Timmy_Foundation/hermes-agent
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cleanup

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Teknium
2025-09-06 22:07:38 -07:00
parent 587d1cf720
commit c7fa4447b8
2 changed files with 40 additions and 52 deletions
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67
model_tools.py
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@@ -8,22 +8,28 @@ for defining tools and executing function calls.
Currently supports:
- Web tools (search, extract, crawl) from web_tools.py
- Terminal tools (command execution with interactive sessions) from terminal_tool.py
- Vision tools (image analysis) from vision_tools.py
- Mixture of Agents tools (collaborative multi-model reasoning) from mixture_of_agents_tool.py
- Image generation tools (text-to-image with upscaling) from image_generation_tool.py
Usage:
from model_tools import get_tool_definitions, handle_function_call
# Get tool definitions for model API
# Get all available tool definitions for model API
tools = get_tool_definitions()
# Get specific toolsets
web_tools = get_tool_definitions(enabled_toolsets=['web_tools'])
# Handle function calls from model
result = handle_function_call("web_search_tool", {"query": "Python", "limit": 3})
result = handle_function_call("web_search", {"query": "Python", "limit": 3})
"""
import json
import asyncio
from typing import Dict, Any, List
# Import toolsets
from web_tools import web_search_tool, web_extract_tool, web_crawl_tool, check_firecrawl_api_key
from terminal_tool import terminal_tool, check_hecate_requirements, TERMINAL_TOOL_DESCRIPTION
from vision_tools import vision_analyze_tool, check_vision_requirements
@@ -75,11 +81,6 @@ def get_web_tool_definitions() -> List[Dict[str, Any]]:
"items": {"type": "string"},
"description": "List of URLs to extract content from (max 5 URLs per call)",
"maxItems": 5
},
"format": {
"type": "string",
"enum": ["markdown", "html"],
"description": "Desired output format for extracted content (optional)"
}
},
"required": ["urls"]
@@ -101,12 +102,6 @@ def get_web_tool_definitions() -> List[Dict[str, Any]]:
"instructions": {
"type": "string",
"description": "Specific instructions for what to crawl/extract using AI intelligence (e.g., 'Find pricing information', 'Get documentation pages', 'Extract contact details')"
},
"depth": {
"type": "string",
"enum": ["basic", "advanced"],
"description": "Depth of extraction - 'basic' for surface content, 'advanced' for deeper analysis (default: basic)",
"default": "basic"
}
},
"required": ["url"]
@@ -185,12 +180,7 @@ def get_vision_tool_definitions() -> List[Dict[str, Any]]:
},
"question": {
"type": "string",
"description": "Your specific question or request about the image to resolve. The AI will automatically provide a complete image description AND answer your specific question. Examples: 'What text can you read?', 'What architectural style is this?', 'Describe the mood and emotions', 'What safety hazards do you see?'"
},
"model": {
"type": "string",
"description": "The vision model to use for analysis (optional, default: gemini-2.5-flash)",
"default": "gemini-2.5-flash"
"description": "Your specific question or request about the image to resolve. The AI will automatically provide a complete image description AND answer your specific question."
}
},
"required": ["image_url", "question"]
@@ -212,7 +202,7 @@ def get_moa_tool_definitions() -> List[Dict[str, Any]]:
"type": "function",
"function": {
"name": "mixture_of_agents",
"description": "Process extremely difficult problems requiring intense reasoning using the Mixture-of-Agents methodology. This tool leverages multiple frontier language models to collaboratively solve complex tasks that single models struggle with. Uses a fixed 2-layer architecture: reference models (claude-opus-4, gemini-2.5-pro, o4-mini, deepseek-r1) generate diverse responses, then an aggregator synthesizes the best solution. Best for: complex mathematical proofs, advanced coding problems, multi-step analytical reasoning, precise and complex STEM problems, algorithm design, and problems requiring diverse domain expertise.",
"description": "Process extremely difficult problems requiring intense reasoning using a Mixture-of-Agents. This tool leverages multiple frontier language models to collaboratively solve complex tasks that single models struggle with. Uses a fixed 2-layer architecture: reference models generate diverse responses, then an aggregator synthesizes the best solution. Best for: complex mathematical proofs, advanced coding problems, multi-step analytical reasoning, precise and complex STEM problems, algorithm design, and problems requiring diverse domain expertise.",
"parameters": {
"type": "object",
"properties": {
@@ -240,13 +230,13 @@ def get_image_tool_definitions() -> List[Dict[str, Any]]:
"type": "function",
"function": {
"name": "image_generate",
"description": "Generate high-quality images from text prompts using FAL.ai's FLUX.1 Krea model with automatic 2x upscaling. Creates detailed, artistic images that are automatically enhanced for superior quality. Returns a single upscaled image URL that can be displayed using <img src=\"{URL}\"></img> tags.",
"description": "Generate high-quality images from text prompts using FLUX Krea model with automatic 2x upscaling. Creates detailed, artistic images that are automatically enhanced for superior quality. Returns a single upscaled image URL that can be displayed using <img src=\"{URL}\"></img> tags.",
"parameters": {
"type": "object",
"properties": {
"prompt": {
"type": "string",
"description": "The text prompt describing the desired image. Be detailed and descriptive for best results."
"description": "The text prompt describing the desired image. Be detailed and descriptive."
