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).
Raise the default httpx stream read timeout from 60s to 120s for all
providers. Additionally, auto-detect local LLM endpoints (Ollama,
llama.cpp, vLLM) and raise the read timeout to HERMES_API_TIMEOUT
(1800s) since local models can take minutes for prefill on large
contexts before producing the first token.
The stale stream timeout already had this local auto-detection pattern;
the httpx read timeout was missing it — causing a hard 60s wall that
users couldn't find (HERMES_STREAM_READ_TIMEOUT was undocumented).
Changes:
- Default HERMES_STREAM_READ_TIMEOUT: 60s -> 120s
- Auto-detect local endpoints -> raise to 1800s (user override respected)
- Document HERMES_STREAM_READ_TIMEOUT and HERMES_STREAM_STALE_TIMEOUT
- Add 10 parametrized tests
Reported-by: Pavan Srinivas (@pavanandums)
When the model mentions <think> as literal text in its response (e.g.
"(/think not producing <think> tags)"), the streaming display treated it
as a reasoning block opener and suppressed everything after it. The
response box would close with truncated content and no error — the API
response was complete but the display ate it.
Root cause: _stream_delta() matched <think> anywhere in the text stream
regardless of position. Real reasoning blocks always start at the
beginning of a line; mentions in prose appear mid-sentence.
Fix: track line position across streaming deltas with a
_stream_last_was_newline flag. Only enter reasoning suppression when
the tag appears at a block boundary (start of stream, after a newline,
or after only whitespace on the current line). Add a _flush_stream()
safety net that recovers buffered content if no closing tag is found
by end-of-stream.
Also fixes three related issues discovered during investigation:
- anthropic_adapter: _get_anthropic_max_output() now normalizes dots to
hyphens so 'claude-opus-4.6' matches the 'claude-opus-4-6' table key
(was returning 32K instead of 128K)
- run_agent: send explicit max_tokens for Claude models on Nous Portal,
same as OpenRouter — both proxy to Anthropic's API which requires it.
Without it the backend defaults to a low limit that truncates responses.
- run_agent: reset truncated_tool_call_retries after successful tool
execution so a single truncation doesn't poison the entire conversation.
Previously /fast only supported gpt-5.4 and forced a provider switch to
openai-codex. Now supports all 13 models from OpenAI's Priority Processing
pricing table (gpt-5.4, gpt-5.4-mini, gpt-5.2, gpt-5.1, gpt-5, gpt-5-mini,
gpt-4.1, gpt-4.1-mini, gpt-4.1-nano, gpt-4o, gpt-4o-mini, o3, o4-mini).
Key changes:
- Replaced _FAST_MODE_BACKEND_CONFIG with _PRIORITY_PROCESSING_MODELS frozenset
- Removed provider-forcing logic — service_tier is now injected into whatever
API path the user is already on (Codex Responses, Chat Completions, or
OpenRouter passthrough)
- Added request_overrides support to chat_completions path in run_agent.py
- Updated messaging from 'Codex inference tier' to 'Priority Processing'
- Expanded test coverage for all supported models
Add /fast slash command to toggle OpenAI Codex service_tier between
normal and priority ('fast') inference. Only exposed for models
registered in _FAST_MODE_BACKEND_CONFIG (currently gpt-5.4).
- Registry-based backend config for extensibility
- Dynamic command visibility (hidden from help/autocomplete for
non-supported models) via command_filter on SlashCommandCompleter
- service_tier flows through request_overrides from route resolution
- Omit max_output_tokens for Codex backend (rejects it)
- Persists to config.yaml under agent.service_tier
Salvage cleanup: removed simple_term_menu/input() menu (banned),
bare /fast now shows status like /reasoning. Removed redundant
override resolution in _build_api_kwargs — single source of truth
via request_overrides from route.
Co-authored-by: Hermes Agent <hermes@nousresearch.com>
When OpenRouter returns 'No endpoints found that support tool use'
(HTTP 404), display a hint explaining that provider routing restrictions
may be filtering out tool-capable providers. Links the user directly
to the model's OpenRouter page to check which providers support tools.
The hint fires in the error display block that runs regardless of whether
fallback succeeds — so the user always understands WHY the model failed,
not just that it fell back.
Reported via Discord: GLM-5.1 on OpenRouter with US-based provider
restrictions eliminated all 4 tool-supporting endpoints (DeepInfra,
Z.AI, Friendli, Venice), leaving only 7 non-tool providers.
When a streaming response is cut mid-tool-call (connection drop, timeout),
the accumulated function.arguments is invalid JSON. The mock response
builder defaulted finish_reason to 'stop', so the agent loop treated it
as a valid completed turn and tried to execute tools with broken args.
Fix: validate tool call arguments with json.loads() during mock response
reconstruction. If any are invalid JSON, override finish_reason to
'length'. In the main loop's length handler, if tool calls are present,
refuse to execute and return partial=True with a clear error instead of
silently failing or wasting retries.
Also fixes _thinking_exhausted to not short-circuit when tool calls are
present — truncated tool calls are not thinking exhaustion.
Original cherry-picked from PR #6776 by AIandI0x1.
Closes#6638.
When the API returns "max_tokens too large given prompt" (input tokens
are within the context window, but input + requested output > window),
the old code incorrectly routed through the same handler as "prompt too
long" errors, calling get_next_probe_tier() and permanently halving
context_length. This made things worse: the window was fine, only the
requested output size needed trimming for that one call.
Two distinct error classes now handled separately:
Prompt too long — input itself exceeds context window.
Fix: compress history + halve context_length (existing behaviour,
unchanged).
Output cap too large — input OK, but input + max_tokens > window.
Fix: parse available_tokens from the error message, set a one-shot
_ephemeral_max_output_tokens override for the retry, and leave
context_length completely untouched.
