* fix(datadog): pass callback_specific_params so DatadogCostManagementLogger receives cost_tag_keys (#29590)
* fix(datadog): pass callback_specific_params so DatadogCostManagementLogger receives cost_tag_keys
* test(proxy): regression test that load_config forwards callback_specific_params
* fix(proxy): guard lakera_prompt_injection callback_specific_params against non-dict
Addresses review feedback: forwarding callback_settings as callback_specific_params
(so DatadogCostManagementLogger receives cost_tag_keys) exposed the
lakera_prompt_injection branch, which did lakeraAI_Moderation(**callback_specific_params
["lakera_prompt_injection"]) with no type guard. A config like
`callback_settings: {lakera_prompt_injection: "any-string"}` then hit `**"any-string"`
-> TypeError: argument after ** must be a mapping, not str.
Guard the lakera branch with isinstance(dict), matching the existing presidio and
datadog_cost_management branches (non-dict values fall back to {}). Add a regression
test asserting initialize_callbacks_on_proxy ignores a non-dict value instead of crashing.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* test: inject fake lakera_ai module to avoid importing the real one
CI fix for the lakera regression test: it stubbed litellm.proxy.proxy_server with
a SimpleNamespace and then monkeypatch.setattr'd the real lakera_ai module, which
forces importing it — and lakera_ai does `from litellm.proxy.proxy_server import
LiteLLM_TeamTable`, absent on the stub -> ImportError under proxy-infra tests.
Inject a fake lakera_ai module into sys.modules instead, so the callbacks branch's
`from ...lakera_ai import lakeraAI_Moderation` resolves to the stub without loading
the real module. The guard under test (isinstance(dict) in the lakera branch) is
unchanged.
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(callbacks): guard compression/websearch interceptors against non-dict callback_settings (#30153)
#29590 forwards the full callback_settings dict into initialize_callbacks_on_proxy, which activates the compression_interception and websearch_interception consumers. Their initialize_from_proxy_config read the callback_settings subkey without an isinstance(dict) guard, so a non-dict value such as `compression_interception: true` reached from_config_yaml(...).get(...) and aborted proxy startup with AttributeError. #29590 added that guard for lakera_prompt_injection but not for these two
Mirror the isinstance(dict) guard already used by the lakera, presidio, and datadog branches so a non-dict value is ignored and the callback initializes with defaults. A parametrized test feeds every callback_settings consumer a non-dict value through initialize_callbacks_on_proxy to catch a future consumer that forgets the guard
* fix(callbacks): normalize non-dict callback_specific_params to empty dict
A blank callback_settings: key in YAML loads as None, and
config.get('callback_settings', {}) returns None because dict.get only
falls back to the default when the key is absent. Forwarding that value
verbatim to initialize_callbacks_on_proxy made the first
'<name>' in callback_specific_params membership test raise
TypeError: argument of type 'NoneType' is not iterable, aborting proxy
startup. Same failure for any non-dict root such as callback_settings: true.
Normalize the value at the function boundary so both callsites (and any
future ones) initialize callbacks with their defaults instead of crashing.
---------
Co-authored-by: Hedi Daoud <150018939+hdaoud23@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(proxy): strip LiteLLM policy tracking from OpenAI batch metadata
Batch create was failing with `Invalid type for 'metadata.applied_policies':
expected a string, but got an array instead` whenever a policy attachment
matched the request. The policy engine helpers wrote `applied_policies`,
`applied_guardrails`, and `policy_sources` into `data["metadata"]`
unconditionally, and `/v1/batches` forwarded that dict straight to OpenAI,
which only accepts string values.
- Route proxy-internal tracking into `litellm_metadata` for batch/file
routes via a shared `_get_or_create_proxy_metadata_bucket` helper.
- Sanitize `data["metadata"]` in `create_batch` to drop known internal
keys and non-string values before building the OpenAI request.
- Cover both behaviors with unit + endpoint tests.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(proxy): merge metadata buckets for batch policy response headers
Ensure get_logging_caching_headers reads both metadata and litellm_metadata so policy/guardrail headers are emitted on batch routes with user metadata, and log dropped non-string OpenAI metadata at debug level.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
* refactor: new agentic loop event hook
simplifies how to create logic for tool based multi llm calls
* fix: compress - make it work on anthropic input as well
* fix(compress.py): working prompt compression for claude code
ensures claude code messages can run through proxy easily
* docs: add agentic loop hook guide
* docs: add agentic_loop_hook to sidebar
* fix: fix multiple arguments error
* fix: fix tool call loop for compression on streaming /v1/messages
* fix: fix linting errors
* fix: fix ci/cd errors
* feat(litellm_pre_call_utils.py): use claude code session for litellm session id
allows claude code logs to be stitched together, making it easy to know they were all part of the same conversation
* fix: suppress incorrect mypy warning rE: module
* revert: drop PR's changes to litellm/proxy/_experimental/out/
Restores the 34 HTML files under _experimental/out/ to their pre-PR
paths (X/index.html -> X.html). All renames are R100 (content
unchanged); no other files are touched.
* fix: address greptile review comments on PR #25729
- Skip ``kwargs["tools"] = []`` injection when compression is a no-op —
Anthropic Messages rejects empty tool arrays on requests that did not
originally declare tools.
- Move agentic-loop safety guards (fingerprint cycle / max depth) out of
the per-callback try/except so they propagate instead of being swallowed
by the generic exception handler. Extracted _check_agentic_loop_safety.
- Gate generic ``x-<vendor>-session-id`` capture behind the
LITELLM_CAPTURE_VENDOR_SESSION_HEADERS env var (off by default) to
preserve backwards compatibility; explicit x-litellm-* headers are
unaffected.
- Fix monkeypatch target in pre-call-hook test to patch the actual
module-level binding
(litellm.integrations.compression_interception.handler.compress).
- Add regression tests for empty-tools skip and opt-in session capture.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* revert: drop LITELLM_CAPTURE_VENDOR_SESSION_HEADERS flag
Generic x-<vendor>-session-id header capture is a new feature and only
runs *after* the explicit x-litellm-trace-id / x-litellm-session-id
checks, so it does not change behavior for any existing caller that was
already using the LiteLLM headers — no backwards-incompatibility to gate.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor(compress): replace input_type with CallTypes call_type
Drop the bespoke ``CompressionInputType`` literal and use the existing
``litellm.types.utils.CallTypes`` enum instead. ``litellm.compress()``
now takes ``call_type: Union[CallTypes, str]`` (default
``CallTypes.completion``) — no new concept to learn, and the enum is
already the way the rest of the codebase talks about request shapes.
Supported values: ``completion`` / ``acompletion`` (OpenAI chat-completions
shape) and ``anthropic_messages`` (Anthropic structured content blocks).
Updated: compress(), the compression_interception handler, tests, docs,
and the two eval scripts.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
yuneng-jiang