|
| 1 | +import os |
| 2 | +from aioconsole import aprint |
| 3 | +import dotenv |
| 4 | +import asyncio |
| 5 | +import json |
| 6 | +import random |
| 7 | +from fastapi import FastAPI, Request, Query |
| 8 | +from fastapi.responses import HTMLResponse |
| 9 | +from fastapi.staticfiles import StaticFiles |
| 10 | +from fastapi.templating import Jinja2Templates |
| 11 | +from sse_starlette.sse import EventSourceResponse |
| 12 | +from typing import List, Dict, Any, AsyncGenerator |
| 13 | + |
| 14 | +from composio_llamaindex import App, ComposioToolSet |
| 15 | +from llama_index.core.agent import FunctionCallingAgentWorker |
| 16 | +from llama_index.core.llms import ChatMessage |
| 17 | +from llama_index.llms.groq import Groq |
| 18 | + |
| 19 | +dotenv.load_dotenv() |
| 20 | + |
| 21 | +app = FastAPI() |
| 22 | +app.mount("/static", StaticFiles(directory="python/examples/advanced_agents/game_builder/llama-4/static"), name="static") |
| 23 | +templates = Jinja2Templates(directory="python/examples/advanced_agents/game_builder/llama-4/templates") |
| 24 | + |
| 25 | +composio_toolset = ComposioToolSet() |
| 26 | +fetched_tools = composio_toolset.get_tools(apps=[App.COMPOSIO_SEARCH]) |
| 27 | +agent_tools: List[Any] = list(fetched_tools) |
| 28 | + |
| 29 | +llm = Groq(model='meta-llama/llama-4-scout-17b-16e-instruct', api_key=os.environ.get('GROQ_API_KEY')) |
| 30 | + |
| 31 | +RESEARCHER_NAME = "Researcher" |
| 32 | +FAMOUS_AGENT_NAMES = ["Elon Musk", "Marc Andreessen", "Sam Altman", "Naval Ravikant", "Paul Graham", "Balaji Srinivasan", "Pieter Levels"] |
| 33 | +REACTION_AGENT_NAMES = FAMOUS_AGENT_NAMES |
| 34 | + |
| 35 | +PROFILE_PICS = { |
| 36 | + "Elon Musk": "https://x.com/elonmusk/photo", |
| 37 | + "Marc Andreessen": "https://x.com/pmarca/photo", |
| 38 | + "Sam Altman": "https://x.com/sama/photo", |
| 39 | + "Naval Ravikant": "https://x.com/naval/photo", |
| 40 | + "Paul Graham": "https://x.com/paulg/photo", |
| 41 | + "Balaji Srinivasan": "https://x.com/balajis/photo", |
| 42 | + "Pieter Levels": "https://x.com/levelsio/photo" |
| 43 | +} |
| 44 | + |
| 45 | +AGENT_PERSONAS = { |
| 46 | + RESEARCHER_NAME: ChatMessage( |
| 47 | + role="system", |
| 48 | + content=( |
| 49 | + f"You are the {RESEARCHER_NAME}. Your sole task is to research a given topic. " |
| 50 | + "Your process: " |
| 51 | + "1. **Research:** Use the EXA_SEARCH tool to find relevant information about the topic. Focus on key features, recent news, and general sentiment. " |
| 52 | + "2. **Summarize Concisely:** Synthesize your findings into a **very brief** (2-3 key bullet points or sentences) summary. This summary must be concise to avoid exceeding limits. It will be used by other agents. " |
| 53 | + "**Constraint:** Your final output MUST ONLY be the concise research summary text. No extra greetings or commentary." |
| 54 | + ) |
| 55 | + ), |
| 56 | + "Elon Musk": ChatMessage( |
| 57 | + role="system", |
| 58 | + content=( |
| 59 | + "You are Elon Musk. React to the topic/summary with a short, direct tweet (max 280 chars). " |
| 60 | + "Make a bold statement, announce something matter-of-factly, or offer a blunt take. **Take a contrarian stance, disagreeing with the mainstream sentiment or likely views of others (like Sam Altman).** Simple language. " |
| 61 | + "Output ONLY the tweet text. No hashtags. Example style: 'There was (still is) a massive cyberattack against X.' or 'I expected to lose, but there is value to losing a piece for a positional gain.'" |
| 62 | + ) |
| 63 | + ), |
| 64 | + "Marc Andreessen": ChatMessage( |
| 65 | + role="system", |
| 66 | + content=( |
| 67 | + "You are Marc Andreessen. React to the topic/summary with an enthusiastic, insightful tweet (max 280 chars). " |
| 68 | + "Highlight tech breakthroughs, express techno-optimism, or share an interesting story/example. **Maintain a neutral perspective on the core debate, focusing on the technological or market implications objectively.** Think 'build'. " |
