_render_tool_calls only handled dict-shaped entries; a SimpleNamespace-
shaped tool_call (SDK-style stream-stitched responses) rendered as
'[called tool: tool]', silently losing the function name and arguments
from the advisory view. Handle both shapes (including a namespace-shaped
nested function inside a dict entry).
One-hunk hardening salvaged from closed#59712.
Co-authored-by: SquabbyZ <601709253@qq.com>
Reference models may have a smaller context window than the aggregator
(e.g. kimi-k2.7-code @ 262K advising a glm-5.2 @ 1M conversation).
Without context-length protection, a reference whose window is exceeded
gets a hard HTTP 400 from the provider, which _run_reference's
try/except silently converts to a [failed: …] note — the MoA turn
silently degrades to fewer references (#60345).
Redesigned implementation of #60387:
- Estimate AFTER the advisory system prompt is prepended, so the
request that is actually sent is what gets budgeted.
- Reserve output headroom: the preset's reference_max_tokens when set,
else an 8192-token constant, plus a 10% estimator-error fraction.
- Trim on advisory-view boundaries (text-only user/assistant turns; no
tool-result frames to orphan), preserving the system prompt, the
user-first invariant after every pop (never assistant-first), and the
trailing synthetic user turn.
- Cache get_model_context_length per (provider, model) in a per-fan-out
dict shared across the worker threads, so a turn resolves each
window once instead of probing metadata sources
per-reference-per-iteration (failures are cached too).
Co-authored-by: webtecnica <75556242+webtecnica@users.noreply.github.com>
Follow-ups for salvaged #56344:
- A reference that completes between the interrupt check and the reap
keeps its REAL output and accounting (the provider call billed) instead
of being zeroed with a placeholder.
- A reference still in flight at interrupt time gets a placeholder in the
results, but its future now carries a done-callback that folds the
eventual real usage/cost into the facade's pending accounting
(late_accounting_sink -> _record_late_reference_accounting), so billed
spend is never silently dropped. Pending totals are folded (not
overwritten) and guarded by a lock since done-callbacks fire on
executor worker threads.
- Interrupted placeholder results are no longer written into the facade's
turn-scoped reference cache: a cache HIT never re-runs references, so
caching a partial snapshot would replay '[skipped: interrupted by
user]' notes for the rest of the turn. The cache is left empty and the
next create() re-runs the fan-out.
agent/tool_executor.py's concurrent tool batch checks agent._interrupt_requested
and aborts the wait early; agent/moa_loop.py's _run_references_parallel had
no equivalent, so a MoA-enabled turn blocked on ThreadPoolExecutor.result()
until every reference model finished or hit its own individual
auxiliary.moa_reference timeout -- there was no way for the user to abort a
live turn mid-fanout.
Thread an optional `agent` parameter through aggregate_moa_context ->
_run_references_parallel (used when MoA references run alongside the main
model) and MoAClient/MoAChatCompletions (used when the MoA preset itself is
the acting model), then poll concurrent.futures.wait() in
_REFERENCE_POLL_INTERVAL_S slices instead of blocking on future.result() per
reference, checking agent._interrupt_requested each cycle.
Deliberately scoped to interrupt/cancel only -- no new or changed timeout
value, so this doesn't overlap open PRs #53784/#53875 (which lower the
per-reference timeout default but don't add interrupt support). `agent` is
optional and defaults to None, so any caller that doesn't pass it keeps
today's uninterruptible blocking behavior unchanged.
Extends the all-references-failed short-circuit (#56975) to the
persistent `provider: moa` facade path: MoAChatCompletions.create()
previously attached 'use the reference responses below' guidance built
entirely from failure sentinels and called the aggregator with it. Now
an all-failed turn attaches either the sanitized unavailability notice
(loud policy) or nothing (silent policy), and the aggregator — which IS
the acting model — simply acts alone. Advisor accounting for the failed
fan-out is still recorded.
Co-authored-by: liuhao1024 <sunsky.lau@gmail.com>
When every MoA reference model returns a failure (HTTP error, timeout,
etc.) or is skipped by the recursion guard, the one-shot aggregator
synthesis call is now skipped entirely. Previously it would try to
synthesise a wall of failure sentinels, which could block for the full
provider timeout (observed ~6 min on SenseNova) before returning a
non-retryable error that left the session hanging.
The early return carries the sanitized unavailability notice (never raw
provider error text, per the failed-reference containment) so the main
agent loop can still act in single-model mode.