Changes:
- agent/model_metadata.py: add parse_available_output_tokens_from_error()
that detects Anthropic's "available_tokens: N" error format and returns
the available output budget, or None for all other error types.
- run_agent.py: call the new parser first in the is_context_length_error
block; if it fires, set _ephemeral_max_output_tokens (with a 64-token
safety margin) and break to retry without touching context_length.
_build_api_kwargs consumes the ephemeral value exactly once then clears
it so subsequent calls use self.max_tokens normally.
- agent/anthropic_adapter.py: expand build_anthropic_kwargs docstring to
clearly document the max_tokens (output cap) vs context_length (total
window) distinction, which is a persistent source of confusion due to
the OpenAI-inherited "max_tokens" name.
- cli-config.yaml.example: add inline comments explaining both keys side
by side where users are most likely to look.
- website/docs/integrations/providers.md: add a callout box at the top
of "Context Length Detection" and clarify the troubleshooting entry.
- tests/test_ctx_halving_fix.py: 24 tests across four classes covering
the parser, build_anthropic_kwargs clamping, ephemeral one-shot
consumption, and the invariant that context_length is never mutated
on output-cap errors.
When _try_activate_fallback() swaps to a new provider (e.g.
kimi-coding), resolve_provider_client() correctly injects
provider-specific default_headers (like KimiCLI User-Agent) into the
returned OpenAI client. However, _client_kwargs was saved with only
api_key and base_url, dropping those headers.
Every subsequent API call rebuilds the client from _client_kwargs via
_create_request_openai_client(), producing a bare OpenAI client without
the required headers. Kimi Coding rejects this with 403; Copilot would
lose its auth headers similarly.
This patch reads _custom_headers from the fallback client (where the
OpenAI SDK stores the default_headers kwarg) and includes them in
_client_kwargs so any client rebuild preserves provider-specific headers.
Fixes#6075
Every turn now logs WHY the agent loop ended to agent.log with a
structured INFO line capturing: exit reason, model, api_calls/max,
budget usage, tool turn count, last message role, response length,
and session ID.
When the last message is a tool result and the turn was NOT
interrupted, emits WARNING level (visible in errors.log) — this is
the 'just stops' scenario users report where a tool call completes
but no continuation or final response follows.
10 tracked exit reasons: text_response, interrupted_by_user,
interrupted_during_api_call, budget_exhausted, max_iterations_reached,
all_retries_exhausted_no_response, fallback_prior_turn_content,
empty_response_exhausted, error_near_max_iterations, unknown.
Parse x-ratelimit-* headers from inference API responses (Nous Portal,
OpenRouter, OpenAI-compatible) and display them in the /usage command.
- New agent/rate_limit_tracker.py: parse 12 rate limit headers (RPM/RPH/
TPM/TPH limits, remaining, reset timers), format as progress bars (CLI)
or compact one-liner (gateway)
- Hook into streaming path in run_agent.py: stream.response.headers is
available on the OpenAI SDK Stream object before chunks are consumed
- CLI /usage: appends rate limit section with progress bars + warnings
when any bucket exceeds 80%
- Gateway /usage: appends compact rate limit summary
- 24 unit tests covering parsing, formatting, edge cases
Headers captured per response:
x-ratelimit-{limit,remaining,reset}-{requests,tokens}{,-1h}
Example CLI display:
Nous Rate Limits (captured just now):
Requests/min [░░░░░░░░░░░░░░░░░░░░] 0.1% 1/800 used (799 left, resets in 59s)
Tokens/hr [░░░░░░░░░░░░░░░░░░░░] 0.0% 49/336.0M (336.0M left, resets in 52m)
When a model returns no content, no structured reasoning, and no tool
calls (common with open models), the agent now silently retries up to
3 times before falling through to (empty).
Silent retry (no synthetic messages) keeps the conversation history
clean, preserves prompt caching, and respects the no-synthetic-user-
injection invariant. Most empty responses from open models are
transient (provider hiccups, rate limits, sampling flukes) so a
simple retry is sufficient.
This fills the last gap in the empty-response recovery chain:
1. _last_content_with_tools fallback (prior tool turn had content)
2. Thinking-only prefill continuation (#5931 — structured reasoning)
3. Empty response silent retry (NEW — truly empty, no reasoning)
4. (empty) terminal (last resort after all retries exhausted)
Inline <think> blocks are excluded — the model chose to reason, it
just produced no visible text. That differs from truly empty.
Tests:
- Updated test_truly_empty to expect 4 API calls (1 + 3 retries)
- Added test_truly_empty_response_succeeds_on_nudge
`_cleanup_task_resources` was unconditionally calling `cleanup_vm()` at
the end of every `run_conversation` (i.e. every user turn), tearing down
the docker/daytona/modal sandbox container regardless of its
`persistent_filesystem` setting. This contradicted the documented intent
of `terminal.lifetime_seconds` (idle reaper) and `container_persistent`,
and caused per-turn loss of `/workspace`, `~/.config`, agent CLI auth
state, and any other content living inside the sandbox.
The unconditional teardown was introduced in fbd3a2fd ("prevent leakage
of morph instances between tasks", 2025-11-04) to plug a Morph backend
leak, two days after `lifetime_seconds` shipped in faecbddd. It was
later refactored into `_cleanup_task_resources` in 70dd3a16 without
changing semantics. Code and docs have disagreed since.
Fix: introduce `terminal_tool.is_persistent_env(task_id)` and skip the
per-turn `cleanup_vm` when the active env is persistent. The idle reaper
(`_cleanup_inactive_envs`) still tears persistent envs down once
`terminal.lifetime_seconds` is exceeded. Non-persistent backends (Morph)
are unchanged — still torn down per turn, preserving the original
leak-prevention intent.
