Follow-up to the salvaged contributor commit, closing the three gaps
flagged in the sweeper review:
1. Init ordering: assign compression.model_thresholds to a selected
plugin context engine BEFORE the initial update_model() call in
agent_init.py, so the initial model's override applies from init
(previously it only took effect after the first /model switch).
Base-class ContextEngine.update_model() now snapshots the
pre-override percent once so repeated switches fall back to the
engine's configured threshold, not a previous model's override.
2. DEFAULT_CONFIG: add compression.model_thresholds (empty map) to
hermes_cli/config.py — additive key, no _config_version bump.
3. Docs: document the key in
website/docs/developer-guide/context-compression-and-caching.md
(yaml example, parameter table, dedicated section) and update the
plugin-boundary note in context-engine-plugin.md to state the
explicit context-engine contract for model_thresholds.
Adds tests/run_agent/test_per_model_threshold_init_ordering.py:
plugin-engine AIAgent init regression (override applies at init,
empty map unchanged), DEFAULT_CONFIG key presence, floor interaction
on the model-switch path (override below the small-context floor is
raised to the floor; above the floor wins), and base-class config
snapshot across repeated switches. Also maps @bennybuoy in
contributors/emails/.
Addresses teknium1 review feedback on PR #60781:
1. Gateway cache invalidation: added ('compression', 'model_thresholds')
to _CACHE_BUSTING_CONFIG_KEYS so a live config edit to the map
invalidates the cached compressor (previously kept stale thresholds).
2. Integrated resolver with small-context floor: per-model overrides are
resolved FIRST, then the existing 75% floor for <512K models is applied
on top. The floor is no longer replaced — it stacks. An override below
75% on a small-context model still gets floored to 75% (raise-only);
an override above 75% wins.
3. Clean rebase on upstream main — no unrelated deletions or anti-thrashing
changes. Only the per-model threshold feature is added.
Changes:
- resolve_model_threshold() module-level helper (longest substring match)
- ContextCompressor.__init__ accepts model_thresholds dict
- _base_threshold_percent stores the per-model resolved value
- _config_threshold_percent stores the raw config value (fallback base)
- update_model() re-resolves on /model switch, falls back to config value
- ContextEngine base class update_model() applies overrides for plugin engines
- agent_init.py reads compression.model_thresholds from config, passes to ctor
- gateway/run.py cache busting key added
- cli-config.yaml.example documents the feature
- 17 tests covering resolve helper, compressor init (large/small context,
override above/below floor), update_model (re-resolve, fallback), base class
Co-authored-by: Copilot <copilot@github.com>
skip_memory=True was meant to skip the external memory *provider* for flush/
background agents, but it also suppressed creation of the built-in file-backed
MemoryStore. When a caller still enables the "memory" toolset, the memory tool
dispatched with store=None and every call failed with "Memory is not available",
silently losing the main automatic memory-capture path.
Now the built-in store is created whenever memory is enabled in config OR the
memory toolset is explicitly enabled, while the external-provider block stays
gated on skip_memory (preserving flush-agent intent).
Follow-up to the salvaged #64010 (Kenmege) and #63870 (dombejar) commits,
making one resolved compression.max_attempts cap govern ALL per-turn
compression attempt sites:
- conversation_loop: resolve max_compression_attempts ONCE at turn start
(it was previously re-resolved inside the API-call loop) and route the
pre-API pressure gate through it — that gate still hardcoded
'compression_attempts < 3' and logged 'attempt=%s/3'.
- conversation_loop: the salvaged post-tool compaction gate now uses the
resolved cap instead of a hardcoded 3.
- turn_context: the preflight compaction loop was 'for _pass in range(3)';
it now sizes itself from the same resolved cap.
- agent_init: harden the max_attempts parser — reject booleans (bool
subclasses int; 'true' would coerce to 1), reject fractional floats
instead of truncating them, keep accepting integral floats and numeric
strings; anything else falls back to 3 (floor 1, ceiling 10 unchanged).
- tests: replace #63870's inspect.getsource source-shape test with
behavioral loop tests (post-tool compaction fires <= cap times per turn,
shares its budget with the pre-API gate, resets between turns); add an
e2e test proving a 4th preflight pass runs at config cap=6 while the
unset default still stops at 3; extend the #64010 config tests with the
bool/float parser semantics.
Salvages #64010 by @Kenmege and #63870 by @dombejar.
The conversation loop hardcodes max_compression_attempts = 3. Sessions
that legitimately need more rounds are stranded: on a restart history
reload, incompressible tool schemas can keep the per-request estimate
above the compressor threshold even though the message floor compresses
correctly, so three rounds cannot clear it and the turn dies with
"Context length exceeded: max compression attempts (3) reached" — the
same failure class as #62605, where the rough estimate similarly leaves
3 retries short.
