Claude Opus 4.7 introduced several breaking API changes that the current
codebase partially handled but not completely. This patch finishes the
migration per the official migration guide at
https://platform.claude.com/docs/en/about-claude/models/migration-guideFixesNousResearch/hermes-agent#11137
Breaking-change coverage:
1. Adaptive thinking + output_config.effort — 4.7 is now recognized by
_supports_adaptive_thinking() (extends previous 4.6-only gate).
2. Sampling parameter stripping — 4.7 returns 400 for any non-default
temperature / top_p / top_k. build_anthropic_kwargs drops them as a
safety net; the OpenAI-protocol auxiliary path (_build_call_kwargs)
and AnthropicCompletionsAdapter.create() both early-exit before
setting temperature for 4.7+ models. This keeps flush_memories and
structured-JSON aux paths that hardcode temperature from 400ing
when the aux model is flipped to 4.7.
3. thinking.display = "summarized" — 4.7 defaults display to "omitted",
which silently hides reasoning text from Hermes's CLI activity feed
during long tool runs. Restoring "summarized" preserves 4.6 UX.
4. Effort level mapping — xhigh now maps to xhigh (was xhigh→max, which
silently over-efforted every coding/agentic request). max is now a
distinct ceiling per Anthropic's 5-level effort model.
5. New stop_reason values — refusal and model_context_window_exceeded
were silently collapsed to "stop" (end_turn) by the adapter's
stop_reason_map. Now mapped to "content_filter" and "length"
respectively, matching upstream finish-reason handling already in
bedrock_adapter.
6. Model catalogs — claude-opus-4-7 added to the Anthropic provider
list, anthropic/claude-opus-4.7 added at top of OpenRouter fallback
catalog (recommended), claude-opus-4-7 added to model_metadata
DEFAULT_CONTEXT_LENGTHS (1M, matching 4.6 per migration guide).
7. Prefill docstrings — run_agent.AIAgent and BatchRunner now document
that Anthropic Sonnet/Opus 4.6+ reject a trailing assistant-role
prefill (400).
8. Tests — 4 new tests in test_anthropic_adapter covering display
default, xhigh preservation, max on 4.7, refusal / context-overflow
stop_reason mapping, plus the sampling-param predicate. test_model_metadata
accepts 4.7 at 1M context.
Tested on macOS 15.5 (darwin). 119 tests pass in
tests/agent/test_anthropic_adapter.py, 1320 pass in tests/agent/.
Shallow-copy client_kwargs at the top of _create_openai_client() to
prevent in-place mutation from leaking back into self._client_kwargs.
Defensive fix that locks the contract for future httpx/transport work.
Cherry-picked from #10978 by @taeuk178.
The gateway compression notifications were already removed in commit cc63b2d1
(PR #4139), but the agent-level context pressure warnings (85%/95% tiered
alerts via _emit_context_pressure) were still firing on both CLI and gateway.
Removed:
- _emit_context_pressure method and all call sites in run_conversation()
- Class-level dedup state (_context_pressure_last_warned, _CONTEXT_PRESSURE_COOLDOWN)
- Instance attribute _context_pressure_warned_at
- Pressure reset logic in _compress_context
- format_context_pressure and format_context_pressure_gateway from agent/display.py
- Orphaned ANSI constants that only served these functions
- tests/run_agent/test_context_pressure.py (all 361 lines)
Compression itself continues to run silently in the background.
Closes#3784
When a model returns an empty response after tool calls with no new
tool_calls in the follow-up turn, the code enters the "nudge" recovery
path which referenced `assistant_msg` before it was assigned. This
variable is only set in the tool-calls branch (line 10098), but the
nudge code lives in the no-tool-calls branch (line 10263+).
The fix builds a fresh assistant message dict via `_build_assistant_message()`
instead of reusing the unbound variable, consistent with the exhausted-
retries path at line 10457.
Three targeted fixes for the 'agent stuck on terminal command' report:
1. **Concurrent tool wait loop now checks interrupts** (run_agent.py)
The sequential path checked _interrupt_requested before each tool call,
but the concurrent path's wait loop just blocked with 30s timeouts.
Now polls every 5s and cancels pending futures on interrupt, giving
already-running tools 3s to notice the per-thread interrupt signal.
