diff --git a/pyproject.toml b/pyproject.toml
index 9a453a0b..fd7023f3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -66,6 +66,7 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
+litellm = ["litellm>=1.80.0,<2.0"]
 multimodal = ["everalgo-parser[svg]>=0.1.0"]  # [svg] bundles cairosvg → SVG works by default
 
 [build-system]
diff --git a/src/everos/component/embedding/__init__.py b/src/everos/component/embedding/__init__.py
index 98f0e1e6..ecd96d10 100644
--- a/src/everos/component/embedding/__init__.py
+++ b/src/everos/component/embedding/__init__.py
@@ -19,6 +19,7 @@
 from .accessor import EmbeddingNotConfiguredError as EmbeddingNotConfiguredError
 from .accessor import get_embedder as get_embedder
 from .factory import build_embedding_provider as build_embedding_provider
+from .litellm_provider import LiteLLMEmbeddingProvider as LiteLLMEmbeddingProvider
 from .openai_provider import OpenAIEmbeddingProvider as OpenAIEmbeddingProvider
 from .protocol import EmbeddingError as EmbeddingError
 from .protocol import EmbeddingProvider as EmbeddingProvider
@@ -27,6 +28,7 @@
     "EmbeddingError",
     "EmbeddingNotConfiguredError",
     "EmbeddingProvider",
+    "LiteLLMEmbeddingProvider",
     "OpenAIEmbeddingProvider",
     "build_embedding_provider",
     "get_embedder",
diff --git a/src/everos/component/embedding/factory.py b/src/everos/component/embedding/factory.py
index 97967c44..a9fdf0be 100644
--- a/src/everos/component/embedding/factory.py
+++ b/src/everos/component/embedding/factory.py
@@ -1,4 +1,11 @@
-"""Factory for building an embedding provider from :class:`EmbeddingSettings`."""
+"""Factory for building an embedding provider from :class:`EmbeddingSettings`.
+
+Dispatches on ``settings.provider``:
+
+- ``"openai"`` — :class:`OpenAIEmbeddingProvider` (requires ``base_url``).
+- ``"litellm"`` — :class:`LiteLLMEmbeddingProvider` (routes via
+  model-id prefix; ``base_url`` optional).
+"""
 
 from __future__ import annotations
 
@@ -7,7 +14,6 @@
 from .openai_provider import OpenAIEmbeddingProvider
 from .protocol import EmbeddingProvider
 
-# Vector dim for the LanceDB index column — see ``17_lancedb_tables_design.md``.
 _DEFAULT_DIM = 1024
 
 
@@ -16,7 +22,7 @@ def build_embedding_provider(
     *,
     dim: int = _DEFAULT_DIM,
 ) -> EmbeddingProvider:
-    """Build an OpenAI-compatible embedding provider from settings.
+    """Build an embedding provider from settings.
 
     Args:
         settings: The :class:`EmbeddingSettings` slice from
@@ -29,8 +35,14 @@ def build_embedding_provider(
         ``embed_batch``.
 