Salvaged from #56975, reworked atop the _is_failed_reference helpers.
Follow-ups for salvaged #53784:
- reference_timeout now defaults to None = no per-preset override, so the
reference fan-out inherits auxiliary.moa_reference.timeout (900s default)
via call_llm's own per-task timeout resolution. The PR's 30.0s default
would have cut off long-thinking advisors mid-response, and its 300s max
cap capped legitimate explicit values — both removed. Explicit per-preset
values are still honored as-is.
- _is_failed_reference also treats '[skipped: …]' recursion-guard notes as
internal sentinels, keeping them out of both aggregator prompts.
- Dashboard/desktop TS types updated to number | null; web_server validator
accepts null/empty as 'inherit'.
Adds per-reference progress events and a phase-transition marker to the
MoA display pipeline so TUI / CLI / desktop surfaces can render a status
bar like `MOA: 2/3 refs done` and surface which phase (reference vs
aggregator) is currently active.
- `moa.progress` — fired once per reference completion with
`refs_done`, `refs_total`, and the source label
- `moa.phase` — fired on phase transitions (currently the single
`phase="aggregator"` transition once the fan-out
finishes)
Plumbed through the existing `reference_callback` →
`tool_progress_callback` → gateway path; no new UI surface. The legacy
`moa.reference` / `moa.aggregating` events are unchanged for backwards
compatibility.
AI-assisted fix by https://github.com/SquabbyZ/peaks-loop
Extends the fanout enum with 'every_n:<N>' (N >= 2): advisors run on the
first iteration of each user turn and every Nth tool iteration after it;
off-cadence iterations REUSE the cached guidance from the last on-cadence
run via the same cache mechanism the user_turn fanout uses, so the
aggregator still gets advice on every step. The cadence counter is scoped
per user turn (resets on a new user message) and only advances when the
advisory state actually changes, so streaming retries never consume a
cadence slot. Mapping form {mode: every_n, n: N} normalizes to the
canonical string. Unknown/degenerate values fall back to per_iteration.
Addresses issue #63393 (advisor fan-out multiplies turn latency/cost by
the tool-iteration count). Redesigned from PR #63448: the submitted shape
skipped references entirely on off-cadence iterations (aggregator ran
advice-less); this version keeps the last advice in play, credited for
the idea and cadence framing.
Config-gated, default-off (default fanout remains per_iteration).
Co-authored-by: webtecnica <75556242+webtecnica@users.noreply.github.com>
Follow-up to the salvaged core of #53802: a naive MoAClient(preset) rebuild
restores a working facade but silently drops the reference_callback relay
wired in agent_init, so moa.reference / moa.aggregating display events stop
reaching every frontend for the rest of the session.
Introduce agent.moa_loop.build_moa_facade(agent, preset) as the single
construction point for the MoA facade and use it at:
- initial client construction (agent_init.py)
- turn-start fallback restore (restore_primary_runtime)
- transient transport recovery (try_recover_primary_transport — previously
fell through to _create_openai_client with MoA's empty client_kwargs and
died with 'api_key client option must be set')
- mid-session model switches (switch_model)
The relay reads agent.tool_progress_callback at emit time, so callbacks
attached after construction are picked up automatically.
Adds test_moa_restored_facade_still_emits_reference_events covering event
delivery through a restored facade.
MoA reference_max_tokens is preset-level — one cap for all reference
models. When mixing a verbose model with a terse one, a single cap is
either too tight for the terse model or too loose for the verbose one.
Now each reference slot can optionally carry its own max_tokens:
reference_models:
- provider: openrouter
model: deepseek/deepseek-v4-pro
max_tokens: *** # per-slot cap, overrides preset-level
- provider: openai-codex
model: gpt-5.5
# no max_tokens → falls back to preset-level reference_max_tokens
_clean_slot (moa_config.py) preserves an optional max_tokens field on
the slot dict, coerced via _coerce_int_or_none. _run_reference
(moa_loop.py) reads slot-level max_tokens first, falling back to the
preset-level cap passed by the caller. Slots without the field are
unaffected — backward compatible.
Type hints on slot-handling functions updated from dict[str, str] to
dict[str, Any] to reflect the now-heterogeneous slot shape.
aggregate_moa_context's single max_tokens parameter was applied to
both the reference fan-out (_run_references_parallel) and the
aggregator's own synthesis call_llm. #53580 explicitly removed a
hardcoded cap from the aggregator call because it truncated long
aggregator syntheses; #56756 (reference_max_tokens, added to speed up
the advisor fan-out) reintroduced the same shared cap by passing it to
both calls, silently regressing #53580's fix.
