OpenCode Zen was in _DOT_TO_HYPHEN_PROVIDERS, causing all dotted model
names (minimax-m2.5-free, gpt-5.4, glm-5.1) to be mangled. The fix:
Layer 1 (model_normalize.py): Remove opencode-zen from the blanket
dot-to-hyphen set. Add an explicit block that preserves dots for
non-Claude models while keeping Claude hyphenated (Zen's Claude
endpoint uses anthropic_messages mode which expects hyphens).
Layer 2 (run_agent.py _anthropic_preserve_dots): Add opencode-zen and
zai to the provider allowlist. Broaden URL check from opencode.ai/zen/go
to opencode.ai/zen/ to cover both Go and Zen endpoints. Add bigmodel.cn
for ZAI URL detection.
Also adds glm-5.1 to ZAI model lists in models.py and setup.py.
Closes#7710
Salvaged from contributions by:
- konsisumer (PR #7739, #7719)
- DomGrieco (PR #8708)
- Esashiero (PR #7296)
- sharziki (PR #7497)
- XiaoYingGee (PR #8750)
- APTX4869-maker (PR #8752)
- kagura-agent (PR #7157)
_check_compression_model_feasibility() called get_model_context_length()
without passing config_context_length, so custom endpoints that do not
support /models API queries always fell through to the 128K default,
ignoring auxiliary.compression.context_length in config.yaml.
Fix: read auxiliary.compression.context_length from config and pass it
as config_context_length (highest-priority hint) so the user-configured
value is always respected regardless of API availability.
Fixes#8499
Three fixes for the (empty) response bug affecting open reasoning models:
1. Allow retries after prefill exhaustion — models like mimo-v2-pro always
populate reasoning fields via OpenRouter, so the old 'not _has_structured'
guard on the retry path blocked retries for EVERY reasoning model after
the 2 prefill attempts. Now: 2 prefills + 3 retries = 6 total attempts
before (empty).
2. Reset prefill/retry counters on tool-call recovery — the counters
accumulated across the entire conversation, never resetting during
tool-calling turns. A model cycling empty→prefill→tools→empty burned
both prefill attempts and the third empty got zero recovery. Now
counters reset when prefill succeeds with tool calls.
3. Strip think blocks before _truly_empty check — inline <think> content
made the string non-empty, skipping both retry paths.
Reported by users on Telegram with xiaomi/mimo-v2-pro and qwen3.5 models.
Reproduced: qwen3.5-9b emits tool calls as XML in reasoning field instead
of proper function calls, causing content=None + tool_calls=None + reasoning
with embedded <tool_call> XML. Prefill recovery works but counter
accumulation caused permanent (empty) in long sessions.
Previously, all invalid API responses (choices=None) were diagnosed
as 'fast response often indicates rate limiting' regardless of actual
response time or error code. A 738s Cloudflare 524 timeout was labeled
as 'fast response' and 'possible rate limit'.
Now extracts the error code from response.error and classifies:
- 524: upstream provider timed out (Cloudflare)
- 504: upstream gateway timeout
- 429: rate limited by upstream provider
- 500/502: upstream server error
- 503/529: upstream provider overloaded
- Other codes: shown with code number
- No code + <10s: likely rate limited (timing heuristic)
- No code + >60s: likely upstream timeout
- No code + 10-60s: neutral response time
All downstream messages (retry status, final error, interrupt message)
now use the classified hint instead of generic rate-limit language.
Reported by community member Lumen Radley (MiMo provider timeouts).
Gemma 4 (26B/31B) uses <thought>...</thought> to wrap its reasoning
output. This tag was not included in the existing list of reasoning tag
variants stripped by _strip_think_blocks(), causing raw thinking blocks
to leak into the visible response.
Added a new re.sub() line for <thought> and extended the cleanup regex
to include 'thought' alongside the existing variants.
