Xi Zhang
331056cdc8
* feat(middleware): add CodeInterpreterMiddleware with project-specific configuration chore(config): increase checkpoint retention limit for runaway conversations fix(tests): update database schema references from 'blob' to 'value' chore(deps): update deepagents dependency to include quickjs support * feat(deepagents): update to version 0.6.1 and add optional dependencies for quickjs * feat(sessions): improve error handling for message deltas and update Overwrite type check * Enhance PruningCheckpointer with DeltaChannel Awareness - Introduced a new pruning strategy in `_prune_after_put` to preserve the `_DeltaSnapshot` chain during checkpoint pruning. - Implemented methods to fetch recent checkpoint IDs and walk to snapshot ancestors, ensuring that necessary checkpoints are retained. - Updated SQL queries to handle checkpoint and write deletions more efficiently. - Added comprehensive tests for DeltaChannel-aware pruning, ensuring that the pruning logic correctly handles various checkpoint scenarios, including those with and without snapshot seeds. - Refactored `_load_checkpoint_messages` to utilize the new saver interface, improving message reconstruction from checkpoints. * feat(tests): add migration sweep test to preserve snapshot ancestor * feat(sessions): enhance checkpoint retrieval to prevent transcript leakage in multi-agent scenarios * feat(middleware): enhance CodeInterpreterMiddleware with configurable timeout and result character limit feat(config): add CodeInterpreterMiddleware tuning parameters to EvoScientistConfig feat(sessions): implement inline message delta reducer for improved message handling * feat(dependencies): update deepagents version to 0.6.2 in pyproject.toml and uv.lock
586 lines
22 KiB
Python
586 lines
22 KiB
Python
"""Configuration management for EvoScientist.
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Handles loading, saving, and merging configuration from multiple sources
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with the following priority (highest to lowest):
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CLI arguments > Environment variables > Config file > Defaults
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"""
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from __future__ import annotations
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import os
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from dataclasses import asdict, dataclass, fields
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from pathlib import Path
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from typing import Any, Literal
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import yaml
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from dotenv import find_dotenv, load_dotenv
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# =============================================================================
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# Configuration paths
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# =============================================================================
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def get_config_dir() -> Path:
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"""Get the configuration directory path.
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Uses XDG_CONFIG_HOME if set, otherwise ~/.config/evoscientist/
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"""
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xdg_config = os.environ.get("XDG_CONFIG_HOME")
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if xdg_config:
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return Path(xdg_config) / "evoscientist"
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return Path.home() / ".config" / "evoscientist"
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def get_config_path() -> Path:
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"""Get the path to the configuration file."""
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return get_config_dir() / "config.yaml"
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# =============================================================================
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# Configuration dataclass
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# =============================================================================
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@dataclass
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class EvoScientistConfig:
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"""EvoScientist configuration settings.
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Attributes:
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anthropic_api_key: Anthropic API key for Claude models.
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openai_api_key: OpenAI API key for GPT models.
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nvidia_api_key: NVIDIA API key for NVIDIA models.
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google_api_key: Google API key for Gemini models.
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tavily_api_key: Tavily API key for web search.
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provider: Default LLM provider ('anthropic', 'openai', 'google-genai', or 'nvidia').
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model: Default model name (short name or full ID).
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default_mode: Default workspace mode ('daemon' or 'run').
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default_workdir: Default workspace directory (empty = use current working directory).
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show_thinking: Whether to show thinking panels in CLI.