Rename the parameter to reference_max_tokens (matching the caller's
own moa_config key) and stop forwarding it to the aggregator's
call_llm invocation, which now always runs uncapped as intended.
Extends the conversation=<id> Portal tag (salvaged from PR #65183 by
@J-SUPHA) from main-loop-only to every LLM call in a conversation:
- agent/portal_tags.py: ContextVar-based conversation context.
nous_portal_tags() falls back to the ambient id when no explicit
session_id is passed, so every aux tag site (auxiliary_client,
chat_completion_helpers summary path, web_tools) inherits the tag
with zero per-call-site plumbing. Ambient id wins over explicit
per-segment ids since it carries the lineage root.
- hermes_state.py: SessionDB.get_conversation_root() — public wrapper
over the lineage walk; returns the ROOT session id, so one
user-facing conversation keeps a single conversation= value across
context-compression rotation, and delegate subagent trees tag as
their parent conversation.
- run_agent.py: run_conversation() publishes the root id for the turn
and resets it in finally. _conversation_root_id() resolves via
_parent_session_id for subagents.
- agent/moa_loop.py: MoA reference fan-out workers now run under
propagate_context_to_thread so advisor slots attribute to the acting
conversation (also fixes approval-callback propagation on that path).
- agent/title_generator.py: bare title thread republishes the context
from its session id (spawned after turn reset).
Tests: ContextVar semantics, cross-context isolation, thread-hop
propagation, lineage-root resolution incl. cycle guard.
The aggregator is MoA's acting model, but the main loop's reasoning
gates key off the virtual moa://local identity and never fire — so with
no per-slot reasoning_effort the aggregator silently ran at the backend
default, ignoring the user's reasoning config entirely (#64187).
New _aggregator_reasoning_config(): slot value > full acting-model
resolution via the shared chokepoint (agent.reasoning_overrides for the
slot's model > global agent.reasoning_effort; YAML False stays
'disabled'). Applied to both aggregator call sites (acting turn +
one-shot /moa synthesis).
Reference advisors intentionally keep slot-or-default: inheriting a
global xhigh into every advisor fan-out would silently multiply cost.
Fixes#64187.
Follow-up to the cherry-picked empty-user-turn drop: the placeholder
introduced in 8582f35d9 fired for whitespace-only STRING turns too
(content=' ' flattens to non-stripping text but isn't in the
(None, '', []) exclusion set), fabricating an attachment note for a turn
that carried nothing. Gate the placeholder on isinstance(content, list)
so only genuinely structured (e.g. image-only) turns get it; empty and
whitespace-only string turns now fall through to the drop path.
Edge cases verified: trailing empty user turn still ends the view on the
synthetic advisory marker; an all-empty transcript degenerates to [].
MoA's _reference_messages() unconditionally appended every user-role
message to the advisory view sent to reference models, even when the
message content was an empty string or a non-string/multimodal payload
that the text-extraction step flattens to "".
Strict providers (Kimi/Moonshot, and others that enforce non-empty user
content) reject such a message with:
400 Invalid request: the message at position N with role 'user'
must not be empty
Lenient providers (DeepSeek) accept it, so an identical rendered view
passes on one reference and 400s on another within the same fan-out —
the user sees "kimi doesn't support MoA" when the real cause is an empty
user turn leaking into the advisory transcript.
Skip empty user turns, mirroring the existing behavior for empty
assistant turns (which are already dropped when they carry no parts).
The end-on-user invariant is preserved: the synthetic advisory-request
user turn is still appended when the view would otherwise end on an
assistant turn.
Adds a regression test asserting the advisory view contains no empty
user turn and still ends on a user turn.
Cache-decorated turns (apply_anthropic_cache_control converts string
content to [{type: text, ..., cache_control}] lists — applied BEFORE the
MoA facade since the #57675 cache-cold fix) and multimodal turns
(text + image_url parts) flattened to empty strings in
_reference_messages, which only read str content. On turn 1 of a
provider:moa session with a Claude aggregator the references received a
single EMPTY user message: Anthropic-side providers 400'd ('messages: at
least one message is required') while tolerant models answered 'no user
request is present' (live incident Jul 14 2026, preset 'closed').
Fixes, in totality:
- _reference_messages: extract visible text via
agent/message_content.flatten_message_text for user/assistant/tool
turns (skips image parts, so no base64 leaks into the advisory view);
decorated and undecorated transcripts now produce a byte-identical
advisory view (advisor cache prefix stays stable).
