* fix(ui): clearing the organization picker no longer sends organization_id="" on key create
* fix(proxy): paginate Request Logs by conversation and aggregate session type counts and models server-side
* fix(proxy): keep access groups in sync when a model is renamed or deleted
* fix(proxy): cap the Request Logs conversation total like the row total
* fix(proxy): judge access group backing by the database for db models
A worker whose router has not polled the database yet still lists a sibling under its old
name, so a delete or rename handled there kept the stale name in every access group. Only
config-sourced deployments count as router backing now; db models are counted in the table.
* fix(ui): keep the conversation badge when an MCP call represents a conversation
A conversation that straddles the bounded page window can be represented by one of its MCP
rows, which showed a plain MCP badge and hid the session counts. The badge now reads the
server aggregates whenever the conversation has more than one call.
* fix(proxy): list every model of a conversation in Request Logs and keep the conversation badge for MCP representatives
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): type session spend aggregates
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(ui): satisfy request logs lint budget
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): cap per-session model aggregation in request logs
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore: ratchet type-discipline budget after staging merge
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(ui): send an explicit null when the key edit form clears the organization
Clearing the Organization picker in the key edit form wrote undefined into
the form value, and JSON.stringify drops undefined-valued keys, so
/key/update never saw the field and the key kept its old organization.
Writing null instead survives serialization, and the backend's
model_dump(exclude_unset=True) preserves it, so the column is set to NULL.
---------
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(ui): paginate request logs by session groups server-side
The logs table server-paginated raw spend logs and then collapsed
multi-call sessions client-side, so a page could render 3 rows while
the footer claimed 25 and sessions straddled pages. Adds an opt-in
group_by_session param to /spend/logs/ui that pages and counts one
representative row per session (DISTINCT ON, newest non-MCP call),
keeps the bounded count contract, enriches whole-session llm/agent
composition counts, and deletes the client-side collapse pipeline.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QxT89fiygmzz2ALcjpu7Ve
* feat(ui): add a 10 rows-per-page option and default request logs to it
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QxT89fiygmzz2ALcjpu7Ve
* fix(ui): key session aggregates per api key in the logs enrichment
Grouped pagination splits a reused session id into one row per api key,
but the enrichment still aggregated by session_id alone, so both rows
showed combined spend and counts. The aggregate query now groups by
(session_id, api_key), the count folds into it (the separate group_by
query is deleted), and each row reads its own key's totals.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QxT89fiygmzz2ALcjpu7Ve
* fix(ui): treat an empty api_key as a real session group value
The spend-log schema defaults api_key to an empty string; truthiness
guards in the enrichment treated it as missing, so keyless multi-call
sessions lost their count and spend. Only None means missing now.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QxT89fiygmzz2ALcjpu7Ve
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* feat(spend_tracking): persist router metadata in spend logs for internal router models
* test(spend_tracking): expect router_metadata key in exact-payload tests, type the routed-kwargs helper
/v1/responses/input_tokens returned 200 with a count for an empty
"input" ("" or []), while OpenAI returns a 400 missing_required_parameter.
The route also went through optimistic budget reservation, which is only
released by LLM success/failure callbacks that a token count never
reaches, so every call leaked a reservation until TTL expiry and could
429 real traffic. Both routes plus the /openai alias now join
/utils/token_counter in the reservation exemption set.
The LLM classifier's cost was recorded on the routing decision but never
reached any savings surface: per-request autorouter_savings stayed gross
and the session rollup recorded only the served request's spend, so
/auto_router/benchmarks overstated savings and understated routed spend.
Net the classifier cost into the savings figure at its one computation
owner and fold it into the rollup turn's spend, keeping
baseline_spend = spend + saved_spend. The response header's numeric
guard now shares the same reader.
Fixes#38816
Every complexity router now derives and records its savings baseline from
its hardest configured tier, and the spend writer always prices against the
decision-recorded baseline model and deployment id. A leftover
litellm_settings.autorouter_savings_baseline_model key is inert
* feat(spend): report prompt caching savings as total and gateway-attributed
`prompt_caching_savings_spend` credited every cached request, including caching a
client asked for with its own `cache_control` and caching a provider does implicitly,
so the number overstated what the gateway had any hand in.
Gating that column in place would have fixed the overstatement by changing what the
column means, leaving rows written before the change saying "all caching savings" and
rows after saying "gateway-injected only" with nothing to tell them apart, and forcing
a decision about rewriting history. It also breaks the cache-leakage estimate on the
dashboard, whose numerator would be gated while its denominator, the cached token
counts, would not, so the rate it extrapolates from would be quietly diluted.