Combines the approaches from PR #6309 (duan78) and PR #5963 (KUSH42):
Tiered warnings (from #5963):
- Replaces boolean _context_pressure_warned with float _context_pressure_warned_at
- Fires at 85% (orange) and re-fires at 95% (red/critical)
- Adds 'compacting context...' status message before compression
Gateway dedup (from #6309):
- Class-level dict _context_pressure_last_warned survives across AIAgent
instances (gateway creates a new instance per message)
- 5-minute cooldown per session prevents warning spam
- Higher-tier warnings bypass the cooldown (85% → 95% always fires)
- Compression reset clears the dedup entry for the session
- Stale entries evicted (older than 2x cooldown) to prevent memory leak
Does NOT inject into messages — purely user-facing via _safe_print (CLI)
and status_callback (gateway). Zero prompt cache impact.
Fixes#6309. Fixes#5963.
Three targeted improvements to the compression system:
1. Replace hardcoded truncation limits with named class constants
(_CONTENT_MAX=6000, _CONTENT_HEAD=4000, _CONTENT_TAIL=1500,
_TOOL_ARGS_MAX=1500, _TOOL_ARGS_HEAD=1200). Previous limits
(3000/500) heavily truncated the summarizer's input — a 200-line
edit got cut to 3000 chars before the summarizer ever saw it.
2. Add '## Tools & Patterns' section to both compression prompt
templates (first-pass and iterative). Preserves working tool
invocations, preferred flags, and tool-specific discoveries
across compaction boundaries.
3. Warn users on 2nd+ compression: 'Session compressed N times —
accuracy may degrade. Consider /new to start fresh.'
Ref #499
Local inference providers (Ollama, oMLX, llama-cpp) can take 300+ seconds
for prefill on large contexts. The 180s stale stream detector was killing
these connections while the provider was still processing.
Uses the existing is_local_endpoint() (proper URL parsing with RFC-1918,
localhost, WSL detection) instead of ad-hoc substring matching. The stale
timeout is only disabled when the user hasn't explicitly set
HERMES_STREAM_STALE_TIMEOUT — explicit user config is always honored.
Fixes#5889
The _call_llm() and direct OpenAI fallback paths in flush_memories() both
hardcoded timeout=30.0, ignoring the user-configurable value at
auxiliary.flush_memories.timeout in config.yaml.
Remove the explicit timeout from the auxiliary _call_llm() call so that
_get_task_timeout('flush_memories') reads from config. For the direct
OpenAI fallback, import and use _get_task_timeout() instead of the
hardcoded value.
Add two regression tests verifying both code paths respect the config.
Fixes#6154
Two linked fixes for MiniMax Anthropic-compatible fallback:
1. Normalize httpx.URL to str before calling .rstrip() in auth/provider
detection helpers. Some client objects expose base_url as httpx.URL,
not str — crashed with AttributeError in _requires_bearer_auth() and
_is_third_party_anthropic_endpoint(). Also fixes _try_activate_fallback()
to use the already-stringified fb_base_url instead of raw httpx.URL.
2. Strip Anthropic-proprietary thinking block signatures when targeting
third-party Anthropic-compatible endpoints (MiniMax, Azure AI Foundry,
self-hosted proxies). These endpoints cannot validate Anthropic's
signatures and reject them with HTTP 400 'Invalid signature in
thinking block'. Now threads base_url through convert_messages_to_anthropic()
→ build_anthropic_kwargs() so signature management is endpoint-aware.
Based on PR #4945 by kshitijk4poor (rstrip fix).
Fixes#4944.
Based on #6079 by @tunamitom with critical fixes and comprehensive tests.
Changes from #6079:
- Fix: sanitization overwrite bug — Qwen message prep now runs AFTER codex
field sanitization, not before (was silently discarding Qwen transforms)
- Fix: missing try/except AuthError in runtime_provider.py — stale Qwen
credentials now fall through to next provider on auto-detect
- Fix: 'qwen' alias conflict — bare 'qwen' stays mapped to 'alibaba'
(DashScope); use 'qwen-portal' or 'qwen-cli' for the OAuth provider
- Fix: hardcoded ['coder-model'] replaced with live API fetch + curated
fallback list (qwen3-coder-plus, qwen3-coder)
- Fix: extract _is_qwen_portal() helper + _qwen_portal_headers() to replace
5 inline 'portal.qwen.ai' string checks and share headers between init
and credential swap
- Fix: add Qwen branch to _apply_client_headers_for_base_url for mid-session
credential swaps
- Fix: remove suspicious TypeError catch blocks around _prompt_provider_choice
- Fix: handle bare string items in content lists (were silently dropped)
- Fix: remove redundant dict() copies after deepcopy in message prep
- Revert: unrelated ai-gateway test mock removal and model_switch.py comment deletion
New tests (30 test functions):
- _qwen_cli_auth_path, _read_qwen_cli_tokens (success + 3 error paths)
- _save_qwen_cli_tokens (roundtrip, parent creation, permissions)
- _qwen_access_token_is_expiring (5 edge cases: fresh, expired, within skew,
None, non-numeric)
- _refresh_qwen_cli_tokens (success, preserve old refresh, 4 error paths,
default expires_in, disk persistence)
- resolve_qwen_runtime_credentials (fresh, auto-refresh, force-refresh,
missing token, env override)
- get_qwen_auth_status (logged in, not logged in)
- Runtime provider resolution (direct, pool entry, alias)
- _build_api_kwargs (metadata, vl_high_resolution_images, message formatting,
max_tokens suppression)
Anthropic signs thinking blocks against the full turn content. Any
upstream mutation (context compression, session truncation, orphan
stripping, message merging) invalidates the signature, causing HTTP 400
'Invalid signature in thinking block' — especially in long-lived
gateway sessions.
Strategy (following clawdbot/OpenClaw pattern):
1. Strip thinking/redacted_thinking from all assistant messages EXCEPT
the last one — preserves reasoning continuity on the current
tool-use chain while avoiding stale signature errors on older turns.