- image-only user turns get a placeholder instead of an empty message
(Anthropic rejects empty text blocks) or a silently dropped turn
(would break user/assistant alternation).
- degenerate-case fallback flattens structured content too.
- _attach_reference_guidance: a decorated/multimodal trailing user turn
now receives the guidance as a NEW text part appended AFTER the
cache_control-marked part (cached prefix byte-stable) instead of
falling through to a second consecutive user message (strict providers
reject user/user).
- conversation_loop MoA injection: multimodal user turns get the MoA
context appended as a trailing text part instead of being dropped;
user_prompt for the one-shot path flattens content lists instead of
str()-ing them (which leaked base64 payloads into the prompt).
Live-verified on the 'closed' preset (real OpenRouter wire, 2 user
turns, tool loop): all 4 reference calls carry the full document +
rendered tool state, end on user, zero tool-role/tool_calls; advisor
cache_write 7968 then cache_read 5909+; aggregator cache_read
14880-15237 on iterations 2+.
Co-authored-by: bo.fu <bo.fu@meituan.com>
Sibling sites of the salvaged #55997 fix, all reading user-editable
config values through .get(key, '').method(): MoA slot provider/model
labels, gateway quick-command alias targets (2 sites), gateway.proxy_url,
and gateway.relay_url. Regression tests for the contributor's two sites
plus the MoA labels.
22c5048d9 restored Anthropic-style cache_control for two of MoA's three
call paths: the acting aggregator (MoAChatCompletions.create, the
persistent `provider: moa` model) and the advisor fan-out (_run_reference).
aggregate_moa_context() -- the /moa <prompt> one-shot command's synthesis
call -- is the third, independent call path and was never covered: its
call_llm(task="moa_aggregator", ...) sent a single undecorated user message
containing the full joined reference output, re-billing the entire input on
every invocation even when the resolved aggregator slot is a cache-honoring
route (Claude on OpenRouter/native Anthropic, MiniMax, Qwen/DashScope).
- Generalize _maybe_apply_advisor_cache_control to
_maybe_apply_moa_cache_control (it never had advisor-specific logic --
same policy function, same breakpoint layout as the main loop, judged
purely on the passed-in runtime) and reuse it in aggregate_moa_context
the same way _run_reference already does.
- Compute _slot_runtime(aggregator) once and reuse it for both the
decoration call and the call_llm kwargs, instead of calling it twice.
Mutation-verified: reverting the moa_loop.py change makes the new
regression test fail by asserting a plain string aggregator-message
content where the cache-honoring case expects native cache_control
content blocks.
Two caching holes made MoA re-bill essentially its entire input stream:
1. AGGREGATOR: anthropic_prompt_cache_policy() judged the agent's own
model/provider — on the MoA path those are the virtual preset name and
'moa', which match no caching branch, so _use_prompt_caching was False
and the acting aggregator (Claude on OpenRouter) ran with ZERO
cache_control breakpoints. Measured on identical opus-4.8 sessions:
85% cache share solo vs 2% via MoA — ~30M re-billed input tokens on one
132-task benchmark run. Fix: when provider == 'moa', resolve the policy
from the preset's real aggregator slot (provider/model/base_url/api_mode
via resolve_runtime_provider).
2. ADVISORS: _run_reference never applied cache_control at all, and
Anthropic caching is opt-in per request — Claude advisors served 0
cache reads across 1,227 benchmark calls (11.5M re-billed input tokens)
even though the advisory view is append-only across iterations (stable
prefix; the synthetic end marker is last so it never pollutes it). Fix:
_maybe_apply_advisor_cache_control() reuses the SAME policy function and
SAME system_and_3 layout as the main loop, judged on the advisor slot's
own resolved runtime — advisor requests are now decorated exactly like
an acting agent on that provider. Auto-caching routes (OpenAI-family)
are left untouched by policy.
Live-verified on the wire (per-iteration opus+gpt5.5 preset, 4 fan-outs):
claude advisor fan-out 2-3 cache_write=2161/2344, fan-out 4
cache_read=2206 / fresh_in=2; aggregator session cache share 84%/77%
(vs 2%/0% before). Sub-1024-token prompts correctly stay uncached
(Anthropic minimum).
