Merge branch 'dev' into kpczerwinski/secrt-1726-move-completion-backend

Author: kcze

autogpt_platform/backend/backend/blocks/llm.py

@@ -129,6 +129,7 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     OPENAI_GPT_OSS_120B = "openai/gpt-oss-120b"
     OPENAI_GPT_OSS_20B = "openai/gpt-oss-20b"
     GEMINI_2_5_PRO = "google/gemini-2.5-pro-preview-03-25"
+    GEMINI_3_PRO_PREVIEW = "google/gemini-3-pro-preview"
     GEMINI_2_5_FLASH = "google/gemini-2.5-flash"
     GEMINI_2_0_FLASH = "google/gemini-2.0-flash-001"
     GEMINI_2_5_FLASH_LITE_PREVIEW = "google/gemini-2.5-flash-lite-preview-06-17"
@@ -241,6 +242,7 @@ def max_output_tokens(self) -> int | None:
     LlmModel.OLLAMA_DOLPHIN: ModelMetadata("ollama", 32768, None),
     # https://openrouter.ai/models
     LlmModel.GEMINI_2_5_PRO: ModelMetadata("open_router", 1050000, 8192),
+    LlmModel.GEMINI_3_PRO_PREVIEW: ModelMetadata("open_router", 1048576, 65535),
     LlmModel.GEMINI_2_5_FLASH: ModelMetadata("open_router", 1048576, 65535),
     LlmModel.GEMINI_2_0_FLASH: ModelMetadata("open_router", 1048576, 8192),
     LlmModel.GEMINI_2_5_FLASH_LITE_PREVIEW: ModelMetadata(
@@ -252,12 +254,12 @@ def max_output_tokens(self) -> int | None:
     LlmModel.COHERE_COMMAND_R_PLUS_08_2024: ModelMetadata("open_router", 128000, 4096),
     LlmModel.DEEPSEEK_CHAT: ModelMetadata("open_router", 64000, 2048),
     LlmModel.DEEPSEEK_R1_0528: ModelMetadata("open_router", 163840, 163840),
-    LlmModel.PERPLEXITY_SONAR: ModelMetadata("open_router", 127000, 127000),
+    LlmModel.PERPLEXITY_SONAR: ModelMetadata("open_router", 127000, 8000),
     LlmModel.PERPLEXITY_SONAR_PRO: ModelMetadata("open_router", 200000, 8000),
     LlmModel.PERPLEXITY_SONAR_DEEP_RESEARCH: ModelMetadata(
         "open_router",
         128000,
-        128000,
+        16000,
     ),
     LlmModel.NOUSRESEARCH_HERMES_3_LLAMA_3_1_405B: ModelMetadata(
         "open_router", 131000, 4096
@@ -797,7 +799,7 @@ class Input(BlockSchemaInput):
             default="",
             description="The system prompt to provide additional context to the model.",
         )
-        conversation_history: list[dict] = SchemaField(
+        conversation_history: list[dict] | None = SchemaField(
             default_factory=list,
             description="The conversation history to provide context for the prompt.",
         )
@@ -904,7 +906,7 @@ async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         logger.debug(f"Calling LLM with input data: {input_data}")
-        prompt = [json.to_dict(p) for p in input_data.conversation_history]
+        prompt = [json.to_dict(p) for p in input_data.conversation_history or [] if p]
 
         values = input_data.prompt_values
         if values:

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -121,13 +121,16 @@ def _convert_raw_response_to_dict(raw_response: Any) -> dict[str, Any]:
         return json.to_dict(raw_response)
 
 
-def get_pending_tool_calls(conversation_history: list[Any]) -> dict[str, int]:
+def get_pending_tool_calls(conversation_history: list[Any] | None) -> dict[str, int]:
     """
     All the tool calls entry in the conversation history requires a response.
     This function returns the pending tool calls that has not generated an output yet.
 
     Return: dict[str, int] - A dictionary of pending tool call IDs with their count.
     """
+    if not conversation_history:
+        return {}
+
     pending_calls = Counter()
     for history in conversation_history:
         for call_id in _get_tool_requests(history):
@@ -173,7 +176,7 @@ class Input(BlockSchemaInput):
             "Function parameters that has no default value and not optional typed has to be provided. ",
             description="The system prompt to provide additional context to the model.",
         )
-        conversation_history: list[dict] = SchemaField(
+        conversation_history: list[dict] | None = SchemaField(
             default_factory=list,
             description="The conversation history to provide context for the prompt.",
         )
@@ -605,10 +608,10 @@ async def run(
         tool_functions = await self._create_tool_node_signatures(node_id)
         yield "tool_functions", json.dumps(tool_functions)
 
