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https://github.com/NousResearch/hermes-agent.git
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Teknium
9aae59feab
PR #28102 made the summary-failure abort path the unconditional default, changing established behavior. Gate it behind config.yaml flag `compression.abort_on_summary_failure` (default False = historical fallback-placeholder behavior). - hermes_cli/config.py: new `compression.abort_on_summary_failure` key, default False, documented inline. - agent/agent_init.py: read the flag from compression config and pass to ContextCompressor. - agent/context_compressor.py: `__init__` accepts `abort_on_summary_failure` (default False). `compress()` failure branch gates the abort on the flag; when False, falls through to the restored legacy fallback path (static "summary unavailable" placeholder + drop middle window). - tests: restore original fallback expectations as default; add new TestAbortOnSummaryFailure class for the opt-in mode. Gateway/CLI plumbing (force=True on /compress, hygiene/handler abort detection, locale `gateway.compress.aborted` key) from PR #28102 stays intact — those paths only fire when `_last_compress_aborted` is True, which now only happens when the flag is enabled.
1495 lines
74 KiB
Python
1495 lines
74 KiB
Python
"""Implementation of :meth:`AIAgent.__init__` — extracted as a module function.
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``AIAgent.__init__`` is one of the longest methods in the codebase (60+
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parameters, ~1,400 lines of attribute initialization, provider
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auto-detection, credential resolution, context-engine bootstrap, etc.).
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Keeping it in ``run_agent.py`` bloats that file with code that's mostly
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"setup state, then forget".
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After this extraction the body lives here as ``init_agent(agent, ...)``
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and :meth:`AIAgent.__init__` is a thin wrapper that calls
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``init_agent(self, ...)``. All imports the body needs at module-load
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time are listed below; the body also performs many lazy imports inside
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its own scope that come along unchanged.
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Symbols that tests patch on ``run_agent.*`` (``OpenAI``, ``cleanup_vm``,
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etc.) are resolved through :func:`_ra` so the patch contract is
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preserved.
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"""
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from __future__ import annotations
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import logging
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import os
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import re
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import sys
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import threading
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import time
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import uuid
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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from urllib.parse import urlparse, parse_qs, urlunparse
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from agent.context_compressor import ContextCompressor
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from agent.iteration_budget import IterationBudget
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from agent.memory_manager import StreamingContextScrubber
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from agent.model_metadata import (
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MINIMUM_CONTEXT_LENGTH,
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fetch_model_metadata,
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get_model_context_length,
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is_local_endpoint,
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query_ollama_num_ctx,
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)
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from agent.process_bootstrap import _install_safe_stdio
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from agent.subdirectory_hints import SubdirectoryHintTracker
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from agent.think_scrubber import StreamingThinkScrubber
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from agent.tool_guardrails import (
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ToolCallGuardrailConfig,
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ToolCallGuardrailController,
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ToolGuardrailDecision,
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)
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from hermes_cli.config import cfg_get
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from hermes_cli.timeouts import get_provider_request_timeout
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from hermes_constants import get_hermes_home
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from model_tools import check_toolset_requirements, get_tool_definitions
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from utils import base_url_host_matches
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# Use the same logger name as run_agent so tests patching ``run_agent.logger``
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# capture our warnings. (run_agent.py also does
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# ``logger = logging.getLogger(__name__)``, which resolves to "run_agent"
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# from inside that module.)
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logger = logging.getLogger("run_agent")
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def _ra():
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"""Lazy reference to ``run_agent`` so callers can patch
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``run_agent.OpenAI`` / ``run_agent.cleanup_vm`` / ... and have those
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patches reach this code path.
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"""
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import run_agent
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return run_agent
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def init_agent(
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agent,
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base_url: str = None,
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api_key: str = None,
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provider: str = None,
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api_mode: str = None,
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acp_command: str = None,
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acp_args: list[str] | None = None,
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command: str = None,
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args: list[str] | None = None,
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model: str = "",
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max_iterations: int = 90, # Default tool-calling iterations (shared with subagents)
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tool_delay: float = 1.0,
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enabled_toolsets: List[str] = None,
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disabled_toolsets: List[str] = None,
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save_trajectories: bool = False,
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verbose_logging: bool = False,
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quiet_mode: bool = False,
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ephemeral_system_prompt: str = None,
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log_prefix_chars: int = 100,
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log_prefix: str = "",
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providers_allowed: List[str] = None,
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providers_ignored: List[str] = None,
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providers_order: List[str] = None,
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provider_sort: str = None,
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provider_require_parameters: bool = False,
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provider_data_collection: str = None,
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openrouter_min_coding_score: Optional[float] = None,
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session_id: str = None,
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tool_progress_callback: callable = None,
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tool_start_callback: callable = None,
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tool_complete_callback: callable = None,
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thinking_callback: callable = None,
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reasoning_callback: callable = None,
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clarify_callback: callable = None,
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step_callback: callable = None,
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stream_delta_callback: callable = None,
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interim_assistant_callback: callable = None,
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tool_gen_callback: callable = None,
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status_callback: callable = None,
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max_tokens: int = None,
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reasoning_config: Dict[str, Any] = None,
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service_tier: str = None,
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request_overrides: Dict[str, Any] = None,
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prefill_messages: List[Dict[str, Any]] = None,
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platform: str = None,
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user_id: str = None,
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user_name: str = None,
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chat_id: str = None,
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chat_name: str = None,
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chat_type: str = None,
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thread_id: str = None,
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gateway_session_key: str = None,
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skip_context_files: bool = False,
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load_soul_identity: bool = False,
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skip_memory: bool = False,
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session_db=None,
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parent_session_id: str = None,
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iteration_budget: "IterationBudget" = None,
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fallback_model: Dict[str, Any] = None,
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credential_pool=None,
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checkpoints_enabled: bool = False,
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checkpoint_max_snapshots: int = 20,
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checkpoint_max_total_size_mb: int = 500,
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checkpoint_max_file_size_mb: int = 10,
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pass_session_id: bool = False,
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):
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"""
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Initialize the AI Agent.
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Args:
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base_url (str): Base URL for the model API (optional)
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api_key (str): API key for authentication (optional, uses env var if not provided)
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provider (str): Provider identifier (optional; used for telemetry/routing hints)
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api_mode (str): API mode override: "chat_completions" or "codex_responses"
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model (str): Model name to use (default: "anthropic/claude-opus-4.6")
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max_iterations (int): Maximum number of tool calling iterations (default: 90)
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tool_delay (float): Delay between tool calls in seconds (default: 1.0)
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enabled_toolsets (List[str]): Only enable tools from these toolsets (optional)
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disabled_toolsets (List[str]): Disable tools from these toolsets (optional)
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save_trajectories (bool): Whether to save conversation trajectories to JSONL files (default: False)
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verbose_logging (bool): Enable verbose logging for debugging (default: False)
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quiet_mode (bool): Suppress progress output for clean CLI experience (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: 100)
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log_prefix (str): Prefix to add to all log messages for identification in parallel processing (default: "")
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providers_allowed (List[str]): OpenRouter providers to allow (optional)
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providers_ignored (List[str]): OpenRouter providers to ignore (optional)
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providers_order (List[str]): OpenRouter providers to try in order (optional)
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provider_sort (str): Sort providers by price/throughput/latency (optional)
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openrouter_min_coding_score (float): Coding-score floor (0.0-1.0) for the
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openrouter/pareto-code router. Only applied when model == "openrouter/pareto-code".
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None or empty = let OpenRouter pick the strongest available coder.
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session_id (str): Pre-generated session ID for logging (optional, auto-generated if not provided)
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tool_progress_callback (callable): Callback function(tool_name, args_preview) for progress notifications
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clarify_callback (callable): Callback function(question, choices) -> str for interactive user questions.
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Provided by the platform layer (CLI or gateway). If None, the clarify tool returns an error.
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max_tokens (int): Maximum tokens for model responses (optional, uses model default if not set)
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reasoning_config (Dict): OpenRouter reasoning configuration override (e.g. {"effort": "none"} to disable thinking).
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If None, defaults to {"enabled": True, "effort": "medium"} for OpenRouter. Set to disable/customize reasoning.
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prefill_messages (List[Dict]): Messages to prepend to conversation history as prefilled context.
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Useful for injecting a few-shot example or priming the model's response style.
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Example: [{"role": "user", "content": "Hi!"}, {"role": "assistant", "content": "Hello!"}]
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NOTE: Anthropic Sonnet 4.6+ and Opus 4.6+ reject a conversation that ends on an
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assistant-role message (400 error). For those models use structured outputs or
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output_config.format instead of a trailing-assistant prefill.
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platform (str): The interface platform the user is on (e.g. "cli", "telegram", "discord", "whatsapp").
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Used to inject platform-specific formatting hints into the system prompt.
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skip_context_files (bool): If True, skip auto-injection of SOUL.md, AGENTS.md, and .cursorrules
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into the system prompt. Use this for batch processing and data generation to avoid
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polluting trajectories with user-specific persona or project instructions.
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load_soul_identity (bool): If True, still use ~/.hermes/SOUL.md as the primary
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identity even when skip_context_files=True. Project context files from the cwd
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remain skipped.
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"""
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_install_safe_stdio()
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agent.model = model
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agent.max_iterations = max_iterations
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# Shared iteration budget — parent creates, children inherit.
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# Consumed by every LLM turn across parent + all subagents.