Report both instead. `prompt_caching_savings_spend` keeps meaning every net dollar
caching saved, which is what a customer means by "what did caching save me", and the
new `gateway_injected_caching_savings_spend` carries the subset litellm caused by
injecting the breakpoints itself. Both are derived from the same marker, so this
changes what is done with it rather than how it is obtained.
The attributed figure is normally the smaller of the two, being a subset of the same
requests, but not always: a request that writes cache it never reads has negative net
savings, and excluding such a request can lift the attributed figure above the total.
Also stops the marker riding into a fallback leg. The fallback rebuild spread the
failed attempt's metadata forward, so a deployment that injected nothing inherited the
marker and was credited anyway, which silently restored the very overstatement this
separates out.
* fix(bedrock): credit gateway caching where the tool cachePoint is placed (#38478)
The savings marker records breakpoints litellm placed, and a tool_config
injection point becomes one only in the converse transform, and only when the
request carries tools. The prompt hook cannot see either condition, so marking
on the point's presence credited request shapes that cached nothing, while
Bedrock tool caching the gateway did cause went uncredited.
Record it at the placement site instead. The marker's reader also resolves its
bucket by value now: litellm_params declares litellm_metadata as None on every
request, so asking the shared name resolver named a bucket that was not there
and the mark was dropped.
* feat(ui): session-level cache observability in request logs
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix: guard cache_hit filter against non-string defaults in direct calls
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(ui): drop redundant cache_hit field comment
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(logging): stop billing and logging response reads as LLM calls
Retrieving, deleting or cancelling a stored response, and vector store management calls, run through the same logging lifecycle as inference. A retrieved response replays the usage of the call that created it, so every read priced it again and wrote a second spend log row for the same tokens. Non-inference calls now cost 0, report no usage, log no placeholder chat message, and get a litellm.responses_management operation name instead of reading as chat.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): keep billing background response jobs after the poll
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(logging): use an empty list for read-call messages
A tuple matches no branch in the loggers that walk this value, so lunary's
parse_messages falls through to clean_message and raises AttributeError on the
success hook. An empty list reads as no messages everywhere: it satisfies the
isinstance(list) checks in newrelic, mlflow and datadog, iterates zero times in
traceloop and helicone, and is what StandardLoggingPayload.messages is typed to
hold. None would be type-legal too but is not iterable, so it trades one crash
for another in mlflow and traceloop.
* fix(otel): stop the legacy emitter reporting replayed tokens on response reads
The zeroing so far lands in the standard logging payload, which the legacy
OpenTelemetry emitter does not read for usage: it takes prompt, completion and
total tokens straight off the response object, so a retrieval span still carried
the token counts of the call that produced the response, and the token usage
histogram still recorded them. That emitter is the default, so the spend row said
zero while the trace said otherwise. The background cost poller keeps its counts,
the same exemption the pricing path already makes.
* fix(logging): keep billing a background response when its retrieval is read
A response created with background=true comes back queued and carries no usage, so
its create bills nothing. The retrieval that first sees the finished job is the only
place that job's tokens are ever visible, and pricing every read at zero therefore
loses the spend outright rather than deduplicating it. On a proxy without the
enterprise cost poller a background job ended up costing $0 end to end.
is_unbilled_non_inference_call now takes the response it is deciding about and treats
a background response the same way it already treats the poller's own read, which is
the same exemption seen from the other side. The legacy OpenTelemetry emitter's time
per output token metric picks up the read gate it was missing, so it stops dividing a
read's latency by the replayed completion token count.
* test(proxy): pass the read response to the non-inference predicate
The poller test called is_unbilled_non_inference_call with the pre-background signature, so it broke when the predicate gained the response it classifies. It now hands the predicate a foreground read, and asserts that the same read is free without the origin stamp, so the stamp is what the test proves.
* fix(otel): stop the v2 metrics recorder reporting replayed tokens on response reads
The v2 span builder sources usage from the standard logging payload, so the
earlier fix already zeroes it there. The metrics recorder reads response_obj
directly, so a responses-management read still recorded the original
generation's tokens into gen_ai.client.token.usage and divided generation time
by them for gen_ai.server.time_per_output_token.
The read still records operation and response duration, under the
litellm.responses_management operation, so it stays observable.
