Auth gate rejections are raised before add_litellm_data_to_request stamps the caller User-Agent and SpendLogsMetadata dropped the field, so failure spend logs and prometheus labels could not identify an abusive client. Stamp requester_ip_address and user_agent on the failure hook payload and carry user_agent through spend log metadata. Request scopes without a headers entry are tolerated.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Keeps the base's rule that a non-admin id lookup matching no spend-log row answers 403, so the detail route never consults cold storage without an owner row
* perf(auth): prefetch user, team, membership, org and project in one MGET, one query and one pipeline
Auth read each object with its own Redis GET and, on a miss, its own DB
query, then the admission spend counters with one GET each. The prefetch
warms every entry the checks read with one MGET, one raw query for the
Redis misses and one pipeline write, and a per-request batch serves the
spend counter reads from one MGET. The per-object getters stay the
readers and the fallback, so enforcement does not depend on the prefetch
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(auth): keep prefetch and spend batch collections immutable
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* perf(auth): let the cold spend-counter reseed reuse the admission MGET instead of one GET per counter
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* perf(auth): prefetch referenced auth objects only after the key's model access check passes
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(auth): give the prefetch-ordering test's patches their test-quality reasons
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(auth): move the real-Postgres prefetch join test to the proxy_behavior shard
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(auth): read NULL nested permission and budget lists as [] in the prefetch join
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* perf(proxy): batch post-call spend counter reads and carry budget state through the request
Post-call warm checks, reservation reads and reconcile reads for one request now go through a task-local spend counter batch: one MGET answers every counter, successful increments write their result back into the batch so no second Redis read follows, and invalidation forgets the key. RedisCache.async_increment sends INCRBYFLOAT and its TTL command in one pipeline round trip.
Auth pins frozen team, user and org budget snapshots on UserAPIKeyAuth, the pre-call setup writes them into the request metadata, and Prometheus reads them back instead of calling get_key_object, get_team_object, get_user_object and get_org_object on the response path. The getters stay as the fallback for requests that carried nothing (custom auth, unauthenticated routes, skipped checks).
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* perf(proxy): reconcile the budget reservation and the post-call warm checks from one MGET and one pipeline
A scope opened inside an open spend counter batch binds into it instead of starting its own, so the reservation reconcile and the post-call warm checks share the request's single MGET. The reconcile reads every reserved counter concurrently, sends the consistent adjustments in one INCRBYFLOAT+EXPIRE pipeline and settles a flushed or reseeded counter on its own afterwards, keeping the pre-call resize fail-closed. PendingSpendIncrement moves to spend_counter_batch so budget_reservation can build a pipeline without importing a private name
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(proxy): drop the dataclass import left behind by the PendingSpendIncrement move
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(types): import Self from typing_extensions so the proxy imports on Python 3.10
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): use a neutral organization alias in the carried budget state tests
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): cover recorded and forgotten spend counter values in the request batch
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(caching): assert async_set_cache_pipeline_with_ttls keeps per-entry TTLs
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(proxy): type the reservation entry carried through reconcile adjustments
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(auth): map the model table's aliases column to model_aliases in the prefetch join and read user memberships the way get_user_object does
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>
* perf(auth): prefetch user, team, membership, org and project in one MGET, one query and one pipeline
Auth read each object with its own Redis GET and, on a miss, its own DB
query, then the admission spend counters with one GET each. The prefetch
warms every entry the checks read with one MGET, one raw query for the
Redis misses and one pipeline write, and a per-request batch serves the
spend counter reads from one MGET. The per-object getters stay the
readers and the fallback, so enforcement does not depend on the prefetch
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(auth): keep prefetch and spend batch collections immutable
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* perf(auth): let the cold spend-counter reseed reuse the admission MGET instead of one GET per counter
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* perf(auth): prefetch referenced auth objects only after the key's model access check passes
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(auth): give the prefetch-ordering test's patches their test-quality reasons
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(auth): move the real-Postgres prefetch join test to the proxy_behavior shard
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(auth): read NULL nested permission and budget lists as [] in the prefetch join
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(caching): assert async_set_cache_pipeline_with_ttls keeps per-entry TTLs
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(auth): map the model table's aliases column to model_aliases in the prefetch join and read user memberships the way get_user_object does
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>
* feat(rust): count tiktoken cl100k_base admission tokens in Rust
The Rust admission token counter only had the Anthropic tokenizer, so every
other model (OpenAI gpt-4 family, Azure, Gemini, Bedrock non-Claude, Mistral)
tokenized with tiktoken on the Python inference worker.
Add an exact cl100k_base counter to litellm-token-counter: the vendored rank
file (base64 token / rank lines, the bytes Python's tiktoken uses) is parsed
into a byte-level BPE model and the cl100k split pattern is a handwritten
scanner over the shared Unicode classes, so no regex engine runs per request.
