_MINI = "gpt-4.1-mini-2025-04-14"
 
     # Anthropic
-    CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219"
+    CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
 
     @property
     def provider_name(self) -> str:
@@ -137,7 +137,7 @@ class StagehandObserveBlock(Block):
         model: StagehandRecommendedLlmModel = SchemaField(
             title="LLM Model",
             description="LLM to use for Stagehand (provider is inferred)",
-            default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET,
+            default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET,
             advanced=False,
         )
         model_credentials: AICredentials = AICredentialsField()
@@ -230,7 +230,7 @@ class StagehandActBlock(Block):
         model: StagehandRecommendedLlmModel = SchemaField(
             title="LLM Model",
             description="LLM to use for Stagehand (provider is inferred)",
-            default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET,
+            default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET,
             advanced=False,
         )
         model_credentials: AICredentials = AICredentialsField()
@@ -330,7 +330,7 @@ class StagehandExtractBlock(Block):
         model: StagehandRecommendedLlmModel = SchemaField(
             title="LLM Model",
             description="LLM to use for Stagehand (provider is inferred)",
-            default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET,
+            default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET,
             advanced=False,
         )
         model_credentials: AICredentials = AICredentialsField()

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -81,7 +81,6 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.CLAUDE_4_5_HAIKU: 4,
     LlmModel.CLAUDE_4_5_OPUS: 14,
     LlmModel.CLAUDE_4_5_SONNET: 9,
-    LlmModel.CLAUDE_3_7_SONNET: 5,
     LlmModel.CLAUDE_3_HAIKU: 1,
     LlmModel.AIML_API_QWEN2_5_72B: 1,
     LlmModel.AIML_API_LLAMA3_1_70B: 1,

autogpt_platform/backend/backend/data/model.py

@@ -666,10 +666,16 @@ class CredentialsFieldInfo(BaseModel, Generic[CP, CT]):
         if not (self.discriminator and self.discriminator_mapping):
             return self
 
+        try:
+            provider = self.discriminator_mapping[discriminator_value]
+        except KeyError:
+            raise ValueError(
+                f"Model '{discriminator_value}' is not supported. "
+                "It may have been deprecated. Please update your agent configuration."
+            )
+
         return CredentialsFieldInfo(
-            credentials_provider=frozenset(
-                [self.discriminator_mapping[discriminator_value]]
-            ),
+            credentials_provider=frozenset([provider]),
             credentials_types=self.supported_types,
             credentials_scopes=self.required_scopes,
             discriminator=self.discriminator,

autogpt_platform/backend/migrations/20260126120000_migrate_claude_3_7_to_4_5_sonnet/migration.sql

@@ -0,0 +1,22 @@
+-- Migrate Claude 3.7 Sonnet to Claude 4.5 Sonnet
+-- This updates all AgentNode blocks that use the deprecated Claude 3.7 Sonnet model
+-- Anthropic is retiring claude-3-7-sonnet-20250219 on February 19, 2026
+
+-- Update AgentNode constant inputs
+UPDATE "AgentNode"
+SET    "constantInput" = JSONB_SET(
+         "constantInput"::jsonb,
+         '{model}',
+         '"claude-sonnet-4-5-20250929"'::jsonb
+       )
+WHERE  "constantInput"::jsonb->>'model' = 'claude-3-7-sonnet-20250219';
+
+-- Update AgentPreset input overrides (stored in AgentNodeExecutionInputOutput)
+UPDATE "AgentNodeExecutionInputOutput"
+SET    "data" = JSONB_SET(
+         "data"::jsonb,
+         '{model}',
+         '"claude-sonnet-4-5-20250929"'::jsonb
+       )
+WHERE  "agentPresetId" IS NOT NULL
+  AND  "data"::jsonb->>'model' = 'claude-3-7-sonnet-20250219';

autogpt_platform/backend/test/e2e_test_data.py

@@ -43,19 +43,24 @@ faker = Faker()
 # Constants for data generation limits (reduced for E2E tests)
 NUM_USERS = 15
 NUM_AGENT_BLOCKS = 30
-MIN_GRAPHS_PER_USER = 15
-MAX_GRAPHS_PER_USER = 15
+MIN_GRAPHS_PER_USER = 25
+MAX_GRAPHS_PER_USER = 25
 MIN_NODES_PER_GRAPH = 3
 MAX_NODES_PER_GRAPH = 6
 MIN_PRESETS_PER_USER = 2
 MAX_PRESETS_PER_USER = 3
-MIN_AGENTS_PER_USER = 15
-MAX_AGENTS_PER_USER = 15
+MIN_AGENTS_PER_USER = 25
+MAX_AGENTS_PER_USER = 25
 MIN_EXECUTIONS_PER_GRAPH = 2
 MAX_EXECUTIONS_PER_GRAPH = 8
 MIN_REVIEWS_PER_VERSION = 2
 MAX_REVIEWS_PER_VERSION = 5
 
+# Guaranteed minimums for marketplace tests (deterministic)
+GUARANTEED_FEATURED_AGENTS = 8
+GUARANTEED_FEATURED_CREATORS = 5
+GUARANTEED_TOP_AGENTS = 10
+
 
 def get_image():
     """Generate a consistent image URL using picsum.photos service."""
@@ -385,7 +390,7 @@ class TestDataCreator:
 
         library_agents = []
         for user in self.users:
-            num_agents = 10  # Create exactly 10 agents per user
+            num_agents = random.randint(MIN_AGENTS_PER_USER, MAX_AGENTS_PER_USER)
 
             # Get available graphs for this user
             user_graphs = [
@@ -507,14 +512,17 @@ class TestDataCreator:
             existing_profiles, min(num_creators, len(existing_profiles))
         )
 
-        # Mark about 50% of creators as featured (more for testing)
-        num_featured = max(2, int(num_creators * 0.5))
+        # Guarantee at least GUARANTEED_FEATURED_CREATORS featured creators
+        num_featured = max(GUARANTEED_FEATURED_CREATORS, int(num_creators * 0.5))
         num_featured = min(
             num_featured, len(selected_profiles)
         )  # Don't exceed available profiles
         featured_profile_ids = set(
             random.sample([p.id for p in selected_profiles], num_featured)
         )
+        print(
+            f"🎯 Creating {num_featured} featured creators (min: {GUARANTEED_FEATURED_CREATORS})"
+        )
 
         for profile in selected_profiles:
             try:
@@ -545,21 +553,25 @@ class TestDataCreator:
         return profiles
 
     async def create_test_store_submissions(self) -> List[Dict[str, Any]]:
-        """Create test store submissions using the API function."""
+        """Create test store submissions using the API function.
+
+        DETERMINISTIC: Guarantees minimum featured agents for E2E tests.
+        """
         print("Creating test store submissions...")
 
         submissions = []
         approved_submissions = []
+        featured_count = 0
+        submission_counter = 0
 
-        # Create a special test submission for test123@gmail.com
+        # Create a special test submission for test123@gmail.com (ALWAYS approved + featured)
         test_user = next(
             (user for user in self.users if user["email"] == "test123@gmail.com"), None
         )
-        if test_user:
-            # Special test data for consistent testing
+        if test_user and self.agent_graphs:
             test_submission_data = {
                 "user_id": test_user["id"],
-                "agent_id": self.agent_graphs[0]["id"],  # Use first available graph
+                "agent_id": self.agent_graphs[0]["id"],
                 "agent_version": 1,
                 "slug": "test-agent-submission",
                 "name": "Test Agent Submission",
@@ -580,37 +592,24 @@ class TestDataCreator:
                 submissions.append(test_submission.model_dump())
                 print("✅ Created special test store submission for test123@gmail.com")
 
-                # Randomly approve, reject, or leave pending the test submission
+                # ALWAYS approve and feature the test submission
                 if test_submission.store_listing_version_id:
-                    random_value = random.random()
-                    if random_value < 0.4:  # 40% chance to approve
-                        approved_submission = await review_store_submission(
-                            store_listing_version_id=test_submission.store_listing_version_id,
-                            is_approved=True,
-                            external_comments="Test submission approved",
-                            internal_comments="Auto-approved test submission",
-                            reviewer_id=test_user["id"],
-                        )
-                        approved_submissions.append(approved_submission.model_dump())
-                        print("✅ Approved test store submission")
+                    approved_submission = await review_store_submission(
+                        store_listing_version_id=test_submission.store_listing_version_id,
+                        is_approved=True,
+                        external_comments="Test submission approved",
+                        internal_comments="Auto-approved test submission",
+                        reviewer_id=test_user["id"],
+                    )
+                    approved_submissions.append(approved_submission.model_dump())
+                    print("✅ Approved test store submission")
 
-                        # Mark approved submission as featured
-                        await prisma.storelistingversion.update(
-                            where={"id": test_submission.store_listing_version_id},
-                            data={"isFeatured": True},
-                        )
-                        print("🌟 Marked test agent as FEATURED")
-                    elif random_value < 0.7:  # 30% chance to reject (40% to 70%)
-                        await review_store_submission(
-                            store_listing_version_id=test_submission.store_listing_version_id,
-                            is_approved=False,
-                            external_comments="Test submission rejected - needs improvements",
-                            internal_comments="Auto-rejected test submission for E2E testing",
-                            reviewer_id=test_user["id"],
-                        )
-                        print("❌ Rejected test store submission")
-                    else:  # 30% chance to leave pending (70% to 100%)
-                        print("⏳ Left test submission pending for review")
+                    await prisma.storelistingversion.update(
+                        where={"id": test_submission.store_listing_version_id},
+                        data={"isFeatured": True},
+                    )
+                    featured_count += 1
+                    print("🌟 Marked test agent as FEATURED")
 
             except Exception as e:
                 print(f"Error creating test store submission: {e}")
@@ -620,7 +619,6 @@ class TestDataCreator:
 
