Addresses the review on #51714: the trace/span layer was declared but unwired —
tel_spans was never written, call rows had no timestamp, and nothing set parent
lineage, so the store was metrics-only and couldn't reconstruct a trace.
Wire the span layer (keeping the praised star-schema shape):
- New SpanEvent (span_id/trace_id/run_id/parent_span_id/name/kind/start_ns/end_ns)
mapped into tel_spans via the emitter's _TABLE_COLUMNS.
- The plugin mints a root span per run and, on each model/tool call, emits a
SpanEvent (timing + parent = the run's root) keyed by the SAME span_id as the
detail row, so tel_model_calls / tel_tool_calls JOIN to their span.
- Call hooks fire on completion, so end_ns = now and start_ns is reconstructed
from the measured latency/duration. The run's root span is emitted at finalize
with the true run start/end.
Result: tel_spans is a connected, single-trace_id, run -> calls tree a desktop
waterfall (or any reader) can render directly, ordered by start_ns. Existing
metrics rows (tel_runs/model_calls/tool_calls) are unchanged.
OTLP: spans now flow to the exporter with their trace/parent/timing attributes.
The exporter still emits one OTel span per event rather than reconstructing OTel
SpanContexts into a connected trace tree; that projection is left for a follow-up
and the module docstring now says so plainly instead of over-claiming.
Adds test_spans_trace.py (connected-tree + detail-row JOIN) over the real dispatch
path. Accurate (pre-hook) start times, real OTLP SpanContexts, and subagent
cross-run lineage remain follow-ups.
Aggregate metrics are derived from the local tel_* tables — they're a coarsened
view of local data, not an independent capture path. With telemetry.local=false
nothing is written, so an aggregate opt-in had nothing to aggregate, yet
may_upload_aggregate() returned True and `status` showed "Aggregate metrics: on".
The config could claim a state it couldn't fulfill.
Gate aggregate on local being enabled:
- may_upload_aggregate() now requires local_enabled AND allow_aggregate AND
consent_state == aggregate.
- `telemetry status` computes aggregate_enabled the same way and, when consent is
aggregate but local is off, prints "inert: local telemetry is off — nothing to
aggregate" instead of the opt-in hint.
Happy path is unchanged (local on + consent aggregate -> on). Adds policy and CLI
tests for the inert combo.
Rename the telemetry tiers away from the borrowed control-plane/data-plane
jargon to plain language, across code, CLI output, config, and docs:
- "local plane" -> "local telemetry"
- "aggregate plane" -> "aggregate metrics"
- "trajectories plane" -> "trajectories" / "telemetry.trajectories"
- "three planes with a hard wall" -> "three settings, isolated from each other"
User-facing `hermes telemetry status` now reads "Local telemetry: on" /
"Aggregate metrics: off" / "Content export: off (trajectories disabled)".
The OTLP resource attribute key telemetry.plane is renamed to telemetry.scope
(wire-level identifier; nothing consumes it yet).
No behavior change — wording only. Status renders identically apart from the
labels; tests updated to match the new strings.
policy.resolve() / TelemetryDecision was a read-only projection used only by
`hermes telemetry status` for display. The actual behavior gates already read
telemetry.* straight from config: the emitter (whether to write) and the plugin
loader (whether to auto-load) each call .get("local", True) on the loaded config,
never through policy.
Make config the single chokepoint the status command reads too: it now resolves
local/allow_aggregate/consent_state inline from the loaded config, the same way
the other gates do. policy.py keeps only what config can't express on its own —
the consent constants, ensure_install_id(), and may_upload_aggregate(config) as a
pure function (the gate a future uploader must consult). resolve() and the
TelemetryDecision dataclass are removed; policy.py drops 107 -> 70 lines.
No behavior change: status renders identically, and the default-on local plane is
still defaulted in DEFAULT_CONFIG plus a fail-safe .get(..., True) at each gate.
The session database records billing_provider and billing_base_url using
COALESCE(column, ?) in update_token_counts(), making them write-once.
When a user switches models mid-session via /model, the runtime (agent.provider,
agent.base_url) updates correctly, but the session row never reflects the new
provider. This causes the dashboard Models page to display a stale provider
badge and misattributes token usage / cost analytics.
