some users do not use make pre-commit as it is a multi-minute process. I personally use it but I want users themselves to decide whether to pre-commit before each commit or not, based on what works best for them
LIT001/LIT002 and the typing.Dict ban all steered dict-shaped values to
frozen dataclasses or suppression even though the checker already accepts
MappingProxyType as a freezing wrapper; the messages now name it so the
dict-shaped freeze path is actually discoverable at fix time.
The 15-25 word cap previously applied only to replies/rebuttals to AI PR
review bots; it now covers every GitHub comment (issue comments and PR
discussion comments included). The public-writing punctuation bullet also
gains a warning that a word cap is not a one-sentence cap, so tight budgets
should be met with period splits or conjunctions rather than ";" splices,
at most one ";" per message
* feat(pre-commit): save full lint output to a per-worktree log file
* docs(claude): point agents at the pre-commit log instead of rerunning
* fix(pre-commit): warn when the log cannot be created or fully written
For a backend-only commit, staging the regenerated schema.d.ts newly
satisfies the ui file triggers, so the folder-wide dashboard lint
budgets run locally for the first time and CI's frontend-lint job
(budgets plus knip) activates on the PR. Those can only fail from
pre-existing dashboard-tree state, never from the regenerated file,
but the guidance should say so instead of implying a re-run is
always redundant.
The stale-types failure already writes the regenerated schema.d.ts to the
working tree, and staging it cannot introduce a new failure: the file is
listed in .prettierignore and the eslint config ignores, so no lint pass
sees it, and gen:api derives it purely from the Python proxy code, so a
second regeneration is a no-op. The only reason left to re-run is when
other checks also failed, so say exactly that in the script message and
CLAUDE.md instead of prescribing an unconditional re-run.
Removes the scheduled workflow that cut litellm_oss_daily_YYYY_MM_DD
branches and the guardrails workflow that only ran on them. The secret
scan and ruff checks that workflow duplicated already run on PRs to
litellm_internal_staging via test-linting.yml, so no coverage is lost.
Retargets contributor-facing messaging in CONTRIBUTING.md, CLAUDE.md,
and the guard-main-branch error output at litellm_internal_staging.
The guard-main-branch error messages and the contributor docs still
pointed people at litellm_oss_staging. Redirect them to the current
daily OSS branch (litellm_oss_daily_YYYY_MM_DD), a fresh one of which
is cut each weekday, so contributors should target the most recent
* chore: add latest model rule to CLAUDE.md
* chore: correct grammar mistake
* chore: make the rule more concise
* chore: replace rule instead
* chore: revise wording to override memories, etc.
* chore: slightly adjust wording to be more precise
* fix: zero out crash-class basedpyright rules across litellm/
* feat(lint): add LIT009 banning inert type: ignore comments
* docs: require bracketed rule and reason on every suppression
* chore(lint): ratchet budgets down and zero crash-class pyright limits
* fix: narrow auto router routelayer through a local before calling
* test: add regression tests for crash-class fixes
* fix: drop dead AZURE_AD_TOKEN lookups and word-bound the type-ignore regex
* chore: clarify the linear ticket instruction in pr template
* fix: make it more concise
* Update CLAUDE.md
lol
* chore: tell claude not to search for it if it doesn't have it
---------
Co-authored-by: ryan-crabbe-berri <ryan@berri.ai>
* chore(e2e): untrack gateway config and document e2e test location
Stop tracking tests/e2e/gateway/litellm-config.yml so the local proxy config stays on the machine
Add a note to CLAUDE.md that new e2e tests belong in tests/e2e/ and must follow that directory's conventions
* chore(e2e): add self-contained docker compose stack for local runs
Ship a docker-compose.yml that starts the proxy with a throwaway Postgres and Redis and inlines the proxy config with example models, so contributors can bring up a local gateway with nothing but a .env. Update CONTRIBUTING.md to match the inline-config flow
* chore(e2e): drop the second gemini deployment; one key is enough locally
* docs(e2e): make pre-commit steps ordered and require flagging internally found issues
* chore(lint): remove dead E501 config, fix stale blame-ignore SHAs, note 120 width in CLAUDE.md