* fix(proxy): keep the response-cost headers on calls priced at zero
Pricing responses reads and vector-store management routes at zero dropped the whole
x-litellm-response-cost family off those replies. The header build reads a falsy zero as
a cost this response never recorded and filters it out, and a call that returns before
pricing stores no cost breakdown for the component headers to read, so a client parsing
the cost off a read got a KeyError where it had previously been handed a number.
Those calls now advertise the family at zero. Retrieving a background response, and the
cost poller's read of one, still report their real cost.
The params-taking form of the predicate moves from opentelemetry into
internal_call_metadata so the proxy header build and the OTEL recorders share one copy.
* fix(proxy): report a zero cost split only under a zero cost total
The component headers were filled from call-type membership alone, while the
total they sit beside keeps its real value when the read priced normally, so a
breakdown that had not landed by the time headers were built could advertise a
real total next to an all-zero split. The split is now reported as zero only
when the total agrees with it, and is otherwise left absent.
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Yucheng Zhu <yucheng@berri.ai>
Staging split batch output-line costing into _safe_output_line_stats /
_compute_output_line_stats / _output_line_cost so one uncostable line can no
longer zero a whole batch, and added _provider_output_file_id so model-encoded
output file ids decode before the fetch. This branch's pass/fail counting was
written against the pre-split shape, where every None line meant a provider
failure.
Keep staging's structure and layer the counts on a three-way classification: a
provider-reported failure yields PROVIDER_FAILED, a provider-successful line
litellm cannot price yields UNCOSTABLE and stays in successful_requests billed
at $0. Without that split a litellm-side pricing gap would be reported to the
customer as a failed request and the counts would stop reconciling with the
provider's own request_counts.
Route the error-file fetch through _provider_output_file_id too, and carry the
new dataclass return through the callers staging added after this branch
forked.
* test: drop the cwd-relative sys.path.insert calls from the test suite
TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.
Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.
Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.
* test: drop the duplicate imports the sys.path sweep exposed to F811
* test(pre-call-utils): restore the os import the new bedrock tests need
The auto-router savings figure was computed only inside the spend-update
writer, downstream of where logging callbacks consume the standard logging
payload, so Datadog-style callbacks never received it. Compute it once in
the payload builder, stamp it as a top-level payload field beside
cost_breakdown, thread it into the spend log metadata, and have both
spend-writer call sites read the recorded value with recomputation as the
fallback for rows written before the field shipped. Internal sub-calls
(classifier, shadow eval) are never stamped, and a caller-forged metadata
value is discarded by the unconditional overwrite.
Resolves LIT-5973
The sweep ran unbounded whenever no config.yaml deployment was present,
deleting the day's sentinel rows for deployments absent from the run's own
view. A written charge records capacity that was reserved, so the only rows
a run may retract are the ones it can reassess: a deployment it scanned and
then declined to charge, because the window closed or the PTU config was
removed. It is now always bounded to the ids it scanned
The rollup read litellm.proxy.proxy_server.llm_router out of sys.modules, so a run
priced and swept whatever deployments anything else in the process had left on that
module. Under xdist the shard's module-to-worker assignment varies per run, which made
three rollup tests fail or pass on the same commit depending on ordering.
Callers now hand the router in, and the proxy's scheduled job passes its own.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Master-key auth stamps the stable alias litellm_proxy_master_key instead of the
raw key, so spend logs carry a readable, non-secret identifier for those rows.
The new redaction path only recognized sha256 and hashed-jwt shapes, so it
hashed that alias and broke continuity with every master-key row written
before this change. The alias joins the recognized non-secret values, still
behind the same provenance gate, so a caller who sends the alias string as
their own bearer token still gets it hashed.
The hashed-jwt branch trusted the value's shape alone, so a caller-supplied key in that shape was stored unhashed. Both pass-throughs now require the value to match the auth-time user_api_key_hash, and the shape check is a full match.
The spend-log helper no longer treats a 64-hex shape as proof a value was already hashed, so this case has to say where the hash came from. Reconciles the test that came in with #31799 against that change.
`pytest.raises(Exception)` with no `match=` passes on any error that broad. A
TypeError from a refactor, a botched fixture, an import that moved: all of them
read as the rejection the test claims to police, so the test goes green for the
wrong reason and stays green after the behaviour it guards is gone.
