* fix(deriver): eliminate create_documents deadlock and stop silently burning batches on transient errors
Two concurrent work units writing the same (workspace, observer, observed)
collection deadlocked on times_derived reinforcement UPDATEs issued in
batch order (DEV-1975, 682 events in 90 days). The deadlock was swallowed
per-document, the loop cascaded PendingRollbackErrors against the dead
session, the whole batch was lost, and the queue item was marked processed.
- serialize writers per collection with a transaction-scoped advisory lock
(pg_advisory_xact_lock + SET LOCAL lock_timeout), skipped for insert-only
batches; covers all three row-lock sites in one move
- hoist external-vector-store dup-candidate resolution ahead of the first
DB statement so the lock's critical section contains no network calls
- abort the batch on SQLAlchemyError instead of continuing through an
aborted transaction; per-document skip semantics kept for non-DB errors
- classify transient errors (new src/utils/retryable_errors.py) and retry
them via a bounded in-process counter instead of marking items errored
* fix(deriver): replace create_documents advisory lock with id-ordered row locks
Advisory locks are database-scoped and would serialize every writer to a
collection, including across Groudon tenants that share names. Collect
reinforcement and replace ops during the loop, lock target rows with
SELECT ... ORDER BY id FOR UPDATE, then apply. populate_existing reloads
times_derived so a prefetched identity-map row cannot lose a concurrent
increment.
* fix(deriver): harden create_documents candidate hoist and test isolation
Skip empty embeddings on the external-store path, isolate per-document
resolve failures, and keep replacement times_derived in the in-batch
ledger. Patch get_external_vector_store in the hoist test and cover
in-loop SQLAlchemyError abort.
* fix(deriver): address CodeRabbit findings on create_documents deadlock fix
- Distinguish external resolve failure ([] skip) from pgvector fallback (None)
so _semantic_dup_decision never re-enters external I/O under an open session
- Bound external candidate hoist concurrency with a semaphore
- Map in-loop IntegrityError to ValidationException for a uniform contract
- Persist transient retry attempts on the oldest unprocessed queue item so
every deriver instance shares one MAX_RETRYABLE_ATTEMPTS budget
- Cover resolve-failure skip and multi-manager reclaim of the retry budget
* fix(deriver): harden retry metadata cleanup and stale reinforce fallback
- Strip _retry_attempts from payloads in the same transaction as
mark_queue_items_as_processed / mark_queue_item_as_errored
- Clear shared retry metadata only after a successful terminal mark
- On reinforce, if the locked target is gone or soft-deleted, insert the
incoming document instead of dropping it
- Skip pgvector semantic lookup when embedding is empty so query_documents
cannot embed under an open session
* fix(deriver): address review on deadlock retry and row-lock apply
Strip _retry_attempts before payload validation so non-representation
tasks are not burned as extra_forbidden. Re-raise retryable observer
save errors after telemetry so the queue actually retries. Skip
same-batch reinforce fallbacks after a replace. Revert unordered
FOR UPDATE on mark processed/errored and drop post-commit retry
cleanup from the success path.
* fix: add test and simplify queue query
---------
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
* fix(deriver): strip NUL bytes from model-generated observations
Postgres rejects NUL (0x00) in text columns and in jsonb strings. API
ingress has always stripped it from user-supplied content, but the
deriver's own output did not go through any equivalent: a model can emit
a \u0000 escape in its tool-call arguments, which the JSON parser decodes
into a real NUL byte. Seen in production when models transcribe shell
output (`tr '\x00' '\n'`) or Windows paths (`c:\<NUL>users\amal`).
The NUL reached the exact-content dedup pre-fetch in create_documents as
a bind parameter, so the query raised DataError before any row was
written and the whole batch for that observer was dropped.
Strip in _normalized_observation and _normalized_observation_input --
the points that already normalize text for persistence and embedding --
so the embedded text matches the stored text. premises and sources are
covered too, since they ride along in internal_metadata. The emptiness
check now runs after normalization, because str.strip() does not remove
NUL and all-NUL content would otherwise be stored as an empty string.
DocumentCreate.content gets a mode="before" validator as a backstop for
callers that bypass those paths; running before the length constraint
makes all-NUL content fail min_length rather than silently empty out.
The NUL helpers move out of schemas/api.py into utils/sanitization.py as
a single recursive strip_nul, so ingress and internal paths share one
implementation. It is overloaded to keep str -> str for the callers that
chain .strip(), and passes None through so optional fields need no guard.
Fixes HONCHO-4XZ
* fix: broaden nul strip check
* chore: code simplification
---------
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
* fix: stop top_k=0 from reaching Turbopuffer on message search
HONCHO-19Q: dreamer search_messages passed LLM limit=0 through to
Turbopuffer (top_k must be 1..10000). #970 guarded documents; this
closes the message path and floors tool limits at 1.
* fix: preserve pgvector None sentinel on zero top_k
query_external_vector_document_ids must return None when on the
pgvector path before applying the top_k<=0 empty-list guard.
* fix(filter): make ne on jsonb metadata keys null-safe
* test(filter): cover null-safe ne on nested metadata keys
* test(filter): count actual rows for nested-metadata ne null-safety
Compile-only checks lock the operator map entry but don't catch wrong
row sets under three-valued logic. Adds a live messages/list case with
a message missing the key and one with empty metadata, following the
scalar-column pattern in test_negation_includes_conclusions_with_no_session.
* test(filter): type message_configs with a TypedDict
basedpyright couldn't narrow the heterogeneous metadata dict literals,
so indexing message_configs["content"] came back partially unknown and
broke the sorted() calls under type checking.
* fix(deriver): truncate oversize observations so one cannot drop the batch
simple_batch_embed raised ValueError when any input exceeded the per-input
token cap, which failed the entire deriver save when a single observation
was over-length. Add on_oversize="truncate": oversize inputs are embedded
from a token-capped prefix (re-encoded until it fits, with a warning),
preserving one vector per input. Default stays "raise" so existing callers
are unchanged. RepresentationManager opts into truncate.
Also add a live embedding test that fails on main (raise / missing kwarg)
and passes once a mixed short+oversize batch survives.
Refs #569
* fix(deriver): surface failure when all observer saves fail
When every observer's save_representation failed (e.g. embedding retries
exhausted under a sustained 429), the deriver logged the error and returned
normally, so the queue marked the work unit processed with zero documents
saved. Collect per-observer errors and, after telemetry is emitted, raise
RepresentationSaveError when no observer succeeded. Partial failures stay
processed (saved observers must not be discarded) and are recorded via an
additive failed_observer_count on RepresentationCompletedEvent.
Refs #728
* fix(embedding): guarantee truncation progress and truncate on re-embed
The retry slice in _truncate_to_token_limit always recomputed the same
keep count, so a slice whose re-encode grew past the cap could oscillate.
Decrement keep after each unsuccessful retry.
Document re-embed in the reconciler used the default on_oversize="raise",
so one oversize document failed every other document in the batch.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
* chore: drop ticket ids and shrink comments to one sentence
Comments and docstrings describe current behavior, not the PR that
introduced them. Ticket numbers stay in the commit/PR.
* chore: annotate RepresentationSaveError and assert truncate on re-embed
* fix(embedding): truncate on conclusion create paths and document BPE loop
Storage callers in create_observations (API + agent tools) now pass
on_oversize="truncate" so a single oversize item cannot drop the batch.
Docstring on _truncate_to_token_limit notes why decode/re-encode is load-bearing.
---------
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
* fix: honor DERIVER_DEDUPLICATE in create_observations
The agent-tool path hardcoded deduplicate=True, so DERIVER_DEDUPLICATE=false
could not disable dedup for observations created through this path. Pass
settings.DERIVER.DEDUPLICATE, matching crud/representation.py.
* test: cover deduplicate setting is forwarded in create_observations
* fix(filter): reject unknown operator dicts on scalar columns
An unrecognized operator dict on a non-JSONB column (e.g.
{"session_id": {"operator": "null"}}) fell through to `column == value`,
binding a dict to a VARCHAR parameter. That compiles, then fails in the
driver at execute time with "cannot adapt type 'dict'" — an unhandled
500 for what is invalid input.
Raise FilterError (422) instead. The guard lives in the shared
_build_field_condition, so every route through apply_filter is covered.
It keys on the actual column type rather than the JSONB_COLUMNS name
list, so dict equality still works on JSONB columns reachable through
Document's raw-key fallback (e.g. source_ids), where the driver adapts
dicts fine.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* chore(filter): name psycopg explicitly in comment
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* fix(filter): handle null operands and non-numeric columns in comparisons
Two defects in _build_comparison_conditions, both reachable from any
route that accepts filters:
1. A null operand hit float(None), raising TypeError where only
ValueError was caught — an unhandled 500. A null operand is a null
check, not a value comparison, so {"ne": null} now compiles to
IS NOT NULL and the other operators reject null with a 422. Equality
against null already produced IS NULL via _build_field_condition.
2. Numeric operators float()-cast on every column type, so a string
inequality on a text column ({"session_id": {"ne": "abc"}}) was
rejected as an invalid number. Coercion is now gated on the column
actually being numeric; text columns compare as text. Numeric columns
still validate, and TypeError is caught alongside ValueError.
Existing ne coverage only exercised the JSONB metadata path, which uses
_safe_numeric_cast and handles strings — the scalar column path was
untested. Adds cases for both.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* fix(filter): fail closed on unrecognized filter shapes
The filter body is arbitrary client JSON with no schema, so validation
was emergent: any shape the DSL didn't recognize surfaced as an
unhandled 500 from somewhere in SQLAlchemy or psycopg. Fixing individual
shapes doesn't converge — a fuzz over the DSL found five more families
beyond the three already fixed here:
{"AND": [None]} TypeError, non-dict in a logical list
{"AND": [[]]} AttributeError on .items()
{"session_id": {"gte": true}} SQLAlchemy ArgumentError
{"embedding": []} NotImplementedError, no python_type
{"session_id": {"ne": {...}}} execute-time "cannot adapt type 'dict'"
Two generic guards instead:
1. Any operand bound to a non-JSONB column must be a scalar, checked
element-wise for `in`. A dict or list bound to a scalar column
compiles cleanly and only fails in the driver at execute time, so it
has to be rejected during construction. JSONB columns are exempt —
a dict there is a containment match.
2. apply_filter fails closed: FilterError propagates, anything else is
logged with logger.exception (filter shape included) and re-raised as
FilterError. Unknown filter failures become 422s while staying fully
visible as errors rather than being swallowed.
Adds two invariant tests over a generated matrix of filter shapes: every
shape either compiles or raises FilterError, and no non-scalar is ever
bound to a scalar column. Both fail without the guards above. They cover
shapes nobody enumerated, so the next unimagined body fails in CI rather
than in production.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* chore: clear the two remaining basedpyright warnings
`uv run basedpyright tests/ src/` reported two warnings in files that
predate this change. The pre-commit hook is file-scoped, so neither was
visible unless the owning file was touched.
- src/vector_store/__init__.py: join the lancedb error message with
explicit `+` instead of adjacent literals (reportImplicitStringConcatenation).
- tests/test_cache_redaction.py: the test covers a private helper
deliberately, so annotate the import (reportPrivateUsage).
No behavior change; whole-tree check is now clean.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* fix(filter): keep numeric operands exact instead of coercing to float
float() rounds any integer past 2**53 and flattens a Decimal, so
{"token_count": {"gt": 9007199254740993}} silently compared against
9007199254740992 — a different row set than the client asked for.
_coerce_numeric passes already-numeric operands through untouched and
only parses strings, trying int() before float() so "5" stays exact
while "5.5" still parses. bool narrows to int: it is an int subclass,
but binding it as a boolean against a numeric column produces SQL
Postgres has no operator for.
Not coerced to the column's own type: int(5.5) would turn
{"token_count": {"lt": 5.5}} into `lt 5`, changing which rows match.
Also fixes a vacuous assertion in test_dict_on_jsonb_column_still_works.
It checked for "internal_metadata" in the whole statement, but that name
is in the SELECT projection either way, so the test passed even when no
WHERE clause was applied. Now asserts on stmt.whereclause and that the
filter payload is actually bound.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* fix(filter): bind boolean columns as booleans, numerics without a cast
Boolean columns were treated as numeric because bool subclasses int, so
`{"is_active": {"ne": true}}` coerced true to 1 and Postgres rejected
`boolean <> integer` at execute time. Confirmed against a live database:
every form except bare equality with a native boolean was a 500.
Boolean columns now get their own branch: native true/false bind as
booleans, and any other operand is a FilterError. SQLAlchemy types the
bind from the operand rather than the column, so "true" renders
`is_active = %(param)s::VARCHAR` and Postgres has no such operator — a
422 is the honest answer. String booleans have never worked, are absent
from the docs (every documented boolean is inside metadata, which is
JSONB containment and unaffected), and produced no Sentry events in 90
days, so nothing can depend on the current behavior.