Fix: add update_session_billing_route() that unconditionally sets
billing_provider, billing_base_url, and billing_mode (no COALESCE), and call
it from switch_model() in agent_runtime_helpers.py after the swap succeeds.
This follows the same pattern as update_session_model() which already
unconditionally updates the model column (added for the identical COALESCE
problem on the model field).
Closes#48248
* 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.
The salvaged context-window screen (#52392) skips fallback candidates that
are too small, and the rate-limit/403 fixes skip candidates that are at
capacity. A third hard failure remained uncovered: a fallback that builds a
client fine but returns a 400 because it structurally cannot run the model.
The canonical case is a configured openai-codex / ChatGPT-account fallback
asked to compress a glm-5.2 conversation:
400 - {'detail': "The 'glm-5.2' model is not supported when using
Codex with a ChatGPT account."}
This is a request-validation error, so should_fallback was False and the
explicit-provider gate blocked it — the auxiliary task (compression) aborted
every turn, dropping middle turns without a summary and churning the session,
which is exactly what destroys the prompt cache.
Adds _is_model_incompatible_error() (400 + capability phrasing, excluding
not-found and billing 400s which the sibling predicates own) and treats it as
a fallback-worthy capacity error in both sync and async call_llm, so the chain
skips the incapable route and continues to the next viable candidate.
The runtime auxiliary fallback chain (_try_configured_fallback_chain and
_try_main_fallback_chain) returned the first reachable candidate without
checking whether the candidate's context window was large enough for the
task. For task='compression' this meant a reachable but undersized
fallback (e.g. 32K) could be selected and then fail, even when a later
larger-context fallback was available.
This adds two small helpers:
_task_minimum_context_length(task)
Returns MINIMUM_CONTEXT_LENGTH (64K) for compression, None for
other tasks (vision, web_extract, etc.).
_candidate_context_window(provider, model, ...)
Thin wrapper around get_model_context_length that returns None on
probe failure so unknown/custom endpoints pass through unchanged
(preserves the existing fallback surface).
Both fallback loops now skip reachable candidates whose resolved context
is below the task minimum and continue iterating. The success path
(first viable candidate wins) is unchanged. Return shape and ordering
for healthy candidates are preserved.
Six regression tests cover:
L2 configured chain skips too-small candidate
L2 chain continues after skipping, returns last viable
L3 main chain skips too-small candidate
L4 unknown-context candidate passes through
L5 non-compression task is not filtered
L6 minimum constant matches MINIMUM_CONTEXT_LENGTH (64K)
3/6 fail on upstream/main without the production change (verified); all
6 pass with the fix. Full test_auxiliary_client.py suite (231 tests)
and related compression tests (130 tests) remain green.
When an explicit aux provider cannot build a client before any request is
sent (missing raw env key, exhausted/unavailable OAuth or credential-pool
auth, resolver returning (None, None)), call_llm raised a misleading
"no API key was found" error and bypassed the configured fallback_chain
entirely. A provider authenticated through Hermes auth / the credential
pool (e.g. ollama-cloud) whose pool entry is exhausted hit this path, so
compression failed instead of routing to the configured fallback.
Adds _try_configured_fallback_for_unavailable_client() and wires it into
both sync and async call_llm before the raise, and into the startup
compression feasibility check.
Salvaged from #51835 by @herbalizer404.
Rate-limit (429) errors on explicit-provider auxiliary tasks were
silently failing instead of triggering the fallback chain. The
is_capacity_error gate only checked payment and connection errors,
excluding rate limits — so when a configured provider like
openai-codex hit its rate limit, auxiliary tasks (kanban_decomposer,
vision, web_extract, approval, etc.) had zero resilience.
Add _is_rate_limit_error() to is_capacity_error at both call sites
(sync and async paths) so rate limits trigger fallback regardless
of whether the provider was auto-detected or explicitly configured.
Fixes#52228
Ollama Cloud (and similar) return 403 with bodies like "this model requires
a subscription, upgrade for access" or "you have reached your session usage
limit, upgrade for higher limits". These are capacity/billing conditions
semantically identical to credit exhaustion, but _is_payment_error() did not
recognize them (403 missing from the status set; keywords missing), so the
configured fallback_chain was never tried and compression failed outright.
Adds 403 to the status set and the subscription/session-usage keywords.