Make the cap a config key, compression.max_attempts:
- default 3 = identical to today, so an unset key is behavior-neutral;
- parsed and validated in agent_init alongside the other compression.*
keys (>= 1, hard-capped at 10, non-integer values fall back to 3),
attached as agent.max_compression_attempts;
- the loop reads it via getattr(agent, "max_compression_attempts", 3),
so objects without the attribute keep the prior behavior;
- documented in the DEFAULT_CONFIG compression block.
Tests pin the parse/validate/attach seam: default preserved, custom
value honored, floor and ceiling enforced, garbage tolerated, and the
loop-side getattr degradation.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The autoraise banner hardcoded '272K' for the gpt-5.4/5.5/5.6 family, but
the Codex /models catalog is authoritative and shifts server-side (gpt-5.6
served 372K during July 9-18, 2026 before OpenAI rolled it back). Pass the
compressor's live-resolved context_length through so the notice reports the
window the session actually got; the static 272K/128K text remains as the
fallback when no resolved value is available.
When the stale-stream detector reconnects past a stream whose socket abort
raced (the close never actually stopped the old worker), the superseded stream
and the retry's stream both write deltas into the same turn. The persisted
transcript is then two coherent responses interleaved token-by-token —
de-interleaving the stored text by alternation yields two complete, independent
answers to the same prompt, which is a dual-writer race in the harness, not a
model/context failure (#65991).
The interrupt path already positively cancels before force-closing (#6600), but
the stale-kill path relies only on the socket abort, and nothing fenced late
chunks from a superseded stream out of the shared delta sink.
Enforce a single-writer invariant on the sink itself, guarded by attempt id
rather than only socket state: every streaming attempt (chat_completions,
anthropic_messages, and bedrock paths) claims a monotonic writer token before
it begins consuming its stream. A newer claim supersedes any older one, so the
consume loop bails the instant it is superseded and _fire_stream_delta /
_fire_reasoning_delta / _record_streamed_assistant_text drop chunks from a
stale writer. The token is stored per-thread, so a thread that never claimed
(a non-streaming delta caller) is never fenced — the guard can only ever drop a
superseded stream, never the single legitimate writer. Discards are counted and
logged sparsely so a real provider problem stays visible instead of being
silently swallowed.
Commentary delivery is on by default; users who find the extra mid-turn
narration noisy can set display.show_commentary: false to restore the
previous behavior (commentary routed to the reasoning channel, visible
only with show_reasoning).
- hermes_cli/config.py: display.show_commentary default true
- agent/agent_init.py: wire config -> agent.show_commentary
- run_agent.py: gate structured commentary extraction on the flag
- agent/codex_runtime.py: gate live-stream commentary callback (falls
back to legacy reasoning-channel routing when off)
- docs + 2 tests (interim path off, live stream fallback)
Also adds AUTHOR_MAP entries for davidrobertson and 100yenadmin.
Provider auto-detection (URL-based inference for Anthropic, OpenAI Codex,
and xAI endpoints) runs before credential-pool validation in AIAgent init,
but #63048 placed the pool validation before auto-detection. When the agent
is constructed with provider=None and a recognized endpoint URL, the pool
is validated against an empty provider identity and discarded, even though
auto-detection correctly resolves the provider moments later.
Fix: move the credential-pool validation block to after the URL-based
auto-detection chain. The pool is stored on the agent before
auto-detection; validation now checks the resolved provider and only
nullifies agent._credential_pool when the pool's scoped provider genuinely
doesn't match.
Regression test covers all three auto-detection paths:
- Anthropic (api.anthropic.com)
- OpenAI Codex (chatgpt.com/backend-api/codex)
- xAI (api.x.ai)
Fixes#63425.
Preserve one durable staged input across terminal close and the worker's early turn flush, without duplicating resumed transcripts or creating a session with a null prompt. Fixes#63766.
_custom_provider_model_matches() only compared the session model
against the entry's single 'model' field. A custom provider declaring
a multi-model catalog (providers.<name>.models mapping / models list)
whose default model differed from the session model silently failed to
match — dropping the entry's extra_body entirely. Real impact: an
OpenAI custom provider pinning service_tier=flex via extra_body ran
every request at STANDARD tier (~2.3x billing) with zero signal.
- Model matching now accepts the session model when it appears in the
entry's models catalog (dict keys or list), case-insensitive;
single-model 'model' field behavior unchanged; entries with neither
still match everything.