Also corrects the previous commit. Coercing operands to exact ints made
SQLAlchemy render an ::INTEGER cast, so any value past int4 — not 2**53
— started failing with "integer out of range" where float() had silently
compared as a double. Decimal keeps the value exact and renders no cast,
matching what float() did. The `in` branch never went through coercion
at all, so {"token_count": {"in": [1, 2147483648]}} was a 500 before
this PR too; it now takes the same path.
Verified end to end against the live database, not just at compile time.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* fix(filter): coerce every operand against its column's type in one place
The DSL had two operand paths with different rules. Comparison operators
parsed datetimes and coerced numbers; bare equality bound whatever it was
handed. SQLAlchemy types a bind from the operand rather than the column
and the psycopg dialect renders that type as an explicit cast, so a
mismatch compiled into valid-looking SQL and failed at execute time:
"operator does not exist: timestamp with time zone = character varying".
A matrix of column type x operand type x operator against a live
database found 462 combinations, of which 55 built cleanly and then
failed. The most plausible was a filter someone would write first try:
{"created_at": "2026-01-01"} 500
{"created_at": {"gte": "2026-01-01"}} worked
_coerce_operand now handles every operand, whatever the operator, keyed
on the column's real type: JSONB takes an object, boolean takes only
true/false, datetime parses strings, numeric goes through _coerce_numeric,
text requires a string, and a column with no python_type (pgvector) is
not filterable. eq/ne/gt/in cannot drift apart because they share the
one call; `in` coerces element-wise, since a single element's type
decides the cast rendered for that parameter. The matrix is now clean.
This is a net deletion: the separate datetime, numeric, in-datetime and
boolean branches, plus _require_bindable_operand, all collapse into it.
Two more execute-time failures fixed on the way. `contains` was keyed on
column_name == "h_metadata", so Document's equally-JSONB
internal_metadata fell through to ILIKE and produced `jsonb ~~* text`;
it now keys on the column type. And {"source_ids": "abc"} was
`jsonb = character varying`.
Closed-set columns are validated against the Literal that defines them,
so declaring a new level or sync state updates filter validation with no
change here. {"level": "banana"} was silently matching nothing.
Empty IN is now always applied rather than skipped. Unifying the branches
inherited a guard that had only ever wrapped the datetime path, which
dropped the condition entirely and widened the query to every row —
fail-open on an empty allowlist, which session scoping relies on to fail
closed (see extract_session_allowlist). Caught by an existing test that
asserts returned rows; the fuzz and the type matrix only check for
errors, so neither would have seen it.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* fix(filter): make NOT and ne include rows where the field is unset
NOT (col = v) and col <> v are NULL when col is NULL, so negation
dropped rows whose column is unset — a conclusion with no session is
not "some other session", but was excluded anyway. IS NOT TRUE and
IS DISTINCT FROM behave identically when no NULL is involved.
Adds a row-count test, since this class of bug builds and executes
cleanly, and documents {"ne": null} for excluding unset fields.
* fix(filter): reject session ids that can't name a real session
extract_session_allowlist accepted any non-empty string, so "*" reached
the three consumers of the allowlist — direct IN, the filter DSL, and a
Python membership test — which disagree about it. The DSL reads "*" as
"drop the condition" and matches every session; the others treat it as a
literal name and match none. One /chat request could have some recall
sources unscoped and others scoped to nothing.
Entries are now validated against RESOURCE_NAME_PATTERN, the same pattern
the API requires of session ids, so no session could ever be named "*"
anyway. Wildcards were never part of this endpoint's documented contract
(an id, a list of ids, or {"in": [...]}), and a wildcard alongside a
top-level session_id already 422'd via must_include.
* fix(filter): treat a bare null operand as a null check
Routing every operand through _coerce_operand made bare `None` a type
error rather than a null check, so {"session_id": null} raised FilterError
where it previously built IS NULL: _build_field_condition used to end in
`column == value`, which SQLAlchemy renders as IS NULL. Confirmed 422 on
all five column families (text, numeric, boolean, datetime, JSONB).
Nothing caught it. The docs added in this branch promise
`{"session_id": None}` matches unset rows, the comment in
_build_comparison_conditions claimed the equality path already covered it,
and the DSL-wide invariant test accepts "compiles OR raises FilterError",
so a 422 passed. _coerce_operand's docstring already stated the contract
its caller wasn't honoring — "Callers handle None (a null check) and `*`
(a wildcard) before calling" — so the guard restores that rather than
adding a new rule.
The three null forms now agree: {"col": null} is IS NULL, {"col": {"ne":
null}} is IS NOT NULL, NOT [{"col": null}] is (IS NULL) IS NOT true.
Also from review of #947:
- Log filter keys, not the body. That log line is new in this branch and
operands carry peer/session ids and free-text `contains` values; the
traceback plus the entry shape is what locates a builder bug.
- Assert whereclause in the _where test helper, so a dropped condition
fails instead of returning the whole statement to substring-match.
- Cover the raw-key JSONB path via source_ids, a JSONB column outside
JSONB_COLUMNS reachable through Document's raw-key fallback.
- Drop the orphaned comment left above ENUM_COLUMN_VALUES when
_coerce_operand replaced SCALAR_OPERAND_TYPES.
- Document that a JSONB column takes an object bare or under `contains`
and nothing else. Bare {"metadata": X} is containment, so the `ne` this
branch removed was never its inverse: a row with {"status":"done","x":1}
satisfied both it and {"metadata": {"status":"done"}}. Per-key operators
and NOT cover the real intents.
- Rewrite "Negation and Unset Fields" to lead with the operator rule and a
truth table, forward-linking to Filtering Conclusions instead of using
conclusions ~525 lines before they are introduced. A conclusion's
session_id is the only nullable documented filterable field, verified
across Message/Document/Session/Peer/Workspace.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
* fix: apply session scoping to all working-representation query paths
session_name was only applied to the recent-documents query in
RepresentationManager; the semantic and most-derived paths ignored it,
so limit_to_session leaked cross-session conclusions into perspectives.
- Thread a session allowlist (session_names) uniformly through all
three query paths; pushed down to pgvector and external vector stores
- Accept a list so the upcoming session-allowlist API reuses this path
- Fail closed on an empty allowlist (downstream stores drop empty IN
clauses, which would silently widen scope)
Fixes DEV-1994
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* feat: bare-list membership sugar in the filter DSL
{"session_id": ["s1", "s2"]} is now shorthand for
{"session_id": {"in": [...]}} on regular columns, generically
(peer_id, etc.). JSONB metadata columns are excluded — a bare list
there keeps JSONB containment semantics, unchanged.
Previously a bare list on a regular column compiled to a type-mismatched
equality that matched nothing, so this is strictly additive.
Also translates the same shape in the turbopuffer/lancedb filter
builders, and fixes lancedb dropping empty IN clauses (fail-open) —
an empty membership list now emits an always-false condition.
Groundwork for DEV-1995 (session allowlist via the existing filters
DSL, no new API params)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* feat: session allowlist on dialectic and representation via filters
Adds a constrained 'filters' body to peer.chat and /representation —
the same DSL search and conclusions already accept, supporting only the
session_id key (a session id, a bare list, or {"in": [...]}).
Unsupported keys and shapes are rejected with 422, never silently
ignored. Composes with session_id (must be included in the allowlist
when both are given). Capped at 1,000 sessions per request.
Enforcement is uniform at every recall chokepoint, fail-closed:
- dialectic prefetch + search_memory: conclusion recall restricted to
the allowlist; dream docs (session_name IS NULL) excluded
- message tools (search/grep/date-range/temporal/context/history):
strict intersection of allowlist and observer session membership
- get_reasoning_chain: unavailable under an allowlist (chains traverse
provenance across sessions and cannot be scoped without leaking)
- empty allowlist short-circuits to empty results everywhere
Auth: workspace keys pass the allowlist as-given; peer-scoped JWTs must
be a member of every allowlisted session (403 otherwise), mirroring the
existing single-session check.
Fixes DEV-1995
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix: fail closed on empty session allowlist across all filter builders
Empty session allowlists relied solely on the early-return guard in
_get_working_representation_internal. The layers below it were
inconsistent, so a future direct caller (the DEV-1995 allowlist API)
would silently widen scope instead of failing closed:
- _build_filter_conditions used a truthiness check; an empty list was
treated like None and dropped the filter. Now uses `is not None`,
matching the recent/most-derived SQL paths.
- turbopuffer emitted a bare `In []` with undocumented (possibly
fail-open) semantics. Now emits an explicit always-false predicate,
mirroring lancedb's `1 = 0`.
Also extract the duplicated JSONB column tuple in filter.py to a
JSONB_COLUMNS constant.
Tests exercise each fail-closed guarantee at the layer it lives, rather
than masking it behind the early-return guard.
* fix: address tests
* fix(crud): fail closed when session_name is outside the allowlist
search/grep/history helpers scoped to a single session_name ignored the
session_names allowlist entirely — a caller could read a session the
allowlist forbids. The API routes guarded this with a 422, but the
dialectic tools call these CRUD functions directly and bypassed it.
Enforce it at the boundary: return [] when session_name is set and not in
the allowlist, across _semantic_search_messages (covers search_messages +
search_messages_temporal), grep_messages, get_messages_by_date_range,
get_recent_history, and get_observation_context.
Also rename the public param allowed_sessions -> session_names for
consistency with representation.py / chat.py / peers.py; the resolved
intersection keeps its distinct name allowed_session_names.
* fix: test
* fix(scopes): tighten and consolidate session allowlist per review
Addresses review feedback on the session allowlist (DEV-1995).
Behavior changes:
- Auth gate on peers.chat now uses active membership (left_at IS NULL)
via get_peer_session_names(active_only=True), matching the adjacent
is_peer_in_session check on options.session_id. Previously a peer that
had left a session was denied when naming it directly but permitted
when naming it in filters.session_id.
- Scoped conclusion recall is restricted to level == "explicit"
(ALLOWLIST_SAFE_LEVELS). Dream-derived conclusions are stamped with a
single session_name but synthesized across all sessions, so that stamp
can't be scoped on. Applied at all four recall paths. Unscoped recall
is unchanged. Follow-up to give conclusions an authoritative
source-session set is tracked in DEV-2201.
- The allowlist gate checks `is not None` rather than truthiness, so
filters={"session_id": []} reaches it instead of being skipped.
Refactors:
- New crud.message.resolve_session_scope replaces four near-identical
copies of the allowlist-membership intersection. Returns
(allowlist, deny) and never returns an empty list, so the None vs []
distinction that external stores fail open on lives in one tested
place. Takes db=None and opens its own short-lived session only when
distinction that external stores fail open on lives in one tested
place. Takes db=None and opens its own short-lived session only when
an observer lookup is needed, preserving external-lookup-first
ordering on the vector-store path.
- extract_session_allowlist takes must_include, collapsing the
session_id-in-allowlist check duplicated across both peer routes.
- DialecticAgent._select_tools dedupes the two toolset-selection blocks
and drops get_reasoning_chain under an allowlist, rather than paying
for the schema plus a wasted turn to return a refusal.
- Rename session_names -> session_allowlist across crud, agent tools,
dialectic and routes, to remove the one-character ambiguity with
session_name. Internal only; the public filters.session_id surface is
unchanged.
Docs:
- session_allowlist documented across all message and recall entry
points, including the None / [] / populated contract.
- session_name marked deprecated for scoping. Not removed and not
aliased: it also pins the query to one session, bypasses observer
scoping, and drives session-history injection into the dialectic
prompt, so it has no drop-in replacement.
- Note at the Document branch in utils/filter.py that the raw-key
fallback is load-bearing for session scoping.
Tests: 20 -> 39 in tests/test_session_allowlist.py, covering the
peer-scoped JWT gate (member, non-member, left-session, workspace-key
bypass, empty allowlist), the resolve_session_scope tri-state including
the no-DB-checkout path, must_include, and the level narrowing.
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* fix: enforce explicit-document session purity in dedup/merge paths
Audit for DEV-2000 (Scopes RFC prerequisite): explicit-level documents must
stay session-pure so scope memory can be built by copying explicit documents
between collections. Two classes of violation were possible:
- Exact-content and semantic dedup in crud/document.py matched candidates
with no level or session scoping, so an explicit document could be
reinforced by — or soft-deleted in favor of — a same-content document from
a different session or a different level (silently merging cross-session
derivations into one row).
- The generic create_observations tool handler accepted level='explicit'
from agents with no message context (dreamer/dialectic), which would mint
session-less explicit documents.