     Raises:
-        ValueError: If ``model``, ``api_key`` or ``base_url`` is unset.
+        ValueError: If required fields are unset for the chosen provider.
     """
+    if settings.provider == "litellm":
+        return _build_litellm(settings, dim=dim)
+    return _build_openai(settings, dim=dim)
+
+
+def _build_openai(settings: EmbeddingSettings, *, dim: int) -> EmbeddingProvider:
     if not settings.model:
         raise ValueError(
             "Embedding model is not configured "
@@ -54,3 +66,26 @@ def build_embedding_provider(
         batch_size=settings.batch_size,
         max_concurrent=settings.max_concurrent,
     )
+
+
+def _build_litellm(settings: EmbeddingSettings, *, dim: int) -> EmbeddingProvider:
+    from .litellm_provider import LiteLLMEmbeddingProvider
+
+    if not settings.model:
+        raise ValueError(
+            "Embedding model is not configured "
+            "(set EVEROS_EMBEDDING__MODEL or [embedding] model in user toml)"
+        )
+    return LiteLLMEmbeddingProvider(
+        model=settings.model,
+        api_key=(
+            settings.api_key.get_secret_value()
+            if settings.api_key is not None
+            else None
+        ),
+        base_url=settings.base_url,
+        dim=dim,
+        timeout=settings.timeout_seconds,
+        batch_size=settings.batch_size,
+        max_concurrent=settings.max_concurrent,
+    )
diff --git a/src/everos/component/embedding/litellm_provider.py b/src/everos/component/embedding/litellm_provider.py
new file mode 100644
index 00000000..c5209bfd
--- /dev/null
+++ b/src/everos/component/embedding/litellm_provider.py
@@ -0,0 +1,99 @@
+"""LiteLLM AI gateway embedding provider.
+
+Wraps ``litellm.aembedding`` so any provider litellm supports (OpenAI,
+Cohere, Bedrock, HuggingFace, …) can serve embeddings without
+per-provider forks.  Mirrors :class:`OpenAIEmbeddingProvider`'s
+batching + concurrency model: inputs are chunked by ``batch_size``
+and an :class:`asyncio.Semaphore` bounds in-flight requests.
+"""
+
+from __future__ import annotations
+
+import asyncio
+from collections.abc import Sequence
+from typing import Any
+
+from .protocol import EmbeddingError
+
+
+class LiteLLMEmbeddingProvider:
+    """LiteLLM-backed embedding provider with batching + concurrency.
+
+    Args:
+        model: LiteLLM embedding model id
+            (e.g. ``"openai/text-embedding-3-small"``).
+        api_key: Bearer credential forwarded to litellm. When *not* set
+            litellm falls back to provider-specific env vars.
+        base_url: Optional proxy / gateway URL.
+        dim: Target vector dimension. Vectors longer than this are
+            truncated client-side.
+        timeout: Per-request timeout, seconds.
+        batch_size: How many inputs per ``/embeddings`` call.
+        max_concurrent: Cap on in-flight chunked requests.
+    """
+
+    def __init__(
+        self,
+        *,
+        model: str,
+        api_key: str | None = None,
+        base_url: str | None = None,
+        dim: int = 1024,
+        timeout: float = 30.0,
+        batch_size: int = 10,
+        max_concurrent: int = 5,
+    ) -> None:
+        self.dim = dim
+        self._model = model
+        self._api_key = api_key
+        self._base_url = base_url
+        self._timeout = timeout
+        self._batch_size = batch_size
+        self._semaphore = asyncio.Semaphore(max_concurrent)
+
+    async def embed(self, text: str) -> list[float]:
+        """Embed a single string."""
+        vectors = await self._embed_chunk([text])
+        return vectors[0]
+
+    async def embed_batch(self, texts: Sequence[str]) -> list[list[float]]:
+        """Embed many strings, preserving input order."""
+        if not texts:
+            return []
+        chunks = [
+            list(texts[i : i + self._batch_size])
+            for i in range(0, len(texts), self._batch_size)
+        ]
+        results = await asyncio.gather(*(self._embed_chunk(c) for c in chunks))
+        return [vec for chunk in results for vec in chunk]
+
+    async def _embed_chunk(self, chunk: list[str]) -> list[list[float]]:
+        """One embedding call, semaphore-guarded."""
+        try:
+            import litellm
+        except ImportError as exc:
+            raise EmbeddingError(
+                "litellm is not installed. Install with: pip install 'everos[litellm]'"
+            ) from exc
+
+        kwargs: dict[str, Any] = {
+            "model": self._model,
+            "input": chunk,
+            "drop_params": True,
+            "timeout": self._timeout,
+        }
+        if self._api_key is not None:
+            kwargs["api_key"] = self._api_key
+        if self._base_url:
+            kwargs["api_base"] = self._base_url
+
+        async with self._semaphore:
+            try:
+                response = await litellm.aembedding(**kwargs)
+            except Exception as exc:
+                qualname = f"{type(exc).__module__}.{type(exc).__name__}"
+                if qualname.startswith("litellm.") or qualname.startswith("openai."):
+                    raise EmbeddingError(str(exc)) from exc
+                raise
+
+        return [list(item.embedding[: self.dim]) for item in response.data]
diff --git a/src/everos/component/llm/__init__.py b/src/everos/component/llm/__init__.py
index c82baf5e..3648b1af 100644
--- a/src/everos/component/llm/__init__.py
+++ b/src/everos/component/llm/__init__.py
@@ -24,6 +24,7 @@
 from .client import get_llm_client as get_llm_client
 from .client import get_multimodal_llm_client as get_multimodal_llm_client
 from .factory import build_llm_provider as build_llm_provider
+from .litellm_provider import LiteLLMProvider as LiteLLMProvider
 from .openai_provider import OpenAIProvider as OpenAIProvider
 from .protocol import ChatMessage as ChatMessage
 from .protocol import ChatResponse as ChatResponse
@@ -37,6 +38,7 @@
     "LLMClient",
     "LLMError",
     "LLMNotConfiguredError",
+    "LiteLLMProvider",
     "OpenAIProvider",
     "Usage",
     "build_llm_provider",
diff --git a/src/everos/component/llm/client.py b/src/everos/component/llm/client.py
index 846dcf1c..8e5c9a7d 100644
--- a/src/everos/component/llm/client.py
+++ b/src/everos/component/llm/client.py
@@ -1,10 +1,14 @@
 """Process-wide LLM client accessor.
 
-Lazy singleton — first call reads settings and builds the algo LLM
+Lazy singleton -- first call reads settings and builds the algo LLM
 client; subsequent calls return the cached instance. Raises
 :class:`LLMNotConfiguredError` when no credentials are present so
 misconfiguration surfaces at app startup (via the LLM lifespan
 provider) instead of silently failing per-request downstream.
+
+When ``settings.llm.provider`` is ``"litellm"``, the factory-built
+:class:`LiteLLMProvider` is used instead of the everalgo default
+client, giving users access to 100+ LLM providers.
 """
 
 from __future__ import annotations
@@ -39,6 +43,17 @@ def get_llm_client() -> LLMClient:
         return _llm_client
 
     llm_cfg = load_settings().llm
+
+    if llm_cfg.provider == "litellm":
+        from .factory import build_llm_provider
+
+        try:
+            _llm_client = build_llm_provider(llm_cfg)
+        except ValueError as exc:
+            raise LLMNotConfiguredError(str(exc)) from exc
+        logger.info("llm_client_built", model=llm_cfg.model, provider="litellm")
+        return _llm_client
+
     api_key = (
         llm_cfg.api_key.get_secret_value() if llm_cfg.api_key is not None else None
     )
@@ -60,7 +75,7 @@ def get_llm_client() -> LLMClient:
 def get_multimodal_llm_client() -> LLMClient:
     """Return the singleton multimodal LLM client (for everalgo.parser).
 