- Usage report ('hermes -z --usage-file') now carries service_tier
(the tier requested via request_overrides.extra_body) so batch
pipelines can audit the billed tier per run.
Validation: 8 new tests; live E2E via real 'hermes -p sweeper -z'
with httpx-level wire capture — service_tier=flex present in the
outgoing /v1/responses body and in the usage report.
Retain the provider-boundary core of #52799 while reusing the pool reload and handoff paths already landed in #53591 and #62417.
Co-authored-by: Flownium <157689911+itsflownium@users.noreply.github.com>
Add a token-free, curated affection matcher (agent/reactions.py) — the single
source of truth for detecting user "vibes" (ily / <3 / good bot / heart emoji).
No model call, no tokens. Generalized to return a reaction *kind* so future
reactions can ride the same signal.
Wire an opt-in AIAgent.reaction_callback that fires from build_turn_context on
the incoming user message. It never touches the conversation (cache-safe) and
never fatal — a purely cosmetic side-beat each host can consume.
Close the remaining end-to-end gaps so the full gpt-5.6 family (sol/
terra/luna + their -pro high-effort modes, 6 slugs) works on every
surface a user can reach them through:
- agent/auxiliary_client.py: the Codex OAuth backend hard-caps context
at 272K for gpt-5.6 exactly as it does for 5.4/5.5, but the default
50% compaction trigger would summarize at ~136K and waste half the
usable window. Extend the existing _is_codex_gpt54_or_gpt55 chokepoint
(single enforced predicate feeding _compression_threshold_for_model)
to match gpt-5.6* on the openai-codex route so those sessions get the
same 0.85 auto-raise. Direct-API/OpenRouter routes (full 1.05M window)
are unaffected; the historical codex_gpt55_autoraise opt-out still
applies. The one-time notice banner is model-dynamic and already
renders the correct slug/cap.
- hermes_cli/config.py, agent/agent_init.py: refresh the autoraise
comments/notice to mention the 5.6 family.
- hermes_cli/codex_models.py: add the -pro variants to DEFAULT_CODEX_MODELS
+ forward-compat so ChatGPT-OAuth (openai-codex) Pro users see the full
family in /model, not just the base tiers.
Supersedes the earlier commit's note that 5.6 was intentionally kept out
of the codex catalog: the slugs are confirmed routable (OpenRouter live
+ codex backend), so they belong there like every other codex-capable
gpt-5.x slug.
E2E verified across all 6 slugs: direct-API ctx 1.05M, codex ctx 272K,
pricing reachable from openai + openai-api routes, codex compaction
override 0.85 (and None on direct-API + when opted out), present in
openai-api picker + codex catalog, /model gpt resolves to sol on both
native routes. Guard tests added for the compaction route matrix.
When context.engine selects a plugin engine (e.g. LCM), the host
compression threshold — including the Codex gpt-5.5 50% -> 85%
autoraise — only configures the built-in ContextCompressor and never
reaches the plugin. The autoraise notice still fired, telling the user
auto-compaction was raised when nothing actually changed, and the
startup context-limit line printed the host percent next to the
engine's own threshold_tokens, contradicting itself.
- Clear _compression_threshold_autoraised when a plugin engine is
selected, suppressing both the CLI startup notice and the gateway
turn-1 replay via _compression_warning.
- Print the active engine's own threshold_percent in the startup
context-limit line so percent and token count agree.
- Built-in behavior is preserved, including the fallback path where a
configured engine fails to load and the built-in compressor takes
over.
Fixes#44439
The Codex gpt-5.5 compaction-threshold autoraise notice re-fired on every
agent init. Because the gateway rebuilds the agent per inbound message, the
notice spammed long-running Discord/Telegram/etc. sessions, and the only
documented remedy (`compression.codex_gpt55_autoraise false`) disables the
useful autoraise behavior itself.
Gate both emission surfaces — the CLI startup print and the gateway
`_compression_warning` replay — on a persisted per-profile marker under
`$HERMES_HOME` (`.codex_gpt55_autoraise_notice`), keyed on the from→to
percentages the notice displays. The notice now shows at most once per
profile; the autoraise still fires and `codex_gpt55_autoraise: false` still
disables it; and a later change to the raised threshold re-notifies once.
Docs updated to match.
The Codex gpt-5.5 compaction autoraise (#40957) overrode the effective
threshold unconditionally. If a user had set compression.threshold above
0.85, agent_init dropped them down to 0.85. That wastes usable window and
contradicts the feature's whole point: use more of the context, not less.
It happened silently too, since the one-time notice is suppressed when the
override doesn't raise.
