#!/usr/bin/env python3
"""
AI Agent Runner with Tool Calling
This module provides a clean, standalone agent that can execute AI models
with tool calling capabilities. It handles the conversation loop, tool execution,
and response management.
Features:
- Automatic tool calling loop until completion
- Configurable model parameters
- Error handling and recovery
- Message history management
- Support for multiple model providers
Usage:
from run_agent import AIAgent
agent = AIAgent(base_url="http://localhost:30000/v1", model="claude-opus-4-20250514")
response = agent.run_conversation("Tell me about the latest Python updates")
"""
import copy
import json
import logging
import os
import random
import sys
import time
import threading
import uuid
from typing import List, Dict, Any, Optional
from openai import OpenAI
import fire
from datetime import datetime
from pathlib import Path
# Load environment variables from .env file
from dotenv import load_dotenv
# Load .env file if it exists
env_path = Path(__file__).parent / '.env'
if env_path.exists():
load_dotenv(dotenv_path=env_path)
if not os.getenv("HERMES_QUIET"):
print(f"✅ Loaded environment variables from {env_path}")
elif not os.getenv("HERMES_QUIET"):
print(f"ℹ️ No .env file found at {env_path}. Using system environment variables.")
# Import our tool system
from model_tools import get_tool_definitions, handle_function_call, check_toolset_requirements
from tools.terminal_tool import cleanup_vm, set_interrupt_event as _set_terminal_interrupt
from tools.browser_tool import cleanup_browser
import requests
# =============================================================================
# Default Agent Identity & Platform Hints
# =============================================================================
# The default identity prompt is prepended to every conversation so the agent
# knows who it is and behaves consistently across platforms.
DEFAULT_AGENT_IDENTITY = (
"You are Hermes Agent, an intelligent AI assistant created by Nous Research. "
"You are helpful, knowledgeable, and direct. You assist users with a wide "
"range of tasks including answering questions, writing and editing code, "
"analyzing information, creative work, and executing actions via your tools. "
"You communicate clearly, admit uncertainty when appropriate, and prioritize "
"being genuinely useful over being verbose unless otherwise directed below."
)
# Platform-specific formatting hints appended to the system prompt.
# These tell the agent how to format its output for the current interface.
PLATFORM_HINTS = {
"whatsapp": (
"You are on a text messaging communication platform, WhatsApp. "
"Please do not use markdown as it does not render."
),
"telegram": (
"You are on a text messaging communication platform, Telegram. "
"Please do not use markdown as it does not render."
),
"discord": (
"You are in a Discord server or group chat communicating with your user."
),
"cli": (
"You are a CLI AI Agent. Try not to use markdown but simple text "
"renderable inside a terminal."
),
}
# =============================================================================
# Model Context Management
# =============================================================================
# Cache for model metadata from OpenRouter
_model_metadata_cache: Dict[str, Dict[str, Any]] = {}
_model_metadata_cache_time: float = 0
_MODEL_CACHE_TTL = 3600 # 1 hour cache TTL
# Default context lengths for common models (fallback if API fails)
DEFAULT_CONTEXT_LENGTHS = {
"anthropic/claude-opus-4": 200000,
"anthropic/claude-opus-4.5": 200000,
"anthropic/claude-opus-4.6": 200000,
"anthropic/claude-sonnet-4": 200000,
"anthropic/claude-sonnet-4-20250514": 200000,
"anthropic/claude-haiku-4.5": 200000,
"openai/gpt-4o": 128000,
"openai/gpt-4-turbo": 128000,
"openai/gpt-4o-mini": 128000,
"google/gemini-2.0-flash": 1048576,
"google/gemini-2.5-pro": 1048576,
"meta-llama/llama-3.3-70b-instruct": 131072,
"deepseek/deepseek-chat-v3": 65536,
"qwen/qwen-2.5-72b-instruct": 32768,
}
def fetch_model_metadata(force_refresh: bool = False) -> Dict[str, Dict[str, Any]]:
"""
Fetch model metadata from OpenRouter's /api/v1/models endpoint.
Results are cached for 1 hour to minimize API calls.
Returns:
Dict mapping model_id to metadata (context_length, max_completion_tokens, etc.)
"""
global _model_metadata_cache, _model_metadata_cache_time
# Return cached data if fresh
if not force_refresh and _model_metadata_cache and (time.time() - _model_metadata_cache_time) < _MODEL_CACHE_TTL:
return _model_metadata_cache
try:
response = requests.get(
"https://openrouter.ai/api/v1/models",
timeout=10
)
response.raise_for_status()
data = response.json()
# Build cache mapping model_id to relevant metadata
cache = {}
for model in data.get("data", []):
model_id = model.get("id", "")
cache[model_id] = {
"context_length": model.get("context_length", 128000),
"max_completion_tokens": model.get("top_provider", {}).get("max_completion_tokens", 4096),
"name": model.get("name", model_id),
"pricing": model.get("pricing", {}),
}
# Also cache by canonical slug if different
canonical = model.get("canonical_slug", "")
if canonical and canonical != model_id:
cache[canonical] = cache[model_id]
_model_metadata_cache = cache
_model_metadata_cache_time = time.time()
if not os.getenv("HERMES_QUIET"):
logging.debug(f"Fetched metadata for {len(cache)} models from OpenRouter")
return cache
except Exception as e:
logging.warning(f"Failed to fetch model metadata from OpenRouter: {e}")
# Return cached data even if stale, or empty dict
return _model_metadata_cache or {}
def get_model_context_length(model: str) -> int:
"""
Get the context length for a specific model.
Args:
model: Model identifier (e.g., "anthropic/claude-sonnet-4")
Returns:
Context length in tokens (defaults to 128000 if unknown)
"""
# Try to get from OpenRouter API
metadata = fetch_model_metadata()
if model in metadata:
return metadata[model].get("context_length", 128000)
# Check default fallbacks (handles partial matches)
for default_model, length in DEFAULT_CONTEXT_LENGTHS.items():
if default_model in model or model in default_model:
return length
# Conservative default
return 128000
def estimate_tokens_rough(text: str) -> int:
"""
Rough token estimate for pre-flight checks (before API call).
Uses ~4 chars per token heuristic.
For accurate counts, use the `usage.prompt_tokens` from API responses.
Args:
text: Text to estimate tokens for
Returns:
Rough estimated token count
"""
if not text:
return 0
return len(text) // 4
def estimate_messages_tokens_rough(messages: List[Dict[str, Any]]) -> int:
"""
Rough token estimate for messages (pre-flight check only).
For accurate counts, use the `usage.prompt_tokens` from API responses.
Args:
messages: List of message dicts
Returns:
Rough estimated token count
"""
total_chars = sum(len(str(msg)) for msg in messages)
return total_chars // 4
class ContextCompressor:
"""
Compresses conversation context when approaching model's context limit.
Uses similar logic to trajectory_compressor but operates in real-time:
1. Protects first few turns (system, initial user, first assistant response)
2. Protects last N turns (recent context is most relevant)
3. Summarizes middle turns when threshold is reached
Token tracking uses actual counts from API responses (usage.prompt_tokens)
rather than estimates for accuracy.
"""
def __init__(
self,
model: str,
threshold_percent: float = 0.85,
summary_model: str = "google/gemini-3-flash-preview",
protect_first_n: int = 3,
protect_last_n: int = 4,
summary_target_tokens: int = 500,
quiet_mode: bool = False,
):
"""
Initialize the context compressor.
Args:
model: The main model being used (to determine context limit)
threshold_percent: Trigger compression at this % of context (default 85%)
summary_model: Model to use for generating summaries (cheap/fast)
protect_first_n: Number of initial turns to always keep
protect_last_n: Number of recent turns to always keep
summary_target_tokens: Target token count for summaries
quiet_mode: Suppress compression notifications
"""
self.model = model
self.threshold_percent = threshold_percent
self.summary_model = summary_model
self.protect_first_n = protect_first_n
self.protect_last_n = protect_last_n
self.summary_target_tokens = summary_target_tokens
self.quiet_mode = quiet_mode
self.context_length = get_model_context_length(model)
self.threshold_tokens = int(self.context_length * threshold_percent)
self.compression_count = 0
# Track actual token usage from API responses
self.last_prompt_tokens = 0
self.last_completion_tokens = 0
self.last_total_tokens = 0
# Initialize OpenRouter client for summarization
api_key = os.getenv("OPENROUTER_API_KEY", "")
self.client = OpenAI(
api_key=api_key,
base_url="https://openrouter.ai/api/v1"
) if api_key else None
def update_from_response(self, usage: Dict[str, Any]):
"""
Update tracked token usage from API response.
Args:
usage: The usage dict from response (contains prompt_tokens, completion_tokens, total_tokens)
"""
self.last_prompt_tokens = usage.get("prompt_tokens", 0)
self.last_completion_tokens = usage.get("completion_tokens", 0)
self.last_total_tokens = usage.get("total_tokens", 0)
def should_compress(self, prompt_tokens: int = None) -> bool:
"""
Check if context exceeds the compression threshold.
Uses actual token count from API response for accuracy.
Args:
prompt_tokens: Actual prompt tokens from last API response.
If None, uses last tracked value.
Returns:
True if compression should be triggered
"""
tokens = prompt_tokens if prompt_tokens is not None else self.last_prompt_tokens
return tokens >= self.threshold_tokens
def should_compress_preflight(self, messages: List[Dict[str, Any]]) -> bool:
"""
Quick pre-flight check using rough estimate (before API call).
Use this to avoid making an API call that would fail due to context overflow.
For post-response compression decisions, use should_compress() with actual tokens.
Args:
messages: Current conversation messages
Returns:
True if compression is likely needed
"""
rough_estimate = estimate_messages_tokens_rough(messages)
return rough_estimate >= self.threshold_tokens
def get_status(self) -> Dict[str, Any]:
"""
Get current compression status for display/logging.
Returns:
Dict with token usage and threshold info
"""
return {
"last_prompt_tokens": self.last_prompt_tokens,
"threshold_tokens": self.threshold_tokens,
"context_length": self.context_length,
"usage_percent": (self.last_prompt_tokens / self.context_length * 100) if self.context_length else 0,
"compression_count": self.compression_count,
}
def _generate_summary(self, turns_to_summarize: List[Dict[str, Any]]) -> str:
"""
Generate a concise summary of conversation turns using a fast model.
Args:
turns_to_summarize: List of message dicts to summarize
Returns:
Summary string
"""
if not self.client:
# Fallback if no API key
return "[CONTEXT SUMMARY]: Previous conversation turns have been compressed to save space. The assistant performed various actions and received responses."
# Format turns for summarization
parts = []
for i, msg in enumerate(turns_to_summarize):
role = msg.get("role", "unknown")
content = msg.get("content", "")
# Truncate very long content
if len(content) > 2000:
content = content[:1000] + "\n...[truncated]...\n" + content[-500:]
# Include tool call info if present
tool_calls = msg.get("tool_calls", [])
if tool_calls:
tool_names = [tc.get("function", {}).get("name", "?") for tc in tool_calls if isinstance(tc, dict)]
content += f"\n[Tool calls: {', '.join(tool_names)}]"
parts.append(f"[{role.upper()}]: {content}")
content_to_summarize = "\n\n".join(parts)
prompt = f"""Summarize these conversation turns concisely. This summary will replace these turns in the conversation history.
Write from a neutral perspective describing:
1. What actions were taken (tool calls, searches, file operations)
2. Key information or results obtained
3. Important decisions or findings
4. Relevant data, file names, or outputs
Keep factual and informative. Target ~{self.summary_target_tokens} tokens.
---
TURNS TO SUMMARIZE:
{content_to_summarize}
---
Write only the summary, starting with "[CONTEXT SUMMARY]:" prefix."""
try:
response = self.client.chat.completions.create(
model=self.summary_model,
messages=[{"role": "user", "content": prompt}],
temperature=0.3,
max_tokens=self.summary_target_tokens * 2,
timeout=30.0,
)
summary = response.choices[0].message.content.strip()
if not summary.startswith("[CONTEXT SUMMARY]:"):
summary = "[CONTEXT SUMMARY]: " + summary
return summary
except Exception as e:
logging.warning(f"Failed to generate context summary: {e}")
return "[CONTEXT SUMMARY]: Previous conversation turns have been compressed. The assistant performed tool calls and received responses."
def compress(self, messages: List[Dict[str, Any]], current_tokens: int = None) -> List[Dict[str, Any]]:
"""
Compress conversation messages by summarizing middle turns.
Algorithm:
1. Keep first N turns (system prompt, initial context)
2. Keep last N turns (recent/relevant context)
3. Summarize everything in between
4. Insert summary as a user message
Args:
messages: Current conversation messages
current_tokens: Actual token count from API (for logging). If None, uses estimate.