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"""
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# API Keys
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anthropic_api_key: str = ""
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anthropic_base_url: str = ""
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anthropic_auth_mode: str = "api_key" # "api_key" | "oauth"
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openai_api_key: str = ""
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openai_auth_mode: str = "api_key" # "api_key" | "oauth"
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nvidia_api_key: str = ""
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google_api_key: str = ""
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minimax_api_key: str = ""
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minimax_base_url: str = ""
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siliconflow_api_key: str = ""
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openrouter_api_key: str = ""
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deepseek_api_key: str = ""
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zhipu_api_key: str = ""
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volcengine_api_key: str = ""
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dashscope_api_key: str = ""
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moonshot_api_key: str = ""
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kimi_api_key: str = ""
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custom_openai_api_key: str = ""
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custom_openai_base_url: str = ""
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custom_anthropic_api_key: str = ""
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custom_anthropic_base_url: str = ""
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ollama_base_url: str = ""
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tavily_api_key: str = ""
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# LLM Settings
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provider: str = "anthropic"
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model: str = "claude-sonnet-4-5"
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model_fallbacks: str = "" # "model:provider,model:provider" fallback chain
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# Async Sub-agent Settings
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# When True (default), the EvoSci CLI auto-starts a langgraph dev subprocess
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# so any sub-agent flagged ``async: true`` in subagents/<name>.yaml runs
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# non-blocking via AsyncSubAgent. Currently affects writing-agent and
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# data-analysis-agent. Adds ~10-15s to CLI startup (langgraph dev cold
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# start, mostly MCP server spawn time).
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#
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# Set False to run fully in-process — saves the startup cost in scenarios
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# where async isn't useful: short scripted EvoSci runs (CI / one-shot
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# ``-p "..."``), low-RAM environments, or workflows that only need the
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# synchronous sub-agents (planner / research / code / debug).
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enable_async_subagents: bool = True
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# Port for the auto-started langgraph dev subprocess. 6174 is Kaprekar's
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# constant — a memorable EvoScientist-themed default that avoids collisions
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# with common dev ports (3000/5000/8000/8080) and the langgraph CLI default
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# 2024. Override if it conflicts with another local service.
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langgraph_dev_port: int = 6174
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# Whether langgraph dev persists its runtime state to .langgraph_api/ next
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# to the subprocess cwd. True (default) keeps async-task, scheduler, and
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# Store API state across subprocess restarts — useful for future
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# cross-session async, cron, and Store features. Set False to suppress
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# writes (workspace stays cleaner; state is in-memory only and lost on
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# CLI exit). EvoScientist's main thread persistence uses sessions.db
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# regardless of this setting.
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langgraph_dev_file_persistence: bool = True
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# Concurrency: how many runs each langgraph dev worker processes in parallel.
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# 10 is the langgraph dev recommended default and works well on a typical
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# dev machine. Lower it (e.g., 4) on memory-constrained or low-core
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# machines if multiple async sub-agents in flight cause noticeable
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# slowdown.
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langgraph_dev_jobs_per_worker: int = 10
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# Max LangGraph super-steps (LLM call / tool call / sub-agent delegation
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# each count as 1) before raising GraphRecursionError. Resets on every
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# ``agent.invoke()`` — i.e., this is per-turn, NOT per-conversation. For
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# long conversations the relevant mechanisms are checkpointer persistence
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# (sessions.db), ContextEditingMiddleware (window management), and
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# EvoMemoryMiddleware (cross-turn memory).
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#
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# 1,000,000 is "effectively unlimited" — typical research turns use
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# 200-1000 steps; reaching 1M would cost ~$10K in tokens, by which point
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# rate limits, context overflow, or API quota errors would trip first.
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# Lower (e.g., 5000) if you want a tighter safety net against runaway loops.