The override is an autoraise. It must only raise. Pulled the apply logic
into a small pure helper that clamps the Codex case to never lower a
higher-or-equal user threshold, and emits the notice only when it actually
fires. Other overrides (Arcee Trinity) keep their existing unconditional
behavior.
Fixes the Codex gpt-5.5 compaction autoraise lowering a user's higher
configured threshold. A user on the Codex OAuth route with
compression.threshold > 0.85 was silently clamped to 0.85, compacting
earlier than they asked and using less of the 272K window the feature was
meant to unlock. The autoraise now only ever raises.
N/A
- [x] 🐛 Bug fix (non-breaking change that fixes an issue)
- [ ] ✨ New feature (non-breaking change that adds functionality)
- [ ] 🔒 Security fix
- [ ] 📝 Documentation update
- [ ] ✅ Tests (adding or improving test coverage)
- [ ] ♻️ Refactor (no behavior change)
- [ ] 🎯 New skill (bundled or hub)
- `agent/agent_init.py`: added `_resolve_compression_threshold()`, a pure
helper that combines the global threshold with a per-model override. The
Codex gpt-5.5 autoraise never lowers a higher-or-equal user threshold;
the notice is returned only when it actually raises. Rewired `init_agent`
to call it, replacing the unconditional `compression_threshold = _model_cthresh`.
- `tests/agent/test_arcee_trinity_overrides.py`: added 5 cases for the
helper — raise from default, never-lower regression, equal-is-noop,
no-override passthrough, and non-codex (Trinity) unconditional apply.
1. Set `compression.threshold: 0.90` and run gpt-5.5 on provider `openai-codex`.
2. Before: effective threshold drops to 0.85, no notice. After: stays 0.90.
3. Run `scripts/run_tests.sh tests/agent/test_arcee_trinity_overrides.py`.
Stash `agent/agent_init.py` and the new cases fail; restore and they pass.
- [x] I've read the [Contributing Guide](https://github.com/NousResearch/hermes-agent/blob/main/CONTRIBUTING.md)
- [x] My commit messages follow [Conventional Commits](https://www.conventionalcommits.org/) (`fix(scope):`, `feat(scope):`, etc.)
- [x] I searched for [existing PRs](https://github.com/NousResearch/hermes-agent/pulls) to make sure this isn't a duplicate
- [x] My PR contains **only** changes related to this fix/feature (no unrelated commits)
- [x] I've run `pytest tests/ -q` and all tests pass
- [x] I've added tests for my changes (required for bug fixes, strongly encouraged for features)
- [x] I've tested on my platform: macOS 15 (Darwin 25.5)
- [x] I've updated relevant documentation (README, `docs/`, docstrings) — or N/A
- [x] I've updated `cli-config.yaml.example` if I added/changed config keys — or N/A
- [x] I've updated `CONTRIBUTING.md` or `AGENTS.md` if I changed architecture or workflows — or N/A
- [x] I've considered cross-platform impact (Windows, macOS) per the [compatibility guide](https://github.com/NousResearch/hermes-agent/blob/main/CONTRIBUTING.md#cross-platform-compatibility) — or N/A
- [x] I've updated tool descriptions/schemas if I changed tool behavior — or N/A
The ChatGPT Codex OAuth backend caps both gpt-5.4 and gpt-5.5 at a 272K
context window, but the autoraise that lifts the compaction trigger to 85%
only matched gpt-5.5. On gpt-5.4 the global 50% threshold fired at ~136K —
half the usable window — compacting far earlier than necessary.
Rename _is_codex_gpt55 -> _is_codex_gpt54_or_gpt55 and match both families.
The one-time user notice is now model-aware (shows the actual slug). The
config key codex_gpt55_autoraise is kept as-is for backward compatibility.
Adds gpt-5.4 coverage to the autoraise tests.
Four independent pre-request stalls sat on the critical path between
prompt submission and the first streamed token, measured with cProfile
against a live process:
1. Discord capability detection (~2.0s, worst 5s): get_tool_definitions
-> _get_dynamic_schema made a BLOCKING https call to discord.com
inside AIAgent.__init__ for any user with DISCORD_BOT_TOKEN set, on
every platform, every cold process. Now non-blocking: memory cache ->
24h disk cache -> permissive default + one background detection that
seeds the disk cache for the next process. The permissive default is
pinned per-process so tool schemas never flip mid-conversation
(prompt-cache safety); it mirrors the existing detection-failure
fallback (all actions exposed, 403s enriched at call time).
2. Ollama /api/show probe (~0.3s): get_model_context_length step 5e
POSTed to <base_url>/api/show for KNOWN providers (openrouter etc.),
got a 404, and never cached the miss - so every fresh process paid a
full HTTP round-trip. Known non-Ollama providers now skip the probe;
local/custom/unknown endpoints keep the exact previous behavior.