         # Create regular submissions for all users
         for user in self.users:
-            # Get available graphs for this specific user
             user_graphs = [
                 g for g in self.agent_graphs if g.get("userId") == user["id"]
             ]
@@ -631,18 +629,17 @@ class TestDataCreator:
                 )
                 continue
 
-            # Create exactly 4 store submissions per user
             for submission_index in range(4):
                 graph = random.choice(user_graphs)
+                submission_counter += 1
 
                 try:
                     print(
-                        f"Creating store submission for user {user['id']} with graph {graph['id']} (owner: {graph.get('userId')})"
+                        f"Creating store submission for user {user['id']} with graph {graph['id']}"
                     )
 
-                    # Use the API function to create store submission with correct parameters
                     submission = await create_store_submission(
-                        user_id=user["id"],  # Must match graph's userId
+                        user_id=user["id"],
                         agent_id=graph["id"],
                         agent_version=graph.get("version", 1),
                         slug=faker.slug(),
@@ -651,22 +648,24 @@ class TestDataCreator:
                         video_url=get_video_url() if random.random() < 0.3 else None,
                         image_urls=[get_image() for _ in range(3)],
                         description=faker.text(),
-                        categories=[
-                            get_category()
-                        ],  # Single category from predefined list
+                        categories=[get_category()],
                         changes_summary="Initial E2E test submission",
                     )
                     submissions.append(submission.model_dump())
                     print(f"✅ Created store submission: {submission.name}")
 
-                    # Randomly approve, reject, or leave pending the submission
                     if submission.store_listing_version_id:
-                        random_value = random.random()
-                        if random_value < 0.4:  # 40% chance to approve
-                            try:
-                                # Pick a random user as the reviewer (admin)
-                                reviewer_id = random.choice(self.users)["id"]
+                        # DETERMINISTIC: First N submissions are always approved
+                        # First GUARANTEED_FEATURED_AGENTS of those are always featured
+                        should_approve = (
+                            submission_counter <= GUARANTEED_TOP_AGENTS
+                            or random.random() < 0.4
+                        )
+                        should_feature = featured_count < GUARANTEED_FEATURED_AGENTS
 
+                        if should_approve:
+                            try:
+                                reviewer_id = random.choice(self.users)["id"]
                                 approved_submission = await review_store_submission(
                                     store_listing_version_id=submission.store_listing_version_id,
                                     is_approved=True,
@@ -681,16 +680,7 @@ class TestDataCreator:
                                     f"✅ Approved store submission: {submission.name}"
                                 )
 
-                                # Mark some agents as featured during creation (30% chance)
-                                # More likely for creators and first submissions
-                                is_creator = user["id"] in [
-                                    p.get("userId") for p in self.profiles
-                                ]
-                                feature_chance = (
-                                    0.5 if is_creator else 0.2
-                                )  # 50% for creators, 20% for others
-
-                                if random.random() < feature_chance:
+                                if should_feature:
                                     try:
                                         await prisma.storelistingversion.update(
                                             where={
@@ -698,8 +688,25 @@ class TestDataCreator:
                                             },
                                             data={"isFeatured": True},
                                         )
+                                        featured_count += 1
                                         print(
-                                            f"🌟 Marked agent as FEATURED: {submission.name}"
+                                            f"🌟 Marked agent as FEATURED ({featured_count}/{GUARANTEED_FEATURED_AGENTS}): {submission.name}"
+                                        )
+                                    except Exception as e:
+                                        print(
+                                            f"Warning: Could not mark submission as featured: {e}"
+                                        )
+                                elif random.random() < 0.2:
+                                    try:
+                                        await prisma.storelistingversion.update(
+                                            where={
+                                                "id": submission.store_listing_version_id
+                                            },
+                                            data={"isFeatured": True},
+                                        )
+                                        featured_count += 1
+                                        print(
+                                            f"🌟 Marked agent as FEATURED (bonus): {submission.name}"
                                         )
                                     except Exception as e:
                                         print(
@@ -710,11 +717,9 @@ class TestDataCreator:
                                 print(
                                     f"Warning: Could not approve submission {submission.name}: {e}"
                                 )
-                        elif random_value < 0.7:  # 30% chance to reject (40% to 70%)
+                        elif random.random() < 0.5:
                             try:
-                                # Pick a random user as the reviewer (admin)
                                 reviewer_id = random.choice(self.users)["id"]
-
                                 await review_store_submission(
                                     store_listing_version_id=submission.store_listing_version_id,
                                     is_approved=False,
@@ -729,7 +734,7 @@ class TestDataCreator:
                                 print(
                                     f"Warning: Could not reject submission {submission.name}: {e}"
                                 )
-                        else:  # 30% chance to leave pending (70% to 100%)
+                        else:
                             print(
                                 f"⏳ Left submission pending for review: {submission.name}"
                             )
@@ -743,9 +748,13 @@ class TestDataCreator:
                     traceback.print_exc()
                     continue
 
+        print("\n📊 Store Submissions Summary:")
+        print(f"   Created: {len(submissions)}")
+        print(f"   Approved: {len(approved_submissions)}")
         print(
-            f"Created {len(submissions)} store submissions, approved {len(approved_submissions)}"
+            f"   Featured: {featured_count} (guaranteed min: {GUARANTEED_FEATURED_AGENTS})"
         )
+
         self.store_submissions = submissions
         return submissions
 
@@ -825,12 +834,15 @@ class TestDataCreator:
         print(f"✅ Agent blocks available: {len(self.agent_blocks)}")
         print(f"✅ Agent graphs created: {len(self.agent_graphs)}")
         print(f"✅ Library agents created: {len(self.library_agents)}")
-        print(f"✅ Creator profiles updated: {len(self.profiles)} (some featured)")
-        print(
-            f"✅ Store submissions created: {len(self.store_submissions)} (some marked as featured during creation)"
-        )
+        print(f"✅ Creator profiles updated: {len(self.profiles)}")
+        print(f"✅ Store submissions created: {len(self.store_submissions)}")
         print(f"✅ API keys created: {len(self.api_keys)}")
         print(f"✅ Presets created: {len(self.presets)}")
+        print("\n🎯 Deterministic Guarantees:")
+        print(f"   • Featured agents: >= {GUARANTEED_FEATURED_AGENTS}")
+        print(f"   • Featured creators: >= {GUARANTEED_FEATURED_CREATORS}")
+        print(f"   • Top agents (approved): >= {GUARANTEED_TOP_AGENTS}")
+        print(f"   • Library agents per user: >= {MIN_AGENTS_PER_USER}")
         print("\n🚀 Your E2E test database is ready to use!")
 
 

autogpt_platform/frontend/src/app/(platform)/library/components/__tests__/FavoritesSection.test.tsx