The advisory view appends a synthetic user marker when it ends on an
assistant turn (Anthropic end-on-user rule) — i.e. on every tool iteration
after the first. The user_turn prefix hash treated that marker as the last
user message, so the hashed prefix included the grown mid-turn context and
the signature changed every iteration: advisors re-ran per iteration,
silently defeating the once-per-turn cadence (live smoke test: 2 fan-outs
for a 2-iteration task; expected 1). Hoist the marker to a module constant
and skip it when locating the last REAL user message. Verified: iteration-2
signature now equals iteration-1 (cache HIT); a new real user message still
re-triggers the fan-out.
New preset key 'fanout': 'per_iteration' (default, unchanged behavior)
re-runs the reference fan-out whenever the advisory view changes — every
tool iteration. 'user_turn' runs the advisors ONCE per user turn and lets
the aggregator act alone for the rest of the tool loop — the original MoA
shape (upfront multi-model synthesis, then a single acting model), and the
obvious lever on MoA's wall/cost multiplier (advisor generation dominates
per-turn latency).
Implementation reuses the existing turn-scoped reference cache: in
user_turn mode the cache signature hashes only the prefix up to the LAST
user message, so mid-turn advisory-view growth doesn't change the key and
iteration 2+ is a cache HIT (advice reused, zero advisor spend, no
re-trace). A new user message changes the prefix and re-triggers the
fan-out. Unknown fanout values normalize to per_iteration.
A single-model Hermes agent never sends temperature; the provider default
applies. MoA hardcoded reference_temperature=0.6 / aggregator_temperature=0.4,
and the coercion float(preset.get(key, 0.6) or 0.6) made unset IMPOSSIBLE to
express: absent, null, empty, and even an explicit 0 all collapsed to the
baked-in default. Every MoA advisor and aggregator therefore ran at 0.6/0.4
while the same model running solo used the provider default — silently
skewing solo-vs-MoA comparisons and overriding provider-tuned defaults.
- moa_config normalization: temperatures coerce to None when absent/blank/
invalid (new _coerce_float_or_none); explicit values incl. 0 honored.
- moa_loop: _preset_temperature() resolves preset values; None flows to
call_llm, which already omits the parameter when None (same contract as
max_tokens). Aggregator still inherits the acting agent's own configured
temperature when the preset doesn't pin one.
- conversation_loop (context-mode MoA): same resolution, no more hardcoded
0.6/0.4 at the call site.
- DEFAULT_CONFIG preset + web_server payload models + docs updated: unset
is the default, pinning stays available.
MoA per-turn latency is dominated by advisor GENERATION: turn wall time
correlates ~0.88 with output tokens and ~-0.03 with input tokens (measured over
52 turns). Each turn waits for the slowest advisor to finish writing, and
advisors were uncapped — writing multi-thousand-token essays the aggregator
only needs the gist of.
Add an opt-in per-preset reference_max_tokens knob (mirrors reference_temperature)
that caps ADVISOR output only; the acting aggregator is never capped. Default
None = uncapped, so existing presets are byte-for-byte unchanged (no regression).
Wired through both MoA execution paths (MoAChatCompletions.create and
aggregate_moa_context).
E2E: same task, closed preset uncapped vs reference_max_tokens=600 -> 59s to 33s
(~44% faster), final answer identical/correct.
- hermes_cli/moa_config.py: _coerce_int_or_none helper + reference_max_tokens
in _normalize_preset/_default_preset/flattened view
- agent/moa_loop.py: read preset.reference_max_tokens, pass to reference fan-out
- agent/conversation_loop.py: pass reference_max_tokens on the per-turn path
- tests + docs
On the MoA path agent.model/provider are the virtual preset name (e.g.
"closed") and "moa", which have no pricing entry. estimate_usage_cost()
returned None for the aggregator turn, so the `if amount_usd is not None`
guard skipped it and the session's estimated_cost_usd reflected only the
advisor fan-out — a ~50% undercount when the aggregator does the full acting
loop (verified: $0.91 advisor-only vs $1.96 true, aggregator = 54%).
MoAChatCompletions.create() now stashes the resolved aggregator slot as
last_aggregator_slot (exposed via MoAClient); conversation_loop reads it to
price the aggregator turn at its real model/provider. cost_source flips from
'none' to 'provider_models_api'.
MoA full-turn traces (moa.save_traces) recorded the aggregator's acting
output only on the non-streaming path, where it's captured inline at
call time. On the streaming path — which every hermes chat --query run
and every live gateway/CLI turn takes — the aggregator's raw token
stream is handed to the live consumer, so the trace left output=null and
only pointed at the session-db assistant row. An offline audit of a
benchmark run (HermesBench drives --query) then couldn't see what the
aggregator produced without hand-joining to state.db.
