diff --git a/burr/integrations/litellm.py b/burr/integrations/litellm.py
new file mode 100644
index 00000000..f546101b
--- /dev/null
+++ b/burr/integrations/litellm.py
@@ -0,0 +1,298 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""LiteLLM integration for Burr.
+
+This module provides Action classes for invoking LLM models through
+the LiteLLM AI gateway within Burr applications. LiteLLM supports
+100+ providers (OpenAI, Anthropic, Google, Azure, AWS Bedrock, Ollama,
+Groq, Mistral, and more) through a unified interface.
+
+Example usage:
+    from burr.integrations.litellm import LiteLLMAction
+
+    def prompt_mapper(state):
+        return {
+            "messages": [
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": state["user_input"]},
+            ],
+        }
+
+    action = LiteLLMAction(
+        model="anthropic/claude-sonnet-4-6",
+        input_mapper=prompt_mapper,
+        reads=["user_input"],
+        writes=["response"],
+    )
+"""
+
+import logging
+from typing import Any, Generator, Optional, Protocol
+
+from burr.core.action import SingleStepAction, StreamingAction
+from burr.core.state import State
+from burr.integrations.base import require_plugin
+
+logger = logging.getLogger(__name__)
+
+try:
+    import litellm
+except ImportError as e:
+    require_plugin(e, "litellm")
+
+
+class StateToMessagesMapper(Protocol):
+    """Protocol for mapping Burr state to LiteLLM messages format."""
+
+    def __call__(self, state: State) -> dict[str, Any]:
+        ...  # noqa: E704
+
+
+class _LiteLLMCore:
+    """Shared LiteLLM configuration and request building."""
+
+    def __init__(
+        self,
+        model: str,
+        input_mapper: StateToMessagesMapper,
+        reads: list[str],
+        writes: list[str],
+        name: str,
+        api_key: Optional[str],
+        temperature: Optional[float],
+        max_tokens: Optional[int],
+        extra_kwargs: Optional[dict[str, Any]],
+    ):
+        self._model = model
+        self._input_mapper = input_mapper
+        self._reads = reads
+        self._writes = writes
+        self._name = name
+        self._api_key = api_key
+        self._temperature = temperature
+        self._max_tokens = max_tokens
+        self._extra_kwargs = extra_kwargs or {}
+
+    @property
+    def reads(self) -> list[str]:
+        return self._reads
+
+    @property
+    def writes(self) -> list[str]:
+        return self._writes
+
+    @property
+    def name(self) -> str:
+        return self._name
+
+    def build_completion_kwargs(self, state: State, stream: bool = False) -> dict[str, Any]:
+        """Build kwargs for litellm.completion from current state."""
+        prompt = self._input_mapper(state)
+        kwargs: dict[str, Any] = {
+            "model": self._model,
+            "messages": prompt["messages"],
+            "stream": stream,
+            "drop_params": True,
+        }
+        if self._api_key:
+            kwargs["api_key"] = self._api_key
+        if self._temperature is not None:
+            kwargs["temperature"] = self._temperature
+        if self._max_tokens is not None:
+            kwargs["max_tokens"] = self._max_tokens
+        kwargs.update(self._extra_kwargs)
+        return kwargs
+
+
+def _result_for_writes(
+    text: str,
+    usage: dict[str, Any],
+    writes: list[str],
+) -> dict[str, Any]:
+    result: dict[str, Any] = {
+        "response": text,
+        "usage": usage,
+    }
+    for w in writes:
+        if w == "usage":
+            result[w] = usage
+        else:
+            result[w] = text
+    return result
+
+
+class _LiteLLMBase:
+    """Shared LiteLLM wiring for action subclasses."""
+
+    _llm: _LiteLLMCore
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+
+    def _init_litellm_core(
+        self,
+        model: str,
+        input_mapper: StateToMessagesMapper,
+        reads: list[str],
+        writes: list[str],
+        name: str,
+        api_key: Optional[str],
+        temperature: Optional[float],
+        max_tokens: Optional[int],
+        extra_kwargs: Optional[dict[str, Any]],
+    ) -> None:
+        self._llm = _LiteLLMCore(
+            model=model,
+            input_mapper=input_mapper,
+            reads=reads,
+            writes=writes,
+            name=name,
+            api_key=api_key,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            extra_kwargs=extra_kwargs,
+        )
+
+    @property
+    def reads(self) -> list[str]:
+        return self._llm.reads
+
+    @property
+    def writes(self) -> list[str]:
+        return self._llm.writes
+
+    @property
+    def name(self) -> str:
+        return self._llm.name
+
+
+class LiteLLMAction(_LiteLLMBase, SingleStepAction):
+    """Action that invokes LLM models through the LiteLLM AI gateway.