Fixes#6148
When running inside WSL (Windows Subsystem for Linux), inject a hint into
the system prompt explaining that the Windows host filesystem is mounted
at /mnt/c/, /mnt/d/, etc. This lets the agent naturally translate Windows
paths (Desktop, Documents) to their /mnt/ equivalents without the user
needing to configure anything.
Uses the existing is_wsl() detection from hermes_constants (cached,
checks /proc/version for 'microsoft'). Adds build_environment_hints()
in prompt_builder.py — extensible for Termux, Docker, etc. later.
Closes the UX gap where WSL users had to manually explain path
translation to the agent every session.
Adds an optional focus topic to /compress: `/compress database schema`
guides the summariser to preserve information related to the focus topic
(60-70% of summary budget) while compressing everything else more aggressively.
Inspired by Claude Code's /compact <focus>.
Changes:
- context_compressor.py: focus_topic parameter on _generate_summary() and
compress(); appends FOCUS TOPIC guidance block to the LLM prompt
- run_agent.py: focus_topic parameter on _compress_context(), passed through
to the compressor
- cli.py: _manual_compress() extracts focus topic from command string,
preserves existing manual_compression_feedback integration (no regression)
- gateway/run.py: _handle_compress_command() extracts focus from event args
and passes through — full gateway parity
- commands.py: args_hint="[focus topic]" on /compress CommandDef
Salvaged from PR #7459 (CLI /compress focus only — /context command deferred).
15 new tests across CLI, compressor, and gateway.
* feat: component-separated logging with session context and filtering
Phase 1 — Gateway log isolation:
- gateway.log now only receives records from gateway.* loggers
(platform adapters, session management, slash commands, delivery)
- agent.log remains the catch-all (all components)
- errors.log remains WARNING+ catch-all
- Moved gateway.log handler creation from gateway/run.py into
hermes_logging.setup_logging(mode='gateway') with _ComponentFilter
Phase 2 — Session ID injection:
- Added set_session_context(session_id) / clear_session_context() API
using threading.local() for per-thread session tracking
- _SessionFilter enriches every log record with session_tag attribute
- Log format: '2026-04-11 10:23:45 INFO [session_id] logger.name: msg'
- Session context set at start of run_conversation() in run_agent.py
- Thread-isolated: gateway conversations on different threads don't leak
Phase 3 — Component filtering in hermes logs:
- Added --component flag: hermes logs --component gateway|agent|tools|cli|cron
- COMPONENT_PREFIXES maps component names to logger name prefixes
- Works with all existing filters (--level, --session, --since, -f)
- Logger name extraction handles both old and new log formats
Files changed:
- hermes_logging.py: _SessionFilter, _ComponentFilter, COMPONENT_PREFIXES,
set/clear_session_context(), gateway.log creation in setup_logging()
- gateway/run.py: removed redundant gateway.log handler (now in hermes_logging)
- run_agent.py: set_session_context() at start of run_conversation()
- hermes_cli/logs.py: --component filter, logger name extraction
- hermes_cli/main.py: --component argument on logs subparser
Addresses community request for component-separated, filterable logging.
Zero changes to existing logger names — __name__ already provides hierarchy.
* fix: use LogRecord factory instead of per-handler _SessionFilter
The _SessionFilter approach required attaching a filter to every handler
we create. Any handler created outside our _add_rotating_handler (like
the gateway stderr handler, or third-party handlers) would crash with
KeyError: 'session_tag' if it used our format string.
Replace with logging.setLogRecordFactory() which injects session_tag
into every LogRecord at creation time — process-global, zero per-handler
wiring needed. The factory is installed at import time (before
setup_logging) so session_tag is available from the moment hermes_logging
is imported.
