Merge branch 'dev' of https://github.com/Significant-Gravitas/AutoGPT …

…into dev

autogpt_platform/backend/.env.example

@@ -121,6 +121,18 @@ REPLICATE_API_KEY=
 # Ideogram
 IDEOGRAM_API_KEY=
 
+# Fal
+FAL_API_KEY=
+
+# Exa
+EXA_API_KEY=
+
+# E2B
+E2B_API_KEY=
+
+# Nvidia
+NVIDIA_API_KEY=
+
 # Logging Configuration
 LOG_LEVEL=INFO
 ENABLE_CLOUD_LOGGING=false

autogpt_platform/backend/backend/blocks/basic.py

@@ -241,7 +241,7 @@ class Input(BlockSchema):
             advanced=True,
         )
         format: str = SchemaField(
-            description="The format string to be used to format the recorded_value.",
+            description="The format string to be used to format the recorded_value. Use Jinja2 syntax.",
             default="",
             advanced=True,
         )

autogpt_platform/backend/backend/blocks/llm.py

@@ -26,8 +26,10 @@
 )
 from backend.util import json
 from backend.util.settings import BehaveAs, Settings
+from backend.util.text import TextFormatter
 
 logger = logging.getLogger(__name__)
+fmt = TextFormatter()
 
 LLMProviderName = Literal[
     ProviderName.ANTHROPIC,
@@ -109,6 +111,7 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     LLAMA3_1_70B = "llama-3.1-70b-versatile"
     LLAMA3_1_8B = "llama-3.1-8b-instant"
     # Ollama models
+    OLLAMA_LLAMA3_2 = "llama3.2"
     OLLAMA_LLAMA3_8B = "llama3"
     OLLAMA_LLAMA3_405B = "llama3.1:405b"
     OLLAMA_DOLPHIN = "dolphin-mistral:latest"
@@ -163,6 +166,7 @@ def context_window(self) -> int:
     # Limited to 16k during preview
     LlmModel.LLAMA3_1_70B: ModelMetadata("groq", 131072),
     LlmModel.LLAMA3_1_8B: ModelMetadata("groq", 131072),
+    LlmModel.OLLAMA_LLAMA3_2: ModelMetadata("ollama", 8192),
     LlmModel.OLLAMA_LLAMA3_8B: ModelMetadata("ollama", 8192),
     LlmModel.OLLAMA_LLAMA3_405B: ModelMetadata("ollama", 8192),
     LlmModel.OLLAMA_DOLPHIN: ModelMetadata("ollama", 32768),
@@ -234,7 +238,9 @@ class Input(BlockSchema):
             description="Number of times to retry the LLM call if the response does not match the expected format.",
         )
         prompt_values: dict[str, str] = SchemaField(
-            advanced=False, default={}, description="Values used to fill in the prompt."
+            advanced=False,
+            default={},
+            description="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}}.",
         )
         max_tokens: int | None = SchemaField(
             advanced=True,
@@ -448,8 +454,8 @@ def trim_prompt(s: str) -> str:
 
         values = input_data.prompt_values
         if values:
-            input_data.prompt = input_data.prompt.format(**values)
-            input_data.sys_prompt = input_data.sys_prompt.format(**values)
+            input_data.prompt = fmt.format_string(input_data.prompt, values)
+            input_data.sys_prompt = fmt.format_string(input_data.sys_prompt, values)
 
         if input_data.sys_prompt:
             prompt.append({"role": "system", "content": input_data.sys_prompt})
@@ -576,7 +582,9 @@ class Input(BlockSchema):
             description="Number of times to retry the LLM call if the response does not match the expected format.",
         )
         prompt_values: dict[str, str] = SchemaField(
-            advanced=False, default={}, description="Values used to fill in the prompt."
+            advanced=False,
+            default={},
+            description="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}}.",
         )
         ollama_host: str = SchemaField(
             advanced=True,

autogpt_platform/backend/backend/blocks/text.py

@@ -141,10 +141,10 @@ def run(self, input_data: Input, **kwargs) -> BlockOutput:
 class FillTextTemplateBlock(Block):
     class Input(BlockSchema):
         values: dict[str, Any] = SchemaField(
-            description="Values (dict) to be used in format"
+            description="Values (dict) to be used in format. These values can be used by putting them in double curly braces in the format template. e.g. {{value_name}}.",
         )
         format: str = SchemaField(
-            description="Template to format the text using `values`"
+            description="Template to format the text using `values`. Use Jinja2 syntax."
         )
 
     class Output(BlockSchema):
@@ -160,7 +160,7 @@ def __init__(self):
             test_input=[
                 {
                     "values": {"name": "Alice", "hello": "Hello", "world": "World!"},
-                    "format": "{hello}, {world} {{name}}",
+                    "format": "{{hello}}, {{ world }} {{name}}",
                 },
                 {
                     "values": {"list": ["Hello", " World!"]},