E501 sat in both lint.ignore and lint.extend-select in ruff.toml; ignore wins,
so no line length was linted at all (verified: a 130-char line passes ruff
check while T201 fires). Remove it from both lists so the config tells the
truth: the formatter's wrap width is the only line-length control, matching
how the repo has actually behaved since E501 was ignored in Oct 2024
.git-blame-ignore-revs listed the pre-squash PR-head SHAs for the two ruff
reformat commits (#31317, #31518), which never landed on the branch, so git
blame ignored nothing. Replace them with the squash-merge SHAs that are
actually in history
Also document in CLAUDE.md that the line length is 120 (ruff.toml), not 88,
so agents stop wrapping to the old Black width
* fix: make CLAUDE.md more concise
* fix: make the guideline more clear
* chore(lint): raise basedpyright per-rule slack to 50% of baseline
The per-rule ceilings in basedpyright-code-budget.json sat at roughly 10% slack over baseline, which several in-flight PRs are already bumping into. Raise the slack on every rule to at least 50% of its baseline so there is ample headroom for a long while, while never lowering any rule that already had more generous slack (e.g. reportReturnType stays at 100).
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* refactor(lint): collapse type/lint budgets to a single per-rule limit
The three non-frontend budget files (ruff-strict, type-discipline, basedpyright-code) tracked a per-rule baseline and slack whose sum was the ceiling. Nothing consumed the split beyond that sum, so this replaces both keys with a single limit equal to the old baseline + slack; the original baselines live in git history if anyone needs them.
The gate scripts and the ratchet guard now read limit directly. lint-budget-update no longer re-captures raw counts; it ratchets each rule's limit down by the number of violations this branch cleared since its branch point (the merge-base), so the granted headroom shrinks by exactly what was fixed and a limit never rises. The ratchet guard reads either schema so it still compares correctly across the migration boundary.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* chore(lint): surface staged-vs-working parity for pre-commit and budget-update
make pre-commit selects which checks to run from the staged index but runs the linters over the working tree, so unstaged edits to tracked files and untracked files skew a green/red away from what a commit of only the staged changes would produce. There is no safe in-place way to lint the index, so the script now warns when unstaged or untracked changes are present, and CLAUDE.md documents that you must stage everything first for both make pre-commit and make lint-budget-update to predict CI correctly.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* docs(lint): list type-discipline budget in lint-budget-update instruction
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* feat(guardrails/headroom): add CCR (compress-cache-retrieve) support via agentic loop
When Headroom's /v1/compress returns messages containing hash markers
(hash=[a-f0-9]{24}), inject a headroom_retrieve tool into the request.
When the LLM calls that tool, intercept via async_should_run_agentic_loop
and async_build_agentic_loop_plan, call GET /v1/retrieve/{hash} on the
Headroom sidecar, and replay the LLM with the original content as a tool
result -- all transparent to the caller.
* style: run ruff format on headroom guardrail and tests
* fix(guardrails/headroom): detect headroom_retrieve calls in both OpenAI and Anthropic response formats
* test(guardrails/headroom): add test for Anthropic content block format detection in CCR loop
* ci: trigger CI checks
* fix(guardrails/headroom): replace List/Dict with list/dict to fix UP006 ruff violations
* fix(guardrails/headroom): replace except Exception with except ValueError to fix BLE001
* fix(guardrails/headroom): add Responses API output format detection for CCR tool calls
* refactor(guardrails/headroom): extract format-specific helpers to fix C901 complexity
* fix(guardrails/headroom): scope CCR retrieval to hashes produced by current request
Previously any LLM-supplied hash in a headroom_retrieve tool call was
forwarded to the Headroom retrieve API, letting a crafted tool call
fetch arbitrary cached content. Validate the hash against the set
produced by compressing the current request's messages before calling
retrieve.