@@ -0,0 +1,185 @@
+import { describe, expect, test, afterEach } from "vitest";
+import { render, screen, waitFor } from "@/tests/integrations/test-utils";
+import { FavoritesSection } from "../FavoritesSection/FavoritesSection";
+import { server } from "@/mocks/mock-server";
+import { http, HttpResponse } from "msw";
+import {
+  mockAuthenticatedUser,
+  resetAuthState,
+} from "@/tests/integrations/helpers/mock-supabase-auth";
+
+const mockFavoriteAgent = {
+  id: "fav-agent-id",
+  graph_id: "fav-graph-id",
+  graph_version: 1,
+  owner_user_id: "test-owner-id",
+  image_url: null,
+  creator_name: "Test Creator",
+  creator_image_url: "https://example.com/avatar.png",
+  status: "READY",
+  created_at: new Date().toISOString(),
+  updated_at: new Date().toISOString(),
+  name: "Favorite Agent Name",
+  description: "Test favorite agent",
+  input_schema: {},
+  output_schema: {},
+  credentials_input_schema: null,
+  has_external_trigger: false,
+  has_human_in_the_loop: false,
+  has_sensitive_action: false,
+  new_output: false,
+  can_access_graph: true,
+  is_latest_version: true,
+  is_favorite: true,
+};
+
+describe("FavoritesSection", () => {
+  afterEach(() => {
+    resetAuthState();
+  });
+
+  test("renders favorites section when there are favorites", async () => {
+    mockAuthenticatedUser();
+
+    server.use(
+      http.get("*/api/library/agents/favorites*", () => {
+        return HttpResponse.json({
+          agents: [mockFavoriteAgent],
+          pagination: {
+            total_items: 1,
+            total_pages: 1,
+            current_page: 1,
+            page_size: 20,
+          },
+        });
+      }),
+    );
+
+    render(<FavoritesSection searchTerm="" />);
+
+    await waitFor(() => {
+      expect(screen.getByText(/favorites/i)).toBeInTheDocument();
+    });
+  });
+
+  test("renders favorite agent cards", async () => {
+    mockAuthenticatedUser();
+
+    server.use(
+      http.get("*/api/library/agents/favorites*", () => {
+        return HttpResponse.json({
+          agents: [mockFavoriteAgent],
+          pagination: {
+            total_items: 1,
+            total_pages: 1,
+            current_page: 1,
+            page_size: 20,
+          },
+        });
+      }),
+    );
+
+    render(<FavoritesSection searchTerm="" />);
+
+    await waitFor(() => {
+      expect(screen.getByText("Favorite Agent Name")).toBeInTheDocument();
+    });
+  });
+
+  test("shows agent count", async () => {
+    mockAuthenticatedUser();
+
+    server.use(
+      http.get("*/api/library/agents/favorites*", () => {
+        return HttpResponse.json({
+          agents: [mockFavoriteAgent],
+          pagination: {
+            total_items: 1,
+            total_pages: 1,
+            current_page: 1,
+            page_size: 20,
+          },
+        });
+      }),
+    );
+
+    render(<FavoritesSection searchTerm="" />);
+
+    await waitFor(() => {
+      expect(screen.getByTestId("agents-count")).toBeInTheDocument();
+    });
+  });
+
+  test("does not render when there are no favorites", async () => {
+    mockAuthenticatedUser();
+
+    server.use(
+      http.get("*/api/library/agents/favorites*", () => {
+        return HttpResponse.json({
+          agents: [],
+          pagination: {
+            total_items: 0,
+            total_pages: 0,
+            current_page: 1,
+            page_size: 20,
+          },
+        });
+      }),
+    );
+
+    const { container } = render(<FavoritesSection searchTerm="" />);
+
+    // Wait for loading to complete
+    await waitFor(() => {
+      // Component should return null when no favorites
+      expect(container.textContent).toBe("");
+    });
+  });
+
+  test("filters favorites based on search term", async () => {
+    mockAuthenticatedUser();
+
+    // Mock that returns different results based on search term
+    server.use(
+      http.get("*/api/library/agents/favorites*", ({ request }) => {
+        const url = new URL(request.url);
+        const searchTerm = url.searchParams.get("search_term");
+
+        if (searchTerm === "nonexistent") {
+          return HttpResponse.json({
+            agents: [],
+            pagination: {
+              total_items: 0,
+              total_pages: 0,
+              current_page: 1,
+              page_size: 20,
+            },
+          });
+        }
+
+        return HttpResponse.json({
+          agents: [mockFavoriteAgent],
+          pagination: {
+            total_items: 1,
+            total_pages: 1,
+            current_page: 1,
+            page_size: 20,
+          },
+        });
+      }),
+    );
+
+    const { rerender } = render(<FavoritesSection searchTerm="" />);
+
+    await waitFor(() => {
+      expect(screen.getByText("Favorite Agent Name")).toBeInTheDocument();
+    });
+
+    // Rerender with search term that yields no results
+    rerender(<FavoritesSection searchTerm="nonexistent" />);
+
+    await waitFor(() => {
+      expect(screen.queryByText("Favorite Agent Name")).not.toBeInTheDocument();
+    });
+  });
+});

autogpt_platform/frontend/src/app/(platform)/library/components/__tests__/LibraryAgentCard.test.tsx

@@ -0,0 +1,122 @@
+import { describe, expect, test, afterEach } from "vitest";
+import { render, screen } from "@/tests/integrations/test-utils";
+import { LibraryAgentCard } from "../LibraryAgentCard/LibraryAgentCard";
+import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
+import {
+  mockAuthenticatedUser,
+  resetAuthState,
+} from "@/tests/integrations/helpers/mock-supabase-auth";
+
+const mockAgent: LibraryAgent = {
+  id: "test-agent-id",
+  graph_id: "test-graph-id",
+  graph_version: 1,
+  owner_user_id: "test-owner-id",
+  image_url: null,
+  creator_name: "Test Creator",
+  creator_image_url: "https://example.com/avatar.png",
+  status: "READY",
+  created_at: new Date().toISOString(),
+  updated_at: new Date().toISOString(),
+  name: "Test Agent Name",
+  description: "Test agent description",
+  input_schema: {},
+  output_schema: {},
+  credentials_input_schema: null,
+  has_external_trigger: false,
+  has_human_in_the_loop: false,
+  has_sensitive_action: false,
+  new_output: false,
+  can_access_graph: true,
+  is_latest_version: true,
+  is_favorite: false,
+};
+
+describe("LibraryAgentCard", () => {
+  afterEach(() => {
+    resetAuthState();
+  });
+
+  test("renders agent name", () => {
+    mockAuthenticatedUser();
+    render(<LibraryAgentCard agent={mockAgent} />);
+
+    expect(screen.getByText("Test Agent Name")).toBeInTheDocument();
+  });
+
+  test("renders see runs link", () => {
+    mockAuthenticatedUser();
+    render(<LibraryAgentCard agent={mockAgent} />);
+
+    expect(screen.getByText(/see runs/i)).toBeInTheDocument();
+  });
+
+  test("renders open in builder link when can_access_graph is true", () => {
+    mockAuthenticatedUser();
+    render(<LibraryAgentCard agent={mockAgent} />);
+
+    expect(screen.getByText(/open in builder/i)).toBeInTheDocument();
+  });
+
+  test("does not render open in builder link when can_access_graph is false", () => {
+    mockAuthenticatedUser();
+    const agentWithoutAccess = { ...mockAgent, can_access_graph: false };
+    render(<LibraryAgentCard agent={agentWithoutAccess} />);
+
+    expect(screen.queryByText(/open in builder/i)).not.toBeInTheDocument();
+  });
+
+  test("shows 'FROM MARKETPLACE' label for marketplace agents", () => {
+    mockAuthenticatedUser();
+    const marketplaceAgent = {
+      ...mockAgent,
+      marketplace_listing: {
+        id: "listing-id",
+        name: "Marketplace Agent",
+        slug: "marketplace-agent",
+        creator: {
+          id: "creator-id",
+          name: "Creator Name",
+          slug: "creator-slug",
+        },
+      },
+    };
+    render(<LibraryAgentCard agent={marketplaceAgent} />);
+
+    expect(screen.getByText(/from marketplace/i)).toBeInTheDocument();
+  });
+
+  test("shows 'Built by you' label for user's own agents", () => {
+    mockAuthenticatedUser();
+    render(<LibraryAgentCard agent={mockAgent} />);
+
+    expect(screen.getByText(/built by you/i)).toBeInTheDocument();
+  });
+
+  test("renders favorite button", () => {
+    mockAuthenticatedUser();
+    render(<LibraryAgentCard agent={mockAgent} />);
+
+    // The favorite button should be present (as a heart icon button)
+    const card = screen.getByTestId("library-agent-card");
+    expect(card).toBeInTheDocument();
+  });
+
+  test("links to correct agent detail page", () => {
+    mockAuthenticatedUser();
+    render(<LibraryAgentCard agent={mockAgent} />);
+
+    const link = screen.getByTestId("library-agent-card-see-runs-link");
+    expect(link).toHaveAttribute("href", "/library/agents/test-agent-id");
+  });
+
+  test("links to correct builder page", () => {
+    mockAuthenticatedUser();
+    render(<LibraryAgentCard agent={mockAgent} />);
+
+    const builderLink = screen.getByTestId(
+      "library-agent-card-open-in-builder-link",
+    );
+    expect(builderLink).toHaveAttribute("href", "/build?flowID=test-graph-id");
+  });
+});

autogpt_platform/frontend/src/app/(platform)/library/components/__tests__/LibrarySearchBar.test.tsx

@@ -0,0 +1,53 @@
+import { describe, expect, test, vi } from "vitest";
+import { render, screen, fireEvent, waitFor } from "@/tests/integrations/test-utils";
+import { LibrarySearchBar } from "../LibrarySearchBar/LibrarySearchBar";
+
+describe("LibrarySearchBar", () => {
+  test("renders search input", () => {
+    const setSearchTerm = vi.fn();
+    render(<LibrarySearchBar setSearchTerm={setSearchTerm} />);
+
+    expect(screen.getByPlaceholderText(/search agents/i)).toBeInTheDocument();
+  });
+
+  test("renders search icon", () => {
+    const setSearchTerm = vi.fn();
+    const { container } = render(
+      <LibrarySearchBar setSearchTerm={setSearchTerm} />,
+    );
+
+    // Check for the magnifying glass icon (SVG element)
+    const searchIcon = container.querySelector("svg");
+    expect(searchIcon).toBeInTheDocument();
+  });
+
+  test("calls setSearchTerm on input change", async () => {
+    const setSearchTerm = vi.fn();
+    render(<LibrarySearchBar setSearchTerm={setSearchTerm} />);
+
+    const input = screen.getByPlaceholderText(/search agents/i);
+    fireEvent.change(input, { target: { value: "test query" } });
+
+    // The search bar uses debouncing, so we need to wait
+    await waitFor(
+      () => {
+        expect(setSearchTerm).toHaveBeenCalled();
+      },
+      { timeout: 1000 },
+    );
+  });
+
+  test("has correct test id", () => {
+    const setSearchTerm = vi.fn();
+    render(<LibrarySearchBar setSearchTerm={setSearchTerm} />);
+
+    expect(screen.getByTestId("search-bar")).toBeInTheDocument();
+  });
+
+  test("input has correct test id", () => {
+    const setSearchTerm = vi.fn();
+    render(<LibrarySearchBar setSearchTerm={setSearchTerm} />);
+
+    expect(screen.getByTestId("library-textbox")).toBeInTheDocument();
+  });
+});

autogpt_platform/frontend/src/app/(platform)/library/components/__tests__/LibrarySortMenu.test.tsx