-    Reads the flat ``[multimodal]`` config — kept separate from the main
+    Reads the flat ``[multimodal]`` config -- kept separate from the main
     ``[llm]`` so parsing can target a vision/audio-capable endpoint.
 
     Raises:
diff --git a/src/everos/component/llm/factory.py b/src/everos/component/llm/factory.py
index d0db74f2..2574d9db 100644
--- a/src/everos/component/llm/factory.py
+++ b/src/everos/component/llm/factory.py
@@ -1,4 +1,11 @@
-"""Factory for building an LLM provider from :class:`LLMSettings`."""
+"""Factory for building an LLM provider from :class:`LLMSettings`.
+
+Dispatches on ``settings.provider``:
+
+- ``"openai"`` — :class:`OpenAIProvider` (requires ``base_url``).
+- ``"litellm"`` — :class:`LiteLLMProvider` (routes via model-id
+  prefix; ``base_url`` optional).
+"""
 
 from __future__ import annotations
 
@@ -9,12 +16,10 @@
 
 
 def build_llm_provider(settings: LLMSettings) -> LLMClient:
-    """Build an OpenAI-compatible LLM provider from settings.
+    """Build an LLM provider from settings.
 
     Unwraps :class:`pydantic.SecretStr` here so downstream callers never
-    touch the raw key directly. Fails fast if either ``api_key`` or
-    ``base_url`` is missing — caller is expected to set them via
-    ``.env`` / user toml / programmatic init before calling.
+    touch the raw key directly.
 
     Args:
         settings: The :class:`LLMSettings` slice from
@@ -26,8 +31,15 @@ def build_llm_provider(settings: LLMSettings) -> LLMClient:
         operators via ``llm=``.
 