Both tokenizers share the message, tool and reply-priming accounting.
The PyO3 TokenCounter gains a from_cl100k_ranks constructor; Python reads the
rank file and passes it in, the way claude_json_str already works. The bridge
selects the counter through the same predicates litellm.token_counter uses
(huggingface_tokenizer_kind, openai_tokenizer_encoding), declines o200k_base,
downloaded HuggingFace and custom tokenizers to Python, and budget reservation
counts once per distinct tokenizer a request names.
The legacy gpt-3.5-turbo-0301 message accounting (4 per message, -1 per name)
stays in Python: the selector declines it through the predicate token_counter
itself uses.
* feat(rust): count tiktoken o200k_base admission tokens in Rust (#40794)
Add a handwritten o200k_base split scanner and TokenCounter::from_o200k_ranks
next to the cl100k_base counter, sharing MergeRanks and the request
accounting. The Python bridge selects it when openai_tokenizer_encoding
names o200k_base, so gpt-4o, gpt-4.1, gpt-5, o1/o3/o4 and chatgpt-4o
requests stop tokenizing on the Python worker under LITELLM_RUST=true
Co-authored-by: yassin <yassin@berri.ai>
---------
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
An out-of-range cursorless page returns nothing, and reading its total off the
offset reported more sessions than exist (page 4 of 100 sessions at page size 50
claimed 150). Only a page that holds rows, or the first page, ends the list;
anything past it falls back to the bounded count.
Claude-Session: https://claude.ai/code/session_01ESi9JwaXDww1vP3Qsrr4Mz
A cursorless page that comes back without its lookahead row is the end of the
list, so the total is offset + len(page) and the bounded grouped COUNT over the
whole spend-log table is skipped. First pages on small deployments and every
offset last page now cost one query less.
Moves the count into _count_grouped_sessions and reworks the query-optimization
test that asserted the count always runs second onto a full page, where it does.
Claude-Session: https://claude.ai/code/session_01ESi9JwaXDww1vP3Qsrr4Mz
A page size that does not divide SPEND_LOGS_PAGINATION_COUNT_CAP left the last
page starting inside the capped window and reading past it, so the rows
disagreed with the total reported next to them. The page limit now stops at the
end of that window, and has_more plus next_session_cursor still hand back a
cursor for walking further.
Claude-Session: https://claude.ai/code/session_01ESi9JwaXDww1vP3Qsrr4Mz
A page starting at or past SPEND_LOGS_PAGINATION_COUNT_CAP lies outside the
total the client is given, so it now returns no rows without running the page
query and the grouped top-N sort bound stays capped.
Rewrites the offset test to page a fake session store instead of asserting on
the generated SQL, and covers the last page inside the cap next to the first
page past it.
Claude-Session: https://claude.ai/code/session_01ESi9JwaXDww1vP3Qsrr4Mz
* fix(proxy): keep call_type and request start time on failed-request spend logs
post_call_failure_hook pops litellm_logging_obj before the failure callbacks
run, so the spend row built from request_data had a blank call_type and used
datetime.now() as the start time. A guardrail-blocked MCP tool call therefore
showed up in the Logs page as an LLM row with no call type and a 0s duration.
Lift call_type and start_time off the logging object alongside the fields
already lifted, and have the DB failure hook prefer the lifted start time.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): inject the spend writer into _ProxyDBLogger instead of patching a module global
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: yucheng <yucheng@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(proxy): offload spend tracking to a pod-local spend worker sidecar
py-spy on the gateway showed the post-response _PROXY_track_cost_callback,
spend-log and DBSpendUpdateWriter work running on the inference workers'
event loop, so a DB or Redis stall backed up the request path.
When LITELLM_SPEND_WORKER_ENABLED=true, _ProxyDBLogger serializes one compact
typed SpendEvent per success and hands it to a SpendEventProducer that ships
it over a unix socket (default) or loopback-only TCP to a sidecar started as
`python -m gateway.spend_worker`. The sidecar runs the unchanged
_ProxyDBLogger pipeline against the pod's PgBouncer (pooled_database_url).
When the sidecar is unreachable, the buffer is full, or the gateway shuts
down with events still queued or in flight, the producer applies
LITELLM_SPEND_WORKER_ON_UNAVAILABLE (fallback in-process, or drop). The
sidecar half-closes producers on SIGTERM and drains, the producer treats
EOF as unavailable, and the gateway flushes buffered spend counters on
shutdown. The sidecar honors LITELLM_LOG so its writes are visible in its
own process log.
Helm: both charts gain an opt-in spend-worker sidecar container sharing an
emptyDir socket dir, and the componentized chart's HPA uses a
ContainerResource CPU metric scoped to the gateway container so sidecar
CPU does not drive inference scaling.