Salvaged from #49076 by @herbalizer404.
build_turn_context() created the DB session row via _ensure_db_session()
before the system prompt was restored/built, so a fresh API/gateway agent
carrying client-managed history inserted a row with system_prompt=NULL. That
tripped the misleading 'stored system prompt is null; rebuilding from scratch
... investigate the previous turn's write path' warning and a guaranteed
first-turn prefix cache miss. Move row creation to after _cached_system_prompt
is populated.
Verified live (OpenRouter + claude-sonnet-4.5): persistent-agent turns show
cache_read jumping to the full prefix on turn 2+ (write 24411 -> read 24411),
and the persisted system_prompt is non-NULL so fresh-agent restore keeps the
prefix cache warm.
Tests: turn-context ordering regression asserting _ensure_db_session runs
after _cached_system_prompt is populated.
/learn told the agent to fill the skill `author` field, and the system
prompt environment probe surfaces the OS login name (user=$(whoami) in
prompt_builder.py), so the model wrote the host username into published
SKILL.md frontmatter — a privacy leak the user never opted into, and
inconsistent run to run as the most-salient identity changed.
The /learn authoring prompt now sets `author` to the literal value
`Hermes` and explicitly forbids deriving it from the host environment
(OS/login user, git config, or any probeable identity). The skill names
itself as the tool that wrote it.
Closes#52368.
#48879 closed the tool-call sequence on interrupt inside finalize_turn so a
/stop after a tool no longer persists a `tool` tail that the next user message
turns into a `tool -> user` role-alternation violation (which strict providers
like Gemini/Claude react to by hallucinating a continuation and ignoring prior
context — what users see as "lost context after stop").
But the retry-wait, error-handling, and post-error retry-wait interrupt aborts
in conversation_loop return early and never reach finalize_turn, so they still
persisted and returned a raw `tool` tail. Interrupting during provider
backoff/rate-limiting (common under heavy work) hit exactly this path.
Extract the close into a shared close_interrupted_tool_sequence helper and apply
it at every interrupt abort (finalize_turn + the three early returns) so the
whole bug class is fixed, not just the one site.
The /learn authoring prompt taught a subset of the HARDLINE skill rules,
and stated the <=60-char description rule without making the model enforce
it — so generated descriptions overshot (up to 202 chars), which the
60-char system-prompt skill index then silently truncates.
- description: add the index-truncation rationale, a count-and-trim
self-check, and a good/bad length example so the model actually hits <=60.
- add platforms-gating rule (OS-bound primitives -> declare platforms:).
- add author-credits-human-first rule.
- round out the Hermes-tool framing with the full wrapped-tool mapping and
references/templates layout.
Closes#52367.
Move terminal/execute_code/read_file preview compaction into agent.display so CLI, gateway, and Ink TUI all inherit the same labels that desktop introduced in #52321.
The shared preview keeps raw args intact while trimming display-only shell plumbing (`cd`, pipe tails, banner/status echoes) and read_file line ranges. Desktop now prefers backend `context` for live rows and keeps its TypeScript fallback only for hydrated history.
Recurring cron jobs were prompt-cache-cold on every fire. session_id is
built as cron_<job_id>_<timestamp>, and the Codex/Responses transport used
session_id directly as prompt_cache_key — so the timestamp changed the cache
key on every run and the static prefix (agent identity + tool schemas) was
re-paid each tick.
Derive prompt_cache_key from a SHA-256 of the static prefix (instructions +
sorted tool schemas) instead. Repeated fires of the same job share one
content-addressed key (pck_<hash>) and reuse the warm prefix within the
provider's cache TTL. The key changes exactly when the prefix changes —
edit the job's prompt or toolset and it re-keys; leave it alone and it stays
stable.
session_id is left untouched for transcript isolation, log correlation, and
the Codex/xAI session-scope routing headers (session_id, x-client-request-id,
x-grok-conv-id) — those are the per-fire identity, not the cache key. Only the
prompt_cache_key body field (standard OpenAI/Codex path and the xAI extra_body
field) is content-addressed.
Closes#51395.
Co-authored-by: spiky02plateau <spiky02plateau@users.noreply.github.com>
Co-authored-by: JoaoMarcos44 <JoaoMarcos44@users.noreply.github.com>
OpenRouter/Nous image gen now runs a quality-first model chain by default:
attempt the highest-fidelity OpenAI image model first, then fall back to
Gemini 3 Pro Image when it's access-gated/unavailable/times out. An explicit
OPENROUTER_IMAGE_MODEL / config model override pins one model with no fallback.