2. **Cancelled concurrent tools get proper interrupt messages** (run_agent.py)
When a concurrent tool is cancelled or didn't return a result due to
interrupt, the tool result message says 'skipped due to user interrupt'
instead of a generic error.
3. **Typing indicator fires before follow-up turn** (gateway/run.py)
After an interrupt is acknowledged and the pending message dequeued,
the gateway now sends a typing indicator before starting the recursive
_run_agent call. This gives the user immediate visual feedback that
the system is processing their new message (closing the perceived
'dead air' gap between the interrupt ack and the response).
Reported by @_SushantSays.
When a custom provider drops a connection mid-stream, the TCP socket
can enter CLOSE-WAIT and the httpx read timeout may never fire —
epoll_wait blocks indefinitely because no data or error signal arrives.
The agent hangs until manually killed.
The existing defenses (httpx read timeout, stale stream detector,
_force_close_tcp_sockets) are all time-based and work correctly once
triggered, but they rely on the socket layer reporting the dead
connection. Without TCP keepalives, the kernel has no reason to probe
a silent connection.
Fix: inject SO_KEEPALIVE + TCP_KEEPIDLE/KEEPINTVL/KEEPCNT into the
httpx transport via socket_options. The kernel probes idle connections
after 30s, retries every 10s, gives up after 3 failures — dead peer
detected within ~60s instead of hanging forever.
Platform-aware: uses TCP_KEEPIDLE on Linux, TCP_KEEPALIVE on macOS.
Falls back silently if socket options aren't available (Windows, etc.).
Closes#10324
Skins define waiting_faces, thinking_faces, and thinking_verbs in their
spinner config, but all 7 call sites in run_agent.py used hardcoded class
constants. Add three classmethods on KawaiiSpinner that query the active
skin first and fall back to the class constants, matching the existing
pattern used for wings/tool_prefix/tool_emojis.
Co-authored-by: nosleepcassette <nosleepcassette@users.noreply.github.com>
When a custom/Ollama provider is used and reasoning_effort is set to 'none'
(or enabled: false), inject 'think': false into the request extra_body.
Ollama does not recognise the OpenRouter-style 'reasoning' extra_body field,
so thinking-capable models (Qwen3, etc.) generate <think> blocks regardless
of the reasoning_effort setting. This produces empty-response errors that
corrupt session state.
The fix adds a provider-specific block in _build_api_kwargs() that sets
think=false in extra_body whenever self.provider == 'custom' and reasoning
is explicitly disabled.
Closes#3191
When no provider was set in config.yaml and auto-detection found no
credentials, the agent silently fell back to bare OPENROUTER_API_KEY
from the environment and sent the configured model name to OpenRouter.
This produced undefined behavior -- wrong provider, wrong model routing,
and auxiliary tasks (compression, vision) hitting the wrong endpoint.
Fix: replace the silent fallback with a hard RuntimeError telling
the user to run hermes model or hermes setup. The provider must
be explicitly configured -- env vars are for secrets, not config.
* fix: show correct env var name in provider API key error (#9506)
The error message for missing provider API keys dynamically built
the env var name as PROVIDER_API_KEY (e.g. ALIBABA_API_KEY), but
some providers use different names (alibaba uses DASHSCOPE_API_KEY).
Users following the error message set the wrong variable.
Fix: look up the actual env var from PROVIDER_REGISTRY before
building the error. Falls back to the dynamic name if the registry
lookup fails.
Closes#9506
* fix: five HERMES_HOME profile-isolation leaks (#5947)
Bug A: Thread session_title from session_db to memory provider init kwargs
so honcho can derive chat-scoped session keys instead of falling back to
cwd-based naming that merges all gateway users into one session.
Bug B: Replace 14 hardcoded ~/.hermes/skills/ paths across 10 skill files
with HERMES_HOME-aware alternatives (${HERMES_HOME:-$HOME/.hermes} in
shell, os.environ.get('HERMES_HOME', ...) in Python).
Bug C: install.sh now respects HERMES_HOME env var and adds --hermes-home
flag. Previously --dir only set INSTALL_DIR while HERMES_HOME was always
hardcoded to $HOME/.hermes.
Bug D: Remove hardcoded ~/.hermes/honcho.json fallback in resolve_config_path().
Non-default profiles no longer silently inherit the default profile's honcho
config. Falls through to ~/.honcho/config.json (global) instead.
