Gemini 3 thinking models attach extra_content with thought_signature
to function call responses. This must be echoed back on subsequent
API calls or the server rejects with a 400 error. The assistant
message builder was dropping this field, causing all Gemini 3 Flash/Pro
tool-calling flows to fail after the first function call.
The 413 "Request Entity Too Large" error from the LLM API was caught by the
generic 4xx handler which aborts immediately. This is wrong for 413 — it's a
payload-size issue that can be resolved by compressing conversation history.
- Intercept 413 before the generic 4xx block and route to _compress_context
- Exclude 413 from generic is_client_error detection
- Add 'request entity too large' to context-length phrases as safety net
- Add tests for 413 compression behavior
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When running via the gateway (e.g. Telegram), the session_search tool
returned: {"error": "session_search must be handled by the agent loop"}
Root cause:
- gateway/run.py creates AIAgent without passing session_db=
- self._session_db is None in the agent instance
- The dispatch condition "elif function_name == 'session_search' and self._session_db"
skips when _session_db is None, falling through to the generic error
This fix:
1. Initializes self._session_db in GatewayRunner.__init__()
2. Passes session_db to all AIAgent instantiations in gateway/run.py
3. Adds defensive fallback in run_agent.py to return a clear error when
session_db is unavailable, instead of falling through
Fixes#105
- Added _max_tokens_param method in AIAgent to return appropriate max tokens parameter based on the provider (OpenAI vs. others).
- Updated API calls in AIAgent to utilize the new max tokens handling.
- Introduced auxiliary_max_tokens_param function in auxiliary_client for consistent max tokens management across auxiliary clients.
- Refactored multiple tools to use auxiliary_max_tokens_param for improved compatibility with different models and providers.
USER.md stays in system prompt when Honcho is active -- prefetch is
additive context, not a replacement. Memory tool user observations
write to both USER.md (local) and Honcho (cross-session) simultaneously.
When Honcho is active:
- System prompt uses Honcho prefetch instead of USER.md
- memory tool target=user add routes to Honcho
- MEMORY.md untouched in all cases
When disabled, everything works as before.
Also wires up contextTokens config to cap prefetch size.
Opt-in persistent cross-session user modeling via Honcho. Reads
~/.honcho/config.json as single source of truth (shared with
Claude Code, Cursor, and other Honcho-enabled tools). Zero impact
when disabled or unconfigured.
- honcho_integration/ package (client, session manager, peer resolution)
- Host-based config resolution matching claude-honcho/cursor-honcho pattern
- Prefetch user context into system prompt per conversation turn
- Sync user/assistant messages to Honcho after each exchange
- query_user_context tool for mid-conversation dialectic reasoning
- Gated activation: requires ~/.honcho/config.json with enabled=true
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>
- Simplified the logic for determining support for reasoning based on the base URL by introducing clearer variable names.
- Added product attribution for the Nous Portal to the extra body of requests when applicable, enhancing tagging for better tracking.
- Introduced a new static method `_clean_session_content` in the `AIAgent` class to convert REASONING_SCRATCHPAD tags to <think> blocks and clean up whitespace in session logs.
- Updated the `_save_session_log` method to utilize the cleaned content for assistant messages, ensuring consistency in session logs.
- Changed the default output directory for TTS audio files from `~/voice-memos` to `~/.hermes/audio_cache`, reflecting a more appropriate storage location.
- Updated the `clear_interrupt` method to also reset the global tool interrupt signal, improving the clarity of interrupt management within the agent.
- This change ensures that all interrupt states are properly cleared, enhancing the reliability of the agent's operation.
- Introduced a new configuration option for reasoning effort in the CLI, allowing users to specify the level of reasoning the agent should perform before responding.
- Updated the CLI and agent initialization to incorporate the reasoning configuration, enhancing the agent's responsiveness and adaptability.
- Implemented logic to load reasoning effort from environment variables and configuration files, providing flexibility in agent behavior.
- Enhanced the documentation in the example configuration file to clarify the new reasoning effort options available.
- Implemented functionality to load ephemeral prefill messages from a JSON file, enhancing few-shot priming capabilities for the agent.
- Introduced a mechanism to load an ephemeral system prompt from environment variables or configuration files, ensuring dynamic prompt adjustments at API-call time.
- Updated the CLI and agent initialization to utilize the new prefill messages and system prompt, improving the overall interaction experience.
- Enhanced configuration options with new environment variables for prefill messages and system prompts, allowing for greater customization without persistence.
- Removed static methods for converting and checking <REASONING_SCRATCHPAD> tags, simplifying the codebase.
- Replaced calls to the removed methods with direct function calls for better clarity and maintainability.
- Updated trajectory saving logic to utilize a dedicated function for improved organization and readability.
- Introduced a shared interrupt signaling mechanism to allow tools to check for user interrupts during long-running operations.
- Updated the AIAgent to handle interrupts more effectively, ensuring in-progress tool calls are canceled and multiple interrupt messages are combined into one prompt.
- Enhanced the CLI configuration to include container resource limits (CPU, memory, disk) and persistence options for Docker, Singularity, and Modal environments.
- Improved documentation to clarify interrupt behaviors and container resource settings, providing users with better guidance on configuration and usage.
- Introduced a method to strip <think> blocks from content, improving text visibility.
- Implemented counters to reset nudge intervals when memory and skill tools are used, enhancing user guidance.
- Captured content from turns with tool calls to provide fallback responses, ensuring continuity in conversation.
- Updated nudge logic to remind users about saving memories and creating skills based on interaction patterns.
- Added skills configuration options in cli-config.yaml.example, including a nudge interval for skill creation reminders.