Enforcement (refuse, never rewrite):
- create_documents refuses explicit documents with a null session_name
- exact dedup keys on (content, level, session-for-explicit); derived levels
keep cross-session consolidation
- is_rejected_duplicate scopes candidate search to the same level, and the
same session for explicit documents
- the create_observations tool rejects explicit-level input outside message
ingestion (deriver) context
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* feat: add card_refresh dream type for event-driven peer-card updates
Adds a lightweight dream variant (DEV-2000, Scopes RFC prerequisite) that
runs ONLY the peer-card update — for event-driven refreshes such as scope
membership changes and cold starts:
- DreamType.CARD_REFRESH alongside OMNI; dispatched by process_dream to a
new run_card_refresh_dream orchestration
- CardRefreshSpecialist: restricted to get_recent_observations,
search_memory, and update_peer_card (no observation-mutating tools), with
a low tool-iteration cap of min(6, DREAM.MAX_TOOL_ITERATIONS)
- rebuild=True mode carried in the dream payload: the existing card is NOT
injected into the prompt and the specialist rebuilds it solely from
observations present in the collection (for use after removals)
- enqueue-able via the manual enqueue_dream path (bypasses volume gates);
the work-unit key already embeds the dream type so a card refresh never
collides with a pending omni dream. POST /v3/workspaces/{id}/schedule_dream
accepts dream_type=card_refresh plus the rebuild flag
- card refreshes never advance the omni dream guard pair
(last_dream_at / last_dream_document_count)
- shared PEER CARD prompt section extracted (verbatim) from
DeductionSpecialist for reuse; CallPurpose gains dream.card_refresh
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* chore: fix tests
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* Structured outputs for dialectic
* cleanup
* rename json_schema_to_pydantic to clarify it's not a general schema converter
* clean up schema DoS guards
* simplification and cleanup of schema conversion
* chore: ruff and pyproject toml
* chore: basedpyright cleanup in test
* fix: some needed unrelated test failures
* test(schema_conversion-and-anthropic-backend): expand test coverage
include table tests
* fix(llm): support combined tool calling and structured output across backends
- OpenAI: parse() 500s on non-strict function tools; route tool-carrying
structured requests through create() with an explicit json_schema
response_format (mirrors the streaming path)
- Anthropic: skip the '{' JSON prefill when tools are present so tool_use
blocks stay reachable; make the schema instruction conditional and rely
on parse + repair
- Gemini: native response_schema + function calling is rejected before
Gemini 3; with tools present, inject a schema instruction into the final
turn instead and rely on parse + repair
- All backends: tool-call turns carry no consumable content, so skip
structured-output parsing on them
Extracted from the dialectic structured-output branch (DEV-1652) so the
transport layer can land independently.
DEV-2035
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* test(live_llm): exercise combined tools + structured output per provider
Two-turn live flow per backend: a forced tool-call turn (structured
parsing must be skipped) followed by a replay turn that must return a
schema-conforming answer with tools still attached. Asserts the
provider-specific request shaping: no parse() for OpenAI (500s on
non-strict tools), no '{' prefill for Anthropic, no native
response_schema for Gemini.
Verified against live OpenAI (gpt-4.1, gpt-5, gpt-5.4, gpt-5.4-mini)
and Gemini (gemini-2.5-flash).
DEV-2035
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* test(unified): dialectic chat with response_format schema under tool use
Adds response_format pass-through to the unified runner's chat query and
a test case that forces the dialectic tool loop (reasoning off + global
enumeration question) while requiring a schema-conforming JSON answer —
end-to-end coverage of the combined tools + structured output transport
path on whichever provider each level is configured with.
Verified locally against a full harness run (json_match assertions pass;
the llm_judge assertion additionally runs in CI where the Anthropic key
is available).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix: some needed unrelated test failures
* ci: add label-triggered live LLM test workflow
Adding the run-live-llm label to a PR (or workflow_dispatch) runs
tests/live_llm/ against real provider APIs — the only place the
--live-llm suite runs in CI. Reuses the unified-tests environment and
its Secrets Manager staging-dotenv resolution for provider keys; runs
on ubuntu-latest (no Fly runner, no Docker — the suite only touches the
LLM backends). Pins LIVE_LLM_ANTHROPIC_45_PLUS_MODELS=claude-sonnet-4-5
since the Anthropic family has no default models and would otherwise
silently collect empty.
Opt-in by design: live model behavior is variable, so this is a signal,
not a required check.
DEV-2035
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* ci: run live LLM tests on main pushes touching the transport
Mirrors unified-tests' push trigger, scoped to paths that can affect
the live suite (src/llm/, config, the tests, deps, and the workflow
itself) so provider API calls aren't spent on unrelated changes.
DEV-2035
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* ci: disable auth in live LLM test environment
The staging dotenv sets AUTH_USE_AUTH=true without a usable JWT secret,
and src/config.py validates the pair at import time — the same reason
unified-tests overrides it. This suite never runs the API server.
DEV-2035
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* test(live_llm): fix gpt-5.4 reasoning_effort and gemini replay-turn flake
- test_live_openai: gpt-5.4 dropped 'minimal' from the reasoning_effort
vocabulary, so the gpt5 caching test 400'd — and the OpenAI backend's
BadRequestError terminal swallowed it into an empty CompletionResult.
Pick the effort per model generation.
- test_live_tools_structured_output: use tool_choice='auto' on the
replay turn, matching the production dialectic loop (which never
forces 'none') — NONE mode is what provoked gemini-2.5-flash's empty
candidates. Drop the temperature pin so retries actually resample,
and treat a repeat tool call as a retryable attempt.
Verified live: full suite green, gemini 4/4 consecutive passes.
DEV-2035
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* ci: fail live LLM run when no staging secret was loaded
If the latest-tag fetch fails and no second tag exists, the fallback
step is skipped rather than failed, and the job would proceed without
provider keys — every test then skips via require_provider_key and the
run goes green. Guard on both fetch outcomes so that path fails loudly.
DEV-2035
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* docs(live-llm-tests-GHA): remove extra comments
* feat(structured-output): enable non-recursive schema references
* docs(structured-outputs): clean up new doc
* test(structured-output): fix caching refs memory leak, add tests
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* feat(telemetry): CloudEvents + Langfuse tracing as projections over a captured LLM stream
Capture each LLM call once (CapturedLLMCall) and fan it out to multiple
exporters -- "one data model, two projections": a CloudEvents trace stream
(llm.call.traced / trace.content) and a Langfuse projection, both reconstructing
trace -> run -> step -> generation from the same source of truth.
- Capture seam (src/llm/capture.py): one canonicalization + content-addressed
hashing point, with an O(N) per-span memo so repeated context isn't re-hashed.
- Session correlation threaded telemetry -> captured call -> exporters,
namespaced only at the Langfuse export boundary.
- Span identity consolidated onto LLMTelemetryContext; dropped TRACE_ENDPOINT.
- Canonical generation/step names; dreamer branches nest under one dream trace;
tool calls become spans under their step.
- LANGFUSE_EXPORTER_MODE toggle ("exporter" default; "inline" kept one release
for side-by-side validation), centralized into computed settings predicates.
- Per-run/per-trace dedup registries (trace_session, langfuse_session) bounded
by an LRU so dedup and span grouping survive long-running workers.
- Embedding-call tracing; deterministic high-volume event sampling.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* fix(telemetry): address trace-review findings (span/step_seq collisions, test, logging)
- Dreamer specialists mint a distinct span_id per execution (trace_id stays the
shared dream run_id), so their CloudEvents trace resource ids no longer collide
between deduction and induction.
- Tool-loop no-tool early-return streams the tail with the next ordinal
(iteration+2) instead of reusing the in-loop call's step_seq, avoiding a
colliding trace resource id; mirrors the synthesis path.
- Tighten test_clips_oversized_string to assert output stays within TRACE_MAX_BYTES.
- emit_trace logs the swallowed exception with exc_info for debuggability.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* fix(telemetry): silence exporter-mode Langfuse warning + drop summarizer run_id placeholder
Two CloudEvents/Langfuse correctness fixes, independent of the trace viewer.
Langfuse exporter-mode gating: annotate_current_generation_io (and its two
executor.py call-site guards) were gated on LANGFUSE_PUBLIC_KEY instead of
langfuse_inline_enabled. In the default `exporter` mode they called
get_client().update_current_generation() with no active @observe span, logging
"No active span in current context" (~14 per dialectic run) and building
throwaway model_dump payloads on every LLM call. The LangfuseExporter projects
I/O from the captured stream, so these helpers must no-op in exporter mode.
Gated all three on langfuse_inline_enabled; added a regression test; fixed a
stale conditional_observe docstring.
Summarizer run_id placeholder: AgentToolSummaryCreatedEvent hardcoded
run_id="deriver"/iteration=0 because summarization is a single LLM call, not an
agentic run. That placeholder pollutes run_id grouping in the CloudEvents stream
(any consumer that groups by run_id sees a phantom "deriver" run). Made
run_id/iteration optional (None) and re-keyed get_resource_id on
message_id:summary_type (the real per-summary identity; run_id/iteration can no
longer identify it); bumped schema_version 2->3. Xatu ingestion stores only the
CloudEvent envelope, so the field/resource_id/version changes are transparent to it.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* docs: update docstrings to be less verbose
* fix(telemetry): address PR review on captured-stream tracing
- embedding traces get a fresh span_id under parent_span_id=run_id, so
sibling embeddings in one run no longer share a span/idempotency key
- capture the provider finish_reason from stream chunks instead of
hardcoding "stop" on a successful drain
- gate the Langfuse exporter behind TELEMETRY.ENABLED (master switch) so
disabling telemetry sends no traces at all
- rename _emit_derived_content -> _emit_hashed_content
- inline the _emit_trace wrapper; drop unused trace_session.end_run
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* refactor: rename TELEMETRY_TRACE_PAYLOADS to TELEMETRY_TRACE_PAYLOADS_ENABLED
* fix(telemetry): capture provider tool calls in trace stream
The captured trace stream dropped assistant tool calls for openai/gemini:
build_captured_messages only read {role, content, tool_call_id}, but those
providers keep tool calls outside content (openai's tool_calls, gemini's
parts), so replayed tool-call turns landed as empty content and gemini lost
its text and tool results entirely. Anthropic (tool_use in content) was fine.
Normalize each input message per provider into a unified tool_calls
[{id, name, input}] field on CapturedMessage/TraceContentEvent, recovering
gemini text/results along the way, and fold tool_calls into
compute_content_hash so empty-content openai turns no longer collide in the
dedup store. langfuse_exporter._input now surfaces the calls.
Also fix a silent serialization drop: gemini thought_signature is bytes, so
model_dump(mode="json") on the traced event raised UnicodeDecodeError and
emit_trace swallowed it -- dropping the whole tool-calling iteration from the
trace stream (billing and Langfuse were unaffected). base64-encode the
signature on the telemetry path; replay keeps the raw bytes.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(telemetry): type replay tool-call dict for bytes signature
thought_signature widened to str | bytes | None, but
_tool_call_result_to_dict's literal was inferred as
dict[str, str | dict[str, Any]], so the bytes assignment failed project-wide
basedpyright (the per-file pre-commit hook didn't catch it). Annotate the
dict as dict[str, Any]; the replay path keeps the raw bytes unchanged.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* test: remove 3 tests
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
* fix(llm): stop capturing live LLM clients in Langfuse generation spans
honcho_llm_call_inner is the @observe generation boundary, and default
auto-capture serialized every argument into the span input -- including
client_override (a live AsyncOpenAI/genai client) and selected_config
(which carries api_key). Auto-capture deep-copies the client into a
half-constructed object whose teardown raises:
- AsyncHttpxClientWrapper ... no attribute '_state' (OpenAI, stderr flood)
- BaseApiClient ... no attribute '_http_options' (Gemini, HONCHO-4HA)
and it leaked ModelConfig.api_key into traces.
Switch from auto-capture (denylist) to explicit annotation (allowlist):
disable capture_input/capture_output on the decorator and stamp curated,
serializable input (messages) and output (HonchoLLMCallResponse) via the
new annotate_current_generation_io helper. Full trace fidelity is
preserved; no client object or secret can reach a trace.
Fixes HONCHO-4HA
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* feat(llm): track call tuning knobs as Langfuse model_parameters
Restore full trace fidelity after disabling @observe auto-capture: surface
every tuning knob (temperature, max_tokens, tools, reasoning effort, ...) on
the generation via model_parameters, sourced from the resolved effective
config instead of the raw function args.
Use a deny-list, not an allow-list: dump the whole ModelConfig and exclude
only secret-bearing fields (api_key, base_url, fallback, provider_params), so
new config knobs are traced automatically without keeping a hand-written list
in sync. The live client is never passed -- there is no useful trace
representation of it and serializing it is what triggered HONCHO-4HA.
