Enhanced the AIAgent class to capture and normalize summary information for reasoning items. Implemented logic to handle summaries as lists, ensuring proper formatting for API interactions. Updated tests to validate the inclusion of summaries in reasoning items, both for existing and default cases.
Introduced a new `provider_routing` section in the CLI configuration to control how requests are routed across providers when using OpenRouter. This includes options for sorting providers by throughput, latency, or price, as well as allowing or ignoring specific providers, setting the order of provider attempts, and managing data collection policies. Updated relevant classes and documentation to support these features, enhancing flexibility in provider selection.
Added support for processing encrypted reasoning content within the AIAgent class. Introduced logic to determine reasoning effort and enable/disable reasoning based on configuration settings. Updated the kwargs to reflect these changes, ensuring proper handling of reasoning parameters during agent execution.
Introduced a new command "/usage" in the CLI to show cumulative token usage for the current session. This includes details on prompt tokens, completion tokens, total tokens, API calls, and context state. Updated command documentation to reflect this addition. Enhanced the AIAgent class to track token usage throughout the session.
When subagents run via delegate_task, the user now sees real-time
progress instead of silence:
CLI: tree-view activity lines print above the delegation spinner
🔀 Delegating: research quantum computing
├─ 💭 "I'll search for papers first..."
├─ 🔍 web_search "quantum computing"
├─ 📖 read_file "paper.pdf"
└─ ⠹ working... (18.2s)
Gateway (Telegram/Discord): batched progress summaries sent every
5 tool calls to avoid message spam. Remaining tools flushed on
subagent completion.
Changes:
- agent/display.py: add KawaiiSpinner.print_above() to print
status lines above an active spinner without disrupting animation.
Uses captured stdout (self._out) so it works inside the child's
redirect_stdout(devnull).
- tools/delegate_tool.py: add _build_child_progress_callback()
that creates a per-child callback relaying tool calls and
thinking events to the parent's spinner (CLI) or progress
queue (gateway). Each child gets its own callback instance,
so parallel subagents don't share state. Includes _flush()
for gateway batch completion.
- run_agent.py: fire tool_progress_callback with '_thinking'
event when the model produces text content. Guarded by
_delegate_depth > 0 so only subagents fire this (prevents
gateway spam from main agent). REASONING_SCRATCHPAD/think/
reasoning XML tags are stripped before display.
Tests: 21 new tests covering print_above, callback builder,
thinking relay, SCRATCHPAD filtering, batching, flush, thread
isolation, delegate_depth guard, and prefix handling.
- Introduce a separate error log for capturing warnings and errors related to tool execution, ensuring detailed inspection of issues post-failure.
- Enhance error handling in the AIAgent class to log exceptions with stack traces for better debugging.
- Add a similar error logging mechanism in the gateway to streamline debugging processes.
- Replace `hermes login` with `hermes model` for selecting providers and managing authentication.
- Update documentation and CLI commands to reflect the new provider selection process.
- Introduce a new redaction system for logging sensitive information.
- Enhance Codex model discovery by integrating API fetching and local cache.
- Adjust max turns configuration logic for better clarity and precedence.
- Improve error handling and user feedback during authentication processes.
- 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.
Add a new hooks system allowing users to run custom code at key lifecycle points in the agent's operation. This includes support for events such as `gateway:startup`, `session:start`, `agent:step`, and more. Documentation for creating hooks and available events has been added to `README.md` and a new `hooks.md` file. Additionally, integrate step callbacks in the agent to facilitate hook execution during tool-calling iterations.
Fixes#149
The _strip_think_blocks() method existed but was not applied to the
final_response in the normal completion path. This caused <think>...</think>
XML tags to leak into user-facing responses on all platforms (CLI, Telegram,
Discord, Slack, WhatsApp).
Changes:
- Strip think blocks from final_response before returning in normal path (line ~2600)
- Strip think blocks from fallback content when salvaging from prior tool_calls turn
Notes:
- The raw content with think blocks is preserved in messages[] for trajectory
export - this only affects the user-facing final_response
- The _has_content_after_think_block() check still uses raw content before
stripping, which is correct for detecting think-only responses
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.