},
"image_size": {
"type": "string",
@@ -291,10 +281,6 @@ def get_all_tool_names() -> List[str]:
if check_image_generation_requirements():
tool_names.extend(["image_generate"])
# Future toolsets can be added here:
# if check_file_tools():
# tool_names.extend(["file_read", "file_write"])
return tool_names
@@ -316,7 +302,6 @@ def get_toolset_for_tool(tool_name: str) -> str:
"vision_analyze": "vision_tools",
"mixture_of_agents": "moa_tools",
"image_generate": "image_tools"
# Future tools can be added here
}
return toolset_mapping.get(tool_name, "unknown")
@@ -400,8 +385,6 @@ def get_tool_definitions(
"vision_tools": get_vision_tool_definitions() if check_vision_requirements() else [],
"moa_tools": get_moa_tool_definitions() if check_moa_requirements() else [],
"image_tools": get_image_tool_definitions() if check_image_generation_requirements() else []
# Future toolsets can be added here:
# "file_tools": get_file_tool_definitions() if check_file_tools() else [],
}
# HIGHEST PRIORITY: enabled_tools (overrides everything)
@@ -487,16 +470,14 @@ def handle_web_function_call(function_name: str, function_args: Dict[str, Any])
urls = function_args.get("urls", [])
# Limit URLs to prevent abuse
urls = urls[:5] if isinstance(urls, list) else []
format = function_args.get("format")
# Run async function in event loop
return asyncio.run(web_extract_tool(urls, format))
return asyncio.run(web_extract_tool(urls, "markdown"))
elif function_name == "web_crawl":
url = function_args.get("url", "")
instructions = function_args.get("instructions")
depth = function_args.get("depth", "basic")
# Run async function in event loop
return asyncio.run(web_crawl_tool(url, instructions, depth))
return asyncio.run(web_crawl_tool(url, instructions, "basic"))
else:
return json.dumps({"error": f"Unknown web function: {function_name}"})
@@ -518,7 +499,7 @@ def handle_terminal_function_call(function_name: str, function_args: Dict[str, A
background = function_args.get("background", False)
idle_threshold = function_args.get("idle_threshold", 5.0)
timeout = function_args.get("timeout")
# Session management is handled internally - don't pass session_id from model
return terminal_tool(command, input_keys, None, background, idle_threshold, timeout)
else:
@@ -539,13 +520,11 @@ def handle_vision_function_call(function_name: str, function_args: Dict[str, Any
if function_name == "vision_analyze":
image_url = function_args.get("image_url", "")
question = function_args.get("question", "")
model = function_args.get("model", "gemini-2.5-flash")
# Automatically prepend full description request to user's question
full_prompt = f"Fully describe and explain everything about this image\n\n{question}"
full_prompt = f"Fully describe and explain everything about this image, then answer the following question:\n\n{question}"
# Run async function in event loop
return asyncio.run(vision_analyze_tool(image_url, full_prompt, model))
return asyncio.run(vision_analyze_tool(image_url, full_prompt, "gemini-2.5-flash"))
else:
return json.dumps({"error": f"Unknown vision function: {function_name}"})
@@ -592,7 +571,6 @@ def handle_image_function_call(function_name: str, function_args: Dict[str, Any]
if not prompt:
return json.dumps({"success": False, "image": None})
# Extract only the exposed parameters
image_size = function_args.get("image_size", "landscape_16_9")
# Use fixed internal defaults for all other parameters (not exposed to model)
@@ -662,12 +640,6 @@ def handle_function_call(function_name: str, function_args: Dict[str, Any]) -> s
elif function_name in ["image_generate"]:
return handle_image_function_call(function_name, function_args)
# Future toolsets can be routed here:
# elif function_name in ["file_read_tool", "file_write_tool"]:
# return handle_file_function_call(function_name, function_args)
# elif function_name in ["code_execute_tool", "code_analyze_tool"]:
# return handle_code_function_call(function_name, function_args)
else:
error_msg = f"Unknown function: {function_name}"
print(f"❌ {error_msg}")
@@ -716,7 +688,6 @@ def get_available_toolsets() -> Dict[str, Dict[str, Any]]:
"description": "Generate high-quality images from text prompts using FAL.ai's FLUX.1 Krea model with automatic 2x upscaling for enhanced quality",
"requirements": ["FAL_KEY environment variable", "fal-client package"]
}
# Future toolsets can be added here
}
return toolsets

25
test_run.sh
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@@ -1,12 +1,29 @@
#!/bin/bash
# Check if a prompt argument was provided
if [ $# -eq 0 ]; then
echo "Error: Please provide a prompt as an argument"
echo "Usage: $0 \"your prompt here\""
exit 1
fi
# Get the prompt from the first argument
PROMPT="$1"
# Set debug mode for web tools
export WEB_TOOLS_DEBUG=true
# Run the agent with the provided prompt
python run_agent.py \
--query "Tell me about this animal pictured: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQi1nkrYXY-ijQv5aCxkwooyg2roNFxj0ewJA&s" \
--query "$PROMPT" \
--max_turns 30 \
--model claude-sonnet-4-20250514 \
--base_url https://api.anthropic.com/v1/ \
# --model claude-sonnet-4-20250514 \
# --base_url https://api.anthropic.com/v1/ \
--model hermes-4-70B \
--base_url http://bore.pub:8292/v1 \
--api_key $ANTHROPIC_API_KEY \
--enabled_toolsets=vision_tools
--save_trajectories
#--enabled_toolsets=vision_tools
#Possible Toolsets:
#web_tools