* feat(nous): unified client=hermes-client-v<version> tag on every Portal request
Every Hermes request to Nous Portal now carries the same
client=hermes-client-v<__version__> tag (e.g. client=hermes-client-v0.13.0
on this release), sourced live from hermes_cli.__version__. The release
script's regex bump auto-aligns it on every release.
Centralized in agent/portal_tags.py and wired into all four call sites:
- NousProfile.build_extra_body (main agent loop, every chat completion)
- auxiliary_client.NOUS_EXTRA_BODY + _build_call_kwargs (aux client)
- run_agent.py compression-summary fallback path
- tools/web_tools.py web_extract fallback
Replaces the client=aux marker added in #24194 with the unified version
tag. Tests assert against the helper output (invariant) rather than the
literal string, so they don't need updating on every release.
* feat(nous): cover /goal judge and kanban specify aux paths
Two aux-using surfaces bypassed call_llm by invoking
client.chat.completions.create() directly without extra_body, so they
were missing the unified Portal client tag:
- hermes_cli/goals.py — /goal standing-goal judge
- hermes_cli/kanban_specify.py — kanban triage specifier
Both now pass extra_body=get_auxiliary_extra_body() or None so they
inherit the version tag when the aux client points at Nous Portal, and
emit nothing otherwise (no tag leak to OpenRouter/Anthropic auxes).
The long-lived prefix-cache layout split the system prompt into stable/
context/volatile blocks and re-derived them on every API call. The
volatile tier (timestamp + memory snapshot + USER profile) ticks per
turn, so the system message bytes mutated mid-conversation and broke
upstream prompt caches (OpenRouter, Nous Portal, Anthropic).
Diagnosed via live wire-format diffing: an 8-turn conversation showed
OLD layout flipping system block[1] sha mid-session at the minute
boundary, dropping cached_tokens to 0 on that turn (cumulative
66.6% vs 83.3% for the single-block layout). Hermes invariant:
history (system + all but the last 1-2 messages) must be static.
Fix: drop the long-lived layout entirely. Single layout everywhere —
system_and_3 with one cached system string built once on first turn,
replayed verbatim on every subsequent turn. Loses cross-session 1h
prefix caching for Claude (the feature that motivated the split), but
within-session caching now actually works on every provider.
Removed:
- run_agent.py: _use_long_lived_prefix_cache flag, _long_lived_cache_ttl,
_supports_long_lived_anthropic_cache method, the long-lived branch in
run_conversation, mark_tools_for_long_lived_cache call site
- agent/prompt_caching.py: apply_anthropic_cache_control_long_lived,
mark_tools_for_long_lived_cache, _mark_system_stable_block helper
- hermes_cli/config.py: prompt_caching.long_lived_prefix and
prompt_caching.long_lived_ttl config keys
- tests/agent/test_prompt_caching_live.py (entire file)
- tests/agent/test_prompt_caching.py: TestMarkToolsForLongLivedCache,
TestApplyAnthropicCacheControlLongLived
- tests/run_agent/test_anthropic_prompt_cache_policy.py:
TestSupportsLongLivedAnthropicCache
Targeted tests: 62/62 pass.
When switching models via /model, AIAgent._config_context_length was
never cleared, so the new model inherited the previous model's context
window instead of auto-detecting the correct one via
get_model_context_length().
Clear _config_context_length to None before the runtime field swap so
the full resolution chain (custom_providers per-model, endpoint probe,
models.dev, etc.) is re-evaluated for the newly selected model.
Closes#21509
PR #24151 routed Portal Qwen (qwen3.6-plus) through the prefix_and_2
long-lived cache layout, attaching {"type":"ephemeral","ttl":"1h"}
markers to the tools[-1] entry and the stable system-prefix block.
That layout works for Portal Claude because Anthropic / OpenRouter on
Anthropic routes honour 1h TTL — but Portal Qwen ultimately proxies to
Alibaba DashScope, which documents a single "ephemeral" TTL of 5
minutes on its Context Cache. The ttl="1h" qualifier is silently
dropped upstream, so the two highest-value breakpoints (tools array +
system prefix) never land. Only the rolling-window 5m markers on the
last 2 messages cache, which matches the observed ~25% read rate.
Fix: keep Portal Qwen on cache_control via _anthropic_prompt_cache_policy
returning (True, False), but drop it from _supports_long_lived_anthropic_cache
so it rides the standard system_and_3 5m layout (system + last 3 messages,
all at 5m). Same 4 breakpoints, all in a TTL the upstream actually honours.
Refs: https://www.alibabacloud.com/help/en/model-studio/context-cachehttps://openrouter.ai/docs/features/prompt-caching (Alibaba Qwen
section: "TTL: 5 minutes")
- _supports_long_lived_anthropic_cache: Portal scope narrowed back to Claude
- tests: flip the two qwen long-lived expectations to False, retitle
non_claude_non_qwen_rejected -> non_claude_rejected
Detect when write_file / patch calls fail during a turn and are never
superseded by a successful write to the same path. When the final
text response is delivered, append an advisory footer listing the
files that did NOT change — so models that over-claim 'patched 5 files'
after 4 silent failures can't hide the lie.
Catches the failure mode reported in Ben Eng's llm-wiki session:
grok-4.1-fast issued batches of parallel patches, half failed with
'Could not find old_string', and the agent summarised the turn
claiming every file was edited. The user had to manually run
'git status' each turn to catch it.
The verifier is a pure post-hoc check on tool results — no new LLM
calls, no synthetic messages injected into history (prompt cache
preserved), no changes to tool argument dispatch. Per-turn state is
keyed by path; a later successful write to the same path clears the
failure entry so single-file retry recovery is not flagged.
Wired into both _execute_tool_calls_concurrent and
_execute_tool_calls_sequential, so batched parallel patches and one-at-
a-time edits are both covered. Footer emission happens after the
agent loop exits, before transform_llm_output / post_llm_call plugin
hooks run, so plugins still see (and can modify) the augmented text.
Config: display.file_mutation_verifier (bool, default true) +
HERMES_FILE_MUTATION_VERIFIER env override.
31 unit tests in tests/run_agent/test_file_mutation_verifier.py cover
target extraction (write_file, patch-replace, patch-v4a single and
multi-file), error-preview extraction (JSON .error field and plain
string), per-turn state transitions (first-error-wins on repeated
failure, success supersedes failure), footer rendering (truncation
at 10 entries, user-actionable hint), and env/config precedence.
Companion docs updated: user-guide/configuration.md +
reference/environment-variables.md.
Qwen models on Nous Portal (e.g. qwen3.6-plus) now get the same envelope-layout
cache_control markers and long-lived (1h cross-session) cache treatment as
Portal Claude. Portal proxies to OpenRouter with identical wire-format and
cache_control semantics, but the prior policy left Portal Qwen falling through
to the alibaba-family branch (which only matches provider=opencode/alibaba),
serving 0% cache hits and re-billing the full prompt every turn.
Scope is narrow: Portal Claude OR Portal Qwen. Other models on Portal keep
their existing behavior.