     Raises:
-        ValueError: If ``api_key`` or ``base_url`` is unset.
+        ValueError: If required credentials are unset for the chosen
+            provider.
     """
+    if settings.provider == "litellm":
+        return _build_litellm(settings)
+    return _build_openai(settings)
+
+
+def _build_openai(settings: LLMSettings) -> LLMClient:
     if settings.api_key is None:
         raise ValueError(
             "LLM api_key is not configured "
@@ -43,3 +55,17 @@ def build_llm_provider(settings: LLMSettings) -> LLMClient:
         api_key=settings.api_key.get_secret_value(),
         base_url=settings.base_url,
     )
+
+
+def _build_litellm(settings: LLMSettings) -> LLMClient:
+    from .litellm_provider import LiteLLMProvider
+
+    return LiteLLMProvider(
+        model=settings.model,
+        api_key=(
+            settings.api_key.get_secret_value()
+            if settings.api_key is not None
+            else None
+        ),
+        base_url=settings.base_url,
+    )
diff --git a/src/everos/component/llm/litellm_provider.py b/src/everos/component/llm/litellm_provider.py
new file mode 100644
index 00000000..ce78b6db
--- /dev/null
+++ b/src/everos/component/llm/litellm_provider.py
@@ -0,0 +1,130 @@
+"""LiteLLM AI gateway provider for everos.
+
+Implements the :class:`everalgo.llm.LLMClient` structural contract by
+routing through `litellm <https://github.com/BerriAI/litellm>`_ — a
+unified interface to 100+ LLM providers (OpenAI, Anthropic, Gemini,
+Bedrock, Azure, Ollama, …). Users specify provider-prefixed model ids
+(e.g. ``anthropic/claude-sonnet-4-20250514``) and litellm translates the
+request to the target provider's native API.
+
+``drop_params=True`` is always passed so provider-unsupported kwargs
+(``seed``, ``frequency_penalty``, ``strict``, …) are silently dropped
+rather than raising.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Literal
+
+from .protocol import ChatMessage, ChatResponse, LLMError, Usage
+
+
+class LiteLLMProvider:
+    """Async LLM provider backed by the litellm SDK.
+
+    Structurally satisfies :class:`everalgo.llm.LLMClient` (PEP 544);
+    instances can be passed directly to everalgo operators that accept
+    ``llm: LLMClient | None``.
+
+    Args:
+        model: LiteLLM model id (e.g. ``"anthropic/claude-sonnet-4-20250514"``
+            or ``"gpt-4o-mini"``).
+        api_key: Bearer credential forwarded to litellm. When *not* set
+            litellm falls back to provider-specific env vars
+            (``ANTHROPIC_API_KEY``, ``OPENAI_API_KEY``, …).
+        base_url: Optional proxy / gateway URL. Only needed when routing
+            through a self-hosted LiteLLM proxy; omit for direct
+            provider access.
+        timeout: Per-request timeout in seconds.
+        temperature: Default sampling temperature (overridable per call).
+        max_tokens: Default max-tokens cap (overridable per call).
+    """
+
+    def __init__(
+        self,
+        *,
+        model: str,
+        api_key: str | None = None,
+        base_url: str | None = None,
+        timeout: float = 60.0,
+        temperature: float = 0.0,
+        max_tokens: int | None = None,
+    ) -> None:
+        self._model = model
+        self._temperature = temperature
+        self._max_tokens = max_tokens
+        self._api_key = api_key
+        self._base_url = base_url
+        self._timeout = timeout
+
+    async def chat(
+        self,
+        messages: list[ChatMessage],
+        *,
+        model: str | None = None,
+        temperature: float | None = None,
+        max_tokens: int | None = None,
+        response_format: Any | None = None,
+        **extra: Any,
+    ) -> ChatResponse:
+        """Send a chat completion request via litellm."""
+        try:
+            import litellm
+        except ImportError as exc:
+            raise LLMError(
+                "litellm is not installed. Install with: pip install 'everos[litellm]'"
+            ) from exc
+
+        request: dict[str, Any] = {
+            "model": model or self._model,
+            "messages": [m.model_dump() for m in messages],
+            "temperature": (
+                temperature if temperature is not None else self._temperature
+            ),
+            "drop_params": True,
+            "timeout": self._timeout,
+        }
+        if self._api_key is not None:
+            request["api_key"] = self._api_key
+        if self._base_url:
+            request["api_base"] = self._base_url
+
+        effective_max = max_tokens if max_tokens is not None else self._max_tokens
+        if effective_max is not None:
+            request["max_tokens"] = effective_max
+        if response_format is not None:
+            request["response_format"] = response_format
+        request.update(extra)
+
+        try:
+            completion = await litellm.acompletion(**request)
+        except Exception as exc:
+            qualname = f"{type(exc).__module__}.{type(exc).__name__}"
+            if qualname.startswith("litellm.") or qualname.startswith("openai."):
+                raise LLMError(str(exc)) from exc
+            raise
+
+        if not completion.choices:
+            raise LLMError("LiteLLM returned no choices")
+        choice = completion.choices[0]
+        usage: Usage | None = None
+        if completion.usage is not None:
+            usage = Usage(
+                prompt_tokens=getattr(completion.usage, "prompt_tokens", None),
+                completion_tokens=getattr(completion.usage, "completion_tokens", None),
+            )
+        return ChatResponse(
+            content=choice.message.content or "",
+            model=completion.model,
+            usage=usage,
+            finish_reason=_normalise_finish_reason(choice.finish_reason),
+            raw=None,
+        )
+
+
+def _normalise_finish_reason(
+    value: str | None,
+) -> Literal["stop", "length", "content_filter"] | None:
+    if value in ("stop", "length", "content_filter"):
+        return value  # type: ignore[return-value]
+    return None
diff --git a/src/everos/config/settings.py b/src/everos/config/settings.py
index 98337f47..777fb74e 100644
--- a/src/everos/config/settings.py
+++ b/src/everos/config/settings.py
@@ -113,16 +113,20 @@ class LLMSettings(BaseModel):
     """LLM client configuration.
 
     Read by the service layer when lazily constructing the LLM client
-    handed to algo extractors. Provider-agnostic field names — the
-    project follows the OpenAI API protocol so any OpenAI-compatible
-    endpoint plugs in via ``base_url``.
+    handed to algo extractors. ``provider`` selects the backend
+    implementation: ``"openai"`` (default) wraps ``openai.AsyncOpenAI``
+    against any OpenAI-compatible endpoint; ``"litellm"`` routes through
+    the LiteLLM SDK to 100+ providers via model-id prefix
+    (e.g. ``anthropic/claude-sonnet-4-20250514``).
 
     Env binding (via parent ``Settings``):
+        EVEROS_LLM__PROVIDER
         EVEROS_LLM__MODEL
         EVEROS_LLM__API_KEY
         EVEROS_LLM__BASE_URL
     """
 
+    provider: Literal["openai", "litellm"] = "openai"
     model: str = "gpt-4o-mini"
     api_key: SecretStr | None = None
     base_url: str | None = None
@@ -161,12 +165,12 @@ class MultimodalSettings(BaseModel):
 class EmbeddingSettings(BaseModel):
     """Embedding client configuration.
 
-    OpenAI-compatible embedding endpoint. ``model`` / ``api_key`` /
-    ``base_url`` are required at runtime when the embedding capability
-    is enabled; the runtime knobs (``timeout`` etc.) have sensible
-    defaults.
+    ``provider`` selects the backend: ``"openai"`` (default) wraps
+    ``openai.AsyncOpenAI`` against any OpenAI-compatible endpoint;
+    ``"litellm"`` routes through LiteLLM to 100+ providers.
 