3. env_probe subprocess sweep (~0.5s): the Python-toolchain probe ran
4-8 subprocess calls inside the FIRST system prompt build. Now warmed
off-thread during agent init; the prompt build hits the cache (same
lock, so a mid-flight warm just joins instead of recomputing).
4. tools.mcp_tool import (~0.4s): the between-turns MCP refresh in
build_turn_context imported the whole mcp package even with zero MCP
servers configured. MCP tools can only exist if tools.mcp_tool was
already imported (discovery/reload paths), so gate the import on
sys.modules membership - no behavior change for MCP users.
CLI additionally pre-imports run_agent + openai off-thread during the
idle banner window (same pattern as the /model picker prewarm), hiding
the remaining ~1.5s of module imports while the user types. Fixes 1-4
apply to every interaction layer (CLI, gateway, TUI, desktop, cron).
Measured cold first turn (submit -> request dispatched, openrouter,
discord token set): 4.3s before -> 0.9s after CLI prewarm (~80%); the
agent-side non-import cost drops 2.9s -> 0.36s (init) + 0.27s (turn
prologue).
* fix(cli): set correct x-initiator header per Copilot turn
copilot_default_headers() always hardcoded x-initiator: agent, but
GitHub Copilot billing requires "user" for user-initiated prompts and
"agent" for tool/follow-up calls. This caused premium requests to never
be consumed correctly, risking billing issues or account bans.
Adds is_agent_turn param to copilot_default_headers() and injects
extra_headers={"x-initiator": "user"} on the first API call of each
user turn when targeting Copilot URLs. The flag flips to False after
injection so subsequent calls (tool use, streaming fallback) default
back to "agent".
Fixes#3040
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* chore(release): add AUTHOR_MAP entry for @tjp2021 (PR #4097 salvage)
---------
Co-authored-by: Tim <tim@iteachyouai.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Self-review (ruff+ty lint diff = 0 net-new; 2-agent deep review) surfaced one
Warning + comment-accuracy nits; no Critical:
- W1: the local-probe TTL cache memoized None (probe failure) for 30s, so a
probe that failed during a startup race would suppress a legit retry once
the server came up. Cache only positive results — still fully bounds the
hot-path probe rate (reachable servers cache their value) while an
unreachable one re-probes on the next call. Add a regression test asserting
a None result is NOT cached (retry re-probes); mutation-verified.
- Tighten the platform-guard comment: gateway/TUI/cron already construct with
quiet_mode=True (gated by `not agent.quiet_mode`), so the guard's active job
is CLI dedup vs show_banner, not "filling the gateway/TUI gap" as originally
worded.
Verified not-issues (per review): positive-value 30s cache does not break the
reconcile-after-restart freshness contract (restart = fresh process, empty
cache); cache key is collision-safe; platform guard is correct in both
directions (no runtime path leaves platform None on a non-CLI surface).
Tests: 149 passed. ruff clean; ty 0 net-new vs base.
Salvage review of #56431 surfaced one Critical + two Warning issues; fix
them on top of the contributor's cherry-picked commits:
1. Critical — duplicate non-agentic warning on the interactive CLI. The new
agent_init warning fires on every platform, but cli.py show_banner()
already warns on CLI (richer output + /model hint), so a CLI user saw the
warning twice per startup. Guard the agent_init emit to skip platform=="cli"
— it now fills exactly the gateway/TUI gap the PR intended, no duplication.
2. Warning — vLLM error-parse regex under-matched. The patterns required a
literal space before the number, so "max_model_len: 32768", "=32768",
"(32768)", and "... is 32768" all returned None. Broaden both patterns to
accept :/=/(/ 'is' delimiters. Add a parametrized test over all delimiter
variants.
3. Warning — per-call live probe latency on local endpoints. The new
reconcile-on-hit + pre-defaults step-7 probe made every local resolution
fire a synchronous network probe (banner + /model switch + compressor
update_model each within one startup). Add a 30s in-process TTL cache
keyed by (model, base_url) around _query_local_context_length so back-to-
back resolutions reuse one round-trip; not persisted to disk, so the
reconcile freshness contract (re-probe after restart) is preserved. Add an
autouse fixture clearing the cache between tests + TTL coverage.
Tests: 148 passed (was 138). ruff clean.
Reconcile stale local disk cache against live vLLM/Ollama max_model_len
probes, probe local servers before the llama hardcoded default, parse
vLLM max_model_len overflow errors, and surface the non-agentic Hermes 3/4
warning at agent init on gateway/TUI.