@@ -0,0 +1,53 @@
+import { describe, expect, test, vi } from "vitest";
+import { render, screen, fireEvent, waitFor } from "@/tests/integrations/test-utils";
+import { LibrarySortMenu } from "../LibrarySortMenu/LibrarySortMenu";
+
+describe("LibrarySortMenu", () => {
+  test("renders sort dropdown", () => {
+    const setLibrarySort = vi.fn();
+    render(<LibrarySortMenu setLibrarySort={setLibrarySort} />);
+
+    expect(screen.getByTestId("sort-by-dropdown")).toBeInTheDocument();
+  });
+
+  test("shows 'sort by' label on larger screens", () => {
+    const setLibrarySort = vi.fn();
+    render(<LibrarySortMenu setLibrarySort={setLibrarySort} />);
+
+    expect(screen.getByText(/sort by/i)).toBeInTheDocument();
+  });
+
+  test("shows default placeholder text", () => {
+    const setLibrarySort = vi.fn();
+    render(<LibrarySortMenu setLibrarySort={setLibrarySort} />);
+
+    expect(screen.getByText(/last modified/i)).toBeInTheDocument();
+  });
+
+  test("opens dropdown when clicked", async () => {
+    const setLibrarySort = vi.fn();
+    render(<LibrarySortMenu setLibrarySort={setLibrarySort} />);
+
+    const trigger = screen.getByRole("combobox");
+    fireEvent.click(trigger);
+
+    await waitFor(() => {
+      expect(screen.getByText(/creation date/i)).toBeInTheDocument();
+    });
+  });
+
+  test("shows both sort options in dropdown", async () => {
+    const setLibrarySort = vi.fn();
+    render(<LibrarySortMenu setLibrarySort={setLibrarySort} />);
+
+    const trigger = screen.getByRole("combobox");
+    fireEvent.click(trigger);
+
+    await waitFor(() => {
+      expect(screen.getByText(/creation date/i)).toBeInTheDocument();
+      expect(
+        screen.getAllByText(/last modified/i).length,
+      ).toBeGreaterThanOrEqual(1);
+    });
+  });
+});

autogpt_platform/frontend/src/app/(platform)/library/components/__tests__/LibraryUploadAgentDialog.test.tsx

@@ -0,0 +1,78 @@
+import { describe, expect, test, afterEach } from "vitest";
+import { render, screen, fireEvent, waitFor } from "@/tests/integrations/test-utils";
+import LibraryUploadAgentDialog from "../LibraryUploadAgentDialog/LibraryUploadAgentDialog";
+import {
+  mockAuthenticatedUser,
+  resetAuthState,
+} from "@/tests/integrations/helpers/mock-supabase-auth";
+
+describe("LibraryUploadAgentDialog", () => {
+  afterEach(() => {
+    resetAuthState();
+  });
+
+  test("renders upload button", () => {
+    mockAuthenticatedUser();
+    render(<LibraryUploadAgentDialog />);
+
+    expect(
+      screen.getByRole("button", { name: /upload agent/i }),
+    ).toBeInTheDocument();
+  });
+
+  test("opens dialog when upload button is clicked", async () => {
+    mockAuthenticatedUser();
+    render(<LibraryUploadAgentDialog />);
+
+    const uploadButton = screen.getByRole("button", { name: /upload agent/i });
+    fireEvent.click(uploadButton);
+
+    await waitFor(() => {
+      expect(screen.getByText("Upload Agent")).toBeInTheDocument();
+    });
+  });
+
+  test("dialog contains agent name input", async () => {
+    mockAuthenticatedUser();
+    render(<LibraryUploadAgentDialog />);
+
+    const uploadButton = screen.getByRole("button", { name: /upload agent/i });
+    fireEvent.click(uploadButton);
+
+    await waitFor(() => {
+      expect(screen.getByLabelText(/agent name/i)).toBeInTheDocument();
+    });
+  });
+
+  test("dialog contains agent description input", async () => {
+    mockAuthenticatedUser();
+    render(<LibraryUploadAgentDialog />);
+
+    const uploadButton = screen.getByRole("button", { name: /upload agent/i });
+    fireEvent.click(uploadButton);
+
+    await waitFor(() => {
+      expect(screen.getByLabelText(/agent description/i)).toBeInTheDocument();
+    });
+  });
+
+  test("upload button is disabled when form is incomplete", async () => {
+    mockAuthenticatedUser();
+    render(<LibraryUploadAgentDialog />);
+
+    const triggerButton = screen.getByRole("button", { name: /upload agent/i });
+    fireEvent.click(triggerButton);
+
+    await waitFor(() => {
+      const submitButton = screen.getByRole("button", { name: /^upload$/i });
+      expect(submitButton).toBeDisabled();
+    });
+  });
+
+  test("has correct test id on trigger button", () => {
+    mockAuthenticatedUser();
+    render(<LibraryUploadAgentDialog />);
+
+    expect(screen.getByTestId("upload-agent-button")).toBeInTheDocument();
+  });
+});

autogpt_platform/frontend/src/app/(platform)/library/components/__tests__/auth-state.test.tsx

@@ -0,0 +1,40 @@
+import { describe, expect, test, afterEach } from "vitest";
+import { render, screen } from "@/tests/integrations/test-utils";
+import LibraryPage from "../../page";
+import {
+  mockAuthenticatedUser,
+  mockUnauthenticatedUser,
+  resetAuthState,
+} from "@/tests/integrations/helpers/mock-supabase-auth";
+
+describe("LibraryPage - Auth State", () => {
+  afterEach(() => {
+    resetAuthState();
+  });
+
+  test("renders page correctly when logged in", async () => {
+    mockAuthenticatedUser();
+    render(<LibraryPage />);
+
+    // Wait for upload button text to appear (indicates page is rendered)
+    expect(
+      await screen.findByText("Upload agent", { exact: false }),
+    ).toBeInTheDocument();
+
+    // Search bar should be visible
+    expect(screen.getByTestId("search-bar")).toBeInTheDocument();
+  });
+
+  test("renders page correctly when logged out", async () => {
+    mockUnauthenticatedUser();
+    render(<LibraryPage />);
+
+    // Wait for upload button text to appear (indicates page is rendered)
+    expect(
+      await screen.findByText("Upload agent", { exact: false }),
+    ).toBeInTheDocument();
+
+    // Search bar should still be visible
+    expect(screen.getByTestId("search-bar")).toBeInTheDocument();
+  });
+});

autogpt_platform/frontend/src/app/(platform)/library/components/__tests__/empty-state.test.tsx

@@ -0,0 +1,82 @@
+import { describe, expect, test, afterEach } from "vitest";
+import { render, screen, waitFor } from "@/tests/integrations/test-utils";
+import LibraryPage from "../../page";
+import { server } from "@/mocks/mock-server";
+import { http, HttpResponse } from "msw";
+import {
+  mockAuthenticatedUser,
+  resetAuthState,
+} from "@/tests/integrations/helpers/mock-supabase-auth";
+
+describe("LibraryPage - Empty State", () => {
+  afterEach(() => {
+    resetAuthState();
+  });
+
+  test("handles empty agents list gracefully", async () => {
+    mockAuthenticatedUser();
+
+    server.use(
+      http.get("*/api/library/agents*", () => {
+        return HttpResponse.json({
+          agents: [],
+          pagination: {
+            total_items: 0,
+            total_pages: 0,
+            current_page: 1,
+            page_size: 20,
+          },
+        });
+      }),
+      http.get("*/api/library/agents/favorites*", () => {
+        return HttpResponse.json({
+          agents: [],
+          pagination: {
+            total_items: 0,
+            total_pages: 0,
+            current_page: 1,
+            page_size: 20,
+          },
+        });
+      }),
+    );
+
+    render(<LibraryPage />);
+
+    // Page should still render without crashing
+    // Search bar should be visible even with no agents
+    expect(
+      await screen.findByPlaceholderText(/search agents/i),
+    ).toBeInTheDocument();
+
+    // Upload button should be visible
+    expect(
+      screen.getByRole("button", { name: /upload agent/i }),
+    ).toBeInTheDocument();
+  });
+
+  test("handles empty favorites gracefully", async () => {
+    mockAuthenticatedUser();
+
+    server.use(
+      http.get("*/api/library/agents/favorites*", () => {
+        return HttpResponse.json({
+          agents: [],
+          pagination: {
+            total_items: 0,
+            total_pages: 0,
+            current_page: 1,
+            page_size: 20,
+          },
+        });
+      }),
+    );
+
+    render(<LibraryPage />);
+
+    // Page should still render without crashing
+    expect(
+      await screen.findByPlaceholderText(/search agents/i),
+    ).toBeInTheDocument();
+  });
+});

autogpt_platform/frontend/src/app/(platform)/library/components/__tests__/error-handling.test.tsx

