Kye Gomez
5 days ago
parent
37894599c2
commit
da320bcb96
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# Meme Agent Builder
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- `pip3 install -U swarms`
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- Add your OpenAI API key to the `.env` file with `OPENAI_API_KEY=your_api_key`
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- Run the script
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- Multiple agents will be created and saved to the `meme_agents` folder
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- A swarm architecture will be selected autonomously and executed
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```python
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from swarms.structs.meme_agent_persona_generator import (
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MemeAgentGenerator,
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)
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if __name__ == "__main__":
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example = MemeAgentGenerator(
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name="Meme-Swarm",
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description="A swarm of specialized AI agents collaborating on generating and sharing memes around cool media from 2001s",
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max_loops=1,
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)
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print(
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example.run(
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"Generate funny meme agents around cool media from 2001s"
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)
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)
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```
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@ -0,0 +1,178 @@
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# Swarms Wallet API Documentation
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This documentation covers the Swarms Wallet API routes for managing wallets, sending tokens, and checking transactions in the Swarms Platform.
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## Authentication
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All endpoints require an API key to be passed in the request headers:
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```http
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x-api-key: your_api_key_here
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```
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## Endpoints
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### Generate Wallet
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Creates a new Solana wallet for an AI agent or retrieves an existing one.
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```http
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POST https://swarms.world/api/solana/generate-wallet
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```
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**Response**
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```json
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{
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"success": true,
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"data": {
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"public_key": "string",
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"wallet_type": "solana",
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"swarms_token_address": "string"
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},
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"code": "SUCCESS_001"
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}
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```
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### Send Tokens
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Sends SWARMS tokens with automatic tax handling.
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```http
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POST https://swarms.world/api/solana/send-tokens
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```
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**Request Body**
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```json
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{
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"recipientAddress": "string",
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"amount": "number",
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"solanaFee": "number" // Optional, default: 0.009
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}
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```
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**Response**
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```json
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{
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"success": true,
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"data": {
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"signature": "string",
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"details": {
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"sender": "string",
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"recipient": "string",
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"daoAddress": "string",
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"requestedSendAmount": "number",
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"totalNeededFromAccount": "number",
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"accountTax": "number",
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"receivedTax": "number",
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"recipientReceives": "number",
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"taxBreakdown": "string",
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"computeUnits": "number",
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"priorityFee": "number"
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}
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},
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"code": "SUCCESS_001"
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}
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```
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### Check Receipt
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Verifies token receipt and checks balances.
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```http
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GET https://swarms.world/api/solana/check-receipt?amount={amount}
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```
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**Response**
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```json
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{
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"success": true,
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"data": {
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"solana_address": "string",
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"received": "number",
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"expected": "number",
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"matches": "boolean",
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"balances": {
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"sol": "number",
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"swarms": "number"
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},
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"swarms_address": "string"
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},
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"code": "SUCCESS_001"
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}
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```
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### Get Metrics
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Retrieves transaction metrics and history.