- _anthropic_prompt_cache_policy: add (is_nous_portal and qwen) -> (True, False)
- _supports_long_lived_anthropic_cache: drop Claude-only gate for Portal so
Qwen also gets the validated 1h cross-session layout
- tests cover both functions, both bare and vendored qwen slug forms, and
the rejection of non-Claude non-Qwen Portal traffic
Set HERMES_SESSION_ID using the existing session_context.py ContextVar
system for concurrency safety (multiple gateway sessions in one process
won't cross-talk). Also writes os.environ as fallback for CLI mode.
Touchpoints:
- gateway/session_context.py: Add _SESSION_ID ContextVar + _VAR_MAP entry
- run_agent.py: Set both ContextVar and os.environ at init and on
context-compression rotation
- tools/environments/local.py: Bridge ContextVars into subprocess env
in _make_run_env() (ContextVars don't propagate to child processes)
- tests/run_agent/test_session_id_env.py: 3 tests covering env, provided
ID, and ContextVar paths
execute_code subprocess already passes HERMES_* prefixed vars through
_scrub_child_env (line 82: _SAFE_ENV_PREFIXES includes 'HERMES_').
Primary use case: webhook-triggered agents that need to include a
`--resume <session_id>` takeover command in their output.
Cuts input cost for first-turn Claude requests by ~85-90% on subsequent
sessions within an hour. Tools array (~13k tokens for default toolset) +
stable system prefix (~5-8k tokens) get a 1h cache_control marker; the
volatile suffix (memory, USER profile, timestamp, session id) sits in a
separate non-cached block at the end so it doesn't poison the cross-session
prefix when it changes.
Provider gate: Claude on native Anthropic (incl. OAuth subscription),
OpenRouter, and Nous Portal (which proxies to OpenRouter). All other
providers keep today's system_and_3 layout unchanged.
Layout (4 cache_control breakpoints, Anthropic max):
1. tools[-1] -> 1h (cross-session)
2. system content[0] -> 1h (cross-session, stable prefix)
3. messages[-2] -> 5m (within-session rolling)
4. messages[-1] -> 5m (within-session rolling)
Within-session rolling shrinks from 3 messages to 2 to free the breakpoint
budget. On Claude with realistic tool loadouts the long-lived tier carries
the bulk of cross-session value anyway.
System prompt is now always assembled cache-friendly: stable identity /
guidance / skills / platform hints first, then session-stable context
files (AGENTS.md, .cursorrules), then per-call volatile content. Old
single-string callers see the same logical content (same join order),
just reordered so volatile lives at the end.
Config knobs (defaults shown):
prompt_caching:
cache_ttl: "5m" # rolling-window TTL (unchanged)
long_lived_prefix: true # opt-out switch
long_lived_ttl: "1h" # cross-session prefix TTL
Live E2E (tests/agent/test_prompt_caching_live.py, gated on
OPENROUTER_API_KEY) on anthropic/claude-haiku-4.5 with default toolset:
Call 1 (cold): cache_write=13,415 cache_read=0
Call 2 (NEW agent + msg): cache_write=391 cache_read=13,025
Cross-session reuse: 97.09%
Implementation:
* agent/prompt_caching.py: new apply_anthropic_cache_control_long_lived()
+ mark_tools_for_long_lived_cache(); existing apply_anthropic_cache_control()
preserved verbatim for the fallback path.
* agent/anthropic_adapter.py: convert_tools_to_anthropic() now forwards
cache_control onto each Anthropic-format tool dict.
* run_agent.py: _build_system_prompt_parts() returns the 3-tier dict;
_build_system_prompt() joins them (backward compatible).
_supports_long_lived_anthropic_cache() policy added next to the existing
_anthropic_prompt_cache_policy() (which now also recognises Nous Portal
Claude — pre-existing gap fixed in passing).
_build_api_kwargs() resolves tools_for_api once and propagates the
marker through all four build paths (anthropic_messages, bedrock,
codex_responses, profile/legacy chat completions).
Long-lived flag plumbed into the runtime snapshot/restore + model-switch
+ fallback-promotion paths.
Tests:
* tests/agent/test_prompt_caching.py: +8 tests (TestMarkToolsForLongLivedCache,
TestApplyAnthropicCacheControlLongLived).
* tests/run_agent/test_anthropic_prompt_cache_policy.py: +9 tests
(TestSupportsLongLivedAnthropicCache matrix across 8 endpoint classes
+ a fallback-target case).
* tests/agent/test_prompt_caching_live.py: new live E2E (skipif when
OPENROUTER_API_KEY is unset; runs outside the hermetic suite).
* Targeted suites: 327/327 pass (caching/adapter/policy/builder).
* tests/agent/ + tests/run_agent/: 3992 pass, 17 skip, 1 pre-existing
flake (test_async_httpx_del_neuter::test_same_key_replaces_stale_loop_entry,
verified failing on pristine origin/main).
Replace with for all literal-tuple
membership tests. Set lookup is O(1) vs O(n) for tuple — consistent
micro-optimization across the codebase.
608 instances fixed via `ruff --fix --unsafe-fixes`, 0 remaining.
133 files, +626/-626 (net zero).
When the user's main provider is openai-codex on the ChatGPT-account
backend (https://chatgpt.com/backend-api/codex), sending a native image
attachment encodes it as data:image/...base64,... in the input_image
field. The OpenAI Responses API on the public endpoint accepts that, but
the ChatGPT-account variant rejects it with HTTP 400:
Invalid 'input[N].content[K].image_url'. Expected a valid URL, but got
a value with an invalid format.
Hermes' image-rejection phrase list didn't include this wording, so the
error escaped the strip-and-retry branch and fell through to the generic
recovery path: model fallback → context-too-large → compression cascade
→ auxiliary OpenRouter 402 spam (issue #23570).
Add a NARROW phrase keyed on the field-path apostrophe used by the Codex
Responses error format: "image_url'. expected". This matches the actual
error format without false-tripping on generic 'Expected a valid URL'
errors from unrelated tools (webhooks, redirect_uri, etc.). Once matched,
the existing branch strips images from history, sets _vision_supported=
False for the session, and retries text-only.
Refs #23570 (1 of 3 image-replay improvements; persistence rewrite to
store image PATHS instead of inlined base64 is a separate follow-up)
* Revert "fix(goals): force judge to use tool calls instead of JSON-text replies (#23547)"
This reverts commit a63a2b7c78.
* Revert "fix(goals): forward standing /goal state on auto-compression session rotation (#23530)"
This reverts commit 4a080b1d5a.
* Revert "feat(goals): /goal checklist + /subgoal user controls (#23456)"
This reverts commit 404640a2b7.
When a kanban worker subprocess hits the iteration budget, the agent
loop strips tools and asks the model for a summary. The model cannot
call kanban_block itself at that point, so the process exits rc=0
without calling kanban_complete or kanban_block — a protocol violation
that the dispatcher detects as a fatal error, giving up after 1 failure
and stranding downstream tasks.
Fix: after _handle_max_iterations() returns, check HERMES_KANBAN_TASK
and call kanban_block with a reason describing the exhaustion. The
dispatcher then sees a clean block transition instead of a protocol
violation, and the task can be retried or escalated by a human.