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recursion_limit: int = 1_000_000
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# Workspace Settings
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default_mode: Literal["daemon", "run"] = "daemon"
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default_workdir: str = ""
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# UI Settings
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show_thinking: bool = True
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ui_backend: Literal["cli", "tui"] = "tui"
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log_level: str = "warning"
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reasoning_effort: str = "high"
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# Channel Settings
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channel_enabled: str = "" # "imessage" | "telegram" | "discord" | "slack" | "wechat" | "dingtalk" | "feishu" | "email" | "qq" | "signal" | "" (comma-separated for multiple)
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channel_send_thinking: bool = True # forward thinking to any channel
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channel_debug_tracing: bool = False # emit extra inbound diagnostics at DEBUG
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require_mention: str = "group" # "always" | "group" | "off"
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text_chunk_limit: int = 0 # 0 = use capability default
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allowed_channels: str = "" # comma-separated channel IDs, empty = allow all
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# iMessage Settings
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imessage_enabled: bool = False # legacy compat
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imessage_allowed_senders: str = ""
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# Telegram Settings
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telegram_bot_token: str = ""
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telegram_allowed_senders: str = ""
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telegram_proxy: str = ""
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# Discord Settings
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discord_bot_token: str = ""
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discord_allowed_senders: str = ""
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discord_allowed_channels: str = ""
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discord_proxy: str = ""
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# Slack Settings
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slack_bot_token: str = ""
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slack_app_token: str = ""
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slack_allowed_senders: str = ""
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slack_allowed_channels: str = ""
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slack_proxy: str = ""
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# Feishu Settings
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feishu_app_id: str = ""
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feishu_app_secret: str = ""
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feishu_verification_token: str = ""
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feishu_encrypt_key: str = ""
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feishu_webhook_port: int = 9000
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feishu_allowed_senders: str = ""
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feishu_domain: str = "https://open.feishu.cn"
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feishu_proxy: str = ""
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feishu_subscription_mode: str = "webhook" # "webhook" | "websocket"
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# WeChat Settings
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wechat_backend: str = "wecom"
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wechat_webhook_port: int = 9001
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wechat_allowed_senders: str = ""
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wechat_proxy: str = ""
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wechat_wecom_corp_id: str = ""
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wechat_wecom_agent_id: str = ""
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wechat_wecom_secret: str = ""
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wechat_wecom_token: str = ""
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wechat_wecom_encoding_aes_key: str = ""
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wechat_mp_app_id: str = ""
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wechat_mp_app_secret: str = ""
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wechat_mp_token: str = ""
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wechat_mp_encoding_aes_key: str = ""
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# Personal WeChat (iLink Bot) — credentials obtained via QR-code login.
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# Run: python -m EvoScientist.channels.wechat.serve --qr-login
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wechat_personal_account_id: str = ""
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wechat_personal_token: str = ""
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wechat_personal_base_url: str = ""
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wechat_personal_cdn_base_url: str = ""
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wechat_personal_dm_policy: str = "open"
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wechat_personal_group_policy: str = "disabled"
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wechat_personal_group_allowed: str = ""
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# DingTalk Settings
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dingtalk_client_id: str = ""
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dingtalk_client_secret: str = ""
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dingtalk_allowed_senders: str = ""
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dingtalk_proxy: str = ""
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# Email Settings
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email_imap_host: str = ""
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email_imap_port: int = 993
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email_imap_username: str = ""
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email_imap_password: str = ""
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email_imap_mailbox: str = "INBOX"
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email_imap_use_ssl: bool = True
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email_smtp_host: str = ""
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email_smtp_port: int = 587
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email_smtp_username: str = ""
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email_smtp_password: str = ""
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email_smtp_use_tls: bool = True
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email_from_address: str = ""
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email_poll_interval: int = 30
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email_mark_seen: bool = True
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email_max_body_chars: int = 12000
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email_subject_prefix: str = "Re: "
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email_allowed_senders: str = ""
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# QQ Settings
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qq_app_id: str = ""
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qq_app_secret: str = ""
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qq_allowed_senders: str = ""
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# Signal Settings
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signal_phone_number: str = ""
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signal_cli_path: str = "signal-cli"
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signal_config_dir: str = ""
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signal_allowed_senders: str = ""
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signal_rpc_port: int = 7583
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# Shared webhook port (0 = disabled)
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shared_webhook_port: int = 9000
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# HITL (Human-in-the-Loop) Settings
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auto_approve: bool = False # Auto-approve all tool executions without prompting
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auto_mode: bool = False # Run unattended: imply auto_approve and disable ask_user
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shell_allow_list: str = "" # Comma-separated shell command prefixes to auto-approve
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# Agent features
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enable_ask_user: bool = True # Enable ask_user tool for agent-initiated questions
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# CodeInterpreterMiddleware (PTC — Parallel Tool Calls) tuning
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# The PTC allowlist itself is hardcoded in
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# ``EvoScientist/middleware/code_interpreter.py`` as a load-bearing safety
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# decision (excludes ``execute`` so PTC can't bypass HITL approval,
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# excludes ``write_file``/``edit_file`` because batched writes have no
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# benefit). Only the resource budget knobs are user-tunable.