PT011 closes that gap for the 317 sites B017 could not reach, because B017 only
fires on a single-statement body with no `as e` binding. Each pattern here is the
message the code actually raised, recorded by running the sites under a plugin
that logged the concrete type and text per call site, so the assertions describe
observed behaviour rather than a guess. Where a site raises more than one message
across its parametrize cases, the pattern is an alternation of what was seen;
where the exception carries an empty `str()` and puts the text on `.message`, the
site keeps a narrow `noqa` with the reason.
PT014 removes four parametrize cases that were listed twice. The duplicate re-runs
an assertion that already passed, and it usually marks a case someone meant to
vary and forgot to edit.
`assert False` inside a `try:` raises AssertionError, which the `except
Exception` right below it catches, so several tests reported green no matter
what the code did. `pytest.fail` raises Failed, a BaseException, and escapes.
A bare `a == b` statement is evaluated and discarded. Nine of those sat in
tests, and one was comparing against a model name the router never produces.
Selects B011, B015, B018, PT015, PLR0133 and PLW0127 in ruff-tests.toml
alongside F821, with all 50 existing violations fixed, so no budget file or
ratchet is needed. CI already runs this config over tests/.
* fix(ptu): hand the prune a plain delete filter the query builder can serialise
The bounded sweep built its predicate as a read-only mapping view, which the query
builder refuses to serialise, so the nightly job raised as soon as a config-declared
deployment was priced. The charges were already written by then, which is why the run
looked like it had produced its rows.
The in-memory table these tests run against accepts any mapping, so only a live run
caught it. A predicate builder now returns a plain dict and is asserted as one, and the
catch-up pass has a test covering a config-declared reservation.
* refactor(ptu): build the prune predicate in one shot
Both filter shapes are known upfront, so the bounded one is constructed
directly rather than by mutating a value already declared Final.
The catch-up test took two independent clock reads, which disagree across
UTC midnight; it now derives both the reservation start and the expected
last charged day from a single read, matching the three sibling tests.
* feat(ptu): accrue flat cost for PTU deployments declared in config.yaml
The flat-cost rollup reads deployments from LiteLLM_ProxyModelTable, and config.yaml
models never reach that table by design, so a PTU deployment declared there accrued no
flat cost at all while still billing its traffic per token. The provider bills the
reservation whichever file declared it.
The rollup now also reads the deployments the router holds that no database row owns,
identified by db_model, skipping the per-request credential clones that carry
original_model_id and reuse their source's PTU config under a fresh id. Registering such
a deployment zeroes its pricing, since reserved capacity already pays for the traffic it
serves, and leaving a rate unset falls back to the public cost map, which makes the double
charge the default rather than an opt-in.
The rules both halves apply now live in one module. The rollup's test for what it will
charge and the router's test for what to zero have to agree, or a deployment one accepts
and the other declines serves its traffic for free. That module also owns the fields the
write endpoints already zero, so the two paths cannot drift: tiered_pricing is emptied
rather than zeroed because its tiers outrank the rates beside them, the search context
table is written zeroed because an absent one means the provider default, and any further
rate the deployment itself declares is zeroed alongside the standing set.
The prune is bounded to the deployments a run scanned, but only for a run that priced a
config-declared deployment. Deciding a row is garbage on staleness alone stays correct
while every run derives its charges from the same table, so a database-only run sweeps
exactly as it did before; once one host's charges come from a file the others cannot read,
a row it never considered is not evidence of anything.
Behaviour change worth calling out: a zeroed deployment sorts ahead of an unpriced sibling
in QualityRouter's cost tiebreak, where an unset rate previously sorted last. Reserved
capacity really is the cheaper choice, but the ordering moves.
* refactor(ptu): drop a Final rebind and two redundant isinstance guards
The basedpyright budget rejected reassigning a Final in the datetime coercion and
two isinstance calls the router entry's own type already guarantees. Filtering the
built records rather than the raw entries removes both guards and leaves
_router_deployment as the single validator.
The flat-cost rollup reads deployments only from LiteLLM_ProxyModelTable, so a PTU
deployment declared in config.yaml never accrues flat cost. Those deployments live in
llm_router.model_list as plain dicts whose id sits in model_info rather than on the entry,
so they do not satisfy the shape _parse_ptu_model reads.
Adds a frozen record in that shape and a factory that maps a router entry onto it, leaving
_parse_ptu_model byte-identical so the existing cases stand as evidence of no behaviour
change. Nothing calls the factory yet; the caller lands with the loader union.
_decode_model_info also stops handing back valid JSON that is not an object. It decoded
a list or a scalar and returned it as a mapping, so the caller read fields off it and
raised, losing the whole run rather than the one bad deployment.