Capture the resolved streamed acting text at trace-flush time (the agent
already holds it in _current_streamed_assistant_text) and fold it into
the trace, so the record is self-contained in both modes. New
output_location value inline_from_stream marks a streamed turn whose text
was captured this way; a genuinely empty acting turn (pure tool call)
still points at the session db, matching state.db exactly.
Touches only the trace side-channel — no change to the acting path,
message history, role alternation, or prompt cache.
- agent/moa_loop.py: consume_and_save_trace(..., aggregator_output_fallback)
on both the facade and the MoAClient wrapper; prefer inline capture,
fall back to the resolved streamed text.
- agent/moa_trace.py: embed the fallback; add inline_from_stream location.
- agent/conversation_loop.py: pass _current_streamed_assistant_text at flush.
- tests: 5 cases across streaming / non-streaming / empty-fallback / no-double-write.
The MoA aggregator received the per-turn reference block merged into the most
recent `user` message. In an agentic tool loop that message is the original
task near the top of the context (everything after it is assistant/tool turns),
so injecting text that changes every iteration diverges the prompt prefix early.
The server's KV cache then cannot be reused and the entire conversation
re-prefills on every tool-loop step — full prefill each step, which dominates
latency on long contexts.
Append the reference block at the end of the prompt instead (merging into the
last message only when it is already a trailing user turn, i.e. plain chat).
This keeps the [system][task][tool-history] prefix stable and cache-reusable so
only the new block re-prefills, and gives the aggregator the references with
recency. Extracted as `_attach_reference_guidance` with unit tests.
Measured on a local llama.cpp aggregator over a long agentic task: KV-cache
reuse on follow-up steps went from ~0.3% to ~93-95% and per-step prefill on an
~80k-token context dropped from ~44s to <1s, with no change to output.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds moa.save_traces (default off). When on, every MoA turn that runs the
reference fan-out appends one JSON line to
<hermes_home>/moa-traces/<session_id>.jsonl capturing the TRUE FULL turn:
each reference model's exact input messages (system advisory prompt + full
advisory view, not the truncated display preview) + full output + usage +
per-advisor cost, and the aggregator's exact input (including the injected
reference-context guidance block) + output. Lets MoA runs be audited and
improved offline — what every model saw, said, and cost.
- agent/moa_trace.py: config-gated JSONL writer, profile-aware path via
get_hermes_home(), best-effort (never breaks a turn), moa.trace_dir override.
- agent/moa_loop.py: _RefAccounting now carries full input/output/model/
provider/temperature; create() stashes the full turn on a cache MISS
(once per turn, never on the cache-HIT repeat iterations); non-streaming
aggregator output captured inline, streaming marked + pointed at the
session assistant message. consume_and_save_trace(session_id) flushes it.
- agent/conversation_loop.py: flushes the trace with the live session_id
right after MoA usage consumption. No-op for non-MoA clients.
- hermes_cli/config.py: moa.save_traces + moa.trace_dir defaults.
Traces are a side channel — NOT the messages table, never in replay, safe
to delete. Off by default; only overhead when off is one config read on a
MoA cache-MISS turn.
Tests: full-trace-when-enabled (per-ref input+output+cost, aggregator
input-with-guidance + output), nothing-when-disabled. Live E2E through
run_conversation confirmed the loop wiring writes the file.
MoA ran the reference models before the aggregator but returned only the
aggregator's usage to the loop — _run_reference discarded each advisor
response's .usage entirely. Session accounting (state.db, /insights, cost)
therefore undercounted every MoA turn by the whole reference fan-out, which
is usually the bulk of the spend and scales with advisor count.
- _run_reference normalizes each advisor's usage with ITS OWN resolved
provider/api_mode and prices it at ITS OWN model rate (correct cache-read/
cache-write split), returning a _RefAccounting(usage, cost).
- create() sums advisor usage + cost once per turn (cache MISS only, so a
repeat tool-iteration reusing cached advice does not double-charge) and
exposes it via MoAClient.consume_reference_usage().
- conversation_loop folds advisor tokens into the reported/persisted token
counts and adds advisor cost (priced per-advisor) on top of the
aggregator cost, in both the in-memory session totals and the state.db
per-call delta. Aggregator cost is still priced on aggregator-only usage
so advisor tokens are never repriced at the aggregator rate.
- CanonicalUsage gains __add__ for per-bucket summing.