Returns:
Compressed message list
"""
n_messages = len(messages)
# Not enough messages to compress
if n_messages <= self.protect_first_n + self.protect_last_n + 1:
if not self.quiet_mode:
print(f"⚠️ Cannot compress: only {n_messages} messages (need > {self.protect_first_n + self.protect_last_n + 1})")
return messages
# Determine compression boundaries
compress_start = self.protect_first_n
compress_end = n_messages - self.protect_last_n
# Nothing to compress
if compress_start >= compress_end:
return messages
# Extract turns to summarize
turns_to_summarize = messages[compress_start:compress_end]
# Use actual token count if provided, otherwise estimate
display_tokens = current_tokens if current_tokens else self.last_prompt_tokens or estimate_messages_tokens_rough(messages)
if not self.quiet_mode:
print(f"\n📦 Context compression triggered ({display_tokens:,} tokens ≥ {self.threshold_tokens:,} threshold)")
print(f" 📊 Model context limit: {self.context_length:,} tokens ({self.threshold_percent*100:.0f}% = {self.threshold_tokens:,})")
print(f" 🗜️ Summarizing turns {compress_start+1}-{compress_end} ({len(turns_to_summarize)} turns)")
# Generate summary
summary = self._generate_summary(turns_to_summarize)
# Build compressed messages
compressed = []
# Keep protected head turns
for i in range(compress_start):
msg = messages[i].copy()
# Add notice to system message on first compression
if i == 0 and msg.get("role") == "system" and self.compression_count == 0:
msg["content"] = msg.get("content", "") + "\n\n[Note: Some earlier conversation turns may be summarized to preserve context space.]"
compressed.append(msg)
# Add summary as user message
compressed.append({
"role": "user",
"content": summary
})
# Keep protected tail turns
for i in range(compress_end, n_messages):
compressed.append(messages[i].copy())
self.compression_count += 1
if not self.quiet_mode:
# Estimate new size (actual will be known after next API call)
new_estimate = estimate_messages_tokens_rough(compressed)
saved_estimate = display_tokens - new_estimate
print(f" ✅ Compressed: {n_messages} → {len(compressed)} messages (~{saved_estimate:,} tokens saved)")
print(f" 💡 Compression #{self.compression_count} complete")
return compressed
# =============================================================================
# Anthropic Prompt Caching (system_and_3 strategy)
# =============================================================================
# Reduces input token costs by ~75% on multi-turn conversations by caching
# the conversation prefix. Uses 4 cache_control breakpoints (Anthropic max):
# 1. System prompt (stable across all turns)
# 2-4. Last 3 non-system messages (rolling window)
#
# Cached tokens are read at 0.1x input price. Cache writes cost 1.25x (5m TTL)
# or 2x (1h TTL). Only applied to Claude models via OpenRouter.
def _apply_cache_marker(msg: dict, cache_marker: dict) -> None:
"""
Add cache_control to a single message, handling all format variations.
- tool messages: cache_control at message level (Anthropic API quirk)
- string content: converted to multipart content array
- list content: marker added to last item
- None content (assistant with tool_calls): message level
"""
role = msg.get("role", "")
content = msg.get("content")
if role == "tool":
msg["cache_control"] = cache_marker
return
if content is None:
msg["cache_control"] = cache_marker
return
if isinstance(content, str):
msg["content"] = [{"type": "text", "text": content, "cache_control": cache_marker}]
return
if isinstance(content, list) and content:
last = content[-1]
if isinstance(last, dict):
last["cache_control"] = cache_marker
def apply_anthropic_cache_control(
api_messages: List[Dict[str, Any]],
cache_ttl: str = "5m",
) -> List[Dict[str, Any]]:
"""
Apply system_and_3 caching strategy to messages for Anthropic models.
Places up to 4 cache_control breakpoints:
1. System prompt (index 0, stable across all turns)
2-4. Last 3 non-system messages (rolling cache frontier)
Each breakpoint tells Anthropic "cache everything from the start up to here."
Multiple breakpoints create a ladder of cached prefixes at different depths,
which provides robust cache hits even when the most recent cache entry hasn't
propagated yet.
Args:
api_messages: Fully assembled message list (system prompt first).
cache_ttl: "5m" (default, 1.25x write cost) or "1h" (2x write cost).
Returns:
Deep copy of messages with cache_control breakpoints injected.
"""
messages = copy.deepcopy(api_messages)
if not messages:
return messages
marker = {"type": "ephemeral"}
if cache_ttl == "1h":
marker["ttl"] = "1h"
breakpoints_used = 0
# Breakpoint 1: System prompt (always stable, gives a guaranteed minimum hit)
if messages[0].get("role") == "system":
_apply_cache_marker(messages[0], marker)
breakpoints_used += 1
# Breakpoints 2-4: Last 3 non-system messages (rolling window)
remaining = 4 - breakpoints_used
non_sys = [i for i in range(len(messages)) if messages[i].get("role") != "system"]
for idx in non_sys[-remaining:]:
_apply_cache_marker(messages[idx], marker)
return messages
# =============================================================================
# Default System Prompt Components
# =============================================================================
# Skills guidance - embeds a compact skill index in the system prompt so
# the model can match skills at a glance without extra tool calls.
def build_skills_system_prompt() -> str:
"""
Build a dynamic skills system prompt by scanning the skills/ directory.
Returns a prompt section that lists all skill categories (with descriptions
from DESCRIPTION.md) and their skill names inline, so the model can
immediately see if a relevant skill exists and load it with a single
skill_view(name) call -- no discovery tool calls needed.
Returns:
str: The skills system prompt section, or empty string if no skills found.
"""
import re
from pathlib import Path
skills_dir = Path(__file__).parent / "skills"
if not skills_dir.exists():
return ""
# Scan for SKILL.md files grouped by category
skills_by_category = {}
for skill_file in skills_dir.rglob("SKILL.md"):
rel_path = skill_file.relative_to(skills_dir)
parts = rel_path.parts
if len(parts) >= 2:
category = parts[0]
skill_name = parts[-2] # Folder containing SKILL.md
else:
category = "general"
skill_name = skill_file.parent.name
skills_by_category.setdefault(category, []).append(skill_name)
if not skills_by_category:
return ""
# Load category descriptions from DESCRIPTION.md files (YAML frontmatter)
category_descriptions = {}
for category in skills_by_category:
desc_file = skills_dir / category / "DESCRIPTION.md"
if desc_file.exists():
try:
content = desc_file.read_text(encoding="utf-8")
# Parse description from YAML frontmatter: ---\ndescription: ...\n---
match = re.search(r"^---\s*\n.*?description:\s*(.+?)\s*\n.*?^---", content, re.MULTILINE | re.DOTALL)
if match:
category_descriptions[category] = match.group(1).strip()
except Exception:
pass
# Build compact index: category with description + skill names
index_lines = []
for category in sorted(skills_by_category.keys()):
desc = category_descriptions.get(category, "")
names = ", ".join(sorted(skills_by_category[category]))
if desc:
index_lines.append(f" {category}: {desc}")
else:
index_lines.append(f" {category}:")
index_lines.append(f" skills: {names}")
return (
"## Skills (mandatory)\n"
"Before replying, scan the skills below. If one clearly matches your task, "
"load it with skill_view(name) and follow its instructions.\n"
"\n"
"\n"
+ "\n".join(index_lines) + "\n"
"\n"
"\n"
"If none match, proceed normally without loading a skill."
)
# =============================================================================
# Context File Injection (SOUL.md, AGENTS.md, .cursorrules)
# =============================================================================
# Maximum characters per context file before truncation
CONTEXT_FILE_MAX_CHARS = 20_000
# Truncation strategy: keep 70% from the head, 20% from the tail
CONTEXT_TRUNCATE_HEAD_RATIO = 0.7
CONTEXT_TRUNCATE_TAIL_RATIO = 0.2
def _truncate_content(content: str, filename: str, max_chars: int = CONTEXT_FILE_MAX_CHARS) -> str:
"""
Truncate content if it exceeds max_chars using a head/tail strategy.
Keeps 70% from the start and 20% from the end, with a truncation
marker in the middle so the model knows content was cut.
"""
if len(content) <= max_chars:
return content
head_chars = int(max_chars * CONTEXT_TRUNCATE_HEAD_RATIO)
tail_chars = int(max_chars * CONTEXT_TRUNCATE_TAIL_RATIO)
head = content[:head_chars]
tail = content[-tail_chars:]
marker = f"\n\n[...truncated {filename}: kept {head_chars}+{tail_chars} of {len(content)} chars. Use file tools to read the full file.]\n\n"
return head + marker + tail
def build_context_files_prompt(cwd: str = None) -> str:
"""
Discover and load context files (SOUL.md, AGENTS.md, .cursorrules)
for injection into the system prompt.
Discovery rules:
- AGENTS.md: Recursively search from cwd (only if top-level exists).
Each file becomes a ## section with its relative path.
- .cursorrules: Check cwd for .cursorrules file and .cursor/rules/*.mdc
- SOUL.md: Check cwd first, then ~/.hermes/SOUL.md as global fallback
Args:
cwd: Working directory to search from. Defaults to os.getcwd().
Returns:
str: The context files prompt section, or empty string if none found.
"""
import os
import glob as glob_mod
from pathlib import Path
if cwd is None:
cwd = os.getcwd()
cwd_path = Path(cwd).resolve()
sections = []
# ----- AGENTS.md (hierarchical, recursive) -----
top_level_agents = None
for name in ["AGENTS.md", "agents.md"]:
candidate = cwd_path / name
if candidate.exists():
top_level_agents = candidate
break
if top_level_agents:
# Recursively find all AGENTS.md files (case-insensitive)
agents_files = []
for root, dirs, files in os.walk(cwd_path):
# Skip hidden directories and common non-project dirs
dirs[:] = [d for d in dirs if not d.startswith('.') and d not in ('node_modules', '__pycache__', 'venv', '.venv')]
for f in files:
if f.lower() == "agents.md":
agents_files.append(Path(root) / f)
# Sort by path depth (top-level first, then deeper)
agents_files.sort(key=lambda p: len(p.parts))
total_agents_content = ""
for agents_path in agents_files:
try:
content = agents_path.read_text(encoding="utf-8").strip()
if content:
rel_path = agents_path.relative_to(cwd_path)
total_agents_content += f"## {rel_path}\n\n{content}\n\n"
except Exception:
pass
if total_agents_content:
total_agents_content = _truncate_content(total_agents_content, "AGENTS.md")
sections.append(total_agents_content)
# ----- .cursorrules -----
cursorrules_content = ""
# Check for .cursorrules file
cursorrules_file = cwd_path / ".cursorrules"
if cursorrules_file.exists():
try:
content = cursorrules_file.read_text(encoding="utf-8").strip()
if content:
cursorrules_content += f"## .cursorrules\n\n{content}\n\n"
except Exception:
pass
# Check for .cursor/rules/*.mdc files
cursor_rules_dir = cwd_path / ".cursor" / "rules"
if cursor_rules_dir.exists() and cursor_rules_dir.is_dir():
mdc_files = sorted(cursor_rules_dir.glob("*.mdc"))
for mdc_file in mdc_files:
try:
content = mdc_file.read_text(encoding="utf-8").strip()
if content:
cursorrules_content += f"## .cursor/rules/{mdc_file.name}\n\n{content}\n\n"
except Exception:
pass
if cursorrules_content:
cursorrules_content = _truncate_content(cursorrules_content, ".cursorrules")
sections.append(cursorrules_content)
# ----- SOUL.md (cwd first, then ~/.hermes/ fallback) -----
soul_content = ""
soul_path = None
for name in ["SOUL.md", "soul.md"]:
candidate = cwd_path / name
if candidate.exists():
soul_path = candidate
break
if not soul_path:
# Global fallback
global_soul = Path.home() / ".hermes" / "SOUL.md"
if global_soul.exists():
soul_path = global_soul
if soul_path:
try:
content = soul_path.read_text(encoding="utf-8").strip()
if content:
content = _truncate_content(content, "SOUL.md")
soul_content = f"## SOUL.md\n\nIf SOUL.md is present, embody its persona and tone. Avoid stiff, generic replies; follow its guidance unless higher-priority instructions override it.\n\n{content}"
sections.append(soul_content)
except Exception:
pass
# ----- Assemble -----
if not sections:
return ""
return "# Project Context\n\nThe following project context files have been loaded and should be followed:\n\n" + "\n".join(sections)
def _build_tool_preview(tool_name: str, args: dict, max_len: int = 40) -> str:
"""
Build a short preview of a tool call's primary argument for display.
Returns a truncated string showing the most informative argument,
or None if no meaningful preview is available.
Args:
tool_name: Name of the tool being called
args: The tool call arguments dict
max_len: Maximum preview length before truncation
Returns:
str or None: Short preview string, or None
"""
# Map tool names to their primary argument key(s)
primary_args = {
"terminal": "command",
"web_search": "query",
"web_extract": "urls",
"read_file": "path",
"write_file": "path",
"patch": "path",
"search": "pattern",
"browser_navigate": "url",
"browser_click": "ref",
"browser_type": "text",
"image_generate": "prompt",
"text_to_speech": "text",
"vision_analyze": "question",
"mixture_of_agents": "user_prompt",
"skill_view": "name",
"skills_list": "category",
"schedule_cronjob": "name",
}
key = primary_args.get(tool_name)
if not key:
# Try common arg names as fallback
for fallback_key in ("query", "text", "command", "path", "name", "prompt"):
if fallback_key in args:
key = fallback_key
break
if not key or key not in args:
return None
value = args[key]
# Handle list values (e.g., urls)
if isinstance(value, list):
value = value[0] if value else ""
preview = str(value).strip()
if not preview:
return None
# Truncate
if len(preview) > max_len:
preview = preview[:max_len - 3] + "..."
return preview
class KawaiiSpinner:
"""
Animated spinner with kawaii faces for CLI feedback during tool execution.
Runs in a background thread and can be stopped when the operation completes.
Uses stdout with carriage return to animate in place.