Sub-64K live probes are returned for startup rejection but are not
persisted to the context cache — preserving the 64K minimum-context
contract instead of normalizing undersized windows as valid config.
(cherry picked from commit c3a02db4fd)
Named providers / custom_providers entries in config.yaml now accept an
extra_headers dict scoped to that endpoint — for reverse proxies, API
gateways, and custom auth schemes (e.g. Cloudflare Access service tokens).
- hermes_cli/config.py: normalize extra_headers on provider entries
(_normalize_custom_provider_entry + providers-dict translation), add
get_custom_provider_extra_headers /
apply_custom_provider_extra_headers_to_client_kwargs helpers keyed on
base_url (case/trailing-slash insensitive, no substring bypass —
mirrors the TLS helpers)
- hermes_cli/runtime_provider.py: surface extra_headers in the resolved
runtime for named custom providers (providers dict, legacy
custom_providers list, and the credential-pool path)
- run_agent.py / agent/agent_init.py: merge per-provider extra_headers
onto the OpenAI client default_headers at construction and on every
_apply_client_headers_for_base_url re-application (credential swaps,
rebuilds), most-specific level wins; OpenAI-wire only (native
Anthropic/Bedrock scoped out)
- agent/auxiliary_client.py: accept model.extra_headers as an alias of
model.default_headers for the global variant
- cli-config.yaml.example: documented commented example
- Header values are treated as secrets and never logged
Salvaged from PR #3526 by @jneeee, reimplemented against current main.
Co-authored-by: Teknium <127238744+teknium1@users.noreply.github.com>
The salvaged fix wired per-provider ssl_ca_cert / ssl_verify (and
HERMES_CA_BUNDLE) into the MAIN OpenAI client. This follow-up:
- Auxiliary client parity: process_bootstrap.build_keepalive_http_client
accepts and forwards verify; auxiliary_client._resolve_aux_verify mirrors
the main-client TLS resolution (via load_config_readonly, the read-only
fast path) so compression/vision/web_extract/title-gen/session_search
honor the same per-provider CA. Without this, chat worked against a
private-CA endpoint but every auxiliary call still failed APIConnectionError.
- switch_model now reads custom_providers from live config (load_config_readonly)
instead of the init-time agent._custom_providers snapshot, so ssl_ca_cert /
ssl_verify edits are honored on mid-session model switch — matching the
context-length reload (#15779).
- Drop the dead client-level verify= where a custom httpx transport is used
(httpx ignores it there); verify lives on the transport. Fix docstrings.
Applies to both run_agent._build_keepalive_http_client and process_bootstrap.
- resolve_httpx_verify: add CURL_CA_BUNDLE to the env chain (consistency with
agent/ssl_guard._CA_BUNDLE_ENV_VARS) and emit a loud logger.warning naming
the endpoint whenever ssl_verify:false disables verification.
- get_custom_provider_tls_settings: case-insensitive base_url match (config
dedup already lowercases; scheme/host are case-insensitive) so a mixed-case
entry doesn't silently drop its CA. Exact match preserved — no prefix bypass.
- Demote best-effort except Exception: pass in agent_init/switch_model to
logger.debug(exc_info=True).
- Tests for aux verify forwarding, _resolve_aux_verify, case-insensitive
match, and prefix-bypass rejection.
Wire ssl_ca_cert and ssl_verify through custom_providers config and env
vars into the keepalive httpx client, fixing APIConnectionError against
mkcert/self-signed Ollama proxies behind HTTPS.
The forked skill/memory review agent shares the parent's session_id for
prompt-cache warmth. Without isolation it wrote its harness turn ('Review the
conversation above and update the skill library…') plus its curator-mode reply
straight into the user's REAL session in state.db; the next live turn re-read
that injected user message as a standing instruction and the agent 'became' the
curator, refusing the actual task.
Root fix: a _persist_disabled flag on the fork that hard-stops every DB write
and lazy-open path (_flush_messages_to_session_db, _ensure_db_session,
_get_session_db_for_recall) — the review writes only to the skill/memory stores
via its tools. Defense-in-depth: _strip_background_review_harness drops any
stray harness message (and the assistant reply that followed) at load time in
get_messages_as_conversation, so an already-polluted session resumes clean.
Salvaged from #50296.
Co-authored-by: arminanton <29869547+arminanton@users.noreply.github.com>
@janrenz's PR #35862 added prompt_caching.enabled=false at init only. But
_anthropic_prompt_cache_policy re-derives _use_prompt_caching on every /model
switch (agent_runtime_helpers) and fallback-model swap (chat_completion_helpers),
which re-enabled markers and re-broke the strict proxy the toggle was meant to fix.