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agent.iteration_budget = iteration_budget or IterationBudget(max_iterations)
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agent.tool_delay = tool_delay
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agent.save_trajectories = save_trajectories
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agent.verbose_logging = verbose_logging
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agent.quiet_mode = quiet_mode
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agent.ephemeral_system_prompt = ephemeral_system_prompt
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agent.platform = platform # "cli", "telegram", "discord", "whatsapp", etc.
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agent._user_id = user_id # Platform user identifier (gateway sessions)
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agent._user_name = user_name
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agent._chat_id = chat_id
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agent._chat_name = chat_name
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agent._chat_type = chat_type
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agent._thread_id = thread_id
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agent._gateway_session_key = gateway_session_key # Stable per-chat key (e.g. agent:main:telegram:dm:123)
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# Pluggable print function — CLI replaces this with _cprint so that
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# raw ANSI status lines are routed through prompt_toolkit's renderer
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# instead of going directly to stdout where patch_stdout's StdoutProxy
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# would mangle the escape sequences. None = use builtins.print.
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agent._print_fn = None
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agent.background_review_callback = None # Optional sync callback for gateway delivery
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agent.skip_context_files = skip_context_files
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agent.load_soul_identity = load_soul_identity
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agent.pass_session_id = pass_session_id
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agent._credential_pool = credential_pool
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agent.log_prefix_chars = log_prefix_chars
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agent.log_prefix = f"{log_prefix} " if log_prefix else ""
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# Store effective base URL for feature detection (prompt caching, reasoning, etc.)
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agent.base_url = base_url or ""
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provider_name = provider.strip().lower() if isinstance(provider, str) and provider.strip() else None
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agent.provider = provider_name or ""
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agent.acp_command = acp_command or command
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agent.acp_args = list(acp_args or args or [])
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if api_mode in {"chat_completions", "codex_responses", "anthropic_messages", "bedrock_converse", "codex_app_server"}:
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agent.api_mode = api_mode
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elif agent.provider == "openai-codex":
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agent.api_mode = "codex_responses"
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elif agent.provider in {"xai", "xai-oauth"}:
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agent.api_mode = "codex_responses"
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elif (provider_name is None) and (
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agent._base_url_hostname == "chatgpt.com"
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and "/backend-api/codex" in agent._base_url_lower
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):
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agent.api_mode = "codex_responses"
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agent.provider = "openai-codex"
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elif (provider_name is None) and agent._base_url_hostname == "api.x.ai":
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agent.api_mode = "codex_responses"
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agent.provider = "xai"
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elif agent.provider == "anthropic" or (provider_name is None and agent._base_url_hostname == "api.anthropic.com"):
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agent.api_mode = "anthropic_messages"
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agent.provider = "anthropic"
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elif agent._base_url_lower.rstrip("/").endswith("/anthropic"):
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# Third-party Anthropic-compatible endpoints (e.g. MiniMax, DashScope)
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# use a URL convention ending in /anthropic. Auto-detect these so the
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# Anthropic Messages API adapter is used instead of chat completions.
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agent.api_mode = "anthropic_messages"
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elif agent.provider == "bedrock" or (
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agent._base_url_hostname.startswith("bedrock-runtime.")
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and base_url_host_matches(agent._base_url_lower, "amazonaws.com")
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):
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# AWS Bedrock — auto-detect from provider name or base URL
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# (bedrock-runtime.<region>.amazonaws.com).
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agent.api_mode = "bedrock_converse"
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else:
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agent.api_mode = "chat_completions"
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# Eagerly warm the transport cache so import errors surface at init,
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# not mid-conversation. Also validates the api_mode is registered.
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try:
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agent._get_transport()
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except Exception:
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pass # Non-fatal — transport may not exist for all modes yet
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try:
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from hermes_cli.model_normalize import (
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_AGGREGATOR_PROVIDERS,
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normalize_model_for_provider,
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)
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if agent.provider not in _AGGREGATOR_PROVIDERS:
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agent.model = normalize_model_for_provider(agent.model, agent.provider)
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except Exception:
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pass
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# GPT-5.x models usually require the Responses API path, but some
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# providers have exceptions (for example Copilot's gpt-5-mini still
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# uses chat completions). Also auto-upgrade for direct OpenAI URLs
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# (api.openai.com) since all newer tool-calling models prefer
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# Responses there. ACP runtimes are excluded: CopilotACPClient
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# handles its own routing and does not implement the Responses API
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# surface.
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# When api_mode was explicitly provided, respect it — the user
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# knows what their endpoint supports (#10473).
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# Exception: Azure OpenAI serves gpt-5.x on /chat/completions and
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# does NOT support the Responses API — skip the upgrade for Azure
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# (openai.azure.com), even though it looks OpenAI-compatible.
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if (
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api_mode is None
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and agent.api_mode == "chat_completions"
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and agent.provider != "copilot-acp"
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and not str(agent.base_url or "").lower().startswith("acp://copilot")
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and not str(agent.base_url or "").lower().startswith("acp+tcp://")
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and not agent._is_azure_openai_url()
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and (
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agent._is_direct_openai_url()
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or agent._provider_model_requires_responses_api(
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agent.model,
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provider=agent.provider,
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)
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)
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):
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agent.api_mode = "codex_responses"
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# Invalidate the eager-warmed transport cache — api_mode changed
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# from chat_completions to codex_responses after the warm at __init__.
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if hasattr(agent, "_transport_cache"):
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agent._transport_cache.clear()
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# Pre-warm OpenRouter model metadata cache in a background thread.
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# fetch_model_metadata() is cached for 1 hour; this avoids a blocking
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# HTTP request on the first API response when pricing is estimated.
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# Use a process-level Event so this thread is only spawned once — a new
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# AIAgent is created for every gateway request, so without the guard
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# each message leaks one OS thread and the process eventually exhausts
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# the system thread limit (RuntimeError: can't start new thread).
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if (agent.provider == "openrouter" or agent._is_openrouter_url()) and \
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not _ra()._openrouter_prewarm_done.is_set():
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_ra()._openrouter_prewarm_done.set()
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threading.Thread(
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target=fetch_model_metadata,
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daemon=True,
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name="openrouter-prewarm",
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).start()
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agent.tool_progress_callback = tool_progress_callback
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agent.tool_start_callback = tool_start_callback
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agent.tool_complete_callback = tool_complete_callback
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agent.suppress_status_output = False
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agent.thinking_callback = thinking_callback
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agent.reasoning_callback = reasoning_callback
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agent.clarify_callback = clarify_callback
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agent.step_callback = step_callback
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agent.stream_delta_callback = stream_delta_callback
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agent.interim_assistant_callback = interim_assistant_callback
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agent.status_callback = status_callback
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agent.tool_gen_callback = tool_gen_callback
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# Tool execution state — allows _vprint during tool execution
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# even when stream consumers are registered (no tokens streaming then)
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agent._executing_tools = False
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agent._tool_guardrails = ToolCallGuardrailController()
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agent._tool_guardrail_halt_decision: ToolGuardrailDecision | None = None
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# Interrupt mechanism for breaking out of tool loops
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agent._interrupt_requested = False
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agent._interrupt_message = None # Optional message that triggered interrupt
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agent._execution_thread_id: int | None = None # Set at run_conversation() start
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agent._interrupt_thread_signal_pending = False
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agent._client_lock = threading.RLock()
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# /steer mechanism — inject a user note into the next tool result
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# without interrupting the agent. Unlike interrupt(), steer() does
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# NOT set _interrupt_requested; it waits for the current tool batch
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# to finish naturally, then the drain hook appends the text to the
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# last tool result's content so the model sees it on its next
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# iteration. Message-role alternation is preserved (we modify an
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# existing tool message rather than inserting a new user turn).
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agent._pending_steer: Optional[str] = None
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agent._pending_steer_lock = threading.Lock()
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# Concurrent-tool worker thread tracking. `_execute_tool_calls_concurrent`
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# runs each tool on its own ThreadPoolExecutor worker — those worker
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# threads have tids distinct from `_execution_thread_id`, so
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# `_set_interrupt(True, _execution_thread_id)` alone does NOT cause
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# `is_interrupted()` inside the worker to return True. Track the
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# workers here so `interrupt()` / `clear_interrupt()` can fan out to
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# their tids explicitly.