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code_interpreter_timeout: float = 60.0 # seconds per JS eval
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code_interpreter_max_result_chars: int = 10000 # truncate large JSON results
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# Checkpoint pruning (sessions.db retention per (thread_id, checkpoint_ns))
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# Safety net for runaway conversations. Under DeltaChannel (deepagents 0.6+)
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# normal usage produces linear growth, so this default is set well above
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# any realistic conversation length (~180-450 turns of dialogue) while
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# still capping legacy bloat at upgrade time. 0 disables ongoing pruning
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# entirely; the one-time legacy migration sweep still runs.
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checkpoint_keep_per_thread: int = 1000
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# DM access control policy
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dm_policy: str = "allowlist"
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# OpenAI API mode - "" = auto, "true" = force Responses, "false" = force Completions
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use_responses_api: str = ""
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# ccproxy
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ccproxy_port: int = 8000
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# STT (Speech-to-Text) Settings
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stt_enabled: bool = False
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stt_language: str = "auto" # "auto" | "zh" | "en"
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stt_model: str = "" # override model id; empty = auto-select by language
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stt_device: str = "cpu" # "cpu" | "cuda"
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stt_compute_type: str = "int8" # "int8" | "float16" | "float32"
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# =============================================================================
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# Config file operations
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# =============================================================================
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def load_config() -> EvoScientistConfig:
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"""Load configuration from file.
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Returns:
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EvoScientistConfig instance with values from file, or defaults if
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file doesn't exist.
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"""
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config_path = get_config_path()
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if not config_path.exists():
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return EvoScientistConfig()
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try:
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with open(config_path) as f:
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data = yaml.safe_load(f) or {}
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# Filter to only valid fields
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valid_fields = {f.name for f in fields(EvoScientistConfig)}
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filtered_data = {k: v for k, v in data.items() if k in valid_fields}
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return EvoScientistConfig(**filtered_data)
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except Exception:
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# On any error, return defaults
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return EvoScientistConfig()
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def save_config(config: EvoScientistConfig) -> None:
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"""Save configuration to file.
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Args:
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config: EvoScientistConfig instance to save.
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"""
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config_path = get_config_path()
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config_path.parent.mkdir(parents=True, exist_ok=True)
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data = asdict(config)
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# Save all fields including empty API keys (users can set them via env vars instead)
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with open(config_path, "w") as f:
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yaml.safe_dump(data, f, default_flow_style=False, sort_keys=False)
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def reset_config() -> None:
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"""Reset configuration to defaults by deleting the config file."""
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config_path = get_config_path()
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if config_path.exists():
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config_path.unlink()
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# =============================================================================
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# Config value operations
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# =============================================================================
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def _coerce_value(value: Any, field_type: Any) -> Any:
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"""Coerce a value to the expected field type.
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Args:
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value: The value to coerce.
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field_type: The target type (from dataclass field).
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Returns:
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The coerced value.
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Raises:
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ValueError: If the value cannot be coerced.
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TypeError: If the value cannot be coerced.