* feat(terraform): opt-in spend-worker sidecar for the AWS and GCP gateway stacks
Adds spend_worker_* inputs to both modules. On ECS Fargate the sidecar is a second, non-essential container in the gateway task; on Cloud Run it is a second container in the gateway service. Both listen on loopback TCP, share the gateway's DB/Redis/secret env, and set LITELLM_JOB_ROLE=spend_worker. Disabled by default. Plan-only tests cover both, and the terraform CI workflow now runs the gcp module too
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): retrieve a completed batch in the in-process spend path test
The base now defers cost tracking for batches that are still in flight, so an in_progress batch never reaches update_database
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(proxy): rename the spend worker sidecar to collector
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): run the collector from the installed litellm package and finish in-flight fallbacks on shutdown
The sidecar command becomes python -m litellm.proxy.collector so the classic image, whose runtime
stage copies only the installed package, can run it. The module now assembles DATABASE_URL and the
pod-local pgbouncer URL itself, replacing gateway/collector.py
The componentized collector sidecar inherits gateway.volumeMounts so custom CA mounts reach it.
SpendEventProducer shields an in-progress fallback from the writer task cancellation so close()
no longer loses an event already handed to the in-process pipeline
Helpers used across modules (address_argument, should_store_prompts_and_responses_in_spend_logs,
flush_spend_counters_on_shutdown) become public so the change adds no reportPrivateUsage errors
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci(terraform): drop the gcp job duplicated by the aws/gcp matrix
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(collector): keep metrics env off the classic sidecar and reject shared loopback ports
The classic chart no longer hands PROMETHEUS_METRICS_PORT and the billing metrics env to the collector container, and gives it the same /.npm scratch mount as the proxy on a read-only root. AWS and GCP now refuse a plan where the spend collector and the metrics sidecar bind the same loopback port. A regression test drives a sidecar crash mid-stream on asyncio and uvloop and checks no event is billed by both the sidecar and the in-process fallback; the producer docstring spells out why a failed drain() cannot double count
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style(proxy): format pooled_database_url after the pgbouncer rebase
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): keep the cache-hit preset key and survive dead producers on collector drain
Cache hits updated the logging object after the early return, so the offloaded spend event carried
preset_cache_key=None and the collector re-hashed reconstructed kwargs. Also guard write_eof() against
producer transports uvloop already closed so one dead connection cannot abort the drain
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(terraform): keep the gcp collector port off the metrics sidecar health port
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): collector connects to Postgres directly under IAM or Entra token auth
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): mark the collector's DATABASE_URL as pooled when it uses the pod's pgbouncer
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>
The Logs drawer gets messages/response from GET /spend/logs/ui/{request_id};
the list endpoint omits those heavy columns for every caller, admins included.
That detail route was missing from LiteLLMRoutes.spend_tracking_routes, and
check_route_access anchors patterns, so /spend/logs/ui never matched it. Every
internal_user got a 403 before the handler ran and the UI fell back to the
"Request/Response Data Not Available" banner, even on their own requests
Adds the route to spend_tracking_routes so internal_user, internal_user_view_only,
admin_viewer and org_admin all inherit it, and drops the now-redundant explicit
entry from admin_viewer_routes. The handler already authorizes non-admins per row
via _assert_user_can_view_request_id, so no handler-side scoping change is needed
That helper returned silently when no spend-log row existed, which the detail
handler treats as authorized before asking every custom logger for the payload by
raw request_id. With retention pruning the row can be gone while the payload is
still in cold storage, so opening the route would have let a non-admin read
another tenant's prompt out of S3/GCS. A missing row now falls through to the
same 403 as a foreign row, which also removes the exists-but-not-yours oracle
Fixes#34099
A request whose model field matched no configured model was rejected with 400 but its failure row still persisted the raw client string as the model, so a client that concatenated its prompt into the model field wrote that prompt into LiteLLM_SpendLogs and the daily spend tables, where /user/daily/activity/aggregated returned it as a breakdown.models key. The spend log payload now records such rejections under the constant unknown-model, keeping the failed request counted without persisting client input as a model name.
* feat(rust_bridge): count budget-check input tokens in Rust on all LLM routes
Rust counts input tokens from the raw JSON body with the GIL released inside the existing budget reservation, covering every LLM route the auth dependency guards. It only fires for models on the Anthropic tokenizer when a budget is set, and Python counts whenever Rust is off, missing, or declines a body shape.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* perf(rust): count byte-level BPE tokens without the GPT-2 split regex (#40594)
The oniguruma run of the ByteLevel pre-tokenizer regex is about 90% of
encode_fast on a 100k token body (100 ms of the ~110 ms Rust admission
count in the gateway pod). A hand-written scanner that yields the same
pieces, then feeds the model directly, counts the same text in 10 ms.