Fixes [Bug] kanban-worker exits cleanly (rc=0) on iteration-budget
exhaustion without calling kanban_complete or kanban_block #23216
Salvages the three substantive low-severity fixes from Gutslabs' #1974
"misc bug fixes" bundle. The other 8 claims in that PR were either
already fixed on main with superior implementations (state lock,
firecrawl lazy import, fcntl/msvcrt guard, path normalization, schema
migrations) or did not survive review.
- run_agent: `_materialize_data_url_for_vision` uses
`NamedTemporaryFile(delete=False)`; if `base64.b64decode` raises on a
corrupt data URL the temp file would persist forever. Wrap the
write in try/except and `os.unlink` the temp on failure.
- gateway/session: `append_to_transcript` JSONL write had no error
handling, so disk-full / read-only-fs / permission errors crashed the
message handler. The SQLite write above is the primary store, so
swallow OSError on the JSONL fallback with a debug log.
- gateway/status: `_read_pid_record` reads `pid_path.read_text()` after
an `exists()` check; if the PID file is deleted between the two
calls (concurrent gateway restart) we hit an unhandled OSError.
Catch it and return None.
Adds a regression test for the tempfile cleanup; the other two paths
are defensive try/excepts on infrequent OSError that don't warrant
dedicated tests.
Co-authored-by: Teknium <127238744+teknium1@users.noreply.github.com>
When run_agent's _compress_context fires mid-turn it ends the parent
session in SessionDB and creates a new continuation session with a
fresh session_id. The /goal state is keyed on session_id in
state_meta ("goal:<sid>"), so without forwarding the goal silently
disappears: _get_goal_manager() rebinds for the new session_id,
load_goal() returns None, mgr.is_active() is False, and the
continuation loop dies with no user-visible signal.
Fix: in the same SessionDB transaction block that creates the
continuation session, copy state_meta[goal:<old>] →
state_meta[goal:<new>] when present. No-op when the user has no
active goal. Logged at INFO so a stuck loop is debuggable.
Tests cover the round-trip via SessionDB and the no-op path.
Affects all three run-conversation surfaces (CLI, gateway, TUI
gateway) because _compress_context is the single rotation site.
Closes#6051.
Reported failure mode: agent migrated to WSL2, browser launch failed
because Playwright wasn't installed yet. Background reviewer captured
the failure as a durable skill (`browser-tool-launch-issue`) and the
agent kept refusing the browser tool for weeks after Playwright was
installed and verified working. Negative claims also propagated into
unrelated skills ("browser tools do not work", "cannot use Y from
execute_code").
Root cause: `_SKILL_REVIEW_PROMPT` and `_COMBINED_REVIEW_PROMPT` both
lean hard on "be active, save things, a pass that does nothing is a
missed learning opportunity." Neither distinguished durable knowledge
from transient environment state. The reviewer was doing what it was
told.
Fix at the write site — both prompts now carry a "Do NOT capture"
section calling out:
• Environment-dependent failures (missing binaries, fresh-install
errors, post-migration path mismatches, 'command not found',
unconfigured credentials, uninstalled packages)
• Negative claims about tools or features ("X does not work")
that harden into self-cited refusals
• Session-specific transient errors that resolved before the
conversation ended
• One-off task narratives ("summarize today's market", "analyze
this PR") — also addresses the #12812 / #4538 family
Plus a positive-reframing line: when a tool fails because of setup
state, capture the FIX (install command, config step, env var)
under an existing setup/troubleshooting skill — never "this tool
doesn't work" as a standalone constraint.
Targeted tests: 24/24 passing in tests/run_agent/test_review_prompt_class_first.py
(2 new + all existing review-prompt assertions). Substring-based
checks so future prompt edits don't false-fail.
The previous PR (#22993) gave us a structured WARNING per stream drop
but the only diagnostic was 'error_type=APIError error=Network
connection lost.' — same nothing the user started with. To actually
diagnose why subagents drop streams disproportionately we need to know
WHERE the drop happened.
Adds three breadcrumbs to the agent.log WARNING:
1. Inner exception chain. openai SDK wraps httpx errors as
APIConnectionError / APIError so the catch site only sees the
wrapper. _flatten_exception_chain walks __cause__/__context__ up to
4 levels deep and renders 'Outer(msg) <- Inner(msg)' so we can
tell ConnectError vs RemoteProtocolError vs ReadError vs
ProxyError without enabling verbose mode.
2. Upstream HTTP headers. Snapshots cf-ray, x-openrouter-provider,
x-openrouter-model, x-openrouter-id, x-request-id, server, via,
etc. from stream.response immediately after open (so they survive
even when the stream dies before the first chunk). These answer
'is one CF edge / one downstream provider responsible, or random?'
3. Per-attempt counters. bytes streamed, chunk count, elapsed time on
the dying attempt, and time-to-first-byte. Distinguishes 'couldn't
connect at all' (0s, 0 bytes) from 'died after 30s mid-stream'
(very different root causes — first is auth/routing, second is
upstream idle-kill or proxy timeout).
Plumbing:
- _stream_diag_init / _stream_diag_capture_response live on AIAgent
and produce a per-attempt dict held on request_client_holder['diag']
for closure access from the retry block.
- _call_chat_completions and _call_anthropic both initialize the diag
and increment counters per chunk/event (best-effort, never raises in
the streaming hot path).
- _log_stream_retry / _emit_stream_drop accept an optional diag and
render the new fields. Final-exhaustion log goes through the same
helper so it gets the same diagnostic dump.
- UI status line gains a brief 'after Xs' suffix when timing is
available — distinguishes 'connect failed' from 'died mid-stream'
at a glance without grepping logs.
Sample WARNING after this change:
Stream drop mid tool-call on attempt 2/3 — retrying.
subagent_id=sa-2-cafef00d depth=1 provider=openrouter
base_url=https://openrouter.ai/api/v1
error_type=APIError error=Connection error.
chain=APIError(Connection error.) <- RemoteProtocolError(peer
closed connection without sending complete message body)
http_status=200 bytes=12400 chunks=47 elapsed=12.00s ttfb=0.83s
upstream=[cf-ray=8f1a2b3c4d5e6f7g-LAX
x-openrouter-provider=Anthropic
x-openrouter-id=gen-abc123 server=cloudflare]
Tests: 10 covering diag init, header capture (whitelist enforced for
PII), exception-chain walking + depth cap, log content with full diag,
log content without diag (placeholders), UI elapsed-suffix on/off.
Subagent stream drops were spamming the parent terminal with two lines
per blip ('Connection dropped...' + 'Reconnected...') while leaving zero
breadcrumb in agent.log to debug them.
Two underlying bugs, fixed together:
1. quiet_mode raised the run_agent/tools/etc. loggers to ERROR, which
filters records before root-logger file handlers see them. The comment
claimed 'File handlers still capture everything' — that was wrong.
Removed in both run_agent.py and cli.py; console quietness already
comes from hermes_logging not installing a console StreamHandler in
non-verbose mode.
2. The stream-retry blocks emitted two _emit_status calls per drop
('⚠️ Connection dropped... Reconnecting...' + '🔄 Reconnected —
resuming…') with no provider name, so multi-provider sessions had to
dig through agent.log to attribute a drop. Replaced both call sites
with a single _emit_stream_drop helper that emits ONE line naming the
provider and error class, and always writes a structured WARNING to
agent.log with subagent_id, depth, provider, base_url, error_type.