"""
# Different spinner animation sets
SPINNERS = {
'dots': ['⠋', '⠙', '⠹', '⠸', '⠼', '⠴', '⠦', '⠧', '⠇', '⠏'],
'bounce': ['⠁', '⠂', '⠄', '⡀', '⢀', '⠠', '⠐', '⠈'],
'grow': ['▁', '▂', '▃', '▄', '▅', '▆', '▇', '█', '▇', '▆', '▅', '▄', '▃', '▂'],
'arrows': ['←', '↖', '↑', '↗', '→', '↘', '↓', '↙'],
'star': ['✶', '✷', '✸', '✹', '✺', '✹', '✸', '✷'],
'moon': ['🌑', '🌒', '🌓', '🌔', '🌕', '🌖', '🌗', '🌘'],
'pulse': ['◜', '◠', '◝', '◞', '◡', '◟'],
'brain': ['🧠', '💭', '💡', '✨', '💫', '🌟', '💡', '💭'],
'sparkle': ['⁺', '˚', '*', '✧', '✦', '✧', '*', '˚'],
}
# General waiting faces
KAWAII_WAITING = [
"(｡◕‿◕｡)", "(◕‿◕✿)", "٩(◕‿◕｡)۶", "(✿◠‿◠)", "( ˘▽˘)っ",
"♪(´ε` )", "(◕ᴗ◕✿)", "ヾ(＾∇＾)", "(≧◡≦)", "(★ω★)",
]
# Thinking-specific faces and messages
KAWAII_THINKING = [
"(｡•́︿•̀｡)", "(◔_◔)", "(¬‿¬)", "( •_•)>⌐■-■", "(⌐■_■)",
"(´･_･`)", "◉_◉", "(°ロ°)", "( ˘⌣˘)♡", "ヽ(>∀<☆)☆",
"٩(๑❛ᴗ❛๑)۶", "(⊙_⊙)", "(¬_¬)", "( ͡° ͜ʖ ͡°)", "ಠ_ಠ",
]
THINKING_VERBS = [
"pondering", "contemplating", "musing", "cogitating", "ruminating",
"deliberating", "mulling", "reflecting", "processing", "reasoning",
"analyzing", "computing", "synthesizing", "formulating", "brainstorming",
]
def __init__(self, message: str = "", spinner_type: str = 'dots'):
self.message = message
self.spinner_frames = self.SPINNERS.get(spinner_type, self.SPINNERS['dots'])
self.running = False
self.thread = None
self.frame_idx = 0
self.start_time = None
self.last_line_len = 0
def _animate(self):
"""Animation loop that runs in background thread."""
while self.running:
# Check for pause signal (e.g., during sudo password prompt)
if os.getenv("HERMES_SPINNER_PAUSE"):
time.sleep(0.1)
continue
frame = self.spinner_frames[self.frame_idx % len(self.spinner_frames)]
elapsed = time.time() - self.start_time
# Build the spinner line
line = f" {frame} {self.message} ({elapsed:.1f}s)"
# Clear previous line and write new one
clear = '\r' + ' ' * self.last_line_len + '\r'
print(clear + line, end='', flush=True)
self.last_line_len = len(line)
self.frame_idx += 1
time.sleep(0.12) # ~8 FPS animation
def start(self):
"""Start the spinner animation."""
if self.running:
return
self.running = True
self.start_time = time.time()
self.thread = threading.Thread(target=self._animate, daemon=True)
self.thread.start()
def stop(self, final_message: str = None):
"""Stop the spinner and optionally print a final message."""
self.running = False
if self.thread:
self.thread.join(timeout=0.5)
# Clear the spinner line
print('\r' + ' ' * (self.last_line_len + 5) + '\r', end='', flush=True)
# Print final message if provided
if final_message:
print(f" {final_message}", flush=True)
def __enter__(self):
self.start()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.stop()
return False
class AIAgent:
"""
AI Agent with tool calling capabilities.
This class manages the conversation flow, tool execution, and response handling
for AI models that support function calling.
"""
def __init__(
self,
base_url: str = None,
api_key: str = None,
model: str = "anthropic/claude-opus-4.6", # OpenRouter format
max_iterations: int = 60, # Default tool-calling iterations
tool_delay: float = 1.0,
enabled_toolsets: List[str] = None,
disabled_toolsets: List[str] = None,
save_trajectories: bool = False,
verbose_logging: bool = False,
quiet_mode: bool = False,
ephemeral_system_prompt: str = None,
log_prefix_chars: int = 100,
log_prefix: str = "",
providers_allowed: List[str] = None,
providers_ignored: List[str] = None,
providers_order: List[str] = None,
provider_sort: str = None,
session_id: str = None,
tool_progress_callback: callable = None,
max_tokens: int = None,
reasoning_config: Dict[str, Any] = None,
prefill_messages: List[Dict[str, Any]] = None,
platform: str = None,
):
"""
Initialize the AI Agent.
Args:
base_url (str): Base URL for the model API (optional)
api_key (str): API key for authentication (optional, uses env var if not provided)
model (str): Model name to use (default: "gpt-4")
max_iterations (int): Maximum number of tool calling iterations (default: 10)
tool_delay (float): Delay between tool calls in seconds (default: 1.0)
enabled_toolsets (List[str]): Only enable tools from these toolsets (optional)
disabled_toolsets (List[str]): Disable tools from these toolsets (optional)
save_trajectories (bool): Whether to save conversation trajectories to JSONL files (default: False)
verbose_logging (bool): Enable verbose logging for debugging (default: False)
quiet_mode (bool): Suppress progress output for clean CLI experience (default: False)
ephemeral_system_prompt (str): System prompt used during agent execution but NOT saved to trajectories (optional)
log_prefix_chars (int): Number of characters to show in log previews for tool calls/responses (default: 20)
log_prefix (str): Prefix to add to all log messages for identification in parallel processing (default: "")
providers_allowed (List[str]): OpenRouter providers to allow (optional)
providers_ignored (List[str]): OpenRouter providers to ignore (optional)
providers_order (List[str]): OpenRouter providers to try in order (optional)
provider_sort (str): Sort providers by price/throughput/latency (optional)
session_id (str): Pre-generated session ID for logging (optional, auto-generated if not provided)
tool_progress_callback (callable): Callback function(tool_name, args_preview) for progress notifications
max_tokens (int): Maximum tokens for model responses (optional, uses model default if not set)
reasoning_config (Dict): OpenRouter reasoning configuration override (e.g. {"effort": "none"} to disable thinking).
If None, defaults to {"enabled": True, "effort": "xhigh"} for OpenRouter. Set to disable/customize reasoning.
prefill_messages (List[Dict]): Messages to prepend to conversation history as prefilled context.
Useful for injecting a few-shot example or priming the model's response style.
Example: [{"role": "user", "content": "Hi!"}, {"role": "assistant", "content": "Hello!"}]
platform (str): The interface platform the user is on (e.g. "cli", "telegram", "discord", "whatsapp").
Used to inject platform-specific formatting hints into the system prompt.
"""
self.model = model
self.max_iterations = max_iterations
self.tool_delay = tool_delay
self.save_trajectories = save_trajectories
self.verbose_logging = verbose_logging
self.quiet_mode = quiet_mode
self.ephemeral_system_prompt = ephemeral_system_prompt
self.platform = platform # "cli", "telegram", "discord", "whatsapp", etc.
self.log_prefix_chars = log_prefix_chars
self.log_prefix = f"{log_prefix} " if log_prefix else ""
# Store effective base URL for feature detection (prompt caching, reasoning, etc.)
# When no base_url is provided, the client defaults to OpenRouter, so reflect that here.
self.base_url = base_url or "https://openrouter.ai/api/v1"
self.tool_progress_callback = tool_progress_callback
self._last_reported_tool = None # Track for "new tool" mode
# Interrupt mechanism for breaking out of tool loops
self._interrupt_requested = False
self._interrupt_message = None # Optional message that triggered interrupt
# Store OpenRouter provider preferences
self.providers_allowed = providers_allowed
self.providers_ignored = providers_ignored
self.providers_order = providers_order
self.provider_sort = provider_sort
# Store toolset filtering options
self.enabled_toolsets = enabled_toolsets
self.disabled_toolsets = disabled_toolsets
# Model response configuration
self.max_tokens = max_tokens # None = use model default
self.reasoning_config = reasoning_config # None = use default (xhigh for OpenRouter)
self.prefill_messages = prefill_messages or [] # Prefilled conversation turns
# Anthropic prompt caching: auto-enabled for Claude models via OpenRouter.
# Reduces input costs by ~75% on multi-turn conversations by caching the
# conversation prefix. Uses system_and_3 strategy (4 breakpoints).
is_openrouter = "openrouter" in self.base_url.lower()
is_claude = "claude" in self.model.lower()
self._use_prompt_caching = is_openrouter and is_claude
self._cache_ttl = "5m" # Default 5-minute TTL (1.25x write cost)
# Configure logging
if self.verbose_logging:
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
datefmt='%H:%M:%S'
)
# Keep third-party libraries at WARNING level to reduce noise
# We have our own retry and error logging that's more informative
logging.getLogger('openai').setLevel(logging.WARNING)
logging.getLogger('openai._base_client').setLevel(logging.WARNING)
logging.getLogger('httpx').setLevel(logging.WARNING)
logging.getLogger('httpcore').setLevel(logging.WARNING)
logging.getLogger('asyncio').setLevel(logging.WARNING)
# Suppress Modal/gRPC related debug spam
logging.getLogger('hpack').setLevel(logging.WARNING)
logging.getLogger('hpack.hpack').setLevel(logging.WARNING)
logging.getLogger('grpc').setLevel(logging.WARNING)
logging.getLogger('modal').setLevel(logging.WARNING)
logging.getLogger('rex-deploy').setLevel(logging.INFO) # Keep INFO for sandbox status
if not self.quiet_mode:
print("🔍 Verbose logging enabled (third-party library logs suppressed)")
else:
# Set logging to INFO level for important messages only
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
datefmt='%H:%M:%S'
)
# Suppress noisy library logging
logging.getLogger('openai').setLevel(logging.ERROR)
logging.getLogger('openai._base_client').setLevel(logging.ERROR)
logging.getLogger('httpx').setLevel(logging.ERROR)
logging.getLogger('httpcore').setLevel(logging.ERROR)
# Initialize OpenAI client - defaults to OpenRouter
client_kwargs = {}
# Default to OpenRouter if no base_url provided
if base_url:
client_kwargs["base_url"] = base_url
else:
client_kwargs["base_url"] = "https://openrouter.ai/api/v1"
# Handle API key - OpenRouter is the primary provider
if api_key:
client_kwargs["api_key"] = api_key
else:
# Primary: OPENROUTER_API_KEY, fallback to direct provider keys
client_kwargs["api_key"] = os.getenv("OPENROUTER_API_KEY", "")
try:
self.client = OpenAI(**client_kwargs)
if not self.quiet_mode:
print(f"🤖 AI Agent initialized with model: {self.model}")
if base_url:
print(f"🔗 Using custom base URL: {base_url}")
# Always show API key info (masked) for debugging auth issues
key_used = client_kwargs.get("api_key", "none")
if key_used and key_used != "dummy-key" and len(key_used) > 12:
print(f"🔑 Using API key: {key_used[:8]}...{key_used[-4:]}")
else:
print(f"⚠️ Warning: API key appears invalid or missing (got: '{key_used[:20] if key_used else 'none'}...')")
