feat(llms): add deepinfra as an openai-compatible provider

docs/edge/ar/concepts/llms.mdx

@@ -1012,6 +1012,39 @@ mode: "wide"
     uv add 'crewai[litellm]'
     ```
   </Accordion>
+
+  <Accordion title="DeepInfra">
+    يتصل CrewAI بـ DeepInfra عبر عميل OpenAI الأصلي، لذا لا حاجة لتثبيت LiteLLM.
+
+    عيّن متغيرات البيئة التالية في ملف `.env`:
+    ```toml Code
+    # Required
+    DEEPINFRA_API_KEY=<your-api-key>
+
+    # Optional, point at a proxy or a private deployment endpoint
+    DEEPINFRA_BASE_URL=https://api.deepinfra.com/v1/openai
+    ```
+
+    مثال الاستخدام في مشروع CrewAI:
+    ```python Code
+    llm = LLM(
+        model="deepinfra/deepseek-ai/DeepSeek-V4-Flash-0731",
+        temperature=0.7
+    )
+    ```
+
+    <Info>
+      ميزات DeepInfra:
+      - نماذج مفتوحة الأوزان (Llama وQwen وDeepSeek وGLM وMistral وغيرها) خلف API واحد متوافق مع OpenAI
+      - استدعاء الأدوات والبث والمخرجات المنظمة بمخطط JSON
+      - تسعير لكل token وطول السياق منشوران في كتالوج النماذج
+    </Info>
+
+    معرّفات النماذج بصيغة `org/model` أصلًا، لذا يتكوّن المرجع الكامل من ثلاثة أجزاء،
+    مثل `deepinfra/meta-llama/Llama-4-Scout-17B-16E-Instruct`. راجع
+    [كتالوج النماذج المنسّق لـ CrewAI](https://api.deepinfra.com/v1/openai/models?filter=with_meta&sort_by=crewai)،
+    وهو عام ولا يتطلب مصادقة، للحصول على المعرّفات الحالية وأطوال السياق والأسعار.
+  </Accordion>
 </AccordionGroup>
 
 ## بث الاستجابات

docs/edge/en/concepts/llms.mdx

@@ -1155,6 +1155,39 @@ In this section, you'll find detailed examples that help you select, configure,
     uv add 'crewai[litellm]'
     ```
   </Accordion>
+
+  <Accordion title="DeepInfra">
+    CrewAI talks to DeepInfra through its native OpenAI client, so no LiteLLM install is needed.
+
+    Set the following environment variables in your `.env` file:
+    ```toml Code
+    # Required
+    DEEPINFRA_API_KEY=<your-api-key>
+
+    # Optional, point at a proxy or a private deployment endpoint
+    DEEPINFRA_BASE_URL=https://api.deepinfra.com/v1/openai
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    llm = LLM(
+        model="deepinfra/deepseek-ai/DeepSeek-V4-Flash-0731",
+        temperature=0.7
+    )
+    ```
+
+    <Info>
+      DeepInfra features:
+      - Open-weight models (Llama, Qwen, DeepSeek, GLM, Mistral and more) behind one OpenAI-compatible API
+      - Tool calling, streaming and JSON schema structured outputs
+      - Per-token pricing and context length published in the model catalog
+    </Info>
+
+    Model ids are already in `org/model` form, so a full reference carries three segments,
+    for example `deepinfra/meta-llama/Llama-4-Scout-17B-16E-Instruct`. See the
+    [model catalog curated for CrewAI](https://api.deepinfra.com/v1/openai/models?filter=with_meta&sort_by=crewai),
+    which is public and needs no authentication, for current ids, context lengths and pricing.
+  </Accordion>
 </AccordionGroup>
 