Adds a deny-list test proving secrets never leak even when the config carries
a real api_key/base_url/provider_params (the production override-client path).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* feat(llm): duplicate token usage to Langfuse + skip payload build when disabled
Mirror per-call token usage (input, output, prompt-cache read/creation) onto
the Langfuse generation via usage_details, so Langfuse renders native tokens
and cost in addition to the CloudEvents accounting.
Also guard both generation-annotation blocks behind settings.LANGFUSE_PUBLIC_KEY
so the model_dump-backed model_parameters payload (and the usage dict) are only
built when Langfuse is actually configured (addresses CodeRabbit: the annotate
helper no-ops when disabled, but the payload was still being constructed every
call).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(agent_tools): strip display-format "id:" prefix from model-supplied observation IDs
Observations are presented to agents as [id:xxx], and models sometimes
copy the prefix verbatim despite tool-schema instructions to pass the
bare ID. This silently corrupts source_ids provenance on
create_observations_* (broken links stored in document metadata) and
breaks get_reasoning_chain lookups.
Normalize at both entry points. delete_observations is intentionally
not touched here since #746 already covers it.
Only the "id:" prefix is stripped: document IDs are nanoids whose
alphabet includes "-" and "_", so more aggressive cleanup could mangle
legitimate IDs.
Related to #719.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(agent_tools): strip whitespace remaining after "id:" prefix removal
Addresses CodeRabbit review: defends against "id: xxx" with a space
after the colon, and matches the docstring, which already promised
surrounding-whitespace stripping.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* telemetry: use session and user IDs in langfuse
* test: update old span test
* fix: disable langfuse in unit tests
* fix: add post-loop synthesis span
* refactor: address PR review feedback on langfuse tracing
- Consolidate track_name onto LLMTelemetryContext as the sole home;
remove the honcho_llm_call kwarg and update 4 callers to set it on
telemetry directly. Sentry ai_track now reads telemetry.track_name.
- Decouple escaped-stream self-stamping from run-context exit ordering:
stream_final_response now resets _in_agent_run explicitly around drain.
- Narrow langfuse_agent_step wrap in the tool loop — between-turn
bookkeeping (iteration_callback, choice switch, increment) lifted
outside the span so it scopes only the LLM call + tools.
- Reword test conftest comment to behavior-only language.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: switch langfuse spans to imperative handles
Replaces the context-manager-based langfuse_agent_run/step with imperative
LangfuseAgentRun/Step handles so the run span can outlive the function that
opens it. Streaming responses now own the run handle from construction and
close it after drain, stamping the accumulated streamed text as trace output
(previously blank). Multi-turn generations always stamp provider/model and
step metadata, fixing the regression where only the first turn was annotated.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* fix(llm): record effective prompt-only input on run span
The run-level Langfuse span recorded the raw messages parameter, which is
None for prompt-only calls. Mirror execute_tool_loop's handling and record
the synthesized user message so the trace input isn't blank.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* fix(llm): drop StreamingResponseWithMetadata.__anext__ to prevent span leak
The standalone __anext__ delegated straight to the inner stream, bypassing
the token-folding and Langfuse run-handle close that live only in the
__aiter__ generator. Any caller driving the wrapper via anext() instead of
`async for` would leak the run span and lose final-stream token accounting.
Latent today (all callers use `async for`), removed to close the footgun.
Add tests covering the run-handle drain path: full drain stamps the
accumulated streamed text as the span output and closes once; an abandoned
stream still closes via the finally rather than leaking.
* chore(llm): document intentional empty-body propagate_attributes block
The `with propagate_attributes(...): pass` stamps the active @observe trace
root via the context manager's __enter__ side effect; the empty body reads
as deletable dead code. Add a comment so it isn't removed. Addresses PR review.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(llm): restore api.py types after __anext__ removal
Dropping StreamingResponseWithMetadata.__anext__ made it stop satisfying
the AsyncIterator protocol, breaking the result annotation and the
isinstance narrowing in honcho_llm_call. Widen the tool-less result
annotation to include StreamingResponseWithMetadata and narrow positively
to HonchoLLMCallResponse before reading .content.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
* feat: add new cloudevents for api routes
* fix: add total input tokens to RepresentationCompletedEvent
* feat(telemetry): inject honcho_version + emitter health metrics
* feat(telemetry): per-LLM-call event with try/finally emission + sampler
Adds LLMCallCompletedEvent (llm.call.completed) — fires once per provider hit
with full cost-attribution context: transport/provider_label, model, token
counts with cache breakdown, finish_reason, outcome (success or error),
is_final_attempt flag, retry/fallback state, duration, tool-call shape,
streaming flag, and agent correlation (run_id + iteration).
- src/telemetry/events/llm.py: new event class + CallPurpose closed enum
(deriver.representation, dialectic.answer, dream.deduction|induction,
summary.short|long). Resource id includes attempt so multi-attempt retries
in one iteration get distinct deterministic ids.
- src/telemetry/events/base.py: BaseEvent._volume_class ClassVar (default
"ground_truth"); the new event opts into "high_volume".
- src/config.py: TelemetrySettings.HIGH_VOLUME_SAMPLE_RATE (default 1.0).
- src/telemetry/emitter.py: deterministic sampler keyed on run_id (so an
entire agent trace is kept or dropped together). Aggregate envelopes
bypass the sampler. Sampled-out events increment the dedicated counter
separate from buffer_full/send_failed drops.
- src/llm/runtime.py: AttemptPlan gains attempt/retry_attempts/is_fallback
so the executor reads retry state without re-deriving it.
- src/llm/types.py: LLMTelemetryContext dataclass carrying workspace,
call_purpose, run_id, iteration, peer fields. Iteration is mutable so
the tool loop can set it per inner call.
- src/llm/executor.py: honcho_llm_call_inner wraps the backend call in
try/finally — emits on success AND on exception, with is_final_attempt
computed from AttemptPlan. Stream path emits a was_stream=True placeholder
(token totals deferred until streaming completion is wired through).
Telemetry failures swallowed.
- src/llm/api.py: threads telemetry kwarg through all 4 signatures into
both honcho_llm_call_inner and execute_tool_loop.
- src/llm/tool_loop.py: _telemetry_for_iteration helper copies the caller
context with iteration set per call — covers both the normal iteration
loop AND the max-iteration synthesis call (iteration N+1).
Tests cover success/error emission, sampler trace-coherence (same run_id →
same decision), volume_class enforcement, unknown call_purpose tolerance,
provider_label inference, and telemetry failure isolation. 378/378 pass.
* feat(telemetry): emit agent.iteration on every LLM response + synthesis
AgentIterationEvent was defined but never emitted on this branch. Phase 2
wires it up in execute_tool_loop so every LLM call inside an agentic loop
produces one event — including the no-tool terminating iteration and the
max-iteration synthesis call — and threads LLMTelemetryContext from dialectic
and dreamer specialists down through honcho_llm_call.
- src/telemetry/events/agent.py: AgentIterationEvent opts into
_volume_class="high_volume" so the Phase 1 sampler throttles it.
- src/llm/tool_loop.py: _emit_agent_iteration() helper fires once per
honcho_llm_call_inner response, BEFORE the no-tool early return so the
terminating iteration is counted. A second emission fires for the
max-iteration synthesis call BEFORE final_response is mutated with
cumulative totals (otherwise the per-iteration counts would double-count).
Emission is defensively skipped when telemetry context lacks run_id /
agent_type / parent_category / workspace_name; emit failures are swallowed.
- src/dreamer/specialists.py: BaseSpecialist.run passes LLMTelemetryContext
with parent_category="dream", agent_type=self.name, observer/observed,
call_purpose=f"dream.{self.name}".
- src/dialectic/core.py: _telemetry_context() builds a shared context for
both answer() and answer_stream(), using self._run_id (always set) +
workspace + observed peer.
Tests cover fresh-copy semantics, per-iteration vs terminating emission,
defensive skip cases, telemetry-failure isolation, and volume_class. 408/408
pass across telemetry + llm + utils + dreamer + dialectic.
* feat(telemetry): agent.tool.call.completed event + ToolResult metadata
Adds the missing generic per-tool-call event so read-only tools (search_*,
get_recent_history, get_observation_context, etc.) and the four existing
state-change tools all produce a telemetry record. Built on a new internal
ToolResult(content, metadata) contract so handlers can surface
search-specific fields (top_k/used_embedding/query_tokens/results_count)
to Phase 3 and create/delete counts to Phase 5's specialist rollups.
- src/telemetry/events/agent.py: AgentToolCallCompletedEvent at v1 with
_volume_class="high_volume". Resource id = {run_id}:{iteration}:{tool_call_seq}
so two calls to the same tool in one iteration don't collide
deterministic ids and get dedup-dropped downstream.
- src/utils/types.py: ToolResult dataclass; two new ContextVars
(_current_tool_call_seq + _last_tool_metadata) so tool_loop and the
execute_tool closure can communicate per-call telemetry without changing
the public Callable[[str, dict], Any] signature.
- src/utils/agent_tools.py: execute_tool times handlers, unwraps ToolResult,
publishes metadata, emits the event. Handlers updated to ToolResult
where useful: create/delete observations, update_peer_card, search_memory,
search_messages. Other handlers continue to return str.
- src/llm/tool_loop.py: set_current_tool_call_seq before each executor call;
read get_last_tool_metadata after and stash on all_tool_calls[i] for
Phase 5 rollups.
Tests cover ToolResult str-likeness, ContextVar round-trip, full-context
emission with search metadata, resource-id disambiguation, defensive skip
cases, telemetry isolation, truncation metadata, volume_class. 420/420 pass.
* feat(telemetry): RepresentationCompletedEvent v2 token breakdown + tool-less truncation
Bulks out the deriver's per-batch telemetry without bumping the event schema
version. New additive fields capture the full token breakdown (queued vs.
extra-context vs. scaffold), the cap configuration (batch_max_tokens,
max_input_tokens, was_flush_enabled), real cap-hit flags, and observer
fanout. `input_tokens` stays unchanged as the queued-message-tokens billing
key Xatu's Stripe meter reads.
The big enabler: src/llm/api.py now actually enforces max_input_tokens on
the tool-less LLM path. Before this, the deriver passed the kwarg but the
path silently dropped it — so the configured cap was advisory and
hit_input_token_cap couldn't be measured. Phase 4 wires truncation through
the same truncate_messages_to_fit helper the tool loop uses and surfaces
input_was_truncated on HonchoLLMCallResponse.
- src/telemetry/events/representation.py: 12 additive fields, schema_version
stays at 2.
- src/llm/types.py: input_was_truncated on HonchoLLMCallResponse.
- src/llm/api.py: tool-less path truncates messages before dispatch, flips
input_was_truncated on the response when clamping occurs. Split into
Literal[True]/Literal[False] branches for typecheck.
- src/deriver/queue_manager.py: QueueBatchResult dataclass replaces the
3-tuple return from get_queue_item_batch; carries hit_batch_token_cap
(computed from cumulative token sum vs cap), was_flush_enabled snapshot,
and batch_max_tokens. Worker loop unpacks + forwards.
- src/deriver/consumer.py: process_representation_batch gains the three
flag kwargs and forwards.
- src/deriver/deriver.py: derives the breakdown fields locally, populates
the new fields on emit, sources hit_input_token_cap from
response.input_was_truncated.
Tests cover schema stability, defaultable fields, input_tokens semantic
preservation, cap-hit flag round-trip, model_dump completeness, and
HonchoLLMCallResponse.input_was_truncated mutability. Existing
test_queue_processing.py tests updated for QueueBatchResult and mock
process_representation_batch signature. 479/479 pass.
* feat(telemetry): DreamRunEvent v2 scheduler reasons + DreamSpecialistEvent v2 rollups
Bumps both dream events to v2 with additive fields. DreamRunEvent gains
scheduler context (threshold_reason / delay_reason / documents_since_last_dream_at_schedule /
document_threshold / dream_type / enabled_types_count) threaded through the
dream queue payload — the two scheduler gates stay as separate fields rather
than collapsing into one trigger_reason, preserving the WHY-vs-WHEN
semantics. DreamSpecialistEvent gains denormalized rollups
(created_observation_count / deleted_observation_count / peer_card_updated /
search_tool_calls_count) sourced from Phase 3's ToolResult.metadata so the
counts reflect observation truth, not call truth.
- src/telemetry/events/dream.py: schema_version → 2 for both events; new
fields all defaultable so older producers still construct valid events.
- src/utils/queue_payload.py: DreamPayload + create_dream_payload accept
threshold_reason / delay_reason / documents_since_last_dream_at_schedule /
document_threshold.
- src/dreamer/dream_scheduler.py: check_and_schedule_dream computes the two
reasons at decision time and threads them through schedule_dream →
_delayed_dream → execute_dream → enqueue_dream.