Net UX change: 6 lines per triple-subagent drop → 3 lines, each
naming the provider. agent.log now has a structured breadcrumb per
retry that didn't exist before.
Tests: 6 new tests in tests/run_agent/test_stream_drop_logging.py
covering the logger-level guard, structured WARNING content, single
status line per drop (no Reconnected follow-up), and provider naming.
When the active main model has native vision and the provider supports
multimodal tool results (Anthropic, OpenAI Chat, Codex Responses, Gemini
3, OpenRouter, Nous), vision_analyze loads the image bytes and returns
them to the model as a multimodal tool-result envelope. The model then
sees the pixels directly on its next turn instead of receiving a lossy
text description from an auxiliary LLM.
Falls back to the legacy aux-LLM text path for non-vision models and
unverified providers.
Mirrors the architecture used in OpenCode, Claude Code, Codex CLI, and
Cline. All four converge on the same pattern: tool results carry image
content blocks for vision-capable provider/model combinations.
Changes
- tools/vision_tools.py: _vision_analyze_native fast path + provider
capability table (_supports_media_in_tool_results). Schema description
updated to reflect new behaviour.
- agent/codex_responses_adapter.py: function_call_output.output now
accepts the array form for multimodal tool results (was string-only).
Preflight validates input_text/input_image parts.
- agent/auxiliary_client.py: _RUNTIME_MAIN_PROVIDER/_MODEL globals so
tools see the live CLI/gateway override, not the stale config.yaml
default. set_runtime_main()/clear_runtime_main() helpers.
- run_agent.py: AIAgent.run_conversation calls set_runtime_main at turn
start so vision_analyze's fast-path check sees the actual runtime.
- tests/conftest.py: clear runtime-main override between tests.
Tests
- tests/tools/test_vision_native_fast_path.py: provider capability
table, envelope shape, fast-path gating (vision-capable model uses
fast path; non-vision model falls through to aux).
- tests/run_agent/test_codex_multimodal_tool_result.py: list tool
content becomes function_call_output.output array; preflight
preserves arrays and drops unknown part types.
Live verified
- Opus 4.6 + Sonnet 4.6 on OpenRouter: model calls vision_analyze on a
typed filepath, gets pixels back, reads exact text from images that
no aux description could capture (font color irony, multi-line
fruit-count list, etc.).
PR replaces the closed prior efforts (#16506 shipped the inbound user-
attached path; this PR closes the gap for tool-discovered images).
Fallback chain entries with 'api_key_env: ENV_VAR_NAME' weren't being
resolved by either the init-time fallback path (line ~1660) or the
runtime _try_activate_fallback path (line ~8045). Only literal
'api_key' was honored; the snake_case 'api_key_env' alias documented
elsewhere in the config was silently dropped, so a 'provider: custom'
fallback with base_url + api_key_env worked as primary but failed as
fallback with 'no endpoint credentials found' / 401.
Adds 'or fb.get("api_key_env")' to the existing 'key_env' lookup in
both call sites, with empty-string-to-None coercion so unset env vars
don't poison the resolver.
Salvage of #22665's fallback portion. The original PR also bundled
gateway-degrade-on-no-adapters changes (those land via the carve-out
in #22853 which is the same code) and run_agent.py memory-nudge
counter hydration (issue #22357 territory, not mentioned in the
title). Drops both bundled pieces; keeps just the api_key_env fix.
Closes#5392.
Pick openrouter/pareto-code as your model and OpenRouter auto-routes each
request to the cheapest model meeting your coding-quality bar (ranked by
Artificial Analysis). The new openrouter.min_coding_score config key (0.0-1.0,
default 0.65) tunes the floor.
- hermes_cli/models.py: add openrouter/pareto-code to OPENROUTER_MODELS so
it shows up in the picker with a description
- hermes_cli/config.py: add openrouter.min_coding_score (default 0.65 — lands
on a mid-tier coder on the current Pareto frontier)
- plugins/model-providers/openrouter: emit extra_body.plugins =
[{id: pareto-router, min_coding_score: X}] when model is openrouter/pareto-code
AND the score is a valid float in [0.0, 1.0]
- agent/transports/chat_completions.py: same emission on the legacy flag
path (when no provider profile is loaded)
- run_agent.py: openrouter_min_coding_score kwarg + storage; plumbed into
both build_kwargs() invocations and the context-summary extra_body path
- cli.py: read openrouter.min_coding_score once at init, validate float in
[0,1], pass to AIAgent constructions (CLI + background-task paths)
- cron/scheduler.py, batch_runner.py, tools/delegate_tool.py,
tui_gateway/server.py: propagate the kwarg (mirrors providers_order
plumbing — subagents inherit, cron/batch read from config)
- tests: profile-level + transport-level coverage of the model gating,
unset/empty/out-of-range handling, and the legacy flag path
- docs: new 'OpenRouter Pareto Code Router' section in providers.md
Verified end-to-end against api.openrouter.ai: at score=0.65 we land on a
mid-tier coder, at omission we get the strongest. Score is silently dropped
on any model other than openrouter/pareto-code, so it's safe to leave set.
DeepSeek V4 Pro returns thinking content as typed blocks inside the
content array rather than as a top-level reasoning_content field:
[{"type": "thinking", "thinking": "..."}, {"type": "output", ...}]
_extract_reasoning only handled content as a plain string, so the
thinking text was silently dropped. On the next turn the session was
replayed without the thinking block, causing:
HTTP 400: The content[].thinking in the thinking mode must be
passed back to the API.
Fix: when content is a list and no structured reasoning field was
found, scan for items with type=='thinking' and accumulate their
'thinking' (or 'text') value into reasoning_parts. Structured fields
(reasoning, reasoning_content, reasoning_details) still take priority
so existing provider behaviour is unchanged.
Closes#21944
_try_activate_fallback() walked the chain by index without comparing
the candidate entry against the currently-failing backend. So a
misconfigured chain that listed the same provider+model as the primary,
or two custom_providers entries pointing at the same shim URL, would
loop the same failure 3x for the same backend.
After the fix, advance() skips:
- entries where (provider, model) match the current agent's
- entries with a base_url + model matching the current backend
(catches two custom_providers names pointing at the same shim)
Recursing through self._try_activate_fallback() continues to the next
chain entry; if everything matches, returns False and the caller
moves on without retrying the same broken path.
3 regression tests covering same-provider-same-model skip, same-base_url-
same-model skip, and the all-self-matching-returns-False exhaustion path.
Closes#22548 (the Hermes-side portion). The 120s timeout itself in
the downstream claude-cli shim is a deployment concern documented in
that issue's wherewolf87 comment.
Gateway creates a fresh AIAgent per inbound message in several common
scenarios: cache miss, idle eviction (1h TTL), config-signature
mismatch, process restart. A freshly-built AIAgent has
_turns_since_memory=0 and _user_turn_count=0, so the
memory.nudge_interval trigger ('_turns_since_memory >=
_memory_nudge_interval') can never be reached when these reconstructions
happen on roughly the cadence of the interval. A user can chat for hours
on Telegram without ever seeing a self-improvement review fire.
