mirror of
https://github.com/NousResearch/hermes-agent.git
synced 2026-04-25 00:51:20 +00:00
add prokletor formatter
This commit is contained in:
hjc-puro
parent
e91d9e839a
commit
7d9a1e119d
2 changed files with 142 additions and 49 deletions
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@ -327,7 +327,9 @@ async def _process_single_prompt(
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save_trajectories=False, # We handle saving ourselves
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verbose_logging=config.get("verbose", False),
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ephemeral_system_prompt=config.get("ephemeral_system_prompt"),
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log_prefix_chars=config.get("log_prefix_chars", 100)
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log_prefix_chars=config.get("log_prefix_chars", 100),
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prokletor_client=config.get("prokletor_client"),
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prokletor_formatter=config.get("prokletor_formatter")
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)
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# Run the agent with task_id to ensure each task gets its own isolated VM
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@ -439,6 +441,8 @@ class BatchRunner:
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max_tool_failure_rate: float = 0.5,
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keep_recent_errors: int = 5,
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min_tool_calls_for_rate: int = 10,
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prokletor_client: str = None,
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prokletor_formatter: str = None,
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):
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"""
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Initialize the batch runner.
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@ -459,6 +463,8 @@ class BatchRunner:
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max_tool_failure_rate (float): Maximum tool failure rate (0.0-1.0) before stopping (default: 0.5)
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keep_recent_errors (int): Number of recent errors to keep per tool (default: 5)
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min_tool_calls_for_rate (int): Minimum number of tool calls before checking failure rate (default: 10)
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prokletor_client (str): Name of the prokletor client to use
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prokletor_formatter (str): Name of the prokletor formatter to use
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"""
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self.dataset_file = Path(dataset_file)
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self.run_name = run_name
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@ -475,6 +481,8 @@ class BatchRunner:
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self.max_tool_failure_rate = max_tool_failure_rate
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self.keep_recent_errors = keep_recent_errors
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self.min_tool_calls_for_rate = min_tool_calls_for_rate
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self.prokletor_client = prokletor_client
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self.prokletor_formatter = prokletor_formatter
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# Validate distribution
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if not validate_distribution(distribution):
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@ -719,7 +727,9 @@ class BatchRunner:
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"api_key": self.api_key,
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"verbose": self.verbose,
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"ephemeral_system_prompt": self.ephemeral_system_prompt,
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"log_prefix_chars": self.log_prefix_chars
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"log_prefix_chars": self.log_prefix_chars,
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"prokletor_client": self.prokletor_client,
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"prokletor_formatter": self.prokletor_formatter
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}
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# Start workers
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@ -882,6 +892,8 @@ def main(
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max_tool_failure_rate: float = 0.5,
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keep_recent_errors: int = 5,
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min_tool_calls_for_rate: int = 10,
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prokletor_client: str = None,
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prokletor_formatter: str = None,
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):
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"""
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Run batch processing of agent prompts from a dataset.
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@ -904,6 +916,8 @@ def main(
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max_tool_failure_rate (float): Maximum tool failure rate (0.0-1.0) before stopping (default: 0.5)
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keep_recent_errors (int): Number of recent errors to keep per tool for reporting (default: 5)
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min_tool_calls_for_rate (int): Minimum number of tool calls before checking failure rate (default: 10)
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prokletor_client (str): Name of the prokletor client to use
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prokletor_formatter (str): Name of the prokletor formatter to use
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Examples:
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# Basic usage
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@ -953,7 +967,9 @@ def main(
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max_tool_failures=max_tool_failures,
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max_tool_failure_rate=max_tool_failure_rate,
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keep_recent_errors=keep_recent_errors,
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min_tool_calls_for_rate=min_tool_calls_for_rate
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min_tool_calls_for_rate=min_tool_calls_for_rate,
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prokletor_client=prokletor_client,
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prokletor_formatter=prokletor_formatter
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)
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runner.run(resume=resume)
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169
run_agent.py
169
run_agent.py
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@ -33,8 +33,14 @@ from datetime import datetime
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from pathlib import Path
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from rich import print
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from prokletor.formatters.hermes_formatter import HermesToolFormatterWithReasoning
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from prokletor.clients.hermes import HermesToolClientWithReasoning
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from prokletor.formatters.hermes import HermesToolFormatterWithReasoning
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from prokletor.formatters.hermes import HermesToolFormatterWithReasoning
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from prokletor.clients.hermes import HermesToolClientWithReasoning, HermesToolClient
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from prokletor.clients.claude import AsyncClaudeClient
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try:
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from anthropic import AsyncAnthropic
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except ImportError:
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AsyncAnthropic = None
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# Load environment variables from .env file
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from dotenv import load_dotenv
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@ -76,6 +82,8 @@ class AIAgent:
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verbose_logging: bool = False,
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ephemeral_system_prompt: str = None,
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log_prefix_chars: int = 100,
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prokletor_client: str = None,
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prokletor_formatter: str = None,
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):
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"""
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Initialize the AI Agent.
