Tool call previews (paths, commands, queries) were hardcoded to truncate
at 35-40 chars across CLI spinners, completion lines, and gateway progress
messages. Users could not see full file paths in tool output.
New config option: display.tool_preview_length (default 0 = no limit).
Set a positive number to truncate at that length.
Changes:
- display.py: module-level _tool_preview_max_len with getter/setter;
build_tool_preview() and get_cute_tool_message() _trunc/_path respect it
- cli.py: reads config at startup, spinner widget respects config
- gateway/run.py: reads config per-message, progress callback respects config
- run_agent.py: removed redundant 30-char quiet-mode spinner truncation
- config.py: added display.tool_preview_length to DEFAULT_CONFIG
Reported by kriskaminski
Add skills.external_dirs config option — a list of additional directories
to scan for skills alongside ~/.hermes/skills/. External dirs are read-only:
skill creation/editing always writes to the local dir. Local skills take
precedence when names collide.
This lets users share skills across tools/agents without copying them into
Hermes's own directory (e.g. ~/.agents/skills, /shared/team-skills).
Changes:
- agent/skill_utils.py: add get_external_skills_dirs() and get_all_skills_dirs()
- agent/prompt_builder.py: scan external dirs in build_skills_system_prompt()
- tools/skills_tool.py: _find_all_skills() and skill_view() search external dirs;
security check recognizes configured external dirs as trusted
- agent/skill_commands.py: /skill slash commands discover external skills
- hermes_cli/config.py: add skills.external_dirs to DEFAULT_CONFIG
- cli-config.yaml.example: document the option
- tests/agent/test_external_skills.py: 11 tests covering discovery, precedence,
deduplication, and skill_view for external skills
Requested by community member primco.
Background agent's KawaiiSpinner wrote \r-based animation and stop()
messages through StdoutProxy, colliding with prompt_toolkit's status bar.
Two fixes:
- display.py: use isinstance(out, StdoutProxy) instead of fragile
hasattr+name check for detecting prompt_toolkit's stdout wrapper
- cli.py: silence bg agent's raw spinner (_print_fn=no-op) and route
thinking updates through the TUI widget only when no foreground
agent is active; clear spinner text in finally block with same guard
Closes#2718
Co-authored-by: kshitijk4poor <kshitijk4poor@users.noreply.github.com>
Salvage of PR #3533 (binhnt92). Follow-up to #3480 — applies min(100, ...) to 5 remaining unclamped percentage display sites in context_compressor, cli /stats, gateway /stats, and memory tool. Defensive clamps now that the root cause (estimation heuristic) was already removed in #3480.
Co-Authored-By: binhnt92 <binhnt92@users.noreply.github.com>
Add per-task timeout settings under auxiliary.{task}.timeout in config.yaml
instead of hardcoded values. Users with slow local models (Ollama, llama.cpp)
can now increase timeouts for compression, vision, session search, etc.
Defaults:
- auxiliary.compression.timeout: 120s (was hardcoded 45s)
- auxiliary.vision.timeout: 30s (unchanged)
- all other aux tasks: 30s (was hardcoded 30s)
- title_generator: 30s (was hardcoded 15s)
call_llm/async_call_llm now auto-resolve timeout from config when not
explicitly passed. Callers can still override with an explicit timeout arg.
Based on PR #3406 by alanfwilliams. Converted from env vars to config.yaml
per project conventions.
Co-authored-by: alanfwilliams <alanfwilliams@users.noreply.github.com>
Use atomic_json_write() from utils.py instead of plain open()/json.dump()
for the models.dev disk cache. Prevents corrupted cache if the process is
killed mid-write — _load_disk_cache() silently returns {} on corrupt JSON,
losing all model metadata until the next successful API fetch.