Atlas validation rejects malformed model output instead of shipping it: adds a
per-state collapse guard (a single sliver/fragment row no longer passes because
other rows are healthy), on top of the existing postage-stamp + multi-pose
checks.
Desktop: pet-gen native notifications are now "global" (not tied to a chat
session), so a background generation started from the command center fires an
OS notification when the user is away even with no active session. Adds a
neutral "This can take up to 5 minutes." banner on step 1, and lets the
provider picker auto-size.
Tests updated/added for the OpenRouter fallback chain, the collapse guard, and
the global notification path.
Make verification closure the default coding behavior after landed file edits while keeping bounded retries and config/env switches for users who need to disable it.
Remove cute/chibi-biased wording from base draft variations and explicitly preserve the requested mood across base and row prompts so scary, eerie, or other non-cute concepts are honored while keeping sprite constraints.
Ship the final pet-generation UX polish (provider picker behavior, step-2 cancel flow, banner integration, and visual consistency) and make saturated-chroma background removal C-op driven so hatch processing no longer hammers the machine during long runs.
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).
The preflight-compression gate only ran the (expensive) token estimate when
the message COUNT exceeded protect_first_n + protect_last_n + 1. A session
with a handful of very large messages never tripped the count condition, so
compression was never attempted and the turn eventually hit a hard
context-overflow error.
Add _should_run_preflight_estimate() with OR semantics: run the estimate when
either the message count exceeds the protected ranges (the historical gate)
OR a cheap char-based estimate already crosses the configured threshold. The
downstream estimate_request_tokens_rough() stays authoritative — this is only
a hint that decides whether to pay for the full estimate.
Salvaged from #27435 by @texhy (authorship preserved). Re-applied on current
main: the preflight gate moved from conversation_loop.py to turn_context.py
since the PR was opened, so the helper + gate are placed there; the test
imports the real MINIMUM_CONTEXT_LENGTH instead of a hardcoded literal.
Closes#27405.
Turn a text prompt into a petdex-spec spritesheet (8×9 grid of 192×208
cells), grounded so every animation row stays the same creature:
- orchestrate: base drafts (distinct variation nudges) → per-row grounded
generation → atlas compose; one image call per row, rows fan out in parallel.
- atlas: frame-perfect registration in normalize_cells — 1-D cross-correlation
of each frame's column-mass profile locks the body (robust to limbs/cape),
one shared per-state scale, bottom-anchored; plus alpha-hole repair, gutter
severing, and interior-seeded chroma-pocket clearing.
- prompts: pixel-art-by-default style hints + registration constraints.
- store: local pet write (register_local_pet), slugify/unique_slug,
export_pet, slug-realigning rename_pet, createdBy provenance.
When context compaction's summary generation fails, the compressor's default
path (abort_on_summary_failure=False) drops the middle window and inserts a
static 'summary unavailable' marker — destroying the compacted turns. #29559
reported the field impact: a Connection error at the compaction moment dropped
124->15 messages (110 lost) for a long browser-automation task; #25585 is the
same failure mode (failed summary commits a destructive compaction anyway).
compress() already has an EXCEPTION to the historical drop default: auth
failures (401/403) ALWAYS abort and preserve the session, because rotating into
a placeholder-summary child on a broken credential strands the user. A transient
network/connection error is the same situation in reverse: it WILL recover, and
retrying then is strictly better than discarding context for a momentary blip.
Extend the always-abort carve-out to terminal connection/network failures:
- new _last_summary_network_failure flag, set in _generate_summary's terminal
failure branch when _is_connection_error(e) (reached only after any main-model
fallback is exhausted), reset alongside the auth flag;
- compress() aborts when it's set (returns messages unchanged,
_last_compress_aborted=True), independent of abort_on_summary_failure;
- a network-specific operator warning (distinct from the auth + config-flag
messages).
Scoped to connection errors only: a generic 500/400 still takes the historical
fallback-drop path (test_non_auth_failure_still_uses_fallback_path stays green).
Tests: network-failure detection + abort-despite-flag-false, both mutation-checked
(removing the flag-set fails detection; removing the carve-out fails the abort).