except Exception as e:
raise RuntimeError(f"Failed to initialize OpenAI client: {e}")
# Get available tools with filtering
self.tools = get_tool_definitions(
enabled_toolsets=enabled_toolsets,
disabled_toolsets=disabled_toolsets,
quiet_mode=self.quiet_mode,
)
# Show tool configuration and store valid tool names for validation
self.valid_tool_names = set()
if self.tools:
self.valid_tool_names = {tool["function"]["name"] for tool in self.tools}
tool_names = sorted(self.valid_tool_names)
if not self.quiet_mode:
print(f"🛠️ Loaded {len(self.tools)} tools: {', '.join(tool_names)}")
# Show filtering info if applied
if enabled_toolsets:
print(f" ✅ Enabled toolsets: {', '.join(enabled_toolsets)}")
if disabled_toolsets:
print(f" ❌ Disabled toolsets: {', '.join(disabled_toolsets)}")
elif not self.quiet_mode:
print("🛠️ No tools loaded (all tools filtered out or unavailable)")
# Check tool requirements
if self.tools and not self.quiet_mode:
requirements = check_toolset_requirements()
missing_reqs = [name for name, available in requirements.items() if not available]
if missing_reqs:
print(f"⚠️ Some tools may not work due to missing requirements: {missing_reqs}")
# Show trajectory saving status
if self.save_trajectories and not self.quiet_mode:
print("📝 Trajectory saving enabled")
# Show ephemeral system prompt status
if self.ephemeral_system_prompt and not self.quiet_mode:
prompt_preview = self.ephemeral_system_prompt[:60] + "..." if len(self.ephemeral_system_prompt) > 60 else self.ephemeral_system_prompt
print(f"🔒 Ephemeral system prompt: '{prompt_preview}' (not saved to trajectories)")
# Show prompt caching status
if self._use_prompt_caching and not self.quiet_mode:
print(f"💾 Prompt caching: ENABLED (Claude via OpenRouter, {self._cache_ttl} TTL)")
# Session logging setup - auto-save conversation trajectories for debugging
self.session_start = datetime.now()
if session_id:
# Use provided session ID (e.g., from CLI)
self.session_id = session_id
else:
# Generate a new session ID
timestamp_str = self.session_start.strftime("%Y%m%d_%H%M%S")
short_uuid = uuid.uuid4().hex[:6]
self.session_id = f"{timestamp_str}_{short_uuid}"
# Setup logs directory
self.logs_dir = Path(__file__).parent / "logs"
self.logs_dir.mkdir(exist_ok=True)
self.session_log_file = self.logs_dir / f"session_{self.session_id}.json"
# Track conversation messages for session logging
self._session_messages: List[Dict[str, Any]] = []
# Initialize context compressor for automatic context management
# Compresses conversation when approaching model's context limit
# Configuration via environment variables (can be set in .env or cli-config.yaml)
compression_threshold = float(os.getenv("CONTEXT_COMPRESSION_THRESHOLD", "0.85"))
compression_model = os.getenv("CONTEXT_COMPRESSION_MODEL", "google/gemini-3-flash-preview")
compression_enabled = os.getenv("CONTEXT_COMPRESSION_ENABLED", "true").lower() in ("true", "1", "yes")
self.context_compressor = ContextCompressor(
model=self.model,
threshold_percent=compression_threshold,
summary_model=compression_model,
protect_first_n=3, # Keep system, first user, first assistant
protect_last_n=4, # Keep recent context
summary_target_tokens=500,
quiet_mode=self.quiet_mode,
)
self.compression_enabled = compression_enabled
if not self.quiet_mode:
if compression_enabled:
print(f"📊 Context limit: {self.context_compressor.context_length:,} tokens (compress at {int(compression_threshold*100)}% = {self.context_compressor.threshold_tokens:,})")
else:
print(f"📊 Context limit: {self.context_compressor.context_length:,} tokens (auto-compression disabled)")
# Pools of kawaii faces for random selection
KAWAII_SEARCH = [
"♪(´ε` )", "(｡◕‿◕｡)", "ヾ(＾∇＾)", "(◕ᴗ◕✿)", "( ˘▽˘)っ",
"٩(◕‿◕｡)۶", "(✿◠‿◠)", "♪～(´ε｀ )", "(ノ´ヮ`)ノ*:・゚✧", "＼(◎o◎)／",
]
KAWAII_READ = [
"φ(゜▽゜*)♪", "( ˘▽˘)っ", "(⌐■_■)", "٩(｡•́‿•̀｡)۶", "(◕‿◕✿)",
"ヾ(＠⌒ー⌒＠)ノ", "(✧ω✧)", "♪(๑ᴖ◡ᴖ๑)♪", "(≧◡≦)", "( ´ ▽ ` )ノ",
]
KAWAII_TERMINAL = [
"ヽ(>∀<☆)ノ", "(ノ°∀°)ノ", "٩(^ᴗ^)۶", "ヾ(⌐■_■)ノ♪", "(•̀ᴗ•́)و",
"┗(＾0＾)┓", "(｀・ω・´)", "＼(￣▽￣)／", "(ง •̀_•́)ง", "ヽ(´▽`)/",
]
KAWAII_BROWSER = [
"(ノ°∀°)ノ", "(☞゚ヮ゚)☞", "( ͡° ͜ʖ ͡°)", "┌( ಠ_ಠ)┘", "(⊙_⊙)？",
"ヾ(•ω•`)o", "(￣ω￣)", "( ˇωˇ )", "(ᵔᴥᵔ)", "＼(◎o◎)／",
]
KAWAII_CREATE = [
"✧*。٩(ˊᗜˋ*)و✧", "(ﾉ◕ヮ◕)ﾉ*:・ﾟ✧", "ヽ(>∀<☆)ノ", "٩(♡ε♡)۶", "(◕‿◕)♡",
"✿◕ ‿ ◕✿", "(*≧▽≦)", "ヾ(＾-＾)ノ", "(☆▽☆)", "°˖✧◝(⁰▿⁰)◜✧˖°",
]
KAWAII_SKILL = [
"ヾ(＠⌒ー⌒＠)ノ", "(๑˃ᴗ˂)ﻭ", "٩(◕‿◕｡)۶", "(✿╹◡╹)", "ヽ(・∀・)ノ",
"(ノ´ヮ`)ノ*:・ﾟ✧", "♪(๑ᴖ◡ᴖ๑)♪", "(◠‿◠)", "٩(ˊᗜˋ*)و", "(＾▽＾)",
"ヾ(＾∇＾)", "(★ω★)/", "٩(｡•́‿•̀｡)۶", "(◕ᴗ◕✿)", "＼(◎o◎)／",
"(✧ω✧)", "ヽ(>∀<☆)ノ", "( ˘▽˘)っ", "(≧◡≦) ♡", "ヾ(￣▽￣)",
]
KAWAII_THINK = [
"(っ°Д°;)っ", "(；′⌒`)", "(・_・ヾ", "( ´_ゝ`)", "(￣ヘ￣)",
"(。-`ω´-)", "( ˘︹˘ )", "(¬_¬)", "ヽ(ー_ー )ノ", "(；一_一)",
]
KAWAII_GENERIC = [
"♪(´ε` )", "(◕‿◕✿)", "ヾ(＾∇＾)", "٩(◕‿◕｡)۶", "(✿◠‿◠)",
"(ノ´ヮ`)ノ*:・ﾟ✧", "ヽ(>∀<☆)ノ", "(☆▽☆)", "( ˘▽˘)っ", "(≧◡≦)",
]
def _get_cute_tool_message(self, tool_name: str, args: dict, duration: float) -> str:
"""
Generate a kawaii ASCII/unicode art message for tool execution in CLI mode.
Args:
tool_name: Name of the tool being called
args: Arguments passed to the tool
duration: How long the tool took to execute
Returns:
A cute ASCII art message about what the tool did
"""
time_str = f"⏱ {duration:.1f}s"
# Web tools - show what we're searching/reading
if tool_name == "web_search":
query = args.get("query", "the web")
if len(query) > 40:
query = query[:37] + "..."
face = random.choice(self.KAWAII_SEARCH)
return f"{face} 🔍 Searching for '{query}'... {time_str}"
elif tool_name == "web_extract":
urls = args.get("urls", [])
face = random.choice(self.KAWAII_READ)
if urls:
url = urls[0] if isinstance(urls, list) else str(urls)
domain = url.replace("https://", "").replace("http://", "").split("/")[0]
if len(domain) > 25:
domain = domain[:22] + "..."
if len(urls) > 1:
return f"{face} 📖 Reading {domain} +{len(urls)-1} more... {time_str}"
return f"{face} 📖 Reading {domain}... {time_str}"
return f"{face} 📖 Reading pages... {time_str}"
elif tool_name == "web_crawl":
url = args.get("url", "website")
domain = url.replace("https://", "").replace("http://", "").split("/")[0]
if len(domain) > 25:
domain = domain[:22] + "..."
face = random.choice(self.KAWAII_READ)
return f"{face} 🕸️ Crawling {domain}... {time_str}"
# Terminal tool
elif tool_name == "terminal":
command = args.get("command", "")
if len(command) > 30:
command = command[:27] + "..."
face = random.choice(self.KAWAII_TERMINAL)
return f"{face} 💻 $ {command} {time_str}"
# Browser tools
elif tool_name == "browser_navigate":
url = args.get("url", "page")
domain = url.replace("https://", "").replace("http://", "").split("/")[0]
if len(domain) > 25:
domain = domain[:22] + "..."
face = random.choice(self.KAWAII_BROWSER)
return f"{face} 🌐 → {domain} {time_str}"
elif tool_name == "browser_snapshot":
face = random.choice(self.KAWAII_BROWSER)
return f"{face} 📸 *snap* {time_str}"
elif tool_name == "browser_click":
element = args.get("ref", "element")
face = random.choice(self.KAWAII_BROWSER)
return f"{face} 👆 *click* {element} {time_str}"
elif tool_name == "browser_type":
text = args.get("text", "")
if len(text) > 15:
text = text[:12] + "..."
face = random.choice(self.KAWAII_BROWSER)
return f"{face} ⌨️ typing '{text}' {time_str}"
elif tool_name == "browser_scroll":
direction = args.get("direction", "down")
arrow = "↓" if direction == "down" else "↑"
face = random.choice(self.KAWAII_BROWSER)
return f"{face} {arrow} scrolling {direction}... {time_str}"
elif tool_name == "browser_back":
face = random.choice(self.KAWAII_BROWSER)
return f"{face} ← going back... {time_str}"
elif tool_name == "browser_vision":
face = random.choice(self.KAWAII_BROWSER)
return f"{face} 👁️ analyzing visually... {time_str}"
# Image generation
elif tool_name == "image_generate":
prompt = args.get("prompt", "image")
if len(prompt) > 20:
prompt = prompt[:17] + "..."
face = random.choice(self.KAWAII_CREATE)
return f"{face} 🎨 creating '{prompt}'... {time_str}"
# Skills - use large pool for variety
elif tool_name == "skills_list":
category = args.get("category", "skills")
face = random.choice(self.KAWAII_SKILL)
return f"{face} 📋 listing {category} skills... {time_str}"
elif tool_name == "skill_view":
name = args.get("name", "skill")
face = random.choice(self.KAWAII_SKILL)
return f"{face} 📖 loading {name}... {time_str}"
# File tools
elif tool_name == "read_file":
path = args.get("path", "file")
if len(path) > 30:
path = "..." + path[-27:]
face = random.choice(self.KAWAII_READ)
return f"{face} 📖 reading \"{path}\" {time_str}"
elif tool_name == "write_file":
path = args.get("path", "file")
if len(path) > 30:
path = "..." + path[-27:]
face = random.choice(self.KAWAII_CREATE)
return f"{face} ✍️ writing \"{path}\" {time_str}"
elif tool_name == "patch":
path = args.get("path", "file")
if path and len(path) > 30:
path = "..." + path[-27:]
face = random.choice(self.KAWAII_CREATE)
return f"{face} 🔧 patching \"{path}\" {time_str}"
elif tool_name == "search":
pattern = args.get("pattern", "")
if len(pattern) > 25:
pattern = pattern[:22] + "..."
face = random.choice(self.KAWAII_SEARCH)
return f"{face} 🔎 searching \"{pattern}\" {time_str}"
# TTS
elif tool_name == "text_to_speech":
text = args.get("text", "")
if len(text) > 25:
text = text[:22] + "..."
face = random.choice(self.KAWAII_CREATE)
return f"{face} 🔊 speaking \"{text}\" {time_str}"
# Vision tools
elif tool_name == "vision_analyze":
question = args.get("question", "")
if len(question) > 25:
question = question[:22] + "..."
face = random.choice(self.KAWAII_BROWSER)
return f"{face} 👁️✨ analyzing \"{question}\" {time_str}"
# Mixture of agents
elif tool_name == "mixture_of_agents":
prompt = args.get("user_prompt", "")
if len(prompt) > 25:
prompt = prompt[:22] + "..."
face = random.choice(self.KAWAII_THINK)
return f"{face} 🧠💭 deep thinking \"{prompt}\" {time_str}"
# Default fallback - random generic kawaii with primary arg preview
else:
face = random.choice(self.KAWAII_GENERIC)
preview = _build_tool_preview(tool_name, args)
if preview:
return f"{face} ⚡ {tool_name}... \"{preview}\" {time_str}"
return f"{face} ⚡ {tool_name}... {time_str}"
def _has_content_after_think_block(self, content: str) -> bool:
"""
Check if content has actual text after any blocks.
This detects cases where the model only outputs reasoning but no actual
response, which indicates an incomplete generation that should be retried.
Args:
content: The assistant message content to check
Returns:
True if there's meaningful content after think blocks, False otherwise
"""
if not content:
return False
import re
# Remove all ... blocks (including nested ones, non-greedy)
cleaned = re.sub(r'.*?', '', content, flags=re.DOTALL)
# Check if there's any non-whitespace content remaining
return bool(cleaned.strip())
def _extract_reasoning(self, assistant_message) -> Optional[str]:
"""
Extract reasoning/thinking content from an assistant message.
OpenRouter and various providers can return reasoning in multiple formats:
1. message.reasoning - Direct reasoning field (DeepSeek, Qwen, etc.)
2. message.reasoning_content - Alternative field (Moonshot AI, Novita, etc.)
3. message.reasoning_details - Array of {type, summary, ...} objects (OpenRouter unified)
Args:
assistant_message: The assistant message object from the API response
Returns:
Combined reasoning text, or None if no reasoning found
"""
reasoning_parts = []
# Check direct reasoning field
if hasattr(assistant_message, 'reasoning') and assistant_message.reasoning:
reasoning_parts.append(assistant_message.reasoning)
# Check reasoning_content field (alternative name used by some providers)
if hasattr(assistant_message, 'reasoning_content') and assistant_message.reasoning_content:
# Don't duplicate if same as reasoning
if assistant_message.reasoning_content not in reasoning_parts:
reasoning_parts.append(assistant_message.reasoning_content)
# Check reasoning_details array (OpenRouter unified format)
# Format: [{"type": "reasoning.summary", "summary": "...", ...}, ...]
if hasattr(assistant_message, 'reasoning_details') and assistant_message.reasoning_details:
for detail in assistant_message.reasoning_details:
if isinstance(detail, dict):
# Extract summary from reasoning detail object
summary = detail.get('summary') or detail.get('content') or detail.get('text')
if summary and summary not in reasoning_parts:
reasoning_parts.append(summary)
# Combine all reasoning parts
if reasoning_parts:
return "\n\n".join(reasoning_parts)
return None
def _get_messages_up_to_last_assistant(self, messages: List[Dict]) -> List[Dict]:
"""
Get messages up to (but not including) the last assistant turn.
This is used when we need to "roll back" to the last successful point
in the conversation, typically when the final assistant message is
incomplete or malformed.
Args:
messages: Full message list
Returns:
Messages up to the last complete assistant turn (ending with user/tool message)
"""
if not messages:
return []
# Find the index of the last assistant message
last_assistant_idx = None
for i in range(len(messages) - 1, -1, -1):
if messages[i].get("role") == "assistant":
last_assistant_idx = i
break
if last_assistant_idx is None:
# No assistant message found, return all messages
return messages.copy()
# Return everything up to (not including) the last assistant message
return messages[:last_assistant_idx]
def _format_tools_for_system_message(self) -> str:
"""
Format tool definitions for the system message in the trajectory format.
Returns:
str: JSON string representation of tool definitions
"""
if not self.tools:
return "[]"
# Convert tool definitions to the format expected in trajectories
formatted_tools = []
for tool in self.tools:
func = tool["function"]
formatted_tool = {
"name": func["name"],
"description": func.get("description", ""),
"parameters": func.get("parameters", {}),
"required": None # Match the format in the example
}
formatted_tools.append(formatted_tool)
return json.dumps(formatted_tools, ensure_ascii=False)
@staticmethod
def _convert_scratchpad_to_think(content: str) -> str:
"""
Convert tags to tags in content.
When native thinking/reasoning is disabled and the model is prompted to
reason inside XML tags instead, this converts those
to the standard format used in our trajectory storage.
Args:
content: Assistant message content that may contain scratchpad tags
Returns:
Content with scratchpad tags replaced by think tags
"""
if not content or "" not in content:
return content
return content.replace("", "").replace("", "")
@staticmethod
def _has_incomplete_scratchpad(content: str) -> bool:
"""
Check if content has an opening without a closing tag.
This indicates the model ran out of output tokens mid-reasoning, producing
a broken turn that shouldn't be saved. The caller should retry or discard.
