- Add 'prompt exceeds max length' to context overflow detection for
Z.AI/GLM 400 errors
- Extract inline reasoning blocks from assistant content as fallback
when no structured reasoning fields are present
- Guard inline extraction so structured API reasoning takes priority
- Update test for reasoning-only response salvage behavior
Cherry-picked from PR #2993 by kshitijk4poor. Added priority guard
to fix test_structured_reasoning_takes_priority failure.
Co-authored-by: kshitijk4poor <kshitijk4poor@users.noreply.github.com>
Each subagent now gets its own IterationBudget instead of sharing the
parent's. The per-subagent cap is controlled by delegation.max_iterations
in config.yaml (default 50). Total iterations across parent + subagents
can exceed the parent's max_iterations, but the user retains control via
the config setting.
Previously, subagents shared the parent's budget, so three parallel
subagents configured for max_iterations=50 racing against a parent that
already used 60 of 90 would each only get ~10 iterations.
Inspired by PR #2928 (Bartok9) which identified the issue (#2873).
* fix(session): surface silent SessionDB failures that cause session data loss
SessionDB initialization and operation failures were logged at debug level
or silently swallowed, causing sessions to never be indexed in the FTS5
database. This made session_search unable to find affected conversations.
In practice, ~48% of sessions can be lost without any visible indication.
The JSON session files are still written (separate code path), but the
SQLite/FTS5 index gets nothing — making session_search return empty results
for affected sessions.
Changes:
- cli.py: Log warnings (not debug) when SessionDB init fails at both
__init__ and _start_session entry points
- run_agent.py: Log warnings on create_session, append_message, and
compression split failures
- run_agent.py: Set _session_db = None after create_session failure to
fail fast instead of silently dropping every message for the session
Root cause: When gateway restarts or DB lock contention occurs during
SessionDB() init, the exception is caught and swallowed. The agent
continues running normally — JSON session logs are written to disk —
but no messages reach the FTS5 index.
* fix: use module logger instead of root logging for SessionDB warnings
Follow-up to cherry-picked PR #2939 — the original used logging.warning()
(root logger) instead of logger.warning() (module logger) in the 5 new
warning calls. Module logger preserves the logger hierarchy and shows the
correct module name in log output.
---------
Co-authored-by: LucidPaths <lc77@outlook.de>
feat: persist reasoning across gateway session turns (schema v6)
Tested against OpenAI Codex (direct), Anthropic (direct + OAI-compat), and OpenRouter → 6 backends. All reasoning field types (reasoning, reasoning_details, codex_reasoning_items) round-trip through the DB correctly.
* fix(run_agent): ensure _fire_first_delta() is called for tool generation events
Added calls to _fire_first_delta() in the AIAgent class to improve the handling of tool generation events, ensuring timely notifications during the processing of function calls and tool usage.
* fix(run_agent): improve timeout handling for chat completions
Enhanced the timeout configuration for chat completions in the AIAgent class by introducing customizable connection, read, and write timeouts using environment variables. This ensures more robust handling of API requests during streaming operations.
* fix(run_agent): reduce default stream read timeout for chat completions
Updated the default stream read timeout from 120 seconds to 60 seconds in the AIAgent class, enhancing the timeout configuration for chat completions. This change aims to improve responsiveness during streaming operations.
* fix(run_agent): enhance streaming error handling and retry logic
Improved the error handling and retry mechanism for streaming requests in the AIAgent class. Introduced a configurable maximum number of stream retries and refined the handling of transient network errors, allowing for retries with fresh connections. Non-transient errors now trigger a fallback to non-streaming only when appropriate, ensuring better resilience during API interactions.
* fix: skills-sh install fails for deeply nested repo structures
Skills in repos with deep directory nesting (e.g.
cli-tool/components/skills/development/senior-backend/) could not be
installed because the candidate path generation and shallow root-dir
scan never reached them.
Added GitHubSource._find_skill_in_repo_tree() which uses the GitHub
Trees API to recursively search the entire repo tree in a single API
call. This is used as a final fallback in
SkillsShSource._discover_identifier() when the standard candidate
paths and shallow scan both fail.
Fixes installation of skills from repos like davila7/claude-code-templates
where skills are nested 4+ levels deep.
Reported by user Samuraixheart.
* docs: unify hooks documentation — add plugin hooks to hooks page, add session:end event
The hooks page only documented gateway event hooks (HOOK.yaml system).