Anthropic migrated the OAuth token endpoint from
console.anthropic.com/v1/oauth/token (now returns HTTP 404) to
platform.claude.com/v1/oauth/token. The token *refresh* path already
iterated both hosts, but the two initial code-exchange call sites were
hardcoded to the dead console host, so every new Claude OAuth login
failed with 'Token exchange failed: HTTP Error 404: Not Found' and saved
no credentials.
Fix the whole bug class:
- Add _OAUTH_TOKEN_URLS [platform.claude.com, console.anthropic.com] in
agent/anthropic_adapter.py; _OAUTH_TOKEN_URL now points at the live
host for backward-compat with existing imports.
- run_hermes_oauth_login_pure() (CLI flow) iterates the list, first
success wins, mirroring the refresh path.
- hermes_cli/web_server.py (desktop dashboard flow) imports the list and
iterates it too, so the GUI login path is fixed identically.
Probe: console.anthropic.com/v1/oauth/token -> HTTP 404 (gone),
platform.claude.com/v1/oauth/token -> HTTP 400 (alive). Verified a real
Claude MAX OAuth login now succeeds end-to-end.
deliver=origin (or omitted) from a TUI or classic-CLI session produces a
job with origin=null, because those sessions never populate the
HERMES_SESSION_PLATFORM/CHAT_ID context vars that _origin_from_env reads.
The scheduler then resolves no delivery target and skips delivery — the
job runs and saves output to last_output, but nothing reaches the user
and they only find out by polling cronjob(action='list') (#51568).
This is by design (local sessions have no live-delivery channel), so the
fix surfaces it instead of silently dropping the intent:
- cronjob create now appends an informational notice to its result when
a created job resolves to zero delivery targets and the user did not
explicitly ask for deliver='local'. The check uses the scheduler's own
_resolve_delivery_targets so it accounts for origin, home channels,
'all', and explicit platform targets — no false positives.
- PLATFORM_HINTS gains a 'tui' entry (the TUI had none) and the 'cli'
hint now states that cron jobs from these sessions are local-only and
that deliver must target a gateway-connected platform to notify the
user. This stops the agent promising a delivery that never happens.
No scheduler/delivery behavior change; no new env var; cron isolation
invariant untouched.
Add a built-in telemetry system that records what the agent does — workflows,
model calls, tool calls, errors — to the local machine, powers `/insights`, and
can export to an operator-chosen destination. Default-on locally; nothing leaves
the machine unless the user exports it or opts into the aggregate plane.
Three planes with a hard wall between them:
- local: full-fidelity observability (real model/provider/tool names), on by
default, never leaves the machine.
- aggregate: opt-in metadata, default off. No uploader ships — consent is
recorded via telemetry.consent_state, and `preview` shows what would be
produced, computed locally.
- trajectories: full message content, opt-in, exported only to the operator's
own destination.
Mechanism:
- Bundled `telemetry` plugin registers observational lifecycle hooks
(on_session_start / post_api_request / post_tool_call / on_session_finalize).
No core call sites are edited; hooks already carry the data.
- Fire-and-forget emitter: emit() returns in microseconds, never blocks or
raises into a model/tool call. A daemon thread writes events to an
append-only JSONL log and the tel_* tables in state.db (its own sqlite
connection, separate from SessionDB).
- tel_runs / tel_model_calls / tel_tool_calls live in the declarative
SCHEMA_SQL and are reconciled automatically; SCHEMA_VERSION 16 -> 17.
- metrics derives rollups for /usage and /insights; rollup builds per-run
summaries for `hermes telemetry preview`.
Consent is config, not a parallel command surface. The config file is the root
of trust: set telemetry.consent_state with `hermes config set`, or pin any
telemetry.* key (including allow_aggregate) via managed scope, which overrides
the user's value per key. `hermes telemetry` exposes only what config cannot:
status (report), preview (query), and export.
Export:
- exporter_bulk writes telemetry (and, when the trajectories plane is enabled,
session content) to ndjson/json.
- otlp_exporter streams spans to a configured OpenTelemetry Collector over
OTLP/HTTP. The SDK is an optional extra (hermes-agent[otlp]), lazily
installed via tools.lazy_deps on first use.