Move the kill switch into anthropic_prompt_cache_policy so it returns (False, False)
on every path. Drop the now-redundant init-time override (kept @janrenz's isinstance
hardening on the cache_ttl read). Add policy-level tests + docs for the toggle.
Follow-up to salvaged PR #35862.
The earlier enterprise base URL change (proxy-ep parsing) gave us URLs
like `api.enterprise.githubcopilot.com`, but ~15 host-matching call
sites still hard-coded `api.githubcopilot.com`. Enterprise users would
therefore drop the `Copilot-Integration-Id: vscode-chat` header at
client-build time, and upstream rejected requests with:
The requested model is not available for integrator "zed"
(or "copilot-language-server") — verify the correct
Copilot-Integration-Id header is being sent.
The header was correct in copilot_default_headers(); it just never
made it into default_headers for non-default hostnames because every
detector compared against the exact string "api.githubcopilot.com".
This commit broadens all those checks to "githubcopilot.com" via
base_url_host_matches (which already does proper subdomain matching),
so api.enterprise.githubcopilot.com, api.business.githubcopilot.com,
etc. all share the same headers, vision routing, max_completion_tokens
selection, and reasoning-effort detection as the default endpoint.
Also adds ".githubcopilot.com" to _URL_TO_PROVIDER so context-window
resolution via models.dev works for enterprise base URLs, and tightens
_is_github_copilot_url to use suffix matching instead of strict equality.
Tests:
- New: enterprise Copilot endpoint preserves Copilot-Integration-Id
- New: enterprise endpoint returns max_completion_tokens (not max_tokens)
- Existing 333 base_url / copilot / aux-client / credential-pool tests pass
Parts 5 of #7731.
* fix(agent): config-driven intent-ack continuation for all api_modes (#27881)
The agent could end a turn after only stating intent ('I will run a health
check...') without executing the announced tool call, forcing the user to
re-prompt. A continuation guard that catches this and nudges the model to
proceed already existed but was hard-gated to the codex_responses api_mode,
so Gemini/Claude/OpenRouter turns never benefited.
- New agent.intent_ack_continuation config (default 'auto' = codex-only,
byte-stable for existing conversations). 'true'/model-list opts every
api_mode in; 'false' disables. Mirrors agent.tool_use_enforcement's shape.
- looks_like_codex_intermediate_ack gains require_workspace (default True).
The opted-in path drops the codebase/filesystem requirement so general
autonomous workflows (server ops, deploys, API calls) are caught, not just
coding tasks. Future-ack + action-verb + short-content + no-prior-tool
guards still apply; the 2-nudge-per-turn cap is unchanged.
- Resolution centralized in intent_ack_continuation_mode (off/codex_only/all).
* docs(infographic): intent-ack continuation (#27881)
When a MoA preset is selected, each reference model's answer now renders in the
CLI as a thinking-style block labelled with its source model, BEFORE the
aggregator responds — so the mixture-of-agents process is visible instead of a
silent pause. The aggregator's response (and its tool actions) follow as normal.
Mechanism (shared seam, all surfaces):
- MoAChatCompletions/MoAClient take an optional reference_callback and emit
'moa.reference' (index/count/label/text) per reference, then 'moa.aggregating'
(aggregator label) once. agent_init wires this to the agent's
tool_progress_callback, which every surface already consumes — so the events
reach CLI/TUI/desktop/gateway with no new plumbing.
- CLI _on_tool_progress renders 'moa.reference' as a labelled '┊ ◇ Reference
i/n — <model>' header + a thinking-style preview (reusing _emit_reasoning_
preview), and 'moa.aggregating' as a spinner transition. Display-only; never
touches message history (cache-safe).
Turn-scoped reference cache: the agent loop calls the facade once per tool-loop
iteration, but the advisory message view is identical across iterations within a
turn, so references are now run AND displayed once per user turn (keyed by the
advisory view's signature) instead of re-running/re-spamming on every iteration.
This also cuts reference API cost from O(iterations) back to O(turns).
Verified live via interactive PTY on the opus-gpt preset (gpt-5.5 + opus refs):
reference blocks render once per turn, labelled by model, before the aggregator;
fresh blocks on each new turn; aggregator tool actions still execute.
Follow-up: TUI/desktop rich rendering + gateway batched-summary already receive
the events via tool_progress_callback; their surface-specific renderers are a
separate change.
The error raised when a model's context window is below the 64K minimum
advertised "or set model.context_length in config.yaml to override" — but
the guard intentionally has no sub-64K escape hatch. Sub-64K models are
rejected by design (tool schemas + system prompt need the headroom).