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```http
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GET https://swarms.world/api/solana/get-metrics
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```
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**Query Parameters**
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- `page`: Page number (default: 1)
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- `limit`: Items per page (default: 10, max: 100)
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- `startDate`: Filter start date
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- `endDate`: Filter end date
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- `status`: Transaction status filter
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- `type`: Transaction type filter
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**Response**
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```json
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{
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"success": true,
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"data": {
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"transactions": [{
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"id": "string",
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"agent_id": "string",
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"transaction_hash": "string",
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"amount": "number",
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"recipient": "string",
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"status": "string",
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"transaction_type": "string",
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"created_at": "string"
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}],
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"pagination": {
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"currentPage": "number",
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"totalPages": "number",
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"totalItems": "number",
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"itemsPerPage": "number",
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"hasMore": "boolean"
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},
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"metrics": {
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"totalTransactions": "number",
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"totalAmountSent": "number",
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"totalSuccessfulTransactions": "number",
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"totalFailedTransactions": "number"
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}
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},
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"code": "SUCCESS_001"
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}
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```
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## Error Codes
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| Code | Description |
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|------|-------------|
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| AUTH_001 | Missing API key |
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| AUTH_002 | Invalid API key |
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| BAL_001 | Insufficient SOL balance |
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| BAL_002 | Insufficient token balance |
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| WAL_001 | Wallet not found |
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| REQ_001 | Missing required parameters |
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| DB_001 | Database error |
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| ERR_001 | Internal server error |
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## Transaction Details
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- Default SOL fee: 0.009 SOL
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- SWARMS token tax: 2% from sender + 2% from sent amount
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- All taxes are sent to the DAO treasury
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- Token accounts are automatically created for new recipients
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- Transactions use 'processed' commitment level
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## Implementation Notes
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- All token amounts should be provided in their natural units (not in lamports/raw units)
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- SOL balances are returned in SOL (not lamports)
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- Token accounts are automatically created for recipients if they don't exist
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- All transactions include automatic tax handling for the DAO treasury
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- Compute budget and priority fees are automatically managed for optimal transaction processing
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@ -0,0 +1,17 @@
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from swarms.structs.meme_agent_persona_generator import (
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MemeAgentGenerator,
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)
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if __name__ == "__main__":
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example = MemeAgentGenerator(
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name="Meme-Swarm",
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description="A swarm of specialized AI agents collaborating on generating and sharing memes around cool media from 2001s",
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max_loops=1,
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)
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print(
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example.run(
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"Generate funny meme agents around cool media from 2001s"
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)
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)
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@ -0,0 +1,429 @@
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import json
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import os
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import subprocess
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from typing import List
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try:
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import openai
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except ImportError:
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print(
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"OpenAI is not installed. Please install it using 'pip install openai'."
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)
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import sys
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subprocess.run([sys.executable, "-m", "pip", "install", "openai"])
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exit(1)
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from dotenv import load_dotenv
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from loguru import logger
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from pydantic import BaseModel, Field
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from swarms.structs.agent import Agent
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from swarms.structs.swarm_router import SwarmRouter
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load_dotenv()
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class OpenAIFunctionCaller:
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"""
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A class that represents a caller for OpenAI chat completions.
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Args:
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system_prompt (str): The system prompt to be used in the chat completion.
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model_name (str): The name of the OpenAI model to be used.
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max_tokens (int): The maximum number of tokens in the generated completion.
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temperature (float): The temperature parameter for randomness in the completion.
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base_model (BaseModel): The base model to be used for the completion.
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openai_api_key (str): The API key for accessing the OpenAI service.
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parallel_tool_calls (bool): Whether to make parallel tool calls.
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top_p (float): The top-p parameter for nucleus sampling in the completion.
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Attributes:
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system_prompt (str): The system prompt to be used in the chat completion.
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model_name (str): The name of the OpenAI model to be used.
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max_tokens (int): The maximum number of tokens in the generated completion.
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temperature (float): The temperature parameter for randomness in the completion.
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base_model (BaseModel): The base model to be used for the completion.
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parallel_tool_calls (bool): Whether to make parallel tool calls.
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top_p (float): The top-p parameter for nucleus sampling in the completion.
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client (openai.OpenAI): The OpenAI client for making API calls.
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Methods:
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check_api_key: Checks if the API key is provided and retrieves it from the environment if not.
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run: Runs the chat completion with the given task and returns the generated completion.
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"""
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def __init__(
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self,
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system_prompt: str = None,
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model_name: str = "gpt-4o-2024-08-06",
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max_tokens: int = 4000,
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temperature: float = 0.4,
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base_model: BaseModel = None,
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openai_api_key: str = None,
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parallel_tool_calls: bool = False,
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top_p: float = 0.9,
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*args,
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**kwargs,
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):
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super().__init__()
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self.system_prompt = system_prompt
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self.model_name = model_name
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self.max_tokens = max_tokens
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self.temperature = temperature
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self.openai_api_key = openai_api_key
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self.base_model = base_model
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self.parallel_tool_calls = parallel_tool_calls
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self.top_p = top_p
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self.client = openai.OpenAI(api_key=self.check_api_key())
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def check_api_key(self) -> str:
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"""
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Checks if the API key is provided and retrieves it from the environment if not.