Reconstruct the counters from conversation_history at the top of
run_conversation(), right after the existing _hydrate_todo_store call.
Idempotent guard ('if self._user_turn_count == 0') means a cached agent
that already accumulated counters keeps them; only freshly-built agents
hydrate. Modulo arithmetic preserves the original 1-in-N cadence rather
than firing a review immediately on resume.
7 regression tests pinning the contract (mid-cycle history, modulo wrap,
idempotency, zero-interval skip, role==user filtering, production-code
anchor).
Closes#22357.
When session_id rotates (e.g. /new), commit_memory_session was firing
MemoryManager.on_session_end but skipping ContextEngine.on_session_end.
Engines that accumulate per-session state (LCM-style DAGs, summary
stores) leaked that state from the rotated-out session into whatever
continued under the same compressor instance.
Mirror the call shutdown_memory_provider already makes — same
lifecycle moment, same hook contract ("real session boundaries (CLI
exit, /reset, gateway expiry)"). /new is a real boundary for the old
session_id; providers keep their state but the rotated-out session_id
is done.
6 regression tests covering both-hooks-fire, no-memory-manager,
no-context-engine, both failure-tolerant paths.
Closes#22394.
SQLite's WAL mode requires shared-memory (mmap) coordination and fcntl
byte-range locks that don't reliably work on network filesystems. Upstream
documents this explicitly:
https://www.sqlite.org/wal.html#sometimes_queries_return_sqlite_busy_in_wal_mode
On NFS / SMB / some FUSE mounts / WSL1, 'PRAGMA journal_mode=WAL' raises
'sqlite3.OperationalError: locking protocol' (SQLITE_PROTOCOL). Before
this change, every feature backed by state.db or kanban.db broke silently:
- /resume, /title, /history, /branch returned 'Session database not
available.' with no cause
- gateway logged the init failure at DEBUG (invisible in errors.log)
- kanban dispatcher crashed every 60s, driving the known migration race
(duplicate column name: consecutive_failures, #21708 / #21374)
Changes:
- hermes_state.apply_wal_with_fallback(): shared helper that tries WAL
and falls back to DELETE on SQLITE_PROTOCOL-style errors with one
WARNING explaining why
- hermes_state.get_last_init_error() + format_session_db_unavailable():
capture the init failure cause and surface it in user-facing strings
(with an NFS/SMB pointer for 'locking protocol')
- hermes_cli/kanban_db.connect(): use the shared helper
- gateway/run.py: bump SessionDB init failure log DEBUG -> WARNING
(matches cli.py's existing correct behavior)
- cli.py (4 sites) + gateway/run.py (5 sites): replace bare
'Session database not available.' with format_session_db_unavailable()
Tests: 12 new tests in tests/test_hermes_state_wal_fallback.py + 1 new
test in tests/hermes_cli/test_kanban_db.py. Existing suites (state,
kanban, gateway, cli) remain green for all tests unrelated to pre-existing
failures on main.
Evidence: real-world user on NFSv3 mount (172.26.224.200:d2dfac12/home,
local_lock=none) reporting 'Session database not available.' on /resume;
'locking protocol' appears in 4 distinct log entries across backup,
kanban, TUI, and CLI paths in the same session.
closes#22032
teknium1 hit ModuleNotFoundError: No module named 'hermes_bootstrap' after
a code update, on both his Windows machine AND his Linux workstation. The
failure mode is real and affects every user who updates hermes by any path
OTHER than a fully-successful ``hermes update``.
## What happens
hermes_bootstrap.py is a top-level module registered via pyproject.toml's
``py-modules`` list (added by Brooklyn's Windows UTF-8 stdio work). It
must be registered in the venv's editable-install .pth file before Python
can find it as a bare ``import hermes_bootstrap``.
``hermes update`` handles this correctly: (1) git reset --hard, (2) clear
__pycache__, (3) uv pip install -e . (re-registers the package including
the new py-modules list), (4) restart.
BUT if any step AFTER (1) fails — network blip during pip install, PEP 668
on a system Python, venv locked, uv not in PATH, a crash mid-update — the
user is left with new code that references hermes_bootstrap and a venv
that doesn't know about it. Every hermes invocation after that crashes
with ModuleNotFoundError, including ``hermes update`` itself. No recovery
path without manual `uv pip install -e .`.
Also affects users who ``git pull`` the repo directly without running
hermes update — relatively common for developers.
## Fix
Wrap ``import hermes_bootstrap`` in a try/except ModuleNotFoundError
across all 6 entry points (hermes_cli/main, run_agent, gateway/run,
acp_adapter/entry, cli, batch_runner). On Windows, missing bootstrap
means the UTF-8 stdio setup doesn't run — degraded behavior (Unicode
chars may fail to print) but NOT a crash. POSIX is unaffected either way
since the bootstrap is a no-op there.
Once hermes is running again, the user can ``hermes update`` to fully
recover.
## Test update
tests/test_hermes_bootstrap.py::test_entry_point_imports_bootstrap
scans for the first top-level import in each entry point and asserts it
is hermes_bootstrap. Extended the check to accept a Try block whose body
is a lone Import of hermes_bootstrap — that's the recovery-friendly form
we just introduced.
Verified behavior by ``mv hermes_bootstrap.py hermes_bootstrap.py.bak``
and confirming ``python -c "import hermes_cli.main"`` succeeds. 82/82
tests pass (hermes_bootstrap + windows-native + windows-compat).
Closes the last Python-on-Windows UTF-8 exposure by making every
text-mode open() call explicit about its encoding.
Before: on Windows, bare open(path, 'r') defaults to the system
locale encoding (cp1252 on US-locale installs). That means reading
any config/yaml/markdown/json file with non-ASCII content either
crashes with UnicodeDecodeError or silently mis-decodes bytes.
After: all 89 affected call sites in production code now pass
encoding='utf-8' explicitly. Works identically on every platform
and every locale, no surprise behavior.
Mechanical sweep via:
ruff check --preview --extend-select PLW1514 --unsafe-fixes --fix --exclude 'tests,venv,.venv,node_modules,website,optional-skills, skills,tinker-atropos,plugins' .
All 89 fixes have the same shape: open(x) or open(x, mode) became
open(x, encoding='utf-8') or open(x, mode, encoding='utf-8'). Nothing
else changed. Every modified file still parses and the Windows/sandbox
test suite is still green (85 passed, 14 skipped, 0 failed across
tests/tools/test_code_execution_windows_env.py +
tests/tools/test_code_execution_modes.py + tests/tools/test_env_passthrough.py +
tests/test_hermes_bootstrap.py).
Scope notes:
- tests/ excluded: test fixtures can use locale encoding intentionally
(exercising edge cases). If we want to tighten tests later that's
a separate PR.
- plugins/ excluded: plugin-specific conventions may differ; plugin
authors own their code.
- optional-skills/ and skills/ excluded: skill scripts are user-authored
and we don't want to mass-edit them.
- website/ and tinker-atropos/ excluded: vendored / generated content.
46 files touched, 89 +/- lines (symmetric replacement). No behavior
change on POSIX or on Windows when the file is ASCII; bug fix on
Windows when the file contains non-ASCII.