Args:
content: Assistant message content to check
Returns:
True if there's an unclosed scratchpad tag
"""
if not content:
return False
return "" in content and "" not in content
def _convert_to_trajectory_format(self, messages: List[Dict[str, Any]], user_query: str, completed: bool) -> List[Dict[str, Any]]:
"""
Convert internal message format to trajectory format for saving.
Args:
messages (List[Dict]): Internal message history
user_query (str): Original user query
completed (bool): Whether the conversation completed successfully
Returns:
List[Dict]: Messages in trajectory format
"""
trajectory = []
# Add system message with tool definitions
system_msg = (
"You are a function calling AI model. You are provided with function signatures within XML tags. "
"You may call one or more functions to assist with the user query. If available tools are not relevant in assisting "
"with user query, just respond in natural conversational language. Don't make assumptions about what values to plug "
"into functions. After calling & executing the functions, you will be provided with function results within "
" XML tags. Here are the available tools:\n"
f"\n{self._format_tools_for_system_message()}\n\n"
"For each function call return a JSON object, with the following pydantic model json schema for each:\n"
"{'title': 'FunctionCall', 'type': 'object', 'properties': {'name': {'title': 'Name', 'type': 'string'}, "
"'arguments': {'title': 'Arguments', 'type': 'object'}}, 'required': ['name', 'arguments']}\n"
"Each function call should be enclosed within XML tags.\n"
"Example:\n\n{'name': ,'arguments': }\n"
)
trajectory.append({
"from": "system",
"value": system_msg
})
# Add the actual user prompt (from the dataset) as the first human message
trajectory.append({
"from": "human",
"value": user_query
})
# Calculate where agent responses start in the messages list.
# Prefill messages are ephemeral (only used to prime model response style)
# so we skip them entirely in the saved trajectory.
# Layout: [*prefill_msgs, actual_user_msg, ...agent_responses...]
num_prefill = len(self.prefill_messages) if self.prefill_messages else 0
i = num_prefill + 1 # Skip prefill messages + the actual user message (already added above)
while i < len(messages):
msg = messages[i]
if msg["role"] == "assistant":
# Check if this message has tool calls
if "tool_calls" in msg and msg["tool_calls"]:
# Format assistant message with tool calls
# Add tags around reasoning for trajectory storage
content = ""
# Prepend reasoning in tags if available (native thinking tokens)
if msg.get("reasoning") and msg["reasoning"].strip():
content = f"\n{msg['reasoning']}\n\n"
if msg.get("content") and msg["content"].strip():
# Convert any tags to tags
# (used when native thinking is disabled and model reasons via XML)
content += self._convert_scratchpad_to_think(msg["content"]) + "\n"
# Add tool calls wrapped in XML tags
for tool_call in msg["tool_calls"]:
# Parse arguments - should always succeed since we validate during conversation
# but keep try-except as safety net
try:
arguments = json.loads(tool_call["function"]["arguments"]) if isinstance(tool_call["function"]["arguments"], str) else tool_call["function"]["arguments"]
except json.JSONDecodeError:
# This shouldn't happen since we validate and retry during conversation,
# but if it does, log warning and use empty dict
logging.warning(f"Unexpected invalid JSON in trajectory conversion: {tool_call['function']['arguments'][:100]}")
arguments = {}
tool_call_json = {
"name": tool_call["function"]["name"],
"arguments": arguments
}
content += f"\n{json.dumps(tool_call_json, ensure_ascii=False)}\n\n"
# Ensure every gpt turn has a block (empty if no reasoning)
# so the format is consistent for training data
if "" not in content:
content = "\n\n" + content
trajectory.append({
"from": "gpt",
"value": content.rstrip()
})
# Collect all subsequent tool responses
tool_responses = []
j = i + 1
while j < len(messages) and messages[j]["role"] == "tool":
tool_msg = messages[j]
# Format tool response with XML tags
tool_response = f"\n"
# Try to parse tool content as JSON if it looks like JSON
tool_content = tool_msg["content"]
try:
if tool_content.strip().startswith(("{", "[")):
tool_content = json.loads(tool_content)
except (json.JSONDecodeError, AttributeError):
pass # Keep as string if not valid JSON
tool_response += json.dumps({
"tool_call_id": tool_msg.get("tool_call_id", ""),
"name": msg["tool_calls"][len(tool_responses)]["function"]["name"] if len(tool_responses) < len(msg["tool_calls"]) else "unknown",
"content": tool_content
}, ensure_ascii=False)
tool_response += "\n"
tool_responses.append(tool_response)
j += 1
# Add all tool responses as a single message
if tool_responses:
trajectory.append({
"from": "tool",
"value": "\n".join(tool_responses)
})
i = j - 1 # Skip the tool messages we just processed
else:
# Regular assistant message without tool calls
# Add tags around reasoning for trajectory storage
content = ""
# Prepend reasoning in tags if available (native thinking tokens)
if msg.get("reasoning") and msg["reasoning"].strip():
content = f"\n{msg['reasoning']}\n\n"
# Convert any tags to tags
# (used when native thinking is disabled and model reasons via XML)
raw_content = msg["content"] or ""
content += self._convert_scratchpad_to_think(raw_content)
# Ensure every gpt turn has a block (empty if no reasoning)
if "" not in content:
content = "\n\n" + content
trajectory.append({
"from": "gpt",
"value": content.strip()
})
elif msg["role"] == "user":
trajectory.append({
"from": "human",
"value": msg["content"]
})
i += 1
return trajectory
def _save_trajectory(self, messages: List[Dict[str, Any]], user_query: str, completed: bool):
"""
Save conversation trajectory to JSONL file.
Args:
messages (List[Dict]): Complete message history
user_query (str): Original user query
completed (bool): Whether the conversation completed successfully
"""
if not self.save_trajectories:
return
# Convert messages to trajectory format
trajectory = self._convert_to_trajectory_format(messages, user_query, completed)
# Determine which file to save to
filename = "trajectory_samples.jsonl" if completed else "failed_trajectories.jsonl"
# Create trajectory entry
entry = {
"conversations": trajectory,
"timestamp": datetime.now().isoformat(),
"model": self.model,
"completed": completed
}
# Append to JSONL file
try:
with open(filename, "a", encoding="utf-8") as f:
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
print(f"💾 Trajectory saved to {filename}")
except Exception as e:
print(f"⚠️ Failed to save trajectory: {e}")
def _log_api_payload(self, turn_number: int, api_kwargs: Dict[str, Any], response=None):
"""
[TEMPORARY DEBUG] Log the full API payload and response token metrics
for each agent turn to a per-session JSONL file for inspection.
Writes one JSON line per turn to logs/payload_.jsonl.
Tool schemas are summarized (just names) to keep logs readable.
Args:
turn_number: Which API call this is (1-indexed)
api_kwargs: The full kwargs dict being passed to chat.completions.create
response: The API response object (optional, added after the call completes)
"""
try:
payload_log_file = self.logs_dir / f"payload_{self.session_id}.jsonl"
# Build a serializable copy of the request payload
payload = {
"turn": turn_number,
"timestamp": datetime.now().isoformat(),
"model": api_kwargs.get("model"),
"max_tokens": api_kwargs.get("max_tokens"),
"extra_body": api_kwargs.get("extra_body"),
"num_tools": len(api_kwargs.get("tools") or []),
"tool_names": [t["function"]["name"] for t in (api_kwargs.get("tools") or [])],
"messages": api_kwargs.get("messages", []),
}
# Add response token metrics if available
if response is not None:
try:
usage_raw = response.usage.model_dump() if hasattr(response.usage, 'model_dump') else {}
payload["response"] = {
# Core token counts
"prompt_tokens": usage_raw.get("prompt_tokens"),
"completion_tokens": usage_raw.get("completion_tokens"),
"total_tokens": usage_raw.get("total_tokens"),
# Completion breakdown (reasoning tokens, etc.)
"completion_tokens_details": usage_raw.get("completion_tokens_details"),
# Prompt breakdown (cached tokens, etc.)
"prompt_tokens_details": usage_raw.get("prompt_tokens_details"),
# Cost tracking
"cost": usage_raw.get("cost"),
"is_byok": usage_raw.get("is_byok"),
"cost_details": usage_raw.get("cost_details"),
# Provider info (top-level field from OpenRouter)
"provider": getattr(response, 'provider', None),
"response_model": getattr(response, 'model', None),
}
except Exception:
payload["response"] = {"error": "failed to extract usage"}
with open(payload_log_file, "a", encoding="utf-8") as f:
f.write(json.dumps(payload, ensure_ascii=False, default=str) + "\n")
except Exception as e:
# Silent fail - don't interrupt the agent for debug logging
if self.verbose_logging:
logging.warning(f"Failed to log API payload: {e}")
def _save_session_log(self, messages: List[Dict[str, Any]] = None):
"""
Save the current session trajectory to the logs directory.
Automatically called after each conversation turn to maintain
a complete log of the session for debugging and inspection.
Args:
messages: Message history to save (uses self._session_messages if not provided)
"""
messages = messages or self._session_messages
if not messages:
return
try:
# Extract the actual user query for the trajectory format.
# Skip prefill messages (they're ephemeral and shouldn't appear in trajectories)
# so the first user message we find is the real task prompt.
first_user_query = ""
start_idx = len(self.prefill_messages) if self.prefill_messages else 0
for msg in messages[start_idx:]:
if msg.get("role") == "user":
first_user_query = msg.get("content", "")
break
# Convert to trajectory format
trajectory = self._convert_to_trajectory_format(messages, first_user_query, True)
# Build the session log entry
entry = {
"session_id": self.session_id,
"model": self.model,
"session_start": self.session_start.isoformat(),
"last_updated": datetime.now().isoformat(),
"message_count": len(messages),
"conversations": trajectory,
}
# Write to session log file (overwrite with latest state)
with open(self.session_log_file, "w", encoding="utf-8") as f:
json.dump(entry, f, indent=2, ensure_ascii=False)
except Exception as e:
# Silent fail - don't interrupt the user experience for logging issues
if self.verbose_logging:
logging.warning(f"Failed to save session log: {e}")
def interrupt(self, message: str = None) -> None:
"""
Request the agent to interrupt its current tool-calling loop.
Call this from another thread (e.g., input handler, message receiver)
to gracefully stop the agent and process a new message.
Also signals long-running tool executions (e.g. terminal commands)
to terminate early, so the agent can respond immediately.
Args:
message: Optional new message that triggered the interrupt.
If provided, the agent will include this in its response context.
Example (CLI):
# In a separate input thread:
if user_typed_something:
agent.interrupt(user_input)
Example (Messaging):
# When new message arrives for active session:
if session_has_running_agent:
running_agent.interrupt(new_message.text)
"""
self._interrupt_requested = True
self._interrupt_message = message
# Signal the terminal tool to kill any running subprocess immediately
_set_terminal_interrupt(True)
if not self.quiet_mode:
print(f"\n⚡ Interrupt requested" + (f": '{message[:40]}...'" if message and len(message) > 40 else f": '{message}'" if message else ""))
def clear_interrupt(self) -> None:
"""Clear any pending interrupt request."""
self._interrupt_requested = False
self._interrupt_message = None
_set_terminal_interrupt(False)
@property
def is_interrupted(self) -> bool:
"""Check if an interrupt has been requested."""
return self._interrupt_requested
def run_conversation(
self,
user_message: str,
system_message: str = None,
conversation_history: List[Dict[str, Any]] = None,
task_id: str = None
) -> Dict[str, Any]:
"""
Run a complete conversation with tool calling until completion.
Args:
user_message (str): The user's message/question
system_message (str): Custom system message (optional, overrides ephemeral_system_prompt if provided)
conversation_history (List[Dict]): Previous conversation messages (optional)
task_id (str): Unique identifier for this task to isolate VMs between concurrent tasks (optional, auto-generated if not provided)
Returns:
Dict: Complete conversation result with final response and message history
"""
# Generate unique task_id if not provided to isolate VMs between concurrent tasks
import uuid
effective_task_id = task_id or str(uuid.uuid4())
# Reset retry counters at the start of each conversation to prevent state leakage
self._invalid_tool_retries = 0
self._invalid_json_retries = 0
self._empty_content_retries = 0
# Initialize conversation
messages = conversation_history or []
# Inject prefill messages at the start of conversation (before user's actual prompt)
# This is used for few-shot priming, e.g., a greeting exchange to set response style
if self.prefill_messages and not conversation_history:
for prefill_msg in self.prefill_messages:
messages.append(prefill_msg.copy())
# Add user message
messages.append({
"role": "user",
"content": user_message
})
if not self.quiet_mode:
print(f"💬 Starting conversation: '{user_message[:60]}{'...' if len(user_message) > 60 else ''}'")
# ── Build the full system prompt ──
# Layers (in order):
# 1. Default agent identity (always present)
# 2. User / gateway system prompt (if provided)
# 3. Skills guidance (if skills tools are loaded)
# 4. Context files (SOUL.md, AGENTS.md, .cursorrules)
# 5. Current date & time
# 6. Platform-specific formatting hint
prompt_parts = [DEFAULT_AGENT_IDENTITY]
# Layer in the caller-supplied system prompt (explicit > ephemeral).
caller_prompt = system_message if system_message is not None else self.ephemeral_system_prompt
if caller_prompt:
prompt_parts.append(caller_prompt)
# Auto-include skills guidance if skills tools are available.
has_skills_tools = any(name in self.valid_tool_names for name in ['skills_list', 'skill_view'])
skills_prompt = build_skills_system_prompt() if has_skills_tools else ""
if skills_prompt:
prompt_parts.append(skills_prompt)
# Auto-include context files (SOUL.md, AGENTS.md, .cursorrules).
context_files_prompt = build_context_files_prompt()
if context_files_prompt:
prompt_parts.append(context_files_prompt)
# Current local date and time so the model is never confused about
# what day/time it is (LLM training cutoffs can otherwise mislead it).
now = datetime.now()
prompt_parts.append(
f"Current local date and time: {now.strftime('%A, %B %d, %Y %I:%M %p')}"
)
# Platform-specific formatting hint (no markdown on WhatsApp, etc.).
platform_key = (self.platform or "").lower().strip()
if platform_key in PLATFORM_HINTS:
prompt_parts.append(PLATFORM_HINTS[platform_key])
active_system_prompt = "\n\n".join(prompt_parts)
# Main conversation loop
api_call_count = 0
final_response = None
interrupted = False
# Clear any stale interrupt state at start
self.clear_interrupt()
while api_call_count < self.max_iterations:
# Check for interrupt request (e.g., user sent new message)
if self._interrupt_requested:
interrupted = True
if not self.quiet_mode:
print(f"\n⚡ Breaking out of tool loop due to interrupt...")
