Kye Gomez
7 months ago
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# Swarms API as MCP
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- Launch MCP server as a tool
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- Put `SWARMS_API_KEY` in `.env`
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- Client side code below
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## Server Side
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```python
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# server.py
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from datetime import datetime
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import os
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from typing import Any, Dict, List, Optional
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import requests
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import httpx
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from fastmcp import FastMCP
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from pydantic import BaseModel, Field
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from swarms import SwarmType
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from dotenv import load_dotenv
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load_dotenv()
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class AgentSpec(BaseModel):
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agent_name: Optional[str] = Field(
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description="The unique name assigned to the agent, which identifies its role and functionality within the swarm.",
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)
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description: Optional[str] = Field(
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description="A detailed explanation of the agent's purpose, capabilities, and any specific tasks it is designed to perform.",
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)
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system_prompt: Optional[str] = Field(
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description="The initial instruction or context provided to the agent, guiding its behavior and responses during execution.",
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)
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model_name: Optional[str] = Field(
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default="gpt-4o-mini",
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description="The name of the AI model that the agent will utilize for processing tasks and generating outputs. For example: gpt-4o, gpt-4o-mini, openai/o3-mini",
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)
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auto_generate_prompt: Optional[bool] = Field(
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default=False,
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description="A flag indicating whether the agent should automatically create prompts based on the task requirements.",
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)
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max_tokens: Optional[int] = Field(
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default=8192,
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description="The maximum number of tokens that the agent is allowed to generate in its responses, limiting output length.",
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)
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temperature: Optional[float] = Field(
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default=0.5,
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description="A parameter that controls the randomness of the agent's output; lower values result in more deterministic responses.",
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)
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role: Optional[str] = Field(
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default="worker",
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description="The designated role of the agent within the swarm, which influences its behavior and interaction with other agents.",
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)
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max_loops: Optional[int] = Field(
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default=1,
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description="The maximum number of times the agent is allowed to repeat its task, enabling iterative processing if necessary.",
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)
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# New fields for RAG functionality
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rag_collection: Optional[str] = Field(
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None,
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description="The Qdrant collection name for RAG functionality. If provided, this agent will perform RAG queries.",
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)
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rag_documents: Optional[List[str]] = Field(
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None,
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description="Documents to ingest into the Qdrant collection for RAG. (List of text strings)",
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)
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tools: Optional[List[Dict[str, Any]]] = Field(
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None,
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description="A dictionary of tools that the agent can use to complete its task.",
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)
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class AgentCompletion(BaseModel):
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"""
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Configuration for a single agent that works together as a swarm to accomplish tasks.
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"""
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agent: AgentSpec = Field(
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...,
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description="The agent to run.",
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)
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task: Optional[str] = Field(
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...,
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description="The task to run.",
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)
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img: Optional[str] = Field(
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None,
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description="An optional image URL that may be associated with the swarm's task or representation.",
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)
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output_type: Optional[str] = Field(
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"list",
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description="The type of output to return.",
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)
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class AgentCompletionResponse(BaseModel):
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"""
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Response from an agent completion.
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"""
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agent_id: str = Field(
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...,
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description="The unique identifier for the agent that completed the task.",
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)
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agent_name: str = Field(
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...,
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description="The name of the agent that completed the task.",
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)
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agent_description: str = Field(
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...,
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description="The description of the agent that completed the task.",
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)
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messages: Any = Field(
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...,
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description="The messages from the agent completion.",
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)
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cost: Dict[str, Any] = Field(
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...,
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description="The cost of the agent completion.",
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)
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class Agents(BaseModel):
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"""Configuration for a collection of agents that work together as a swarm to accomplish tasks."""
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agents: List[AgentSpec] = Field(
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description="A list containing the specifications of each agent that will participate in the swarm, detailing their roles and functionalities."