- Idempotent: marker attribute prevents double-wrapping on module reload
- Chains with existing factory: won't break third-party record factories
- Removes _SessionFilter from _add_rotating_handler and setup_verbose_logging
- Adds tests: record factory injection, idempotency, arbitrary handler compat
The _get_budget_warning() method already returned None unconditionally —
the entire budget warning system was disabled. Remove all dead code:
- _BUDGET_WARNING_RE regex
- _strip_budget_warnings_from_history() function and its call site
- Both injection blocks (concurrent + sequential tool execution)
- _get_budget_warning() method
- 7 tests for the removed functions
The budget exhaustion grace call system (_budget_exhausted_injected,
_budget_grace_call) is a separate recovery mechanism and is preserved.
Normalize api_messages before each API call for consistent prefix
matching across turns:
1. Strip leading/trailing whitespace from system prompt parts
2. Strip leading/trailing whitespace from message content strings
3. Normalize tool-call arguments to compact sorted JSON
This enables KV cache reuse on local inference servers (llama.cpp,
vLLM, Ollama) and improves cache hit rates for cloud providers.
All normalization operates on the api_messages copy — the original
conversation history in messages is never mutated. Tool-call JSON
normalization creates new dicts via spread to avoid the shallow-copy
mutation bug in the original PR.
Salvaged from PR #7875 by @waxinz with mutation fix.
Switch estimate_tokens_rough(), estimate_messages_tokens_rough(), and
estimate_request_tokens_rough() from floor division (len // 4) to
ceiling division ((len + 3) // 4). Short texts (1-3 chars) previously
estimated as 0 tokens, causing the compressor and pre-flight checks to
systematically undercount when many short tool results are present.
Also replaced the inline duplicate formula in run_conversation()
(total_chars // 4) with a call to the shared
estimate_messages_tokens_rough() function.
Updated 4 tests that hardcoded floor-division expected values.
Related: issue #6217, PR #6629
Add display.interim_assistant_messages config (enabled by default) that
forwards completed assistant commentary between tool calls to the user
as separate chat messages. Models already emit useful status text like
'I'll inspect the repo first.' — this surfaces it on Telegram, Discord,
and other messaging platforms instead of swallowing it.
Independent from tool_progress and gateway streaming. Disabled for
webhooks. Uses GatewayStreamConsumer when available, falls back to
direct adapter send. Tracks response_previewed to prevent double-delivery
when interim message matches the final response.
Also fixes: cursor not stripped from fallback prefix in stream consumer
(affected continuation calculation on no-edit platforms like Signal).
Cherry-picked from PR #7885 by asheriif, default changed to enabled.
Fixes#5016
Three root causes of the 'agent stops mid-task' gateway bug:
1. Compression threshold floor (64K tokens minimum)
- The 50% threshold on a 100K-context model fired at 50K tokens,
causing premature compression that made models lose track of
multi-step plans. Now threshold_tokens = max(50% * context, 64K).
- Models with <64K context are rejected at startup with a clear error.
2. Budget warning removal — grace call instead
- Removed the 70%/90% iteration budget warnings entirely. These
injected '[BUDGET WARNING: Provide your final response NOW]' into
tool results, causing models to abandon complex tasks prematurely.
- Now: no warnings during normal execution. When the budget is
actually exhausted (90/90), inject a user message asking the model
to summarise, allow one grace API call, and only then fall back
to _handle_max_iterations.
3. Activity touches during long terminal execution
- _wait_for_process polls every 0.2s but never reported activity.
The gateway's inactivity timeout (default 1800s) would fire during
long-running commands that appeared 'idle.'
- Now: thread-local activity callback fires every 10s during the
poll loop, keeping the gateway's activity tracker alive.
- Agent wires _touch_activity into the callback before each tool call.
Also: docs update noting 64K minimum context requirement.
Closes#7915 (root cause was agent-loop termination, not Weixin delivery limits).
Replace the verbose_logging-gated logging.exception() with an
unconditional logger.debug(exc_info=True). The full traceback now
always lands in agent.log when debug logging is enabled, without
requiring the verbose_logging flag or spamming the console.
Previously, production errors in the 700-line response processing
block (normalization, tool dispatch, final response handling) were
logged as one-line messages with the traceback hidden behind
verbose_logging — making post-mortem debugging difficult.