The plugins page listed plugin hooks (pre_tool_call, etc.) that weren't
referenced from the hooks page, which was confusing.
Changes:
- hooks.md: Add overview table showing both hook systems
- hooks.md: Add Plugin Hooks section with available hooks, callback
signatures, and example
- hooks.md: Add missing session:end gateway event (emitted but undocumented)
- hooks.md: Mark pre_llm_call, post_llm_call, on_session_start,
on_session_end as planned (defined in VALID_HOOKS but not yet invoked)
- hooks.md: Update info box to cross-reference plugin hooks
- hooks.md: Fix heading hierarchy (gateway content as subsections)
- plugins.md: Add cross-reference to hooks page for full details
- plugins.md: Mark planned hooks as (planned)
* feat(session_search): add recent sessions mode when query is omitted
When session_search is called without a query (or with an empty query),
it now returns metadata for the most recent sessions instead of erroring.
This lets the agent quickly see what was worked on recently without
needing specific keywords.
Returns for each session: session_id, title, source, started_at,
last_active, message_count, preview (first user message).
Zero LLM cost — pure DB query. Current session lineage and child
delegation sessions are excluded.
The agent can then keyword-search specific sessions if it needs
deeper context from any of them.
* docs: clarify two-mode behavior in session_search schema description
* fix(compression): restore sane defaults and cap summary at 12K tokens
- 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
* fix: browser_vision ignores auxiliary.vision.timeout config (#2901)
* docs: unify hooks documentation — add plugin hooks to hooks page, add session:end event
The hooks page only documented gateway event hooks (HOOK.yaml system).
The plugins page listed plugin hooks (pre_tool_call, etc.) that weren't
referenced from the hooks page, which was confusing.
Changes:
- hooks.md: Add overview table showing both hook systems
- hooks.md: Add Plugin Hooks section with available hooks, callback
signatures, and example
- hooks.md: Add missing session:end gateway event (emitted but undocumented)
- hooks.md: Mark pre_llm_call, post_llm_call, on_session_start,
on_session_end as planned (defined in VALID_HOOKS but not yet invoked)
- hooks.md: Update info box to cross-reference plugin hooks
- hooks.md: Fix heading hierarchy (gateway content as subsections)
- plugins.md: Add cross-reference to hooks page for full details
- plugins.md: Mark planned hooks as (planned)
* fix: browser_vision ignores auxiliary.vision.timeout config
browser_vision called call_llm() without passing a timeout parameter,
so it always used the 30-second default in auxiliary_client.py. This
made vision analysis with local models (llama.cpp, ollama) impossible
since they typically need more than 30s for screenshot analysis.
Now browser_vision reads auxiliary.vision.timeout from config.yaml
(same config key that vision_analyze already uses) and passes it
through to call_llm().
Also bumped the default vision timeout from 30s to 120s in both
browser_vision and vision_analyze — 30s is too aggressive for local
models and the previous default silently failed for anyone running
vision locally.
Fixes user report from GamerGB1988.
* fix(skills): agent-created skills were incorrectly treated as untrusted community content
_resolve_trust_level() didn't handle 'agent-created' source, so it
fell through to 'community' trust level. Community policy blocks on
any caution or dangerous findings, which meant common patterns like
curl with env vars, systemctl, crontab, cloudflared references etc.
would block skill creation/patching.
The agent-created policy row already existed in INSTALL_POLICY with
permissive settings (allow caution, ask on dangerous) but was never
reached. Now it is.
Fixes reports of skill_manage being blocked by security scanner.
* fix(cli): enhance real-time reasoning output by forcing flush of long partial lines
Updated the reasoning output mechanism to emit complete lines and force-flush long partial lines, ensuring reasoning is visible in real-time even without newlines. This improves user experience during reasoning sessions.
* fix: skip KawaiiSpinner when TUI handles tool progress
In the interactive CLI, the agent runs with quiet_mode=True and
tool_progress_callback set. The quiet_mode condition triggered
KawaiiSpinner for every tool call, but the TUI was already handling
progress display via the spinner widget.
The KawaiiSpinner writes carriage-return animation through StdoutProxy,
triggering run_in_terminal() erase/redraw cycles on every flush. These
redundant cycles cause the status bar to ghost into terminal scrollback.
The thinking spinner already had this guard (checks thinking_callback).
This extends the same pattern to the three tool spinner creation sites:
concurrent tools, delegate_task, and single tool execution.