@@ -0,0 +1,59 @@
+import { describe, expect, test, afterEach } from "vitest";
+import { render, screen, waitFor } from "@/tests/integrations/test-utils";
+import LibraryPage from "../../page";
+import { server } from "@/mocks/mock-server";
+import {
+  mockAuthenticatedUser,
+  resetAuthState,
+} from "@/tests/integrations/helpers/mock-supabase-auth";
+import { create500Handler } from "@/tests/integrations/helpers/create-500-handler";
+import {
+  getGetV2ListLibraryAgentsMockHandler422,
+  getGetV2ListFavoriteLibraryAgentsMockHandler422,
+} from "@/app/api/__generated__/endpoints/library/library.msw";
+
+describe("LibraryPage - Error Handling", () => {
+  afterEach(() => {
+    resetAuthState();
+  });
+
+  test("handles API 422 error gracefully", async () => {
+    mockAuthenticatedUser();
+
+    server.use(getGetV2ListLibraryAgentsMockHandler422());
+
+    render(<LibraryPage />);
+
+    // Page should still render without crashing
+    // Search bar should be visible even with error
+    await waitFor(() => {
+      expect(screen.getByPlaceholderText(/search agents/i)).toBeInTheDocument();
+    });
+  });
+
+  test("handles favorites API 422 error gracefully", async () => {
+    mockAuthenticatedUser();
+
+    server.use(getGetV2ListFavoriteLibraryAgentsMockHandler422());
+
+    render(<LibraryPage />);
+
+    // Page should still render without crashing
+    await waitFor(() => {
+      expect(screen.getByPlaceholderText(/search agents/i)).toBeInTheDocument();
+    });
+  });
+
+  test("handles API 500 error gracefully", async () => {
+    mockAuthenticatedUser();
+
+    server.use(create500Handler("get", "*/api/library/agents*"));
+
+    render(<LibraryPage />);
+
+    // Page should still render without crashing
+    await waitFor(() => {
+      expect(screen.getByPlaceholderText(/search agents/i)).toBeInTheDocument();
+    });
+  });
+});

autogpt_platform/frontend/src/app/(platform)/library/components/__tests__/loading-state.test.tsx

@@ -0,0 +1,55 @@
+import { describe, expect, test, afterEach } from "vitest";
+import { render, screen } from "@/tests/integrations/test-utils";
+import LibraryPage from "../../page";
+import { server } from "@/mocks/mock-server";
+import { http, HttpResponse, delay } from "msw";
+import {
+  mockAuthenticatedUser,
+  resetAuthState,
+} from "@/tests/integrations/helpers/mock-supabase-auth";
+
+describe("LibraryPage - Loading State", () => {
+  afterEach(() => {
+    resetAuthState();
+  });
+
+  test("shows loading spinner while agents are being fetched", async () => {
+    mockAuthenticatedUser();
+
+    // Override handlers to add delay to simulate loading
+    server.use(
+      http.get("*/api/library/agents*", async () => {
+        await delay(500);
+        return HttpResponse.json({
+          agents: [],
+          pagination: {
+            total_items: 0,
+            total_pages: 0,
+            current_page: 1,
+            page_size: 20,
+          },
+        });
+      }),
+      http.get("*/api/library/agents/favorites*", async () => {
+        await delay(500);
+        return HttpResponse.json({
+          agents: [],
+          pagination: {
+            total_items: 0,
+            total_pages: 0,
+            current_page: 1,
+            page_size: 20,
+          },
+        });
+      }),
+    );
+
+    const { container } = render(<LibraryPage />);
+
+    // Check for loading spinner (LoadingSpinner component)
+    const loadingElements = container.querySelectorAll(
+      '[class*="animate-spin"]',
+    );
+    expect(loadingElements.length).toBeGreaterThan(0);
+  });
+});

autogpt_platform/frontend/src/app/(platform)/library/components/__tests__/rendering.test.tsx

@@ -0,0 +1,65 @@
+import { describe, expect, test, afterEach } from "vitest";
+import { render, screen, waitFor } from "@/tests/integrations/test-utils";
+import LibraryPage from "../../page";
+import {
+  mockAuthenticatedUser,
+  resetAuthState,
+} from "@/tests/integrations/helpers/mock-supabase-auth";
+
+describe("LibraryPage - Rendering", () => {
+  afterEach(() => {
+    resetAuthState();
+  });
+
+  test("renders search bar", async () => {
+    mockAuthenticatedUser();
+    render(<LibraryPage />);
+
+    expect(
+      await screen.findByPlaceholderText(/search agents/i),
+    ).toBeInTheDocument();
+  });
+
+  test("renders upload agent button", async () => {
+    mockAuthenticatedUser();
+    render(<LibraryPage />);
+
+    expect(
+      await screen.findByRole("button", { name: /upload agent/i }),
+    ).toBeInTheDocument();
+  });
+
+  test("renders agent cards when data is loaded", async () => {
+    mockAuthenticatedUser();
+    render(<LibraryPage />);
+
+    // Wait for agent cards to appear (from mock data)
+    await waitFor(() => {
+      const agentCards = screen.getAllByTestId("library-agent-card");
+      expect(agentCards.length).toBeGreaterThan(0);
+    });
+  });
+
+  test("agent cards display agent name", async () => {
+    mockAuthenticatedUser();
+    render(<LibraryPage />);
+
+    // Wait for agent cards and check they have names
+    await waitFor(() => {
+      const agentNames = screen.getAllByTestId("library-agent-card-name");
+      expect(agentNames.length).toBeGreaterThan(0);
+    });
+  });
+
+  test("agent cards have see runs link", async () => {
+    mockAuthenticatedUser();
+    render(<LibraryPage />);
+
+    await waitFor(() => {
+      const seeRunsLinks = screen.getAllByTestId(
+        "library-agent-card-see-runs-link",
+      );
+      expect(seeRunsLinks.length).toBeGreaterThan(0);
+    });
+  });
+});

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/useChatContainerAiSdk.ts

@@ -0,0 +1,246 @@
+/**
+ * useChatContainerAiSdk - ChatContainer hook using Vercel AI SDK
+ *
+ * This is a drop-in replacement for useChatContainer that uses @ai-sdk/react
+ * instead of the custom streaming implementation. The API surface is identical
+ * to enable easy A/B testing and gradual migration.
+ */
+
+import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
+import { useEffect, useMemo, useRef } from "react";
+import type { UIMessage } from "ai";
+import { useAiSdkChat } from "../../useAiSdkChat";
+import { usePageContext } from "../../usePageContext";
+import type { ChatMessageData } from "../ChatMessage/useChatMessage";
+import {
+  filterAuthMessages,
+  hasSentInitialPrompt,
+  markInitialPromptSent,
+  processInitialMessages,
+} from "./helpers";
+
+// Helper to convert backend messages to AI SDK UIMessage format
+function convertToUIMessages(
+  messages: SessionDetailResponse["messages"],
+): UIMessage[] {
+  const result: UIMessage[] = [];
+
+  for (const msg of messages) {
+    if (!msg.role || !msg.content) continue;
+
+    // Create parts based on message type
+    const parts: UIMessage["parts"] = [];
+
+    if (msg.role === "user" || msg.role === "assistant") {
+      if (typeof msg.content === "string") {
+        parts.push({ type: "text", text: msg.content });
+      }
+    }
+
+    // Handle tool calls in assistant messages
+    if (msg.role === "assistant" && msg.tool_calls) {
+      for (const toolCall of msg.tool_calls as Array<{
+        id: string;
+        type: string;
+        function: { name: string; arguments: string };
+      }>) {
+        if (toolCall.type === "function") {
+          let args = {};
+          try {
+            args = JSON.parse(toolCall.function.arguments);
+          } catch {
+            // Keep empty args
+          }
+          parts.push({
+            type: `tool-${toolCall.function.name}` as `tool-${string}`,
+            toolCallId: toolCall.id,
+            toolName: toolCall.function.name,
+            state: "input-available",
+            input: args,
+          } as UIMessage["parts"][number]);
+        }
+      }
+    }
+
+    // Handle tool responses
+    if (msg.role === "tool" && msg.tool_call_id) {
+      // Find the matching tool call to get the name
+      const toolName = "unknown";
+      let output: unknown = msg.content;
+      try {
+        output =
+          typeof msg.content === "string"
+            ? JSON.parse(msg.content)
+            : msg.content;
+      } catch {
+        // Keep as string
+      }
+
+      parts.push({
+        type: `tool-${toolName}` as `tool-${string}`,
+        toolCallId: msg.tool_call_id as string,
+        toolName,
+        state: "output-available",
+        output,
+      } as UIMessage["parts"][number]);
+    }
+
+    if (parts.length > 0) {
+      result.push({
+        id: msg.id || `msg-${Date.now()}-${Math.random()}`,
+        role: msg.role === "tool" ? "assistant" : (msg.role as "user" | "assistant"),
+        parts,
+        createdAt: msg.created_at ? new Date(msg.created_at as string) : new Date(),
+      });
+    }
+  }
+
+  return result;
+}
+
+interface Args {
+  sessionId: string | null;
+  initialMessages: SessionDetailResponse["messages"];
+  initialPrompt?: string;
+  onOperationStarted?: () => void;
+}
+
+export function useChatContainerAiSdk({
+  sessionId,
+  initialMessages,
+  initialPrompt,
+  onOperationStarted,
+}: Args) {
+  const { capturePageContext } = usePageContext();
+  const sendMessageRef = useRef<
+    (
+      content: string,
+      isUserMessage?: boolean,
+      context?: { url: string; content: string },
+    ) => Promise<void>
+  >();
+
+  // Convert initial messages to AI SDK format
+  const uiMessages = useMemo(
+    () => convertToUIMessages(initialMessages),
+    [initialMessages],
+  );
+
+  const {
+    messages: aiSdkMessages,
+    streamingChunks,
+    isStreaming,
+    error,
+    isRegionBlockedModalOpen,
+    setIsRegionBlockedModalOpen,
+    sendMessage,
+    stopStreaming,
+  } = useAiSdkChat({
+    sessionId,
+    initialMessages: uiMessages,
+    onOperationStarted,
+  });
+
+  // Keep ref updated for initial prompt handling
+  sendMessageRef.current = sendMessage;
+
+  // Merge AI SDK messages with processed initial messages
+  // This ensures we show both historical messages and new streaming messages
+  const allMessages = useMemo(() => {
+    const processedInitial = processInitialMessages(initialMessages);
+
+    // Build a set of message keys for deduplication
+    const seenKeys = new Set<string>();
+    const result: ChatMessageData[] = [];
+
+    // Add processed initial messages first
+    for (const msg of processedInitial) {
+      const key = getMessageKey(msg);
+      if (!seenKeys.has(key)) {
+        seenKeys.add(key);
+        result.push(msg);
+      }
+    }
+
+    // Add AI SDK messages that aren't duplicates
+    for (const msg of aiSdkMessages) {
+      const key = getMessageKey(msg);
+      if (!seenKeys.has(key)) {
+        seenKeys.add(key);
+        result.push(msg);
+      }
+    }
+
+    return result;
+  }, [initialMessages, aiSdkMessages]);
+
+  // Handle initial prompt
+  useEffect(
+    function handleInitialPrompt() {
+      if (!initialPrompt || !sessionId) return;
+      if (initialMessages.length > 0) return;
+      if (hasSentInitialPrompt(sessionId)) return;
+
+      markInitialPromptSent(sessionId);
+      const context = capturePageContext();
+      sendMessageRef.current?.(initialPrompt, true, context);
+    },
+    [initialPrompt, sessionId, initialMessages.length, capturePageContext],
+  );
+
+  // Send message with page context
+  async function sendMessageWithContext(
+    content: string,
+    isUserMessage: boolean = true,
+  ) {
+    const context = capturePageContext();
+    await sendMessage(content, isUserMessage, context);
+  }
+
+  function handleRegionModalOpenChange(open: boolean) {
+    setIsRegionBlockedModalOpen(open);
+  }
+
+  function handleRegionModalClose() {
+    setIsRegionBlockedModalOpen(false);
+  }
+
+  return {
+    messages: filterAuthMessages(allMessages),
+    streamingChunks,
+    isStreaming,
+    error,
+    isRegionBlockedModalOpen,
+    setIsRegionBlockedModalOpen,
+    sendMessageWithContext,
+    handleRegionModalOpenChange,
+    handleRegionModalClose,
+    sendMessage,
+    stopStreaming,
+  };
+}
+
+// Helper to generate deduplication key for a message
+function getMessageKey(msg: ChatMessageData): string {
+  if (msg.type === "message") {
+    return `msg:${msg.role}:${msg.content}`;
+  } else if (msg.type === "tool_call") {
+    return `toolcall:${msg.toolId}`;
+  } else if (msg.type === "tool_response") {
+    return `toolresponse:${(msg as { toolId?: string }).toolId}`;
+  } else if (
+    msg.type === "operation_started" ||
+    msg.type === "operation_pending" ||
+    msg.type === "operation_in_progress"
+  ) {
+    const typedMsg = msg as {
+      toolId?: string;
+      operationId?: string;
+      toolCallId?: string;
+      toolName?: string;
+    };
+    return `op:${typedMsg.toolId || typedMsg.operationId || typedMsg.toolCallId || ""}:${typedMsg.toolName || ""}`;
+  } else {
+    return `${msg.type}:${JSON.stringify(msg).slice(0, 100)}`;
+  }
+}