Bug E: Guard _edit_skill, _patch_skill, _delete_skill, _write_file, and
_remove_file against writing to skills found in external_dirs. Skills
outside the local SKILLS_DIR are now read-only from the agent's perspective.
Closes#5947
When Nous returns a 429, the retry amplification chain burns up to 9
API requests per conversation turn (3 SDK retries × 3 Hermes retries),
each counting against RPH and deepening the rate limit. With multiple
concurrent sessions (cron + gateway + auxiliary), this creates a spiral
where retries keep the limit tapped indefinitely.
New module: agent/nous_rate_guard.py
- Shared file-based rate limit state (~/.hermes/rate_limits/nous.json)
- Parses reset time from x-ratelimit-reset-requests-1h, x-ratelimit-
reset-requests, retry-after headers, or error context
- Falls back to 5-minute default cooldown if no header data
- Atomic writes (tempfile + rename) for cross-process safety
- Auto-cleanup of expired state files
run_agent.py changes:
- Top-of-retry-loop guard: when another session already recorded Nous
as rate-limited, skip the API call entirely. Try fallback provider
first, then return a clear message with the reset time.
- On 429 from Nous: record rate limit state and skip further retries
(sets retry_count = max_retries to trigger fallback path)
- On success from Nous: clear the rate limit state so other sessions
know they can resume
auxiliary_client.py changes:
- _try_nous() checks rate guard before attempting Nous in the auxiliary
fallback chain. When rate-limited, returns (None, None) so the chain
skips to the next provider instead of piling more requests onto Nous.
This eliminates three sources of amplification:
1. Hermes-level retries (saves 6 of 9 calls per turn)
2. Cross-session retries (cron + gateway all skip Nous)
3. Auxiliary fallback to Nous (compression/session_search skip too)
Includes 24 tests covering the rate guard module, header parsing,
state lifecycle, and auxiliary client integration.
The error message for missing provider API keys dynamically built
the env var name as PROVIDER_API_KEY (e.g. ALIBABA_API_KEY), but
some providers use different names (alibaba uses DASHSCOPE_API_KEY).
Users following the error message set the wrong variable.
Fix: look up the actual env var from PROVIDER_REGISTRY before
building the error. Falls back to the dynamic name if the registry
lookup fails.
Closes#9506
The GPT-5 auto-upgrade logic unconditionally overrode api_mode to
codex_responses for any model starting with gpt-5, even when the
user explicitly set api_mode=chat_completions. Custom proxies that
serve GPT-5 via /chat/completions became unusable.
Fix: check api_mode is None before the override fires. If the caller
passed any explicit api_mode, it is final -- no auto-upgrade.
Closes#10473
When proxy env vars (HTTP_PROXY, HTTPS_PROXY, ALL_PROXY) contain
malformed URLs — e.g. 'http://127.0.0.1:6153export' from a broken
shell config — the OpenAI/httpx client throws a cryptic 'Invalid port'
error that doesn't identify the offending variable.
Add _validate_proxy_env_urls() and _validate_base_url() in
auxiliary_client.py, called from resolve_provider_client() and
_create_openai_client() to fail fast with a clear, actionable error
message naming the broken env var or URL.
Closes#6360
Co-authored-by: MestreY0d4-Uninter <MestreY0d4-Uninter@users.noreply.github.com>
The recovery block previously only retried (continue) when one of the
per-component sanitization checks (messages, tools, system prompt,
headers, credentials) found and stripped non-ASCII content. When the
non-ASCII lived only in api_messages' reasoning_content field (which
is built from messages['reasoning'] and not checked by the original
_sanitize_messages_non_ascii), all checks returned False and the
recovery fell through to the normal error path — burning a retry
attempt despite _force_ascii_payload being set.
Now the recovery always continues (retries) when _is_ascii_codec is
detected. The _force_ascii_payload flag guarantees the next iteration
runs _sanitize_structure_non_ascii(api_kwargs) on the full API payload,
catching any remaining non-ASCII regardless of where it lives.
Also adds test for the 'reasoning' field on canonical messages.
Fixes#6843
The ASCII-locale recovery path in run_agent.py sanitized the canonical
'messages' list but left 'api_messages' untouched. api_messages is a
separate API-copy built before the retry loop and may carry extra fields
(reasoning_content, extra_body entries) that are not present in
'messages'. This caused the retry to still raise UnicodeEncodeError even
after the 'System encoding is ASCII — stripped...' log line appeared.