- 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
The gateway process runs from the hermes-agent install directory, so
os.getcwd() picks up the repo's AGENTS.md (16k chars) and other dev
context files — inflating input tokens by ~10k on every gateway message.
Fix: use TERMINAL_CWD (which the gateway sets to MESSAGING_CWD or
$HOME) as the cwd for build_context_files_prompt(). In CLI mode,
TERMINAL_CWD is the user's actual project directory, so behavior
is unchanged.
Before: gateway 15-20k input tokens, CLI 6-8k
After: gateway ~6-8k input tokens (same as CLI)
Reported by keri on Discord.
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 → ...
Reset context pressure warnings and update last_prompt_tokens and last_completion_tokens in the context compressor to prevent stale values from causing excessive warnings and re-triggering compression. This change ensures accurate pressure calculations following the compaction process.
Complete cleanup after dropping the mini-swe-agent submodule (PR #2804):
- Remove MSWEA_SILENT_STARTUP and MSWEA_GLOBAL_CONFIG_DIR env var
settings from cli.py, run_agent.py, hermes_cli/main.py, doctor.py
- Remove mini-swe-agent health check from hermes doctor
- Remove 'minisweagent' from logger suppression lists
- Remove litellm/typer/platformdirs from requirements.txt
- Remove mini-swe-agent install steps from install.ps1 (Windows)
- Remove mini-swe-agent install steps from website docs
- Update all stale comments/docstrings referencing mini-swe-agent
in terminal_tool.py, tools/__init__.py, code_execution_tool.py,
environments/README.md, environments/agent_loop.py
- Remove mini_swe_runner from pyproject.toml py-modules
(still exists as standalone script for RL training use)
- Shrink test_minisweagent_path.py to empty stub
The orphaned mini-swe-agent/ directory on disk needs manual removal:
rm -rf mini-swe-agent/
Drop the mini-swe-agent git submodule. All terminal backends now use
hermes-agent's own environment implementations directly.
Docker backend:
- Inline the `docker run -d` container startup (was 15 lines in
minisweagent's DockerEnvironment). Our wrapper already handled
execute(), cleanup(), security hardening, volumes, and resource limits.
Modal backend:
- Import swe-rex's ModalDeployment directly instead of going through
minisweagent's 90-line passthrough wrapper.
- Bake the _AsyncWorker pattern (from environments/patches.py) directly
into ModalEnvironment for Atropos compatibility without monkey-patching.
Cleanup:
- Remove minisweagent_path.py (submodule path resolution helper)
- Remove submodule init/install from install.sh and setup-hermes.sh
- Remove mini-swe-agent from .gitmodules
- environments/patches.py is now a no-op (kept for backward compat)
- terminal_tool.py no longer does sys.path hacking for minisweagent
- mini_swe_runner.py guards imports (optional, for RL training only)
- Update all affected tests to mock the new direct subprocess calls
- Update README.md, CONTRIBUTING.md
No functionality change — all Docker, Modal, local, SSH, Singularity,
and Daytona backends behave identically. 6093 tests pass.
- Added calls to `_fire_first_delta()` in the `AIAgent` class to ensure that the first delta is triggered for both reasoning and thinking updates. This change improves the handling of delta events during streaming, enhancing the responsiveness of the agent's reasoning capabilities.
- Introduced `_on_tool_gen_start` in `HermesCLI` to indicate when tool-call arguments are being generated, enhancing user feedback during streaming.
- Updated `AIAgent` to support a new `tool_gen_callback`, notifying the display layer when tool generation starts, allowing for better user experience during large payloads.
- Ensured that the callback is triggered appropriately during streaming events to prevent user interface freezing.
Added explicit closing of the OpenAI/httpx client in the background review process to prevent "Event loop is closed" errors. This change ensures that the client is properly cleaned up when the review agent is no longer needed, enhancing stability and resource management.
Follow-up to 669c60a6 (cherry-pick of PR #2187, fixes#2177).
The original fix emits a "\n\n" delta immediately after every
_execute_tool_calls() invocation. When the model runs multiple
consecutive tool iterations before producing text (common with
search → read → analyze flows), each iteration appends its own
paragraph break, resulting in 4-6+ blank lines before the actual
response.
Replace the immediate delta with a deferred flag
(_stream_needs_break). _fire_stream_delta() checks the flag and
prepends a single "\n\n" only when the first real text delta
arrives, so multiple back-to-back tool iterations still produce
exactly one paragraph break.
Reverts the sanitizer addition from PR #2466 (originally #2129).