It only engages for tokenizers with the Anthropic shape (optional NFKC,
ByteLevel without prefix space, no post-processor) and falls back to the
full encoder when the text contains an added token. Parity with
encode_fast is tested on random texts, the pieces are compared with the
real pre-tokenizer, and the \p{L}/\p{N}/\s tables are checked against
oniguruma for every code point.
NFKC runs through unicode-normalization-alignments, the crate and
Unicode tables NormalizedString::nfkc already uses, so the fast path
normalizes exactly what the full encoder would. Using the newer
unicode-normalization crate changed the count for 171 code points that
gained compatibility decompositions after Unicode 9 (U+32FF, U+A7F1..).
The fast normalizer is compared with the tokenizer's for every scalar
value and on random texts.
The scanner is built without mutable state: byte_char and mapped_len replace the const table builders and the reusable mapped buffer, and iter::successors replaces the stateful piece iterator. byte_chars_match_the_byte_level_alphabet checks the byte mapping against ByteLevel for every scalar value.
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(rust_bridge): bound concurrent token-count encodes and share the Anthropic tokenizer predicate
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>
Profiling the sidecar-enabled gateway at 700 rps showed ~2.4% of all samples
in get_model_group_info called per request from budget reservation, plus
get_deployment_model_info for tiered pricing tables. Both are read-only lookups
over the model list, so serve them from the Router's lru caches and clear the
deployment cache alongside the group cache when the model list changes.
The deployment-info cache is a per-router lru_cache built in __init__ rather
than a class-level decorated method, so it does not pin Router instances in a
process-wide cache and is dropped with the router.
A price data reload replaces litellm.model_cost without touching model_list, so
the reload replay also clears both caches; otherwise reservation would keep
pricing against the old catalog until an unrelated model-list change.
Co-authored-by: yassin <yassin@berri.ai>
* feat(mock): report admission-time input token count in mock_response usage
Mock completions always reported prompt_tokens=10, so spend tracking, TPM metrics, budgets and the tokens-per-minute autoscaling signal saw 10 tokens for a 100k-token request. Budget reservation now carries the admission-time input token count in the reservation record, and mock_completion reads it back so mock traffic exercises the same spend and TPM paths as real traffic without any extra tokenizer work.
* fix(mock): keep a zero admission input token count instead of falling back to 10
---------
Co-authored-by: yassin <yassin@berri.ai>
* perf(proxy): pipeline spend counter increments into one redis call
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): apply surviving spend increments before raising scope error
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style(proxy): ruff format spend counter helpers
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): settle inner spend counter gathers and fall back per key on pipeline failure
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): suppress BLE001 on pipeline fallback catch
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): invalidate all batched spend counters on pipeline failure
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>
Concurrent usage reads on one worker now share a single spend-log query
instead of each scanning the same window, and a digest that comes back
nameless a second time is remembered for the full ten minutes rather than
thirty seconds, so a key that never resolves costs at most two scans per
worker per window per ten minutes. The first miss still expires after
thirty seconds so a read that lands between the daily spend flush and the
spend-log flush recovers on the next read
The daily spend rows and the spend logs of one batch are written a moment apart, so a usage read landing between them used to remember the session as nameless for ten minutes on that worker. Found identities keep the ten minute entry
The spend-log lookup for permanently unresolvable digests is back to a single DISTINCT ON scan over the requested window, keeping only rows that carry an alias, user, or team so a newer nameless row cannot hide an older named one. Results and misses are cached per worker for ten minutes keyed by digest and window, failed queries are not cached, and JWT rows keyed hashed-jwt-<sha256> now pass the digest gate. Tests cover the JWT gate, cache reuse and partial misses, window changes, error handling, the with-window guard, and the daily activity wiring
CLI session tokens are in-memory only and never get a LiteLLM_VerificationToken
row, so the usage APIs could not resolve key_alias, team_id, or user_email for
their spend rows: the exact join and the reverse-hash recovery both miss. The
owner is written to LiteLLM_SpendLogs.metadata at request time under the same
hashed api_key, so read it back from there for keys still unresolved after the
token-table passes.
The lookup is sha256-gated like the existing reverse-hash recovery and bounded
to the records' startTime window (min date minus one day, max date plus two) so
it stays on the startTime index. No migration.
Also guard the window parser against the date=None rollup rows GROUPING SETS
aggregation emits, which raised TypeError from strptime and turned the
aggregated usage endpoints into HTTP 500s.
Preserve deployment identity through savings calculation, with canonical model fallback only when either ID is absent. Cover negotiated rates, unchanged deployments, alias/base-model cache accounting and missing IDs.
Fixes#38811. Based on the deployment-identity approach proposed by @QuantumBreakz in #38834.
Co-authored-by: Claude Code <noreply@anthropic.com>