Two changes:
- _sanitize_messages_non_ascii now walks all extra top-level string fields
in each message dict (any key not in {content, name, tool_calls, role})
so reasoning_content and future extras are cleaned in both 'messages'
and 'api_messages'.
- The ASCII-codec recovery block now also calls sanitize on api_messages
and api_kwargs so no non-ASCII survives into the next retry attempt.
Adds regression tests covering:
- reasoning_content with non-ASCII in api_messages
- extra_body with non-ASCII in api_kwargs
- canonical messages clean but api_messages dirty
Fixes#6843
Memory provider plugins (e.g. Mnemosyne) can register tools via two paths:
1. Plugin system (ctx.register_tool) → tool registry → get_tool_definitions()
2. Memory manager → get_all_tool_schemas() → direct append in AIAgent.__init__
Path 2 blindly appended without checking if path 1 already added the same
tool names. This created duplicate function names in the tools array sent
to the API. Most providers silently handle duplicates, but Xiaomi MiMo
(via Nous Portal) strictly rejects them with a 400 Bad Request.
Fix: build a set of existing tool names before memory manager injection
and skip any tool whose name is already present.
Confirmed via live testing against Nous Portal:
- Unique tool names → 200 OK
- Duplicate tool names → 400 'Provider returned error'
The on_memory_write bridge that notifies external memory providers
(ClawMem, retaindb, supermemory, etc.) of built-in memory writes was
only present in the concurrent tool execution path (_invoke_tool).
The sequential path (_execute_tool_calls_sequential) — which handles
all single tool calls, the common case — was missing it entirely.
This meant external memory providers silently missed every single-call
memory write, which is the vast majority of memory operations.
Fix: add the identical bridge block to the sequential path, right
after the memory_tool call returns.
Closes#10174
Multiple gaps in activity tracking could cause the gateway's inactivity
timeout to fire while the agent is actively working:
1. Streaming wait loop had no periodic heartbeat — the outer thread only
touched activity when the stale-stream detector fired (180-300s), and
for local providers (Ollama) the stale timeout was infinity, meaning
zero heartbeats. Now touches activity every 30s.
2. Concurrent tool execution never set the activity callback on worker
threads (threading.local invisible across threads) and never set
_current_tool. Workers now set the callback, and the concurrent wait
uses a polling loop with 30s heartbeats.
3. Modal backend's execute() override had its own polling loop without
any activity callback. Now matches _wait_for_process cadence (10s).
The _last_content_with_tools fallback was firing indiscriminately for ALL
content+tool turns, including mid-task narration alongside substantive
tools (terminal, search_files, etc.). This caused the agent to exit
the loop with 'I'll scan the directory...' as the final answer instead
of nudging the model to continue processing tool results.
The fix restricts the fallback to housekeeping-only turns (memory, todo,
skill_manage, session_search) where the content genuinely IS the final
answer. When substantive tools are present, the existing post-tool
nudge mechanism now fires instead, prompting the model to continue.
Affected models: xiaomi/mimo-v2-pro, GLM-5, and other weaker models
that intermittently return empty after tool results.
Reported by user Renaissance on Discord.
OV transparently handles message history across /new and /compress: old
messages stay in the same session and extraction is idempotent, so there's
no need to rebind providers to a new session_id. The only thing the
session boundary actually needs is to trigger extraction.
- MemoryProvider / MemoryManager: remove on_session_reset hook
- OpenViking: remove on_session_reset override (nothing to do)
- AIAgent: replace rotate_memory_session with commit_memory_session
(just calls on_session_end, no rebind)
- cli.py / run_agent.py: single commit_memory_session call at the
session boundary before session_id rotates
- tests: replace on_session_reset coverage with routing tests for
MemoryManager.on_session_end
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace hasattr-forked OpenViking-specific paths with a proper base-class
hook. Collapse the two agent wrappers into a single rotate_memory_session
so callers don't orchestrate commit + rebind themselves.