Co-authored-by: memosr <memosr@users.noreply.github.com>
Cherry-pick of feat/gpt-tool-steering with modifications:
1. Tool-use enforcement prompt (refactored from GPT-specific):
- Renamed GPT_TOOL_USE_GUIDANCE -> TOOL_USE_ENFORCEMENT_GUIDANCE
- Added TOOL_USE_ENFORCEMENT_MODELS tuple: ('gpt', 'codex')
- Injection logic now checks against the tuple instead of hardcoding
'gpt' — adding new model families is a one-line change
- Addresses models describing actions instead of making tool calls
2. Budget warning history stripping:
- _strip_budget_warnings_from_history() strips _budget_warning JSON
keys and [BUDGET WARNING: ...] text from tool results at the start
of run_conversation()
- Prevents old budget warnings from poisoning subsequent turns
Based on PR #3479 by teknium1.
* fix: cap context pressure percentage at 100% in display
The forward-looking token estimate can overshoot the compaction threshold
(e.g. a large tool result pushes it from 70% to 109% in one step). The
progress bar was already capped via min(), but pct_int was not — causing
the user to see '109% to compaction' which is confusing.
Cap pct_int at 100 in both CLI and gateway display functions.
Reported by @JoshExile82.
* refactor: use real API token counts for compression decisions
Replace the rough chars/3 estimation with actual prompt_tokens +
completion_tokens from the API response. The estimation was needed to
predict whether tool results would push context past the threshold, but
the default 50% threshold leaves ample headroom — if tool results push
past it, the next API call reports real usage and triggers compression
then.
This removes all estimation from the compression and context pressure
paths, making both 100% data-driven from provider-reported token counts.
Also removes the dead _msg_count_before_tools variable.
_expand_git_reference() and _rg_files() called subprocess.run()
without a timeout. On a large repository, @diff, @staged, or
@git:N references could hang the agent indefinitely while git
or ripgrep processes slow output.
- Add timeout=30 to git subprocess in _expand_git_reference()
with a user-friendly error message on TimeoutExpired
- Add timeout=10 to rg subprocess in _rg_files() returning
None on timeout (falls back to os.walk folder listing)
Co-authored-by: memosr.eth <96793918+memosr@users.noreply.github.com>
Salvage of #3389 by @binhnt92 with reasoning fallback and retry logic added on top.
All 7 auxiliary LLM call sites now use extract_content_or_reasoning() which mirrors the main agent loop's behavior: extract content, strip think blocks, fall back to structured reasoning fields, retry on empty.
Closes#3389.
Show only agentic models that map to OpenRouter defaults:
Qwen/Qwen3.5-397B-A17B ↔ qwen/qwen3.5-plus
Qwen/Qwen3.5-35B-A3B ↔ qwen/qwen3.5-35b-a3b
deepseek-ai/DeepSeek-V3.2 ↔ deepseek/deepseek-chat
moonshotai/Kimi-K2.5 ↔ moonshotai/kimi-k2.5
MiniMaxAI/MiniMax-M2.5 ↔ minimax/minimax-m2.5
zai-org/GLM-5 ↔ z-ai/glm-5
XiaomiMiMo/MiMo-V2-Flash ↔ xiaomi/mimo-v2-pro
moonshotai/Kimi-K2-Thinking ↔ moonshotai/kimi-k2-thinking
Users can still pick any HF model via Enter custom model name.
The Anthropic adapter defaulted to max_tokens=16384 when no explicit value
was configured. This severely limits thinking-enabled models where thinking
tokens count toward max_tokens:
- Claude Opus 4.6 supports 128K output but was capped at 16K
- Claude Sonnet 4.6 supports 64K output but was capped at 16K
With extended thinking (adaptive or budget-based), the model could exhaust
the entire 16K on reasoning, leaving zero tokens for the actual response.
This caused two user-visible errors:
- 'Response truncated (finish_reason=length)' — thinking consumed most tokens
- 'Response only contains think block with no content' — thinking consumed all
Fix: add _ANTHROPIC_OUTPUT_LIMITS lookup table (sourced from Anthropic docs
and Cline's model catalog) and use the model's actual output limit as the
default. Unknown future models default to 128K (the current maximum).