- src/deriver/enqueue.py: create_dream_record / enqueue_dream gain the
kwargs and persist on the queue payload.
- src/dreamer/orchestrator.py: process_dream unpacks the payload; run_dream
accepts the kwargs and stamps them on DreamRunEvent.
- src/dreamer/specialists.py: BaseSpecialist.run walks response.tool_calls_made
and sums ToolResult.metadata.created_count / .deleted_count, sets
peer_card_updated, counts search-tool calls by name.
Tests cover schema_version bumps, defaultable Phase 5 fields,
threshold-vs-delay semantics, observation-vs-call-count rollup distinction,
and DreamPayload round-trip. Existing tests updated for the schema bump
and the new enqueue_dream kwargs. 488/488 pass.
* feat(telemetry): AgentToolSummaryCreatedEvent v2 token breakdown
Bumps schema_version to 2 and adds three additive breakdown fields so
analytics can answer "how much of a summary call's cost was the previous-
summary rollup vs. the new messages vs. the scaffold instructions".
- src/telemetry/events/agent.py: previous_summary_tokens, message_tokens,
prompt_scaffold_tokens added with sensible 0 defaults. input_tokens
retains its current semantic (provider-side LLM tokens) — the plan's
proposed `provider_input_tokens` was omitted because input_tokens
already serves that purpose and a duplicate would fork queries.
- src/utils/summarizer.py: emit now populates the three new fields from
values already in scope (messages_tokens, previous_summary_tokens,
prompt_tokens). Hoisted prompt_tokens calculation out of the
is_fallback conditional so both the save-summary path and the emit
share one binding — basedpyright couldn't prove the sibling-scope
binding was safe, and the compute is cheap + idempotent.
Tests cover schema bump, defaultable fields, input_tokens semantic
preservation, first-summary edge case, and breakdown round-trip.
493/493 pass.
* feat(telemetry): embedding.call.completed event + call-purpose ContextVar
Adds the final piece of cost-attribution telemetry: per-embedding-call
events covering every provider hit (single + batch + retry attempts).
Embedding calls are real provider spend that was invisible before this
phase; search-heavy paths (dialectic agentic) can produce more embedding
calls than LLM calls, so the new event participates in the shared
HIGH_VOLUME_SAMPLE_RATE.
- src/telemetry/events/llm.py: EmbeddingCallCompletedEvent at v1 with
_volume_class="high_volume". EmbeddingCallPurpose closed enum
(search_memory / search_messages / create_observations / vector_sync /
summary / message_create). Resource id = run:purpose:provider:model:input_count
so per-iteration calls in one agentic run don't collide.
- src/utils/types.py: _embedding_call_purpose ContextVar plus
@contextmanager wrapper. Nesting-safe via ContextVar.reset(token).
Callers wrap embedding-driving operations in
`with embedding_call_purpose("search_memory"): ...` — no changes to
the embedding client signature.
- src/embedding_client.py: _emit_embedding_call wraps each provider hit
with try/finally so success AND error paths emit. Errors propagate
unchanged. Each retry attempt of _process_batch emits its own event.
Unknown call_purpose slugs drop to None (validation against the enum
happens at emit time, not at context-manager-set time).
- src/utils/agent_tools.py: search_memory / search_messages /
search_messages_temporal / create_observations (batch + fallback) all
tag their embedding calls.
- src/crud/representation.py: save_representation tags with
CREATE_OBSERVATIONS; get_working_representation precompute tags with
SEARCH_MEMORY.
- src/crud/message.py: create_messages batch embed tags with
MESSAGE_CREATE; search_messages/temporal fallback tags with
SEARCH_MESSAGES.
Tests cover event shape, enum closure, ContextVar nesting/exception
cleanup, wrapper success+error emission, unknown-purpose graceful
fallback, telemetry-failure isolation. 550/550 pass across the full
telemetry+llm+utils+dreamer+dialectic+deriver+crud test set.
* chore: fix tests
* fix(telemetry): address review findings on stream events, context propagation, and cap detection
Five findings from a post-Phase-7 review (one resolved by the merge from
main, four addressed here):
- src/llm/executor.py: stream-path LLMCallCompletedEvent now fires AFTER
the stream is set up and drained (or on exception), with real duration
and accurate outcome. Previously the event was emitted before
execute_stream() ran and was always recorded as outcome="success" with
duration_ms=0, which silently masked stream-setup and stream-drain
failures. Wrapping the async generator in try/finally surfaces the real
outcome; token counts stay 0 because we still don't have them at stream
end (aggregate envelopes carry totals).
- src/deriver/deriver.py + src/utils/summarizer.py: deriver and summarizer
LLM calls now thread LLMTelemetryContext into honcho_llm_call. Before
this, the closed CallPurpose enum had DERIVER_REPRESENTATION /
SUMMARY_SHORT / SUMMARY_LONG slugs but those production call sites
didn't actually pass `telemetry=`, so their LLMCallCompletedEvents lost
workspace_name, parent_category, and call_purpose. summarizer threads
workspace_name through _create_and_save_summary → _create_summary →
create_short_summary / create_long_summary.
- src/utils/types.py + src/embedding_client.py: embedding_call_purpose
ctx manager now accepts workspace_name and run_id kwargs, backed by
two new ContextVars. EmbeddingCallCompletedEvent's publisher reads
both via get_embedding_workspace_name / get_embedding_run_id so
embedding events carry workspace and run correlation. All call sites
updated: search_memory / search_messages / search_messages_temporal /
_handle_create_observations_impl pass ctx.workspace_name +
ctx.run_id; create_observations standalone and create_messages pass
workspace_name; RepresentationManager.save_representation and
get_working_representation pass self.workspace_name.
- src/deriver/queue_manager.py: hit_batch_token_cap detection rewritten.
Previously summed kept-rows' token_count and checked against
batch_max_tokens, but the SQL filter `cumulative_token_count <= cap`
guarantees kept rows stay under the cap, so the flag almost never
fired. Now uses two follow-up queries: total token_count across the
included id range + EXISTS check for any session message past the
last-kept id. Both true → cap was actually binding.
(The fifth finding — deriver scaffold-token computation needing
estimate_deriver_prompt_tokens(custom_instructions) — was resolved by
the merge from main; the Phase 4 emit at src/deriver/deriver.py:283
already sources prompt_scaffold_tokens from the wrapped helper.)
567/567 telemetry+llm+utils+dreamer+dialectic+deriver+crud tests pass.
ruff + basedpyright clean.
* chore: ruff linting
* chore: clean AI generated comments references specs
* fix: address coderabbit changes
* fix(telemetry): address remaining PR review findings
Six findings from the PR 637 telemetry review batched into one commit.
- src/llm/executor.py + src/embedding_client.py: asyncio.CancelledError
now surfaces as outcome="cancelled" on both stream and sync paths,
distinct from "error". Client disconnects mid-stream and server
shutdowns are normal control flow and should not feed error-rate
alerting. LLMCallCompletedEvent and EmbeddingCallCompletedEvent
outcome Literal extended; docstrings + tests cover the new state.
- src/utils/types.py + src/llm/tool_loop.py: new iteration_scope()
context manager captures and resets the four per-tool-loop
ContextVars (_current_iteration, _current_tool_call_seq,
_current_provider_tool_call_id, _last_tool_metadata). Applied as a
typed decorator to execute_tool_loop so back-to-back loops in the
same asyncio Task (worker batches, tests) don't observe stale state.
- src/telemetry/events/api.py + src/routers/messages.py:
MessageCreatedEvent schema v1 → v2. Added required last_message_id
(nanoid public_id of the trailing message); get_resource_id now keys
on it instead of message_count, eliminating the collision case where
two same-size batches in the same session+source produced identical
event ids. message_count stays on the body for analytics.
- src/deriver/queue_manager.py: hit_batch_token_cap now computed from
the FINAL post-config-filter batch. Previously the flag used the
pre-filter messages_context[-1].id, which produced false positives
when _resolve_batch_configuration trimmed the trailing queue item —
telemetry reported a cap-hit when the actual returned batch was
short for unrelated reasons. Cap-detection block moved inside the
async with after the filter; no extra DB connection.
- src/config.py + src/telemetry/emitter.py: documented the
HIGH_VOLUME_SAMPLE_RATE orphan trade-off (rate<1.0 keeps aggregates
but drops children, so JOIN ON run_id queries see partial traces).
Behavior unchanged — rate defaults to 1.0.
- src/deriver/deriver.py: WARNING-level invariant logs when
response.input_tokens < messages_tokens (provider tokenization
drift) or prompt_scaffold_tokens <= 0 (estimator silent failure).
Best-effort — telemetry never bleeds into the deriver path
* fix(telemetry): stream retry, embed attempts, truncation, dedup
Address remaining audit findings on the cloudevents PR:
- Stream setup now runs inside the awaited honcho_llm_call_inner so
tenacity's retry wrapper in stream_final_response catches transient
setup failures (rate-limit, auth, network). Previously the returned
generator deferred execute_stream until first iteration — outside
the retry wrapper — crashing the request and bypassing telemetry.
- Embedding _emit_embedding_call gains an is_final_attempt parameter;
_process_batch threads the real retry index so dashboards stop
conflating one-shot, mid-retry, and exhausted-retry calls.
- _truncate_tool_output returns (text, original_chars, was_truncated)
and a new _maybe_truncated_result helper wraps in ToolResult when
truncation happens. Five handlers migrated. AgentToolCallCompletedEvent
fields was_truncated and result_chars_before_truncation are now
populated instead of always None/False.
- execute_tool_loop tracks any_iteration_truncated and stamps
input_was_truncated on the final response (both HonchoLLMCallResponse
and StreamingResponseWithMetadata). Dialectic now reports
hit_input_token_cap correctly.
- GetContextEvent.get_resource_id uses empty-string sentinel instead
of literal "none" so a peer named "none" can't collide with absent.
- generate_event_id folds honcho_version into the deterministic id so
same logical event from different deploys produces distinct ids.
* fix(telemetry): address audit findings across LLM/embed/event paths
Three rounds of telemetry audit findings, grouped by area:
Retry correctness
- Stream LLM setup now runs inside the awaited honcho_llm_call_inner so
tenacity's outer retry catches setup failures (Fix 1). Previously the
inner generator deferred execute_stream past the retry wrapper.
- stream_final_response bumps the per-retry attempt index via
dataclasses.replace so emitted events show [1, 2, 3] instead of
[1, 1, 1] (Fix 13).
- Embedding _emit_embedding_call takes is_final_attempt; _process_batch
threads the real retry index (Fix 2).
Token + cost reporting
- HonchoLLMCallResponse.hit_input_token_cap (renamed from
input_was_truncated) uses a token-based rule so single-message
over-cap inputs are correctly flagged — the deriver's prompt-only
path used to silently fly through. Propagated through tool_loop's
per-iteration check (Fix 4) and into RepresentationCompletedEvent.
- DialecticCompletedEvent gains hit_input_token_cap; output_tokens now
folds in the final-stream's cumulative usage via
StreamingResponseWithMetadata.__aiter__ (Fix 7).
Event emission completeness
- AgentToolCallCompletedEvent's was_truncated /
result_chars_before_truncation populated by _truncate_tool_output via
a new _maybe_truncated_result wrapper; 5 handlers migrated (Fix 3).
- DreamSpecialistEvent emits on failure with success=False + new
error_class field, via try/finally (Fix 11).
- DeletionCompletedEvent emits on failure paths via try/finally
(Fix 12).
- CleanupStaleItemsCompletedEvent.queue_items_cleaned populated from
deleted_count (Fix 8).
Embedding call attribution (Fix 9)
- embedding_call_purpose context manager accepts parent_category.
- 4 new EmbeddingCallPurpose enum values: DIALECTIC_PREFETCH,
SESSION_CONTEXT_SEARCH, PREFERENCE_EXTRACTION, GENERIC_DOCUMENT_SEARCH.
- Wrapped previously-unattributed sites: dialectic prefetch, session
context search, preference extraction, conclusions search, vector
sync (×2).
Deterministic event ID + dedup
- generate_event_id folds honcho_version into the hash so cross-deploy
events don't silently collide on ID (Fix 6).
- GetContextEvent resource_id uses empty-string sentinel instead of
"none" so a peer literally named "none" can't collide (Fix 5).
Queue batch cap detection (P2.1)
- hit_batch_token_cap keys on the pre-config-filter SQL boundary so the
"kept=900 of 1000 cap, next=300 excluded by cap" case reports True
while still avoiding the config-filter false positive.
Tool result metadata
- search_messages_temporal returns ToolResult with the same search_meta
shape as search_memory / search_messages (P2.3) — top_k,
used_embedding, embedding_query_count, query_tokens, results_count.