All retry counters (_invalid_tool_retries, _invalid_json_retries,
_empty_content_retries, _incomplete_scratchpad_retries,
_codex_incomplete_retries) are initialized to 0 at the top of
run_conversation() (lines 7566-7570). The hasattr guards added before
the reset block existed are now dead code — the attributes always exist.
Removed 7 redundant hasattr checks (5 original targets + 2 bonus for
_codex_incomplete_retries found during cleanup).
When _try_activate_fallback() switches to a new provider, retry_count was
reset to 0 but compression_attempts and primary_recovery_attempted were
not. This meant a fallback provider that hit context overflow would only
get the leftover compression budget from the failed primary provider,
and transport recovery was blocked because the flag was still True from
the old provider's attempt.
Reset both counters at all 5 fallback activation sites inside the retry
loop so each fallback provider gets a fresh compression budget (3 attempts)
and its own transport recovery opportunity.
When replaying codex_reasoning_items from previous turns,
duplicate item IDs (rs_*) could appear in the input array,
causing HTTP 400 "Duplicate item found" errors from the
OpenAI Responses API.
Add seen_item_ids tracking in both _chat_messages_to_responses_input()
and _preflight_codex_input_items() to skip already-added reasoning
items by their ID.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The interrupt mechanism in tools/interrupt.py used a process-global
threading.Event. In the gateway, multiple agents run concurrently in
the same process via run_in_executor. When any agent was interrupted
(user sends a follow-up message), the global flag killed ALL agents'
running tools — terminal commands, browser ops, web requests — across
all sessions.
Changes:
- tools/interrupt.py: Replace single threading.Event with a set of
interrupted thread IDs. set_interrupt() targets a specific thread;
is_interrupted() checks the current thread. Includes a backward-
compatible _ThreadAwareEventProxy for legacy _interrupt_event usage.
- run_agent.py: Store execution thread ID at start of run_conversation().
interrupt() and clear_interrupt() pass it to set_interrupt() so only
this agent's thread is affected.
- tools/code_execution_tool.py: Use is_interrupted() instead of
directly checking _interrupt_event.is_set().
- tools/process_registry.py: Same — use is_interrupted().
- tests: Update interrupt tests for per-thread semantics. Add new
TestPerThreadInterruptIsolation with two tests verifying cross-thread
isolation.
Models that do not use <think> tags (e.g. GLM-4.7 on NVIDIA Build,
minimax) may return content=None or empty string when truncated. The
previous _thinking_exhausted check treated any None/empty content as
thinking-budget exhaustion, causing these models to always show the
'Thinking Budget Exhausted' error instead of attempting continuation.
Fix: gate the exhaustion check on _has_think_tags — only trigger the
exhaustion path when the model actually produced reasoning blocks
(<think>, <thinking>, <reasoning>, <REASONING_SCRATCHPAD>). Models
without think tags now fall through to the normal continuation retry
logic (up to 3 attempts).
Fixes#7729
When API routers rewrite finish_reason from "length" to "tool_calls",
truncated JSON arguments bypassed the length handler and wasted 3
retry attempts in the generic JSON validation loop. Now detects
truncation patterns in tool call arguments regardless of finish_reason.
Fixes#7680
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Two-phase design so the warning fires before the user's first message
on every platform:
Phase 1 (__init__):
_check_compression_model_feasibility() runs during agent construction.
Resolves the auxiliary compression model (same chain as call_llm with
task='compression'), compares its context length to the main model's
compression threshold. If too small, emits via _emit_status() (prints
for CLI) and stores the warning in _compression_warning.
Phase 2 (run_conversation, first call):
_replay_compression_warning() re-sends the stored warning through
status_callback — which the gateway wires AFTER construction. The
warning is then cleared so it only fires once.