2. Downgrade unsigned thinking blocks to plain text — Anthropic can't
validate them, but the reasoning content is preserved.
3. Strip cache_control from thinking/redacted_thinking blocks to
prevent cache markers from interfering with signature validation.
4. Drop thinking blocks from the second message when merging
consecutive assistant messages (role alternation enforcement).
5. Error recovery: on HTTP 400 mentioning 'signature' and 'thinking',
strip all reasoning_details from the conversation and retry once.
This is the safety net for edge cases the proactive stripping
misses.
Addresses the issue reported in PR #6086 by @mingginwan while
preserving reasoning continuity (their PR stripped ALL thinking
blocks unconditionally).
Files changed:
- agent/anthropic_adapter.py: thinking block management in
convert_messages_to_anthropic (strip old turns, downgrade unsigned,
strip cache_control, merge-time strip)
- run_agent.py: one-shot signature error recovery in retry loop
- tests/test_anthropic_adapter.py: 10 new tests covering all cases
Salvaged fixes from community PRs:
- fix(model_switch): _read_auth_store → _load_auth_store + fix auth store
key lookup (was checking top-level dict instead of store['providers']).
OAuth providers now correctly detected in /model picker.
Cherry-picked from PR #5911 by Xule Lin (linxule).
- fix(ollama): pass num_ctx to override 2048 default context window.
Ollama defaults to 2048 context regardless of model capabilities. Now
auto-detects from /api/show metadata and injects num_ctx into every
request. Config override via model.ollama_num_ctx. Fixes#2708.
Cherry-picked from PR #5929 by kshitij (kshitijk4poor).
- fix(aux): normalize provider aliases for vision/auxiliary routing.
Adds _normalize_aux_provider() with 17 aliases (google→gemini,
claude→anthropic, glm→zai, etc). Fixes vision routing failure when
provider is set to 'google' instead of 'gemini'.
Cherry-picked from PR #5793 by e11i (Elizabeth1979).
- fix(aux): rewrite MiniMax /anthropic base URLs to /v1 for OpenAI SDK.
MiniMax's inference_base_url ends in /anthropic (Anthropic Messages API),
but auxiliary client uses OpenAI SDK which appends /chat/completions →
404 at /anthropic/chat/completions. Generic _to_openai_base_url() helper
rewrites terminal /anthropic to /v1 for OpenAI-compatible endpoint.
Inspired by PR #5786 by Lempkey.
Added debug logging to silent exception blocks across all fixes.
Co-authored-by: Hermes Agent <hermes@nousresearch.com>
The response validation stage unconditionally marked Codex Responses API
replies as invalid when response.output was empty, triggering unnecessary
retries and fallback chains. However, _normalize_codex_response can
recover from this state by synthesizing output from response.output_text.
Now the validation stage checks for output_text before marking the
response invalid, matching the normalization logic. Also fixes
logging.warning → logger.warning for consistency with the rest of the
file.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
When the model produces structured reasoning (via API fields like .reasoning,
.reasoning_content, .reasoning_details) but no visible text content, append
the assistant message as prefill and continue the loop. The model sees its own
reasoning context on the next turn and produces the text portion.
Inspired by clawdbot's 'incomplete-text' recovery pattern. Up to 2 prefill
attempts before falling through to the existing '(empty)' terminal.
Key design decisions:
- Only triggers for structured reasoning (API fields), NOT inline <think> tags
- Prefill messages are popped on success to maintain strict role alternation
- _thinking_prefill marker stripped from all API message building paths
- Works across all providers: OpenAI (continuation), Anthropic (native prefill)
Verified with E2E tests: simulated thinking-only → real OpenRouter continuation
produces correct content. Also confirmed Qwen models consistently produce
structured-reasoning-only responses under token pressure.
Memory plugins (Mem0, Honcho) used static identifiers ('hermes-user',
config peerName) meaning all gateway users shared the same memory bucket.
Changes:
- AIAgent.__init__: add user_id parameter, store as self._user_id
- run_agent.py: include user_id in _init_kwargs passed to memory providers
- gateway/run.py: pass source.user_id to AIAgent in primary + background paths
- Mem0 plugin: prefer kwargs user_id over config default
- Honcho plugin: override cfg.peer_name with gateway user_id when present
CLI sessions (user_id=None) preserve existing defaults. Only gateway
sessions with a real platform user_id get per-user memory scoping.
Reported by plev333.
Comprehensive cleanup across 80 files based on automated (ruff, pyflakes, vulture)
and manual analysis of the entire codebase.
Changes by category:
Unused imports removed (~95 across 55 files):
- Removed genuinely unused imports from all major subsystems
- agent/, hermes_cli/, tools/, gateway/, plugins/, cron/
- Includes imports in try/except blocks that were truly unused
(vs availability checks which were left alone)
Unused variables removed (~25):
- Removed dead variables: connected, inner, channels, last_exc,
source, new_server_names, verify, pconfig, default_terminal,
result, pending_handled, temperature, loop
- Dropped unused argparse subparser assignments in hermes_cli/main.py
(12 instances of add_parser() where result was never used)
Dead code removed:
- run_agent.py: Removed dead ternary (None if False else None) and
surrounding unreachable branch in identity fallback
- run_agent.py: Removed write-only attribute _last_reported_tool
- hermes_cli/providers.py: Removed dead @property decorator on
module-level function (decorator has no effect outside a class)
- gateway/run.py: Removed unused MCP config load before reconnect
- gateway/platforms/slack.py: Removed dead SessionSource construction
Undefined name bugs fixed (would cause NameError at runtime):
- batch_runner.py: Added missing logger = logging.getLogger(__name__)
- tools/environments/daytona.py: Added missing Dict and Path imports
Unnecessary global statements removed (14):
- tools/terminal_tool.py: 5 functions declared global for dicts
they only mutated via .pop()/[key]=value (no rebinding)
- tools/browser_tool.py: cleanup thread loop only reads flag
- tools/rl_training_tool.py: 4 functions only do dict mutations
- tools/mcp_oauth.py: only reads the global
- hermes_time.py: only reads cached values
Inefficient patterns fixed:
- startswith/endswith tuple form: 15 instances of
x.startswith('a') or x.startswith('b') consolidated to
x.startswith(('a', 'b'))
- len(x)==0 / len(x)>0: 13 instances replaced with pythonic
truthiness checks (not x / bool(x))
- in dict.keys(): 5 instances simplified to in dict
- Redefined unused name: removed duplicate _strip_mdv2 import in
send_message_tool.py
Other fixes:
- hermes_cli/doctor.py: Replaced undefined logger.debug() with pass
- hermes_cli/config.py: Consolidated chained .endswith() calls
Test results: 3934 passed, 17 failed (all pre-existing on main),
19 skipped. Zero regressions.