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agent._tool_worker_threads: set[int] = set()
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agent._tool_worker_threads_lock = threading.Lock()
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# Subagent delegation state
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agent._delegate_depth = 0 # 0 = top-level agent, incremented for children
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agent._active_children = [] # Running child AIAgents (for interrupt propagation)
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agent._active_children_lock = threading.Lock()
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# Store OpenRouter provider preferences
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agent.providers_allowed = providers_allowed
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agent.providers_ignored = providers_ignored
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agent.providers_order = providers_order
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agent.provider_sort = provider_sort
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agent.provider_require_parameters = provider_require_parameters
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agent.provider_data_collection = provider_data_collection
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agent.openrouter_min_coding_score = openrouter_min_coding_score
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# Store toolset filtering options
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agent.enabled_toolsets = enabled_toolsets
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agent.disabled_toolsets = disabled_toolsets
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|
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# Model response configuration
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agent.max_tokens = max_tokens # None = use model default
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agent.reasoning_config = reasoning_config # None = use default (medium for OpenRouter)
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agent.service_tier = service_tier
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agent.request_overrides = dict(request_overrides or {})
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agent.prefill_messages = prefill_messages or [] # Prefilled conversation turns
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agent._force_ascii_payload = False
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|
|
# Anthropic prompt caching: auto-enabled for Claude models on native
|
|
# Anthropic, OpenRouter, and third-party gateways that speak the
|
|
# Anthropic protocol (``api_mode == 'anthropic_messages'``). Reduces
|
|
# input costs by ~75% on multi-turn conversations. Uses system_and_3
|
|
# strategy (4 breakpoints). See ``_anthropic_prompt_cache_policy``
|
|
# for the layout-vs-transport decision.
|
|
agent._use_prompt_caching, agent._use_native_cache_layout = (
|
|
agent._anthropic_prompt_cache_policy()
|
|
)
|
|
# Anthropic supports "5m" (default) and "1h" cache TTL tiers. Read from
|
|
# config.yaml under prompt_caching.cache_ttl; unknown values keep "5m".
|
|
# 1h tier costs 2x on write vs 1.25x for 5m, but amortizes across long
|
|
# sessions with >5-minute pauses between turns (#14971).
|
|
agent._cache_ttl = "5m"
|
|
try:
|
|
from hermes_cli.config import load_config as _load_pc_cfg
|
|
|
|
_pc_cfg = _load_pc_cfg().get("prompt_caching", {}) or {}
|
|
_ttl = _pc_cfg.get("cache_ttl", "5m")
|
|
if _ttl in {"5m", "1h"}:
|
|
agent._cache_ttl = _ttl
|
|
except Exception:
|
|
pass
|
|
|
|
# Iteration budget: the LLM is only notified when it actually exhausts
|
|
# the iteration budget (api_call_count >= max_iterations). At that
|
|
# point we inject ONE message, allow one final API call, and if the
|
|
# model doesn't produce a text response, force a user-message asking
|
|
# it to summarise. No intermediate pressure warnings — they caused
|
|
# models to "give up" prematurely on complex tasks (#7915).
|
|
agent._budget_exhausted_injected = False
|
|
agent._budget_grace_call = False
|
|
|
|
# Activity tracking — updated on each API call, tool execution, and
|
|
# stream chunk. Used by the gateway timeout handler to report what the
|
|
# agent was doing when it was killed, and by the "still working"
|
|
# notifications to show progress.
|
|
agent._last_activity_ts: float = time.time()
|
|
agent._last_activity_desc: str = "initializing"
|
|
agent._current_tool: str | None = None
|
|
agent._api_call_count: int = 0
|
|
|
|
# Rate limit tracking — updated from x-ratelimit-* response headers
|
|
# after each API call. Accessed by /usage slash command.
|
|
agent._rate_limit_state: Optional["RateLimitState"] = None
|
|
|
|
# OpenRouter response cache hit counter — incremented when
|
|
# X-OpenRouter-Cache-Status: HIT is seen in streaming response headers.
|
|
agent._or_cache_hits: int = 0
|
|
|
|
# Centralized logging — agent.log (INFO+) and errors.log (WARNING+)
|
|
# both live under ~/.hermes/logs/. Idempotent, so gateway mode
|
|
# (which creates a new AIAgent per message) won't duplicate handlers.
|
|
from hermes_logging import setup_logging, setup_verbose_logging
|
|
setup_logging(hermes_home=_ra()._hermes_home)
|
|
|
|
if agent.verbose_logging:
|
|
setup_verbose_logging()
|
|
_ra().logger.info("Verbose logging enabled (third-party library logs suppressed)")
|
|
elif agent.quiet_mode:
|
|
# In quiet mode (CLI default), keep console output clean —
|
|
# but DO NOT raise per-logger levels. Doing so prevents the
|
|
# root logger's file handlers (agent.log, errors.log) from
|
|
# ever seeing the records, because Python checks
|
|
# logger.isEnabledFor() before handler propagation. We rely
|
|
# on the fact that hermes_logging.setup_logging() does not
|
|
# install a console StreamHandler in quiet mode — so INFO
|
|
# records flow to the file handlers but never reach a
|
|
# console. Any future noise reduction belongs at the
|
|
# handler level inside hermes_logging.py, not here.
|
|
pass
|
|
|
|
# Internal stream callback (set during streaming TTS).
|
|
# Initialized here so _vprint can reference it before run_conversation.
|
|
agent._stream_callback = None
|
|
# Deferred paragraph break flag — set after tool iterations so a
|
|
# single "\n\n" is prepended to the next real text delta.
|
|
agent._stream_needs_break = False
|
|
# Stateful scrubber for <memory-context> spans split across stream
|
|
# deltas (#5719). sanitize_context() alone can't survive chunk
|
|
# boundaries because the block regex needs both tags in one string.
|
|
agent._stream_context_scrubber = StreamingContextScrubber()
|
|
# Stateful scrubber for reasoning/thinking tags in streamed deltas
|
|
# (#17924). Replaces the per-delta _strip_think_blocks regex that
|
|
# destroyed downstream state (e.g. MiniMax-M2.7 streaming
|
|
# '<think>' as delta1 and 'Let me check' as delta2 — the regex
|
|
# erased delta1, so downstream state machines never learned a
|
|
# block was open and leaked delta2 as content).
|
|
agent._stream_think_scrubber = StreamingThinkScrubber()
|
|
# Visible assistant text already delivered through live token callbacks
|
|
# during the current model response. Used to avoid re-sending the same
|
|
# commentary when the provider later returns it as a completed interim
|
|
# assistant message.
|
|
agent._current_streamed_assistant_text = ""
|
|
|
|
# Optional current-turn user-message override used when the API-facing
|
|
# user message intentionally differs from the persisted transcript
|
|
# (e.g. CLI voice mode adds a temporary prefix for the live call only).
|
|
agent._persist_user_message_idx = None
|
|
agent._persist_user_message_override = None
|
|
|
|
# Cache anthropic image-to-text fallbacks per image payload/URL so a
|
|
# single tool loop does not repeatedly re-run auxiliary vision on the
|
|
# same image history.
|
|
agent._anthropic_image_fallback_cache: Dict[str, str] = {}
|
|
|
|
# Initialize LLM client via centralized provider router.
|
|
# The router handles auth resolution, base URL, headers, and
|
|
# Codex/Anthropic wrapping for all known providers.
|
|
# raw_codex=True because the main agent needs direct responses.stream()
|
|
# access for Codex Responses API streaming.
|
|
agent._anthropic_client = None
|
|
agent._is_anthropic_oauth = False
|
|
|
|
# Resolve per-provider / per-model request timeout once up front so
|
|
# every client construction path below (Anthropic native, OpenAI-wire,
|
|
# router-based implicit auth) can apply it consistently. Bedrock
|
|
# Claude uses its own timeout path and is not covered here.
|
|
_provider_timeout = get_provider_request_timeout(agent.provider, agent.model)
|
|
|
|
if agent.api_mode == "anthropic_messages":
|
|
from agent.anthropic_adapter import build_anthropic_client, resolve_anthropic_token
|
|
# Bedrock + Claude → use AnthropicBedrock SDK for full feature parity
|
|
# (prompt caching, thinking budgets, adaptive thinking).
|
|
_is_bedrock_anthropic = agent.provider == "bedrock"
|
|
if _is_bedrock_anthropic:
|
|
from agent.anthropic_adapter import build_anthropic_bedrock_client
|
|
_region_match = re.search(r"bedrock-runtime\.([a-z0-9-]+)\.", base_url or "")
|
|
_br_region = _region_match.group(1) if _region_match else "us-east-1"
|
|
agent._bedrock_region = _br_region
|
|
agent._anthropic_client = build_anthropic_bedrock_client(_br_region)
|
|
agent._anthropic_api_key = "aws-sdk"
|
|
agent._anthropic_base_url = base_url
|
|
agent._is_anthropic_oauth = False
|
|
agent.api_key = "aws-sdk"
|
|
agent.client = None
|
|
agent._client_kwargs = {}
|
|
if not agent.quiet_mode:
|
|
print(f"🤖 AI Agent initialized with model: {agent.model} (AWS Bedrock + AnthropicBedrock SDK, {_br_region})")
|
|
else:
|
|
# Only fall back to ANTHROPIC_TOKEN when the provider is actually Anthropic.
|
|
# Other anthropic_messages providers (MiniMax, Alibaba, etc.) must use their own API key.
|
|
# Falling back would send Anthropic credentials to third-party endpoints (Fixes #1739, #minimax-401).
|
|
_is_native_anthropic = agent.provider == "anthropic"
|
|
effective_key = (api_key or resolve_anthropic_token() or "") if _is_native_anthropic else (api_key or "")
|
|
agent.api_key = effective_key
|
|
agent._anthropic_api_key = effective_key
|
|
agent._anthropic_base_url = base_url
|
|
# Only mark the session as OAuth-authenticated when the token
|
|
# genuinely belongs to native Anthropic. Third-party providers
|
|
# (MiniMax, Kimi, GLM, LiteLLM proxies) that accept the
|
|
# Anthropic protocol must never trip OAuth code paths — doing
|
|
# so injects Claude-Code identity headers and system prompts
|
|
# that cause 401/403 on their endpoints. Guards #1739 and
|
|
# the third-party identity-injection bug.
|
|
from agent.anthropic_adapter import _is_oauth_token as _is_oat
|
|
agent._is_anthropic_oauth = _is_oat(effective_key) if _is_native_anthropic else False
|
|
agent._anthropic_client = build_anthropic_client(effective_key, base_url, timeout=_provider_timeout)
|
|
# No OpenAI client needed for Anthropic mode
|
|
agent.client = None
|
|
agent._client_kwargs = {}
|
|
if not agent.quiet_mode:
|
|
print(f"🤖 AI Agent initialized with model: {agent.model} (Anthropic native)")