- Secrets are always redacted on every export path
(redact_sensitive_text(force=True)); content export is gated by the
trajectories plane, and PII scrubbing follows telemetry.content_redaction.
OTLP auth headers reference environment variable names, never inline values.
No outbound emission to Nous. The aggregate uploader is intentionally not built.
install_pet now refuses spritesheet/pet.json URLs that aren't on a petdex
host (matching thumbnail_png's existing _is_petdex_host guard), so a
spoofed manifest can't redirect a download at an arbitrary host. Slugs
are normalized to a single path segment before indexing into pets_dir(),
closing a path-traversal vector in load_pet/remove_pet/install_pet.
Open-ended skill learning across every surface. /learn <free text> takes a
description of any source — a directory, a URL, the workflow you just walked
the agent through, or pasted notes — and the live agent gathers it with the
tools it already has (read_file/search_files, web_extract, the conversation,
the pasted text), then authors a SKILL.md via skill_manage following the
house authoring standards (<=60-char description, the standard section order,
Hermes-tool framing, no invented commands).
No engine, no model-tool footprint, works on any terminal backend (local,
Docker, remote): /learn builds a standards-guided prompt and hands it to the
agent as a normal turn.
- agent/learn_prompt.py: shared standards-guided prompt builder
- /learn registry entry (both surfaces) + CLI handler (inject onto input
queue) + gateway handler (rewrite turn, fall through, /blueprint pattern)
- tui_gateway command.dispatch returns a send directive -> TUI + dashboard chat
- dashboard Skills page 'Learn a skill' panel (dir + URL + open-ended text)
composes a /learn request and runs it in chat
- docs (slash-commands ref + skills feature page), 11 targeted tests
Inspired by OpenAI Codex's Record & Replay and the /learn concept from #47234
(dir-distillation engine); reworked to be open-ended and engine-free per
review.
A "one-shot" is a single stateless model call that runs OUTSIDE any conversation:
it never touches session history, never breaks prompt caching, and returns plain
text. UI surfaces need this for small generative chores — a commit message from a
diff, a rename suggestion, a summary — where an agent turn would pollute the
thread and hand-rolling an LLM call at every call site would be worse.
- `agent/oneshot.py`: `run_oneshot(...)` over the existing auxiliary-client
plumbing (same path as title generation). Two call shapes: explicit
instructions/input, or a registered `template` + `variables` (templates own the
prompt engineering so it stays consistent across CLI/TUI/desktop). Ships a
`commit_message` template. Model selection inherits the live session via
`main_runtime`, else the configured aux `task` backend.
- `tui_gateway/server.py`: `llm.oneshot` RPC (long-handler) inheriting the
session's model when `session_id` resolves.
Stateless by construction — no session mutation, cache untouched.
Follow-up to the coding-context posture (#43316): that PR detects each repo's
verify loop (manifests, package manager, exact test/lint/build commands, context
files) and bakes it into the system-prompt snapshot — but only as a string, for
the model. Non-prompt consumers (the desktop verify UI) had no way to read it
without re-sniffing and drifting from the prompt.
Split detection from rendering, keeping one source of truth:
- `detect_project_facts(root) -> ProjectFacts` (frozen) holds the structured
facts; `_project_facts()` now renders it into the same snapshot lines, so the
prompt block stays byte-identical (cache-safe).
- `project_facts_for(cwd)` resolves the workspace root (git, else marker) and
returns the structured facts, or None outside a workspace.
- `project.facts` gateway RPC surfaces it to any client (desktop/TUI/ACP).
Tests assert the structured output and that the UI-facing commands never drift
from what the prompt block renders (one detector feeds both).
Adds auxiliary.background_review.{provider,model} (default auto = main chat
model — unchanged). Set it to a different, cheaper model and the post-turn
self-improvement review runs there for ~3-5x lower cost.
Cache-aware by design: the main chat is warm in the prompt cache, so the
default full-history replay on the main model is cheap cache reads — left
exactly as-is. A different model can't reuse that cache (different key), so
when (and only when) routed to a different model the fork replays a compact
digest instead of the full transcript, minimising what it cold-writes on the
aux model. Same model -> full replay; different model -> digest.
Quality holds in benchmarks: memory capture identical, skill near-identical.
Nothing changes unless you opt in by naming a different model.
Co-authored-by: Hermes Agent <noreply@nousresearch.com>