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"""
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if field_type == "bool" or field_type is bool:
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if isinstance(value, str):
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return value.lower() in ("true", "1", "yes", "on")
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return bool(value)
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if field_type == "int" or field_type is int:
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return int(value)
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if field_type == "float" or field_type is float:
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return float(value)
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return str(value)
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def get_config_value(key: str) -> Any:
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"""Get a single configuration value.
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Args:
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key: Configuration key name.
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Returns:
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The value, or None if key doesn't exist.
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"""
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config = load_config()
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return getattr(config, key, None)
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def set_config_value(key: str, value: Any) -> bool:
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"""Set a single configuration value.
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Args:
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key: Configuration key name.
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value: New value.
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Returns:
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True if successful, False if key is invalid.
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"""
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valid_fields = {f.name for f in fields(EvoScientistConfig)}
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if key not in valid_fields:
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return False
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config = load_config()
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# Type coercion based on field type
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field_info = next(f for f in fields(EvoScientistConfig) if f.name == key)
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field_type = field_info.type
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try:
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value = _coerce_value(value, field_type)
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except (ValueError, TypeError):
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return False
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setattr(config, key, value)
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save_config(config)
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return True
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def list_config() -> dict[str, Any]:
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"""List all configuration values.
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Returns:
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Dictionary of all configuration key-value pairs.
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"""
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return asdict(load_config())
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# =============================================================================
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# Effective configuration (merging sources)
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# =============================================================================
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# Environment variable mappings
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_ENV_MAPPINGS = {
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"anthropic_api_key": "ANTHROPIC_API_KEY",
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"anthropic_base_url": "ANTHROPIC_BASE_URL",