- MemoryProvider: add on_session_reset(new_session_id) as a default no-op
- MemoryManager: on_session_reset fans out unconditionally (no hasattr,
no builtin skip — base no-op covers it)
- OpenViking: rename reset_session -> on_session_reset; drop the explicit
POST /api/v1/sessions (OV auto-creates on first message) and the two
debug raise_for_status wrappers
- AIAgent: collapse commit_memory_session + reinitialize_memory_session
into rotate_memory_session(new_sid, messages)
- cli.py / run_agent.py: replace hasattr blocks and the split calls with
a single unconditional rotate_memory_session call; compression path
now passes the real messages list instead of []
- tests: align with on_session_reset, assert reset does NOT POST /sessions
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The OpenViking memory provider extracts memories when its session is
committed (POST /api/v1/sessions/{id}/commit). Before this fix, the
CLI had two code paths that changed the active session_id without ever
committing the outgoing OpenViking session:
1. /new (new_session() in cli.py) — called flush_memories() to write
MEMORY.md, then immediately discarded the old session_id. The
accumulated OpenViking session was never committed, so all context
from that session was lost before extraction could run.
2. /compress and auto-compress (_compress_context() in run_agent.py) —
split the SQLite session (new session_id) but left the OpenViking
provider pointing at the old session_id with no commit, meaning all
messages synced to OpenViking were silently orphaned.
The gateway already handles session commit on /new and /reset via
shutdown_memory_provider() on the cached agent; the CLI path did not.
Fix: introduce a lightweight session-transition lifecycle alongside
the existing full shutdown path:
- OpenVikingMemoryProvider.reset_session(new_session_id): waits for
in-flight background threads, resets per-session counters, and
creates the new OV session via POST /api/v1/sessions — without
tearing down the HTTP client (avoids connection overhead on /new).
- MemoryManager.restart_session(new_session_id): calls reset_session()
on providers that implement it; falls back to initialize() for
providers that do not. Skips the builtin provider (no per-session
state).
- AIAgent.commit_memory_session(messages): wraps
memory_manager.on_session_end() without shutdown — commits OV session
for extraction but leaves the provider alive for the next session.
- AIAgent.reinitialize_memory_session(new_session_id): wraps
memory_manager.restart_session() — transitions all external providers
to the new session after session_id has been assigned.
Call sites:
- cli.py new_session(): commit BEFORE session_id changes, reinitialize
AFTER — ensuring OV extraction runs on the correct session and the
new session is immediately ready for the next turn.
- run_agent._compress_context(): same pattern, inside the
if self._session_db: block where the session_id split happens.
/compress and auto-compress are functionally identical at this layer:
both call _compress_context(), so both are fixed by the same change.
Tests added to tests/agent/test_memory_provider.py:
- TestMemoryManagerRestartSession: reset_session() routing, builtin
skip, initialize() fallback, failure tolerance, empty-manager noop.
- TestOpenVikingResetSession: session_id update, per-session state
clear, POST /api/v1/sessions call, API failure tolerance, no-client
noop.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
With store=False (our default for the Responses API), the API does not
persist response items. When reasoning items with 'id' fields were
replayed on subsequent turns, the API attempted a server-side lookup
for those IDs and returned 404:
Item with id 'rs_...' not found. Items are not persisted when store
is set to false.
The encrypted_content blob is self-contained for reasoning chain
continuity — the id field is unnecessary and triggers the failed lookup.
Fix: strip 'id' from reasoning items in both _chat_messages_to_responses_input
(message conversion) and _preflight_codex_input_items (normalization layer).
The id is still used for local deduplication but never sent to the API.
Reported by @zuogl448 on GPT-5.4.
The existing recovery block sanitized self.api_key and
self._client_kwargs['api_key'] but did not update self.client.api_key.
The OpenAI SDK stores its own copy of api_key and reads it dynamically
via the auth_headers property on every request. Without this fix, the
retry after sanitization would still send the corrupted key in the
Authorization header, causing the same UnicodeEncodeError.
The bug manifests when an API key contains Unicode lookalike characters
(e.g. ʋ U+028B instead of v) from copy-pasting out of PDFs, rich-text
editors, or web pages with decorative fonts. httpx hard-encodes all
HTTP headers as ASCII, so the non-ASCII char in the Authorization
header triggers the error.
Adds TestApiKeyClientSync with two tests verifying:
- All three key locations are synced after sanitization
- Recovery handles client=None (pre-init) without crashing
Three independent fixes:
1. Reset activity timestamp on cached agent reuse (#9051)
When the gateway reuses a cached AIAgent for a new turn, the
_last_activity_ts from the previous turn (possibly hours ago)
carried over. The inactivity timeout handler immediately saw
the agent as idle for hours and killed it.