|
|
# ``effective_key`` may be a callable Entra ID bearer
|
|
# provider for Azure Foundry anthropic_messages mode.
|
|
# The Anthropic adapter installs an httpx event hook
|
|
# that mints a fresh JWT per request — we never
|
|
# invoke or inspect the callable in the banner.
|
|
from agent.azure_identity_adapter import is_token_provider
|
|
|
|
if is_token_provider(effective_key):
|
|
print("🔑 Using credentials: Microsoft Entra ID")
|
|
elif isinstance(effective_key, str) and len(effective_key) > 12:
|
|
print(f"🔑 Using token: {effective_key[:8]}...{effective_key[-4:]}")
|
|
elif agent.api_mode == "bedrock_converse":
|
|
# AWS Bedrock — uses boto3 directly, no OpenAI client needed.
|
|
# Region is extracted from the base_url or defaults to us-east-1.
|
|
_region_match = re.search(r"bedrock-runtime\.([a-z0-9-]+)\.", base_url or "")
|
|
agent._bedrock_region = _region_match.group(1) if _region_match else "us-east-1"
|
|
# Guardrail config — read from config.yaml at init time.
|
|
agent._bedrock_guardrail_config = None
|
|
try:
|
|
from hermes_cli.config import load_config as _load_br_cfg
|
|
_gr = _load_br_cfg().get("bedrock", {}).get("guardrail", {})
|
|
if _gr.get("guardrail_identifier") and _gr.get("guardrail_version"):
|
|
agent._bedrock_guardrail_config = {
|
|
"guardrailIdentifier": _gr["guardrail_identifier"],
|
|
"guardrailVersion": _gr["guardrail_version"],
|
|
}
|
|
if _gr.get("stream_processing_mode"):
|
|
agent._bedrock_guardrail_config["streamProcessingMode"] = _gr["stream_processing_mode"]
|
|
if _gr.get("trace"):
|
|
agent._bedrock_guardrail_config["trace"] = _gr["trace"]
|
|
except Exception:
|
|
pass
|
|
agent.client = None
|
|
agent._client_kwargs = {}
|
|
if not agent.quiet_mode:
|
|
_gr_label = " + Guardrails" if agent._bedrock_guardrail_config else ""
|
|
print(f"🤖 AI Agent initialized with model: {agent.model} (AWS Bedrock, {agent._bedrock_region}{_gr_label})")
|
|
else:
|
|
if api_key and base_url:
|
|
# Explicit credentials from CLI/gateway — construct directly.
|
|
# The runtime provider resolver already handled auth for us.
|
|
# Extract query params (e.g. Azure api-version) from base_url
|
|
# and pass via default_query to prevent loss during SDK URL
|
|
# joining (httpx drops query string when joining paths).
|
|
_parsed_url = urlparse(base_url)
|
|
if _parsed_url.query:
|
|
_clean_url = urlunparse(_parsed_url._replace(query=""))
|
|
_query_params = {
|
|
k: v[0] for k, v in parse_qs(_parsed_url.query).items()
|
|
}
|
|
client_kwargs = {
|
|
"api_key": api_key,
|
|
"base_url": _clean_url,
|
|
"default_query": _query_params,
|
|
}
|
|
else:
|
|
client_kwargs = {"api_key": api_key, "base_url": base_url}
|
|
if _provider_timeout is not None:
|
|
client_kwargs["timeout"] = _provider_timeout
|
|
if agent.provider == "copilot-acp":
|
|
client_kwargs["command"] = agent.acp_command
|
|
client_kwargs["args"] = agent.acp_args
|
|
effective_base = base_url
|
|
if base_url_host_matches(effective_base, "openrouter.ai"):
|
|
from agent.auxiliary_client import build_or_headers
|
|
client_kwargs["default_headers"] = build_or_headers()
|
|
elif base_url_host_matches(effective_base, "integrate.api.nvidia.com"):
|
|
from agent.auxiliary_client import build_nvidia_nim_headers
|
|
client_kwargs["default_headers"] = build_nvidia_nim_headers(effective_base)
|
|
elif base_url_host_matches(effective_base, "api.routermint.com"):
|
|
client_kwargs["default_headers"] = _ra()._routermint_headers()
|
|
elif base_url_host_matches(effective_base, "api.githubcopilot.com"):
|
|
from hermes_cli.models import copilot_default_headers
|
|
|
|
client_kwargs["default_headers"] = copilot_default_headers()
|
|
elif base_url_host_matches(effective_base, "api.kimi.com"):
|
|
client_kwargs["default_headers"] = {
|
|
"User-Agent": "claude-code/0.1.0",
|
|
}
|
|
elif base_url_host_matches(effective_base, "portal.qwen.ai"):
|
|
client_kwargs["default_headers"] = _ra()._qwen_portal_headers()
|
|
elif base_url_host_matches(effective_base, "chatgpt.com"):
|
|
from agent.auxiliary_client import _codex_cloudflare_headers
|
|
client_kwargs["default_headers"] = _codex_cloudflare_headers(api_key)
|
|
elif "default_headers" not in client_kwargs:
|
|
# Fall back to profile.default_headers for providers that
|
|
# declare custom headers (e.g. Vercel AI Gateway attribution,
|
|
# Kimi User-Agent on non-kimi.com endpoints).
|
|
try:
|
|
from providers import get_provider_profile as _gpf
|
|
_ph = _gpf(agent.provider)
|
|
if _ph and _ph.default_headers:
|
|
client_kwargs["default_headers"] = dict(_ph.default_headers)
|
|
except Exception:
|
|
pass
|
|
else:
|
|
# No explicit creds — use the centralized provider router
|
|
from agent.auxiliary_client import resolve_provider_client
|
|
_routed_client, _ = resolve_provider_client(
|
|
agent.provider or "auto", model=agent.model, raw_codex=True)
|
|
if _routed_client is not None:
|
|
client_kwargs = {
|
|
"api_key": _routed_client.api_key,
|
|
"base_url": str(_routed_client.base_url),
|
|
}
|
|
if _provider_timeout is not None:
|
|
client_kwargs["timeout"] = _provider_timeout
|
|
# Preserve provider-specific headers the router set. The
|
|
# OpenAI SDK stores caller-provided default_headers in
|
|
# _custom_headers; older/mocked clients may expose
|
|
# _default_headers instead.
|
|
_routed_headers = getattr(_routed_client, "_custom_headers", None)
|
|
if not _routed_headers:
|
|
_routed_headers = getattr(_routed_client, "_default_headers", None)
|
|
if _routed_headers:
|
|
client_kwargs["default_headers"] = dict(_routed_headers)
|
|
else:
|
|
# When the user explicitly chose a non-OpenRouter provider
|
|
# but no credentials were found, fail fast with a clear
|
|
# message instead of silently routing through OpenRouter.
|
|
_explicit = (agent.provider or "").strip().lower()
|
|
if _explicit and _explicit not in {"auto", "openrouter", "custom"}:
|
|
# Look up the actual env var name from the provider
|
|
# config — some providers use non-standard names
|
|
# (e.g. alibaba → DASHSCOPE_API_KEY, not ALIBABA_API_KEY).
|
|
_env_hint = f"{_explicit.upper()}_API_KEY"
|
|
try:
|
|
from hermes_cli.auth import PROVIDER_REGISTRY
|
|
_pcfg = PROVIDER_REGISTRY.get(_explicit)
|
|
if _pcfg and _pcfg.api_key_env_vars:
|
|
_env_hint = _pcfg.api_key_env_vars[0]
|
|
except Exception:
|
|
pass
|
|
# --- Init-time fallback (#17929) ---
|
|
_fb_entries = []
|
|
if isinstance(fallback_model, list):
|
|
_fb_entries = [
|
|
f for f in fallback_model
|
|
if isinstance(f, dict) and f.get("provider") and f.get("model")
|
|
]
|
|
elif isinstance(fallback_model, dict) and fallback_model.get("provider") and fallback_model.get("model"):
|
|
_fb_entries = [fallback_model]
|
|
_fb_resolved = False
|
|
for _fb in _fb_entries:
|
|
_fb_explicit_key = (_fb.get("api_key") or "").strip() or None
|
|
if not _fb_explicit_key:
|
|
_fb_key_env = (_fb.get("key_env") or _fb.get("api_key_env") or "").strip()
|
|
if _fb_key_env:
|
|
_fb_explicit_key = os.getenv(_fb_key_env, "").strip() or None
|
|
_fb_client, _fb_model = resolve_provider_client(
|
|
_fb["provider"], model=_fb["model"], raw_codex=True,
|
|
explicit_base_url=_fb.get("base_url"),
|
|
explicit_api_key=_fb_explicit_key,
|
|
)
|
|
if _fb_client is not None:
|
|
agent.provider = _fb["provider"]
|
|
agent.model = _fb_model or _fb["model"]
|
|
agent._fallback_activated = True
|
|
client_kwargs = {
|
|
"api_key": _fb_client.api_key,
|
|
"base_url": str(_fb_client.base_url),
|
|
}
|
|
if _provider_timeout is not None:
|
|
client_kwargs["timeout"] = _provider_timeout
|
|
_fb_headers = getattr(_fb_client, "_custom_headers", None)
|
|
if not _fb_headers:
|
|
_fb_headers = getattr(_fb_client, "_default_headers", None)
|
|
if _fb_headers:
|
|
client_kwargs["default_headers"] = dict(_fb_headers)
|
|
_fb_resolved = True
|
|
break
|
|
if not _fb_resolved:
|
|
raise RuntimeError(
|
|
f"Provider '{_explicit}' is set in config.yaml but no API key "
|
|
f"was found. Set the {_env_hint} environment "
|
|
f"variable, or switch to a different provider with `hermes model`."