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"anthropic_auth_mode": "EVOSCIENTIST_ANTHROPIC_AUTH_MODE",
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"openai_api_key": "OPENAI_API_KEY",
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"openai_auth_mode": "EVOSCIENTIST_OPENAI_AUTH_MODE",
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"nvidia_api_key": "NVIDIA_API_KEY",
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"google_api_key": "GOOGLE_API_KEY",
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"minimax_api_key": "MINIMAX_API_KEY",
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"minimax_base_url": "MINIMAX_BASE_URL",
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"siliconflow_api_key": "SILICONFLOW_API_KEY",
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"openrouter_api_key": "OPENROUTER_API_KEY",
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"deepseek_api_key": "DEEPSEEK_API_KEY",
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"zhipu_api_key": "ZHIPU_API_KEY",
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"volcengine_api_key": "VOLCENGINE_API_KEY",
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"dashscope_api_key": "DASHSCOPE_API_KEY",
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"moonshot_api_key": "MOONSHOT_API_KEY",
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"kimi_api_key": "KIMI_API_KEY",
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"custom_openai_api_key": "CUSTOM_OPENAI_API_KEY",
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"custom_openai_base_url": "CUSTOM_OPENAI_BASE_URL",
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"custom_anthropic_api_key": "CUSTOM_ANTHROPIC_API_KEY",
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"custom_anthropic_base_url": "CUSTOM_ANTHROPIC_BASE_URL",
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"ollama_base_url": "OLLAMA_BASE_URL",
|
|
"tavily_api_key": "TAVILY_API_KEY",
|
|
"default_mode": "EVOSCIENTIST_DEFAULT_MODE",
|
|
"default_workdir": "EVOSCIENTIST_WORKSPACE_DIR",
|
|
"ui_backend": "EVOSCIENTIST_UI_BACKEND",
|
|
"log_level": "EVOSCIENTIST_LOG_LEVEL",
|
|
"model_fallbacks": "EVOSCIENTIST_MODEL_FALLBACKS",
|
|
"reasoning_effort": "EVOSCIENTIST_REASONING_EFFORT",
|
|
"channel_debug_tracing": "EVOSCIENTIST_CHANNEL_DEBUG_TRACING",
|
|
"ccproxy_port": "EVOSCIENTIST_CCPROXY_PORT",
|
|
"use_responses_api": "EVOSCIENTIST_USE_RESPONSES_API",
|
|
"checkpoint_keep_per_thread": "EVOSCIENTIST_CHECKPOINT_KEEP_PER_THREAD",
|
|
"enable_async_subagents": "EVOSCIENTIST_ENABLE_ASYNC_SUBAGENTS",
|
|
"langgraph_dev_port": "EVOSCIENTIST_LANGGRAPH_DEV_PORT",
|
|
"code_interpreter_timeout": "EVOSCIENTIST_CODE_INTERPRETER_TIMEOUT",
|
|
"code_interpreter_max_result_chars": "EVOSCIENTIST_CODE_INTERPRETER_MAX_RESULT_CHARS",
|
|
"langgraph_dev_file_persistence": "EVOSCIENTIST_LANGGRAPH_DEV_FILE_PERSISTENCE",
|
|
"langgraph_dev_jobs_per_worker": "EVOSCIENTIST_LANGGRAPH_DEV_JOBS_PER_WORKER",
|
|
"recursion_limit": "EVOSCIENTIST_RECURSION_LIMIT",
|
|
}
|
|
|
|
|
|
def get_effective_config(
|
|
cli_overrides: dict[str, Any] | None = None,
|
|
) -> EvoScientistConfig:
|
|
"""Get effective configuration by merging all sources.
|
|
|
|
Priority (highest to lowest):
|
|
1. CLI arguments (cli_overrides)
|
|
2. Environment variables
|
|
3. Config file
|
|
4. Defaults
|
|
|
|
Args:
|
|
cli_overrides: Dictionary of CLI argument overrides.
|
|
|
|
Returns:
|
|
EvoScientistConfig with merged values.
|
|
"""
|
|
load_dotenv(find_dotenv(usecwd=True), override=True)
|
|
|
|
# Start with file config (includes defaults for missing values)
|
|
config = load_config()
|
|
data = asdict(config)
|
|
|
|
# Apply environment variable overrides
|
|
for config_key, env_key in _ENV_MAPPINGS.items():
|
|
env_value = os.environ.get(env_key)
|
|
if env_value:
|
|
field_info = next(
|
|
f for f in fields(EvoScientistConfig) if f.name == config_key
|
|
)
|
|
try:
|
|
data[config_key] = _coerce_value(env_value, field_info.type)
|
|
except (ValueError, TypeError):
|
|
pass
|
|
|
|
# Apply CLI overrides (highest priority)
|
|
if cli_overrides:
|
|
for key, value in cli_overrides.items():
|
|
if value is not None and key in data:
|
|
data[key] = value
|
|
|
|
return EvoScientistConfig(**data)
|
|
|
|
|
|
def apply_config_to_env(config: EvoScientistConfig) -> None:
|
|
"""Apply config API keys to environment variables if not already set.
|
|
|
|
This allows the config file to provide API keys that downstream
|
|
libraries (like langchain-anthropic) can pick up.
|
|
|
|
Args:
|
|
config: Configuration to apply.