This ensures:
- CLI users see the warning immediately at startup (right after the
context limit line)
- Gateway users (Telegram, Discord, Slack, WhatsApp, Signal, Matrix,
Mattermost, Home Assistant, DingTalk, etc.) receive it via
status_callback('lifecycle', ...) on their first message
- logger.warning() always hits agent.log regardless of platform
Also warns when no auxiliary LLM provider is configured at all.
Entire check wrapped in try/except — never blocks startup.
11 tests covering: core warning logic, boundary conditions, exception
safety, two-phase store+replay, gateway callback wiring, and
single-delivery guarantee.
Matrix gateway: fix sync loop never dispatching events (#5819)
- _sync_loop() called client.sync() but never called handle_sync()
to dispatch events to registered callbacks — _on_room_message was
registered but never fired for new messages
- Store next_batch token from initial sync and pass as since= to
subsequent incremental syncs (was doing full initial sync every time)
- 17 comments, confirmed by multiple users on matrix.org
Feishu docs: add interactive card configuration for approvals (#6893)
- Error 200340 is a Feishu Developer Console configuration issue,
not a code bug — users need to enable Interactive Card capability
and configure Card Request URL
- Added required 3-step setup instructions to feishu.md
- Added troubleshooting entry for error 200340
- 17 comments from Feishu users
Copilot provider drift: detect GPT-5.x Responses API requirement (#3388)
- GPT-5.x models are rejected on /v1/chat/completions by both OpenAI
and OpenRouter (unsupported_api_for_model error)
- Added _model_requires_responses_api() to detect models needing
Responses API regardless of provider
- Applied in __init__ (covers OpenRouter primary users) and in
_try_activate_fallback() (covers Copilot->OpenRouter drift)
- Fixed stale comment claiming gateway creates fresh agents per message
(it caches them via _agent_cache since the caching was added)
- 7 comments, reported on Copilot+Telegram gateway
Based on PR #7285 by @kshitijk4poor.
Two bugs affecting Qwen OAuth users:
1. Wrong context window — qwen3-coder-plus showed 128K instead of 1M.
Added specific entries before the generic qwen catch-all:
- qwen3-coder-plus: 1,000,000 (corrected from PR's 1,048,576 per
official Alibaba Cloud docs and OpenRouter)
- qwen3-coder: 262,144
2. Random stopping — max_tokens was suppressed for Qwen Portal, so the
server applied its own low default. Reasoning models exhaust that on
thinking tokens. Now: honor explicit max_tokens, default to 65536
when unset.
Co-authored-by: kshitijk4poor <82637225+kshitijk4poor@users.noreply.github.com>
Aligns MiniMax provider with official API documentation. Fixes 6 bugs:
transport mismatch (openai_chat -> anthropic_messages), credential leak
in switch_model(), prompt caching sent to non-Anthropic endpoints,
dot-to-hyphen model name corruption, trajectory compressor URL routing,
and stale doctor health check.
Also corrects context window (204,800), thinking support (manual mode),
max output (131,072), and model catalog (M2 family only on /anthropic).
Source: https://platform.minimax.io/docs/api-reference/text-anthropic-api
Co-authored-by: kshitijk4poor <kshitijk4poor@users.noreply.github.com>
The pre_llm_call plugin hook receives session_id, user_message,
conversation_history, is_first_turn, model, and platform — but not
the sender's user_id. This means plugins cannot perform per-user
access control (e.g. restricting knowledge base recall to authorized
users).
The gateway already passes source.user_id as user_id to AIAgent,
which stores it in self._user_id. This change forwards it as
sender_id in the pre_llm_call kwargs so plugins can use it for
ACL decisions.
For CLI sessions where no user_id exists, sender_id defaults to
empty string. Plugins can treat empty sender_id as a trusted local
call (the owner is at the terminal) or deny it depending on their
ACL policy.