Two gaps in the codex empty-output handling:
1. _run_codex_create_stream_fallback() skipped all non-terminal events,
so when the fallback path was used and the terminal response had
empty output, there was no recovery. Now collects output_item.done
and text deltas during the fallback stream, backfills on empty output.
2. _normalize_codex_response() hard-crashed with RuntimeError when
output was empty, even when the response had output_text set. The
function already had fallback logic at line 3562 to use output_text,
but the guard at line 3446 killed it first. Now checks output_text
before raising and synthesizes a minimal output item.
Salvages the core fix from PR #5673 (egerev) onto current main.
The chatgpt.com/backend-api/codex endpoint streams valid output items
via response.output_item.done events, but the OpenAI SDK's
get_final_response() returns an empty output list. This caused every
Codex response to be rejected as invalid.
Fix: collect output_item.done events during streaming and backfill
response.output when get_final_response() returns empty. Falls back
to synthesizing from text deltas when no done events were received.
Also moves the synthesis logic from the validation loop (too late, from
#5681) into _run_codex_stream() (before the response leaves the
streaming function), and simplifies the validation to just log
diagnostics since recovery now happens upstream.
Co-authored-by: Egor <egerev@users.noreply.github.com>
Three bugs causing OpenAI Codex sessions to fail silently:
1. Credential pool vs legacy store disconnect: hermes auth and hermes
model store device_code tokens in the credential pool, but
get_codex_auth_status(), resolve_codex_runtime_credentials(), and
_model_flow_openai_codex() only read from the legacy provider state.
Fresh pool tokens were invisible to the auth status checks and model
selection flow.
2. _import_codex_cli_tokens() imported expired tokens from ~/.codex/
without checking JWT expiry. Combined with _login_openai_codex()
saying 'Login successful!' for expired credentials, users got stuck
in a loop of dead tokens being recycled.
3. _login_openai_codex() accepted expired tokens from
resolve_codex_runtime_credentials() without validating expiry before
telling the user login succeeded.
Fixes:
- get_codex_auth_status() now checks credential pool first, falls back
to legacy provider state
- _model_flow_openai_codex() uses pool-aware auth status for token
retrieval when fetching model lists
- _import_codex_cli_tokens() validates JWT exp claim, rejects expired
- _login_openai_codex() verifies resolved token isn't expiring before
accepting existing credentials
- _run_codex_stream() logs response.incomplete/failed terminal events
with status and incomplete_details for diagnostics
- Codex empty output recovery: captures streamed text during streaming
and synthesizes a response when get_final_response() returns empty
output (handles chatgpt.com backend-api edge cases)
Two fixes:
1. Replace all stale 'hermes login' references with 'hermes auth' across
auth.py, auxiliary_client.py, delegate_tool.py, config.py, run_agent.py,
and documentation. The 'hermes login' command was deprecated; 'hermes auth'
now handles OAuth credential management.
2. Fix credential removal not persisting for singleton-sourced credentials
(device_code for openai-codex/nous, hermes_pkce for anthropic).
auth_remove_command already cleared env vars for env-sourced credentials,
but singleton credentials stored in the auth store were re-seeded by
_seed_from_singletons() on the next load_pool() call. Now clears the
underlying auth store entry when removing singleton-sourced credentials.
When the Codex CLI (or VS Code extension) consumes a refresh token before
Hermes can use it, Hermes previously surfaced a generic 401 error with no
actionable guidance.
- In `refresh_codex_oauth_pure`: detect `refresh_token_reused` from the
OAuth endpoint and raise an AuthError explaining the cause and the exact
steps to recover (run `codex` to refresh, then `hermes login`).
- In `run_agent.py`: when provider is `openai-codex` and HTTP 401 is
received, show Codex-specific recovery steps instead of the generic
"check your API key" message.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When using xAI's API directly (base_url contains x.ai), send the
x-grok-conv-id header set to the Hermes session_id. This routes
consecutive requests to the same server, maximizing automatic
prompt cache hits.
Ref: https://docs.x.ai/developers/advanced-api-usage/prompt-caching
OpenCode Go model names with dots (minimax-m2.7, glm-4.5, kimi-k2.5)
were being mangled to hyphens (minimax-m2-7), causing HTTP 401 errors.
