mirror of https://github.com/NousResearch/hermes-agent.git synced 2026-04-25 00:51:20 +00:00

teknium 7ea17bb957 Update environment configuration and enhance tool definitions

- Modified `.env.example` to set default terminal environment to 'local' and updated Docker, Singularity, and Modal image references to use 'python:3.11-slim'.
- Updated `package.json` to include Node.js engine requirements and modified post-install script for better user guidance.
- Enhanced `pyproject.toml` to reflect new dependencies and optional dependencies for modal and development environments.
- Improved `README.md` with additional setup instructions for Singularity and Node.js dependencies, along with clearer toolset documentation.
- Refactored `model_tools.py` to include new tool definitions and ensure consistency across toolsets.
- Updated architecture documentation to clarify tool structure and registration processes.

2026-01-29 22:36:07 +00:00

2.7 KiB

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LLM Client

Hermes Agent uses the OpenAI Python SDK with OpenRouter as the backend, providing access to many models through a single API.

Configuration

from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("OPENROUTER_API_KEY"),
    base_url="https://openrouter.ai/api/v1"
)

Supported Models

Any model available on OpenRouter:

# Anthropic
model = "anthropic/claude-sonnet-4"
model = "anthropic/claude-opus-4"

# OpenAI
model = "openai/gpt-4o"
model = "openai/o1"

# Google
model = "google/gemini-2.0-flash"

# Open models
model = "meta-llama/llama-3.3-70b-instruct"
model = "deepseek/deepseek-chat-v3"
model = "moonshotai/kimi-k2.5"

Tool Calling

Standard OpenAI function calling format:

response = client.chat.completions.create(
    model=model,
    messages=messages,
    tools=[
        {
            "type": "function",
            "function": {
                "name": "web_search",
                "description": "Search the web",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "query": {"type": "string"}
                    },
                    "required": ["query"]
                }
            }
        }
    ],
)

# Check for tool calls
if response.choices[0].message.tool_calls:
    for tool_call in response.choices[0].message.tool_calls:
        name = tool_call.function.name
        args = json.loads(tool_call.function.arguments)
        # Execute tool...

Reasoning Models

Some models return reasoning/thinking content:

# Access reasoning if available
message = response.choices[0].message
if hasattr(message, 'reasoning_content') and message.reasoning_content:
    reasoning = message.reasoning_content
    # Store for trajectory export

Provider Selection

OpenRouter allows selecting specific providers:

response = client.chat.completions.create(
    model=model,
    messages=messages,
    extra_body={
        "provider": {
            "order": ["Anthropic", "Google"],  # Preferred providers
            "ignore": ["Novita"],              # Providers to skip
        }
    }
)

Error Handling

Common errors and handling:

try:
    response = client.chat.completions.create(...)
except openai.RateLimitError:
    # Back off and retry
except openai.APIError as e:
    # Check e.code for specific errors
    # 400 = bad request (often provider-specific)
    # 502 = bad gateway (retry with different provider)

Cost Tracking

OpenRouter returns usage info:

usage = response.usage
print(f"Tokens: {usage.prompt_tokens} + {usage.completion_tokens}")
print(f"Cost: ${usage.cost:.6f}")  # If available

2.7 KiB Raw Blame History