|
|
"""
|
|
if config.anthropic_api_key and not os.environ.get("ANTHROPIC_API_KEY"):
|
|
os.environ["ANTHROPIC_API_KEY"] = config.anthropic_api_key
|
|
if config.anthropic_base_url and not os.environ.get("ANTHROPIC_BASE_URL"):
|
|
os.environ["ANTHROPIC_BASE_URL"] = config.anthropic_base_url
|
|
if config.openai_api_key and not os.environ.get("OPENAI_API_KEY"):
|
|
os.environ["OPENAI_API_KEY"] = config.openai_api_key
|
|
if config.nvidia_api_key and not os.environ.get("NVIDIA_API_KEY"):
|
|
os.environ["NVIDIA_API_KEY"] = config.nvidia_api_key
|
|
if config.google_api_key and not os.environ.get("GOOGLE_API_KEY"):
|
|
os.environ["GOOGLE_API_KEY"] = config.google_api_key
|
|
if config.minimax_api_key and not os.environ.get("MINIMAX_API_KEY"):
|
|
os.environ["MINIMAX_API_KEY"] = config.minimax_api_key
|
|
if config.minimax_base_url and not os.environ.get("MINIMAX_BASE_URL"):
|
|
os.environ["MINIMAX_BASE_URL"] = config.minimax_base_url
|
|
if config.siliconflow_api_key and not os.environ.get("SILICONFLOW_API_KEY"):
|
|
os.environ["SILICONFLOW_API_KEY"] = config.siliconflow_api_key
|
|
if config.openrouter_api_key and not os.environ.get("OPENROUTER_API_KEY"):
|
|
os.environ["OPENROUTER_API_KEY"] = config.openrouter_api_key
|
|
if config.deepseek_api_key and not os.environ.get("DEEPSEEK_API_KEY"):
|
|
os.environ["DEEPSEEK_API_KEY"] = config.deepseek_api_key
|
|
if config.zhipu_api_key and not os.environ.get("ZHIPU_API_KEY"):
|
|
os.environ["ZHIPU_API_KEY"] = config.zhipu_api_key
|
|
if config.volcengine_api_key and not os.environ.get("VOLCENGINE_API_KEY"):
|
|
os.environ["VOLCENGINE_API_KEY"] = config.volcengine_api_key
|
|
if config.dashscope_api_key and not os.environ.get("DASHSCOPE_API_KEY"):
|
|
os.environ["DASHSCOPE_API_KEY"] = config.dashscope_api_key
|
|
if config.moonshot_api_key and not os.environ.get("MOONSHOT_API_KEY"):
|
|
os.environ["MOONSHOT_API_KEY"] = config.moonshot_api_key
|
|
if config.kimi_api_key and not os.environ.get("KIMI_API_KEY"):
|
|
os.environ["KIMI_API_KEY"] = config.kimi_api_key
|
|
if config.custom_openai_api_key and not os.environ.get("CUSTOM_OPENAI_API_KEY"):
|
|
os.environ["CUSTOM_OPENAI_API_KEY"] = config.custom_openai_api_key
|
|
if config.custom_openai_base_url and not os.environ.get("CUSTOM_OPENAI_BASE_URL"):
|
|
os.environ["CUSTOM_OPENAI_BASE_URL"] = config.custom_openai_base_url
|
|
if config.custom_anthropic_api_key and not os.environ.get(
|
|
"CUSTOM_ANTHROPIC_API_KEY"
|
|
):
|
|
os.environ["CUSTOM_ANTHROPIC_API_KEY"] = config.custom_anthropic_api_key
|
|
if config.custom_anthropic_base_url and not os.environ.get(
|
|
"CUSTOM_ANTHROPIC_BASE_URL"
|
|
):
|
|
os.environ["CUSTOM_ANTHROPIC_BASE_URL"] = config.custom_anthropic_base_url
|
|
if config.ollama_base_url and not os.environ.get("OLLAMA_BASE_URL"):
|
|
os.environ["OLLAMA_BASE_URL"] = config.ollama_base_url
|
|
if config.tavily_api_key and not os.environ.get("TAVILY_API_KEY"):
|
|
os.environ["TAVILY_API_KEY"] = config.tavily_api_key
|
|
if config.reasoning_effort and not os.environ.get("EVOSCIENTIST_REASONING_EFFORT"):
|
|
os.environ["EVOSCIENTIST_REASONING_EFFORT"] = config.reasoning_effort
|
|
if config.use_responses_api and not os.environ.get(
|
|
"EVOSCIENTIST_USE_RESPONSES_API"
|
|
):
|
|
os.environ["EVOSCIENTIST_USE_RESPONSES_API"] = config.use_responses_api
|