Also adds context_length clamping: if the user configured a smaller context
window (e.g. custom endpoint), max_tokens is clamped to context_length - 1
to avoid exceeding the window.
Closes#2706
Salvage of PR #1747 (original PR #1171 by @davanstrien) onto current main.
Registers Hugging Face Inference Providers (router.huggingface.co/v1) as a named provider:
- hermes chat --provider huggingface (or --provider hf)
- 18 curated open models via hermes model picker
- HF_TOKEN in ~/.hermes/.env
- OpenAI-compatible endpoint with automatic failover (Groq, Together, SambaNova, etc.)
Files: auth.py, models.py, main.py, setup.py, config.py, model_metadata.py, .env.example, 5 docs pages, 17 new tests.
Co-authored-by: Daniel van Strien <davanstrien@gmail.com>
The OpenAI SDK's AsyncHttpxClientWrapper.__del__ schedules aclose() via
asyncio.get_running_loop().create_task(). When an AsyncOpenAI client is
garbage-collected while prompt_toolkit's event loop is running (the common
CLI idle state), the aclose() task runs on prompt_toolkit's loop but the
underlying TCP transport is bound to a different (dead) worker loop.
The transport's self._loop.call_soon() then raises RuntimeError('Event
loop is closed'), which prompt_toolkit surfaces as the disruptive
'Unhandled exception in event loop ... Press ENTER to continue...' error.
Three-layer fix:
1. neuter_async_httpx_del(): Monkey-patches __del__ to a no-op at CLI
startup before any AsyncOpenAI clients are created. Safe because
cached clients are explicitly cleaned via _force_close_async_httpx,
and uncached clients' TCP connections are cleaned by the OS on exit.
2. Custom asyncio exception handler: Installed on prompt_toolkit's event
loop to silently suppress 'Event loop is closed' RuntimeError.
Defense-in-depth for SDK upgrades that might change the class name.
3. cleanup_stale_async_clients(): Called after each agent turn (when the
agent thread joins) to proactively evict cache entries whose event
loop is closed, preventing stale clients from accumulating.
When user messages have empty content (e.g., Discord @mention-only
messages, unrecognized attachments), the Anthropic API rejects the
request with 'user messages must have non-empty content'.
Changes:
- anthropic_adapter.py: Add empty content validation for user messages
(string and list formats), matching the existing pattern for assistant
and tool messages. Empty content gets '(empty message)' placeholder.
- discord.py: Defense-in-depth check at gateway layer to catch empty
messages before they enter session history.
- Add 4 regression tests covering empty string, whitespace-only,
empty list, and empty text block scenarios.
Fixes#3143
Co-authored-by: Bartok9 <bartok9@users.noreply.github.com>
_try_anthropic() caught ImportError on the module import (line 667-669)
but not on the build_anthropic_client() call (line 696). When the
anthropic_adapter module imports fine but the anthropic SDK is missing,
build_anthropic_client() raises ImportError at call time. This escaped
_try_anthropic() entirely, killing get_available_vision_backends() and
cascading to 7 test failures:
- 4 setup wizard tests hit unexpected 'Configure vision:' prompt
- 3 codex-auth-as-vision tests failed check_vision_requirements()
The fix wraps the build_anthropic_client call in try/except ImportError,
returning (None, None) when the SDK is unavailable — consistent with the
existing guard at the top of the function.
* fix(gateway): silence flush agent terminal output
quiet_mode=True only suppresses AIAgent init messages.
Tool call output still leaks to the terminal through
_safe_print → _print_fn during session reset/expiry.
Since #2670 injected live memory state into the flush prompt,
the flush agent now reliably calls memory tools — making the
output leak noticeable for the first time.
Set _print_fn to a no-op so the background flush is fully silent.