Tests: stream-setup retry, stream-retry attempt sequence, post-stream
output_tokens write-back, is_final_attempt matrix, truncation E2E,
tool-loop hit_input_token_cap propagation, honcho_version in event id,
GetContextEvent disambiguation, queue_items_cleaned round-trip.
* fix(telemetry): address audit findings across LLM/embed/event paths
Four rounds of telemetry audit findings (initial + 3 follow-ups), grouped
by area:
Retry correctness
- Stream LLM setup now runs inside the awaited honcho_llm_call_inner so
tenacity's outer retry catches setup failures (Fix 1). The inner
generator previously deferred execute_stream past the retry wrapper.
- stream_final_response bumps the per-retry attempt index via
dataclasses.replace so emitted events show [1, 2, 3] instead of
[1, 1, 1] (Fix 13).
- Embedding _emit_embedding_call takes is_final_attempt; _process_batch
threads the real retry index (Fix 2).
Token + cost reporting
- HonchoLLMCallResponse.hit_input_token_cap (renamed from
input_was_truncated) uses a token-based rule so single-message
over-cap inputs are correctly flagged — the deriver's prompt-only
path used to silently fly through. Propagated through tool_loop's
per-iteration check (Fix 4) and into RepresentationCompletedEvent.
- DialecticCompletedEvent gains hit_input_token_cap; output_tokens now
folds in the final-stream's cumulative usage via
StreamingResponseWithMetadata.__aiter__ (Fix 7).
Queue batch cap detection
- hit_batch_token_cap previously required total_in_range >= cap, which
produced false negatives whenever the kept range didn't fully exhaust
the budget. Replaced with a pre-config-filter SQL boundary check
(P2.1), then further refined to a queue-item boundary comparison
(Fix 14) so trailing-context trimming doesn't false-negative either.
Event emission completeness
- AgentToolCallCompletedEvent's was_truncated /
result_chars_before_truncation now populated by _truncate_tool_output
via _maybe_truncated_result; 5 handlers migrated (Fix 3).
- DreamSpecialistEvent emits on failure with success=False + new
error_class field, via try/finally (Fix 11). except BaseException
catches cancellations too (Fix 16).
- DeletionCompletedEvent emits on failure paths via try/finally
(Fix 12), and uses ValidationException for unsupported types per
project guideline (Fix 17).
- CleanupStaleItemsCompletedEvent.queue_items_cleaned populated from
deleted_count (Fix 8).
Embedding call attribution (Fix 9)
- embedding_call_purpose accepts parent_category.
- 4 new EmbeddingCallPurpose values: DIALECTIC_PREFETCH,
SESSION_CONTEXT_SEARCH, PREFERENCE_EXTRACTION, GENERIC_DOCUMENT_SEARCH.
- Wrapped previously-unattributed sites: dialectic prefetch, session
context search, preference extraction, conclusions search, vector
sync (×2).
Reconciler no longer holds DB session during embedding (Fix 15)
- _sync_documents and _sync_message_embeddings refactored into
three phases per CLAUDE.md guideline: fetch+detach in a small DB
scope, external embedding call without DB locks, writes in a fresh
short-lived DB scope. New _apply_*_sync helpers; orchestrators
expunge ORM objects before invoking. Vector store upsert + sync_state
updates stay in the apply phase together.
Deterministic event ID + dedup
- generate_event_id folds honcho_version into the hash so cross-deploy
events don't silently collide on ID (Fix 6).
- GetContextEvent resource_id uses empty-string sentinel instead of
"none" so a peer literally named "none" can't collide (Fix 5).
Tool result metadata
- search_messages_temporal returns ToolResult with the same search_meta
shape as search_memory / search_messages (P2.3).
- Dialectic.prefetched_conclusion_count uses Representation.len() so
inductive + contradiction observations count too (Fix 10).
* fix(telemetry): orchestrator emit + review feedback
Three more rounds of audit findings + inline PR review, grouped:
Orchestration / emit reliability
- run_dream wrapped in try/finally so DreamRunEvent always emits, even
on unexpected exceptions including CancelledError (`finally` still
runs while cancellation propagates). Specialist except clauses
broadened from SpecialistExecutionError (never raised in src/) to
Exception so provider/DB/tool failures are recorded with
deduction_success=False / induction_success=False instead of crashing
past the emit.
- BaseSpecialist.run() telemetry state initialization + try/finally
hoisted above the preflight phase (peer lookup, peer-card preload,
create_tool_executor, get_model_config, prompt construction) so
preflight failures emit DreamSpecialistEvent(success=False) instead
of being dropped on the floor.
- Reverted the Round-4 _sync_documents / _sync_message_embeddings
phase split. The split introduced a race: rows were released from
FOR UPDATE SKIP LOCKED before the embed call, allowing two workers
to claim and clobber the same batch. Long-held DB transaction
restored (pre-existing CLAUDE.md violation accepted as a deliberate
trade-off; proper fix requires a claim/in_flight migration tracked
separately).
Schema + naming (PR-internal — none of these have shipped)
- threshold_reason → trigger_reason on DreamRunEvent, DreamPayload, and
every emit/scheduler/router/test call site (~45 src + 21 test lines).
Name now accurately reflects the field's role across "manual",
"surprisal", and "document_threshold" values.
- MessageCreatedEvent reset to schema v1 (was internally bumped to v2
for last_message_id but never shipped at v1 — downstream sees it
for the first time at merge).
- DreamSpecialistEvent gains created_counts_by_level /
deleted_counts_by_level: dict[str, int] keyed on the closed
level taxonomy. Per-tool-call events use list[str] (≤10 items),
but specialist runs aggregate 20+ — dict keeps emissions compact.
- QueueBatchResult marked frozen=True.
Per-call embedding attribution
- Agent tool embedding_call_purpose wraps for search_memory,
search_messages, search_messages_temporal, create_observations now
driven embedding cost rolls up under the right workflow.
- create_observations() signature gains parent_category kwarg
(mirrors existing run_id pattern).
Manual dream scheduling
- Manual /schedule_dream route now passes trigger_reason="manual" and
delay_reason="immediate". Previously both arrived as null in
DreamRunEvent, breaking analytics joins.
Queue-batch SQL perf
- next_exists_check folded into the main CTE query via
bool_or(cumulative_token_count > batch_max_tokens) OVER () in a
nested subquery. Cap detection is now one roundtrip per batch
instead of two.
Code/doc cleanup
- representation.py docstring uses generic "downstream metering key"
language (was "Xatu's Stripe meter"). bench runner --base-url help
uses a generic example host (was "groudon.fly.dev"). Public-facing
code/docs shouldn't reference internal service names.
Tests added for: orchestrator failure-path DreamRunEvent emission,
specialists preflight try/finally coverage, manual-dream
trigger_reason/delay_reason round-trip, dict-rollup accumulation across
multiple tool calls in a specialist run, CTE-fold one-roundtrip
behavior. Full Python suite passes (1236).
* fix(telemetry): correctness + attribution + emitter robustness
- Dreamer iteration count: read response.iterations directly so
one-shot runs no longer report iterations=0 and tool-using runs
include the terminal/synthesis LLM call.
- RepresentationCompletedEvent.observer_count counts successful
saves, not attempts.
- search_memory empty-memory fallback reports the snippet count when
message context is returned (was always 0).
- Wire parent_category through every embedding emit path: message
create (api), save_representation (representation), per-observation
fallback (caller-supplied), and the peer/session context routes
(api). get_working_representation accepts parent_category and
embedding_purpose so the internal fallback embed lands in the same
analytics bucket as the route-level precompute even when the
precompute is suppressed.
- BatchItem carries token_count so _process_batch reuses chunk-prep
counts instead of re-encoding every chunk for the telemetry proxy.
- Drop vestigial EmbeddingCallCompletedEvent.batch_size (always ==
input_count).
- Emitter: release the lock during HTTP send so a failing endpoint's
retry+backoff (~36s worst case) doesn't block other flushers;
edge-trigger the 80%-capacity warning so sustained backpressure
doesn't flood logs; defer event_id generation past the high-volume
sampler for events with run_id so sampled-out children don't pay
the sha256; harden emit() against sync callers with no running
loop; track threshold-flush tasks so shutdown() drains in-flight
sends before closing the HTTP client.
* fix(telemetry): tool cancellation emit, nanoid run_ids, version unification
- execute_tool: wrap post-work in finally so AgentToolCallCompletedEvent
fires on CancelledError; explicit handler sets is_error/result_str
before re-raising.
- run_id: replace str(uuid.uuid4())[:8] with generate_nanoid() across
dialectic/dreamer/specialists; matches project-wide nanoid convention.
- Bump _schema_version on events touched by run_id widening:
DialecticCompletedEvent v1→v2 (also covers hit_input_token_cap field),
AgentIterationEvent v1→v2, AgentToolConclusionsCreatedEvent v1→v2,
AgentToolConclusionsDeletedEvent v2→v3, AgentToolPeerCardUpdatedEvent
v1→v2.
- Unify honcho_version: single HONCHO_VERSION constant in src/_version.py
read from pyproject.toml (importlib.metadata fallback). Drop
TELEMETRY.HONCHO_VERSION setting. Use the constant for the FastAPI app
version (no more hardcoded "3.0.6") and for emitter body injection.
- Delete 17 tautological per-event test_schema_version methods; the
parametrized contract test still enforces version >= 1 across all events.
---------
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
* fix(deriver): ignore blank observations before embedding
* Address PR review on observation normalization
* Harden mock await arg access in tests
* Unify blank observation filtering across tool paths
* Move soft-delete query test back to fixture class
* fix: Add JSON repair for truncated LLM responses across all providers and Gemini thinking budget support
LengthFinishReasonError from OpenAI-compatible providers (custom, openai, groq) was crashing the deriver
with 14k+ occurrences in production. The vLLM path already had repair logic but it was gated on
provider=="vllm", unreachable when routing through litellm as a custom provider.
- Extract shared _repair_response_model_json() helper for all providers
- Catch LengthFinishReasonError in OpenAI/custom parse() path and repair truncated JSON
- Add repair fallback to Anthropic and Gemini response_model paths
- Add repair fallback to Groq response_model path
- Pass thinking_budget_tokens to Gemini 2.5 models via thinking_config
- Add 14 tests covering repair paths for all providers and Gemini thinking budget
Fixes HONCHO-YC
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat: live llm integration tests
* feat: Consistent Model Config Protocol
* fix: migrate the remaining app callers off the legacy llm_settings path
* fix: Docs and regression tests
* fix: refactor llm runtime path to model-config-only API
* fix: refactor config to nested model-config source of truth
* fix: refactor llm streaming and tool dispatch through backends
* fix: cut over llm config to nested model_config only
* fix: collapse vllm and custom into openai_compatible transport
* feat: refactor llm config to explicit transports and bare model ids
* feat: (embed) Add configurability for embedding model
* fix: tests for embedding provider
* fix: Address Review Comments
* fix: (llm) remove Groq backend and per-vendor base URLs
* chore: move llm tests
* fix: (llm) address review findings — config regressions, backend bugs, dead code
* fix: address backend end silly errors
* chore: (docs) update configuration and self-hosting guides
* chore: fix tests
* fix: address code rabbit comments
* fix: add validation to the dream settings
* fix: further address code rabbit comments
* fix: Address Code Rabbit Comments
* fix: Another round of code rabbit
* fix: Address Code Rabbit Nits
* fix: tests
* refactor: rename thinking validator to reflect transport scope
_validate_anthropic_thinking_minimum only enforces the >=1024 rule for
Anthropic and no-ops for other transports, so the name was misleading
now that it's shared across ConfiguredModelSettings, FallbackModelSettings,
and ModelConfig. Renamed to _validate_thinking_constraints with a docstring
clarifying per-transport behavior. No logic change.
* fix(config): drop transport-specific thinking params when env override changes transport
_fill_defaults_for_nested_field previously preserved the default MODEL_CONFIG's
thinking_budget_tokens/thinking_effort across a transport override. This leaked
Gemini-family defaults (e.g. thinking_budget_tokens=1024) into OpenAI-transport
overrides, and the OpenAI backend then correctly rejected the unsupported param
at call time (OpenAI uses reasoning.effort, not a token budget).
The helper now strips thinking_budget_tokens and thinking_effort from the
default dict when the env override supplies a transport different from the
default's. Explicit thinking params in the override are preserved.
* fix(config): apply thinking-param strip to dialectic level merge too
DialecticSettings._merge_level_defaults does its own inline MODEL_CONFIG
merge (parallel to _fill_defaults_for_nested_field), so the previous fix
missed dialectic-level overrides. E.g. flipping
DIALECTIC_LEVELS__minimal__MODEL_CONFIG__TRANSPORT from gemini (default)
to openai still leaked the default thinking_budget_tokens=0 into the
openai config, which the OpenAI backend then rejected at call time.