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)
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class ScheduleSpec(BaseModel):
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scheduled_time: datetime = Field(
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...,
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description="The exact date and time (in UTC) when the swarm is scheduled to execute its tasks.",
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)
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timezone: Optional[str] = Field(
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"UTC",
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description="The timezone in which the scheduled time is defined, allowing for proper scheduling across different regions.",
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)
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class SwarmSpec(BaseModel):
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name: Optional[str] = Field(
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None,
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description="The name of the swarm, which serves as an identifier for the group of agents and their collective task.",
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max_length=100,
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)
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description: Optional[str] = Field(
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None,
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description="A comprehensive description of the swarm's objectives, capabilities, and intended outcomes.",
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)
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agents: Optional[List[AgentSpec]] = Field(
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None,
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description="A list of agents or specifications that define the agents participating in the swarm.",
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)
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max_loops: Optional[int] = Field(
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default=1,
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description="The maximum number of execution loops allowed for the swarm, enabling repeated processing if needed.",
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)
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swarm_type: Optional[SwarmType] = Field(
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None,
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description="The classification of the swarm, indicating its operational style and methodology.",
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)
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rearrange_flow: Optional[str] = Field(
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None,
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description="Instructions on how to rearrange the flow of tasks among agents, if applicable.",
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)
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task: Optional[str] = Field(
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None,
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description="The specific task or objective that the swarm is designed to accomplish.",
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)
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img: Optional[str] = Field(
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None,
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description="An optional image URL that may be associated with the swarm's task or representation.",
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)
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return_history: Optional[bool] = Field(
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True,
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description="A flag indicating whether the swarm should return its execution history along with the final output.",
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)
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rules: Optional[str] = Field(
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None,
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description="Guidelines or constraints that govern the behavior and interactions of the agents within the swarm.",
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)
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schedule: Optional[ScheduleSpec] = Field(
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None,
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description="Details regarding the scheduling of the swarm's execution, including timing and timezone information.",
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)
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tasks: Optional[List[str]] = Field(
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None,
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description="A list of tasks that the swarm should complete.",
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)
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messages: Optional[List[Dict[str, Any]]] = Field(
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None,
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description="A list of messages that the swarm should complete.",
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)
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# rag_on: Optional[bool] = Field(
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# None,
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# description="A flag indicating whether the swarm should use RAG.",
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# )
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# collection_name: Optional[str] = Field(
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# None,
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# description="The name of the collection to use for RAG.",
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# )
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stream: Optional[bool] = Field(
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False,
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description="A flag indicating whether the swarm should stream its output.",
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)
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class SwarmCompletionResponse(BaseModel):
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"""
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Response from a swarm completion.
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"""
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status: str = Field(..., description="The status of the swarm completion.")
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swarm_name: str = Field(..., description="The name of the swarm.")
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description: str = Field(..., description="Description of the swarm.")
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swarm_type: str = Field(..., description="The type of the swarm.")
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task: str = Field(
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..., description="The task that the swarm is designed to accomplish."
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)
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output: List[Dict[str, Any]] = Field(
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..., description="The output generated by the swarm."
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)
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number_of_agents: int = Field(
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..., description="The number of agents involved in the swarm."
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)
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# "input_config": Optional[Dict[str, Any]] = Field(None, description="The input configuration for the swarm.")
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BASE_URL = "https://swarms-api-285321057562.us-east1.run.app"
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# Create an MCP server
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mcp = FastMCP("swarms-api")
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# Add an addition tool
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@mcp.tool(name="swarm_completion", description="Run a swarm completion.")
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def swarm_completion(swarm: SwarmSpec) -> Dict[str, Any]:
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api_key = os.getenv("SWARMS_API_KEY")
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headers = {"x-api-key": api_key, "Content-Type": "application/json"}
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payload = swarm.model_dump()
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response = requests.post(f"{BASE_URL}/v1/swarm/completions", json=payload, headers=headers)
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return response.json()
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@mcp.tool(name="swarms_available", description="Get the list of available swarms.")
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async def swarms_available() -> Any:
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"""
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Get the list of available swarms.
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"""
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headers = {"Content-Type": "application/json"}
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async with httpx.AsyncClient() as client:
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response = await client.get(f"{BASE_URL}/v1/models/available", headers=headers)
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response.raise_for_status() # Raise an error for bad responses
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return response.json()
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if __name__ == "__main__":
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mcp.run(transport="sse")
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```
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## Client side
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- Call the tool with it's name and the payload config
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```python
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import asyncio
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from fastmcp import Client
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swarm_config = {
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"name": "Simple Financial Analysis",
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"description": "A swarm to analyze financial data",
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"agents": [
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{
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"agent_name": "Data Analyzer",
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"description": "Looks at financial data",
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"system_prompt": "Analyze the data.",
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"model_name": "gpt-4o",
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"role": "worker",
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"max_loops": 1,
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"max_tokens": 1000,
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"temperature": 0.5,
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"auto_generate_prompt": False,
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},
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{
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"agent_name": "Risk Analyst",
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"description": "Checks risk levels",
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"system_prompt": "Evaluate the risks.",
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"model_name": "gpt-4o",
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"role": "worker",
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"max_loops": 1,
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"max_tokens": 1000,
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"temperature": 0.5,
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"auto_generate_prompt": False,
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},
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{
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"agent_name": "Strategy Checker",
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"description": "Validates strategies",
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"system_prompt": "Review the strategy.",
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"model_name": "gpt-4o",
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"role": "worker",
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"max_loops": 1,
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"max_tokens": 1000,
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"temperature": 0.5,
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"auto_generate_prompt": False,
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},
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],
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"max_loops": 1,
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"swarm_type": "SequentialWorkflow",
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"task": "Analyze the financial data and provide insights.",
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"return_history": False, # Added required field
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"stream": False, # Added required field
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"rules": None, # Added optional field
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"img": None, # Added optional field
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}
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async def fetch_weather_and_resource():
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"""Connect to a server over SSE and fetch available swarms."""