     Env binding:
+        EVEROS_EMBEDDING__PROVIDER
         EVEROS_EMBEDDING__MODEL
         EVEROS_EMBEDDING__API_KEY
         EVEROS_EMBEDDING__BASE_URL
@@ -176,6 +180,7 @@ class EmbeddingSettings(BaseModel):
         EVEROS_EMBEDDING__MAX_CONCURRENT
     """
 
+    provider: Literal["openai", "litellm"] = "openai"
     model: str | None = None
     api_key: SecretStr | None = None
     base_url: str | None = None
diff --git a/tests/unit/test_component/test_embedding/test_factory.py b/tests/unit/test_component/test_embedding/test_factory.py
index d75a1789..e37b817c 100644
--- a/tests/unit/test_component/test_embedding/test_factory.py
+++ b/tests/unit/test_component/test_embedding/test_factory.py
@@ -1,4 +1,4 @@
-"""``build_embedding_provider`` — settings validation + provider build."""
+"""``build_embedding_provider`` -- settings validation + provider build."""
 
 from __future__ import annotations
 
@@ -40,7 +40,35 @@ def test_custom_dim_passes_through() -> None:
     s = EmbeddingSettings(model="m", api_key=SecretStr("k"), base_url="https://x")
     p = build_embedding_provider(s, dim=512)
     assert isinstance(p, OpenAIEmbeddingProvider)
-    # Provider stores dim on a private attr; assert via the public output shape
-    # only if straightforward. Skip introspection if attr name differs.
     if hasattr(p, "_dim"):
         assert p._dim == 512
+
+
+def test_builds_litellm_embedding_provider() -> None:
+    from everos.component.embedding.litellm_provider import LiteLLMEmbeddingProvider
+
+    s = EmbeddingSettings(
+        provider="litellm",
+        model="openai/text-embedding-3-small",
+        api_key=SecretStr("k"),
+    )
+    p = build_embedding_provider(s)
+    assert isinstance(p, LiteLLMEmbeddingProvider)
+
+
+def test_litellm_embedding_without_base_url() -> None:
+    from everos.component.embedding.litellm_provider import LiteLLMEmbeddingProvider
+
+    s = EmbeddingSettings(
+        provider="litellm",
+        model="openai/text-embedding-3-small",
+        base_url=None,
+    )
+    p = build_embedding_provider(s)
+    assert isinstance(p, LiteLLMEmbeddingProvider)
+
+
+def test_litellm_embedding_raises_when_model_missing() -> None:
+    s = EmbeddingSettings(provider="litellm", model=None)
+    with pytest.raises(ValueError, match="EVEROS_EMBEDDING__MODEL"):
+        build_embedding_provider(s)
diff --git a/tests/unit/test_component/test_embedding/test_litellm_provider.py b/tests/unit/test_component/test_embedding/test_litellm_provider.py
new file mode 100644
index 00000000..7d24da50
--- /dev/null
+++ b/tests/unit/test_component/test_embedding/test_litellm_provider.py
@@ -0,0 +1,181 @@
+"""LiteLLMEmbeddingProvider -- embed dispatch, batching, truncation, errors."""
+
+from __future__ import annotations
+
+import types
+from typing import Any
+from unittest import mock
+
+import pytest
+
+from everos.component.embedding.protocol import EmbeddingError
+
+
+def _make_fake_litellm() -> types.ModuleType:
+    mod = types.ModuleType("litellm")
+    mod.aembedding = mock.AsyncMock(name="litellm.aembedding")  # type: ignore[attr-defined]
+    return mod
+
+
+def _patch_litellm(
+    monkeypatch: pytest.MonkeyPatch,
+    fake: types.ModuleType,
+) -> None:
+    monkeypatch.setitem(__import__("sys").modules, "litellm", fake)
+
+
+def _embedding_response(vectors: list[list[float]]) -> Any:
+    """Return an object shaped like ``litellm.EmbeddingResponse``."""
+    items = []
+    for vec in vectors:
+        item = mock.MagicMock()
+        item.embedding = vec
+        items.append(item)
+    resp = mock.MagicMock()
+    resp.data = items
+    return resp
+
+
+async def test_embed_single(monkeypatch: pytest.MonkeyPatch) -> None:
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    from everos.component.embedding.litellm_provider import LiteLLMEmbeddingProvider
+
+    fake.aembedding.return_value = _embedding_response([[0.1, 0.2, 0.3]])  # type: ignore[attr-defined]
+
+    provider = LiteLLMEmbeddingProvider(model="openai/text-embedding-3-small", dim=3)
+    result = await provider.embed("hello")
+
+    assert result == [0.1, 0.2, 0.3]
+    fake.aembedding.assert_awaited_once()  # type: ignore[attr-defined]
+    call_kwargs = fake.aembedding.call_args.kwargs  # type: ignore[attr-defined]
+    assert call_kwargs["drop_params"] is True
+
+
+async def test_embed_batch_preserves_order(monkeypatch: pytest.MonkeyPatch) -> None:
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    from everos.component.embedding.litellm_provider import LiteLLMEmbeddingProvider