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@ -92,6 +100,8 @@ class AIAgent:
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verbose_logging (bool): Enable verbose logging for debugging (default: False)
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ephemeral_system_prompt (str): System prompt used during agent execution but NOT saved to trajectories (optional)
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log_prefix_chars (int): Number of characters to show in log previews for tool calls/responses (default: 20)
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prokletor_client (str): Name of the prokletor client to use (e.g., "AsyncClaudeClient", "HermesToolClient")
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prokletor_formatter (str): Name of the prokletor formatter to use (optional)
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"""
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self.model = model
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self.max_iterations = max_iterations
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@ -100,6 +110,8 @@ class AIAgent:
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self.verbose_logging = verbose_logging
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self.ephemeral_system_prompt = ephemeral_system_prompt
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self.log_prefix_chars = log_prefix_chars
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self.prokletor_client_name = prokletor_client
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self.prokletor_formatter_name = prokletor_formatter
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# Store toolset filtering options
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self.enabled_toolsets = enabled_toolsets
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@ -128,7 +140,7 @@ class AIAgent:
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logging.getLogger('openai').setLevel(logging.WARNING)
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logging.getLogger('httpx').setLevel(logging.WARNING)
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# Initialize OpenAI client
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# Initialize Client
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client_kwargs = {}
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if base_url:
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client_kwargs["base_url"] = base_url
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@ -138,16 +150,45 @@ class AIAgent:
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client_kwargs["api_key"] = os.getenv("ANTHROPIC_API_KEY", "dummy-key")
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try:
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oai_client = AsyncOpenAI(**client_kwargs)
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# self.client = oai_client
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self.client = HermesToolClientWithReasoning(oai_client)
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print(f"🧠 Wrapped OpenAI client with AsyncHermesToolClientWithReasoning")
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if prokletor_client == "AsyncClaudeClient":
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if AsyncAnthropic is None:
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raise ImportError("anthropic package is required for AsyncClaudeClient")
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# AsyncAnthropic kwargs
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anthropic_kwargs = {k: v for k, v in client_kwargs.items() if k in ["api_key", "base_url", "timeout", "max_retries", "default_headers"]}
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anthropic_client = AsyncAnthropic(**anthropic_kwargs)
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self.client = AsyncClaudeClient(anthropic_client)
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print(f"🧠 Wrapped Anthropic client with AsyncClaudeClient")
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elif prokletor_client == "HermesToolClient":
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oai_client = AsyncOpenAI(**client_kwargs)
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self.client = HermesToolClient(oai_client)
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print(f"🧠 Wrapped OpenAI client with HermesToolClient")
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elif prokletor_client == "HermesToolClientWithReasoning":
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oai_client = AsyncOpenAI(**client_kwargs)
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self.client = HermesToolClientWithReasoning(oai_client)
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print(f"🧠 Wrapped OpenAI client with HermesToolClientWithReasoning")
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elif prokletor_client:
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# Fallback for unknown client names or if user provides a custom one (future proofing?)