-        input_data.conversation_history = input_data.conversation_history or []
-        prompt = [json.to_dict(p) for p in input_data.conversation_history if p]
+        conversation_history = input_data.conversation_history or []
+        prompt = [json.to_dict(p) for p in conversation_history if p]
 
-        pending_tool_calls = get_pending_tool_calls(input_data.conversation_history)
+        pending_tool_calls = get_pending_tool_calls(conversation_history)
         if pending_tool_calls and input_data.last_tool_output is None:
             raise ValueError(f"Tool call requires an output for {pending_tool_calls}")
 

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -92,6 +92,7 @@
     LlmModel.OPENAI_GPT_OSS_120B: 1,
     LlmModel.OPENAI_GPT_OSS_20B: 1,
     LlmModel.GEMINI_2_5_PRO: 4,
+    LlmModel.GEMINI_3_PRO_PREVIEW: 5,
     LlmModel.MISTRAL_NEMO: 1,
     LlmModel.COHERE_COMMAND_R_08_2024: 1,
     LlmModel.COHERE_COMMAND_R_PLUS_08_2024: 3,

autogpt_platform/backend/backend/data/execution.py

@@ -460,6 +460,7 @@ def to_node_execution_entry(
 async def get_graph_executions(
     graph_exec_id: Optional[str] = None,
     graph_id: Optional[str] = None,
+    graph_version: Optional[int] = None,
     user_id: Optional[str] = None,
     statuses: Optional[list[ExecutionStatus]] = None,
     created_time_gte: Optional[datetime] = None,
@@ -476,6 +477,8 @@ async def get_graph_executions(
         where_filter["userId"] = user_id
     if graph_id:
         where_filter["agentGraphId"] = graph_id
+    if graph_version is not None:
+        where_filter["agentGraphVersion"] = graph_version
     if created_time_gte or created_time_lte:
         where_filter["createdAt"] = {
             "gte": created_time_gte or datetime.min.replace(tzinfo=timezone.utc),

autogpt_platform/backend/backend/data/graph.py

@@ -18,6 +18,7 @@
     AgentGraphWhereInput,
     AgentNodeCreateInput,
     AgentNodeLinkCreateInput,
+    StoreListingVersionWhereInput,
 )
 from pydantic import BaseModel, Field, create_model
 from pydantic.fields import computed_field
@@ -884,9 +885,9 @@ async def get_graph_metadata(graph_id: str, version: int | None = None) -> Graph
 
 async def get_graph(
     graph_id: str,
-    version: int | None = None,
+    version: int | None,
+    user_id: str | None,
     *,
-    user_id: str | None = None,
     for_export: bool = False,
     include_subgraphs: bool = False,
     skip_access_check: bool = False,
@@ -897,25 +898,43 @@ async def get_graph(
 
     Returns `None` if the record is not found.
     """
-    where_clause: AgentGraphWhereInput = {
-        "id": graph_id,
-    }
+    graph = None
 
-    if version is not None:
-        where_clause["version"] = version
+    # Only search graph directly on owned graph (or access check is skipped)
+    if skip_access_check or user_id is not None:
+        graph_where_clause: AgentGraphWhereInput = {
+            "id": graph_id,
+        }
+        if version is not None:
+            graph_where_clause["version"] = version
+        if not skip_access_check and user_id is not None:
+            graph_where_clause["userId"] = user_id
 
-    graph = await AgentGraph.prisma().find_first(
-        where=where_clause,
-        include=AGENT_GRAPH_INCLUDE,
-        order={"version": "desc"},
-    )
+        graph = await AgentGraph.prisma().find_first(
+            where=graph_where_clause,
+            include=AGENT_GRAPH_INCLUDE,
+            order={"version": "desc"},
+        )
+
+    # Use store listed graph to find not owned graph
     if graph is None:
-        return None
+        store_where_clause: StoreListingVersionWhereInput = {
+            "agentGraphId": graph_id,
+            "submissionStatus": SubmissionStatus.APPROVED,
+            "isDeleted": False,
+        }
+        if version is not None:
+            store_where_clause["agentGraphVersion"] = version
 
-    if not skip_access_check and graph.userId != user_id:
-        # For access, the graph must be owned by the user or listed in the store
-        if not await is_graph_published_in_marketplace(graph_id, graph.version):
-            return None
+        if store_listing := await StoreListingVersion.prisma().find_first(
+            where=store_where_clause,
+            order={"agentGraphVersion": "desc"},
+            include={"AgentGraph": {"include": AGENT_GRAPH_INCLUDE}},
+        ):
+            graph = store_listing.AgentGraph
+
+    if graph is None:
+        return None
 
     if include_subgraphs or for_export:
         sub_graphs = await get_sub_graphs(graph)