Tests: advisor usage/cost capture, per-turn sum + consume-clears +
cache-hit no-double-charge, CanonicalUsage.__add__.
_slot_runtime maintained a hand-listed name-preservation set
({nous, anthropic, openai-codex, xai-oauth, bedrock}) that returned bare
provider+model to avoid call_llm collapsing an explicit base_url to the generic
'custom' route. That duplicated _resolve_task_provider_model's
_preserve_provider_with_base_url guard (a provider-catalog capability check)
and had to be extended by hand for every provider with custom auth/signing —
the exact drift that produced the anthropic (#54609) and bedrock (#54912) 429/
empty-response bugs.
Removes the whitelist: _slot_runtime now forwards the resolved base_url/api_key/
api_mode for every slot, and the single chokepoint
(_resolve_task_provider_model -> _preserve_provider_with_base_url) decides
identity preservation. Behavior is unchanged for the five providers — their
provider branches (codex Responses+Cloudflare, xai-oauth, bedrock SigV4,
anthropic OAuth Bearer+anthropic-beta, nous Portal tags) re-resolve their own
credentials by name and ignore a forwarded base_url/api_key, so forwarding is
safe even for bedrock's placeholder 'aws-sdk' key.
Verified via real-import E2E: _slot_runtime -> _resolve_task_provider_model
preserves openai-codex/xai-oauth/bedrock/anthropic/nous (+openrouter control) —
none collapse to custom. Tests updated to assert the pipeline invariant against
the real resolver instead of the removed whitelist's bare-return shape.
_slot_runtime() resolved a bedrock slot to its bedrock-runtime base_url
plus the placeholder api_key "aws-sdk" and forwarded both to call_llm.
call_llm then treated it as a plain OpenAI-compatible endpoint and issued
an UNSIGNED bearer POST (no AWS SigV4 / IAM signing), so Bedrock returned
an empty/malformed ChatCompletion (choices=None) and the MoA aggregator
turn failed validation.
Add 'bedrock' to the name-preserve set alongside nous/openai-codex/
xai-oauth so bedrock slots are passed by provider name only, routing
through call_llm's dedicated SigV4-signed bedrock branch.
Affects any MoA preset using a bedrock aggregator or bedrock reference.
MoA's _slot_runtime() whitelists providers that must keep their provider
identity (so call_llm runs their provider branch) instead of being treated
as a plain custom endpoint via forwarded base_url/api_key. Native anthropic
was missing from this set.
Native anthropic subscription OAuth setup-tokens (sk-ant-oat*) require Bearer
auth plus the 'anthropic-beta: oauth-*' header, which only the anthropic
provider branch adds. Without the whitelist entry, the slot's base_url/api_key
were forwarded and call_llm sent the OAuth token as x-api-key, which Anthropic
rejects with a bare 429 (rate_limit_error with no quota details). This made
anthropic references in MoA presets fail every time.
Add 'anthropic' to the whitelist so native anthropic reference/aggregator
slots route through the provider branch. Extends upstream 9229d0db1 which
added 'nous' for the same reason.
MoA sessions could not stream: the gateway streaming toggle was a no-op for
provider "moa", so users saw nothing until the entire response finished — minutes
of silence on long turns. The aggregator's reply was always fetched whole.
Root cause was twofold:
1. conversation_loop hard-disabled streaming for provider in {"copilot-acp",
"moa"} (MoA grouped with the ACP client, whose facade isn't a stream).
2. MoAChatCompletions.create() fetched the aggregator response whole via
call_llm(), which had no streaming mode.
For provider "moa", _create_request_openai_client() returns the MoAClient facade
itself, so the existing streaming consumer already calls
MoAChatCompletions.create(stream=True). We reuse that battle-tested consumer
(text-delta delivery, tool_call reassembly, stale-stream detection, non-streaming
fallback) instead of adding a parallel streaming path.
Changes:
- call_llm() gains stream/stream_options. When streaming it returns the raw SDK
stream iterator directly, bypassing _validate_llm_response and the
temperature/max_tokens/payment fallback chain (which assume a complete
response). The caller owns reassembly and fallback.
- MoAChatCompletions.create() runs the references first (unchanged), then when
stream=True returns the aggregator's raw stream, forwarding stream_options and
the consumer's per-request read timeout. stream=False is byte-identical to
before (no stream/stream_options/timeout forwarded).
- conversation_loop streams MoA only when a display/TTS consumer is present;
quiet/subagent/health-check paths keep the complete-response path.