* fix(guardrails/headroom): track issued hashes server-side, fix Responses API replay shape
Hash validation now also checks an in-memory cache of hashes actually
returned by /v1/compress, not just whether the hash text appears
somewhere in the request's messages. The message-text check alone is
forgeable: an attacker can plant a hash-shaped string in their own
prompt and have it treated as valid.
Responses API follow-up now emits function_call/function_call_output
items keyed by call_id instead of chat-style assistant/tool messages,
since the Responses API does not accept the latter as input. Also
fixes call_id/id field priority when extracting tool calls from
Responses API output, since call_id (not id) is what must match
between the function_call and its output.
* fix(guardrails/headroom): drop redundant quoted type annotations
UP037 flags quotes on annotations that are already lazily evaluated
via `from __future__ import annotations`.
* test(guardrails/headroom): add missing pytest.mark.asyncio decorators
Functional under asyncio_mode=auto, but every other async test in the
file has the decorator for consistency.
* fix(guardrails/headroom): scope CCR hashes per call_id, fix Anthropic replay shape
Two real gaps found in review:
1. The instance-wide issued-hash cache combined with a message-text
check did not actually scope retrieval to the request that produced
the hash. A hash issued for request A stays in the shared cache
until TTL expiry, and the message-text check is satisfied by any
request whose own messages happen to echo that hash string. Request
B could plant A's hash in its own prompt and retrieve A's content.
Fixed by keying the issued-hash cache by litellm_call_id, matching
the pattern already used in compression_interception: a hash is
only honored when it was issued under the exact call_id resolving
for the current request.
2. The Anthropic Messages replay path fell through to the chat-style
assistant/tool-message builder, which Anthropic does not accept.
Anthropic requires the tool_use block echoed in an assistant message
paired with a tool_result block in a user message, keyed by
tool_use_id. Added a dedicated branch for this shape.
* docs: note proactive API-fragmentation helper convention
Add a bullet to the coding-conventions list: look for or add a shared
helper when logic branches on API surface (chat completions vs
Anthropic Messages vs Responses API), instead of duplicating
format-detection per module.
* fix(guardrails/headroom): fix Anthropic tool-shape detection, extract shared cross-API tool util
Live e2e testing against the real Anthropic API surfaced two bugs the
mocked unit tests couldn't catch because they used MagicMock responses
instead of realistic response shapes:
1. has_headroom_retrieve_tool only recognized OpenAI-shaped function
tools. By the time an Anthropic Messages response reaches the
agentic-loop gate, the tool this guardrail injected has already been
transformed into Anthropic's native shape (type: "custom", top-level
"name"), so the gate never fired for real Anthropic requests.
2. AnthropicMessagesResponse is a TypedDict, so real responses are
plain dicts at runtime, not objects with attribute access. The
extractors and format detectors used bare getattr(), which silently
returns nothing for dict responses instead of reading the actual
key.
Extracted the cross-API-surface tool-call extraction and tool-presence
check into litellm/litellm_core_utils/prompt_templates/factory.py
(get_tool_calls_from_response, has_tool_with_name) so this format
fragmentation is handled in one place instead of being duplicated
per-guardrail, and reused the existing repair-aware
parse_tool_call_arguments from common_utils instead of a naive
json.loads. headroom.py now delegates to these shared helpers.
Confirmed live against the real Anthropic API: the retrieve loop now
fires and successfully retrieves the correct hash's content through
the full compress -> tool-call -> retrieve -> replay round-trip.
* fix(guardrails/headroom): fix ruff-strict UP006/I001 budget violations
Use lowercase list/dict generics in the new factory.py tool-call
helpers instead of typing.List/Dict, drop the now-unused Tuple import
in headroom.py, and reorder the new factory import ahead of the
llms.custom_httpx import to satisfy import sorting.
* fix(guardrails/headroom): match Anthropic tools without a type field
Anthropic's documented client tool format is just name + input_schema;
type: "custom" is only one possible value, not a requirement. Match
any non-OpenAI-shaped tool on its top-level name instead of requiring
type == "custom".