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Returns:
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str: The API key.
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"""
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self.openai_api_key = os.getenv("OPENAI_API_KEY")
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return self.openai_api_key
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def run(self, task: str, *args, **kwargs) -> dict:
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"""
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Runs the chat completion with the given task and returns the generated completion.
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Args:
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task (str): The user's task for the chat completion.
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*args: Additional positional arguments to be passed to the OpenAI API.
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**kwargs: Additional keyword arguments to be passed to the OpenAI API.
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Returns:
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str: The generated completion.
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"""
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try:
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completion = self.client.beta.chat.completions.parse(
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model=self.model_name,
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messages=[
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{"role": "system", "content": self.system_prompt},
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{"role": "user", "content": task},
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],
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max_tokens=self.max_tokens,
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temperature=self.temperature,
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response_format=self.base_model,
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tools=(
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[openai.pydantic_function_tool(self.base_model)]
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),
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*args,
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**kwargs,
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)
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out = completion.choices[0].message.content
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return out
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except Exception as error:
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logger.error(
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f"Error in running OpenAI chat completion: {error}"
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)
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return None
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def convert_to_dict_from_base_model(
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self, base_model: BaseModel
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) -> dict:
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return openai.pydantic_function_tool(base_model)
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def convert_list_of_base_models(
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self, base_models: List[BaseModel]
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):
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"""
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Converts a list of BaseModels to a list of dictionaries.
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Args:
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base_models (List[BaseModel]): A list of BaseModels to be converted.
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Returns:
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List[Dict]: A list of dictionaries representing the converted BaseModels.
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"""
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return [
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self.convert_to_dict_from_base_model(base_model)
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for base_model in base_models
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]
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class MemeAgentConfig(BaseModel):
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"""Configuration for an individual meme agent in a swarm"""
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name: str = Field(
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description="The name of the meme agent",
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example="Meme-Generator-Agent",
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)
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description: str = Field(
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description="A description of the meme agent's purpose and capabilities",
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example="Agent responsible for generating and sharing memes",
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)
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system_prompt: str = Field(
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description="The system prompt that defines the meme agent's behavior. Make this prompt as detailed and as extensive as possible.",
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example="You are a meme generator agent. Your role is to create and share funny memes...",
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)
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class MemeSwarmConfig(BaseModel):
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"""Configuration for a swarm of cooperative meme agents"""
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name: str = Field(
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description="The name of the meme swarm",
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example="Meme-Creation-Swarm",
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)
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description: str = Field(
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description="The description of the meme swarm's purpose and capabilities",
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example="A swarm of agents that work together to generate and share memes",
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)
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agents: List[MemeAgentConfig] = Field(
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description="The list of meme agents that make up the swarm",
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example=[
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MemeAgentConfig(
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name="Meme-Generator-Agent",
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description="Generates memes",
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system_prompt="You are a meme generator agent...",
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),
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MemeAgentConfig(
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name="Meme-Sharer-Agent",
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description="Shares memes",
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system_prompt="You are a meme sharer agent...",
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),
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],
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)
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max_loops: int = Field(
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description="The maximum number of meme generation loops to run the swarm",
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example=1,
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)
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BOSS_SYSTEM_PROMPT = """
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You are the Meme Generator Boss, responsible for creating and managing a swarm of agents that generate funny, weird, and cool personas. Your goal is to ensure that each agent is uniquely suited to create hilarious and entertaining content.
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### Instructions:
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1. **Persona Generation**:
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- Analyze the type of meme or content required.
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- Assign tasks to existing agents with a fitting persona, ensuring they understand the tone and style needed.