Two code paths were affected:
1. model_normalize.py: opencode-go was incorrectly in DOT_TO_HYPHEN_PROVIDERS
2. run_agent.py: _anthropic_preserve_dots() did not check for opencode-go
Fix:
- Remove opencode-go from _DOT_TO_HYPHEN_PROVIDERS (dots are correct for Go)
- Add opencode-go to _anthropic_preserve_dots() provider check
- Add opencode.ai/zen/go to base_url fallback check
- Add regression tests in tests/test_model_normalize.py
Co-authored-by: jacob3712 <jacob3712@users.noreply.github.com>
- Rename per-LLM-call hooks from pre_llm_request/post_llm_request for clarity vs pre_llm_call
- Emit summary kwargs only (counts, usage dict from normalize_usage); keep env_var_enabled for HERMES_DUMP_REQUESTS
- Add is_truthy_value/env_var_enabled to utils; wire hermes_cli.plugins._env_enabled through it
- Update Langfuse local setup doc; add scripts/langfuse_smoketest.py and optional ~/.hermes plugin tests
Made-with: Cursor
Consolidated salvage from PRs #5301 (qaqcvc), #5339 (lance0),
#5058 and #5098 (maymuneth).
Mem0 API v2 compatibility (#5301):
- All reads use filters={user_id: ...} instead of bare user_id= kwarg
- All writes use filters with user_id + agent_id for attribution
- Response unwrapping for v2 dict format {results: [...]}
- Split _read_filters() vs _write_filters() — reads are user-scoped
only for cross-session recall, writes include agent_id
- Preserved 'hermes-user' default (no breaking change for existing users)
- Omitted run_id scoping from #5301 — cross-session memory is Mem0's
core value, session-scoping reads would defeat that purpose
Memory prefetch context fencing (#5339):
- Wraps prefetched memory in <memory-context> fenced blocks with system
note marking content as recalled context, NOT user input
- Sanitizes provider output to strip fence-escape sequences, preventing
injection where memory content breaks out of the fence
- API-call-time only — never persisted to session history
Secret redaction (#5058, #5098):
- Added prefix patterns for Groq (gsk_), Matrix (syt_), RetainDB
(retaindb_), Hindsight (hsk-), Mem0 (mem0_), ByteRover (brv_)
Adds OPENAI_MODEL_EXECUTION_GUIDANCE — XML-tagged behavioral guidance
injected for GPT and Codex models alongside the existing tool-use
enforcement. Targets four specific failure modes:
- <tool_persistence>: retry on empty/partial results instead of giving up
- <prerequisite_checks>: do discovery/lookup before jumping to final action
- <verification>: check correctness/grounding/formatting before finalizing
- <missing_context>: use lookup tools instead of hallucinating
Follows the same injection pattern as GOOGLE_MODEL_OPERATIONAL_GUIDANCE
for Gemini/Gemma models. Inspired by OpenClaw PR #38953 and OpenAI's
GPT-5.4 prompting guide patterns.
Agent activity tracking:
- Add _last_activity_ts, _last_activity_desc, _current_tool to AIAgent
- Touch activity on: API call start/complete, tool start/complete,
first stream chunk, streaming request start
- Public get_activity_summary() method for external consumers
Gateway timeout diagnostics:
- Timeout message now includes what the agent was doing when killed:
actively working vs stuck on a tool vs waiting on API response
- Includes iteration count, last activity description, seconds since
last activity — users can distinguish legitimate long tasks from
genuine hangs
- 'Still working' notifications now show iteration count and current
tool instead of just elapsed time
- Stale lock eviction logs include agent activity state for debugging
Stream stale timeout:
- _emit_status when stale stream is detected (was log-only) — gateway
users now see 'No response from provider for Ns' with model and
context size
- Improved logger.warning with model name and estimated context size
Error path notifications (gateway-visible via _emit_status):
- Context compression attempts now use _emit_status (was _vprint only)
- Non-retryable client errors emit summary before aborting
- Max retry exhaustion emits error summary (was _vprint only)
- Rate limit exhaustion emits specific rate-limit message
These were all CLI-visible but silent to gateway users, which is why
people on Telegram/Discord saw generic 'request failed' messages
without explanation.
Subagent sessions spawned by delegate_task were created with
parent_session_id=NULL and source=cli, making them indistinguishable
from user sessions in hermes sessions list and /resume.
Changes:
- delegate_tool.py: pass parent_agent.session_id to child agent
- run_agent.py: accept parent_session_id param, pass to create_session
- hermes_state.py list_sessions_rich: filter parent_session_id IS NULL
by default (opt-in include_children=True for callers that need them)
- hermes_state.py delete_session: delete child sessions first (FK)
- hermes_state.py prune_sessions: delete children before parents (FK)
session_search already handles parent_session_id correctly — child
sessions are filtered from recent list and resolved to parent root
in full-text search results.
Fixes#5122
As the agent navigates into subdirectories via tool calls (read_file,
terminal, search_files, etc.), automatically discover and load project
context files (AGENTS.md, CLAUDE.md, .cursorrules) from those directories.
Previously, context files were only loaded from the CWD at session start.
If the agent moved into backend/, frontend/, or any subdirectory with its
own AGENTS.md, those instructions were never seen.
Now, SubdirectoryHintTracker watches tool call arguments for file paths
and shell commands, resolves directories, and loads hint files on first
access. Discovered hints are appended to the tool result so the model
gets relevant context at the moment it starts working in a new area —
without modifying the system prompt (preserving prompt caching).
Features:
- Extracts paths from tool args (path, workdir) and shell commands
- Loads AGENTS.md, CLAUDE.md, .cursorrules (first match per directory)
- Deduplicates — each directory loaded at most once per session
- Ignores paths outside the working directory
- Truncates large hint files at 8K chars
- Works on both sequential and concurrent tool execution paths
Inspired by Block/goose SubdirectoryHintTracker.
Add POST /v1/runs to start async agent runs and GET /v1/runs/{run_id}/events
for SSE streaming of typed lifecycle events (tool.started, tool.completed,
message.delta, reasoning.available, run.completed, run.failed).
Changes the internal tool_progress_callback signature from positional
(tool_name, preview, args) to event-type-first
(event_type, tool_name, preview, args, **kwargs). Existing consumers
filter on event_type and remain backward-compatible.