The level-merge path now applies the same 'strip transport-specific
thinking params when transport changes' rule as the generic helper.
Added a regression test exercising the merge validator directly.
* refactor(llm): wire ModelConfig knobs through, prune clients.py migration leftovers
Three connected fixes to finish carving the LLM stack out of src/utils/clients.py
and into src/llm/:
1. Propagate ModelConfig tuning knobs into backend calls.
honcho_llm_call_inner built extra_params from only {json_mode, verbosity},
silently dropping top_p, top_k, frequency_penalty, presence_penalty, seed,
and operator-supplied provider_params from any ModelConfig. Thread the
selected config through ProviderSelection and merge
build_config_extra_params(selected_config) into extra_params; per-call
kwargs still win over provider_params defaults. Makes
_build_config_extra_params public as build_config_extra_params so
clients.py and request_builder.py share one translation. Adds
TestModelConfigExtraParamsPropagation covering OpenAI/Anthropic knob
propagation, provider_params passthrough, and per-call override
precedence.
2. Drop dead extract_openai_* duplicates in clients.py.
extract_openai_reasoning_content, extract_openai_reasoning_details, and
extract_openai_cache_tokens had no callers outside their own definitions
— the live implementations live in src/llm/backends/openai.py. -103
lines from clients.py.
3. Unify on ModelTransport, delete SupportedProviders.
The "google" vs "gemini" split forced a _provider_for_model_config
translation shim in two places. Replace all SupportedProviders usages
with ModelTransport, rename CLIENTS["google"] → CLIENTS["gemini"],
update provider branches + LLMError labels + reasoning-trace entries
accordingly. Trace JSONL now writes "provider": "gemini" instead of
"google" — consistent with the broader env-var rename cutover.
Also tidies up pre-existing basedpyright findings in tests/llm/test_model_config.py
(pydantic before-validator dict inputs + descriptor-proxy call).
ruff: clean. basedpyright: 0 errors, 0 warnings. Tests: 153/153 pass across
tests/utils/test_clients.py, tests/utils/test_length_finish_reason.py,
tests/llm/, tests/dialectic/, tests/deriver/.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* refactor(llm): finish the src/utils/clients.py → src/llm/ migration
honcho_llm_call_inner now delegates to request_builder.execute_completion
and execute_stream instead of re-implementing backend call scaffolding
inline. The new _effective_config_for_call helper carries per-call kwargs
(temperature, stop_seqs, thinking_budget_tokens, reasoning_effort) onto
the selected ModelConfig — or synthesizes a minimal config for the
test-only callers that pass provider+model directly. max_output_tokens
is zeroed on the effective config to preserve the current
"per-call max_tokens wins" semantic; honoring ModelConfig.max_output_tokens
is a separable correctness concern.
Side effect of routing through the new path: ConfiguredModelSettings'
thinking_budget_tokens validator now fires on synthesized configs.
test_anthropic_thinking_budget was asserting that a sub-1024 budget
propagated to Anthropic — bumped to 1024 to match what Anthropic actually
accepts.
Unified client construction. Promoted the cached client factories in
src/llm/__init__.py (get_anthropic_client, get_openai_client,
get_gemini_client, get_{anthropic,openai,gemini}_override_client) to
public API and added them to __all__. Promoted
credentials._default_transport_api_key → default_transport_api_key.
Deleted the duplicate _build_client and _default_credentials_for_provider
from clients.py; _client_for_model_config now falls through to the
public factories. CLIENTS dict and _get_backend_for_provider stay as the
mockable seam for the ~50 patch.dict(CLIENTS, {...}) test call sites.
Wired operator-configurable Gemini cached-content reuse end-to-end.
PromptCachePolicy moved from src/llm/caching.py into src/config.py so
ModelConfig can reference it as a field without a circular import;
caching.py re-exports the name for existing imports. Added
cache_policy: PromptCachePolicy | None on ConfiguredModelSettings,
FallbackModelSettings, ResolvedFallbackConfig, and ModelConfig.
resolve_model_config, _resolve_fallback_config, and
_select_model_config_for_attempt copy the field through.
honcho_llm_call_inner passes effective_config.cache_policy into
execute_completion / execute_stream, so operators opt in via
e.g. DERIVER_MODEL_CONFIG__CACHE_POLICY__MODE=gemini_cached_content
and the selection actually fires instead of sitting on a dead path.
New regression test test_cache_policy_reaches_gemini_backend asserts the
PromptCachePolicy object reaches the Gemini backend's extra_params.
ruff + basedpyright: clean. Tests: 154/154 pass across
tests/utils/test_clients.py, tests/utils/test_length_finish_reason.py,
tests/llm/, tests/dialectic/, tests/deriver/.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* refactor(llm): move all LLM orchestration into src/llm/ and delete clients.py
The 1624-line src/utils/clients.py has been carved up into focused modules
under src/llm/ and deleted. There is now one golden path for LLM
orchestration and no dual entrypoint.
New module layout:
src/llm/
__init__.py thin stable re-export surface
api.py public honcho_llm_call with retry + fallback + tool
loop delegation
executor.py honcho_llm_call_inner (single-call executor); bridges
to request_builder.execute_completion / execute_stream
tool_loop.py execute_tool_loop + stream_final_response, plus
assistant-tool-message and tool-result formatting
runtime.py AttemptPlan dataclass (replaces the loose
ProviderSelection NamedTuple), effective_config_for_call,
plan_attempt, per-retry temperature bump, attempt
ContextVar
registry.py single owner of CLIENTS dict + cached default and
override SDK-client factories + backend/history-adapter
selection + high-level get_backend(config)
conversation.py count_message_tokens, tool-aware message grouping,
truncate_messages_to_fit
types.py HonchoLLMCallResponse, HonchoLLMCallStreamChunk,
StreamingResponseWithMetadata, IterationData,
IterationCallback, ReasoningEffortType, VerbosityType,
ProviderClient
request_builder.py low-level request assembly (ModelConfig → backend
complete/stream); no longer owns credential resolution
credentials.py default_transport_api_key, resolve_credentials
caching.py gemini_cache_store; re-exports PromptCachePolicy
from src.config
backend.py Protocol + normalized result types
history_adapters.py provider-specific assistant/tool message shapes
structured_output.py
backends/ AnthropicBackend, OpenAIBackend, GeminiBackend
handle_streaming_response had no production callers; it is deleted. The
three tests that used it now drive honcho_llm_call_inner(stream=True,
client_override=...) directly, which exercises the same code path the
public API uses.
Dead credential passthrough removed. The ProviderBackend Protocol and
all three concrete backends no longer accept api_key / api_base — those
are baked into the underlying SDK client at registry construction time
and were being del'd everywhere they appeared. request_builder also
stops resolving and forwarding them.
Client construction is unified. The cached default-client factories
(get_anthropic_client, get_openai_client, get_gemini_client) and override
factories (get_*_override_client) are promoted to public API; the
module-level CLIENTS dict populates from them and remains the
patch.dict(CLIENTS, {...}) mocking seam tests rely on. Old duplicate
helpers (_build_client, _default_credentials_for_provider) are gone.
default_transport_api_key is promoted to public.
Application imports now come from src.llm (dreamer, dialectic, deriver,
summarizer, telemetry-adjacent tests). No code imports from
src.utils.clients anywhere in the repo.
ruff: clean. basedpyright: 0 errors, 0 warnings. Tests: 1013/1013 pass
across the entire non-infra test suite (excluding tests/unified,
tests/bench, tests/live_llm, tests/alembic).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(llm): sanitize tool schemas for Gemini's function_declarations validator
Gemini's native-transport function-declarations validator accepts a narrow
subset of JSON-Schema / OpenAPI: type, format, description, nullable, enum,
properties, required, items, minItems, maxItems, minimum, maximum, title.
Anything else — additionalProperties, allOf, if/then/else, $ref, anyOf,
oneOf, $defs, patternProperties — triggers an INVALID_ARGUMENT 400 at call
time.
Our agent tool schemas in src/utils/agent_tools.py use several of those
(additionalProperties: false, allOf + if/then conditionals) because they
were authored for OpenAI strict-mode + Anthropic, which need the richer
vocabulary. GeminiBackend._convert_tools was passing them straight through.
Add _sanitize_schema(): walks the parameters tree and drops unsupported
keywords while preserving semantics for the keywords that hold user data
(properties maps field-name → sub-schema; required / enum are lists of
literals; items is a single sub-schema). Other backends are untouched and
continue to receive the full strict schemas.
Regression tests:
- test_gemini_sanitize_schema_strips_unsupported_keywords: confirms
additionalProperties, allOf + if/then, and $defs are stripped at nested
levels while legitimate fields survive.
- test_gemini_convert_tools_sanitizes_parameters_schema: end-to-end
_convert_tools output has no forbidden keys.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix: fix tool calling syntax for gemini
* refactor(llm): normalize defaults, widen OpenAI reasoning-model routing
* chore: fix test
* fix(llm): address post-migration review feedback
* fix(llm): gemini robustness + dreamer specialist ergonomics
* chore: addres review comments
* chore: (docs) unrelease changelog addition
* chore: (docs) merge commit changes
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Erosika <eri@plasticlabs.ai>
* fix: further remove extraneous transactions
* fix: (search) use 2 phase function to reduce un-needed transaction
* fix: refactor agent search to perform external operations before making a transaction
* fix: reduce scope of queue manager transaction
* fix: (bench) add concurrency to test bench
* fix: address review findings for search dedup, webhook idempotency, and bench throttling
* Fix Leakage in non-session-scoped chat call (#526)
* fix: (search) reduce scope for peer based searches
* fix: tests
* fix: (test) address coderabbit comment
* fix: drop db param from deliver_webhook
---------
Co-authored-by: Rajat Ahuja <rahuja445@gmail.com>
* fix: dialectic held connection
* fix: (agent) pre-compute embeddings for agent tools
* fix: (tests) refactor tests to use smaller test db connections
* fix: Embedding client to branch depending on vector store
* fix: reflect dedup-skipped observations in created counts and isolate DB sessions in extract_preferences
* fix: (tests) update tests to match changes
* fix: expunge docs + don't pass in db to query_documents
---------
Co-authored-by: Rajat Ahuja <rahuja445@gmail.com>
* fix: Add bounds to gemini client
* fix: Prevent empty summaries from being saved to DB (HONCHO-M7)
Raise LLMError on blocked Gemini responses (SAFETY, RECITATION, etc.)
so retry/backup-provider logic triggers. Treat empty LLM responses in
the summarizer as fallback instead of persisting empty strings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: Summary Eval via Locomo
* fix: Code Rabbit Comments
* fix: Code Rabbit Comments
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* fix: remove default max_distance from get_working_rep, get token count from gemini client properly, log summaries better
* fix: defensively get message public id (new field)
* fix: add defensive check to to_schema_summary
---------
Co-authored-by: Rajat Ahuja <rahuja445@gmail.com>
* feat: add optional JWT and webhook secrets to honcho instance creation
* chore: ignore spurious warnings
* feat: add response format if using gpt-5 model family
* feat: add response models to all apis except anthropic
* fix: raise NotImplementedError for response models in AsyncAnthropic client
* chore: address review
* [WIP] representation structure + deriver cleanup
* chore: add tests, cleanup
* feat: [WIP: semi-working] representation object
* fix: alignment
* fix: make observations hashable for dedup
* fix: datetime formatting, observation counting
* fix: switch to int for message id, clean up representation
* feat: remove need for metadata working rep
* chore: cleanup
* fix: use tenacity instead of custom fns
* feat: add representation and card to context if desired
* feat: add semantically relevant observations
* fix: pass all params to streaming, nonblocking streaming
* feat: consolidate document saving, make working representation fetching much smarter
* chore: add 100% test coverage of representation util
* feat: basic dream infra
* feat: dream queue item first pass
* chore: fixes & cleanup from coderabbit
* fix: dreams scheduled when new document count reaches a certain threshold
* feat: wip: timed dreams (not working)
* fix: test
* fix: remove useless pyright ignore
* fix: executing dreams
* feat: dreaming
* feat: [WIP] longmemeval bench
* feat: add USE_PEER_CARD setting, fix longmem test driver
* feat: get full working rep for dialectic in one swoop -- fix representation_from_documents to use the proper timestamp!