* test(gateway): add test for flush agent terminal silence + fix dotenv mock
- Add TestFlushAgentSilenced: verifies _print_fn is set to a no-op on
the flush agent so tool output never leaks to the terminal
- Fix pre-existing test failures: replace patch('run_agent.AIAgent')
with sys.modules mock to avoid importing run_agent (requires openai)
- Add autouse _mock_dotenv fixture so all tests in this file run
without the dotenv package installed
* fix(display): route KawaiiSpinner output through print_fn to fully silence flush agent
The previous fix set tmp_agent._print_fn = no-op on the flush agent but
spinner output and quiet-mode cute messages bypassed _print_fn entirely:
- KawaiiSpinner captured sys.stdout at __init__ and wrote directly to it
- quiet-mode tool results used builtin print() instead of _safe_print()
Add optional print_fn parameter to KawaiiSpinner.__init__; _write routes
through it when set. Pass self._print_fn to all spinner construction sites
in run_agent.py and change the quiet-mode cute message print to _safe_print.
The existing gateway fix (tmp_agent._print_fn = lambda) now propagates
correctly through both paths.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(gateway): silence hygiene and compression background agents
Two more background AIAgent instances in the gateway were created with
quiet_mode=True but without _print_fn = no-op, causing tool output to
leak to the terminal:
- _hyg_agent (in-turn hygiene memory agent)
- tmp_agent (_compress_context path)
Apply the same _print_fn no-op pattern used for the flush agent.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* chore(display): remove unused _last_flush_time from KawaiiSpinner
Attribute was set but never read; upstream already removed it.
Leftover from conflict resolution during rebase onto upstream/main.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Dilee <uzmpsk.dilekakbas@gmail.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Nous Portal now passes through OpenRouter model names and routes from
there. Update the static fallback model list and auxiliary client default
to use OpenRouter-format slugs (provider/model) instead of bare names.
- _PROVIDER_MODELS['nous']: full OpenRouter catalog
- _NOUS_MODEL: google/gemini-3-flash-preview (was gemini-3-flash)
- Updated 4 test assertions for the new default model name
Anthropic migrated their OAuth infrastructure from console.anthropic.com
to platform.claude.com (Claude Code v2.1.81+). Update _refresh_oauth_token()
to try the new endpoint first, falling back to the old one for tokens
issued before the migration.
Also switches Content-Type from application/x-www-form-urlencoded to
application/json to match current Claude Code behavior.
Salvaged from PR #2741 by kshitijk4poor.
Two improvements salvaged from PR #2600 (paraddox):
1. Preflight compression now counts tool schema tokens alongside system
prompt and messages. With 50+ tools enabled, schemas can add 20-30K
tokens that were previously invisible to the estimator, delaying
compression until the API rejected the request.
2. Context probe persistence guard: when the agent steps down context
tiers after a context-length error, only provider-confirmed numeric
limits (parsed from the error message) are cached to disk. Guessed
fallback tiers from get_next_probe_tier() stay in-memory only,
preventing wrong values from polluting the persistent cache.
Co-authored-by: paraddox <paraddox@users.noreply.github.com>
Three categories of cleanup, all zero-behavioral-change:
1. F-strings without placeholders (154 fixes across 29 files)
- Converted f'...' to '...' where no {expression} was present
- Heaviest files: run_agent.py (24), cli.py (20), honcho_integration/cli.py (34)
2. Simplify defensive patterns in run_agent.py
- Added explicit self._is_anthropic_oauth = False in __init__ (before
the api_mode branch that conditionally sets it)
- Replaced 7x getattr(self, '_is_anthropic_oauth', False) with direct
self._is_anthropic_oauth (attribute always initialized now)
- Added _is_openrouter_url() and _is_anthropic_url() helper methods
- Replaced 3 inline 'openrouter' in self._base_url_lower checks
3. Remove dead code in small files
- hermes_cli/claw.py: removed unused 'total' computation
- tools/fuzzy_match.py: removed unused strip_indent() function and
pattern_stripped variable
Full test suite: 6184 passed, 0 failures
E2E PTY: banner clean, tool calls work, zero garbled ANSI
The recursive os.walk for AGENTS.md in subdirectories was undesired.