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/ChatInput.tsx

@@ -57,6 +57,7 @@ export function ChatInput({
     isStreaming,
     value,
     baseHandleKeyDown,
+    inputId,
   });
 
   return (

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/useVoiceRecording.ts

@@ -15,6 +15,7 @@ interface Args {
   isStreaming?: boolean;
   value: string;
   baseHandleKeyDown: (event: KeyboardEvent<HTMLTextAreaElement>) => void;
+  inputId?: string;
 }
 
 export function useVoiceRecording({
@@ -23,6 +24,7 @@ export function useVoiceRecording({
   isStreaming = false,
   value,
   baseHandleKeyDown,
+  inputId,
 }: Args) {
   const [isRecording, setIsRecording] = useState(false);
   const [isTranscribing, setIsTranscribing] = useState(false);
@@ -103,7 +105,7 @@ export function useVoiceRecording({
         setIsTranscribing(false);
       }
     },
-    [handleTranscription],
+    [handleTranscription, inputId],
   );
 
   const stopRecording = useCallback(() => {
@@ -201,6 +203,15 @@ export function useVoiceRecording({
     }
   }, [error, toast]);
 
+  useEffect(() => {
+    if (!isTranscribing && inputId) {
+      const inputElement = document.getElementById(inputId);
+      if (inputElement) {
+        inputElement.focus();
+      }
+    }
+  }, [isTranscribing, inputId]);
+
   const handleKeyDown = useCallback(
     (event: KeyboardEvent<HTMLTextAreaElement>) => {
       if (event.key === " " && !value.trim() && !isTranscribing) {

