Replaces the unsafe 128K fallback for unknown models with a descending
probe strategy (2M → 1M → 512K → 200K → 128K → 64K → 32K). When a
context-length error occurs, the agent steps down tiers and retries.
The discovered limit is cached per model+provider combo in
~/.hermes/context_length_cache.yaml so subsequent sessions skip probing.
Also parses API error messages to extract the actual context limit
(e.g. 'maximum context length is 32768 tokens') for instant resolution.
The CLI banner now displays the context window size next to the model
name (e.g. 'claude-opus-4 · 200K context · Nous Research').
Changes:
- agent/model_metadata.py: CONTEXT_PROBE_TIERS, persistent cache
(save/load/get), parse_context_limit_from_error(), get_next_probe_tier()
- agent/context_compressor.py: accepts base_url, passes to metadata
- run_agent.py: step-down logic in context error handler, caches on success
- cli.py + hermes_cli/banner.py: context length in welcome banner
- tests: 22 new tests for probing, parsing, and caching
Addresses #132. PR #319's approach (8K default) rejected — too conservative.
The flush_memories() and run_conversation() code paths already stripped
finish_reason and reasoning from API messages (added in 7a0b377 via PR
#253), but _handle_max_iterations() was missed. It was sending raw
messages.copy() which could include finish_reason, causing 422 errors
on strict APIs like Mistral when the agent hit max iterations.
Now strips the same internal fields consistently across all three API
call sites.
Authored by ch3ronsa. Fixes#348.
Adds 'context size' (LM Studio) and 'context window' (Ollama) to
context-length error detection phrases so local backend 400 errors
trigger compression instead of aborting. Also removes 'error code: 400'
from the non-retryable error list as defense in depth.
Two fixes for the case where a user switches to a model with a smaller
context window while having a large existing session:
1. Preflight compression in run_conversation(): Before the main loop,
estimate tokens of loaded history + system prompt. If it exceeds the
model's compression threshold (85% of context), compress proactively
with up to 3 passes. This naturally handles model switches because
the gateway creates a fresh AIAgent per message with the current
model's context length.
2. Error handler reordering: Context-length errors (400 with 'maximum
context length' etc.) are now checked BEFORE the generic 4xx handler.
Previously, OpenRouter's 400-status context-length errors were caught
as non-retryable client errors and aborted immediately, never reaching
the compression+retry logic.
Reported by Sonicrida on Discord: 840-message session (2MB+) crashed
after switching from a large-context model to minimax via OpenRouter.
Local backends (LM Studio, Ollama, llama.cpp) return HTTP 400
with messages like "Context size has been exceeded" when the
context window is full. The error phrase list did not include
"context size" or "context window", so these errors fell through
to the generic 4xx abort handler instead of triggering compression.
Changes:
- Move context-length check above generic 4xx handler so it runs
first (same pattern as the existing 413 check)
- Add "context size" and "context window" to the phrase list
- Guard 4xx handler with `not is_context_length_error` to prevent
context-related 400s from being treated as non-retryable
session_search was returning the current session if it matched the
query, which is redundant — the agent already has the current
conversation context. This wasted an LLM summarization call and a
result slot.
Added current_session_id parameter to session_search(). The agent
passes self.session_id and the search filters out any results where
either the raw or parent-resolved session ID matches. Both the raw
match and the parent-resolved match are checked to handle child
sessions from delegation.
Two tests added verifying the exclusion works and that other
sessions are still returned.
Authored by 0xbyt4. Adds smart home control via REST tools (ha_list_entities,
ha_get_state, ha_call_service) with domain blocklist and entity_id validation,
plus WebSocket gateway adapter for real-time event monitoring.
Also includes Gemini 3 thought_signature preservation fix (extra_content on
tool calls) needed for multi-turn tool calling via OpenRouter.
In _handle_max_iterations, the codex_responses path set tools=None to
prevent tool calls during summarization. However, the OpenAI SDK's
_make_tools() treats None as a valid value (not its Omit sentinel) and
tries to iterate over it, causing TypeError: 'NoneType' object is not
iterable.
Fix: use codex_kwargs.pop('tools', None) to remove the key entirely,
so the SDK never receives it and uses its default omit behavior.
Fixes#300
Issue #263: Telegram/Discord/WhatsApp/Slack now show tool call details
based on display.tool_progress in config.yaml.
Changes:
- gateway/run.py: 'verbose' mode shows full args (keys + JSON, 200 char
max). 'all' mode preview increased from 40 to 80 chars. Added missing
tool emojis (execute_code, delegate_task, clarify, skill_manage,
search_files).
- agent/display.py: Added execute_code, delegate_task, clarify,
skill_manage to primary_args. Added 'code' and 'goal' to fallback keys.
- run_agent.py: Pass function_args dict to tool_progress_callback so
gateway can format based on its own verbosity config.
Config usage:
display:
tool_progress: verbose # off | new | all | verbose
The TestFlushSentinelNotLeaked test from PR #227 had two issues:
1. flush_memories() uses get_text_auxiliary_client() which could bypass
agent.client entirely — mock it to return (None, None)
2. No assertion that the API was actually called — added guard assert
Without these fixes the test passed vacuously (API never called).
The OpenAI API returns content: null on assistant messages with tool
calls. msg.get('content', '') returns None when the key exists with
value None, causing TypeError on len(), string concatenation, and
.strip() in downstream code paths.
Fixed 4 locations that process conversation messages:
- agent/auxiliary_client.py:84 — None passed to API calls
- cli.py:1288 — crash on content[:200] and len(content)
- run_agent.py:3444 — crash on None.strip()
- honcho_integration/session.py:445 — 'None' rendered in transcript
13 other instances were verified safe (already protected, only process
user/tool messages, or use the safe pattern).
Pattern: msg.get('content', '') → msg.get('content') or ''
Fixes#276
* fix(agent): skip reasoning param for Mistral API to prevent 422 errors
* fix(agent): strip finish_reason from assistant messages to fix Mistral 422 errors
Updated the AIAgent class to print the full content of assistant messages without truncation, enhancing visibility of the messages during runtime. This change improves the clarity of communication from the agent.
Added the tools attribute to the AIAgent class's status output, ensuring that the current tools used by the agent are included in the status information. This enhancement improves the visibility of the agent's capabilities during runtime.
Added the system prompt to the AIAgent class's status output, ensuring that the current system prompt is included in the agent's status information. This enhancement improves visibility into the agent's configuration during runtime.
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.
The retry exhaustion checks used > instead of >= to compare
retry_count against max_retries. Since the while loop condition is
retry_count < max_retries, the check retry_count > max_retries can
never be true inside the loop. When retries are exhausted, the loop
exits and falls through to response.choices[0] on an invalid response,
crashing with IndexError instead of returning a proper error.
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.
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.