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -51,6 +51,7 @@
     LlmModel.LLAMA3_1_405B: 1,
     LlmModel.LLAMA3_1_70B: 1,
     LlmModel.LLAMA3_1_8B: 1,
+    LlmModel.OLLAMA_LLAMA3_2: 1,
     LlmModel.OLLAMA_LLAMA3_8B: 1,
     LlmModel.OLLAMA_LLAMA3_405B: 1,
     LlmModel.OLLAMA_DOLPHIN: 1,

autogpt_platform/backend/backend/data/execution.py

@@ -136,6 +136,7 @@ async def create_graph_execution(
     graph_version: int,
     nodes_input: list[tuple[str, BlockInput]],
     user_id: str,
+    preset_id: str | None = None,
 ) -> tuple[str, list[ExecutionResult]]:
     """
     Create a new AgentGraphExecution record.
@@ -163,6 +164,7 @@ async def create_graph_execution(
                 ]
             },
             "userId": user_id,
+            "agentPresetId": preset_id,
         },
         include=GRAPH_EXECUTION_INCLUDE,
     )

autogpt_platform/backend/backend/data/graph.py

@@ -6,7 +6,13 @@
 from typing import Any, Literal, Optional, Type
 
 import prisma
-from prisma.models import AgentGraph, AgentGraphExecution, AgentNode, AgentNodeLink
+from prisma.models import (
+    AgentGraph,
+    AgentGraphExecution,
+    AgentNode,
+    AgentNodeLink,
+    StoreListingVersion,
+)
 from prisma.types import AgentGraphWhereInput
 from pydantic.fields import computed_field
 
@@ -529,7 +535,6 @@ async def get_execution(user_id: str, execution_id: str) -> GraphExecution | Non
 async def get_graph(
     graph_id: str,
     version: int | None = None,
-    template: bool = False,
     user_id: str | None = None,
     for_export: bool = False,
 ) -> GraphModel | None:
@@ -543,21 +548,36 @@ async def get_graph(
     where_clause: AgentGraphWhereInput = {
         "id": graph_id,
     }
+
     if version is not None:
         where_clause["version"] = version
-    elif not template:
+    else:
         where_clause["isActive"] = True
 
-    # TODO: Fix hack workaround to get adding store agents to work
-    if user_id is not None and not template:
-        where_clause["userId"] = user_id
-
     graph = await AgentGraph.prisma().find_first(
         where=where_clause,
         include=AGENT_GRAPH_INCLUDE,
         order={"version": "desc"},
     )
-    return GraphModel.from_db(graph, for_export) if graph else None
+
+    # The Graph has to be owned by the user or a store listing.
+    if (
+        graph is None
+        or graph.userId != user_id
+        and not (
+            await StoreListingVersion.prisma().find_first(
+                where=prisma.types.StoreListingVersionWhereInput(
+                    agentId=graph_id,
+                    agentVersion=version or graph.version,
+                    isDeleted=False,
+                    StoreListing={"is": {"isApproved": True}},
+                )
+            )
+        )
+    ):
+        return None
+
+    return GraphModel.from_db(graph, for_export)
 
 
 async def set_graph_active_version(graph_id: str, version: int, user_id: str) -> None:
@@ -611,9 +631,7 @@ async def create_graph(graph: Graph, user_id: str) -> GraphModel:
     async with transaction() as tx:
         await __create_graph(tx, graph, user_id)
 