Adds concurrency limit (_MAX_CONCURRENT_RUNS=10) and orphaned run sweep.
Fixes logic inversion in cli.py _on_tool_progress where the original PR
would have displayed internal tools instead of non-internal ones.
Co-authored-by: Mibayy <mibayy@users.noreply.github.com>
Route AIAgent print output to stderr via _print_fn for ACP stdio sessions.
Gate quiet-mode spinner startup on _should_start_quiet_spinner() so JSON-RPC
on stdout isn't corrupted. Child agents inherit the redirect.
Co-authored-by: Git-on-my-level <Git-on-my-level@users.noreply.github.com>
* feat: coerce tool call arguments to match JSON Schema types
LLMs frequently return numbers as strings ("42" instead of 42) and
booleans as strings ("true" instead of true). This causes silent
failures with MCP tools and any tool with strictly-typed parameters.
Added coerce_tool_args() in model_tools.py that runs before every tool
dispatch. For each argument, it checks the tool registry schema and
attempts safe coercion:
- "42" → 42 when schema says "type": "integer"
- "3.14" → 3.14 when schema says "type": "number"
- "true"/"false" → True/False when schema says "type": "boolean"
- Union types tried in order
- Original values preserved when coercion fails or is not applicable
Inspired by Block/goose tool argument coercion system.
* fix: accept reasoning-only responses without retries — set content to "(empty)"
Previously, when a model returned reasoning/thinking but no visible
content, we entered a 120-line retry/classify/compress/salvage cascade
that wasted 3+ API calls trying to "fix" the response. The model was
done thinking — retrying with the same input just burned money.
Now reasoning-only responses are accepted immediately:
- Reasoning stays in the `reasoning` field (semantically correct)
- Content set to "(empty)" — valid non-empty string every provider accepts
- No retries, no compression triggers, no salvage logic
- Session history contains "(empty)" not "" — prevents #2128 session
poisoning where empty assistant content caused prefill rejections
Removes ~120 lines, adds ~15. Saves 2-3 API calls per reasoning-only
response. Fixes#2128.
- Add OLLAMA_API_KEY to credential resolution chain for ollama.com endpoints
- Update requested_provider/_explicit_api_key/_explicit_base_url after /model
switch so _ensure_runtime_credentials() doesn't revert the switch
- Pass base_url/api_key from fallback config to resolve_provider_client()
- Add DirectAlias system: user-configurable model_aliases in config.yaml
checked before catalog resolution, with reverse lookup by model ID
- Add /model tab completion showing aliases with provider metadata
Co-authored-by: LucidPaths <LucidPaths@users.noreply.github.com>
Previously, tool results exceeding 100K characters were silently chopped
with only a '[Truncated]' notice — the rest of the content was lost
permanently. The model had no way to access the truncated portion.
Now, oversized results are written to HERMES_HOME/cache/tool_responses/
and the model receives:
- A 1,500-char head preview for immediate context
- The file path so it can use read_file/search_files on the full output
This preserves the context window protection (inline content stays small)
while making the full data recoverable. Falls back to the old destructive
truncation if the file write fails.
Inspired by Block/goose's large response handler pattern.
Persist structured exhaustion metadata from provider errors, use explicit reset timestamps when available, and expose label-based credential targeting in the auth CLI. This keeps long-lived Codex cooldowns from being misreported as one-hour waits and avoids forcing operators to manage entries by list position alone.
Constraint: Existing credential pool JSON needs to remain backward compatible with stored entries that only record status code and timestamp
Constraint: Runtime recovery must keep the existing retry-then-rotate semantics for 429s while enriching pool state with provider metadata
Rejected: Add a separate credential scheduler subsystem | too large for the Hermes pool architecture and unnecessary for this fix
Rejected: Only change CLI formatting | would leave runtime rotation blind to resets_at and preserve the serial-failure behavior
Confidence: high
Scope-risk: moderate
Reversibility: clean
Directive: Preserve structured rate-limit metadata when new providers expose reset hints; do not collapse back to status-code-only exhaustion tracking
Tested: Focused pytest slice for auth commands, credential pool recovery, and routing (272 passed); py_compile on changed Python files; hermes -w auth list/remove smoke test with temporary HERMES_HOME
Not-tested: Full repository pytest suite, broader gateway/integration flows outside the touched auth and pool paths
Updates _sanitize_tool_calls_for_strict_api docstring to explicitly
mention Fireworks alongside Mistral as strict APIs requiring sanitization.
Also documents the specific fields that are stripped (call_id, response_item_id).
Replaces hardcoded Mistral check with the new _should_sanitize_tool_calls()
method. Updates comment to mention Fireworks alongside Mistral as strict
APIs requiring tool_call field sanitization.
Replaces hardcoded Mistral check with the new _should_sanitize_tool_calls()
method. Ensures summary generation works correctly with Fireworks and
other strict APIs that reject unknown tool_call fields.
Replaces hardcoded Mistral check with the new _should_sanitize_tool_calls()
method. This ensures tool_calls are sanitized for all strict APIs, not
just Mistral. Prevents 400 errors from Fireworks and other providers.
Adds a centralized method to determine when tool_calls need sanitization
for strict APIs. Returns True for all APIs except codex_responses mode.
This prevents 400 errors from providers like Fireworks that reject unknown
fields (call_id, response_item_id) in tool_calls.
Plugin context from pre_llm_call hooks was injected into the system
prompt, breaking the prompt cache prefix every turn when content
changed (typical for memory plugins). Now all plugin context goes
into the current turn's user message — the system prompt stays
identical across turns, preserving cached tokens.
The system prompt is reserved for Hermes internals. Plugins
contribute context alongside the user's input.
Also adds comprehensive documentation for all 6 plugin hooks:
pre_tool_call, post_tool_call, pre_llm_call, post_llm_call,
on_session_start, on_session_end — each with full callback
signatures, parameter tables, firing conditions, and examples.