|
|
)
|
|
if not getattr(agent, "_fallback_activated", False):
|
|
# No provider configured — reject with a clear message.
|
|
raise RuntimeError(
|
|
"No LLM provider configured. Run `hermes model` to "
|
|
"select a provider, or run `hermes setup` for first-time "
|
|
"configuration."
|
|
)
|
|
|
|
agent._client_kwargs = client_kwargs # stored for rebuilding after interrupt
|
|
|
|
# Enable fine-grained tool streaming for Claude on OpenRouter.
|
|
# Without this, Anthropic buffers the entire tool call and goes
|
|
# silent for minutes while thinking — OpenRouter's upstream proxy
|
|
# times out during the silence. The beta header makes Anthropic
|
|
# stream tool call arguments token-by-token, keeping the
|
|
# connection alive.
|
|
_effective_base = str(client_kwargs.get("base_url", "")).lower()
|
|
if base_url_host_matches(_effective_base, "openrouter.ai") and "claude" in (agent.model or "").lower():
|
|
headers = client_kwargs.get("default_headers") or {}
|
|
existing_beta = headers.get("x-anthropic-beta", "")
|
|
_FINE_GRAINED = "fine-grained-tool-streaming-2025-05-14"
|
|
if _FINE_GRAINED not in existing_beta:
|
|
if existing_beta:
|
|
headers["x-anthropic-beta"] = f"{existing_beta},{_FINE_GRAINED}"
|
|
else:
|
|
headers["x-anthropic-beta"] = _FINE_GRAINED
|
|
client_kwargs["default_headers"] = headers
|
|
|
|
agent.api_key = client_kwargs.get("api_key", "")
|
|
agent.base_url = client_kwargs.get("base_url", agent.base_url)
|
|
try:
|
|
agent.client = agent._create_openai_client(client_kwargs, reason="agent_init", shared=True)
|
|
if not agent.quiet_mode:
|
|
print(f"🤖 AI Agent initialized with model: {agent.model}")
|
|
if base_url:
|
|
print(f"🔗 Using custom base URL: {base_url}")
|
|
# ``api_key`` may be a callable Entra ID bearer
|
|
# provider (Azure Foundry). The OpenAI SDK mints a
|
|
# fresh JWT per request internally — the banner
|
|
# never invokes or inspects the callable.
|
|
from agent.azure_identity_adapter import is_token_provider
|
|
|
|
key_used = client_kwargs.get("api_key", "none")
|
|
if is_token_provider(key_used):
|
|
print("🔑 Using credentials: Microsoft Entra ID")
|
|
elif isinstance(key_used, str) and 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("⚠️ Warning: API key appears invalid or missing")
|
|
except Exception as e:
|
|
raise RuntimeError(f"Failed to initialize OpenAI client: {e}")
|
|
|
|
# Provider fallback chain — ordered list of backup providers tried
|
|
# when the primary is exhausted (rate-limit, overload, connection
|
|
# failure). Supports both legacy single-dict ``fallback_model`` and
|
|
# new list ``fallback_providers`` format.
|
|
if isinstance(fallback_model, list):
|
|
agent._fallback_chain = [
|
|
f for f in fallback_model
|
|
if isinstance(f, dict) and f.get("provider") and f.get("model")
|
|
]
|
|
elif isinstance(fallback_model, dict) and fallback_model.get("provider") and fallback_model.get("model"):
|
|
agent._fallback_chain = [fallback_model]
|
|
else:
|
|
agent._fallback_chain = []
|
|
agent._fallback_index = 0
|
|
agent._fallback_activated = getattr(agent, "_fallback_activated", False)
|
|
# Legacy attribute kept for backward compat (tests, external callers)
|
|
agent._fallback_model = agent._fallback_chain[0] if agent._fallback_chain else None
|
|
if agent._fallback_chain and not agent.quiet_mode:
|
|
if len(agent._fallback_chain) == 1:
|
|
fb = agent._fallback_chain[0]
|
|
print(f"🔄 Fallback model: {fb['model']} ({fb['provider']})")
|
|
else:
|
|
print(f"🔄 Fallback chain ({len(agent._fallback_chain)} providers): " +
|
|
" → ".join(f"{f['model']} ({f['provider']})" for f in agent._fallback_chain))
|
|
|
|
# Get available tools with filtering
|
|
agent.tools = _ra().get_tool_definitions(
|
|
enabled_toolsets=enabled_toolsets,
|
|
disabled_toolsets=disabled_toolsets,
|
|
quiet_mode=agent.quiet_mode,
|
|
)
|
|
|
|
# Show tool configuration and store valid tool names for validation
|
|
agent.valid_tool_names = set()
|
|
if agent.tools:
|
|
agent.valid_tool_names = {tool["function"]["name"] for tool in agent.tools}
|
|
tool_names = sorted(agent.valid_tool_names)
|
|
if not agent.quiet_mode:
|
|
print(f"🛠️ Loaded {len(agent.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 agent.quiet_mode:
|
|
print("🛠️ No tools loaded (all tools filtered out or unavailable)")
|
|
|
|
# Check tool requirements
|
|
if agent.tools and not agent.quiet_mode:
|
|
requirements = _ra().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 agent.save_trajectories and not agent.quiet_mode:
|
|
print("📝 Trajectory saving enabled")
|
|
|
|
# Show ephemeral system prompt status
|
|
if agent.ephemeral_system_prompt and not agent.quiet_mode:
|
|
prompt_preview = agent.ephemeral_system_prompt[:60] + "..." if len(agent.ephemeral_system_prompt) > 60 else agent.ephemeral_system_prompt
|
|
print(f"🔒 Ephemeral system prompt: '{prompt_preview}' (not saved to trajectories)")
|
|
|
|
# Show prompt caching status
|
|
if agent._use_prompt_caching and not agent.quiet_mode:
|
|
if agent._use_native_cache_layout and agent.provider == "anthropic":
|
|
source = "native Anthropic"
|
|
elif agent._use_native_cache_layout:
|
|
source = "Anthropic-compatible endpoint"
|
|
else:
|
|
source = "Claude via OpenRouter"
|
|
print(f"💾 Prompt caching: ENABLED ({source}, {agent._cache_ttl} TTL)")
|
|
|
|
# Session logging setup - auto-save conversation trajectories for debugging
|
|
agent.session_start = datetime.now()
|
|
if session_id:
|
|
# Use provided session ID (e.g., from CLI)
|
|
agent.session_id = session_id
|
|
else:
|
|
# Generate a new session ID
|
|
timestamp_str = agent.session_start.strftime("%Y%m%d_%H%M%S")
|
|
short_uuid = uuid.uuid4().hex[:6]
|
|
agent.session_id = f"{timestamp_str}_{short_uuid}"
|
|
|
|
# Expose session ID to tools (terminal, execute_code) so agents can
|
|
# reference their own session for --resume commands, cross-session
|
|
# coordination, and logging. Uses the ContextVar system from
|
|
# session_context.py for concurrency safety (gateway runs multiple
|
|
# sessions in one process). Also writes os.environ as fallback for
|
|
# CLI mode where ContextVars aren't used.
|
|
os.environ["HERMES_SESSION_ID"] = agent.session_id
|
|
try:
|
|
from gateway.session_context import _SESSION_ID
|
|
_SESSION_ID.set(agent.session_id)
|
|
except Exception:
|
|
pass # CLI/test mode — ContextVar not needed
|
|
|
|
# Session logs go into ~/.hermes/sessions/ alongside gateway sessions
|
|
hermes_home = get_hermes_home()
|
|
agent.logs_dir = hermes_home / "sessions"
|
|
agent.logs_dir.mkdir(parents=True, exist_ok=True)
|
|
agent.session_log_file = agent.logs_dir / f"session_{agent.session_id}.json"
|
|
|
|
# Track conversation messages for session logging
|
|
agent._session_messages: List[Dict[str, Any]] = []
|
|
agent._memory_write_origin = "assistant_tool"
|
|
agent._memory_write_context = "foreground"
|
|
|
|
# Cached system prompt -- built once per session, only rebuilt on compression
|
|
agent._cached_system_prompt: Optional[str] = None
|
|
|
|
# Filesystem checkpoint manager (transparent — not a tool)
|
|
from tools.checkpoint_manager import CheckpointManager
|
|
agent._checkpoint_mgr = CheckpointManager(
|
|
enabled=checkpoints_enabled,
|
|
max_snapshots=checkpoint_max_snapshots,
|
|
max_total_size_mb=checkpoint_max_total_size_mb,
|
|
max_file_size_mb=checkpoint_max_file_size_mb,
|
|
)
|
|
|
|
# SQLite session store (optional -- provided by CLI or gateway)
|
|
agent._session_db = session_db
|
|
agent._parent_session_id = parent_session_id
|
|
agent._last_flushed_db_idx = 0 # tracks DB-write cursor to prevent duplicate writes
|
|
agent._session_db_created = False # DB row deferred to run_conversation()
|
|
agent._session_init_model_config = {
|
|
"max_iterations": agent.max_iterations,
|
|
"reasoning_config": reasoning_config,
|
|
"max_tokens": max_tokens,
|
|
}
|
|
|
|
# In-memory todo list for task planning (one per agent/session)
|
|
from tools.todo_tool import TodoStore
|
|
agent._todo_store = TodoStore()
|
|
|
|
# Load config once for memory, skills, and compression sections
|
|
try:
|
|
from hermes_cli.config import load_config as _load_agent_config
|
|
_agent_cfg = _load_agent_config()
|
|
except Exception:
|
|
_agent_cfg = {}
|
|
try:
|
|
agent._tool_guardrails = ToolCallGuardrailController(
|
|
ToolCallGuardrailConfig.from_mapping(
|
|
_agent_cfg.get("tool_loop_guardrails", {})
|
|
)
|
|
)
|
|
except Exception as _tlg_err:
|
|
_ra().logger.warning("Tool loop guardrail config ignored: %s", _tlg_err)