Fix: reset _last_activity_ts, _last_activity_desc, and
_api_call_count when retrieving an agent from the cache.
2. Detect uv-managed virtual environments (#8620 sub-issue 1)
The systemd unit generator fell back to sys.executable (uv's
standalone Python) when running under 'uv run', because
sys.prefix == sys.base_prefix (uv doesn't set up traditional
venv activation). The generated ExecStart pointed to a Python
binary without site-packages, crashing the service on startup.
Fix: check VIRTUAL_ENV env var before falling back to
sys.executable. uv sets VIRTUAL_ENV even when sys.prefix
doesn't reflect the venv.
3. Nudge model to continue after empty post-tool response (#9400)
Weaker models (GLM-5, mimo-v2-pro) sometimes return empty
responses after tool calls instead of continuing to the next
step. The agent silently abandoned the remaining work with
'(empty)' or used prior-turn fallback text.
Fix: when the model returns empty after tool calls AND there's
no prior-turn content to fall back on, inject a one-time user
nudge message telling the model to process the tool results and
continue. The flag resets after each successful tool round so it
can fire again on later rounds.
Test plan: 97 gateway + CLI tests pass, 9 venv detection tests pass
Previously, non-integer context_length values (e.g. '256K') in
config.yaml were silently ignored, causing the agent to fall back
to 128K auto-detection with no user feedback. This was confusing
for users with custom LiteLLM endpoints expecting larger context.
Now prints a clear stderr warning and logs at WARNING level when
model.context_length or custom_providers[].models.<model>.context_length
cannot be parsed as an integer, telling users to use plain integers
(e.g. 256000 instead of '256K').
Reported by community user ChFarhan via Discord.
When compression fails after max attempts, the agent returns
{completed: False, partial: True} but was missing the 'failed' flag.
The gateway's agent_failed_early guard checked for 'failed' AND
'not final_response', but _run_agent_blocking always converts errors
to final_response — making the guard dead code. This caused the
oversized session to persist, creating an infinite fail loop where
every subsequent message hits the same compression failure.
Changes:
- run_agent.py: add 'failed: True' and 'compression_exhausted: True'
to all 5 compression-exhaustion return paths
- gateway/run.py (_run_agent_blocking): forward 'failed' and
'compression_exhausted' flags through to the caller
- gateway/run.py (_handle_message_with_agent): fix agent_failed_early
to check bool(failed) without the broken 'not final_response' clause;
auto-reset the session when compression is exhausted so the next
message starts fresh
- Update tests to match new guard logic and add
TestCompressionExhaustedFlag test class
Closes#9893
Three bugfixes in the agent loop:
1. Reset retry counters after context compression. Without this,
pre-compression retry counts carry over, causing the model to
hit empty-response recovery immediately after a compression-
induced context loss, wasting API calls on a now-valid context.
2. Unmute output in the final-response (no-tool-call) branch.
_mute_post_response could be left True from a prior housekeeping
turn, silently suppressing empty-response warnings and recovery
status that the user should see.
3. Stop injecting 'Calling the X tools...' into assistant message
content when falling back to prior-turn content. This mutated
conversation history with synthetic text that the model never
produced, poisoning subsequent turns.
API keys containing Unicode lookalike characters (e.g. ʋ U+028B instead
of v) cause UnicodeEncodeError when httpx encodes the Authorization
header as ASCII. This commonly happens when users copy-paste keys from
PDFs, rich-text editors, or web pages with decorative fonts.
Three layers of defense:
1. **Save-time validation** (hermes_cli/config.py):
_check_non_ascii_credential() strips non-ASCII from credential values
when saving to .env, with a clear warning explaining the issue.
2. **Load-time sanitization** (hermes_cli/env_loader.py):
_sanitize_loaded_credentials() strips non-ASCII from credential env
vars (those ending in _API_KEY, _TOKEN, _SECRET, _KEY) after dotenv
loads them, so the rest of the codebase never sees non-ASCII keys.
3. **Runtime recovery** (run_agent.py):
The UnicodeEncodeError recovery block now also sanitizes self.api_key
and self._client_kwargs['api_key'], fixing the gap where message/tool
sanitization succeeded but the API key still caused httpx to fail on
the Authorization header.
Also: hermes_logging.py RotatingFileHandler now explicitly sets
encoding='utf-8' instead of relying on locale default (defensive
hardening for ASCII-locale systems).