break
api_call_count += 1
# Prepare messages for API call
# If we have an ephemeral system prompt, prepend it to the messages
# Note: Reasoning is embedded in content via tags for trajectory storage.
# However, providers like Moonshot AI require a separate 'reasoning_content' field
# on assistant messages with tool_calls. We handle both cases here.
api_messages = []
for msg in messages:
api_msg = msg.copy()
# For ALL assistant messages, pass reasoning back to the API
# This ensures multi-turn reasoning context is preserved
if msg.get("role") == "assistant":
reasoning_text = msg.get("reasoning")
if reasoning_text:
# Add reasoning_content for API compatibility (Moonshot AI, Novita, OpenRouter)
api_msg["reasoning_content"] = reasoning_text
# Remove 'reasoning' field - it's for trajectory storage only
# We've copied it to 'reasoning_content' for the API above
if "reasoning" in api_msg:
api_msg.pop("reasoning")
# Keep 'reasoning_details' - OpenRouter uses this for multi-turn reasoning context
# The signature field helps maintain reasoning continuity
api_messages.append(api_msg)
if active_system_prompt:
# Insert system message at the beginning
api_messages = [{"role": "system", "content": active_system_prompt}] + api_messages
# Apply Anthropic prompt caching for Claude models via OpenRouter.
# Auto-detected: if model name contains "claude" and base_url is OpenRouter,
# inject cache_control breakpoints (system + last 3 messages) to reduce
# input token costs by ~75% on multi-turn conversations.
if self._use_prompt_caching:
api_messages = apply_anthropic_cache_control(api_messages, cache_ttl=self._cache_ttl)
# Calculate approximate request size for logging
total_chars = sum(len(str(msg)) for msg in api_messages)
approx_tokens = total_chars // 4 # Rough estimate: 4 chars per token
# Thinking spinner for quiet mode (animated during API call)
thinking_spinner = None
if not self.quiet_mode:
print(f"\n{self.log_prefix}🔄 Making API call #{api_call_count}/{self.max_iterations}...")
print(f"{self.log_prefix} 📊 Request size: {len(api_messages)} messages, ~{approx_tokens:,} tokens (~{total_chars:,} chars)")
print(f"{self.log_prefix} 🔧 Available tools: {len(self.tools) if self.tools else 0}")
else:
# Animated thinking spinner in quiet mode
face = random.choice(KawaiiSpinner.KAWAII_THINKING)
verb = random.choice(KawaiiSpinner.THINKING_VERBS)
spinner_type = random.choice(['brain', 'sparkle', 'pulse', 'moon', 'star'])
thinking_spinner = KawaiiSpinner(f"{face} {verb}...", spinner_type=spinner_type)
thinking_spinner.start()
# Log request details if verbose
if self.verbose_logging:
logging.debug(f"API Request - Model: {self.model}, Messages: {len(messages)}, Tools: {len(self.tools) if self.tools else 0}")
logging.debug(f"Last message role: {messages[-1]['role'] if messages else 'none'}")
logging.debug(f"Total message size: ~{approx_tokens:,} tokens")
api_start_time = time.time()
retry_count = 0
max_retries = 6 # Increased to allow longer backoff periods
while retry_count <= max_retries:
try:
# Build OpenRouter provider preferences if specified
provider_preferences = {}
if self.providers_allowed:
provider_preferences["only"] = self.providers_allowed
if self.providers_ignored:
provider_preferences["ignore"] = self.providers_ignored
if self.providers_order:
provider_preferences["order"] = self.providers_order
if self.provider_sort:
provider_preferences["sort"] = self.provider_sort
# Make API call with tools - increased timeout for long responses
api_kwargs = {
"model": self.model,
"messages": api_messages,
"tools": self.tools if self.tools else None,
"timeout": 600.0 # 10 minute timeout for very long responses
}
# Add max_tokens if configured (overrides model default)
if self.max_tokens is not None:
api_kwargs["max_tokens"] = self.max_tokens
# Add extra_body for OpenRouter (provider preferences + reasoning)
extra_body = {}
# Add provider preferences if specified
if provider_preferences:
extra_body["provider"] = provider_preferences
# Configure reasoning for OpenRouter
# If reasoning_config is explicitly provided, use it (allows disabling/customizing)
# Otherwise, default to xhigh effort for OpenRouter models
if "openrouter" in self.base_url.lower():
if self.reasoning_config is not None:
extra_body["reasoning"] = self.reasoning_config
else:
extra_body["reasoning"] = {
"enabled": True,
"effort": "xhigh"
}
if extra_body:
api_kwargs["extra_body"] = extra_body
response = self.client.chat.completions.create(**api_kwargs)
api_duration = time.time() - api_start_time
# Stop thinking spinner with cute completion message
if thinking_spinner:
face = random.choice(["(◕‿◕✿)", "ヾ(＾∇＾)", "(≧◡≦)", "✧٩(ˊᗜˋ*)و✧", "(*^▽^*)"])
thinking_spinner.stop(f"{face} got it! ({api_duration:.1f}s)")
thinking_spinner = None
if not self.quiet_mode:
print(f"{self.log_prefix}⏱️ API call completed in {api_duration:.2f}s")
if self.verbose_logging:
# Log response with provider info if available
resp_model = getattr(response, 'model', 'N/A') if response else 'N/A'
logging.debug(f"API Response received - Model: {resp_model}, Usage: {response.usage if hasattr(response, 'usage') else 'N/A'}")
# [DEBUG] Log the full API payload + response token metrics
self._log_api_payload(api_call_count, api_kwargs, response=response)
# Validate response has valid choices before proceeding
if response is None or not hasattr(response, 'choices') or response.choices is None or len(response.choices) == 0:
# Stop spinner before printing error messages
if thinking_spinner:
thinking_spinner.stop(f"(´;ω;`) oops, retrying...")
thinking_spinner = None
# This is often rate limiting or provider returning malformed response
retry_count += 1
error_details = []
if response is None:
error_details.append("response is None")
elif not hasattr(response, 'choices'):
error_details.append("response has no 'choices' attribute")
elif response.choices is None:
error_details.append("response.choices is None")
else:
error_details.append("response.choices is empty")
# Check for error field in response (some providers include this)
error_msg = "Unknown"
provider_name = "Unknown"
if response and hasattr(response, 'error') and response.error:
error_msg = str(response.error)
# Try to extract provider from error metadata
if hasattr(response.error, 'metadata') and response.error.metadata:
provider_name = response.error.metadata.get('provider_name', 'Unknown')
elif response and hasattr(response, 'message') and response.message:
error_msg = str(response.message)
# Try to get provider from model field (OpenRouter often returns actual model used)
if provider_name == "Unknown" and response and hasattr(response, 'model') and response.model:
provider_name = f"model={response.model}"
# Check for x-openrouter-provider or similar metadata
if provider_name == "Unknown" and response:
# Log all response attributes for debugging
resp_attrs = {k: str(v)[:100] for k, v in vars(response).items() if not k.startswith('_')}
if self.verbose_logging:
logging.debug(f"Response attributes for invalid response: {resp_attrs}")
print(f"{self.log_prefix}⚠️ Invalid API response (attempt {retry_count}/{max_retries}): {', '.join(error_details)}")
print(f"{self.log_prefix} 🏢 Provider: {provider_name}")
print(f"{self.log_prefix} 📝 Provider message: {error_msg[:200]}")
print(f"{self.log_prefix} ⏱️ Response time: {api_duration:.2f}s (fast response often indicates rate limiting)")
if retry_count > max_retries:
print(f"{self.log_prefix}❌ Max retries ({max_retries}) exceeded for invalid responses. Giving up.")
logging.error(f"{self.log_prefix}Invalid API response after {max_retries} retries.")
return {
"messages": messages,
"completed": False,
"api_calls": api_call_count,
"error": f"Invalid API response (choices is None/empty). Likely rate limited by provider.",
"failed": True # Mark as failure for filtering
}
# Longer backoff for rate limiting (likely cause of None choices)
wait_time = min(5 * (2 ** (retry_count - 1)), 120) # 5s, 10s, 20s, 40s, 80s, 120s
print(f"{self.log_prefix}⏳ Retrying in {wait_time}s (extended backoff for possible rate limit)...")
logging.warning(f"Invalid API response (retry {retry_count}/{max_retries}): {', '.join(error_details)} | Provider: {provider_name}")
# Sleep in small increments to stay responsive to interrupts
sleep_end = time.time() + wait_time
while time.time() < sleep_end:
if self._interrupt_requested:
print(f"{self.log_prefix}⚡ Interrupt detected during retry wait, aborting.")
return {
"final_response": "Operation interrupted.",
"messages": messages,
"api_calls": api_call_count,
"completed": False,
"interrupted": True,
}
time.sleep(0.2)
continue # Retry the API call
# Check finish_reason before proceeding
finish_reason = response.choices[0].finish_reason
# Handle "length" finish_reason - response was truncated
if finish_reason == "length":
print(f"{self.log_prefix}⚠️ Response truncated (finish_reason='length') - model hit max output tokens")
# If we have prior messages, roll back to last complete state
if len(messages) > 1:
print(f"{self.log_prefix} ⏪ Rolling back to last complete assistant turn")
rolled_back_messages = self._get_messages_up_to_last_assistant(messages)
# Clean up VM and browser
try:
cleanup_vm(effective_task_id)
except Exception as e:
if self.verbose_logging:
logging.warning(f"Failed to cleanup VM for task {effective_task_id}: {e}")
try:
cleanup_browser(effective_task_id)
except Exception as e:
if self.verbose_logging:
logging.warning(f"Failed to cleanup browser for task {effective_task_id}: {e}")
return {
"final_response": None,
"messages": rolled_back_messages,
"api_calls": api_call_count,
"completed": False,
"partial": True,
"error": "Response truncated due to output length limit"
}
else:
# First message was truncated - mark as failed
print(f"{self.log_prefix}❌ First response truncated - cannot recover")
return {
"final_response": None,
"messages": messages,
"api_calls": api_call_count,
"completed": False,
"failed": True,
"error": "First response truncated due to output length limit"
}
# Track actual token usage from response for context management
if hasattr(response, 'usage') and response.usage:
usage_dict = {
"prompt_tokens": getattr(response.usage, 'prompt_tokens', 0),
"completion_tokens": getattr(response.usage, 'completion_tokens', 0),
"total_tokens": getattr(response.usage, 'total_tokens', 0),
}
self.context_compressor.update_from_response(usage_dict)
if self.verbose_logging:
logging.debug(f"Token usage: prompt={usage_dict['prompt_tokens']:,}, completion={usage_dict['completion_tokens']:,}, total={usage_dict['total_tokens']:,}")
# Log cache hit stats when prompt caching is active
if self._use_prompt_caching:
details = getattr(response.usage, 'prompt_tokens_details', None)
cached = getattr(details, 'cached_tokens', 0) or 0 if details else 0
written = getattr(details, 'cache_write_tokens', 0) or 0 if details else 0
prompt = usage_dict["prompt_tokens"]
hit_pct = (cached / prompt * 100) if prompt > 0 else 0
if not self.quiet_mode:
print(f"{self.log_prefix} 💾 Cache: {cached:,}/{prompt:,} tokens ({hit_pct:.0f}% hit, {written:,} written)")
break # Success, exit retry loop
except Exception as api_error:
# Stop spinner before printing error messages
if thinking_spinner:
thinking_spinner.stop(f"(╥_╥) error, retrying...")
thinking_spinner = None
retry_count += 1
elapsed_time = time.time() - api_start_time
# Enhanced error logging
error_type = type(api_error).__name__
error_msg = str(api_error).lower()
print(f"{self.log_prefix}⚠️ API call failed (attempt {retry_count}/{max_retries}): {error_type}")
print(f"{self.log_prefix} ⏱️ Time elapsed before failure: {elapsed_time:.2f}s")
print(f"{self.log_prefix} 📝 Error: {str(api_error)[:200]}")
print(f"{self.log_prefix} 📊 Request context: {len(api_messages)} messages, ~{approx_tokens:,} tokens, {len(self.tools) if self.tools else 0} tools")
# Check for interrupt before deciding to retry
if self._interrupt_requested:
print(f"{self.log_prefix}⚡ Interrupt detected during error handling, aborting retries.")
return {
"final_response": "Operation interrupted.",
"messages": messages,
"api_calls": api_call_count,
"completed": False,
"interrupted": True,
}
# Check for non-retryable client errors (4xx HTTP status codes).
# These indicate a problem with the request itself (bad model ID,
# invalid API key, forbidden, etc.) and will never succeed on retry.
is_client_error = any(phrase in error_msg for phrase in [
'error code: 400', 'error code: 401', 'error code: 403',
'error code: 404', 'error code: 422',
'is not a valid model', 'invalid model', 'model not found',
'invalid api key', 'invalid_api_key', 'authentication',
'unauthorized', 'forbidden', 'not found',
])
if is_client_error:
print(f"{self.log_prefix}❌ Non-retryable client error detected. Aborting immediately.")
print(f"{self.log_prefix} 💡 This type of error won't be fixed by retrying.")
logging.error(f"{self.log_prefix}Non-retryable client error: {api_error}")
return {
"final_response": None,
"messages": messages,
"api_calls": api_call_count,
"completed": False,
"failed": True,
"error": str(api_error),
}
# Check for non-retryable errors (context length exceeded)
is_context_length_error = any(phrase in error_msg for phrase in [
'context length', 'maximum context', 'token limit',
'too many tokens', 'reduce the length', 'exceeds the limit'
])
if is_context_length_error:
print(f"{self.log_prefix}⚠️ Context length exceeded - attempting compression...")