- Implemented skills guidance in AIAgent to prompt users to save reusable workflows after complex tasks.
- Enhanced skills indexing in the prompt builder to include descriptions from SKILL.md files for better context.
- Updated the agent's behavior to periodically remind users about potential skills during tool-calling iterations.
- Added configuration options for memory nudge interval and flush minimum turns in cli-config.yaml.example.
- Implemented memory flushing before conversation reset, clearing, and exit in the CLI to ensure memories are saved.
- Introduced a flush_memories method in AIAgent to handle memory persistence before context loss.
- Added periodic nudges to remind the agent to consider saving memories based on user interactions.
- Introduced MEMORY_GUIDANCE and SESSION_SEARCH_GUIDANCE to improve agent's contextual awareness and proactive assistance.
- Updated AIAgent to conditionally include tool-aware guidance in prompts based on available tools.
- Enhanced descriptions in memory and session search schemas for clearer user instructions on when to utilize these features.
- Eliminated the `compression_model` variable from the AIAgent class, as it was not being utilized.
- Cleaned up the context compressor initialization for improved clarity and maintainability.
- Relocated functions related to model metadata, including fetch_model_metadata, get_model_context_length, estimate_tokens_rough, and estimate_messages_tokens_rough, to agent/model_metadata.py for better organization and maintainability.
- Updated imports in run_agent.py to reflect the new location of these functions.
- Added functionality to suppress logging noise from specific modules when in quiet mode, improving user experience in CLI.
- Updated terminal_tool.py to change the log level for fallback directory usage from warning to debug, providing clearer context without cluttering logs.
- Added methods for handling sudo password and dangerous command approval prompts using a callback mechanism in cli.py.
- Integrated these prompts with the prompt_toolkit UI for improved user experience.
- Updated terminal_tool.py to support callback registration for interactive prompts, enhancing the CLI's interactivity.
- Introduced a background thread for API calls in run_agent.py to allow for interrupt handling during long-running operations.
- Enhanced error handling for interrupted API calls, ensuring graceful degradation of user experience.
- Introduced new methods in run_agent.py for building API keyword arguments and normalizing assistant messages from API responses.
- Added functionality for compressing conversation context and managing session state in SQLite.
- Improved tool call execution handling, including enhanced logging and error management.
- Updated path handling in multiple platform files to utilize pathlib for better compatibility and readability.
- Updated various modules including cli.py, run_agent.py, gateway, and tools to replace silent exception handling with structured logging.
- Improved error messages to provide more context, aiding in debugging and monitoring.
- Ensured consistent logging practices throughout the codebase, enhancing traceability and maintainability.
- Introduced logging functionality in cli.py, run_agent.py, scheduler.py, and various tool modules to replace print statements with structured logging.
- Enhanced error handling and informational messages to improve debugging and monitoring capabilities.
- Ensured consistent logging practices across the codebase, facilitating better traceability and maintenance.
- Eliminated the `_log_api_payload` method used for temporary debugging, streamlining the codebase.
- Updated the `_save_session_log` method to save the full raw session, including all messages and metadata, improving the clarity and completeness of session logs.
- Adjusted session log entry to include additional context such as `base_url` and `platform` for better tracking.
- Changed the session logging directory from `~/.hermes-agent/logs/` to `~/.hermes/sessions/` for consistency.
- Updated the `run_agent.py` to reflect the new logging path, ensuring session logs are stored correctly alongside gateway sessions.
- Incremented schema version to 2 and added a new column `finish_reason` to the `messages` table.
- Implemented a method to flush un-logged messages to the session database, ensuring data integrity during conversation interruptions.
- Enhanced error handling to persist messages in various early-return scenarios, preventing data loss.
- 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.
- Added a spinner to visually indicate task delegation progress in quiet mode, improving user experience during batch processing.
- Implemented a method to update spinner text dynamically based on remaining tasks, providing real-time feedback.
- Enhanced the `delegate_task` function to include per-task completion messages, ensuring clarity on task status during execution.
- Updated the KawaiiSpinner class to allow message updates while running, facilitating better interaction during long-running tasks.
- Introduced the `delegate_task` tool, allowing the main agent to spawn child AIAgent instances with isolated context for complex tasks.
- Supported both single-task and batch processing (up to 3 concurrent tasks) to enhance task management capabilities.
- Updated configuration options for delegation, including maximum iterations and default toolsets for subagents.
- Enhanced documentation to provide clear guidance on using the delegation feature and its configuration.
- Added comprehensive tests to ensure the functionality and reliability of the delegation logic.
- Updated the tool name from "search" to "search_files" across multiple files to better reflect its functionality.
- Adjusted related documentation and descriptions to ensure clarity in usage and expected behavior.
- Enhanced the toolset definitions and mappings to incorporate the new naming convention, improving overall consistency in the codebase.
- Introduced a new `execute_code` tool that allows the agent to run Python scripts that call Hermes tools via RPC, reducing the number of round trips required for tool interactions.
- Added configuration options for timeout and maximum tool calls in the sandbox environment.
- Updated the toolset definitions to include the new code execution capabilities, ensuring integration across platforms.
- Implemented comprehensive tests for the code execution sandbox, covering various scenarios including tool call limits and error handling.
- Enhanced the CLI and documentation to reflect the new functionality, providing users with clear guidance on using the code execution tool.
- Added a new `clarify_tool` to enable the agent to ask structured multiple-choice or open-ended questions to users.
- Implemented callback functionality for user interaction, allowing the platform to handle UI presentation.
- Updated the CLI and agent to support clarify questions, including timeout handling and response management.
- Enhanced toolset definitions and requirements to include the clarify tool, ensuring availability across platforms.