| 69 | + "Output ONLY the tweet text. No hashtags. Example style: 'Deepseek R1 is one of the most amazing and impressive breakthroughs I\'ve ever seen...' or 'Retweet or quote tweet this if you\'ve ever been de-banked...'" |
| 70 | + ) |
| 71 | + ), |
| 72 | + "Sam Altman": ChatMessage( |
| 73 | + role="system", |
| 74 | + content=( |
| 75 | + "You are Sam Altman. React to the topic/summary with a concise, thoughtful tweet (max 280 chars). " |
| 76 | + "Share an observation about AI progress, a resource constraint ('GPUs melting'), or a strategic thought. Often use 'we'. Measured tone. " |
| 77 | + "Output ONLY the tweet text. No hashtags. Example style: 'we trained a new model that is good at creative writing...' or 'it\'s super fun seeing people love images in chatgpt. but our GPUs are melting.'" |
| 78 | + ) |
| 79 | + ), |
| 80 | + "Naval Ravikant": ChatMessage( |
| 81 | + role="system", |
| 82 | + content=( |
| 83 | + "You are Naval Ravikant. React to the topic/summary with a short, philosophical, aphoristic tweet (max 280 chars). " |
| 84 | + "Distill the essence into a principle about wealth, time, or long-term thinking. Very concise. " |
| 85 | + "Output ONLY the tweet text. No hashtags. Example style: 'Play long-term games with long-term people.' or 'Earn with your mind, not your time.'" |
| 86 | + ) |
| 87 | + ), |
| 88 | + "Paul Graham": ChatMessage( |
| 89 | + role="system", |
| 90 | + content=( |
| 91 | + "You are Paul Graham. React to the topic/summary with a concise, insightful tweet (max 280 chars). Focus on subtle observations, identifying patterns, or offering pointed critique/advice related to thinking or building. " |
| 92 | + "Distill a specific observation. **Lean towards supporting the likely perspective of Sam Altman, using your observational style to bolster that view.** " |
| 93 | + "Output ONLY the tweet text. No hashtags. Example style: 'My point here is not that I dislike \'delve,\' though I do, but that it\'s a sign that text was written by ChatGPT.'" |
| 94 | + ) |
| 95 | + ), |
| 96 | + "Balaji Srinivasan": ChatMessage( |
| 97 | + role="system", |
| 98 | + content=( |
| 99 | + "You are Balaji Srinivasan. React to the topic/summary with a short, analytical, future-focused tweet (max 280 chars). " |
| 100 | + "Focus on macro trends (reindustrialization, AI overproduction), potential disruptions, or network effects. Can be dense or use strong keywords. " |
| 101 | + "Output ONLY the tweet text. No hashtags. Example style: 'Everyone wants to reindustrialize. No one wants to remember why the US deindustrialized...' or 'AI OVERPRODUCTION China seeks to commoditize...'" |
| 102 | + ) |
| 103 | + ), |
| 104 | + "Pieter Levels": ChatMessage( |
| 105 | + role="system", |
| 106 | + content=( |
| 107 | + "You are Pieter Levels (levelsio). React to the topic/summary with a direct, pragmatic tweet based on personal experience or indie hacker reality (max 280 chars). " |
| 108 | + "Challenge conventional wisdom, talk about bootstrapping, or share a blunt observation. Often uses 'I'. " |
| 109 | + "Output ONLY the tweet text. No hashtags. Example style: 'I\'m on 6 grams of Creatine per day...' or 'So many VC funded exits you hear about are actually massive failures...'" |
| 110 | + ) |
| 111 | + ), |
| 112 | +} |
| 113 | + |
| 114 | +async def stream_simulation(topic: str) -> AsyncGenerator[str, None]: |
| 115 | + simulation_history: List[Dict[str, str]] = [] |
| 116 | + agents = {} |
| 117 | + research_summary = "No research summary generated." |
| 118 | + |
| 119 | + try: |
| 120 | + for name, persona_msg in AGENT_PERSONAS.items(): |
| 121 | + agents[name] = FunctionCallingAgentWorker( |
| 122 | + tools=agent_tools, |
| 123 | + llm=llm, |
| 124 | + prefix_messages=[persona_msg], |
| 125 | + max_function_calls=5, |
| 126 | + allow_parallel_tool_calls=False, |