_is_oauth_token() returned True for any key not starting with 'sk-ant-api',
which means MiniMax and Alibaba API keys were falsely treated as Anthropic
OAuth tokens. This triggered the Claude Code compatibility path:
- All tool names prefixed with mcp_ (e.g. mcp_terminal, mcp_web_search)
- System prompt injected with 'You are Claude Code' identity
- 'Hermes Agent' replaced with 'Claude Code' throughout
Fix: Make _is_oauth_token() positively identify Anthropic OAuth tokens by
their key format instead of using a broad catch-all:
- sk-ant-* (but not sk-ant-api-*) -> setup tokens, managed keys
- eyJ* -> JWTs from Anthropic OAuth flow
- Everything else -> False (MiniMax, Alibaba, etc.)
Reported by stefan171.
- Remove auto-activation: when context.engine is 'compressor' (default),
plugin-registered engines are NOT used. Users must explicitly set
context.engine to a plugin name to activate it.
- Add curses_radiolist() to curses_ui.py: single-select radio picker
with keyboard nav + text fallback, matching curses_checklist pattern.
- Rewrite cmd_toggle() as composite plugins UI:
Top section: general plugins with checkboxes (existing behavior)
Bottom section: provider plugin categories (Memory Provider, Context Engine)
with current selection shown inline. ENTER/SPACE on a category opens
a radiolist sub-screen for single-select configuration.
- Add provider discovery helpers: _discover_memory_providers(),
_discover_context_engines(), config read/save for memory.provider
and context.engine.
- Add tests: radiolist non-TTY fallback, provider config save/load,
discovery error handling, auto-activation removal verification.
Follow-up fixes for the context engine plugin slot (PR #5700):
- Enhance ContextEngine ABC: add threshold_percent, protect_first_n,
protect_last_n as class attributes; complete update_model() default
with threshold recalculation; clarify on_session_end() lifecycle docs
- Add ContextCompressor.update_model() override for model/provider/
base_url/api_key updates
- Replace all direct compressor internal access in run_agent.py with
ABC interface: switch_model(), fallback restore, context probing
all use update_model() now; _context_probed guarded with getattr/
hasattr for plugin engine compatibility
- Create plugins/context_engine/ directory with discovery module
(mirrors plugins/memory/ pattern) — discover_context_engines(),
load_context_engine()
- Add context.engine config key to DEFAULT_CONFIG (default: compressor)
- Config-driven engine selection in run_agent.__init__: checks config,
then plugins/context_engine/<name>/, then general plugin system,
falls back to built-in ContextCompressor
- Wire on_session_end() in shutdown_memory_provider() at real session
boundaries (CLI exit, /reset, gateway expiry)
- PluginContext.register_context_engine() lets plugins replace the
built-in ContextCompressor with a custom ContextEngine implementation
- PluginManager stores the registered engine; only one allowed
- run_agent.py checks for a plugin engine at init before falling back
to the default ContextCompressor
- reset_session_state() now calls engine.on_session_reset() instead of
poking internal attributes directly
- ContextCompressor.on_session_reset() handles its own internals
(_context_probed, _previous_summary, etc.)
- 19 new tests covering ABC contract, defaults, plugin slot registration,
rejection of duplicates/non-engines, and compressor reset behavior
- All 34 existing compressor tests pass unchanged
When models return empty responses (no content, no tool calls, no
reasoning), Hermes previously retried 3 times silently then fell through
to '(empty)' — without ever trying the fallback provider chain. Users on
GLM-4.5-Air and similar models experienced what appeared to be a
complete hang, especially in gateway (Telegram/Discord) contexts where
the silent retries produced zero feedback.
Changes:
- After exhausting 3 empty retries, attempt _try_activate_fallback()
before giving up with '(empty)'. If fallback succeeds, reset retry
counter and continue the conversation loop with the new provider.
- Replace all _vprint() calls in recovery paths with _emit_status(),
which surfaces messages through both CLI (_vprint with force=True)
and gateway (status_callback -> adapter.send). Users now see:
* '⚠️ Empty response from model — retrying (N/3)' during retries
* '⚠️ Model returning empty responses — switching to fallback...'