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# For now, raise error or default to OpenAI
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print(f"⚠️ Unknown prokletor_client '{prokletor_client}'. Defaulting to HermesToolClientWithReasoning.")
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oai_client = AsyncOpenAI(**client_kwargs)
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self.client = HermesToolClientWithReasoning(oai_client)
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else:
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# Default behavior
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oai_client = AsyncOpenAI(**client_kwargs)
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self.client = oai_client
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print(f"🧠 Using raw OpenAI client (no prokletor wrapper)")
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print(f"🤖 AI Agent initialized with model: {self.model}")
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if base_url:
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print(f"🔗 Using custom base URL: {base_url}")
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except Exception as e:
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raise RuntimeError(f"Failed to initialize OpenAI client: {e}")
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raise RuntimeError(f"Failed to initialize client: {e}")
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# Get available tools with filtering
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self.tools = get_tool_definitions(
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@ -223,7 +264,7 @@ class AIAgent:
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# Use the client wrapper's format method if available to get the exact Hermes format
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# This ensures batch runner also gets the correct formatting
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if hasattr(self, 'client') and hasattr(self.client, 'format'):
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formatted_messages = self.client.format(messages, self.tools, use_tool_role=True)
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formatted_messages = self.client.format(messages, self.tools, render_final=True)
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trajectory = []
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for msg in formatted_messages:
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@ -462,13 +503,23 @@ class AIAgent:
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api_messages = [{"role": "system", "content": active_system_prompt}] + api_messages
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# Make API call with tools
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api_kwargs = {
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"model": self.model,
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"messages": api_messages,
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"tools": self.tools if self.tools else None,
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"timeout": 300.0, # 5 minute timeout for long-running agent tasks
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}
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response = await self.client.chat.completions.create(
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model=self.model,
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messages=api_messages,
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tools=self.tools if self.tools else None,
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timeout=300.0 # 5 minute timeout for long-running agent tasks
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)
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# Enable thinking by default for AsyncClaudeClient if using a supported model
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if self.prokletor_client_name == "AsyncClaudeClient" and self.model.startswith("claude"):
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api_kwargs["thinking"] = {
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"type": "enabled",
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"budget_tokens": 8000
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}
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# Ensure max_tokens is set higher than budget_tokens
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api_kwargs["max_tokens"] = 16000
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response = await self.client.chat.completions.create(**api_kwargs)
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api_duration = time.time() - api_start_time
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print(f"⏱️ OpenAI-compatible API call completed in {api_duration:.2f}s")
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@ -572,7 +623,8 @@ class AIAgent:
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"tool_calls": tool_calls_data
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})
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# Execute each tool call
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# Execute tool calls concurrently
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tool_tasks = []
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for i, tool_call in enumerate(assistant_message.tool_calls, 1):
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function_name = tool_call.function.name
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@ -587,35 +639,54 @@ class AIAgent:
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args_preview = args_str[:self.log_prefix_chars] + "..." if len(args_str) > self.log_prefix_chars else args_str
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print(f" 📞 Tool {i}: {function_name}({list(function_args.keys())}) - {args_preview}")
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# Create coroutine for tool execution
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task = handle_function_call(function_name, function_args, effective_task_id)
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tool_tasks.append(task)
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if tool_tasks:
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tool_start_time = time.time()
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# Execute the tool with task_id to isolate VMs between concurrent tasks
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function_result = await handle_function_call(function_name, function_args, effective_task_id)
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tool_duration = time.time() - tool_start_time
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result_preview = function_result[:200] if len(function_result) > 200 else function_result
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# Record tool timing in profiler
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get_profiler().record_tool_timing(function_name, tool_duration)
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if self.verbose_logging:
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logging.debug(f"Tool {function_name} completed in {tool_duration:.2f}s")
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logging.debug(f"Tool result preview: {result_preview}...")