We already have _empty_content_retries handling for reasoning-only
responses. The trailing strip risks silently eating valid messages
and is redundant with existing empty-content handling.
When subagents run in ThreadPoolExecutor threads, the shared stdout handle
can close between thread teardown and KawaiiSpinner cleanup. Python raises
ValueError (not OSError) for I/O operations on closed files:
ValueError: I/O operation on closed file
The _SafeWriter class was only catching OSError, missing this case.
Changes:
- Add ValueError to exception handling in write(), flush(), and isatty()
- Update docstring to document the ThreadPoolExecutor teardown scenario
Fixes#2428
Two related root causes for the '?[33mTool progress: NEW?[0m' garbling
reported on kitty, alacritty, ghostty and gnome-console:
1. /verbose label printing used self.console.print() with Rich markup
([yellow]...[/]). self.console is a plain Rich Console() whose output
goes directly to sys.stdout, which patch_stdout's StdoutProxy
intercepts and mangles raw ANSI sequences.
2. Context pressure status lines (e.g. 'approaching compaction') from
AIAgent._safe_print() had the same problem -- _safe_print() was a
@staticmethod that always called builtin print(), bypassing the
prompt_toolkit renderer entirely.
Fix:
- Convert AIAgent._safe_print() from @staticmethod to an instance method
that delegates to self._print_fn (defaults to builtin print, preserving
all non-CLI behaviour).
- After the CLI creates its AIAgent instance, wire self.agent._print_fn to
the existing _cprint() helper which routes through
prompt_toolkit.print_formatted_text(ANSI(text)).
- Rewrite the /verbose feedback labels to use hermes_cli.colors.Colors
ANSI constants in f-strings and emit them via _cprint() directly,
removing the Rich-markup-inside-patch_stdout anti-pattern.
Fixes#2262
Co-authored-by: Animesh Mishra <animesh.m.7523@gmail.com>
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>
Cherry-picked from PR #2187 by @devorun. Fixes#2177.
When streaming is enabled, text before and after tool calls gets
concatenated without separation. Adds a paragraph break delta after
_execute_tool_calls() so stream consumers insert proper whitespace
between iteration boundaries.
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
* fix: prevent Anthropic token fallback leaking to third-party anthropic_messages providers
When provider is minimax/alibaba/etc and MINIMAX_API_KEY is not set,
the code fell back to resolve_anthropic_token() sending Anthropic OAuth
credentials to third-party endpoints, causing 401 errors.
Now only provider=="anthropic" triggers the fallback. Generalizes the
Alibaba-specific guard from #1739 to all non-Anthropic providers.
* fix: set provider='anthropic' in credential refresh tests
Follow-up for cherry-picked PR #2383 — existing tests didn't set
agent.provider, which the new guard requires to allow Anthropic
token refresh.
---------
Co-authored-by: 0xbyt4 <35742124+0xbyt4@users.noreply.github.com>
PR #2314 checked for provider names 'alibaba-coding-plan' and
'alibaba-coding-plan-anthropic' which don't exist in the provider
registry. The provider is always 'alibaba' — the condition was dead
code. Fixed to check self.provider == 'alibaba'.
When using Alibaba (DashScope) with an anthropic-compatible endpoint,
model names like qwen3.5-plus were being normalized to qwen3-5-plus.
Alibaba's API expects the dot. Added preserve_dots parameter to
normalize_model_name() and build_anthropic_kwargs().
Also fixed 401 auth: when provider is alibaba or base_url contains
dashscope/aliyuncs, use only the resolved API key (DASHSCOPE_API_KEY).
Never fall back to resolve_anthropic_token(), and skip Anthropic
credential refresh for DashScope endpoints.
Cherry-picked from PR #1748 by crazywriter1. Fixes#1739.
- Add resolve_config_path(): checks $HERMES_HOME/honcho.json first,
falls back to ~/.honcho/config.json. Enables isolated Hermes instances
with independent Honcho credentials and settings.
- Update CLI and doctor to use resolved path instead of hardcoded global.
- Change default session_strategy from per-session to per-directory.
Part 1 of #1962 by @erosika.
Two changes to the error handler in the agent loop:
1. Remove the 'if not pending_handled' block that injected fake
[System error during processing: ...] messages into conversation
history. These polluted history, burned tokens on retries, and
could violate role alternation by injecting as role=user.
The tool_calls error-result path (role=tool) is preserved.