* fix: timestamps for real, handle assistant qs in longmem
* fix: remove old client, add batching to longmem
* perf: remove duplicate detection, will move to background task
* feat: track perf metrics on evals
* feat: adjust deriver prompt to use peer_id, add question date to question, clean up deriver
* fix: label metrics by task for better perf trace
* chore: code review
* feat: add efficiency score to longmem bench
* chore: tuning and cleaning up eval
* chore: bring in the big prompts
* feat: add support for vllm client
* feat: perf: bundle db calls in deriver and dialectic, increase max conns in docker db
* feat: add merge-sessions flag to longmemeval, add SUMMARY_ENABLED flag
* fix: COLLECT_METRICS default false
* chore: display start/end message ids, don't include in metrics
* fix: break large messages apart for eval
* fix: only get/create collection when needed
* feat: properly attribute documents with message id ranges and add session name column to documents
* fix: revert move of get_or_create_collection (need for fkey)
* fix: always get collection with peer name even if it's none
* chore: coderabbit
* fix: give peer card its own config, expand document schema, refactor get_context to be parallel, various cleanup chores and bugfixes
* chore: refactor: reify observer/observed system across entire codebase, including db migration
* refactor: cleanup code organization, make singletons where desired
* refactor: replace embeddings store with representation manager
* chore: coderabbit cleanup
* feat: multi-db longmem harness
* Merge branch 'main' into ben/multi-db-harness
* [WIP] feat: add delete workspace endpoint, use in bench
* chore: move excess logging to debug
feat: improve metrics block logs to include more data
fix: make longmem db deletion configurable
* fix: [CRITICAL] use async genai client
* chore: update core sdk, fix tests to use aio as well
* fix: rollback prompt changes
* chore: update version
* fix: cleanup, coderabbit, wrap delete op in try/except
---------
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
* feat: add optional JWT and webhook secrets to honcho instance creation
* chore: ignore spurious warnings
* feat: add response format if using gpt-5 model family
* feat: add response models to all apis except anthropic
* fix: raise NotImplementedError for response models in AsyncAnthropic client
* chore: address review
* [WIP] representation structure + deriver cleanup
* chore: add tests, cleanup
* feat: [WIP: semi-working] representation object
* fix: alignment
* fix: make observations hashable for dedup
* fix: datetime formatting, observation counting
* fix: switch to int for message id, clean up representation
* feat: remove need for metadata working rep
* chore: cleanup
* fix: use tenacity instead of custom fns
* feat: add representation and card to context if desired
* feat: add semantically relevant observations
* fix: pass all params to streaming, nonblocking streaming
* feat: consolidate document saving, make working representation fetching much smarter
* chore: add 100% test coverage of representation util
* feat: basic dream infra
* feat: dream queue item first pass
* chore: fixes & cleanup from coderabbit
* fix: dreams scheduled when new document count reaches a certain threshold
* feat: wip: timed dreams (not working)
* fix: test
* fix: remove useless pyright ignore
* fix: executing dreams
* feat: dreaming
* feat: [WIP] longmemeval bench
* feat: add USE_PEER_CARD setting, fix longmem test driver
* feat: get full working rep for dialectic in one swoop -- fix representation_from_documents to use the proper timestamp!
* fix: timestamps for real, handle assistant qs in longmem
* fix: remove old client, add batching to longmem
* perf: remove duplicate detection, will move to background task
* feat: track perf metrics on evals
* feat: adjust deriver prompt to use peer_id, add question date to question, clean up deriver
* fix: label metrics by task for better perf trace
* chore: code review
* feat: add efficiency score to longmem bench
* chore: tuning and cleaning up eval
* chore: bring in the big prompts
* feat: add support for vllm client
* feat: perf: bundle db calls in deriver and dialectic, increase max conns in docker db
* feat: add merge-sessions flag to longmemeval, add SUMMARY_ENABLED flag
* fix: COLLECT_METRICS default false
* chore: display start/end message ids, don't include in metrics
* fix: break large messages apart for eval
* fix: only get/create collection when needed
* feat: properly attribute documents with message id ranges and add session name column to documents
* fix: revert move of get_or_create_collection (need for fkey)
* fix: always get collection with peer name even if it's none
* chore: coderabbit
* fix: give peer card its own config, expand document schema, refactor get_context to be parallel, various cleanup chores and bugfixes
* chore: refactor: reify observer/observed system across entire codebase, including db migration
* refactor: cleanup code organization, make singletons where desired
* refactor: replace embeddings store with representation manager
* chore: coderabbit cleanup
* chore: update migration to non-null session param in documents, general review and cleanup
* chore: merge branch 'main' into ben/deriver-tidy
* chore: review fixes
* feat: add optional JWT and webhook secrets to honcho instance creation
* chore: ignore spurious warnings
* feat: add response format if using gpt-5 model family
* feat: add response models to all apis except anthropic
* fix: raise NotImplementedError for response models in AsyncAnthropic client
* chore: address review
* chore: add tests, cleanup
* fix: use tenacity instead of custom fns
* fix: pass all params to streaming, nonblocking streaming
* chore: fix test mock
* type stuff
* add action
* bump python
* Refactor type annotations and update tracking decorators in agent and dependencies modules. Replace ai_track with track from src.utils.types, and enhance type hints for better clarity. Update pyproject.toml to allow untyped libraries.
* type everything basically
* fix migration typing
* type like crazy
* remove usless tests
* Update mocks in tests to use AsyncMock for dialectic_call and dialectic_stream, ensuring proper async behavior in test cases. Adjust mock return values for consistency and clarity.
* Update src/deriver/tom/single_prompt.py
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
* Update src/deriver/tom/long_term.py
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
* Enhance CLAUDE.md documentation with additional details on core concepts, API structure, and development commands. Update command syntax for running server and tests to use 'uv run' for consistency. Improve clarity in configuration and architectural decisions sections.
* Refactor type annotations in CRUD functions to accept more flexible filter types, changing from dict[str, str] to dict[str, Any]. Clean up logging in agent.py by removing unnecessary timing logs for user representation generation and query execution.
* Remove unused import of ai_track from long_term.py and single_prompt.py to clean up the codebase.
* pass tests
* update some stuff
* fix unused
* ruff
* make stuff work again
* Add LLM_GROQ_API_KEY to GitHub Actions and format tom_inference parameters
* test
* test
* Refactor LLM settings to use 'gemini' provider and update related model parameters; remove unused API keys from GitHub Actions workflow.
* Update LLM settings to use 'anthropic' provider and change model to 'claude-3-5-haiku-20241022'; maintain existing summarization provider.
* test
* llm provider stuff
* update
* revert
* Integrate client management for LLM providers across various modules; remove deprecated environment variable setup for API keys.
* only if key avaialble
* Refactor type hints and improve schema definitions for queue processing; remove unused imports and enhance function signatures for clarity.
* fix test
* model
* test
* Update LLM provider type annotations and enhance client management; replace Provider with Providers for better type handling in config and clients modules.
* Refactor LLM provider handling to default to "openai" for custom providers across multiple modules; update type annotations and improve client management for consistency.
---------
Co-authored-by: Dani Balcells <18307962+danibalcells@users.noreply.github.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
* chore: Update versioning for release
* fix: remove db creation at start and sync migrations and models
* fix: Checkpoint changing metamessage schema
* chore: linter fixes
* fix: session cloning working
* Hybrid long-term memory (#92)
* Add TOM method switching
* Add system prompt and note on format
* Add persistence tweaks
* Specify format for each section of user representation
* Parse XML tags before saving representation metamessage
* Clean up
* Use Claude 3.5 Haiku and refine prompt
* Simplify message processing
* chore: update token limit on dialectic and model for deriver
* Add embedding-based long-term fact retrieval
* Fix bug preventing new documents from being created
* Use multiple queries + tweak prompt
* Fix collection name bug + add duplicate removal
* First implementation of on-demand user rep generation
* WIP debug on-demand user rep changes
* Fixed representations not being stored & deriver issue
* Some speed improvements
* Play with number of facts / queries
* WIP prompt caching for Claude
* WIP fix anthropic caching
* Anthropic prompt caching working but messages too short
* Use Cerebras for small inferences
* Make dialectic responses 1000 tokens max
* Make user representation generation model a constant
* Use llama 3.1 8b for query generation
* Update env template
* Add crud.get_or_create_protected_collection
* rabbit comments
* Fix linter issues
* Add Cerebras to stream router method
* Better handling of default-empty string args
* Change prints to debug logs
* Add error handling to TOM inference
* Handle missing/empty client in model responses
* Handle no messages case in get_chat_history
* Fix indent
* Add error handling to single_prompt methods
* Fix get_or_create_user_protected_collection
* Simplify openAI-compatible model client instantiation
* Remove health endpoint
* Remove LocalEmbeddingStore
* Change prints to debug logs
* Change sentry track
* Code review changes
* Add README to ToM module
* Switch to Groq
* Fix inconsistent openai compatible provider list in stream()
* Update env template to include Groq variables
* Add model_client tests
* fix: Fix unit tests
---------
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
* add scoped API keys (#91)
* add AUTH_JWT_SECRET and ADMIN_KEY, use in security middleware (TODO granular keys)
* WIP: convert all API paths to use scoped keys
* add basic unit tests for API keys, ruff formatting
* MVP of route using JWT for payload
* add get_user_from_token
* add key table to postgres, use it to enable key revocation
* add key revocation pt 2 -- fix order of param checks
* finish convenience routes that assume params from JWT
* add tests for key API
* get_keys
* add secrets utility script, add key rotation, fill out tests
* add tiny cache as PoC
* nits, validations, etc
* only create keys table migration if necessary
* fix keys tests to always use auth
* tiny fix to make custom DATABASE_SCHEMA work
* review: add better docs, fix security issue with cache, clear db on rotation, and more
* remove rotation
* remove key database entirely
* Add `/all` path to get all apps (#94)
* add `/all` path for apps
* assert vector extension installed (need this for groudon)
* review
---------
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
* add scoped API keys (#91)
* add AUTH_JWT_SECRET and ADMIN_KEY, use in security middleware (TODO granular keys)
* WIP: convert all API paths to use scoped keys
* add basic unit tests for API keys, ruff formatting
* MVP of route using JWT for payload
* add get_user_from_token
* add key table to postgres, use it to enable key revocation
* add key revocation pt 2 -- fix order of param checks
* finish convenience routes that assume params from JWT
* add tests for key API
* get_keys
* add secrets utility script, add key rotation, fill out tests
* add tiny cache as PoC
* nits, validations, etc
* only create keys table migration if necessary
* fix keys tests to always use auth
* tiny fix to make custom DATABASE_SCHEMA work
* review: add better docs, fix security issue with cache, clear db on rotation, and more
* remove rotation
* remove key database entirely
* Add `/all` path to get all apps (#94)
* add `/all` path for apps
* assert vector extension installed (need this for groudon)
* review
---------
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
* chore: README and CHANGELOG updates
* add JWT expiry
* fix: Consolidate get methods with JWT token resolution
* chore: Add Annotation to Path, Query, and Body params
* chore: run ruff formatter
* chore: nits & add one exhaustive test of a query route
* fix: undo change to fly.toml
* fix: Langfuse tracing
* Consolidate Get Methods (#96)
* fix: Consolidate get methods with JWT token resolution
* chore: Add Annotation to Path, Query, and Body params
* chore: run ruff formatter
* chore: nits & add one exhaustive test of a query route
* fix: undo change to fly.toml
---------
Co-authored-by: dr-frmr <docterformer@protonmail.com>
* fix: dev-667 fix streaming endpoint
* fix: Anthropic Langfuse Tracing
* fix: add scripts folder to dockerfile
* fix: Remove redundant fields from pydantic schemas
* fix: Add deeper protection on reserved collection
* fix: Consolidate chat and stream methods
* docs: Update Mintlify API Reference and Changelog
* remove langchain guide, update architecture diagram
* honcho mcp server
* chore: Update .env template
* update discord, temporarily remove other guides
* Limit dialectic & deriver context usage with two-scale progressive summarization (#97)
* WIP two tiered summaries
* Move to process_item
* Save user rep metamessage even if no message_id
* Change number of messages per short summary
* Fix broken mock
* Remove prints
* chore: fix test
---------
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
* feat: Add Gemini Support, link facts to message, use 8b for dialectic fact queries
* chore: Styling
* chore: coderabbit nitpicks
* keep dialectic guide
* Add streaming guide
* Remove TODO from dialectic guide
* Fix JS snippets that referred to honcho singleton as client
* Add App explanation to architecture page
---------
Co-authored-by: Dani Balcells <18307962+danibalcells@users.noreply.github.com>
Co-authored-by: doria <93405247+dr-frmr@users.noreply.github.com>
Co-authored-by: dr-frmr <docterformer@protonmail.com>
Co-authored-by: vintro <vince@plasticlabs.ai>
Co-authored-by: Daniel Balcells <dbalcells@gmail.com>
Aakash Kattelu
Eugene Eisenstein
Serhii Zghama
Vineeth Voruganti
Ulysse Pence
TcDrozd
Rajat Ahuja
adavyas
Phil
LRRuan
doria
Benjamin McCormick
Ayush Paul