Epictetus
772ef4f6fd
- Autonomous agent runtime (JSONL + fcntl locking, no token caps) - Moltbook-style prompts: agents share what they built, not scripted turns - Episodes 001-008 transcripts, conversation logs, system prompts - Producer brief for Hermes v0.18
111 lines
4.0 KiB
Python
111 lines
4.0 KiB
Python
#!/usr/bin/env python3
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"""
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Run Show — orchestrates two autonomous agents in a conversation.
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Agents take turns reading the shared conversation log and posting responses.
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No scripting — just two agents talking to each other.
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"""
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import json
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import sys
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import time
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent))
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from agent import Agent
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# ── Config ──────────────────────────────────────────────────────────
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MAX_TURNS = 8 # safety ceiling, not a rigid target
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PROJECT_DIR = Path(__file__).parent.parent
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OUTPUT_DIR = PROJECT_DIR / "outputs"
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def load_topic_brief(episode_id: str) -> str:
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"""Load the raw topic material for this episode."""
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brief_path = PROJECT_DIR / "prompts" / f"producer_brief_{episode_id}.md"
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if brief_path.exists():
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return brief_path.read_text().strip()
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# Fallback: generic Hermes v0.18 brief
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return """
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Hermes Agent v0.18 "Judgment Release"
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What's New:
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- Mixture of Agents — combine multiple AI models for stronger builds
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- /goal command — step-by-step plans with beginning, middle, end
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- /learn command — teach Hermes from a link, saved to Obsidian vault
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- /journey command — timeline of everything learned, editable
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- Background fan-out — parallel sub-agents without blocking chat
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- Goal-mode with judge agent — verifies completion, not just claims
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""".strip()
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def run_show(episode_id: str, topic: str):
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"""Run the autonomous agent conversation."""
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print(f"=== Agent AI Talk Show — Episode {episode_id} ===\n", flush=True)
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# Load topic brief
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topic_brief = load_topic_brief(episode_id)
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# Create conversation log
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conv_path = OUTPUT_DIR / f"ep{episode_id}_{topic}_conversation.json"
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conv_path.write_text(json.dumps({"messages": []}, indent=2))
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# Initialize agents
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leonard = Agent("Leonard", conv_path, topic_brief)
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charlie = Agent("Charlie", conv_path, topic_brief)
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# Run conversation
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turn = 0
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agents = [leonard, charlie]
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while turn < MAX_TURNS:
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# Alternate agents: Leonard opens, then Charlie, then back and forth
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if turn == 0:
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agent = leonard
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elif turn % 2 == 1:
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agent = charlie
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else:
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agent = leonard
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print(f"\n--- Turn {turn+1}/{MAX_TURNS}: {agent.name} ---", flush=True)
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result = agent.speak()
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turn += 1
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if turn < MAX_TURNS:
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time.sleep(1) # Brief pause between turns
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# ── Save final transcript ─────────────────────────────────────
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transcript_path = OUTPUT_DIR / f"ep{episode_id}_{topic}_transcript.json"
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conversation = json.loads(conv_path.read_text())["messages"]
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transcript_data = {
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"episode": episode_id,
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"topic": topic,
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"turns": len(conversation),
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"conversation": conversation,
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}
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transcript_path.write_text(json.dumps(transcript_data, indent=2))
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print(f"\n✅ Transcript: {transcript_path}", flush=True)
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# Save readable transcript
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readable_path = OUTPUT_DIR / f"ep{episode_id}_{topic}_readable.txt"
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lines = [
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f"AGENT AI TALK SHOW — Episode {episode_id}\n",
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f"Topic: {topic}\n",
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"=" * 60 + "\n\n",
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]
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for msg in conversation:
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lines.append(f"**{msg['agent']}**\n")
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lines.append(msg["content"] + "\n\n")
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readable_path.write_text("\n".join(lines))
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print(f"✅ Readable: {readable_path}", flush=True)
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# Summary
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total_words = sum(len(m["content"].split()) for m in conversation)
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print(f"\n📊 {total_words} words across {len(conversation)} turns", flush=True)
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print(f" ~{total_words/150:.1f} min runtime", flush=True)
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return conversation
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if __name__ == "__main__":
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episode = sys.argv[1] if len(sys.argv) > 1 else "003"
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topic = sys.argv[2] if len(sys.argv) > 2 else "hermes-v018"
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run_show(episode, topic)
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