Codebase-wide fix for Python-on-Windows UTF-8 footguns, complementing
the earlier execute_code sandbox fixes (which remain load-bearing for
when the sandbox explicitly scrubs child env).
Problem: Python on Windows has two long-standing text-encoding pitfalls:
1. sys.stdout/stderr are bound to the console code page (cp1252 on
US-locale installs) — print('café') crashes with UnicodeEncodeError.
2. Subprocess children don't know to use UTF-8 unless PYTHONUTF8 and/or
PYTHONIOENCODING are set in their env — so any Python we spawn
(linters, sandbox children, delegation workers) hits the same bug.
Solution: A tiny bootstrap module (hermes_bootstrap.py) imported as the
first statement of every Hermes entry point:
- hermes_cli/main.py (hermes / hermes-agent console_script)
- run_agent.py (hermes-agent direct)
- acp_adapter/entry.py (hermes-acp)
- gateway/run.py (messaging gateway)
- batch_runner.py (parallel batch mode)
- cli.py (legacy direct-launch CLI)
On Windows, the bootstrap:
- os.environ.setdefault('PYTHONUTF8', '1') (PEP 540 UTF-8 mode)
- os.environ.setdefault('PYTHONIOENCODING', 'utf-8')
- sys.stdout/stderr/stdin.reconfigure(encoding='utf-8', errors='replace')
Children inherit the env vars → they run in UTF-8 mode.
Current process's stdio is reconfigured → print('café') works now.
On POSIX (Linux/macOS), the bootstrap is a complete no-op. We don't
touch LANG, LC_*, or anything else — users who have intentionally
configured a non-UTF-8 locale aren't affected. POSIX systems are
already UTF-8 by default in 99% of modern setups, so there's nothing
to fix.
setdefault() (not overwrite) means users who explicitly set PYTHONUTF8=0
or PYTHONIOENCODING=cp1252 in their environment are respected.
What this does NOT fix: bare open(path, 'w') calls in the *parent*
process still default to locale encoding because PYTHONUTF8 is only
read at interpreter init. A ruff PLW1514 sweep (separate follow-up)
will add explicit encoding='utf-8' at those ~219 call sites for
belt-and-suspenders.
Tests (17): 16 passed, 1 skipped on Windows.
- Windows: env vars set, stdio reconfigured, child inherits UTF-8 mode
- POSIX: complete no-op (verified on fake POSIX + skipped on real
POSIX since we don't have a Linux box in this session)
- Idempotence: multiple calls safe
- Graceful degradation: non-reconfigurable streams don't crash
- User opt-out: explicit PYTHONUTF8=0 is respected
- Load order: every entry point's FIRST top-level import is
hermes_bootstrap, enforced by an AST-level parametrized test
pyproject.toml: added hermes_bootstrap to py-modules so it ships with
pip installs.
Follow-up to #15328's vision-unsupported retry branch in run_agent.py.
_strip_images_from_messages() previously deleted any message whose content
was entirely images. That's fine for synthetic user messages injected for
attachment delivery, but it breaks providers for tool-role messages — the
paired tool_call_id on the preceding assistant message ends up unmatched,
which OpenAI-compatible APIs reject with HTTP 400.
Fix: tool-role messages whose content becomes empty are replaced with a
plaintext placeholder that preserves the tool_call_id linkage. Only
non-tool messages are dropped. Added 10 tests covering the role-alternation
invariants + image-type coverage.
Image-rejection detector: expanded phrase list (image content not
supported / multimodal input / vision input / model does not support
image) and gated on 4xx status so transient 5xx errors never get
misinterpreted as 'server said no to images'. Detection is documented as
best-effort English phrase matching.
AUTHOR_MAP: mapped 3820588+ddupont808@users.noreply.github.com to
ddupont808 so release notes attribute the salvage correctly.
Tool handlers (e.g. computer_use capture) return a _multimodal envelope
dict when a screenshot is attached. The tool-message builder was passing
this raw dict as the `content` field of role:tool messages, which is an
illegal format — OpenAI-compatible APIs expect a string or a content-parts
list, not a plain Python dict, and would reject it with a 400/422 error.
Fix: unwrap _multimodal results to their `content` list
([{type:text,...},{type:image_url,...}]) in both the parallel and
sequential tool-call paths. The Anthropic adapter already handles content
lists natively; vision-capable OpenAI-compatible servers (mlx-vlm,
GPT-4o, etc.) accept image_url parts in tool messages directly.
Also add a _vision_supported adaptive fallback: on first image-rejection
error ("Only 'text' content type is supported." etc.) the agent strips all
image parts from the message history and retries with text only, so
text-only endpoints degrade gracefully without crashing the session.
Extends the cua-driver computer-use backend to drive backgrounded macOS
windows without stealing keyboard or mouse focus from the foreground app.
All changes target the cua-driver MCP backend and the shared dispatcher.
## cua_backend.py
**Window-aware capture**: capture() now calls list_windows + get_window_state
instead of the removed capture tool. Prefers structuredContent.windows
(MCP 2024-11-05+ cua-driver) for zero-parse window enumeration; falls back
to regex-parsed text for older builds. Stores the selected (pid, window_id)
as sticky context so subsequent action calls do not need a redundant round-trip.
**Action routing**: click/scroll/type_text/key all carry the sticky pid
(and window_id for element-indexed clicks). type_text routes through
type_text_chars (individual key events) rather than AX attribute write --
WebKit AXTextFields reject attribute writes from backgrounded processes.
**Key parsing**: _parse_key_combo splits cmd+s-style strings into
(key, [modifiers]) and routes to hotkey (modifier present) or
press_key (bare key) -- cua-driver actual tool names.
**set_value method**: new set_value(value, element) calls the cua-driver
set_value MCP tool. For AXPopUpButton / HTML select in a backgrounded Safari,
AXPress opens the native macOS popup which closes immediately when the app is
non-frontmost; set_value AX-presses the matching child option directly
(no menu required, no focus steal).
**focus_app**: reimplemented as a pure window-selector (enumerates
list_windows, sets sticky pid/window_id) without ever raising the window
or stealing focus.
**list_apps**: fixed tool name from listApps to list_apps; handles plain-text
response via regex when structured data is absent.
**Structured-content extraction**: _extract_tool_result now surfaces
structuredContent from MCP results, enabling the list_windows window array
without text parsing.
**Helpers**: _parse_windows_from_text, _parse_elements_from_tree,
_split_tree_text, _parse_key_combo extracted as module-level functions.
## schema.py
Added set_value to the action enum with a description explaining when to
prefer it over click (select/popup elements, sliders, no focus steal).
Added value field for set_value payloads.
## tool.py
Routed set_value action through _dispatch to backend.set_value.
Added set_value to _DESTRUCTIVE_ACTIONS (approval-gated).
Fixed MIME-type detection in _capture_response: cua-driver may return
JPEG; detect from base64 magic bytes (/9j/ -> image/jpeg, else image/png)
rather than hardcoding image/png.
## agent/display.py + run_agent.py
Guard _detect_tool_failure and result-preview logic against non-string
function_result values: multimodal tool results (dicts with _multimodal=True)
are not string-sliceable; treat them as successes and fall back to str()
for length/preview.