+
+    :param model: LiteLLM model string (e.g. ``anthropic/claude-sonnet-4-6``,
+        ``openai/gpt-4o``, ``groq/llama-4-scout-17b-16e-instruct``).
+    :param input_mapper: Callable mapping :class:`~burr.core.state.State` to
+        a dict with a ``messages`` key (OpenAI chat format).
+    :param reads: State keys this action reads.
+    :param writes: State keys to update (typically include ``response``).
+    :param name: Action name for the graph.
+    :param api_key: Optional API key (falls back to provider env vars).
+    :param temperature: Optional sampling temperature.
+    :param max_tokens: Optional max tokens for the response.
+    :param extra_kwargs: Additional kwargs passed to ``litellm.completion``.
+    """
+
+    def __init__(
+        self,
+        model: str,
+        input_mapper: StateToMessagesMapper,
+        reads: list[str],
+        writes: list[str],
+        name: str = "litellm_invoke",
+        api_key: Optional[str] = None,
+        temperature: Optional[float] = None,
+        max_tokens: Optional[int] = None,
+        extra_kwargs: Optional[dict[str, Any]] = None,
+    ):
+        super().__init__()
+        self._init_litellm_core(
+            model=model,
+            input_mapper=input_mapper,
+            reads=reads,
+            writes=writes,
+            name=name,
+            api_key=api_key,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            extra_kwargs=extra_kwargs,
+        )
+
+    def run_and_update(self, state: State, **run_kwargs) -> tuple[dict, State]:
+        kwargs = self._llm.build_completion_kwargs(state)
+        response = litellm.completion(**kwargs)
+
+        text = response.choices[0].message.content or ""
+        usage = dict(response.usage) if response.usage else {}
+
+        result = _result_for_writes(text, usage, self._llm.writes)
+        updates = {key: result[key] for key in self._llm.writes if key in result}
+        new_state = state.update(**updates)
+
+        return result, new_state
+
+
+class LiteLLMStreamingAction(_LiteLLMBase, StreamingAction):
+    """Streaming LiteLLM action.
+
+    Parameters match :class:`LiteLLMAction` except the default ``name`` is
+    ``litellm_stream``. Yields chunk dicts from :meth:`stream_run` and merges
+    the final response in :meth:`update`.
+    """
+
+    def __init__(
+        self,
+        model: str,
+        input_mapper: StateToMessagesMapper,
+        reads: list[str],
+        writes: list[str],
+        name: str = "litellm_stream",
+        api_key: Optional[str] = None,
+        temperature: Optional[float] = None,
+        max_tokens: Optional[int] = None,
+        extra_kwargs: Optional[dict[str, Any]] = None,
+    ):
+        super().__init__()
+        self._init_litellm_core(
+            model=model,
+            input_mapper=input_mapper,
+            reads=reads,
+            writes=writes,
+            name=name,
+            api_key=api_key,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            extra_kwargs=extra_kwargs,
+        )
+
+    def stream_run(self, state: State, **run_kwargs) -> Generator[dict, None, None]:
+        kwargs = self._llm.build_completion_kwargs(state, stream=True)
+        response = litellm.completion(**kwargs)
+
+        text_parts: list[str] = []
+        for chunk in response:
+            choices = getattr(chunk, "choices", None)
+            if not choices:
+                continue
+            delta = getattr(choices[0], "delta", None)
+            if delta is None:
+                continue
+            content = getattr(delta, "content", None)
+            if isinstance(content, str) and content:
+                text_parts.append(content)
+                full_response = "".join(text_parts)
+                yield {"chunk": content, "response": full_response}
+
+        full_text = "".join(text_parts)
+        payload = _result_for_writes(full_text, {}, self._llm.writes)
+        yield {"chunk": "", "complete": True, **payload}
+
+    def update(self, result: dict, state: State) -> State:
+        if result.get("complete"):
+            updates = {key: result[key] for key in self._llm.writes if key in result}
+            return state.update(**updates)
+        return state
diff --git a/pyproject.toml b/pyproject.toml
index c2ab5d20..296390ec 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -136,6 +136,10 @@ bedrock = [
   "boto3"
 ]
 
+litellm = [
+  "litellm"
+]
+
 tracking-server-s3 = [
   "aerich",
   "aiobotocore",