+
+    fake.aembedding.return_value = _embedding_response(  # type: ignore[attr-defined]
+        [[1.0, 2.0], [3.0, 4.0]]
+    )
+
+    provider = LiteLLMEmbeddingProvider(
+        model="m", dim=2, batch_size=10, max_concurrent=2
+    )
+    result = await provider.embed_batch(["a", "b"])
+
+    assert len(result) == 2
+    assert result[0] == [1.0, 2.0]
+    assert result[1] == [3.0, 4.0]
+
+
+async def test_embed_batch_empty(monkeypatch: pytest.MonkeyPatch) -> None:
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    from everos.component.embedding.litellm_provider import LiteLLMEmbeddingProvider
+
+    provider = LiteLLMEmbeddingProvider(model="m", dim=2)
+    result = await provider.embed_batch([])
+
+    assert result == []
+    fake.aembedding.assert_not_awaited()  # type: ignore[attr-defined]
+
+
+async def test_dimension_truncation(monkeypatch: pytest.MonkeyPatch) -> None:
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    from everos.component.embedding.litellm_provider import LiteLLMEmbeddingProvider
+
+    fake.aembedding.return_value = _embedding_response(  # type: ignore[attr-defined]
+        [[0.1, 0.2, 0.3, 0.4, 0.5]]
+    )
+
+    provider = LiteLLMEmbeddingProvider(model="m", dim=3)
+    result = await provider.embed("test")
+
+    assert len(result) == 3
+    assert result == [0.1, 0.2, 0.3]
+
+
+async def test_api_key_forwarded(monkeypatch: pytest.MonkeyPatch) -> None:
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    from everos.component.embedding.litellm_provider import LiteLLMEmbeddingProvider
+
+    fake.aembedding.return_value = _embedding_response([[0.1]])  # type: ignore[attr-defined]
+
+    provider = LiteLLMEmbeddingProvider(model="m", api_key="sk-test", dim=1)
+    await provider.embed("x")
+
+    call_kwargs = fake.aembedding.call_args.kwargs  # type: ignore[attr-defined]
+    assert call_kwargs["api_key"] == "sk-test"
+
+
+async def test_api_key_omitted_when_none(monkeypatch: pytest.MonkeyPatch) -> None:
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    from everos.component.embedding.litellm_provider import LiteLLMEmbeddingProvider
+
+    fake.aembedding.return_value = _embedding_response([[0.1]])  # type: ignore[attr-defined]
+
+    provider = LiteLLMEmbeddingProvider(model="m", dim=1)
+    await provider.embed("x")
+
+    call_kwargs = fake.aembedding.call_args.kwargs  # type: ignore[attr-defined]
+    assert "api_key" not in call_kwargs
+
+
+async def test_litellm_error_wrapped(monkeypatch: pytest.MonkeyPatch) -> None:
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    class FakeAPIError(Exception):
+        pass
+
+    FakeAPIError.__module__ = "litellm.exceptions"
+    fake.aembedding.side_effect = FakeAPIError("rate limit")  # type: ignore[attr-defined]
+
+    from everos.component.embedding.litellm_provider import LiteLLMEmbeddingProvider
+
+    provider = LiteLLMEmbeddingProvider(model="m", dim=1)
+    with pytest.raises(EmbeddingError, match="rate limit"):
+        await provider.embed("x")
+
+
+async def test_import_error_raises_embedding_error() -> None:
+    from everos.component.embedding.litellm_provider import LiteLLMEmbeddingProvider
+
+    provider = LiteLLMEmbeddingProvider(model="m", dim=1)
+    with pytest.raises(EmbeddingError, match="litellm is not installed"):
+        await provider.embed("x")
+
+
+async def test_batching_chunks_correctly(monkeypatch: pytest.MonkeyPatch) -> None:
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    from everos.component.embedding.litellm_provider import LiteLLMEmbeddingProvider
+
+    call_count = 0
+
+    async def _fake_embed(**kwargs: Any) -> Any:
+        nonlocal call_count
+        call_count += 1
+        n = len(kwargs["input"])
+        return _embedding_response([[float(i)] for i in range(n)])
+
+    fake.aembedding = _fake_embed  # type: ignore[attr-defined]
+
+    provider = LiteLLMEmbeddingProvider(model="m", dim=1, batch_size=2)
+    result = await provider.embed_batch(["a", "b", "c", "d", "e"])
+
+    assert call_count == 3
+    assert len(result) == 5
diff --git a/tests/unit/test_component/test_llm/test_factory.py b/tests/unit/test_component/test_llm/test_factory.py
index 4329fceb..5720e570 100644
--- a/tests/unit/test_component/test_llm/test_factory.py
+++ b/tests/unit/test_component/test_llm/test_factory.py
@@ -1,4 +1,4 @@
-"""``build_llm_provider`` — settings validation + provider build."""
+"""``build_llm_provider`` -- settings validation + provider build."""
 