Background macOS desktop control via cua-driver MCP — does NOT steal the
user's cursor or keyboard focus, works with any tool-capable model.
Replaces the Anthropic-native `computer_20251124` approach from the
abandoned #4562 with a generic OpenAI function-calling schema plus SOM
(set-of-mark) captures so Claude, GPT, Gemini, and open models can all
drive the desktop via numbered element indices.
- `tools/computer_use/` package — swappable ComputerUseBackend ABC +
CuaDriverBackend (stdio MCP client to trycua/cua's cua-driver binary).
- Universal `computer_use` tool with one schema for all providers.
Actions: capture (som/vision/ax), click, double_click, right_click,
middle_click, drag, scroll, type, key, wait, list_apps, focus_app.
- Multimodal tool-result envelope (`_multimodal=True`, OpenAI-style
`content: [text, image_url]` parts) that flows through
handle_function_call into the tool message. Anthropic adapter converts
into native `tool_result` image blocks; OpenAI-compatible providers
get the parts list directly.
- Image eviction in convert_messages_to_anthropic: only the 3 most
recent screenshots carry real image data; older ones become text
placeholders to cap per-turn token cost.
- Context compressor image pruning: old multimodal tool results have
their image parts stripped instead of being skipped.
- Image-aware token estimation: each image counts as a flat 1500 tokens
instead of its base64 char length (~1MB would have registered as
~250K tokens before).
- COMPUTER_USE_GUIDANCE system-prompt block — injected when the toolset
is active.
- Session DB persistence strips base64 from multimodal tool messages.
- Trajectory saver normalises multimodal messages to text-only.
- `hermes tools` post-setup installs cua-driver via the upstream script
and prints permission-grant instructions.
- CLI approval callback wired so destructive computer_use actions go
through the same prompt_toolkit approval dialog as terminal commands.
- Hard safety guards at the tool level: blocked type patterns
(curl|bash, sudo rm -rf, fork bomb), blocked key combos (empty trash,
force delete, lock screen, log out).
- Skill `apple/macos-computer-use/SKILL.md` — universal (model-agnostic)
workflow guide.
- Docs: `user-guide/features/computer-use.md` plus reference catalog
entries.
44 new tests in tests/tools/test_computer_use.py covering schema
shape (universal, not Anthropic-native), dispatch routing, safety
guards, multimodal envelope, Anthropic adapter conversion, screenshot
eviction, context compressor pruning, image-aware token estimation,
run_agent helpers, and universality guarantees.
469/469 pass across tests/tools/test_computer_use.py + the affected
agent/ test suites.
- `model_tools.py` provider-gating: the tool is available to every
provider. Providers without multi-part tool message support will see
text-only tool results (graceful degradation via `text_summary`).
- Anthropic server-side `clear_tool_uses_20250919` — deferred;
client-side eviction + compressor pruning cover the same cost ceiling
without a beta header.
- macOS only. cua-driver uses private SkyLight SPIs
(SLEventPostToPid, SLPSPostEventRecordTo,
_AXObserverAddNotificationAndCheckRemote) that can break on any macOS
update. Pin with HERMES_CUA_DRIVER_VERSION.
- Requires Accessibility + Screen Recording permissions — the post-setup
prints the Settings path.
Supersedes PR #4562 (pyautogui/Quartz foreground backend, Anthropic-
native schema). Credit @0xbyt4 for the original #3816 groundwork whose
context/eviction/token design is preserved here in generic form.
When switching from a custom local provider (e.g. ollama-launch) to a
cloud provider, two bugs caused the CLI to misbehave:
1. _explicit_api_key/_explicit_base_url were only updated when the switch
result had non-empty values (guarded by `if result.api_key:` etc.).
If the previous provider set these to Ollama values ("ollama",
"http://127.0.0.1:11434/v1"), those stale values leaked into the next
turn's _ensure_runtime_credentials() call and were forwarded to the
new provider's API endpoint, causing authentication/routing failures.
Fix: unconditionally write result.api_key/base_url into the explicit
fields after every successful switch. An empty string is the correct
sentinel — it tells _ensure_runtime_credentials to re-resolve from the
auth store / config rather than forwarding a stale override.
2. In AIAgent.switch_model(), `self.base_url = base_url or self.base_url`
kept the old Ollama localhost URL whenever the incoming base_url was an
empty string. For providers that use a native SDK (not an OpenAI-compat
endpoint), the caller passes base_url="" and expects the agent to clear
the field — not silently inherit Ollama's address.
Fix: only update self.base_url when base_url is truthy.
3. _handle_model_picker_selection() was called from the prompt_toolkit
Enter key binding without any exception guard. Any unexpected error
in the model-selection code path propagated through prompt_toolkit's
key-binding dispatcher and caused the entire TUI to exit — which the
user sees as "the terminal exits when I switch providers".
Fix: wrap the call in try/except and close the picker on failure.
- Add pricing entries for Claude Opus 4.5/4.6/4.7, Sonnet 4.5/4.6, and
Haiku 4.5 with updated source URLs (platform.claude.com)
- Add _normalize_anthropic_model_name() to handle dot-notation variants
(e.g. claude-opus-4.7 → claude-opus-4-7) for pricing lookups
- Fix silent token loss: ensure session row exists before UPDATE in both
run_agent.py and hermes_state.py (INSERT OR IGNORE is idempotent)
- Log token persistence failures at DEBUG level instead of swallowing
them silently — makes undercounted analytics diagnosable
- Surface reasoning tokens in CLI /usage and TUI usage panel
- Add 'reasoning' and 'cost_status' fields to TUI Usage type
When empty-response terminal scaffolding fires on a tool-result turn,
_drop_trailing_empty_response_scaffolding left the live history ending at
a bare 'tool' message. The next user input then landed as [...tool, user],
a protocol-invalid sequence that OpenRouter/Opus and other providers
silently fail on (returns empty content). That retriggered the empty-retry
recovery every turn, and recovery flags never hit SQLite (no column for
them), so history kept looking broken on every reload.
Two fixes:
1. Scaffolding strip rewinds the orphan assistant(tool_calls)+tool pair
after popping sentinels. Only fires when scaffolding flags were
actually present, so mid-iteration tool loops are untouched.
2. _repair_message_sequence runs right before every API call as a
defensive belt: drops stray tool messages with unknown tool_call_ids,
merges consecutive user messages so no user input is lost. Does NOT
rewind assistant(tool_calls)+tool+user — that pattern is valid when
the user redirected before the model got its continuation turn.
Repro: session 20260507_044111_fa7e65. Opus-4.7/OpenRouter returned
content-less response after a 42KB execute_code output, nudge+retry
chain exhausted (no fallback configured), terminal sentinel appended,
scaffolding stripped leaving bare tool tail, user typed 'wtf happened..'
and landed as tool→user violation. Every subsequent turn collapsed in
<50ms with the same 3-retry empty chain because the API request itself
was malformed.