|
|
# Cache only the derived auxiliary compression context override that is
|
|
# needed later by the startup feasibility check. Avoid exposing a
|
|
# broad pseudo-public config object on the agent instance.
|
|
agent._aux_compression_context_length_config = None
|
|
|
|
# Persistent memory (MEMORY.md + USER.md) -- loaded from disk
|
|
agent._memory_store = None
|
|
agent._memory_enabled = False
|
|
agent._user_profile_enabled = False
|
|
agent._memory_nudge_interval = 10
|
|
agent._turns_since_memory = 0
|
|
agent._iters_since_skill = 0
|
|
if not skip_memory:
|
|
try:
|
|
mem_config = _agent_cfg.get("memory", {})
|
|
agent._memory_enabled = mem_config.get("memory_enabled", False)
|
|
agent._user_profile_enabled = mem_config.get("user_profile_enabled", False)
|
|
agent._memory_nudge_interval = int(mem_config.get("nudge_interval", 10))
|
|
if agent._memory_enabled or agent._user_profile_enabled:
|
|
from tools.memory_tool import MemoryStore
|
|
agent._memory_store = MemoryStore(
|
|
memory_char_limit=mem_config.get("memory_char_limit", 2200),
|
|
user_char_limit=mem_config.get("user_char_limit", 1375),
|
|
)
|
|
agent._memory_store.load_from_disk()
|
|
except Exception:
|
|
pass # Memory is optional -- don't break agent init
|
|
|
|
|
|
|
|
# Memory provider plugin (external — one at a time, alongside built-in)
|
|
# Reads memory.provider from config to select which plugin to activate.
|
|
agent._memory_manager = None
|
|
if not skip_memory:
|
|
try:
|
|
_mem_provider_name = mem_config.get("provider", "") if mem_config else ""
|
|
|
|
if _mem_provider_name and _mem_provider_name.strip():
|
|
from agent.memory_manager import MemoryManager as _MemoryManager
|
|
from plugins.memory import load_memory_provider as _load_mem
|
|
agent._memory_manager = _MemoryManager()
|
|
_mp = _load_mem(_mem_provider_name)
|
|
if _mp and _mp.is_available():
|
|
agent._memory_manager.add_provider(_mp)
|
|
if agent._memory_manager.providers:
|
|
_init_kwargs = {
|
|
"session_id": agent.session_id,
|
|
"platform": platform or "cli",
|
|
"hermes_home": str(get_hermes_home()),
|
|
"agent_context": "primary",
|
|
}
|
|
# Thread session title for memory provider scoping
|
|
# (e.g. honcho uses this to derive chat-scoped session keys)
|
|
if agent._session_db:
|
|
try:
|
|
_st = agent._session_db.get_session_title(agent.session_id)
|
|
if _st:
|
|
_init_kwargs["session_title"] = _st
|
|
except Exception:
|
|
pass
|
|
# Thread gateway user identity for per-user memory scoping
|
|
if agent._user_id:
|
|
_init_kwargs["user_id"] = agent._user_id
|
|
if agent._user_name:
|
|
_init_kwargs["user_name"] = agent._user_name
|
|
if agent._chat_id:
|
|
_init_kwargs["chat_id"] = agent._chat_id
|
|
if agent._chat_name:
|
|
_init_kwargs["chat_name"] = agent._chat_name
|
|
if agent._chat_type:
|
|
_init_kwargs["chat_type"] = agent._chat_type
|
|
if agent._thread_id:
|
|
_init_kwargs["thread_id"] = agent._thread_id
|
|
# Thread gateway session key for stable per-chat Honcho session isolation
|
|
if agent._gateway_session_key:
|
|
_init_kwargs["gateway_session_key"] = agent._gateway_session_key
|
|
# Profile identity for per-profile provider scoping
|
|
try:
|
|
from hermes_cli.profiles import get_active_profile_name
|
|
_profile = get_active_profile_name()
|
|
_init_kwargs["agent_identity"] = _profile
|
|
_init_kwargs["agent_workspace"] = "hermes"
|
|
except Exception:
|
|
pass
|
|
agent._memory_manager.initialize_all(**_init_kwargs)
|
|
_ra().logger.info("Memory provider '%s' activated", _mem_provider_name)
|
|
else:
|
|
_ra().logger.debug("Memory provider '%s' not found or not available", _mem_provider_name)
|
|
agent._memory_manager = None
|
|
except Exception as _mpe:
|
|
_ra().logger.warning("Memory provider plugin init failed: %s", _mpe)
|
|
agent._memory_manager = None
|
|
|
|
# Inject memory provider tool schemas into the tool surface.
|
|
# Skip tools whose names already exist (plugins may register the
|
|
# same tools via ctx.register_tool(), which lands in agent.tools
|
|
# through _ra().get_tool_definitions()). Duplicate function names cause
|
|
# 400 errors on providers that enforce unique names (e.g. Xiaomi
|
|
# MiMo via Nous Portal).
|
|
if agent._memory_manager and agent.tools is not None:
|
|
_existing_tool_names = {
|
|
t.get("function", {}).get("name")
|
|
for t in agent.tools
|
|
if isinstance(t, dict)
|
|
}
|
|
for _schema in agent._memory_manager.get_all_tool_schemas():
|
|
_tname = _schema.get("name", "")
|
|
if _tname and _tname in _existing_tool_names:
|
|
continue # already registered via plugin path
|
|
_wrapped = {"type": "function", "function": _schema}
|
|
agent.tools.append(_wrapped)
|
|
if _tname:
|
|
agent.valid_tool_names.add(_tname)
|
|
_existing_tool_names.add(_tname)
|
|
|
|
# Skills config: nudge interval for skill creation reminders
|
|
agent._skill_nudge_interval = 10
|
|
try:
|
|
skills_config = _agent_cfg.get("skills", {})
|
|
agent._skill_nudge_interval = int(skills_config.get("creation_nudge_interval", 10))
|
|
except Exception:
|
|
pass
|
|
|
|
# Tool-use enforcement config: "auto" (default — matches hardcoded
|
|
# model list), true (always), false (never), or list of substrings.
|
|
_agent_section = _agent_cfg.get("agent", {})
|
|
if not isinstance(_agent_section, dict):
|
|
_agent_section = {}
|
|
agent._tool_use_enforcement = _agent_section.get("tool_use_enforcement", "auto")
|
|
|
|
# App-level API retry count (wraps each model API call). Default 3,
|
|
# overridable via agent.api_max_retries in config.yaml. See #11616.
|
|
try:
|
|
_raw_api_retries = _agent_section.get("api_max_retries", 3)
|
|
_api_retries = int(_raw_api_retries)
|
|
_api_retries = max(_api_retries, 1) # 1 = no retry (single attempt)
|
|
except (TypeError, ValueError):
|
|
_api_retries = 3
|
|
agent._api_max_retries = _api_retries
|
|
|
|
# Initialize context compressor for automatic context management
|
|
# Compresses conversation when approaching model's context limit
|
|
# Configuration via config.yaml (compression section)
|
|
_compression_cfg = _agent_cfg.get("compression", {})
|
|
if not isinstance(_compression_cfg, dict):
|
|
_compression_cfg = {}
|
|
compression_threshold = float(_compression_cfg.get("threshold", 0.50))
|
|
try:
|
|
from agent.auxiliary_client import _compression_threshold_for_model as _cthresh_fn
|
|
_model_cthresh = _cthresh_fn(agent.model)
|
|
if _model_cthresh is not None:
|
|
compression_threshold = _model_cthresh
|
|
except Exception:
|
|
pass
|
|
compression_enabled = str(_compression_cfg.get("enabled", True)).lower() in {"true", "1", "yes"}
|
|
compression_target_ratio = float(_compression_cfg.get("target_ratio", 0.20))
|
|
compression_protect_last = int(_compression_cfg.get("protect_last_n", 20))
|
|
# protect_first_n is the number of non-system messages to protect at
|
|
# the head, in addition to the system prompt (which is always
|
|
# implicitly protected by the compressor). Floor at 0 — a value of
|
|
# 0 means "preserve only the system prompt + summary + tail", which
|
|
# is a legitimate (and common) configuration for long-running
|
|
# rolling-compaction sessions.
|
|
compression_protect_first = max(
|
|
0, int(_compression_cfg.get("protect_first_n", 3))
|
|
)
|
|
compression_abort_on_summary_failure = str(
|
|
_compression_cfg.get("abort_on_summary_failure", False)