* '↻ Switched to fallback: <model> (<provider>)' on success
* '❌ Model returned no content after all retries [and fallback]'
- Add logger.warning() throughout empty response paths for log file
visibility (model name, provider, retry counts).
- Upgrade _last_content_with_tools fallback from logger.debug to
logger.info + _emit_status so recovery is visible.
- Upgrade thinking-only prefill continuation to use _emit_status.
Tests:
- test_empty_response_triggers_fallback_provider: verifies fallback
activation after 3 empty retries produces content from fallback model
- test_empty_response_fallback_also_empty_returns_empty: verifies
graceful degradation when fallback also returns empty
- test_empty_response_emits_status_for_gateway: verifies _emit_status
is called during retries so gateway users see feedback
Addresses #7180.
Add a close() method to AIAgent that acts as a single entry point for
releasing all resources held by an agent instance. This prevents zombie
process accumulation on long-running gateway deployments by explicitly
cleaning up:
- Background processes tracked in ProcessRegistry
- Terminal sandbox environments
- Browser daemon sessions
- Active child agents (subagent delegation)
- OpenAI/httpx client connections
Each cleanup step is independently guarded so a failure in one does not
prevent the rest. The method is idempotent and safe to call multiple
times.
Also simplifies the background review cleanup to use close() instead
of manually closing the OpenAI client.
Ref: #7131
When _build_api_kwargs() throws an exception, the except handler in
the retry loop referenced api_kwargs before it was assigned. This
caused an UnboundLocalError that masked the real error, making
debugging impossible for the user.
Two _dump_api_request_debug() calls in the except block (non-retryable
client error path and max-retries-exhausted path) both accessed
api_kwargs without checking if it was assigned.
Fix: initialize api_kwargs = None before the retry loop and guard both
dump calls. Now the real error surfaces instead of the masking
UnboundLocalError.
Reported by Discord user gruman0.
`delegate_task` silently truncated batch tasks to 3 — the model sends
5 tasks, gets results for 3, never told 2 were dropped. Now returns a
clear tool_error explaining the limit and how to fix it.
The limit is configurable via:
- delegation.max_concurrent_children in config.yaml (priority 1)
- DELEGATION_MAX_CONCURRENT_CHILDREN env var (priority 2)
- default: 3
Uses the same _load_config() path as the rest of delegate_task for
consistent config priority. Clamps to min 1, warns on non-integer
config values.
Also removes the hardcoded maxItems: 3 from the JSON schema — the
schema was blocking the model from even attempting >3 tasks before
the runtime check could fire. The runtime check gives a much more
actionable error message.
Backwards compatible: default remains 3, existing configs unchanged.
When delegation.base_url routes subagents to a different endpoint, the
correct URL was passed through _resolve_delegation_credentials() and
_build_child_agent() into AIAgent.__init__(), but self.base_url could
fall out of sync with client_kwargs["base_url"] — the value the OpenAI
client actually uses.
This caused billing_base_url in session records to show the parent's
endpoint while actual API calls went to the correct delegation target.
Keep self.base_url in sync with client_kwargs after the credential
resolution block, matching the existing pattern for self.api_key.
Fixes#6825
Broaden the UnicodeEncodeError recovery to handle systems with ASCII-only
locale (LANG=C, Chromebooks) where ANY non-ASCII character causes encoding
failure, not just lone surrogates.
Changes:
- Add _strip_non_ascii() and _sanitize_messages_non_ascii() helpers that
strip all non-ASCII characters from message content, name, and tool_calls
- Update the UnicodeEncodeError handler to detect ASCII codec errors and
fall back to non-ASCII sanitization after surrogate check fails
- Sanitize tool_calls arguments and name fields (not just content)
- Fix bare .encode() in cli.py suspend handler to use explicit utf-8
- Add comprehensive test suite (17 tests)
When switching models at runtime, the config_context_length override
was not being passed to the new context compressor instance. This
meant the user-specified context length from config.yaml was lost
after a model switch.