Verified live via HTTP mock: pre-fix reproduced 5 api_calls/0.15s exit
'empty_response_exhausted'; post-fix 1 api_call/0.10s exit
'text_response(finish_reason=stop)'. Three-turn session flows cleanly
through the scenario. Full run_agent suite: 1242 passed (0 regressions,
2 pre-existing concurrent_interrupt failures unrelated).
Enables plugins to transform LLM output text after generation,
useful for vocabulary/personality transformation without burning
inference tokens.
Follows same pattern as transform_tool_result and transform_terminal_output:
- First non-empty string result wins
- Fail-open: exceptions logged as warnings, agent continues
- Signature: (response_text, session_id, model, platform)
Replaces the per-directory shadow-repo design with a single shared shadow
git store at ~/.hermes/checkpoints/store/. Object DB is now deduplicated
across every working directory the agent has ever touched; a dozen
worktrees of the same project cost near-zero in additional disk.
Why
---
Pre-v2 design had three compounding problems that let ~/.hermes/checkpoints/
grow to multi-GB on active machines:
1. Each working directory got its own full shadow git repo — no object
dedup across projects or across worktrees of the same project.
2. _prune() was a documented no-op: max_snapshots only limited the
/rollback listing. Loose objects accumulated forever.
3. Defaults: enabled=True, auto_prune=False — users paid the disk cost
without ever asking for /rollback.
Field report on a single workstation: 847 MB across 47 shadow repos,
mostly redundant clones of the hermes-agent source tree.
Changes
-------
- tools/checkpoint_manager.py: full rewrite. Single bare store, per-project
refs (refs/hermes/<hash>), per-project indexes (store/indexes/<hash>),
per-project metadata (store/projects/<hash>.json with workdir +
created_at + last_touch). On first v2 init, any pre-v2 per-directory
shadow repos are auto-migrated into legacy-<timestamp>/ so the new
store starts clean. _prune() now actually rewrites the per-project ref
to the last max_snapshots commits and runs git gc --prune=now. New
_enforce_size_cap() drops oldest commits round-robin across projects
when the store exceeds max_total_size_mb. _drop_oversize_from_index()
filters any single file larger than max_file_size_mb out of the snapshot.
- hermes_cli/checkpoints.py: new 'hermes checkpoints' CLI
(status / list / prune / clear / clear-legacy) for managing the store
outside a session.
- hermes_cli/config.py: flipped defaults — enabled=False, max_snapshots=20,
auto_prune=True. Added max_total_size_mb=500, max_file_size_mb=10.
Tightened DEFAULT_EXCLUDES (added target/, *.so/*.dylib/*.dll,
*.mp4/*.mov, *.zip/*.tar.gz, .worktrees/, .mypy_cache/, etc.).
- run_agent.py / cli.py / gateway/run.py: thread the new kwargs through
AIAgent and the startup auto_prune hooks.
- Tests rewritten to match v2 storage while keeping backwards-compat
coverage for the pre-v2 prune path (per-directory shadow repos under
base/ are still swept correctly for anyone mid-migration).
- Docs updated: user-guide/checkpoints-and-rollback.md explains the
shared store, new defaults, migration, and the new CLI;
reference/cli-commands.md documents 'hermes checkpoints'.
E2E validated
-------------
- Legacy migration: pre-v2 shadow repos auto-archived into legacy-<ts>/.
- Object dedup: two projects with an identical shared.py blob resolve to
7 total objects in the store (v1 would have stored the blob twice).
- max_snapshots=3 actually enforced: after 6 commits, list shows 3.
- Orphan prune: deleting a project's workdir + 'hermes checkpoints prune
--retention-days 0' removes its ref, index, and metadata; GC reclaims
the objects.
- max_file_size_mb=1 excludes a 2 MB weights.bin while keeping the
tracked source code files.
- hermes checkpoints {status,prune,clear,clear-legacy} all work from the
CLI without an agent running.
Breaking / migration
--------------------
No in-place data migration — legacy per-directory shadow repos are moved
into legacy-<timestamp>/ on first run. Old /rollback history is still
accessible by inspecting the archive with git; run
'hermes checkpoints clear-legacy' to reclaim the space when ready. Users
relying on /rollback must now set checkpoints.enabled=true (or pass
--checkpoints) explicitly.
Introduces providers/ package — single source of truth for every
inference provider. Adding a simple api-key provider now requires one
providers/<name>.py file with zero edits anywhere else.
What this PR ships:
- providers/ package (ProviderProfile ABC + 33 profiles across 4 api_modes)
- ProviderProfile declarative fields: name, api_mode, aliases, display_name,
env_vars, base_url, models_url, auth_type, fallback_models, hostname,
default_headers, fixed_temperature, default_max_tokens, default_aux_model
- 4 overridable hooks: prepare_messages, build_extra_body,
build_api_kwargs_extras, fetch_models
- chat_completions.build_kwargs: profile path via _build_kwargs_from_profile,
legacy flag path retained for lmstudio/tencent-tokenhub (which have
session-aware reasoning probing that doesn't map cleanly to hooks yet)
- run_agent.py: profile path for all registered providers; legacy path
variable scoping fixed (all flags defined before branching)
- Auto-wires: auth.PROVIDER_REGISTRY, models.CANONICAL_PROVIDERS,
doctor health checks, config.OPTIONAL_ENV_VARS, model_metadata._URL_TO_PROVIDER
- GeminiProfile: thinking_config translation (native + openai-compat nested)
- New tests/providers/ (79 tests covering profile declarations, transport
parity, hook overrides, e2e kwargs assembly)
Deltas vs original PR (salvaged onto current main):
- Added profiles: alibaba-coding-plan, azure-foundry, minimax-oauth
(were added to main since original PR)
- Skipped profiles: lmstudio, tencent-tokenhub stay on legacy path (their
reasoning_effort probing has no clean hook equivalent yet)
- Removed lmstudio alias from custom profile (it's a separate provider now)
- Skipped openrouter/custom from PROVIDER_REGISTRY auto-extension
(resolve_provider special-cases them; adding breaks runtime resolution)
- runtime_provider: profile.api_mode only as fallback when URL detection
finds nothing (was breaking minimax /v1 override)
- Preserved main's legacy-path improvements: deepseek reasoning_content
preserve, gemini Gemma skip, OpenRouter response caching, Anthropic 1M
beta recovery, etc.
- Kept agent/copilot_acp_client.py in place (rejected PR's relocation —
main has 7 fixes landed since; relocation would revert them)
- _API_KEY_PROVIDER_AUX_MODELS alias kept for backward compat with existing
test imports
Co-authored-by: kshitijk4poor <82637225+kshitijk4poor@users.noreply.github.com>
Closes#14418
Each auxiliary model must be resolved with its own provider so that
provider-specific paths (e.g. Bedrock static table, OpenRouter API)
are invoked for the correct client, not inherited from the main model.
When the main model is Bedrock, passing self.provider unconditionally
to get_model_context_length() for the aux model caused the Bedrock
static table hard-intercept (step 1b) to fire for non-Bedrock models,
returning BEDROCK_DEFAULT_CONTEXT_LENGTH=128K instead of the model's
real context window — triggering a false compression warning every session.
Fix: pass _aux_cfg_provider when explicitly set, falling back to
self.provider only when the aux provider is unset or "auto".
Closes#12977
Related: #13807, #17460