Supersedes #5138 which identified the same cache-busting bug
and proposed an uncached system suffix approach. This fix goes
further by removing system prompt injection entirely.
Co-identified-by: OutThisLife (PR #5138)
When a skill is active, user_message contains the full SKILL.md content
injected by the skill system. This bloated string was being passed to
memory provider sync_all(), queue_prefetch_all(), and prefetch_all(),
causing providers with query size limits (e.g. Honcho's 10K char limit)
to fail.
Both call sites now use original_user_message (the clean user input,
already defined at line 6516) instead of the skill-inflated user_message:
- Pre-turn prefetch (line ~6695): prefetch_all() query
- Post-turn sync (line ~8672): sync_all() + queue_prefetch_all()
Fixes#4889
* fix(gateway): add message deduplication to Discord and Slack adapters (#4777)
Discord RESUME replays events after reconnects (~7/day observed),
and Slack Socket Mode can redeliver events if the ack was lost.
Neither adapter tracked which messages were already processed,
causing duplicate bot responses.
Add _seen_messages dedup cache (message ID → timestamp) with 5-min
TTL and 2000-entry cap to both adapters, matching the pattern already
used by Mattermost, Matrix, WeCom, Feishu, DingTalk, and Email.
The check goes at the very top of the message handler, before any
other logic, so replayed events are silently dropped.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: prevent duplicate messages on partial stream delivery
When streaming fails after tokens are already delivered to the platform,
_interruptible_streaming_api_call re-raised the error into the outer
retry loop, which would make a new API call — creating a duplicate
message.
Now checks deltas_were_sent before re-raising: if partial content was
already streamed, returns a stub response instead. The outer loop treats
the turn as complete (no retry, no fallback, no duplicate).
Inspired by PR #4871 (@trevorgordon981) which identified the bug.
This implementation avoids monkey-patching exception objects and keeps
the fix within the streaming call boundary.
---------
Co-authored-by: Mibayy <mibayy@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add a provider-agnostic role allowlist guard to _sanitize_api_messages()
that drops messages with roles not accepted by the chat-completions API
(e.g. session_meta). This prevents CLI resume/session restore from
leaking transcript-only metadata into the outgoing messages payload.
Two layers of defense:
1. API-boundary guard: _sanitize_api_messages() now filters messages by
role allowlist (system/user/assistant/tool/function/developer) before
the existing orphaned tool-call repair logic. This protects all
current and future call paths.
2. CLI restore defense-in-depth: Both session restore paths in cli.py
now strip session_meta entries before loading history into
conversation_history, matching the existing gateway behavior.
Closes#4715
Memory provider tools (hindsight_retain, honcho_search, etc.) were
advertised to the model via tool schemas but failed with 'Unknown tool'
at execution time. The concurrent path (_invoke_tool) correctly checks
self._memory_manager.has_tool() before falling through to the registry,
but the sequential path (_execute_tool_calls_sequential) was never
updated with this check. Since sequential is the default for single
tool calls, memory provider tools always hit the registry dispatcher
which returns 'Unknown tool' because they're not registered there.
Add the memory_manager dispatch check between the delegate_task handler
and the quiet_mode fallthrough in the sequential path, with proper
spinner/display handling to match the existing pattern.
Reported by KiBenderOP — all memory providers affected (Honcho,
Hindsight, Holographic, etc.).
Three fixes for long-running gateway sessions that enter a death spiral
when API disconnects prevent token data collection, which prevents
compression, which causes more disconnects:
Layer 1 — Stale token counter fallback (run_agent.py in-loop):
When last_prompt_tokens is 0 (stale after API disconnect or provider
returned no usage data), fall back to estimate_messages_tokens_rough()
instead of passing 0 to should_compress(), which would never fire.
Layer 2 — Server disconnect heuristic (run_agent.py error handler):
When ReadError/RemoteProtocolError hits a large session (>60% context
or >200 messages), treat it as a context-length error and trigger
compression rather than burning through retries that all fail the
same way.
Layer 3 — Hard message count limit (gateway/run.py hygiene):
Force compression when a session exceeds 400 messages, regardless of
token estimates. This catches runaway growth even when all token-based
checks fail due to missing API data.
Based on the analysis from PR #2157 by ygd58 — the gateway threshold
direction fix (1.4x multiplier) was already resolved on main.
Anthropic returns HTTP 429 'Extra usage is required for long context
requests' when a Claude Max subscription doesn't include the 1M context
tier. This is NOT a transient rate limit — retrying won't help.
Only applies to Sonnet models (Opus 1M is general access). Detects
this specific error before the generic rate-limit handler and:
1. Reduces context_length from 1M to 200k (the standard tier)
2. Triggers context compression to fit
3. Retries with the reduced context
The reduction is session-scoped (not persisted) so it auto-recovers
if the user later enables extra usage on their subscription.
Fixes: Sonnet 4.6 instant rate limits on Claude Max without extra usage
Anthropic returns HTTP 429 'Extra usage is required for long context
requests' when a Claude Max subscription doesn't include the 1M context
tier. This is NOT a transient rate limit — retrying won't help.
Detect this specific error before the generic rate-limit handler and:
1. Reduce context_length from 1M to 200k (the standard tier)
2. Trigger context compression to fit
3. Retry with the reduced context
The reduction is session-scoped (not persisted) so it auto-recovers
if the user later enables extra usage on their subscription.
Fixes: Sonnet 4.6 instant rate limits on Claude Max without extra usage
The step_callback previously only forwarded tool names as strings,
so build_tool_complete received result=None and ACP tool_call_update
events had empty content/rawOutput. Now prev_tools carries dicts with
both name and result by pairing each tool_call with its matching
tool-role message via tool_call_id.