Only load AGENTS.md from the working directory root, matching the
behavior of CLAUDE.md and .cursorrules.
Remove run_hermes_oauth_login(), refresh_hermes_oauth_token(),
read_hermes_oauth_credentials(), _save_hermes_oauth_credentials(),
_generate_pkce(), and associated constants/credential file path.
This code was added in 63e88326 but never wired into any user-facing
flow (setup wizard, hermes model, or any CLI command). Neither
clawdbot/OpenClaw nor opencode implement PKCE for Anthropic — both
use setup-token or API keys. Dead code that was never tested in
production.
Also removes the credential resolution step that checked
~/.hermes/.anthropic_oauth.json (step 3 in resolve_anthropic_token),
renumbering remaining steps.
In gateway mode, async tools (vision_analyze, web_extract, session_search)
deadlock because _run_async() spawns a thread with asyncio.run(), creating
a new event loop, but _get_cached_client() returns an AsyncOpenAI client
bound to a different loop. httpx.AsyncClient cannot work across event loop
boundaries, causing await client.chat.completions.create() to hang forever.
Fix: include the event loop identity in the async client cache key so each
loop gets its own AsyncOpenAI instance. Also fix session_search_tool.py
which had its own broken asyncio.run()-in-thread pattern — now uses the
centralized _run_async() bridge.
frontmatter.get("metadata", {}) returns None (not {}) when the
key exists with a null value, crashing build_skills_system_prompt
with AttributeError: 'NoneType' object has no attribute 'get'.
Made-with: Cursor
Centralizes two widely-duplicated patterns into hermes_constants.py:
1. get_hermes_home() — Path resolution for ~/.hermes (HERMES_HOME env var)
- Was copy-pasted inline across 30+ files as:
Path(os.getenv("HERMES_HOME", Path.home() / ".hermes"))
- Now defined once in hermes_constants.py (zero-dependency module)
- hermes_cli/config.py re-exports it for backward compatibility
- Removed local wrapper functions in honcho_integration/client.py,
tools/website_policy.py, tools/tirith_security.py, hermes_cli/uninstall.py
2. parse_reasoning_effort() — Reasoning effort string validation
- Was copy-pasted in cli.py, gateway/run.py, cron/scheduler.py
- Same validation logic: check against (xhigh, high, medium, low, minimal, none)
- Now defined once in hermes_constants.py, called from all 3 locations
- Warning log for unknown values kept at call sites (context-specific)
31 files changed, net +31 lines (125 insertions, 94 deletions)
Full test suite: 6179 passed, 0 failed
In gateway/Telegram mode, the stdout fd can be closed by executor
thread cleanup. KawaiiSpinner.stop() called isatty() on the closed fd,
raising ValueError and masking the original error.
Instead of a point fix, add a _is_tty property that centralizes the
closed-stream guard — both _animate() and stop() now use it. Follows
the same (ValueError, OSError) pattern already in _write().
Inspired by PR #2632 by bot-deo88.
format_token_count_compact() used unconditional rstrip("0") to clean up
decimal trailing zeros (e.g. "1.50" → "1.5"), but this also stripped
meaningful trailing zeros from whole numbers ("260" → "26", "100" → "1").
Guard the strip behind a decimal-point check.
Co-authored-by: kshitijk4poor <82637225+kshitijk4poor@users.noreply.github.com>
When the CLI is active, sys.stdout is prompt_toolkit's StdoutProxy which
queues writes and injects newlines around each flush(). This causes every
\r spinner frame to land on its own line instead of overwriting the
previous one, producing visible flickering where the spinner and status
bar repeatedly swap positions.