-    if created_graph := await get_graph(
-        graph.id, graph.version, graph.is_template, user_id=user_id
-    ):
+    if created_graph := await get_graph(graph.id, graph.version, user_id=user_id):
         return created_graph
 
     raise ValueError(f"Created graph {graph.id} v{graph.version} is not in DB")

autogpt_platform/backend/backend/executor/manager.py

@@ -780,7 +780,8 @@ def add_execution(
         graph_id: str,
         data: BlockInput,
         user_id: str,
-        graph_version: int | None = None,
+        graph_version: int,
+        preset_id: str | None = None,
     ) -> GraphExecutionEntry:
         graph: GraphModel | None = self.db_client.get_graph(
             graph_id=graph_id, user_id=user_id, version=graph_version
@@ -829,6 +830,7 @@ def add_execution(
             graph_version=graph.version,
             nodes_input=nodes_input,
             user_id=user_id,
+            preset_id=preset_id,
         )
 
         starting_node_execs = []

autogpt_platform/backend/backend/executor/scheduler.py

@@ -63,7 +63,10 @@ def execute_graph(**kwargs):
     try:
         log(f"Executing recurring job for graph #{args.graph_id}")
         get_execution_client().add_execution(
-            args.graph_id, args.input_data, args.user_id
+            graph_id=args.graph_id,
+            data=args.input_data,
+            user_id=args.user_id,
+            graph_version=args.graph_version,
         )
     except Exception as e:
         logger.exception(f"Error executing graph {args.graph_id}: {e}")

autogpt_platform/backend/backend/integrations/credentials_store.py

@@ -93,6 +93,34 @@
     title="Use Credits for Open Router",
     expires_at=None,
 )
+fal_credentials = APIKeyCredentials(
+    id="6c0f5bd0-9008-4638-9d79-4b40b631803e",
+    provider="fal",
+    api_key=SecretStr(settings.secrets.fal_api_key),
+    title="Use Credits for FAL",
+    expires_at=None,
+)
+exa_credentials = APIKeyCredentials(
+    id="96153e04-9c6c-4486-895f-5bb683b1ecec",
+    provider="exa",
+    api_key=SecretStr(settings.secrets.exa_api_key),
+    title="Use Credits for Exa search",
+    expires_at=None,
+)
+e2b_credentials = APIKeyCredentials(
+    id="78d19fd7-4d59-4a16-8277-3ce310acf2b7",
+    provider="e2b",
+    api_key=SecretStr(settings.secrets.e2b_api_key),
+    title="Use Credits for E2B",
+    expires_at=None,
+)
+nvidia_credentials = APIKeyCredentials(
+    id="96b83908-2789-4dec-9968-18f0ece4ceb3",
+    provider="nvidia",
+    api_key=SecretStr(settings.secrets.nvidia_api_key),
+    title="Use Credits for Nvidia",
+    expires_at=None,
+)
 
 
 DEFAULT_CREDENTIALS = [
@@ -106,6 +134,10 @@
     jina_credentials,
     unreal_credentials,
     open_router_credentials,
+    fal_credentials,
+    exa_credentials,
+    e2b_credentials,
+    nvidia_credentials,
 ]
 
 
@@ -157,6 +189,14 @@ def get_all_creds(self, user_id: str) -> list[Credentials]:
             all_credentials.append(unreal_credentials)
         if settings.secrets.open_router_api_key:
             all_credentials.append(open_router_credentials)
+        if settings.secrets.fal_api_key:
+            all_credentials.append(fal_credentials)
+        if settings.secrets.exa_api_key:
+            all_credentials.append(exa_credentials)
+        if settings.secrets.e2b_api_key:
+            all_credentials.append(e2b_credentials)
+        if settings.secrets.nvidia_api_key:
+            all_credentials.append(nvidia_credentials)
         return all_credentials
 
     def get_creds_by_id(self, user_id: str, credentials_id: str) -> Credentials | None:

autogpt_platform/backend/backend/server/integrations/router.py

@@ -320,7 +320,8 @@ async def webhook_ingress_generic(
             continue
         logger.debug(f"Executing graph #{node.graph_id} node #{node.id}")
         executor.add_execution(
-            node.graph_id,
+            graph_id=node.graph_id,
+            graph_version=node.graph_version,
             data={f"webhook_{webhook_id}_payload": payload},
             user_id=webhook.user_id,
         )

autogpt_platform/backend/backend/server/model.py

@@ -56,3 +56,18 @@ class SetGraphActiveVersion(pydantic.BaseModel):
 
 class UpdatePermissionsRequest(pydantic.BaseModel):
     permissions: List[APIKeyPermission]
+
+
+class Pagination(pydantic.BaseModel):
+    total_items: int = pydantic.Field(
+        description="Total number of items.", examples=[42]
+    )
+    total_pages: int = pydantic.Field(
+        description="Total number of pages.", examples=[2]
+    )
+    current_page: int = pydantic.Field(
+        description="Current_page page number.", examples=[1]
+    )
+    page_size: int = pydantic.Field(
+        description="Number of items per page.", examples=[25]
+    )