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
120 lines
4.3 KiB
Python
120 lines
4.3 KiB
Python
#!/usr/bin/env python3
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"""
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Autonomous Agent — base class for Leonard and Charlie.
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Each agent runs independently, reads the shared conversation log,
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generates a response, and appends it to the log.
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"""
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import json
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import time
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import sys
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import requests
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from pathlib import Path
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from datetime import datetime
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# ── Config ──────────────────────────────────────────────────────────
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BASE_URL = "http://100.64.0.2:39195/v1/chat/completions"
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MODEL = "qwen36-27b-nvfp4-mtp-gguf"
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TEMPERATURE = 0.85
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PROJECT_DIR = Path(__file__).parent.parent
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OUTPUT_DIR = PROJECT_DIR / "outputs"
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PROMPTS_DIR = PROJECT_DIR / "prompts"
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class Agent:
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def __init__(self, name: str, conversation_path: Path, topic_brief: str):
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self.name = name
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self.conversation_path = conversation_path
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self.topic_brief = topic_brief
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self.system_prompt = (PROMPTS_DIR / f"{name.lower()}_system.md").read_text().strip()
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self.is_opener = (name == "Leonard")
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def read_conversation(self) -> list:
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"""Read the current conversation log."""
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if not self.conversation_path.exists():
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return []
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data = json.loads(self.conversation_path.read_text())
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return data.get("messages", [])
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def write_message(self, content: str) -> None:
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"""Append a message to the conversation log."""
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messages = self.read_conversation()
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message = {
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"agent": self.name,
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"content": content,
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"turn": len(messages) + 1,
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"timestamp": datetime.utcnow().isoformat(),
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}
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messages.append(message)
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self.conversation_path.write_text(json.dumps({"messages": messages}, indent=2))
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def build_context(self, is_first_turn: bool) -> list:
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"""Build the message context for the model call."""
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conversation = self.read_conversation()
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# Build conversation history
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history = []
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for msg in conversation:
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speaker = msg["agent"]
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history.append({"role": "user", "content": f"{speaker}: {msg['content']}"})
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# First turn: inject topic brief
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if is_first_turn and self.is_opener:
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topic_msg = {
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"role": "user",
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"content": (
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f"You're on the show now. Here's your topic:\n\n"
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f"{self.topic_brief}\n\n"
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"Welcome your listeners, introduce yourself and Charlie, "
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"and start talking about the topic."
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),
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}
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history.insert(0, topic_msg)
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elif is_first_turn and not self.is_opener:
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# Charlie's first turn after Leonard's opener
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history.insert(0, {
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"role": "user",
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"content": "Leonard just opened the show. Respond naturally to what he said."
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})
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return history
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def speak(self) -> dict:
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"""Generate and post a response."""
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messages = self.read_conversation()
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is_first_turn = len(messages) == 0 if self.is_opener else (
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len(messages) == 1 and messages[0]["agent"] == "Leonard"
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)
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history = self.build_context(is_first_turn)
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# Max tokens: 250 for opener, 200 for others
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max_tokens = 250 if is_first_turn and self.is_opener else 200
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payload = {
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"model": MODEL,
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"messages": [{"role": "system", "content": self.system_prompt}] + history,
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"max_tokens": max_tokens,
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"temperature": TEMPERATURE,
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}
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resp = requests.post(BASE_URL, json=payload, timeout=120)
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resp.raise_for_status()
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data = resp.json()
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content = data["choices"][0]["message"]["content"].strip()
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word_count = len(content.split())
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turn_num = len(messages) + 1
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print(f" {self.name} turn {turn_num}: {word_count} words", flush=True)
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excerpt = content[:100].replace("\n", " ")
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print(f" → {excerpt}...", flush=True)
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self.write_message(content)
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return {
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"agent": self.name,
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"content": content,
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"word_count": word_count,
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"turn": turn_num,
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}
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