The CLI already renders spinner state via a dedicated TUI widget
(_spinner_text / get_spinner_text), so KawaiiSpinner's \r-based loop is
redundant under StdoutProxy. Detect the proxy and suppress the animation
entirely — the thread still runs to preserve start()/stop() semantics.
Also removes the 0.4s flush rate-limit workaround that was papering over
the same issue, and cleans up the unused _last_flush_time attribute.
Salvaged from PR #2908 by Mibayy (fixed _raw -> raw detection, dropped
unrelated bundled changes).
build_skills_system_prompt() was calling _read_skill_conditions() which
re-read each SKILL.md file to extract conditional activation fields.
The frontmatter was already parsed by _parse_skill_file() earlier in
the same loop. Extract conditions inline from the existing frontmatter
dict instead, saving one file read per skill (~80+ on a typical setup).
Salvaged from PR #2827 by InB4DevOps.
- threshold: 0.80 → 0.50 (compress at 50%, not 80%)
- target_ratio: 0.40 → 0.20, now relative to threshold not total context
(20% of 50% = 10% of context as tail budget)
- summary ceiling: 32K → 12K (Gemini can't output more than ~12K)
- Updated DEFAULT_CONFIG, config display, example config, and tests
The summary_target_tokens parameter was accepted in the constructor,
stored on the instance, and never used — the summary budget was always
computed from hardcoded module constants (_SUMMARY_RATIO=0.20,
_MAX_SUMMARY_TOKENS=8000). This caused two compounding problems:
1. The config value was silently ignored, giving users no control
over post-compression size.
2. Fixed budgets (20K tail, 8K summary cap) didn't scale with
context window size. Switching from a 1M-context model to a
200K model would trigger compression that nuked 350K tokens
of conversation history down to ~30K.
Changes:
- Replace summary_target_tokens with summary_target_ratio (default 0.40)
which sets the post-compression target as a fraction of context_length.
Tail token budget and summary cap now scale proportionally:
MiniMax 200K → ~80K post-compression
GPT-5 1M → ~400K post-compression
- Change threshold_percent default: 0.50 → 0.80 (don't fire until
80% of context is consumed)
- Change protect_last_n default: 4 → 20 (preserve ~10 full turns)
- Summary token cap scales to 5% of context (was fixed 8K), capped
at 32K ceiling
- Read target_ratio and protect_last_n from config.yaml compression
section (both are now configurable)
- Remove hardcoded summary_target_tokens=500 from run_agent.py
- Add 5 new tests for ratio scaling, clamping, and new defaults
Move OpenRouter to position 1 in the setup wizard's provider list
to match hermes model ordering. Update default selection index and
fix test expectations for the new ordering.
Setup order: OpenRouter → Nous Portal → Codex → Custom → ...
When AsyncOpenAI clients are garbage-collected after the event loop
closes, their AsyncHttpxClientWrapper.__del__ tries to schedule
aclose() on the dead loop, causing RuntimeError: Event loop is closed.
prompt_toolkit catches this as an unhandled exception and shows
'Press ENTER to continue...' which blocks CLI exit.
Fix: Add shutdown_cached_clients() to auxiliary_client.py that marks
all cached async clients' underlying httpx transport as CLOSED before
GC runs. This prevents __del__ from attempting the aclose() call.
- _force_close_async_httpx(): sets httpx AsyncClient._state to CLOSED
- shutdown_cached_clients(): iterates _client_cache, closes sync clients
normally and marks async clients as closed
- Also fix stale client eviction in _get_cached_client to mark evicted
async clients as closed (was just del-ing them, triggering __del__)
- Call shutdown_cached_clients() from _run_cleanup() in cli.py
The context length resolver was querying the /models endpoint for known
providers like GitHub Copilot, which returns a provider-imposed limit
(128k) instead of the model's actual context window (400k for gpt-5.4).