|
|
).lower() in {"true", "1", "yes"}
|
|
|
|
# Read optional explicit context_length override for the auxiliary
|
|
# compression model. Custom endpoints often cannot report this via
|
|
# /models, so the startup feasibility check needs the config hint.
|
|
try:
|
|
_aux_cfg = cfg_get(_agent_cfg, "auxiliary", "compression", default={})
|
|
except Exception:
|
|
_aux_cfg = {}
|
|
if isinstance(_aux_cfg, dict):
|
|
_aux_context_config = _aux_cfg.get("context_length")
|
|
else:
|
|
_aux_context_config = None
|
|
if _aux_context_config is not None:
|
|
try:
|
|
_aux_context_config = int(_aux_context_config)
|
|
except (TypeError, ValueError):
|
|
_aux_context_config = None
|
|
agent._aux_compression_context_length_config = _aux_context_config
|
|
|
|
# Read explicit model output-token override from config when the
|
|
# caller did not pass one directly.
|
|
_model_cfg = _agent_cfg.get("model", {})
|
|
if agent.max_tokens is None and isinstance(_model_cfg, dict):
|
|
_config_max_tokens = _model_cfg.get("max_tokens")
|
|
if _config_max_tokens is not None:
|
|
try:
|
|
if isinstance(_config_max_tokens, bool):
|
|
raise ValueError
|
|
_parsed_max_tokens = int(_config_max_tokens)
|
|
if _parsed_max_tokens <= 0:
|
|
raise ValueError
|
|
agent.max_tokens = _parsed_max_tokens
|
|
except (TypeError, ValueError):
|
|
_ra().logger.warning(
|
|
"Invalid model.max_tokens in config.yaml: %r — "
|
|
"must be a positive integer (e.g. 4096). "
|
|
"Falling back to provider default.",
|
|
_config_max_tokens,
|
|
)
|
|
print(
|
|
f"\n⚠ Invalid model.max_tokens in config.yaml: {_config_max_tokens!r}\n"
|
|
f" Must be a positive integer (e.g. 4096).\n"
|
|
f" Falling back to provider default.\n",
|
|
file=sys.stderr,
|
|
)
|
|
agent._session_init_model_config["max_tokens"] = agent.max_tokens
|
|
|
|
# Read explicit context_length override from model config
|
|
if isinstance(_model_cfg, dict):
|
|
_config_context_length = _model_cfg.get("context_length")
|
|
else:
|
|
_config_context_length = None
|
|
if _config_context_length is not None:
|
|
try:
|
|
_config_context_length = int(_config_context_length)
|
|
except (TypeError, ValueError):
|
|
_ra().logger.warning(
|
|
"Invalid model.context_length in config.yaml: %r — "
|
|
"must be a plain integer (e.g. 256000, not '256K'). "
|
|
"Falling back to auto-detection.",
|
|
_config_context_length,
|
|
)
|
|
print(
|
|
f"\n⚠ Invalid model.context_length in config.yaml: {_config_context_length!r}\n"
|
|
f" Must be a plain integer (e.g. 256000, not '256K').\n"
|
|
f" Falling back to auto-detected context window.\n",
|
|
file=sys.stderr,
|
|
)
|
|
_config_context_length = None
|
|
|
|
# Resolve custom_providers list once for reuse below (startup
|
|
# context-length override and plugin context-engine init).
|
|
try:
|
|
from hermes_cli.config import get_compatible_custom_providers
|
|
_custom_providers = get_compatible_custom_providers(_agent_cfg)
|
|
except Exception:
|
|
_custom_providers = _agent_cfg.get("custom_providers")
|
|
if not isinstance(_custom_providers, list):
|
|
_custom_providers = []
|
|
|
|
# Store for reuse by _check_compression_model_feasibility (auxiliary
|
|
# compression model context-length detection needs the same list).
|
|
agent._custom_providers = _custom_providers
|
|
|
|
# Check custom_providers per-model context_length
|
|
if _config_context_length is None and _custom_providers:
|
|
try:
|
|
from hermes_cli.config import get_custom_provider_context_length
|
|
_cp_ctx_resolved = get_custom_provider_context_length(
|
|
model=agent.model,
|
|
base_url=agent.base_url,
|
|
custom_providers=_custom_providers,
|
|
)
|
|
if _cp_ctx_resolved:
|
|
_config_context_length = int(_cp_ctx_resolved)
|
|
except Exception:
|
|
_cp_ctx_resolved = None
|
|
|
|
# Surface a clear warning if the user set a context_length but it
|
|
# wasn't a valid positive int — the helper silently skips those.
|
|
if _config_context_length is None:
|
|
_target = agent.base_url.rstrip("/") if agent.base_url else ""
|
|
for _cp_entry in _custom_providers:
|
|
if not isinstance(_cp_entry, dict):
|
|
continue
|
|
_cp_url = (_cp_entry.get("base_url") or "").rstrip("/")
|
|
if _target and _cp_url == _target:
|
|
_cp_models = _cp_entry.get("models", {})
|
|
if isinstance(_cp_models, dict):
|
|
_cp_model_cfg = _cp_models.get(agent.model, {})
|
|
if isinstance(_cp_model_cfg, dict):
|
|
_cp_ctx = _cp_model_cfg.get("context_length")
|
|
if _cp_ctx is not None:
|
|
try:
|
|
_parsed = int(_cp_ctx)
|
|
if _parsed <= 0:
|
|
raise ValueError
|
|
except (TypeError, ValueError):
|
|
_ra().logger.warning(
|
|
"Invalid context_length for model %r in "
|
|
"custom_providers: %r — must be a positive "
|
|
"integer (e.g. 256000, not '256K'). "
|
|
"Falling back to auto-detection.",
|
|
agent.model, _cp_ctx,
|
|
)
|
|
print(
|
|
f"\n⚠ Invalid context_length for model {agent.model!r} in custom_providers: {_cp_ctx!r}\n"
|
|
f" Must be a positive integer (e.g. 256000, not '256K').\n"
|
|
f" Falling back to auto-detected context window.\n",
|
|
file=sys.stderr,
|
|
)
|
|
break
|
|
|
|
# Persist for reuse on switch_model / fallback activation. Must come
|
|
# AFTER the custom_providers branch so per-model overrides aren't lost.
|
|
agent._config_context_length = _config_context_length
|
|
|
|
agent._ensure_lmstudio_runtime_loaded(_config_context_length)
|
|
|
|
|
|
|
|
# Select context engine: config-driven (like memory providers).
|
|
# 1. Check config.yaml context.engine setting
|
|
# 2. Check plugins/context_engine/<name>/ directory (repo-shipped)
|
|
# 3. Check general plugin system (user-installed plugins)
|
|
# 4. Fall back to built-in ContextCompressor
|
|
_selected_engine = None
|
|
_engine_name = "compressor" # default
|
|
try:
|
|
_ctx_cfg = _agent_cfg.get("context", {}) if isinstance(_agent_cfg, dict) else {}
|
|
_engine_name = _ctx_cfg.get("engine", "compressor") or "compressor"
|
|
except Exception:
|
|
pass
|
|
|
|
if _engine_name != "compressor":
|
|
# Try loading from plugins/context_engine/<name>/
|
|
try:
|
|
from plugins.context_engine import load_context_engine
|
|
_selected_engine = load_context_engine(_engine_name)
|
|
except Exception as _ce_load_err:
|
|
_ra().logger.debug("Context engine load from plugins/context_engine/: %s", _ce_load_err)
|
|
|
|
# Try general plugin system as fallback
|
|
if _selected_engine is None:
|
|
try:
|
|
from hermes_cli.plugins import get_plugin_context_engine
|
|
_candidate = get_plugin_context_engine()
|
|
if _candidate and _candidate.name == _engine_name:
|
|
_selected_engine = _candidate
|
|
except Exception:
|
|
pass
|
|
|
|
if _selected_engine is None:
|
|
_ra().logger.warning(
|
|
"Context engine '%s' not found — falling back to built-in compressor",
|
|
_engine_name,
|
|
)
|
|
# else: config says "compressor" — use built-in, don't auto-activate plugins
|
|
|
|
if _selected_engine is not None:
|
|
agent.context_compressor = _selected_engine
|
|
# Resolve context_length for plugin engines — mirrors switch_model() path
|
|
from agent.model_metadata import get_model_context_length
|
|
_plugin_ctx_len = get_model_context_length(
|
|
agent.model,
|
|
base_url=agent.base_url,
|
|
api_key=getattr(agent, "api_key", ""),
|
|
config_context_length=_config_context_length,
|
|
provider=agent.provider,
|
|
custom_providers=_custom_providers,
|
|
)
|
|
agent.context_compressor.update_model(
|
|
model=agent.model,
|
|
context_length=_plugin_ctx_len,
|
|
base_url=agent.base_url,
|
|
api_key=getattr(agent, "api_key", ""),
|
|
provider=agent.provider,
|
|
)
|
|
if not agent.quiet_mode:
|
|
_ra().logger.info("Using context engine: %s", _selected_engine.name)
|
|
else:
|
|
agent.context_compressor = ContextCompressor(
|
|
model=agent.model,
|
|
threshold_percent=compression_threshold,
|
|
protect_first_n=compression_protect_first,
|
|
protect_last_n=compression_protect_last,
|
|
summary_target_ratio=compression_target_ratio,
|
|
summary_model_override=None,
|
|
quiet_mode=agent.quiet_mode,
|
|
base_url=agent.base_url,
|
|
api_key=getattr(agent, "api_key", ""),
|
|
config_context_length=_config_context_length,
|
|
provider=agent.provider,
|
|
api_mode=agent.api_mode,
|
|
abort_on_summary_failure=compression_abort_on_summary_failure,
|
|
)
|
|
agent.compression_enabled = compression_enabled
|
|
|
|
# Reject models whose context window is below the minimum required
|
|
# for reliable tool-calling workflows (64K tokens).
|
|
from agent.model_metadata import MINIMUM_CONTEXT_LENGTH
|
|
_ctx = getattr(agent.context_compressor, "context_length", 0)
|
|
if _ctx and _ctx < MINIMUM_CONTEXT_LENGTH:
|
|
raise ValueError(
|
|
f"Model {agent.model} has a context window of {_ctx:,} tokens, "
|
|
f"which is below the minimum {MINIMUM_CONTEXT_LENGTH:,} required "
|
|
f"by Hermes Agent. Choose a model with at least "
|
|
f"{MINIMUM_CONTEXT_LENGTH // 1000}K context, or set "
|
|
f"model.context_length in config.yaml to override."