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# Add tool result to conversation
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# Note: thought_signature should NOT be in tool responses, only in assistant messages
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messages.append({
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"role": "tool",
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"content": function_result,
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"tool_call_id": tool_call.id
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})
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# Preview tool response
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response_preview = function_result[:self.log_prefix_chars] + "..." if len(function_result) > self.log_prefix_chars else function_result
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print(f" ✅ Tool {i} completed in {tool_duration:.2f}s - {response_preview}")
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# Delay between tool calls
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if self.tool_delay > 0 and i < len(assistant_message.tool_calls):
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# Execute all tools concurrently
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# We use return_exceptions=True to ensure one failure doesn't stop others
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# Order of results corresponds to order of tasks
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results = await asyncio.gather(*tool_tasks, return_exceptions=True)
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tool_duration = time.time() - tool_start_time
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# Process results
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for i, (result, tool_call) in enumerate(zip(results, assistant_message.tool_calls), 1):
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function_name = tool_call.function.name
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# Handle exceptions from asyncio.gather
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if isinstance(result, Exception):
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function_result = json.dumps({"error": str(result)}, ensure_ascii=False)
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print(f"❌ Tool {i} ({function_name}) failed: {result}")
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else:
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function_result = result
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result_preview = function_result[:200] if len(function_result) > 200 else function_result
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# Record tool timing in profiler (approximate since they ran in parallel)
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get_profiler().record_tool_timing(function_name, tool_duration)
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if self.verbose_logging:
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logging.debug(f"Tool {function_name} completed in parallel batch")
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logging.debug(f"Tool result preview: {result_preview}...")
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# Add tool result to conversation
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# Note: thought_signature should NOT be in tool responses, only in assistant messages
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messages.append({
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"role": "tool",
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"content": function_result,
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"tool_call_id": tool_call.id
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})
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# Preview tool response
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response_preview = function_result[:self.log_prefix_chars] + "..." if len(function_result) > self.log_prefix_chars else function_result
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print(f" ✅ Tool {i} completed - {response_preview}")
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# Optional delay after batch execution
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if self.tool_delay > 0:
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await asyncio.sleep(self.tool_delay)
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# Continue loop for next response
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@ -668,7 +739,7 @@ class AIAgent:
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# Use the client wrapper's format method if available to get the exact Hermes format
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if hasattr(self, 'client') and hasattr(self.client, 'format'):
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raise ValueError("reached this point")
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formatted_messages = self.client.format(messages, self.tools, use_tool_role=True)
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formatted_messages = self.client.format(messages, self.tools, render_final=True)
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# We need to adapt this formatted list to the trajectory format expected by _save_trajectory
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# Since _convert_to_trajectory_format expects raw OAI messages, we might need a different approach
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@ -776,7 +847,9 @@ def main(
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save_trajectories: bool = False,
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verbose: bool = False,
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log_prefix_chars: int = 20,
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show_profiling: bool = True
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show_profiling: bool = True,
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prokletor_client: str = None,
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prokletor_formatter: str = None,
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):
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"""
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Main function for running the agent directly.
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@ -796,6 +869,8 @@ def main(
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verbose (bool): Enable verbose logging for debugging. Defaults to False.
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log_prefix_chars (int): Number of characters to show in log previews for tool calls/responses. Defaults to 20.
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show_profiling (bool): Display profiling statistics after conversation. Defaults to True.
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prokletor_client (str): Name of the prokletor client to use (e.g., "AsyncClaudeClient")
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prokletor_formatter (str): Name of the prokletor formatter to use
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Toolset Examples:
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- "research": Web search, extract, crawl + vision tools
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@ -914,7 +989,9 @@ def main(
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disabled_toolsets=disabled_toolsets_list,
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save_trajectories=save_trajectories,
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verbose_logging=verbose,
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log_prefix_chars=log_prefix_chars
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log_prefix_chars=log_prefix_chars,
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prokletor_client=prokletor_client,
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prokletor_formatter=prokletor_formatter
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)
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except RuntimeError as e:
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print(f"❌ Failed to initialize agent: {e}")
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