The misleading clause invited a cluster of dup PRs (#11097, #11110, #8962,
#9142, #37548) all trying to wire an override that we don't want. Reword to
state the real options: pick a >=64K model, or — if your local server
under-reports its true window — declare the real value (which must itself
be >=64K). Guard behavior is unchanged.
* feat(moa): expose MoA presets as selectable virtual models
Reconstructed onto current main (PR #46081's base had diverged with no common
ancestor, marking the PR dirty so CI never dispatched). MoA is now a virtual
provider: each named preset is a selectable model under provider 'moa', and the
preset's aggregator is the acting model that answers and calls tools.
Reference models fan out in parallel via a bounded ThreadPoolExecutor (the same
batch pattern delegate_task uses) — all references dispatched at once, collected
when every one finishes, then handed to the aggregator. Output order is
preserved, failures and the MoA-recursion guard stay isolated per reference.
- Removed the old mixture_of_agents model tool and moa toolset.
- Added moa as a virtual provider in the provider/model inventory.
- /moa is shortcut behavior over model selection (default preset / named preset
/ one-shot prompt).
- Dashboard + Desktop manage named presets; presets appear in model pickers.
- Parallel reference fan-out in agent/moa_loop.py with regression test.
* fix(moa): thread moa_config through _run_agent to _run_agent_inner
The reconstructed gateway MoA wiring declared moa_config on _run_agent (the
profile-scoping wrapper) and used it inside _run_agent_inner, but the wrapper
never forwarded it — _run_agent_inner had no such parameter, so the runtime hit
NameError: name 'moa_config' is not defined on the compression-failure session
sync path. Add moa_config to _run_agent_inner's signature and forward it from
both wrapper call sites (multiplex and non-multiplex). Caught by
tests/gateway/test_compression_failure_session_sync.py on CI shard test(4).
* fix(moa): classify moa as a virtual provider in the catalog
The moa virtual provider has no PROVIDER_REGISTRY/ProviderProfile entry, so
provider_catalog() fell through to the default auth_type="api_key" with no
env vars — tripping two catalog invariants:
- test_provider_catalog: api_key providers must expose a credential env var
- test_provider_parity: every hermes-model provider must be desktop-configurable
moa already declares auth_type="virtual" in HERMES_OVERLAYS; consult that
overlay as an auth_type fallback so the catalog reports moa as virtual (no real
credential, no network endpoint). Exempt virtual providers from the desktop
parity union check the same way 'custom' is exempt — derived from the catalog,
not a hardcoded slug, so future virtual providers are covered too.
Closes#47707
Context engines and memory providers expose tool schemas via
get_tool_schemas(). agent_init.py wrapped each as
{"type":"function","function":_schema} without validating that
_schema carries a top-level name. A provider returning an entry already
in OpenAI tool form ({"type":"function","function":{...}}) was then
double-wrapped into a tool whose function has no name. Strict providers
(e.g. DeepSeek) reject the entire request with HTTP 400
'tools[N].function: missing field name', so one malformed schema
silently disables the whole toolset and breaks every turn. The schema
was also never added to valid_tool_names, so even lenient providers
could not call it.
Add a shared normalize_tool_schema() helper that unwraps an
already-wrapped entry and returns None for anything lacking a resolvable
string name. Wire it into the agent_init context-engine loop and all
three memory_manager surfaces (inject_memory_provider_tools,
add_provider routing index, get_all_tool_schemas), so a single bad
plugin schema is skipped with a warning instead of poisoning the
request.
Verification: 209 targeted agent/memory tests pass (incl. 9 new).
New tests assert the unwrap + skip-nameless behavior and fail without
the fix.
When delegate_task spawns a child agent with a different model/provider, the
child's init_agent loaded the plugin context-engine GLOBAL singleton by
reference (`_selected_engine = _candidate`) and then called update_model() on
it with the child's (smaller) context_length. Because parent and child shared
the same object, this mutated the PARENT's compressor: e.g. DeepSeek 1M ctx
silently dropped to 204800 and the compression threshold from 200K to 40K
after any delegate_task with a different model.
Deepcopy the singleton before assigning/mutating it (agent_init.py) so the
child gets its own instance and the parent's compressor is untouched.
Salvaged from #42452 by @liuhao1024 (authorship preserved). Added a
source-pin regression test that fails if the production line reverts to the
bare alias, plus an end-to-end test driving get_plugin_context_engine() and a
StubEngine.update_model() — the original PR's tests exercised copy.deepcopy in
isolation but did not guard the actual agent_init code path.
Closes#42449. Supersedes #42469, #42474 (same one-line fix, no test).