Tests: tests/run_agent/test_moa_streaming.py — create() stream/non-stream
branches, stream_options + timeout forwarding, call_llm raw-stream return vs
validated non-stream. Existing MoA tests unchanged (20 passed).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Sibling of #15795's context_compressor fix. agent/moa_loop.py used the
same response.choices[0].message.content access; while wrapped in
try/except (so no crash), a dict/str-shaped message silently returned
empty. Coerce defensively so the content is actually extracted.
Slot_runtime resolved the provider's real API surface (including api_mode)
but only forwarded base_url and api_key to call_llm, dropping api_mode.
This caused Copilot GPT-5.x reference slots to hit /chat/completions
instead of the Responses API, returning 400 unsupported_api_for_model.
- _slot_runtime: forward api_mode from resolve_runtime_provider
- call_llm: accept explicit api_mode param, override task config
- 4 regression tests for propagation, omission, and signature
The advisory reference view stripped all tool calls and tool results, so
reference models judged a task whose actions and results they never saw — and
references only fired once per user turn, never re-running as the agent's
state advanced through the tool loop.
Two fixes:
- _reference_messages() now PRESERVES the agent's tool calls and tool results,
rendering them inline as text ([called tool: ...] / [tool result: ...]) so a
reference gives an informed judgement on the real current state. Still emits
zero tool-role messages and zero tool_calls arrays (strict providers reject
those), and large tool results are previewed head+tail (4000-char budget).
The required end-on-user shape is met by APPENDING a synthetic advisory user
turn — not by deleting the agent's latest context (which the prior fix did).
- References now re-run on every state change — each new user message AND each
new tool result — instead of once per user turn. The state-sensitive advisory
signature drives the cache: new tool result = miss (re-run), identical-state
re-call = hit (no re-run, no re-emit).
The acting aggregator still receives the full, untrimmed transcript.
* fix(moa): reference advisory view must end with a user turn
MoA reference calls failed with Anthropic models that don't support
assistant prefill (e.g. Claude Opus 4.8): '400 ... must end with a user
message'. The advisory view built by _reference_messages() kept the last
assistant turn's text while dropping the following tool result, leaving a
trailing assistant turn — which Anthropic (and OpenRouter->Anthropic)
interpret as an assistant prefill to continue. References are advisory and
must end on the user turn they answer.
Strip trailing assistant turns from the advisory view (preserving
intervening ones). Update the existing test that encoded the buggy shape
and add a mid-tool-loop regression test.
* feat(moa): give reference models an advisory-role system prompt
Reference models received the bare trimmed conversation with no role
framing, so they assumed they were the acting agent and refused ("I can't
access repositories/URLs from here") or tried to call tools they don't have.
Prepend a dedicated advisory system prompt to every reference call: the
model is an analyst, not the actor — it cannot execute, should not
apologize for lacking tools, and should reason about the presented state to
advise the aggregator/orchestrator on approach, next steps, tool-use
strategy, risks, and anything the acting agent missed. Its output is private
guidance for the aggregator, not a user-facing answer.
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.
MoA was calling reference and aggregator models through a bare
call_llm(provider=slot["provider"], model=slot["model"]) with a forced
temperature and a forced max_tokens (the preset's hardcoded 4096). That left
base_url/api_key/api_mode unresolved — so the auxiliary auto-detector guessed
the API surface instead of using the provider's real runtime, and the 4096 cap
truncated long aggregator syntheses.
A MoA slot is just a model selection and must be called the same way any model
is called elsewhere. Each slot is now resolved through resolve_runtime_provider
(the canonical provider→api_mode/base_url/api_key resolver the CLI, gateway, and
delegate_task all use) via a new _slot_runtime() helper, and the resolved
endpoint is passed into call_llm. So a reference/aggregator gets its provider's
actual API surface — MiniMax → anthropic_messages, GPT-5/o-series →
max_completion_tokens, custom endpoints → their base_url — identical to how that
model is handled as the acting model.
MoA also no longer imposes its own output cap: max_tokens defaults to None
(omitted → the model's real maximum) for references and is passed through from
the caller for the aggregator. The preset's hardcoded 4096 is gone. The
max_tokens preset config field is left in place (config/web/desktop unchanged);
it is simply no longer applied as a forced cap.
Tests: slots route through resolve_runtime_provider with resolved base_url/
api_key; resolution errors fall back to bare provider/model; neither call
carries an output cap even when the preset config still contains max_tokens.
* 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.