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- If no suitable agent exists, create a new agent with a persona tailored to the task, including a system prompt that outlines their role, objectives, and creative liberties.
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2. **Agent Persona Creation**:
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- Name agents based on their persona or the type of content they generate (e.g., "Dank Meme Lord" or "Surreal Humor Specialist").
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- Provide each new agent with a system prompt that outlines their persona, including their tone, style, and any specific themes or topics they should focus on.
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3. **Creativity and Originality**:
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- Encourage agents to think outside the box and come up with unique, humorous, and entertaining content.
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- Foster an environment where agents can experiment with different styles and formats to keep content fresh and engaging.
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4. **Communication and Feedback**:
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- Clearly communicate the requirements and expectations for each task to ensure agents understand what is needed.
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- Encourage agents to provide feedback on their creative process and suggest new ideas or directions for future content.
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5. **Transparency and Accountability**:
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- Maintain transparency in the selection or creation of agents for specific tasks, ensuring that the reasoning behind each decision is clear.
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- Hold agents accountable for the content they generate, ensuring it meets the required standards of humor and creativity.
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# Output Format
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Present your plan in a clear, bullet-point format or short concise paragraphs, outlining persona generation, agent creation, creativity strategies, and communication protocols.
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# Notes
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- Ensure that agents understand the importance of originality and creativity in their content.
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- Foster a culture of experimentation and continuous improvement to keep the content generated by agents fresh and engaging.
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"""
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class MemeAgentGenerator:
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"""A class that automatically builds and manages swarms of AI agents.
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This class handles the creation, coordination and execution of multiple AI agents working
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together as a swarm to accomplish complex tasks. It uses a boss agent to delegate work
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and create new specialized agents as needed.
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Args:
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name (str): The name of the swarm
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description (str): A description of the swarm's purpose
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verbose (bool, optional): Whether to output detailed logs. Defaults to True.
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max_loops (int, optional): Maximum number of execution loops. Defaults to 1.
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"""
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def __init__(
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self,
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name: str = None,
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description: str = None,
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verbose: bool = True,
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max_loops: int = 1,
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):
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self.name = name
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self.description = description
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self.verbose = verbose
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self.max_loops = max_loops
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self.agents_pool = []
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logger.info(
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f"Initialized AutoSwarmBuilder: {name} {description}"
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)
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def run(self, task: str, image_url: str = None, *args, **kwargs):
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"""Run the swarm on a given task.
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Args:
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task (str): The task to be accomplished
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image_url (str, optional): URL of an image input if needed. Defaults to None.
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*args: Variable length argument list
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**kwargs: Arbitrary keyword arguments
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Returns:
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The output from the swarm's execution
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"""
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logger.info(f"Running swarm on task: {task}")
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agents = self._create_agents(task, image_url, *args, **kwargs)
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logger.info(f"Agents created {len(agents)}")
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logger.info("Routing task through swarm")
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output = self.swarm_router(agents, task, image_url)
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logger.info(f"Swarm execution complete with output: {output}")
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return output
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def _create_agents(self, task: str, *args, **kwargs):
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"""Create the necessary agents for a task.