2. Append the error final_response as an assistant message when
hitting the iteration limit, so session resume doesn't produce
consecutive user messages.
Enhanced the review agent to scan and summarize successful tool actions, providing users with a compact overview of updates made during the review process. This includes actions related to memory and user profiles, improving user feedback and interaction clarity.
- Introduced a mechanism to mute output after the main response is delivered, ensuring that subsequent tool calls run without cluttering the CLI.
- Redirected stdout to devnull during the review agent's execution to prevent any print statements from interfering with the main CLI display.
- Added a new attribute `_mute_post_response` to manage output suppression effectively.
When an API call fails, the error output now shows the provider name,
model, and endpoint URL so users can immediately identify which service
rejected their request. Auth errors (401/403) get actionable guidance:
check key validity, model access, and OpenRouter credits link.
Before: 'API call failed (attempt 1/3): PermissionDeniedError'
After: 'API call failed (attempt 1/3): PermissionDeniedError
Provider: openrouter Model: anthropic/claude-sonnet-4
Endpoint: https://openrouter.ai/api/v1
Your API key was rejected by the provider. Check:
• Is the key valid? Run: hermes setup
• Does your account have access to anthropic/claude-sonnet-4?
• Check credits: https://openrouter.ai/settings/credits'
Remove the memory and skill nudges that were appended directly to user
messages, causing backward-looking system instructions to compete with
forward-looking user tasks. Found in 43% of user messages across 15
sessions, with confirmed cases of the agent spending tool calls on
nudge responses before starting the user's actual request.
Replace with a background review agent that runs AFTER the main agent
finishes responding:
- Spawns a background thread with a snapshot of the conversation
- Uses the main model (not auxiliary) for high-precision memory/skill work
- Only has memory + skill_manage tools (5 iteration budget)
- Shares the memory store for direct writes
- Never modifies the main conversation history
- Never competes with the user's task for model attention
- Zero latency impact (runs after response is delivered)
- Same token cost (processes the same context, just on a separate track)
The trigger conditions are unchanged (every 10 user turns for memory,
after 10+ tool iterations for skills). Only the execution path changes:
from inline injection to background fork.
Closes#2227.
Co-authored-by: Test <test@test.com>
Remove the [Files already read — do NOT re-read these] user message
that was injected into the conversation after context compression.
This message used role='user' for system-generated content, creating
a fake user turn that confused models about conversation state and
could contribute to task-redo behavior.
The file_tools.py read tracker (warn on 3rd consecutive read, block
on 4th+) already handles re-read prevention inline without injecting
synthetic messages.
Closes#2224.
Co-authored-by: Test <test@test.com>
- Updated _stream_delta method in HermesCLI to handle None values, flushing the stream and resetting state for clean tool execution.
- Enhanced quiet mode handling in AIAgent to ensure proper display closure before tool execution, preventing display issues with intermediate streamed content.
These changes improve the robustness of the streaming functionality and ensure a smoother user experience during tool interactions.
Cherry-picked from PR #2169 by @0xbyt4.
1. _strip_provider_prefix: skip Ollama model:tag names (qwen:0.5b)
2. Fuzzy match: remove reverse direction that made claude-sonnet-4
resolve to 1M instead of 200K
3. _has_content_after_think_block: reuse _strip_think_blocks() to
handle all tag variants (thinking, reasoning, REASONING_SCRATCHPAD)
4. models.dev lookup: elif→if so nous provider also queries models.dev
5. Disk cache fallback: use 5-min TTL instead of full hour so network
is retried soon
6. Delegate build: wrap child construction in try/finally so
_last_resolved_tool_names is always restored on exception
* feat: context pressure warnings for CLI and gateway
User-facing notifications as context approaches the compaction threshold.
Warnings fire at 60% and 85% of the way to compaction — relative to
the configured compression threshold, not the raw context window.
CLI: Formatted line with a progress bar showing distance to compaction.
Cyan at 60% (approaching), bold yellow at 85% (imminent).
◐ context ▰▰▰▰▰▰▰▰▰▰▰▰▱▱▱▱▱▱▱▱ 60% to compaction 100k threshold (50%) · approaching compaction
⚠ context ▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▱▱▱ 85% to compaction 100k threshold (50%) · compaction imminent
Gateway: Plain-text notification sent to the user's chat via the new
status_callback mechanism (asyncio.run_coroutine_threadsafe bridge,
same pattern as step_callback).
Does NOT inject into the message stream. The LLM never sees these
warnings. Flags reset after each compaction cycle.