autogpt_platform/frontend/src/components/contextual/Chat/useAiSdkChat.ts

@@ -0,0 +1,421 @@
+"use client";
+
+/**
+ * useAiSdkChat - Vercel AI SDK integration for CoPilot Chat
+ *
+ * This hook wraps @ai-sdk/react's useChat to provide:
+ * - Streaming chat with the existing Python backend (already AI SDK protocol compatible)
+ * - Integration with existing session management
+ * - Custom tool response parsing for AutoGPT-specific types
+ * - Page context injection
+ *
+ * The Python backend already implements the AI SDK Data Stream Protocol (v1),
+ * so this hook can communicate directly without any backend changes.
+ */
+
+import { useChat as useAiSdkChatBase } from "@ai-sdk/react";
+import { DefaultChatTransport, type UIMessage } from "ai";
+import { useCallback, useEffect, useMemo, useRef, useState } from "react";
+import { toast } from "sonner";
+import type { ChatMessageData } from "./components/ChatMessage/useChatMessage";
+
+// Tool response types from the backend
+type OperationType =
+  | "operation_started"
+  | "operation_pending"
+  | "operation_in_progress";
+
+interface ToolOutputBase {
+  type: string;
+  [key: string]: unknown;
+}
+
+interface UseAiSdkChatOptions {
+  sessionId: string | null;
+  initialMessages?: UIMessage[];
+  onOperationStarted?: () => void;
+  onStreamingChange?: (isStreaming: boolean) => void;
+}
+
+/**
+ * Parse tool output from AI SDK message parts into ChatMessageData format
+ */
+function parseToolOutput(
+  toolCallId: string,
+  toolName: string,
+  output: unknown,
+): ChatMessageData | null {
+  if (!output) return null;
+
+  let parsed: ToolOutputBase;
+  try {
+    parsed =
+      typeof output === "string"
+        ? JSON.parse(output)
+        : (output as ToolOutputBase);
+  } catch {
+    return null;
+  }
+
+  const type = parsed.type;
+
+  // Handle operation status types
+  if (
+    type === "operation_started" ||
+    type === "operation_pending" ||
+    type === "operation_in_progress"
+  ) {
+    return {
+      type: type as OperationType,
+      toolId: toolCallId,
+      toolName: toolName,
+      operationId: (parsed.operation_id as string) || undefined,
+      message: (parsed.message as string) || undefined,
+      timestamp: new Date(),
+    } as ChatMessageData;
+  }
+
+  // Handle agent carousel
+  if (type === "agent_carousel" && Array.isArray(parsed.agents)) {
+    return {
+      type: "agent_carousel",
+      toolId: toolCallId,
+      toolName: toolName,
+      agents: parsed.agents,
+      timestamp: new Date(),
+    } as ChatMessageData;
+  }
+
+  // Handle execution started
+  if (type === "execution_started") {
+    return {
+      type: "execution_started",
+      toolId: toolCallId,
+      toolName: toolName,
+      graphId: parsed.graph_id as string,
+      graphVersion: parsed.graph_version as number,
+      graphExecId: parsed.graph_exec_id as string,
+      nodeExecIds: parsed.node_exec_ids as string[],
+      timestamp: new Date(),
+    } as ChatMessageData;
+  }
+
+  // Handle error responses
+  if (type === "error") {
+    return {
+      type: "tool_response",
+      toolId: toolCallId,
+      toolName: toolName,
+      result: parsed,
+      success: false,
+      timestamp: new Date(),
+    } as ChatMessageData;
+  }
+
+  // Handle clarification questions
+  if (type === "clarification_questions" && Array.isArray(parsed.questions)) {
+    return {
+      type: "clarification_questions",
+      toolId: toolCallId,
+      toolName: toolName,
+      questions: parsed.questions,
+      timestamp: new Date(),
+    } as ChatMessageData;
+  }
+
+  // Handle credentials needed
+  if (type === "credentials_needed" || type === "setup_requirements") {
+    const credentials = parsed.credentials as
+      | Array<{
+          provider: string;
+          provider_name: string;
+          credential_type: string;
+          scopes?: string[];
+        }>
+      | undefined;
+    if (credentials && credentials.length > 0) {
+      return {
+        type: "credentials_needed",
+        toolId: toolCallId,
+        toolName: toolName,
+        credentials: credentials,
+        timestamp: new Date(),
+      } as ChatMessageData;
+    }
+  }
+
+  // Default: generic tool response
+  return {
+    type: "tool_response",
+    toolId: toolCallId,
+    toolName: toolName,
+    result: parsed,
+    success: true,
+    timestamp: new Date(),
+  } as ChatMessageData;
+}
+
+/**
+ * Convert AI SDK UIMessage parts to ChatMessageData array
+ */
+function convertMessageToChatData(message: UIMessage): ChatMessageData[] {
+  const result: ChatMessageData[] = [];
+
+  for (const part of message.parts) {
+    switch (part.type) {
+      case "text":
+        if (part.text.trim()) {
+          result.push({
+            type: "message",
+            role: message.role as "user" | "assistant",
+            content: part.text,
+            timestamp: new Date(message.createdAt || Date.now()),
+          });
+        }
+        break;
+
+      default:
+        // Handle tool parts (tool-*)
+        if (part.type.startsWith("tool-")) {
+          const toolPart = part as {
+            type: string;
+            toolCallId: string;
+            toolName: string;
+            state: string;
+            input?: Record<string, unknown>;
+            output?: unknown;
+          };
+
+          // Show tool call in progress
+          if (
+            toolPart.state === "input-streaming" ||
+            toolPart.state === "input-available"
+          ) {
+            result.push({
+              type: "tool_call",
+              toolId: toolPart.toolCallId,
+              toolName: toolPart.toolName,
+              arguments: toolPart.input || {},
+              timestamp: new Date(),
+            });
+          }
+
+          // Parse tool output when available
+          if (
+            toolPart.state === "output-available" &&
+            toolPart.output !== undefined
+          ) {
+            const parsed = parseToolOutput(
+              toolPart.toolCallId,
+              toolPart.toolName,
+              toolPart.output,
+            );
+            if (parsed) {
+              result.push(parsed);
+            }
+          }
+
+          // Handle tool errors
+          if (toolPart.state === "output-error") {
+            result.push({
+              type: "tool_response",
+              toolId: toolPart.toolCallId,
+              toolName: toolPart.toolName,
+              response: {
+                type: "error",
+                message: (toolPart as { errorText?: string }).errorText,
+              },
+              success: false,
+              timestamp: new Date(),
+            } as ChatMessageData);
+          }
+        }
+        break;
+    }
+  }
+
+  return result;
+}
+
+export function useAiSdkChat({
+  sessionId,
+  initialMessages = [],
+  onOperationStarted,
+  onStreamingChange,
+}: UseAiSdkChatOptions) {
+  const [isRegionBlockedModalOpen, setIsRegionBlockedModalOpen] =
+    useState(false);
+  const previousSessionIdRef = useRef<string | null>(null);
+  const hasNotifiedOperationRef = useRef<Set<string>>(new Set());
+
+  // Create transport with session-specific endpoint
+  const transport = useMemo(() => {
+    if (!sessionId) return undefined;
+    return new DefaultChatTransport({
+      api: `/api/chat/sessions/${sessionId}/stream`,
+      headers: {
+        "Content-Type": "application/json",
+      },
+    });
+  }, [sessionId]);
+
+  const {
+    messages: aiMessages,
+    status,
+    error,
+    stop,
+    setMessages,
+    sendMessage: aiSendMessage,
+  } = useAiSdkChatBase({
+    transport,
+    initialMessages,
+    onError: (err) => {
+      console.error("[useAiSdkChat] Error:", err);
+
+      // Check for region blocking
+      if (
+        err.message?.toLowerCase().includes("not available in your region") ||
+        (err as { code?: string }).code === "MODEL_NOT_AVAILABLE_REGION"
+      ) {
+        setIsRegionBlockedModalOpen(true);
+        return;
+      }
+
+      toast.error("Chat Error", {
+        description: err.message || "An error occurred",
+      });
+    },
+    onFinish: ({ message }) => {
+      console.info("[useAiSdkChat] Message finished:", {
+        id: message.id,
+        partsCount: message.parts.length,
+      });
+    },
+  });
+
+  // Track streaming status
+  const isStreaming = status === "streaming" || status === "submitted";
+
+  // Notify parent of streaming changes
+  useEffect(() => {
+    onStreamingChange?.(isStreaming);
+  }, [isStreaming, onStreamingChange]);
+
+  // Handle session changes - reset state
+  useEffect(() => {
+    if (sessionId === previousSessionIdRef.current) return;
+
+    if (previousSessionIdRef.current && status === "streaming") {
+      stop();
+    }
+
+    previousSessionIdRef.current = sessionId;
+    hasNotifiedOperationRef.current = new Set();
+
+    if (sessionId) {
+      setMessages(initialMessages);
+    }
+  }, [sessionId, status, stop, setMessages, initialMessages]);
+
+  // Convert AI SDK messages to ChatMessageData format
+  const messages = useMemo(() => {
+    const result: ChatMessageData[] = [];
+
+    for (const message of aiMessages) {
+      const converted = convertMessageToChatData(message);
+      result.push(...converted);
+
+      // Check for operation_started and notify
+      for (const msg of converted) {
+        if (
+          msg.type === "operation_started" &&
+          !hasNotifiedOperationRef.current.has(
+            (msg as { toolId?: string }).toolId || "",
+          )
+        ) {
+          hasNotifiedOperationRef.current.add(
+            (msg as { toolId?: string }).toolId || "",
+          );
+          onOperationStarted?.();
+        }
+      }
+    }
+
+    return result;
+  }, [aiMessages, onOperationStarted]);
+
+  // Get streaming text chunks from the last assistant message
+  const streamingChunks = useMemo(() => {
+    if (!isStreaming) return [];
+
+    const lastMessage = aiMessages[aiMessages.length - 1];
+    if (!lastMessage || lastMessage.role !== "assistant") return [];
+
+    const chunks: string[] = [];
+    for (const part of lastMessage.parts) {
+      if (part.type === "text" && part.text) {
+        chunks.push(part.text);
+      }
+    }
+
+    return chunks;
+  }, [aiMessages, isStreaming]);
+
+  // Send message with optional context
+  const sendMessage = useCallback(
+    async (
+      content: string,
+      isUserMessage: boolean = true,
+      context?: { url: string; content: string },
+    ) => {
+      if (!sessionId || !transport) {
+        console.error("[useAiSdkChat] Cannot send message: no session");
+        return;
+      }
+
+      setIsRegionBlockedModalOpen(false);
+
+      try {
+        await aiSendMessage(
+          { text: content },
+          {
+            body: {
+              is_user_message: isUserMessage,
+              context: context || null,
+            },
+          },
+        );
+      } catch (err) {
+        console.error("[useAiSdkChat] Failed to send message:", err);
+
+        if (err instanceof Error && err.name === "AbortError") return;
+
+        toast.error("Failed to send message", {
+          description:
+            err instanceof Error ? err.message : "Failed to send message",
+        });
+      }
+    },
+    [sessionId, transport, aiSendMessage],
+  );
+
+  // Stop streaming
+  const stopStreaming = useCallback(() => {
+    stop();
+  }, [stop]);
+
+  return {
+    messages,
+    streamingChunks,
+    isStreaming,
+    error,
+    status,
+    isRegionBlockedModalOpen,
+    setIsRegionBlockedModalOpen,
+    sendMessage,
+    stopStreaming,
+    // Expose raw AI SDK state for advanced use cases
+    aiMessages,
+    setAiMessages: setMessages,
+  };
+}

autogpt_platform/frontend/src/tests/library.spec.ts

@@ -59,12 +59,13 @@ test.describe("Library", () => {
   });
 
   test("pagination works correctly", async ({ page }, testInfo) => {
-    test.setTimeout(testInfo.timeout * 3); // Increase timeout for pagination operations
+    test.setTimeout(testInfo.timeout * 3);
     await page.goto("/library");
 
+    const PAGE_SIZE = 20;
     const paginationResult = await libraryPage.testPagination();
 
-    if (paginationResult.initialCount >= 10) {
+    if (paginationResult.initialCount >= PAGE_SIZE) {
       expect(paginationResult.finalCount).toBeGreaterThanOrEqual(
         paginationResult.initialCount,
       );
@@ -133,7 +134,10 @@ test.describe("Library", () => {
     test.expect(clearedSearchValue).toBe("");
   });
 
-  test("pagination while searching works correctly", async ({ page }) => {
+  test("pagination while searching works correctly", async ({
+    page,
+  }, testInfo) => {
+    test.setTimeout(testInfo.timeout * 3);
     await page.goto("/library");
 
     const allAgents = await libraryPage.getAgents();
@@ -152,9 +156,10 @@ test.describe("Library", () => {
     );
     expect(matchingResults.length).toEqual(initialSearchResults.length);
 
+    const PAGE_SIZE = 20;
     const searchPaginationResult = await libraryPage.testPagination();
 
-    if (searchPaginationResult.initialCount >= 10) {
+    if (searchPaginationResult.initialCount >= PAGE_SIZE) {
       expect(searchPaginationResult.finalCount).toBeGreaterThanOrEqual(
         searchPaginationResult.initialCount,
       );

autogpt_platform/frontend/src/tests/marketplace-creator.spec.ts

@@ -69,9 +69,12 @@ test.describe("Marketplace Creator Page – Basic Functionality", () => {
       await marketplacePage.getFirstCreatorProfile(page);
     await firstCreatorProfile.click();
     await page.waitForURL("**/marketplace/creator/**");
+    await page.waitForLoadState("networkidle").catch(() => {});
+
     const firstAgent = page
       .locator('[data-testid="store-card"]:visible')
       .first();
+    await firstAgent.waitFor({ state: "visible", timeout: 30000 });
 
     await firstAgent.click();
     await page.waitForURL("**/marketplace/agent/**");

autogpt_platform/frontend/src/tests/marketplace.spec.ts

@@ -77,7 +77,6 @@ test.describe("Marketplace – Basic Functionality", () => {
 
     const firstFeaturedAgent =
       await marketplacePage.getFirstFeaturedAgent(page);
-    await firstFeaturedAgent.waitFor({ state: "visible" });
     await firstFeaturedAgent.click();
     await page.waitForURL("**/marketplace/agent/**");
     await matchesUrl(page, /\/marketplace\/agent\/.+/);
@@ -116,7 +115,15 @@ test.describe("Marketplace – Basic Functionality", () => {
     const searchTerm = page.getByText("DummyInput").first();
     await isVisible(searchTerm);
 
