[CLEANUP]

playground/agents/command_r_tool_agent.py

@@ -16,6 +16,7 @@ model = AutoModelForCausalLM.from_pretrained(
 # Load the pre-trained model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
+
 # Initialize the schema for the person's information
 class APIExampleRequestSchema(BaseModel):
     endpoint: str = Field(
@@ -31,22 +32,25 @@ class APIExampleRequestSchema(BaseModel):
         ..., description="The body of the example request"
     )
     response: dict = Field(
-        ..., description="The expected response of the example request"
+        ...,
+        description="The expected response of the example request",
     )
 
+
 # Convert the schema to a JSON string
 api_example_schema = base_model_to_json(APIExampleRequestSchema)
 # Convert the schema to a JSON string
 
 # Define the task to generate a person's information
-task = (
+task = "Generate an example API request using this code:\n"
-    "Generate an example API request using this code:\n"
-)
 
 # Create an instance of the ToolAgent class
 agent = ToolAgent(
     name="Command R Tool Agent",
-    description="An agent that generates an API request using the Command R model.",
+    description=(
+        "An agent that generates an API request using the Command R"
+        " model."
+    ),
     model=model,
     tokenizer=tokenizer,
     json_schema=api_example_schema,

playground/agents/jamba_tool_agent.py

@@ -16,6 +16,7 @@ model = AutoModelForCausalLM.from_pretrained(
 # Load the pre-trained model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
+
 # Initialize the schema for the person's information
 class APIExampleRequestSchema(BaseModel):
     endpoint: str = Field(
@@ -31,22 +32,25 @@ class APIExampleRequestSchema(BaseModel):
         ..., description="The body of the example request"
     )
     response: dict = Field(
-        ..., description="The expected response of the example request"
+        ...,
+        description="The expected response of the example request",
     )
 
+
 # Convert the schema to a JSON string
 api_example_schema = base_model_to_json(APIExampleRequestSchema)
 # Convert the schema to a JSON string
 
 # Define the task to generate a person's information
-task = (
+task = "Generate an example API request using this code:\n"
-    "Generate an example API request using this code:\n"
-)
 
 # Create an instance of the ToolAgent class
 agent = ToolAgent(
     name="Command R Tool Agent",
-    description="An agent that generates an API request using the Command R model.",
+    description=(
+        "An agent that generates an API request using the Command R"
+        " model."
+    ),
     model=model,
     tokenizer=tokenizer,
     json_schema=api_example_schema,

swarms/models/__init__.py

@@ -32,6 +32,7 @@ from swarms.models.popular_llms import (
 from swarms.models.popular_llms import (
     ReplicateLLM as Replicate,
 )
+from swarms.models.popular_llms import OctoAIChat
 from swarms.models.qwen import QwenVLMultiModal  # noqa: E402
 
 from swarms.models.sampling_params import SamplingParams, SamplingType
@@ -79,4 +80,5 @@ __all__ = [
     "AudioModality",
     "ImageModality",
     "VideoModality",
+    "OctoAIChat",
 ]

swarms/models/popular_llms.py

@@ -11,6 +11,7 @@ from langchain_community.llms import (
     OpenAI,
     Replicate,
 )
+from langchain_community.llms.octoai_endpoint import OctoAIEndpoint
 
 
 class AnthropicChat(Anthropic):
@@ -46,3 +47,8 @@ class AzureOpenAILLM(AzureChatOpenAI):
 class OpenAIChatLLM(OpenAIChat):
     def __call__(self, *args, **kwargs):
         return self.invoke(*args, **kwargs)
+
+
+class OctoAIChat(OctoAIEndpoint):
+    def __call__(self, *args, **kwargs):
+        return self.invoke(*args, **kwargs)

swarms/utils/__init__.py

@@ -40,6 +40,7 @@ from swarms.utils.remove_json_whitespace import (
     remove_whitespace_from_yaml,
 )
 from swarms.utils.save_logs import parse_log_file
+
 # from swarms.utils.supervision_visualizer import MarkVisualizer
 from swarms.utils.try_except_wrapper import try_except_wrapper
 from swarms.utils.yaml_output_parser import YamlOutputParser