|
|
)
|
|
|
|
# Inject context engine tool schemas (e.g. lcm_grep, lcm_describe, lcm_expand).
|
|
# Skip names that are already present — the _ra().get_tool_definitions()
|
|
# quiet_mode cache returned a shared list pre-#17335, so a stray
|
|
# mutation here would poison subsequent agent inits in the same
|
|
# Gateway process and trip provider-side 'duplicate tool name'
|
|
# errors. Even with the cache fix, dedup is the right defense
|
|
# against plugin paths that may register the same schemas via
|
|
# ctx.register_tool(). Mirrors the memory tools dedup above.
|
|
agent._context_engine_tool_names: set = set()
|
|
if hasattr(agent, "context_compressor") and agent.context_compressor and agent.tools is not None:
|
|
_existing_tool_names = {
|
|
t.get("function", {}).get("name")
|
|
for t in agent.tools
|
|
if isinstance(t, dict)
|
|
}
|
|
for _schema in agent.context_compressor.get_tool_schemas():
|
|
_tname = _schema.get("name", "")
|
|
if _tname and _tname in _existing_tool_names:
|
|
continue # already registered via plugin/cache path
|
|
_wrapped = {"type": "function", "function": _schema}
|
|
agent.tools.append(_wrapped)
|
|
if _tname:
|
|
agent.valid_tool_names.add(_tname)
|
|
agent._context_engine_tool_names.add(_tname)
|
|
_existing_tool_names.add(_tname)
|
|
|
|
# Notify context engine of session start
|
|
if hasattr(agent, "context_compressor") and agent.context_compressor:
|
|
try:
|
|
agent.context_compressor.on_session_start(
|
|
agent.session_id,
|
|
hermes_home=str(get_hermes_home()),
|
|
platform=agent.platform or "cli",
|
|
model=agent.model,
|
|
context_length=getattr(agent.context_compressor, "context_length", 0),
|
|
)
|
|
except Exception as _ce_err:
|
|
_ra().logger.debug("Context engine on_session_start: %s", _ce_err)
|
|
|
|
agent._subdirectory_hints = SubdirectoryHintTracker(
|
|
working_dir=os.getenv("TERMINAL_CWD") or None,
|
|
)
|
|
agent._user_turn_count = 0
|
|
|
|
# Cumulative token usage for the session
|
|
agent.session_prompt_tokens = 0
|
|
agent.session_completion_tokens = 0
|
|
agent.session_total_tokens = 0
|
|
agent.session_api_calls = 0
|
|
agent.session_input_tokens = 0
|
|
agent.session_output_tokens = 0
|
|
agent.session_cache_read_tokens = 0
|
|
agent.session_cache_write_tokens = 0
|
|
agent.session_reasoning_tokens = 0
|
|
agent.session_estimated_cost_usd = 0.0
|
|
agent.session_cost_status = "unknown"
|
|
agent.session_cost_source = "none"
|
|
|
|
# ── Ollama num_ctx injection ──
|
|
# Ollama defaults to 2048 context regardless of the model's capabilities.
|
|
# When running against an Ollama server, detect the model's max context
|
|
# and pass num_ctx on every chat request so the full window is used.
|
|
# User override: set model.ollama_num_ctx in config.yaml to cap VRAM use.
|
|
# If model.context_length is set, it caps num_ctx so the user's VRAM
|
|
# budget is respected even when GGUF metadata advertises a larger window.
|
|
agent._ollama_num_ctx: int | None = None
|
|
_ollama_num_ctx_override = None
|
|
if isinstance(_model_cfg, dict):
|
|
_ollama_num_ctx_override = _model_cfg.get("ollama_num_ctx")
|
|
if _ollama_num_ctx_override is not None:
|
|
try:
|
|
agent._ollama_num_ctx = int(_ollama_num_ctx_override)
|
|
except (TypeError, ValueError):
|
|
_ra().logger.debug("Invalid ollama_num_ctx config value: %r", _ollama_num_ctx_override)
|
|
if agent._ollama_num_ctx is None and agent.base_url and is_local_endpoint(agent.base_url):
|
|
try:
|
|
# ``agent.api_key`` may be a callable (Entra token provider).
|
|
# Ollama detection makes a manual HTTP request and expects a
|
|
# string — Azure Foundry isn't a local endpoint so this branch
|
|
# never fires for Entra, but guard defensively.
|
|
_key_for_ollama = agent.api_key if isinstance(agent.api_key, str) else ""
|
|
_detected = query_ollama_num_ctx(agent.model, agent.base_url, api_key=_key_for_ollama or "")
|
|
if _detected and _detected > 0:
|
|
agent._ollama_num_ctx = _detected
|
|
except Exception as exc:
|
|
_ra().logger.debug("Ollama num_ctx detection failed: %s", exc)
|
|
# Cap auto-detected ollama_num_ctx to the user's explicit context_length.
|
|
# Without this, GGUF metadata can advertise 256K+ which Ollama honours
|
|
# by allocating that much VRAM — blowing up small GPUs even though the
|
|
# user explicitly set a smaller context_length in config.yaml.
|
|
if (
|
|
agent._ollama_num_ctx
|
|
and _config_context_length
|
|
and _ollama_num_ctx_override is None # don't override explicit ollama_num_ctx
|
|
and agent._ollama_num_ctx > _config_context_length
|
|
):
|
|
_ra().logger.info(
|
|
"Ollama num_ctx capped: %d -> %d (model.context_length override)",
|
|
agent._ollama_num_ctx, _config_context_length,
|
|
)
|
|
agent._ollama_num_ctx = _config_context_length
|
|
if agent._ollama_num_ctx and not agent.quiet_mode:
|
|
_ra().logger.info(
|
|
"Ollama num_ctx: will request %d tokens (model max from /api/show)",
|
|
agent._ollama_num_ctx,
|
|
)
|
|
|
|
if not agent.quiet_mode:
|
|
if compression_enabled:
|
|
print(f"📊 Context limit: {agent.context_compressor.context_length:,} tokens (compress at {int(compression_threshold*100)}% = {agent.context_compressor.threshold_tokens:,})")
|
|
else:
|
|
print(f"📊 Context limit: {agent.context_compressor.context_length:,} tokens (auto-compression disabled)")
|
|
|
|
# Check immediately so CLI users see the warning at startup.
|
|
# Gateway status_callback is not yet wired, so any warning is stored
|
|
# in _compression_warning and replayed in the first run_conversation().
|
|
agent._compression_warning = None
|
|
agent._check_compression_model_feasibility()
|
|
|
|
# Snapshot primary runtime for per-turn restoration. When fallback
|
|
# activates during a turn, the next turn restores these values so the
|
|
# preferred model gets a fresh attempt each time. Uses a single dict
|
|
# so new state fields are easy to add without N individual attributes.
|
|
_cc = agent.context_compressor
|
|
agent._primary_runtime = {
|
|
"model": agent.model,
|
|
"provider": agent.provider,
|
|
"base_url": agent.base_url,
|
|
"api_mode": agent.api_mode,
|
|
"api_key": getattr(agent, "api_key", ""),
|
|
"client_kwargs": dict(agent._client_kwargs),
|
|
"use_prompt_caching": agent._use_prompt_caching,
|
|
"use_native_cache_layout": agent._use_native_cache_layout,
|
|
# Context engine state that _try_activate_fallback() overwrites.
|
|
# Use getattr for model/base_url/api_key/provider since plugin
|
|
# engines may not have these (they're ContextCompressor-specific).
|
|
"compressor_model": getattr(_cc, "model", agent.model),
|
|
"compressor_base_url": getattr(_cc, "base_url", agent.base_url),
|
|
"compressor_api_key": getattr(_cc, "api_key", ""),
|
|
"compressor_provider": getattr(_cc, "provider", agent.provider),
|
|
"compressor_context_length": _cc.context_length,
|
|
"compressor_threshold_tokens": _cc.threshold_tokens,
|
|
}
|
|
if agent.api_mode == "anthropic_messages":
|
|
agent._primary_runtime.update({
|
|
"anthropic_api_key": agent._anthropic_api_key,
|
|
"anthropic_base_url": agent._anthropic_base_url,
|
|
"is_anthropic_oauth": agent._is_anthropic_oauth,
|
|
})
|
|
|
|
|
|
|
|
__all__ = ["init_agent"]
|