Files changed:
- agent/display.py — format_context_pressure(), format_context_pressure_gateway()
- run_agent.py — status_callback param, _context_50/70_warned flags,
_emit_context_pressure(), flag reset in _compress_context()
- gateway/run.py — _status_callback_sync bridge, wired to AIAgent
- tests/test_context_pressure.py — 23 tests
* Merge remote-tracking branch 'origin/main' into hermes/hermes-7ea545bf
---------
Co-authored-by: Test <test@test.com>
Replace the fragile hardcoded context length system with a multi-source
resolution chain that correctly identifies context windows per provider.
Key changes:
- New agent/models_dev.py: Fetches and caches the models.dev registry
(3800+ models across 100+ providers with per-provider context windows).
In-memory cache (1hr TTL) + disk cache for cold starts.
- Rewritten get_model_context_length() resolution chain:
0. Config override (model.context_length)
1. Custom providers per-model context_length
2. Persistent disk cache
3. Endpoint /models (local servers)
4. Anthropic /v1/models API (max_input_tokens, API-key only)
5. OpenRouter live API (existing, unchanged)
6. Nous suffix-match via OpenRouter (dot/dash normalization)
7. models.dev registry lookup (provider-aware)
8. Thin hardcoded defaults (broad family patterns)
9. 128K fallback (was 2M)
- Provider-aware context: same model now correctly resolves to different
context windows per provider (e.g. claude-opus-4.6: 1M on Anthropic,
128K on GitHub Copilot). Provider name flows through ContextCompressor.
- DEFAULT_CONTEXT_LENGTHS shrunk from 80+ entries to ~16 broad patterns.
models.dev replaces the per-model hardcoding.
- CONTEXT_PROBE_TIERS changed from [2M, 1M, 512K, 200K, 128K, 64K, 32K]
to [128K, 64K, 32K, 16K, 8K]. Unknown models no longer start at 2M.
- hermes model: prompts for context_length when configuring custom
endpoints. Supports shorthand (32k, 128K). Saved to custom_providers
per-model config.
- custom_providers schema extended with optional models dict for
per-model context_length (backward compatible).
- Nous Portal: suffix-matches bare IDs (claude-opus-4-6) against
OpenRouter's prefixed IDs (anthropic/claude-opus-4.6) with dot/dash
normalization. Handles all 15 current Nous models.
- Anthropic direct: queries /v1/models for max_input_tokens. Only works
with regular API keys (sk-ant-api*), not OAuth tokens. Falls through
to models.dev for OAuth users.
Tests: 5574 passed (18 new tests for models_dev + updated probe tiers)
Docs: Updated configuration.md context length section, AGENTS.md
Co-authored-by: Test <test@test.com>
When streaming was enabled, two visual feedback mechanisms were
completely suppressed:
1. The thinking spinner (TUI toolbar) was skipped because the entire
spinner block was gated on 'not self._has_stream_consumers()'.
Now the thinking_callback fires in streaming mode too — the
raw KawaiiSpinner is still skipped (would conflict with streamed
tokens) but the TUI toolbar widget works fine alongside streaming.
2. Tool progress lines (the ┊ feed) were invisible because _vprint
was blanket-suppressed when stream consumers existed. But during
tool execution, no tokens are actively streaming, so printing is
safe. Added an _executing_tools flag that _vprint respects to
allow output during tool execution even with stream consumers
registered.
Based on PR #1859 by @magi-morph (too stale to cherry-pick, reimplemented).
GPT-5.x models reject tool calls + reasoning_effort on
/v1/chat/completions with a 400 error directing to /v1/responses.
This auto-detects api.openai.com in the base URL and switches to
codex_responses mode in three places:
- AIAgent.__init__: upgrades chat_completions → codex_responses
- _try_activate_fallback(): same routing for fallback model
- runtime_provider.py: _detect_api_mode_for_url() for both custom
provider and openrouter runtime resolution paths
Also extracts _is_direct_openai_url() helper to replace the inline
check in _max_tokens_param().
Follow-up to PR #2101 (InB4DevOps). Adds three missing context compressor
resets in reset_session_state():
- compression_count (displayed in status bar)
- last_total_tokens
- _context_probed (stale context-error flag)
Also fixes the test_cli_new_session.py prompt_toolkit mock (missing
auto_suggest stub) and adds a regression test for #2099 that verifies
all token counters and compressor state are zeroed on /new.