# Try to compress and retry
original_len = len(messages)
messages = self.context_compressor.compress(messages, current_tokens=approx_tokens)
if len(messages) < original_len:
# Compression was possible, retry
print(f"{self.log_prefix} 🗜️ Compressed {original_len} → {len(messages)} messages, retrying...")
continue # Retry with compressed messages
else:
# Can't compress further
print(f"{self.log_prefix}❌ Context length exceeded and cannot compress further.")
print(f"{self.log_prefix} 💡 The conversation has accumulated too much content.")
logging.error(f"{self.log_prefix}Context length exceeded: {approx_tokens:,} tokens. Cannot compress further.")
return {
"messages": messages,
"completed": False,
"api_calls": api_call_count,
"error": f"Context length exceeded ({approx_tokens:,} tokens). Cannot compress further.",
"partial": True
}
if retry_count > max_retries:
print(f"{self.log_prefix}❌ Max retries ({max_retries}) exceeded. Giving up.")
logging.error(f"{self.log_prefix}API call failed after {max_retries} retries. Last error: {api_error}")
logging.error(f"{self.log_prefix}Request details - Messages: {len(api_messages)}, Approx tokens: {approx_tokens:,}")
raise api_error
wait_time = min(2 ** retry_count, 60) # Exponential backoff: 2s, 4s, 8s, 16s, 32s, 60s, 60s
print(f"⚠️ OpenAI-compatible API call failed (attempt {retry_count}/{max_retries}): {str(api_error)[:100]}")
print(f"⏳ Retrying in {wait_time}s...")
logging.warning(f"API retry {retry_count}/{max_retries} after error: {api_error}")
# Sleep in small increments so we can respond to interrupts quickly
# instead of blocking the entire wait_time in one sleep() call
sleep_end = time.time() + wait_time
while time.time() < sleep_end:
if self._interrupt_requested:
print(f"{self.log_prefix}⚡ Interrupt detected during retry wait, aborting.")
return {
"final_response": "Operation interrupted.",
"messages": messages,
"api_calls": api_call_count,
"completed": False,
"interrupted": True,
}
time.sleep(0.2) # Check interrupt every 200ms
try:
assistant_message = response.choices[0].message
# Handle assistant response
if assistant_message.content and not self.quiet_mode:
print(f"{self.log_prefix}🤖 Assistant: {assistant_message.content[:100]}{'...' if len(assistant_message.content) > 100 else ''}")
# Check for incomplete (opened but never closed)
# This means the model ran out of output tokens mid-reasoning — retry up to 2 times
if self._has_incomplete_scratchpad(assistant_message.content or ""):
if not hasattr(self, '_incomplete_scratchpad_retries'):
self._incomplete_scratchpad_retries = 0
self._incomplete_scratchpad_retries += 1
print(f"{self.log_prefix}⚠️ Incomplete detected (opened but never closed)")
if self._incomplete_scratchpad_retries <= 2:
print(f"{self.log_prefix}🔄 Retrying API call ({self._incomplete_scratchpad_retries}/2)...")
# Don't add the broken message, just retry
continue
else:
# Max retries - discard this turn and save as partial
print(f"{self.log_prefix}❌ Max retries (2) for incomplete scratchpad. Saving as partial.")
self._incomplete_scratchpad_retries = 0
rolled_back_messages = self._get_messages_up_to_last_assistant(messages)
try:
cleanup_vm(effective_task_id)
except Exception:
pass
try:
cleanup_browser(effective_task_id)
except Exception:
pass
return {
"final_response": None,
"messages": rolled_back_messages,
"api_calls": api_call_count,
"completed": False,
"partial": True,
"error": "Incomplete REASONING_SCRATCHPAD after 2 retries"
}
# Reset incomplete scratchpad counter on clean response
if hasattr(self, '_incomplete_scratchpad_retries'):
self._incomplete_scratchpad_retries = 0
# Check for tool calls
if assistant_message.tool_calls:
if not self.quiet_mode:
print(f"{self.log_prefix}🔧 Processing {len(assistant_message.tool_calls)} tool call(s)...")
if self.verbose_logging:
for tc in assistant_message.tool_calls:
logging.debug(f"Tool call: {tc.function.name} with args: {tc.function.arguments[:200]}...")
# Validate tool call names - detect model hallucinations
invalid_tool_calls = [
tc.function.name for tc in assistant_message.tool_calls
if tc.function.name not in self.valid_tool_names
]
if invalid_tool_calls:
# Track retries for invalid tool calls
if not hasattr(self, '_invalid_tool_retries'):
self._invalid_tool_retries = 0
self._invalid_tool_retries += 1
invalid_preview = invalid_tool_calls[0][:80] + "..." if len(invalid_tool_calls[0]) > 80 else invalid_tool_calls[0]
print(f"{self.log_prefix}⚠️ Invalid tool call detected: '{invalid_preview}'")
print(f"{self.log_prefix} Valid tools: {sorted(self.valid_tool_names)}")
if self._invalid_tool_retries < 3:
print(f"{self.log_prefix}🔄 Retrying API call ({self._invalid_tool_retries}/3)...")
# Don't add anything to messages, just retry the API call
continue
else:
print(f"{self.log_prefix}❌ Max retries (3) for invalid tool calls exceeded. Stopping as partial.")
# Return partial result - don't include the bad tool call in messages
self._invalid_tool_retries = 0 # Reset for next conversation
return {
"final_response": None,
"messages": messages, # Messages up to last valid point
"api_calls": api_call_count,
"completed": False,
"partial": True,
"error": f"Model generated invalid tool call: {invalid_preview}"
}
# Reset retry counter on successful tool call validation
if hasattr(self, '_invalid_tool_retries'):
self._invalid_tool_retries = 0
# Validate tool call arguments are valid JSON
# Handle empty strings as empty objects (common model quirk)
invalid_json_args = []
for tc in assistant_message.tool_calls:
args = tc.function.arguments
# Treat empty/whitespace strings as empty object
if not args or not args.strip():
tc.function.arguments = "{}"
continue
try:
json.loads(args)
except json.JSONDecodeError as e:
invalid_json_args.append((tc.function.name, str(e)))
if invalid_json_args:
# Track retries for invalid JSON arguments
self._invalid_json_retries += 1
tool_name, error_msg = invalid_json_args[0]
print(f"{self.log_prefix}⚠️ Invalid JSON in tool call arguments for '{tool_name}': {error_msg}")
if self._invalid_json_retries < 3:
print(f"{self.log_prefix}🔄 Retrying API call ({self._invalid_json_retries}/3)...")
# Don't add anything to messages, just retry the API call
continue
else:
# Instead of returning partial, inject a helpful message and let model recover
print(f"{self.log_prefix}⚠️ Injecting recovery message for invalid JSON...")
self._invalid_json_retries = 0 # Reset for next attempt
# Add a user message explaining the issue
recovery_msg = (
f"Your tool call to '{tool_name}' had invalid JSON arguments. "
f"Error: {error_msg}. "
f"For tools with no required parameters, use an empty object: {{}}. "
f"Please either retry the tool call with valid JSON, or respond without using that tool."
)
messages.append({"role": "user", "content": recovery_msg})
# Continue the loop - model will see this message and can recover
continue
# Reset retry counter on successful JSON validation
self._invalid_json_retries = 0
# Extract reasoning from response if available
# OpenRouter can return reasoning in multiple formats:
# 1. message.reasoning - direct reasoning field
# 2. message.reasoning_content - alternative field (some providers)
# 3. message.reasoning_details - array with {summary: "..."} objects
reasoning_text = self._extract_reasoning(assistant_message)
if reasoning_text and self.verbose_logging:
preview = reasoning_text[:100] + "..." if len(reasoning_text) > 100 else reasoning_text
logging.debug(f"Captured reasoning ({len(reasoning_text)} chars): {preview}")
# Build assistant message with tool calls
# Content stays as-is; reasoning is stored separately and will be passed
# to the API via reasoning_content field when preparing api_messages
assistant_msg = {
"role": "assistant",
"content": assistant_message.content or "",
"reasoning": reasoning_text, # Stored for trajectory extraction & API calls
"tool_calls": [
{
"id": tool_call.id,
"type": tool_call.type,
"function": {
"name": tool_call.function.name,
"arguments": tool_call.function.arguments
}
}
for tool_call in assistant_message.tool_calls
]
}
# Store reasoning_details for multi-turn reasoning context (OpenRouter)
if hasattr(assistant_message, 'reasoning_details') and assistant_message.reasoning_details:
assistant_msg["reasoning_details"] = [
{"type": d.get("type"), "text": d.get("text"), "signature": d.get("signature")}
for d in assistant_message.reasoning_details
if isinstance(d, dict)
]
messages.append(assistant_msg)
# Execute each tool call
for i, tool_call in enumerate(assistant_message.tool_calls, 1):
function_name = tool_call.function.name
# Parse arguments - should always succeed since we validated above
try:
function_args = json.loads(tool_call.function.arguments)
except json.JSONDecodeError as e:
# This shouldn't happen since we validate and retry above
logging.warning(f"Unexpected JSON error after validation: {e}")
function_args = {}
# Preview tool call - cleaner format for quiet mode
if not self.quiet_mode:
args_str = json.dumps(function_args, ensure_ascii=False)
args_preview = args_str[:self.log_prefix_chars] + "..." if len(args_str) > self.log_prefix_chars else args_str
print(f" 📞 Tool {i}: {function_name}({list(function_args.keys())}) - {args_preview}")
# Fire progress callback if registered (for messaging platforms)
if self.tool_progress_callback:
try:
# Build a short preview of the primary argument
preview = _build_tool_preview(function_name, function_args)
self.tool_progress_callback(function_name, preview)
except Exception as cb_err:
logging.debug(f"Tool progress callback error: {cb_err}")
tool_start_time = time.time()
# Execute the tool - with animated spinner in quiet mode
if self.quiet_mode:
# Tool-specific spinner animations
tool_spinners = {
'web_search': ('arrows', ['🔍', '🌐', '📡', '🔎']),
'web_extract': ('grow', ['📄', '📖', '📑', '🗒️']),
'web_crawl': ('arrows', ['🕷️', '🕸️', '🔗', '🌐']),
'terminal': ('dots', ['💻', '⌨️', '🖥️', '📟']),
'browser_navigate': ('moon', ['🌐', '🧭', '🔗', '🚀']),
'browser_click': ('bounce', ['👆', '🖱️', '👇', '✨']),
'browser_type': ('dots', ['⌨️', '✍️', '📝', '💬']),
'browser_screenshot': ('star', ['📸', '🖼️', '📷', '✨']),
'image_generate': ('sparkle', ['🎨', '✨', '🖼️', '🌟']),
'skill_view': ('star', ['📚', '📖', '🎓', '✨']),
'skills_list': ('pulse', ['📋', '📝', '📑', '📜']),
'moa_query': ('brain', ['🧠', '💭', '🤔', '💡']),
'analyze_image': ('sparkle', ['👁️', '🔍', '📷', '✨']),
}
spinner_type, tool_emojis = tool_spinners.get(function_name, ('dots', ['⚙️', '🔧', '⚡', '✨']))
face = random.choice(KawaiiSpinner.KAWAII_WAITING)
tool_emoji = random.choice(tool_emojis)
spinner = KawaiiSpinner(f"{face} {tool_emoji} {function_name}...", spinner_type=spinner_type)
spinner.start()
try:
function_result = handle_function_call(function_name, function_args, effective_task_id)
finally:
tool_duration = time.time() - tool_start_time
cute_msg = self._get_cute_tool_message(function_name, function_args, tool_duration)
spinner.stop(cute_msg)
else:
function_result = handle_function_call(function_name, function_args, effective_task_id)
tool_duration = time.time() - tool_start_time
result_preview = function_result[:200] if len(function_result) > 200 else function_result
if self.verbose_logging:
logging.debug(f"Tool {function_name} completed in {tool_duration:.2f}s")
logging.debug(f"Tool result preview: {result_preview}...")
# Add tool result to conversation
messages.append({
"role": "tool",
"content": function_result,
"tool_call_id": tool_call.id
})
# Preview tool response (only in non-quiet mode)
if not self.quiet_mode:
response_preview = function_result[:self.log_prefix_chars] + "..." if len(function_result) > self.log_prefix_chars else function_result
print(f" ✅ Tool {i} completed in {tool_duration:.2f}s - {response_preview}")
# Check for interrupt between tool calls - skip remaining
# tools so the agent can respond to the user immediately
if self._interrupt_requested and i < len(assistant_message.tool_calls):
remaining = len(assistant_message.tool_calls) - i
print(f"{self.log_prefix}⚡ Interrupt: skipping {remaining} remaining tool call(s)")
# Add placeholder results for skipped tool calls so the
# message sequence stays valid (assistant tool_calls need matching tool results)
for skipped_tc in assistant_message.tool_calls[i:]:
messages.append({
"role": "tool",
"content": "[Tool execution skipped - user sent a new message]",
"tool_call_id": skipped_tc.id
})
break
# Delay between tool calls
if self.tool_delay > 0 and i < len(assistant_message.tool_calls):
time.sleep(self.tool_delay)
# Check if context compression is needed before next API call
# Uses actual token count from last API response
if self.compression_enabled and self.context_compressor.should_compress():
messages = self.context_compressor.compress(
messages,
current_tokens=self.context_compressor.last_prompt_tokens
)
# Save session log incrementally (so progress is visible even if interrupted)
self._session_messages = messages
self._save_session_log(messages)
# Continue loop for next response
continue
else:
# No tool calls - this is the final response
final_response = assistant_message.content or ""
# Check if response only has think block with no actual content after it
if not self._has_content_after_think_block(final_response):
# Track retries for empty-after-think responses
if not hasattr(self, '_empty_content_retries'):
self._empty_content_retries = 0
self._empty_content_retries += 1
content_preview = final_response[:80] + "..." if len(final_response) > 80 else final_response
print(f"{self.log_prefix}⚠️ Response only contains think block with no content after it")
print(f"{self.log_prefix} Content: '{content_preview}'")
if self._empty_content_retries < 3:
print(f"{self.log_prefix}🔄 Retrying API call ({self._empty_content_retries}/3)...")