-    await page.waitForTimeout(10000);
+    await page.waitForLoadState("networkidle").catch(() => {});
+
+    await page
+      .waitForFunction(
+        () =>
+          document.querySelectorAll('[data-testid="store-card"]').length > 0,
+        { timeout: 15000 },
+      )
+      .catch(() => console.log("No search results appeared within timeout"));
 
     const results = await marketplacePage.getSearchResultsCount(page);
     expect(results).toBeGreaterThan(0);

autogpt_platform/frontend/src/tests/pages/library.page.ts

@@ -300,21 +300,27 @@ export class LibraryPage extends BasePage {
   async scrollToLoadMore(): Promise<void> {
     console.log(`scrolling to load more agents`);
 
-    // Get initial agent count
-    const initialCount = await this.getAgentCount();
-    console.log(`Initial agent count: ${initialCount}`);
+    const initialCount = await this.getAgentCountByListLength();
+    console.log(`Initial agent count (DOM cards): ${initialCount}`);
 
-    // Scroll down to trigger pagination
     await this.scrollToBottom();
 
-    // Wait for potential new agents to load
-    await this.page.waitForTimeout(2000);
+    await this.page
+      .waitForLoadState("networkidle", { timeout: 10000 })
+      .catch(() => console.log("Network idle timeout, continuing..."));
 
-    // Check if more agents loaded
-    const newCount = await this.getAgentCount();
-    console.log(`New agent count after scroll: ${newCount}`);
+    await this.page
+      .waitForFunction(
+        (prevCount) =>
+          document.querySelectorAll('[data-testid="library-agent-card"]')
+            .length > prevCount,
+        initialCount,
+        { timeout: 5000 },
+      )
+      .catch(() => {});
 
-    return;
+    const newCount = await this.getAgentCountByListLength();
+    console.log(`New agent count after scroll (DOM cards): ${newCount}`);
   }
 
   async testPagination(): Promise<{

autogpt_platform/frontend/src/tests/pages/marketplace.page.ts

@@ -9,6 +9,7 @@ export class MarketplacePage extends BasePage {
 
   async goto(page: Page) {
     await page.goto("/marketplace");
+    await page.waitForLoadState("networkidle").catch(() => {});
   }
 
   async getMarketplaceTitle(page: Page) {
@@ -109,16 +110,24 @@ export class MarketplacePage extends BasePage {
 
   async getFirstFeaturedAgent(page: Page) {
     const { getId } = getSelectors(page);
-    return getId("featured-store-card").first();
+    const card = getId("featured-store-card").first();
+    await card.waitFor({ state: "visible", timeout: 30000 });
+    return card;
   }
 
   async getFirstTopAgent() {
-    return this.page.locator('[data-testid="store-card"]:visible').first();
+    const card = this.page
+      .locator('[data-testid="store-card"]:visible')
+      .first();
+    await card.waitFor({ state: "visible", timeout: 30000 });
+    return card;
   }
 
   async getFirstCreatorProfile(page: Page) {
     const { getId } = getSelectors(page);
-    return getId("creator-card").first();
+    const card = getId("creator-card").first();
+    await card.waitFor({ state: "visible", timeout: 30000 });
+    return card;
   }
 
   async getSearchResultsCount(page: Page) {

docs/integrations/block-integrations/llm.md

@@ -65,7 +65,7 @@ The result routes data to yes_output or no_output, enabling intelligent branchin
 | condition | A plaintext English description of the condition to evaluate | str | Yes |
 | yes_value | (Optional) Value to output if the condition is true. If not provided, input_value will be used. | Yes Value | No |
 | no_value | (Optional) Value to output if the condition is false. If not provided, input_value will be used. | No Value | No |
-| model | The language model to use for evaluating the condition. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for evaluating the condition. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 
 ### Outputs
 
@@ -103,7 +103,7 @@ The block sends the entire conversation history to the chosen LLM, including sys
 |-------|-------------|------|----------|
 | prompt | The prompt to send to the language model. | str | No |
 | messages | List of messages in the conversation. | List[Any] | Yes |
-| model | The language model to use for the conversation. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for the conversation. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | max_tokens | The maximum number of tokens to generate in the chat completion. | int | No |
 | ollama_host | Ollama host for local  models | str | No |
 
@@ -257,7 +257,7 @@ The block formulates a prompt based on the given focus or source data, sends it
 |-------|-------------|------|----------|
 | focus | The focus of the list to generate. | str | No |
 | source_data | The data to generate the list from. | str | No |
-| model | The language model to use for generating the list. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for generating the list. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | max_retries | Maximum number of retries for generating a valid list. | int | No |
 | force_json_output | Whether to force the LLM to produce a JSON-only response. This can increase the block's reliability, but may also reduce the quality of the response because it prohibits the LLM from reasoning before providing its JSON response. | bool | No |
 | max_tokens | The maximum number of tokens to generate in the chat completion. | int | No |
@@ -424,7 +424,7 @@ The block sends the input prompt to a chosen LLM, along with any system prompts
 | prompt | The prompt to send to the language model. | str | Yes |
 | expected_format | Expected format of the response. If provided, the response will be validated against this format. The keys should be the expected fields in the response, and the values should be the description of the field. | Dict[str, str] | Yes |
 | list_result | Whether the response should be a list of objects in the expected format. | bool | No |
-| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | force_json_output | Whether to force the LLM to produce a JSON-only response. This can increase the block's reliability, but may also reduce the quality of the response because it prohibits the LLM from reasoning before providing its JSON response. | bool | No |
 | sys_prompt | The system prompt to provide additional context to the model. | str | No |
 | conversation_history | The conversation history to provide context for the prompt. | List[Dict[str, Any]] | No |
@@ -464,7 +464,7 @@ The block sends the input prompt to a chosen LLM, processes the response, and re
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
 | prompt | The prompt to send to the language model. You can use any of the {keys} from Prompt Values to fill in the prompt with values from the prompt values dictionary by putting them in curly braces. | str | Yes |
-| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | sys_prompt | The system prompt to provide additional context to the model. | str | No |
 | retry | Number of times to retry the LLM call if the response does not match the expected format. | int | No |
 | prompt_values | Values used to fill in the prompt. The values can be used in the prompt by putting them in a double curly braces, e.g. {{variable_name}}. | Dict[str, str] | No |
@@ -501,7 +501,7 @@ The block splits the input text into smaller chunks, sends each chunk to an LLM
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
 | text | The text to summarize. | str | Yes |
-| model | The language model to use for summarizing the text. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for summarizing the text. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | focus | The topic to focus on in the summary | str | No |
 | style | The style of the summary to generate. | "concise" \| "detailed" \| "bullet points" \| "numbered list" | No |
 | max_tokens | The maximum number of tokens to generate in the chat completion. | int | No |
@@ -763,7 +763,7 @@ Configure agent_mode_max_iterations to control loop behavior: 0 for single decis
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
 | prompt | The prompt to send to the language model. | str | Yes |
-| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
+| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No |
 | multiple_tool_calls | Whether to allow multiple tool calls in a single response. | bool | No |
 | sys_prompt | The system prompt to provide additional context to the model. | str | No |
 | conversation_history | The conversation history to provide context for the prompt. | List[Dict[str, Any]] | No |

docs/integrations/block-integrations/stagehand/blocks.md

@@ -20,7 +20,7 @@ Configure timeouts for DOM settlement and page loading. Variables can be passed
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
 | browserbase_project_id | Browserbase project ID (required if using Browserbase) | str | Yes |
-| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-3-7-sonnet-20250219" | No |
+| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-sonnet-4-5-20250929" | No |
 | url | URL to navigate to. | str | Yes |
 | action | Action to perform. Suggested actions are: click, fill, type, press, scroll, select from dropdown. For multi-step actions, add an entry for each step. | List[str] | Yes |
 | variables | Variables to use in the action. Variables contains data you want the action to use. | Dict[str, str] | No |
@@ -65,7 +65,7 @@ Supports searching within iframes and configurable timeouts for dynamic content
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
 | browserbase_project_id | Browserbase project ID (required if using Browserbase) | str | Yes |
-| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-3-7-sonnet-20250219" | No |
+| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-sonnet-4-5-20250929" | No |
 | url | URL to navigate to. | str | Yes |
 | instruction | Natural language description of elements or actions to discover. | str | Yes |
 | iframes | Whether to search within iframes. If True, Stagehand will search for actions within iframes. | bool | No |
@@ -106,7 +106,7 @@ Use this to explore a page's interactive elements before building automated work
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
 | browserbase_project_id | Browserbase project ID (required if using Browserbase) | str | Yes |
-| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-3-7-sonnet-20250219" | No |
+| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-sonnet-4-5-20250929" | No |
 | url | URL to navigate to. | str | Yes |
 | instruction | Natural language description of elements or actions to discover. | str | Yes |
 | iframes | Whether to search within iframes. If True, Stagehand will search for actions within iframes. | bool | No |