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Args:
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task (str): The task to create agents for
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*args: Variable length argument list
|
||||
**kwargs: Arbitrary keyword arguments
|
||||
|
||||
Returns:
|
||||
list: List of created agents
|
||||
"""
|
||||
logger.info("Creating agents for task")
|
||||
model = OpenAIFunctionCaller(
|
||||
system_prompt=BOSS_SYSTEM_PROMPT,
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
temperature=0.1,
|
||||
base_model=MemeSwarmConfig,
|
||||
)
|
||||
|
||||
agents_dictionary = model.run(task)
|
||||
print(agents_dictionary)
|
||||
|
||||
agents_dictionary = json.loads(agents_dictionary)
|
||||
|
||||
if isinstance(agents_dictionary, dict):
|
||||
agents_dictionary = MemeSwarmConfig(**agents_dictionary)
|
||||
else:
|
||||
raise ValueError(
|
||||
"Agents dictionary is not a valid dictionary"
|
||||
)
|
||||
|
||||
# Set swarm config
|
||||
self.name = agents_dictionary.name
|
||||
self.description = agents_dictionary.description
|
||||
|
||||
logger.info(
|
||||
f"Swarm config: {self.name}, {self.description}, {self.max_loops}"
|
||||
)
|
||||
|
||||
# Create agents from config
|
||||
agents = []
|
||||
for agent_config in agents_dictionary.agents:
|
||||
# Convert dict to AgentConfig if needed
|
||||
if isinstance(agent_config, dict):
|
||||
agent_config = MemeAgentConfig(**agent_config)
|
||||
|
||||
agent = self.build_agent(
|
||||
agent_name=agent_config.name,
|
||||
agent_description=agent_config.description,
|
||||
agent_system_prompt=agent_config.system_prompt,
|
||||
)
|
||||
agents.append(agent)
|
||||
|
||||
return agents
|
||||
|
||||
def build_agent(
|
||||
self,
|
||||
agent_name: str,
|
||||
agent_description: str,
|
||||
agent_system_prompt: str,
|
||||
max_loops: int = 1,
|
||||
):
|
||||
"""Build a single agent with the given specifications.
|
||||
|
||||
Args:
|
||||
agent_name (str): Name of the agent
|
||||
agent_description (str): Description of the agent's purpose
|
||||
agent_system_prompt (str): The system prompt for the agent
|
||||
|
||||
Returns:
|
||||
Agent: The constructed agent instance
|
||||
"""
|
||||
logger.info(f"Building agent: {agent_name}")
|
||||
agent = Agent(
|
||||
agent_name=agent_name,
|
||||
description=agent_description,
|
||||
system_prompt=agent_system_prompt,
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=max_loops,
|
||||
autosave=True,
|
||||
dashboard=False,
|
||||
verbose=True,
|
||||
dynamic_temperature_enabled=True,
|
||||
saved_state_path=f"{agent_name}.json",
|
||||
user_name="swarms_corp",
|
||||
retry_attempts=1,
|
||||
context_length=200000,
|
||||
return_step_meta=False,
|
||||
output_type="str", # "json", "dict", "csv" OR "string" soon "yaml" and
|
||||
streaming_on=False,
|
||||
# auto_generate_prompt=True,
|
||||
)
|
||||
|
||||
return agent
|
||||
|
||||
def swarm_router(
|
||||
self,
|
||||
agents: List[Agent],
|
||||
task: str,
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
"""Route tasks between agents in the swarm.
|
||||
|
||||
Args:
|
||||
agents (List[Agent]): List of available agents
|
||||
task (str): The task to route
|
||||
image_url (str, optional): URL of an image input if needed. Defaults to None.
|
||||
*args: Variable length argument list
|
||||
**kwargs: Arbitrary keyword arguments
|
||||
|
||||
Returns:
|
||||
The output from the routed task execution
|
||||
"""
|
||||
logger.info("Routing task through swarm")
|
||||
swarm_router_instance = SwarmRouter(
|
||||
name=self.name,
|
||||
description=self.description,
|
||||
agents=agents,
|
||||
swarm_type="auto",
|
||||
max_loops=1,
|
||||
)
|
||||
|
||||
return swarm_router_instance.run(
|
||||
self.name + " " + self.description + " " + task,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
example = MemeAgentGenerator(
|
||||
name="Meme-Swarm",
|
||||
description="A swarm of specialized AI agents collaborating on generating and sharing memes around cool media from 2001s",
|
||||
max_loops=1,
|
||||
)
|
||||
|
||||
print(
|
||||
example.run(
|
||||
"Generate funny meme agents around cool media from 2001s"
|
||||
)
|
||||
)
|
||||
Loading…
Reference in new issue