- Store _config_context_length on AIAgent instance during __init__
- Pass _config_context_length when creating new ContextCompressor in switch_model
- Add test to verify config_context_length is preserved across model switches
Fixes: quando estamos alterando o modelo não está alterando o tamanho do contexto
Automated dead code audit using vulture + coverage.py + ast-grep intersection,
confirmed by Opus deep verification pass. Every symbol verified to have zero
production callers (test imports excluded from reachability analysis).
Removes ~1,534 lines of dead production code across 46 files and ~1,382 lines
of stale test code. 3 entire files deleted (agent/builtin_memory_provider.py,
hermes_cli/checklist.py, tests/hermes_cli/test_setup_model_selection.py).
Co-authored-by: alt-glitch <balyan.sid@gmail.com>
The _call_anthropic() streaming path never updated last_chunk_time during
the event loop — only once at stream start. The stale stream detector in
the outer poll loop uses this timer, so any Anthropic stream longer than
180s was killed even when events were actively arriving. This self-inflicted
a RemoteProtocolError that users saw as:
'⚠️ Connection to provider dropped (RemoteProtocolError). Reconnecting…'
The _call_chat_completions() path already updates last_chunk_time on every
chunk (line 4475). This brings _call_anthropic() to parity.
Also adds deltas_were_sent tracking to the Anthropic text_delta path so
the retry loop knows not to retry after partial delivery (prevents
duplicated output on connection drops mid-stream).
Reported-by: Discord users (Castellani, Codename_11)
The hardcoded User-Agent 'KimiCLI/1.3' is outdated — Kimi CLI is now at
v1.30.0. The stale version string causes intermittent 403 errors from
Kimi's coding endpoint ('only available for Coding Agents').
Update all 8 occurrences across run_agent.py, auxiliary_client.py, and
doctor.py to 'KimiCLI/1.30.0' to match the current official Kimi CLI.
Extends the /fast command to support Anthropic's Fast Mode beta in addition
to OpenAI Priority Processing. When enabled on Claude Opus 4.6, adds
speed:"fast" and the fast-mode-2026-02-01 beta header to API requests for
~2.5x faster output token throughput.
Changes:
- hermes_cli/models.py: Add _ANTHROPIC_FAST_MODE_MODELS registry,
model_supports_fast_mode() now recognizes Claude Opus 4.6,
resolve_fast_mode_overrides() returns {speed: fast} for Anthropic
vs {service_tier: priority} for OpenAI
- agent/anthropic_adapter.py: Add _FAST_MODE_BETA constant,
build_anthropic_kwargs() accepts fast_mode=True which injects
speed:fast + beta header via extra_headers (skipped for third-party
Anthropic-compatible endpoints like MiniMax)
- run_agent.py: Pass fast_mode to build_anthropic_kwargs in the
anthropic_messages path of _build_api_kwargs()
- cli.py: Update _handle_fast_command with provider-aware messaging
(shows 'Anthropic Fast Mode' vs 'Priority Processing')
- hermes_cli/commands.py: Update /fast description to mention both
providers
- tests: 13 new tests covering Anthropic model detection, override
resolution, CLI availability, routing, adapter kwargs, and
third-party endpoint safety
After mid-loop compression (triggered by 413, context_overflow, or Anthropic
long-context tier errors), _compress_context() creates a new session in SQLite
and resets _last_flushed_db_idx=0. However, conversation_history was not cleared,
so _flush_messages_to_session_db() computed:
flush_from = max(len(conversation_history=200), _last_flushed_db_idx=0) = 200
messages[200:] → empty (compressed messages < 200)
This resulted in zero messages being written to the new session's SQLite store.
On resume, the user would see 'Session found but has no messages.'
The preflight compression path (line 7311) already had the fix:
conversation_history = None
This commit adds the same clearing to the three mid-loop compression sites:
- Anthropic long-context tier overflow
- HTTP 413 payload too large
- Generic context_overflow error
Reported by Aaryan (Nous community).