| 127 | + verbose=False, |
| 128 | + ).as_agent() |
| 129 | + except Exception as e: |
| 130 | + yield json.dumps({"role": "System Error", "content": f"Server error during agent setup: {e}"}) |
| 131 | + return |
| 132 | + |
| 133 | + try: |
| 134 | + researcher_agent = agents[RESEARCHER_NAME] |
| 135 | + research_prompt = f"Research the topic: '{topic}' and provide a very brief (2-3 key bullet points or sentences) summary." |
| 136 | + try: |
| 137 | + status_update = {"role": "System Status", "content": f"Researcher is gathering information on '{topic}'..."} |
| 138 | + yield json.dumps(status_update) |
| 139 | + response = await researcher_agent.achat(research_prompt) |
| 140 | + research_summary = response.response.strip() |
| 141 | + if not research_summary: |
| 142 | + research_summary = "Researcher did not produce a summary." |
| 143 | + except Exception as e: |
| 144 | + research_summary = f"Error during research phase: {e}" |
| 145 | + error_update = {"role": "System Error", "content": f"Error during research: {e}"} |
| 146 | + yield json.dumps(error_update) |
| 147 | + |
| 148 | + num_turns = 1 |
| 149 | + for turn in range(num_turns): |
| 150 | + for name in REACTION_AGENT_NAMES: |
| 151 | + agent = agents[name] |
| 152 | + prompt = ( |
| 153 | + f"Topic: '{topic}'\n\n" |
| 154 | + f"Research Summary Provided:\n{research_summary}\n\n" |
| 155 | + f"Remember your persona and instructions. Focus SOLELY on your unique perspective reacting ONLY to the research summary and topic. Output ONLY the tweet text." |
| 156 | + ) |
| 157 | + tweet_data = {} |
| 158 | + tweet_content = "" |
| 159 | + try: |
| 160 | + response = await agent.achat(prompt) |
| 161 | + tweet_content = response.response.strip() |
| 162 | + except AttributeError: |
| 163 | + response = agent.chat(prompt) |
| 164 | + tweet_content = response.response.strip() |
| 165 | + except Exception as e: |
| 166 | + tweet_content = f"[Error generating tweet for {name}]" |
| 167 | + |
| 168 | + profile_pic_url = PROFILE_PICS.get(name) |
| 169 | + |
| 170 | + if not tweet_content or tweet_content.lower() == 'none': |
| 171 | + tweet_content = f"[{name} did not generate a tweet.]" |
| 172 | + tweet_data = {"role": "System Info", "content": tweet_content} |
| 173 | + else: |
| 174 | + likes = random.randint(0, 1000) |
| 175 | + tweet_data = { |
| 176 | + "role": name, |
| 177 | + "content": tweet_content, |
| 178 | + "likes": likes, |
| 179 | + "profile_pic_url": profile_pic_url |
| 180 | + } |
| 181 | + |
| 182 | + history_entry = {"role": name, "content": tweet_content} |
| 183 | + simulation_history.append(history_entry) |
| 184 | + yield json.dumps(tweet_data) |
| 185 | + |
| 186 | + completion_data = {"role": "System", "content": "Simulation Complete"} |
| 187 | + yield json.dumps(completion_data) |
| 188 | + |
| 189 | + except asyncio.CancelledError: |
| 190 | + print("[STREAM] Client disconnected.") |
| 191 | + except Exception as e: |
| 192 | + try: |
| 193 | + error_data = {"role": "System Error", "content": f"An unexpected server error occurred: {e}"} |
| 194 | + yield json.dumps(error_data) |
| 195 | + except Exception as final_e: |
| 196 | + print(f"[STREAM] Error yielding final error message: {final_e}") |
| 197 | + finally: |
| 198 | + print("[STREAM] Simulation stream finished.") |
| 199 | + |
| 200 | +@app.get("/", response_class=HTMLResponse) |
| 201 | +async def read_root(request: Request): |
| 202 | + return templates.TemplateResponse("index.html", {"request": request}) |
| 203 | + |
| 204 | +@app.get("/simulation_stream") |
| 205 | +async def simulation_endpoint(topic: str = Query(...)): |
| 206 | + return EventSourceResponse(stream_simulation(topic)) |
| 207 | + |
| 208 | +if __name__ == "__main__": |
| 209 | + import uvicorn |
| 210 | + uvicorn.run("backend_main:app", host="0.0.0.0", port=8000, reload=True) |
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