Add Daytona as a backend choice in the interactive setup wizard with
SDK installation and API key prompts. Show Daytona image in status
output and validate API key + SDK in doctor checks. Add OPTION 6
example in cli-config.yaml.example.
Signed-off-by: rovle <lovre.pesut@gmail.com>
- Enhanced Codex model discovery by fetching available models from the API, with fallback to local cache and defaults.
- Updated the context compressor's summary target tokens to 2500 for improved performance.
- Added external credential detection for Codex CLI to streamline authentication.
- Refactored various components to ensure consistent handling of authentication and model selection across the application.
The `hermes` CLI entry point (hermes_cli/main.py) and the agent runner
(run_agent.py) only loaded .env from the project installation directory.
After the standard installer, code lives at ~/.hermes/hermes-agent/ but
config lives at ~/.hermes/ — so the .env was never found.
Aligns these entry points with the pattern already used by gateway/run.py
and rl_cli.py: load ~/.hermes/.env first, fall back to project root .env
for dev-mode compatibility.
Also fixes:
- status.py checking .env existence and API keys at PROJECT_ROOT
- doctor.py KeyError on tool availability (missing_vars vs env_vars)
- doctor.py checking logs/ and Skills Hub at PROJECT_ROOT instead of HERMES_HOME
- doctor.py redundant logs/ check (already covered by subdirectory loop)
- mini-swe-agent loading config from platformdirs default instead of ~/.hermes/
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Updated the README to include a new banner image and changed the title emoji from 🦋 to ⚕.
- Modified various CLI outputs and scripts to reflect the new branding, ensuring consistency in the use of the ⚕ emoji.
- Added a new banner image asset for enhanced visual appeal during installation and setup processes.
- Implemented a multi-provider authentication system for the Hermes Agent, supporting OAuth for Nous Portal and traditional API key methods for OpenRouter and custom endpoints.
- Enhanced CLI with commands for logging in and out of providers, allowing users to authenticate and manage their credentials easily.
- Updated configuration options to select inference providers, with detailed documentation on usage and setup.
- Improved status reporting to include authentication status and provider details, enhancing user awareness of their current configuration.
- Added new files for authentication handling and updated existing components to integrate the new provider system.
Two config systems used different key names for the terminal backend:
- hermes_cli/config.py, README, and all docs use "terminal.backend"
- cli.py's env var mapping only recognized "terminal.env_type"
Users following the docs who set `backend: modal` in ~/.hermes/config.yaml
had it silently ignored -- TERMINAL_ENV always defaulted to "local".
Additionally, when no config file existed, cli.py's hardcoded defaults
overwrote any TERMINAL_ENV=modal set in .env, despite the comment saying
"env vars take precedence."
Fixes:
- cli.py now normalizes "backend" -> "env_type" (backend takes precedence)
- Defaults no longer overwrite .env when no config file terminal section exists
- hermes status reads from config as fallback when env var isn't set
Also fixes four related bugs found in the Modal/sandbox lifecycle:
- file_tools cache not cleared on sandbox cleanup (stale ops on dead sandbox)
- Global lock held during slow Modal teardown (blocked all tool calls 10-15s)
- Race condition in file_tools between existence check and access (KeyError)
- Per-task creation locks never cleaned up (memory leak)
- Updated `.env.example` to include Tinker and WandB API keys for reinforcement learning training.
- Enhanced `model_tools.py` to clarify configuration options and streamline the RL training process.
- Expanded `README.md` with detailed instructions for setting up RL training using Tinker and WandB.
- Modified `hermes_cli` files to integrate RL training tools and ensure proper configuration checks.
- Improved `rl_training_tool.py` to reflect changes in training parameters and configuration management.
- Updated CLI to load configuration from user-specific and project-specific YAML files, prioritizing user settings.
- Introduced a new command `/platforms` to display the status of connected messaging platforms (Telegram, Discord, WhatsApp).
- Implemented a gateway system for handling messaging interactions, including session management and delivery routing for cron job outputs.
- Added support for environment variable configuration and a dedicated gateway configuration file for advanced settings.
- Enhanced documentation in README.md and added a new messaging.md file to guide users on platform integrations and setup.
- Updated toolsets to include platform-specific capabilities for Telegram, Discord, and WhatsApp, ensuring secure and tailored interactions.
