[CLEANUP][API]

api.py

@@ -2,7 +2,6 @@
 import json
 import os
 
-import tiktoken
 from fastapi import FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
 from swarms import Agent, OpenAIChat
@@ -13,58 +12,14 @@
     AgentOutput,
     ModelList,
     ModelSchema,
+    AllAgentsSchema,
+    AgentCreationOutput,
 )
 from swarms_memory import ChromaDB
+from swarms.models.tiktoken_wrapper import TikTokenizer
 
 logger.info("Starting the agent API server...")
 
-
-def count_tokens(text: str) -> int:
-    try:
-        # Get the encoding for the specific model
-        enc = tiktoken.get_encoding("cl100k_base")
-
-        # Encode the text
-        tokens = enc.encode(text)
-
-        # Count the tokens
-        return len(tokens)
-    except Exception as e:
-        raise HTTPException(status_code=400, detail=f"Error counting tokens: {e}")
-
-
-# async def model_router(model_name: str):
-#     """
-#     Function to switch to the specified model.
-
-#     Parameters:
-#     - model_name (str): The name of the model to switch to.
-
-#     Returns:
-#     - None
-
-#     Raises:
-#     - None
-
-#     """
-#     # Logic to switch to the specified model
-#     if model_name == "gpt-4o":
-#         # Switch to OpenAIChat model
-#         llm = OpenAIChat(max_tokens=4000, model_name="gpt-4o", api_key=os.getenv("OPENAI_API_KEY"))
-#     elif model_name == "gpt-4-vision-preview":
-#         # Switch to GPT4VisionAPI model
-#         llm = GPT4VisionAPI(
-#             max_tokens=4000,
-#         )
-#     elif model_name == "Anthropic":
-#         # Switch to Anthropic model
-#         llm = Anthropic(anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"))
-#     else:
-#         # Invalid model name
-#         raise HTTPException(status_code=400, detail=f"Invalid model name: {model_name}")
-
-#     return llm
-
 llm = OpenAIChat(
     max_tokens=4000,
     model_name="gpt-4o",
@@ -162,6 +117,23 @@ async def list_models():
     return models
 
 
+@app.get("/v1/agents", response_model=AllAgentsSchema)
+async def list_agents(request: Request):
+    """
+    An endpoint to list available models. It returns a list of model names.
+    This is useful for clients to query and understand what models are available for use.
+    """
+    logger.info("Listing available agents...")
+
+    agents = AllAgentsSchema(
+        AgentCreationOutput(
+            name="Agent 1",
+            description="Description 1",
+            created_at=1628584185,
+        )
+    )
+
+
 @app.post("/v1/agent/completions", response_model=AgentOutput)
 async def agent_completions(agent_input: AgentInput):
     try:
@@ -217,14 +189,13 @@ async def agent_completions(agent_input: AgentInput):
         logger.info(f"Agent response: {completions}")
 
         # Costs calculation
-        all_input_tokens = count_tokens(agent_history)
-        output_tokens = count_tokens(completions)
+        all_input_tokens = TikTokenizer().count_tokens(agent_history)
+        output_tokens = TikTokenizer().count_tokens(completions)
         total_costs = all_input_tokens + output_tokens
         logger.info(f"Token counts: {total_costs}")
 
         # Prepare the output
         out = AgentOutput(
-            agent=agent_input,
             completions=ChatCompletionResponse(
                 model=model_name,
                 object="chat.completion",
@@ -238,7 +209,7 @@ async def agent_completions(agent_input: AgentInput):
                         },
                     }
                 ],
-                usage_info=UsageInfo(
+                usage=UsageInfo(
                     prompt_tokens=all_input_tokens,
                     completion_tokens=output_tokens,
                     total_tokens=total_costs,

parallel_swarm_api.py

@@ -100,7 +100,10 @@ def run_parallel_swarm_completions(
 
 
 @app.post("v1/swarms", response_model=AllSwarmsSchema)
-def get_all_swarms(request: Request):
+def get_all_swarms(
+    request: Request,
+    Swa,
+):
     return AllSwarmsSchema(
         swarms=[
             SwarmAPISchema(

swarms_cloud/schema/agent_api_schemas.py

@@ -146,3 +146,23 @@ class ParallelSwarmAPIOutput(BaseModel):
 # )
 
 # print(full_example.dict())
+
+
+class AgentCreationOutput(BaseModel):
+    id: str = uuid.uuid4().hex
+    name: str = Field(description="The name of the agent.")
+    description: str = Field(description="The description of the agent.")
+    tags: str = Field(
+        description="The tags associated with the agent, example: Finance Agent, Chat Agent, Math Agent"
+    )
+    use_cases: Dict[str, str] = Field(
+        description="The use cases of the agent, example: {'use_case_1': 'Use case 1 description', 'use_case_2': 'Use case 2 description'}"
+    )
+    created_at: int = time.time()
+    owned_by: str = "TGSC"
+
+
+class AllAgentsSchema(BaseModel):
+    agents: List[AgentCreationOutput] = Field(
+        description="The list of agents available."
+    )