 from __future__ import annotations
 
@@ -26,3 +26,29 @@ def test_builds_openai_provider() -> None:
     s = LLMSettings(model="m", api_key=SecretStr("k"), base_url="https://x")
     p = build_llm_provider(s)
     assert isinstance(p, OpenAIProvider)
+
+
+def test_builds_litellm_provider() -> None:
+    from everos.component.llm.litellm_provider import LiteLLMProvider
+
+    s = LLMSettings(
+        provider="litellm", model="anthropic/claude-haiku", api_key=SecretStr("k")
+    )
+    p = build_llm_provider(s)
+    assert isinstance(p, LiteLLMProvider)
+
+
+def test_litellm_provider_without_base_url() -> None:
+    from everos.component.llm.litellm_provider import LiteLLMProvider
+
+    s = LLMSettings(provider="litellm", model="anthropic/claude-haiku", base_url=None)
+    p = build_llm_provider(s)
+    assert isinstance(p, LiteLLMProvider)
+
+
+def test_litellm_provider_without_api_key() -> None:
+    from everos.component.llm.litellm_provider import LiteLLMProvider
+
+    s = LLMSettings(provider="litellm", model="gpt-4o-mini", api_key=None)
+    p = build_llm_provider(s)
+    assert isinstance(p, LiteLLMProvider)
diff --git a/tests/unit/test_component/test_llm/test_litellm_provider.py b/tests/unit/test_component/test_llm/test_litellm_provider.py
new file mode 100644
index 00000000..6ec99bbe
--- /dev/null
+++ b/tests/unit/test_component/test_llm/test_litellm_provider.py
@@ -0,0 +1,252 @@
+"""LiteLLMProvider -- chat dispatch, error wrapping, drop_params default."""
+
+from __future__ import annotations
+
+import types
+from typing import Any
+from unittest import mock
+
+import pytest
+
+from everos.component.llm.protocol import ChatMessage, ChatResponse, LLMError
+
+
+def _make_fake_litellm() -> types.ModuleType:
+    """Build a stub ``litellm`` module with a mock ``acompletion``."""
+    mod = types.ModuleType("litellm")
+    mod.acompletion = mock.AsyncMock(name="litellm.acompletion")  # type: ignore[attr-defined]
+    return mod
+
+
+def _patch_litellm(
+    monkeypatch: pytest.MonkeyPatch,
+    fake: types.ModuleType,
+) -> None:
+    monkeypatch.setitem(__import__("sys").modules, "litellm", fake)
+
+
+def _openai_style_response(
+    content: str = "hello",
+    model: str = "anthropic/claude-haiku",
+    prompt_tokens: int = 5,
+    completion_tokens: int = 3,
+    finish_reason: str = "stop",
+) -> Any:
+    """Return an object shaped like ``litellm.ModelResponse``."""
+    msg = mock.MagicMock()
+    msg.content = content
+
+    choice = mock.MagicMock()
+    choice.message = msg
+    choice.finish_reason = finish_reason
+
+    usage = mock.MagicMock()
+    usage.prompt_tokens = prompt_tokens
+    usage.completion_tokens = completion_tokens
+
+    resp = mock.MagicMock()
+    resp.choices = [choice]
+    resp.usage = usage
+    resp.model = model
+    return resp
+
+
+async def test_chat_dispatches_to_litellm(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    from everos.component.llm.litellm_provider import LiteLLMProvider
+
+    fake.acompletion.return_value = _openai_style_response()  # type: ignore[attr-defined]
+
+    provider = LiteLLMProvider(model="anthropic/claude-haiku", api_key="sk-test")
+    msgs = [ChatMessage(role="user", content="hi")]
+    result = await provider.chat(msgs)
+
+    fake.acompletion.assert_awaited_once()  # type: ignore[attr-defined]
+    call_kwargs = fake.acompletion.call_args.kwargs  # type: ignore[attr-defined]
+    assert call_kwargs["model"] == "anthropic/claude-haiku"
+    assert call_kwargs["drop_params"] is True
+    assert call_kwargs["api_key"] == "sk-test"
+
+    assert isinstance(result, ChatResponse)
+    assert result.content == "hello"
+    assert result.finish_reason == "stop"
+    assert result.usage is not None
+    assert result.usage.prompt_tokens == 5
+
+
+async def test_api_key_omitted_when_none(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    from everos.component.llm.litellm_provider import LiteLLMProvider
+
+    fake.acompletion.return_value = _openai_style_response()  # type: ignore[attr-defined]
+
+    provider = LiteLLMProvider(model="gpt-4o-mini")
+    await provider.chat([ChatMessage(role="user", content="hi")])
+
+    call_kwargs = fake.acompletion.call_args.kwargs  # type: ignore[attr-defined]
+    assert "api_key" not in call_kwargs
+
+
+async def test_base_url_forwarded(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    from everos.component.llm.litellm_provider import LiteLLMProvider
+
+    fake.acompletion.return_value = _openai_style_response()  # type: ignore[attr-defined]
+
+    provider = LiteLLMProvider(model="m", api_key="k", base_url="http://proxy:4000")
+    await provider.chat([ChatMessage(role="user", content="x")])
+
+    call_kwargs = fake.acompletion.call_args.kwargs  # type: ignore[attr-defined]
+    assert call_kwargs["api_base"] == "http://proxy:4000"
+
+
+async def test_litellm_error_wrapped_as_llm_error(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    exc_mod = types.ModuleType("litellm.exceptions")
+
+    class FakeAPIError(Exception):
+        pass
+