Since this check happened before the models.dev lookup, the wrong value
won every time.
Fix:
- Add api.githubcopilot.com and models.github.ai to _URL_TO_PROVIDER
- Skip the endpoint metadata probe for known providers — their /models
data is unreliable for context length. models.dev has the correct
per-provider values.
Reported by danny [DUMB] — gpt-5.4 via Copilot was resolving to 128k
instead of the correct 400k from models.dev.
When a non-OpenRouter provider (e.g. minimax, anthropic) is set in
config.yaml but its API key is missing, Hermes silently fell back to
OpenRouter, causing confusing 404 errors.
Now checks if the user explicitly configured a provider before falling
back. Explicit providers raise RuntimeError with a clear message naming
the missing env var. Auto/openrouter/custom providers still fall through
to OpenRouter as before.
Three code paths fixed:
- run_agent.py AIAgent.__init__ — main client initialization
- auxiliary_client.py call_llm — sync auxiliary calls
- auxiliary_client.py call_llm_streaming — async auxiliary calls
Based on PR #2272 by @StefanIsMe. Applied manually to fix a
pconfig NameError in the original and extend to call_llm_streaming.
Co-authored-by: StefanIsMe <StefanIsMe@users.noreply.github.com>
Recent versions of llama.cpp moved the server properties endpoint from
/props to /v1/props (consistent with the /v1 API prefix convention).
The server-type detection path and the n_ctx reading path both used the
old /props URL, which returns 404 on current builds. This caused the
allocated context window size to fall back to a hardcoded default,
resulting in an incorrect (too small) value being displayed in the TUI
context bar.
Fix: try /v1/props first, fall back to /props for backward compatibility
with older llama.cpp builds. Both paths are now handled gracefully.
Two bugs in the auxiliary provider auto-detection chain:
1. Expired Codex JWT blocks the auto chain: _read_codex_access_token()
returned any stored token without checking expiry, preventing fallback
to working providers. Now decodes JWT exp claim and returns None for
expired tokens.
2. Auxiliary Anthropic client missing OAuth identity transforms:
_AnthropicCompletionsAdapter always called build_anthropic_kwargs with
is_oauth=False, causing 400 errors for OAuth tokens. Now detects OAuth
tokens via _is_oauth_token() and propagates the flag through the
adapter chain.
Cherry-picked from PR #2378 by 0xbyt4. Fixed test_api_key_no_oauth_flag
to mock resolve_anthropic_token directly (env var alone was insufficient).
redact_sensitive_text() now returns early for None and coerces other
non-string values to str before applying regex-based redaction,
preventing TypeErrors in logging/tool-output paths.
Cherry-picked from PR #2369 by aydnOktay.
On the native Anthropic Messages API path, convert_messages_to_anthropic()
moves top-level cache_control on role:tool messages inside the tool_result
block. On OpenRouter (chat_completions), no such conversion happens — the
unexpected top-level field causes a silent hang on the second tool call.
Add native_anthropic parameter to _apply_cache_marker() and
apply_anthropic_cache_control(). When False (OpenRouter), role:tool messages
are skipped entirely. When True (native Anthropic), existing behaviour is
preserved.
Fixes#2362
Only honor config.model.base_url for Anthropic resolution when
config.model.provider is actually "anthropic". This prevents a Codex
(or other provider) base_url from leaking into Anthropic runtime and
auxiliary client paths, which would send requests to the wrong
endpoint.
Closes#2384
Add @file:path, @folder:dir, @diff, @staged, @git:N, and @url:
references that expand inline before the message reaches the LLM.
Supports line ranges (@file:main.py:10-50), token budget enforcement
(soft warn at 25%, hard block at 50%), and path sandboxing for gateway.
Core module from PR #2090 by @kshitijk4poor. CLI and gateway wiring
rewritten against current main. Fixed asyncio.run() crash when called
from inside a running event loop (gateway).
Closes#682.