# Don't add the incomplete message, just retry
continue
else:
# Max retries exceeded - roll back to last complete assistant turn
print(f"{self.log_prefix}❌ Max retries (3) for empty content exceeded. Rolling back to last complete turn.")
self._empty_content_retries = 0 # Reset for next conversation
rolled_back_messages = self._get_messages_up_to_last_assistant(messages)
# Clean up VM and browser
try:
cleanup_vm(effective_task_id)
except Exception as e:
if self.verbose_logging:
logging.warning(f"Failed to cleanup VM for task {effective_task_id}: {e}")
try:
cleanup_browser(effective_task_id)
except Exception as e:
if self.verbose_logging:
logging.warning(f"Failed to cleanup browser for task {effective_task_id}: {e}")
return {
"final_response": None,
"messages": rolled_back_messages,
"api_calls": api_call_count,
"completed": False,
"partial": True,
"error": "Model generated only think blocks with no actual response after 3 retries"
}
# Reset retry counter on successful content
if hasattr(self, '_empty_content_retries'):
self._empty_content_retries = 0
# Extract reasoning from response if available
reasoning_text = self._extract_reasoning(assistant_message)
if reasoning_text and self.verbose_logging:
preview = reasoning_text[:100] + "..." if len(reasoning_text) > 100 else reasoning_text
logging.debug(f"Captured final reasoning ({len(reasoning_text)} chars): {preview}")
# Build final assistant message
# Content stays as-is; reasoning stored separately for trajectory extraction
final_msg = {
"role": "assistant",
"content": final_response,
"reasoning": reasoning_text # Stored for trajectory extraction
}
# Store reasoning_details for multi-turn reasoning context (OpenRouter)
if hasattr(assistant_message, 'reasoning_details') and assistant_message.reasoning_details:
final_msg["reasoning_details"] = [
{"type": d.get("type"), "text": d.get("text"), "signature": d.get("signature")}
for d in assistant_message.reasoning_details
if isinstance(d, dict)
]
messages.append(final_msg)
if not self.quiet_mode:
print(f"🎉 Conversation completed after {api_call_count} OpenAI-compatible API call(s)")
break
except Exception as e:
error_msg = f"Error during OpenAI-compatible API call #{api_call_count}: {str(e)}"
print(f"❌ {error_msg}")
if self.verbose_logging:
logging.exception("Detailed error information:")
# Add error to conversation and try to continue
messages.append({
"role": "assistant",
"content": f"I encountered an error: {error_msg}. Let me try a different approach."
})
# If we're near the limit, break to avoid infinite loops
if api_call_count >= self.max_iterations - 1:
final_response = f"I apologize, but I encountered repeated errors: {error_msg}"
break
# Handle max iterations reached - ask model to summarize what it found
if api_call_count >= self.max_iterations and final_response is None:
print(f"⚠️ Reached maximum iterations ({self.max_iterations}). Requesting summary...")
# Inject a user message asking for a summary
summary_request = (
"You've reached the maximum number of tool-calling iterations allowed. "
"Please provide a final response summarizing what you've found and accomplished so far, "
"without calling any more tools."
)
messages.append({"role": "user", "content": summary_request})
# Make one final API call WITHOUT tools to force a text response
try:
api_messages = messages.copy()
if self.ephemeral_system_prompt:
api_messages = [{"role": "system", "content": self.ephemeral_system_prompt}] + api_messages
# Build extra_body for summary call (same reasoning config as main loop)
summary_extra_body = {}
if "openrouter" in self.base_url.lower():
if self.reasoning_config is not None:
summary_extra_body["reasoning"] = self.reasoning_config
else:
summary_extra_body["reasoning"] = {
"enabled": True,
"effort": "xhigh"
}
summary_kwargs = {
"model": self.model,
"messages": api_messages,
# No tools parameter - forces text response
}
if self.max_tokens is not None:
summary_kwargs["max_tokens"] = self.max_tokens
if summary_extra_body:
summary_kwargs["extra_body"] = summary_extra_body
summary_response = self.client.chat.completions.create(**summary_kwargs)
if summary_response.choices and summary_response.choices[0].message.content:
final_response = summary_response.choices[0].message.content
# Strip think blocks from final response
if "" in final_response:
import re
final_response = re.sub(r'.*?\s*', '', final_response, flags=re.DOTALL).strip()
# Add to messages for session continuity
messages.append({"role": "assistant", "content": final_response})
else:
final_response = "I reached the iteration limit and couldn't generate a summary."
except Exception as e:
logging.warning(f"Failed to get summary response: {e}")
final_response = f"I reached the maximum iterations ({self.max_iterations}) but couldn't summarize. Error: {str(e)}"
# Determine if conversation completed successfully
completed = final_response is not None and api_call_count < self.max_iterations
# Save trajectory if enabled
self._save_trajectory(messages, user_message, completed)
# Clean up VM and browser for this task after conversation completes
try:
cleanup_vm(effective_task_id)
except Exception as e:
if self.verbose_logging:
logging.warning(f"Failed to cleanup VM for task {effective_task_id}: {e}")
try:
cleanup_browser(effective_task_id)
except Exception as e:
if self.verbose_logging:
logging.warning(f"Failed to cleanup browser for task {effective_task_id}: {e}")
# Update session messages and save session log
self._session_messages = messages
self._save_session_log(messages)
# Build result with interrupt info if applicable
result = {
"final_response": final_response,
"messages": messages,
"api_calls": api_call_count,
"completed": completed,
"partial": False, # True only when stopped due to invalid tool calls
"interrupted": interrupted,
}
# Include interrupt message if one triggered the interrupt
if interrupted and self._interrupt_message:
result["interrupt_message"] = self._interrupt_message
# Clear interrupt state after handling
self.clear_interrupt()
return result
def chat(self, message: str) -> str:
"""
Simple chat interface that returns just the final response.
Args:
message (str): User message
Returns:
str: Final assistant response
"""
result = self.run_conversation(message)
return result["final_response"]
def main(
query: str = None,
model: str = "anthropic/claude-opus-4.6",
api_key: str = None,
base_url: str = "https://openrouter.ai/api/v1",
max_turns: int = 10,
enabled_toolsets: str = None,
disabled_toolsets: str = None,
list_tools: bool = False,
save_trajectories: bool = False,
save_sample: bool = False,
verbose: bool = False,
log_prefix_chars: int = 20
):
"""
Main function for running the agent directly.
Args:
query (str): Natural language query for the agent. Defaults to Python 3.13 example.
model (str): Model name to use (OpenRouter format: provider/model). Defaults to anthropic/claude-sonnet-4-20250514.
api_key (str): API key for authentication. Uses OPENROUTER_API_KEY env var if not provided.
base_url (str): Base URL for the model API. Defaults to https://openrouter.ai/api/v1
max_turns (int): Maximum number of API call iterations. Defaults to 10.
enabled_toolsets (str): Comma-separated list of toolsets to enable. Supports predefined
toolsets (e.g., "research", "development", "safe").
Multiple toolsets can be combined: "web,vision"
disabled_toolsets (str): Comma-separated list of toolsets to disable (e.g., "terminal")
list_tools (bool): Just list available tools and exit
save_trajectories (bool): Save conversation trajectories to JSONL files (appends to trajectory_samples.jsonl). Defaults to False.
save_sample (bool): Save a single trajectory sample to a UUID-named JSONL file for inspection. Defaults to False.
verbose (bool): Enable verbose logging for debugging. Defaults to False.
log_prefix_chars (int): Number of characters to show in log previews for tool calls/responses. Defaults to 20.
Toolset Examples:
- "research": Web search, extract, crawl + vision tools
"""
print("🤖 AI Agent with Tool Calling")
print("=" * 50)
# Handle tool listing
if list_tools:
from model_tools import get_all_tool_names, get_toolset_for_tool, get_available_toolsets
from toolsets import get_all_toolsets, get_toolset_info
print("📋 Available Tools & Toolsets:")
print("-" * 50)
# Show new toolsets system
print("\n🎯 Predefined Toolsets (New System):")
print("-" * 40)
all_toolsets = get_all_toolsets()
# Group by category
basic_toolsets = []
composite_toolsets = []
scenario_toolsets = []
for name, toolset in all_toolsets.items():
info = get_toolset_info(name)
if info:
entry = (name, info)
if name in ["web", "terminal", "vision", "creative", "reasoning"]:
basic_toolsets.append(entry)
elif name in ["research", "development", "analysis", "content_creation", "full_stack"]:
composite_toolsets.append(entry)
else:
scenario_toolsets.append(entry)
# Print basic toolsets
print("\n📌 Basic Toolsets:")
for name, info in basic_toolsets:
tools_str = ', '.join(info['resolved_tools']) if info['resolved_tools'] else 'none'
print(f" • {name:15} - {info['description']}")
print(f" Tools: {tools_str}")
# Print composite toolsets
print("\n📂 Composite Toolsets (built from other toolsets):")
for name, info in composite_toolsets:
includes_str = ', '.join(info['includes']) if info['includes'] else 'none'
print(f" • {name:15} - {info['description']}")
print(f" Includes: {includes_str}")
print(f" Total tools: {info['tool_count']}")
# Print scenario-specific toolsets
print("\n🎭 Scenario-Specific Toolsets:")
for name, info in scenario_toolsets:
print(f" • {name:20} - {info['description']}")
print(f" Total tools: {info['tool_count']}")
# Show legacy toolset compatibility
print("\n📦 Legacy Toolsets (for backward compatibility):")
legacy_toolsets = get_available_toolsets()
for name, info in legacy_toolsets.items():
status = "✅" if info["available"] else "❌"
print(f" {status} {name}: {info['description']}")
if not info["available"]:
print(f" Requirements: {', '.join(info['requirements'])}")
# Show individual tools
all_tools = get_all_tool_names()
print(f"\n🔧 Individual Tools ({len(all_tools)} available):")
for tool_name in sorted(all_tools):
toolset = get_toolset_for_tool(tool_name)
print(f" 📌 {tool_name} (from {toolset})")
print(f"\n💡 Usage Examples:")
print(f" # Use predefined toolsets")
print(f" python run_agent.py --enabled_toolsets=research --query='search for Python news'")
print(f" python run_agent.py --enabled_toolsets=development --query='debug this code'")
print(f" python run_agent.py --enabled_toolsets=safe --query='analyze without terminal'")
print(f" ")
print(f" # Combine multiple toolsets")
print(f" python run_agent.py --enabled_toolsets=web,vision --query='analyze website'")
print(f" ")
print(f" # Disable toolsets")
print(f" python run_agent.py --disabled_toolsets=terminal --query='no command execution'")
print(f" ")
print(f" # Run with trajectory saving enabled")
print(f" python run_agent.py --save_trajectories --query='your question here'")
return
# Parse toolset selection arguments
enabled_toolsets_list = None
disabled_toolsets_list = None
if enabled_toolsets:
enabled_toolsets_list = [t.strip() for t in enabled_toolsets.split(",")]
print(f"🎯 Enabled toolsets: {enabled_toolsets_list}")
if disabled_toolsets:
disabled_toolsets_list = [t.strip() for t in disabled_toolsets.split(",")]
print(f"🚫 Disabled toolsets: {disabled_toolsets_list}")
if save_trajectories:
print(f"💾 Trajectory saving: ENABLED")
print(f" - Successful conversations → trajectory_samples.jsonl")
print(f" - Failed conversations → failed_trajectories.jsonl")
# Initialize agent with provided parameters
try:
agent = AIAgent(
base_url=base_url,
model=model,
api_key=api_key,
max_iterations=max_turns,
enabled_toolsets=enabled_toolsets_list,
disabled_toolsets=disabled_toolsets_list,
save_trajectories=save_trajectories,
verbose_logging=verbose,
log_prefix_chars=log_prefix_chars
)
except RuntimeError as e:
print(f"❌ Failed to initialize agent: {e}")
return
# Use provided query or default to Python 3.13 example
if query is None:
user_query = (
"Tell me about the latest developments in Python 3.13 and what new features "
"developers should know about. Please search for current information and try it out."
)
else:
user_query = query
print(f"\n📝 User Query: {user_query}")
print("\n" + "=" * 50)
# Run conversation
result = agent.run_conversation(user_query)
print("\n" + "=" * 50)
print("📋 CONVERSATION SUMMARY")
print("=" * 50)
print(f"✅ Completed: {result['completed']}")
print(f"📞 API Calls: {result['api_calls']}")
print(f"💬 Messages: {len(result['messages'])}")
if result['final_response']:
print(f"\n🎯 FINAL RESPONSE:")
print("-" * 30)
print(result['final_response'])
# Save sample trajectory to UUID-named file if requested
if save_sample:
import uuid
sample_id = str(uuid.uuid4())[:8]
sample_filename = f"sample_{sample_id}.json"
# Convert messages to trajectory format (same as batch_runner)
trajectory = agent._convert_to_trajectory_format(
result['messages'],
user_query,
result['completed']
)
entry = {
"conversations": trajectory,
"timestamp": datetime.now().isoformat(),
"model": model,
"completed": result['completed'],
"query": user_query
}
try:
with open(sample_filename, "w", encoding="utf-8") as f:
# Pretty-print JSON with indent for readability
f.write(json.dumps(entry, ensure_ascii=False, indent=2))
print(f"\n💾 Sample trajectory saved to: {sample_filename}")
except Exception as e:
print(f"\n⚠️ Failed to save sample: {e}")
print("\n👋 Agent execution completed!")
if __name__ == "__main__":
fire.Fire(main)