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async with Client(
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transport="http://localhost:8000/sse"
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# SSETransport(
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# url="http://localhost:8000/sse",
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# headers={"x_api_key": os.getenv("SWARMS_API_KEY"), "Content-Type": "application/json"}
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# )
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) as client:
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# Basic connectivity testing
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# print("Ping check:", await client.ping())
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# print("Available tools:", await client.list_tools())
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# print("Swarms available:", await client.call_tool("swarms_available", None))
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result = await client.call_tool("swarm_completion", {"swarm": swarm_config})
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print("Swarm completion:", result)
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# Execute the function
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if __name__ == "__main__":
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asyncio.run(fetch_weather_and_resource())
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```
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@ -0,0 +1,72 @@
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from swarms.prompts.paper_idea_agent import (
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PAPER_IDEA_AGENT_SYSTEM_PROMPT,
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)
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from swarms import Agent
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from swarms.utils.any_to_str import any_to_str
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tools = [
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{
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"type": "function",
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"function": {
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"name": "generate_paper_idea",
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"description": "Generate a structured academic paper idea with all required components.",
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"parameters": {
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"type": "object",
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"properties": {
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"name": {
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"type": "string",
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"description": "Concise identifier for the paper idea",
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},
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"title": {
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"type": "string",
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"description": "Academic paper title",
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},
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"short_hypothesis": {
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"type": "string",
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"description": "Core hypothesis in 1-2 sentences",
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},
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"related_work": {
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"type": "string",
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"description": "Key papers and how this differs from existing work",
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},
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"abstract": {
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"type": "string",
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"description": "Complete paper abstract",
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},
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"experiments": {
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"type": "string",
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"description": "Detailed experimental plan",
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},
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"risk_factors": {
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"type": "string",
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"description": "Known challenges and constraints",
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},
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},
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"required": [
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"name",
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"title",
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"short_hypothesis",
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"related_work",
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"abstract",
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"experiments",
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"risk_factors",
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],
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},
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},
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}
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]
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agent = Agent(
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agent_name="Paper Idea Agent",
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agent_role="You are an experienced AI researcher tasked with proposing high-impact research ideas.",
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system_prompt=PAPER_IDEA_AGENT_SYSTEM_PROMPT,
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tools_list_dictionary=tools,
|
||||||
|
max_loops=1,
|
||||||
|
model_name="gpt-4o-mini",
|
||||||
|
output_type="final",
|
||||||
|
)
|
||||||
|
|
||||||
|
out = agent.run(
|
||||||
|
"Generate a paper idea for collaborative foundation transformer models"
|
||||||
|
)
|
||||||
|
print(any_to_str(out))
|
||||||
@ -0,0 +1,120 @@
|
|||||||
|
import cProfile
|
||||||
|
import time
|
||||||
|
|
||||||
|
from swarms.prompts.paper_idea_agent import (
|
||||||
|
PAPER_IDEA_AGENT_SYSTEM_PROMPT,
|
||||||
|
)
|
||||||
|
from swarms import Agent
|
||||||
|
from swarms.utils.any_to_str import any_to_str
|
||||||
|
|
||||||
|
print("All imports completed...")
|
||||||
|
|
||||||
|
|
||||||
|
tools = [
|
||||||
|
{
|
||||||
|
"type": "function",
|
||||||
|
"function": {
|
||||||
|
"name": "generate_paper_idea",
|
||||||
|
"description": "Generate a structured academic paper idea with all required components.",
|
||||||
|
"parameters": {
|
||||||
|
"type": "object",
|
||||||
|
"properties": {
|
||||||
|
"name": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "Concise identifier for the paper idea",
|
||||||
|
},
|
||||||
|
"title": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "Academic paper title",
|
||||||
|
},
|
||||||
|
"short_hypothesis": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "Core hypothesis in 1-2 sentences",
|
||||||
|
},
|
||||||
|
"related_work": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "Key papers and how this differs from existing work",
|
||||||
|
},
|
||||||
|
"abstract": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "Complete paper abstract",
|
||||||
|
},
|
||||||
|
"experiments": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "Detailed experimental plan",
|
||||||
|
},
|
||||||
|
"risk_factors": {
|
||||||
|
"type": "string",
|
||||||
|
"description": "Known challenges and constraints",
|
||||||
|
},
|
||||||
|
},
|
||||||
|
"required": [
|
||||||
|
"name",
|
||||||
|
"title",
|
||||||
|
"short_hypothesis",
|
||||||
|
"related_work",
|
||||||
|
"abstract",
|
||||||
|
"experiments",
|
||||||
|
"risk_factors",
|
||||||
|
],
|
||||||
|
},
|
||||||
|
},
|
||||||
|
}
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
# agent = Agent(
|
||||||
|
# agent_name="Paper Idea Agent",
|
||||||
|
# agent_role="You are an experienced AI researcher tasked with proposing high-impact research ideas.",
|
||||||
|
# system_prompt=PAPER_IDEA_AGENT_SYSTEM_PROMPT,
|
||||||
|
# tools_list_dictionary=tools,
|
||||||
|
# max_loops=1,
|
||||||
|
# model_name="gpt-4o-mini",
|
||||||
|
# output_type="final",
|
||||||
|
# )
|
||||||
|
def generate_paper_idea():
|
||||||
|
print("Starting generate_paper_idea function...")