+    FakeAPIError.__module__ = "litellm.exceptions"
+    FakeAPIError.__qualname__ = "APIError"
+    exc_mod.APIError = FakeAPIError  # type: ignore[attr-defined]
+    monkeypatch.setitem(__import__("sys").modules, "litellm.exceptions", exc_mod)
+
+    from everos.component.llm.litellm_provider import LiteLLMProvider
+
+    fake.acompletion.side_effect = FakeAPIError("boom")  # type: ignore[attr-defined]
+
+    provider = LiteLLMProvider(model="m")
+    with pytest.raises(LLMError, match="boom"):
+        await provider.chat([ChatMessage(role="user", content="x")])
+
+
+async def test_import_error_raises_llm_error() -> None:
+    from everos.component.llm.litellm_provider import LiteLLMProvider
+
+    provider = LiteLLMProvider(model="m")
+    with pytest.raises(LLMError, match="litellm is not installed"):
+        await provider.chat([ChatMessage(role="user", content="x")])
+
+
+async def test_empty_choices_raises_llm_error(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    from everos.component.llm.litellm_provider import LiteLLMProvider
+
+    resp = mock.MagicMock()
+    resp.choices = []
+    fake.acompletion.return_value = resp  # type: ignore[attr-defined]
+
+    provider = LiteLLMProvider(model="m")
+    with pytest.raises(LLMError, match="no choices"):
+        await provider.chat([ChatMessage(role="user", content="x")])
+
+
+async def test_null_usage_handled(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    from everos.component.llm.litellm_provider import LiteLLMProvider
+
+    resp = _openai_style_response()
+    resp.usage = None
+    fake.acompletion.return_value = resp  # type: ignore[attr-defined]
+
+    provider = LiteLLMProvider(model="m")
+    result = await provider.chat([ChatMessage(role="user", content="x")])
+    assert result.usage is None
+    assert result.content == "hello"
+
+
+async def test_null_content_coerced_to_empty(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    from everos.component.llm.litellm_provider import LiteLLMProvider
+
+    resp = _openai_style_response(content=None)  # type: ignore[arg-type]
+    fake.acompletion.return_value = resp  # type: ignore[attr-defined]
+
+    provider = LiteLLMProvider(model="m")
+    result = await provider.chat([ChatMessage(role="user", content="x")])
+    assert result.content == ""
+
+
+async def test_per_call_model_override(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    from everos.component.llm.litellm_provider import LiteLLMProvider
+
+    fake.acompletion.return_value = _openai_style_response()  # type: ignore[attr-defined]
+
+    provider = LiteLLMProvider(model="default-model")
+    await provider.chat([ChatMessage(role="user", content="x")], model="override-model")
+
+    call_kwargs = fake.acompletion.call_args.kwargs  # type: ignore[attr-defined]
+    assert call_kwargs["model"] == "override-model"
+
+
+async def test_openai_exception_wrapped(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    from everos.component.llm.litellm_provider import LiteLLMProvider
+
+    class FakeOpenAIError(Exception):
+        pass
+
+    FakeOpenAIError.__module__ = "openai._exceptions"
+    fake.acompletion.side_effect = FakeOpenAIError("auth failed")  # type: ignore[attr-defined]
+
+    provider = LiteLLMProvider(model="m")
+    with pytest.raises(LLMError, match="auth failed"):
+        await provider.chat([ChatMessage(role="user", content="x")])
+
+
+async def test_litellm_timeout_wrapped(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """litellm.Timeout lives at litellm.* not litellm.exceptions.*"""
+    fake = _make_fake_litellm()
+    _patch_litellm(monkeypatch, fake)
+
+    from everos.component.llm.litellm_provider import LiteLLMProvider
+
+    class FakeTimeoutError(Exception):
+        pass
+
+    FakeTimeoutError.__module__ = "litellm"
+    fake.acompletion.side_effect = FakeTimeoutError("timed out")  # type: ignore[attr-defined]
+
+    provider = LiteLLMProvider(model="m")
+    with pytest.raises(LLMError, match="timed out"):
+        await provider.chat([ChatMessage(role="user", content="x")])
diff --git a/uv.lock b/uv.lock
index a662c5a2..32ea7018 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,6 +2,128 @@ version = 1
 revision = 3
 requires-python = ">=3.12"
 
+[[package]]
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+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5f/fc/a7bf5b6e4e617b45f90f2d9d2a68519c249c81dd4fc2658c7a2a61c4f4b7/aiohappyeyeballs-2.6.2-py3-none-any.whl", hash = "sha256:4708045e2d7a6c6bdf8aafa8ed39649eaf926a4543b54560659129e3365953c4", size = 15062, upload-time = "2026-05-20T15:12:23.328Z" },
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.14.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohappyeyeballs" },
+    { name = "aiosignal" },
+    { name = "attrs" },
+    { name = "frozenlist" },
+    { name = "multidict" },
+    { name = "propcache" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+    { name = "yarl" },
+]
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 [package.optional-dependencies]
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