Initial commit: autonomous AI talk show (Leonard + Charlie)

- 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

scripts/agent.py

@@ -0,0 +1,119 @@
+#!/usr/bin/env python3
+"""
+Autonomous Agent — base class for Leonard and Charlie.
+Each agent runs independently, reads the shared conversation log,
+generates a response, and appends it to the log.
+"""
+
+import json
+import time
+import sys
+import requests
+from pathlib import Path
+from datetime import datetime
+
+# ── Config ──────────────────────────────────────────────────────────
+BASE_URL = "http://100.64.0.2:39195/v1/chat/completions"
+MODEL = "qwen36-27b-nvfp4-mtp-gguf"
+TEMPERATURE = 0.85
+PROJECT_DIR = Path(__file__).parent.parent
+OUTPUT_DIR = PROJECT_DIR / "outputs"
+PROMPTS_DIR = PROJECT_DIR / "prompts"
+
+class Agent:
+    def __init__(self, name: str, conversation_path: Path, topic_brief: str):
+        self.name = name
+        self.conversation_path = conversation_path
+        self.topic_brief = topic_brief
+        self.system_prompt = (PROMPTS_DIR / f"{name.lower()}_system.md").read_text().strip()
+        self.is_opener = (name == "Leonard")
+    
+    def read_conversation(self) -> list:
+        """Read the current conversation log."""
+        if not self.conversation_path.exists():
+            return []
+        data = json.loads(self.conversation_path.read_text())
+        return data.get("messages", [])
+    
+    def write_message(self, content: str) -> None:
+        """Append a message to the conversation log."""
+        messages = self.read_conversation()
+        message = {
+            "agent": self.name,
+            "content": content,
+            "turn": len(messages) + 1,
+            "timestamp": datetime.utcnow().isoformat(),
+        }
+        messages.append(message)
+        self.conversation_path.write_text(json.dumps({"messages": messages}, indent=2))
+    
+    def build_context(self, is_first_turn: bool) -> list:
+        """Build the message context for the model call."""
+        conversation = self.read_conversation()
+        
+        # Build conversation history
+        history = []
+        for msg in conversation:
+            speaker = msg["agent"]
+            history.append({"role": "user", "content": f"{speaker}: {msg['content']}"})
+        
+        # First turn: inject topic brief
+        if is_first_turn and self.is_opener:
+            topic_msg = {
+                "role": "user",
+                "content": (
+                    f"You're on the show now. Here's your topic:\n\n"
+                    f"{self.topic_brief}\n\n"
+                    "Welcome your listeners, introduce yourself and Charlie, "
+                    "and start talking about the topic."
+                ),
+            }
+            history.insert(0, topic_msg)
+        elif is_first_turn and not self.is_opener:
+            # Charlie's first turn after Leonard's opener
+            history.insert(0, {
+                "role": "user",
+                "content": "Leonard just opened the show. Respond naturally to what he said."
+            })
+        
+        return history
+    
+    def speak(self) -> dict:
+        """Generate and post a response."""
+        messages = self.read_conversation()
+        is_first_turn = len(messages) == 0 if self.is_opener else (
+            len(messages) == 1 and messages[0]["agent"] == "Leonard"
+        )
+        
+        history = self.build_context(is_first_turn)
+        
+        # Max tokens: 250 for opener, 200 for others
+        max_tokens = 250 if is_first_turn and self.is_opener else 200
+        
+        payload = {
+            "model": MODEL,
+            "messages": [{"role": "system", "content": self.system_prompt}] + history,
+            "max_tokens": max_tokens,
+            "temperature": TEMPERATURE,
+        }
+        
+        resp = requests.post(BASE_URL, json=payload, timeout=120)
+        resp.raise_for_status()
+        data = resp.json()
+        content = data["choices"][0]["message"]["content"].strip()
+        
+        word_count = len(content.split())
+        turn_num = len(messages) + 1
+        
+        print(f"  {self.name} turn {turn_num}: {word_count} words", flush=True)
+        excerpt = content[:100].replace("\n", " ")
+        print(f"  → {excerpt}...", flush=True)
+        
+        self.write_message(content)
+        
+        return {
+            "agent": self.name,
+            "content": content,
+            "word_count": word_count,
+            "turn": turn_num,
+        }