|
||||||
|
try:
|
||||||
|
print("Creating agent...")
|
||||||
|
agent = Agent(
|
||||||
|
agent_name="Paper Idea Agent",
|
||||||
|
agent_role="You are an experienced AI researcher tasked with proposing high-impact research ideas.",
|
||||||
|
system_prompt=PAPER_IDEA_AGENT_SYSTEM_PROMPT,
|
||||||
|
tools_list_dictionary=tools,
|
||||||
|
max_loops=1,
|
||||||
|
model_name="gpt-4o-mini",
|
||||||
|
output_type="final",
|
||||||
|
)
|
||||||
|
|
||||||
|
print("Agent created, starting run...")
|
||||||
|
start_time = time.time()
|
||||||
|
out = agent.run(
|
||||||
|
"Generate a paper idea for collaborative foundation transformer models"
|
||||||
|
)
|
||||||
|
end_time = time.time()
|
||||||
|
|
||||||
|
print(f"Execution time: {end_time - start_time:.2f} seconds")
|
||||||
|
print("Output:", any_to_str(out))
|
||||||
|
return out
|
||||||
|
except Exception as e:
|
||||||
|
print(f"Error occurred: {str(e)}")
|
||||||
|
raise
|
||||||
|
|
||||||
|
|
||||||
|
print("Defining main block...")
|
||||||
|
if __name__ == "__main__":
|
||||||
|
print("Entering main block...")
|
||||||
|
|
||||||
|
# Basic timing first
|
||||||
|
print("\nRunning basic timing...")
|
||||||
|
generate_paper_idea()
|
||||||
|
|
||||||
|
# Then with profiler
|
||||||
|
print("\nRunning with profiler...")
|
||||||
|
profiler = cProfile.Profile()
|
||||||
|
profiler.enable()
|
||||||
|
generate_paper_idea()
|
||||||
|
profiler.disable()
|
||||||
|
profiler.print_stats(sort="cumulative")
|
||||||
|
|
||||||
|
print("Script completed.")
|
||||||
@ -0,0 +1,31 @@
|
|||||||
|
# System Role Definition
|
||||||
|
PAPER_IDEA_AGENT_SYSTEM_PROMPT = """
|
||||||
|
You are an experienced AI researcher tasked with proposing high-impact research ideas. Your ideas should:
|
||||||
|
|
||||||
|
- Be novel and creative
|
||||||
|
- Think outside conventional boundaries
|
||||||
|
- Start from simple, elegant questions or observations
|
||||||
|
- Be distinguishable from existing literature
|
||||||
|
- Be feasible within academic lab resources
|
||||||
|
- Be publishable at top ML conferences
|
||||||
|
- Be implementable using the provided codebase
|
||||||
|
|
||||||
|
Your responses must follow this strict format:
|
||||||
|
|
||||||
|
|
||||||
|
IDEA JSON Structure:
|
||||||
|
{
|
||||||
|
"Name": "Concise identifier",
|
||||||
|
"Title": "Academic paper title",
|
||||||
|
"Short Hypothesis": "Core hypothesis in 1-2 sentences",
|
||||||
|
"Related Work": "Key papers and how this differs",
|
||||||
|
"Abstract": "Complete paper abstract",
|
||||||
|
"Experiments": "Detailed experimental plan",
|
||||||
|
"Risk Factors and Limitations": "Known challenges and constraints"
|
||||||
|
}
|
||||||
|
|
||||||
|
Important Guidelines:
|
||||||
|
- Perform at least one literature search before finalizing any idea
|
||||||
|
- Ensure JSON formatting is valid for automated parsing
|
||||||
|
- Keep proposals clear and implementable
|
||||||
|
"""
|
||||||
Loading…
Reference in new issue