 ## Streaming Responses

docs/edge/ko/concepts/llms.mdx

@@ -755,6 +755,39 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
     uv add 'crewai[litellm]'
     ```
   </Accordion>
+
+  <Accordion title="DeepInfra">
+    CrewAI는 네이티브 OpenAI 클라이언트로 DeepInfra에 연결하므로 LiteLLM 설치가 필요하지 않습니다.
+
+    `.env` 파일에 다음 환경 변수를 설정하십시오:
+    ```toml Code
+    # Required
+    DEEPINFRA_API_KEY=<your-api-key>
+
+    # Optional, point at a proxy or a private deployment endpoint
+    DEEPINFRA_BASE_URL=https://api.deepinfra.com/v1/openai
+    ```
+
+    CrewAI 프로젝트에서의 예시 사용법:
+    ```python Code
+    llm = LLM(
+        model="deepinfra/deepseek-ai/DeepSeek-V4-Flash-0731",
+        temperature=0.7
+    )
+    ```
+
+    <Info>
+      DeepInfra 특징:
+      - 하나의 OpenAI 호환 API로 제공되는 오픈 웨이트 모델 (Llama, Qwen, DeepSeek, GLM, Mistral 등)
+      - 도구 호출, 스트리밍, JSON 스키마 구조화 출력 지원
+      - 모델 카탈로그에 공개된 토큰 단위 가격과 컨텍스트 길이
+    </Info>
+
+    모델 ID는 이미 `org/model` 형식이므로 전체 참조는 세 개의 세그먼트로 구성됩니다.
+    예: `deepinfra/meta-llama/Llama-4-Scout-17B-16E-Instruct`. 현재 ID, 컨텍스트 길이, 가격은
+    인증 없이 공개된 [CrewAI용으로 선별된 모델 카탈로그](https://api.deepinfra.com/v1/openai/models?filter=with_meta&sort_by=crewai)를
+    참조하세요.
+  </Accordion>
 </AccordionGroup>
 
 ## 스트리밍 응답

docs/edge/pt-BR/concepts/llms.mdx

@@ -728,6 +728,39 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
     uv add 'crewai[litellm]'
     ```
   </Accordion>
+
+  <Accordion title="DeepInfra">
+    O CrewAI se comunica com o DeepInfra pelo cliente OpenAI nativo, então não é necessário instalar o LiteLLM.
+
+    Defina as seguintes variáveis de ambiente no seu arquivo `.env`:
+    ```toml Code
+    # Required
+    DEEPINFRA_API_KEY=<your-api-key>
+
+    # Optional, point at a proxy or a private deployment endpoint
+    DEEPINFRA_BASE_URL=https://api.deepinfra.com/v1/openai
+    ```
+
+    Exemplo de uso em seu projeto CrewAI:
+    ```python Code
+    llm = LLM(
+        model="deepinfra/deepseek-ai/DeepSeek-V4-Flash-0731",
+        temperature=0.7
+    )
+    ```
+
+    <Info>
+      Recursos do DeepInfra:
+      - Modelos de pesos abertos (Llama, Qwen, DeepSeek, GLM, Mistral e outros) por trás de uma única API compatível com OpenAI
+      - Chamada de ferramentas, streaming e saídas estruturadas com JSON schema
+      - Preço por token e tamanho de contexto publicados no catálogo de modelos
+    </Info>
+
+    Os IDs dos modelos já estão no formato `org/model`, então uma referência completa tem três segmentos,
+    por exemplo `deepinfra/meta-llama/Llama-4-Scout-17B-16E-Instruct`. Consulte o
+    [catálogo de modelos selecionado para o CrewAI](https://api.deepinfra.com/v1/openai/models?filter=with_meta&sort_by=crewai),
+    que é público e não exige autenticação, para obter os IDs atuais, tamanhos de contexto e preços.
+  </Accordion>
 </AccordionGroup>
 
 ## Respostas em streaming

lib/crewai/src/crewai/llm.py

@@ -345,6 +345,7 @@ def _ensure_litellm() -> bool:
     "cerebras",
     "dashscope",
     "snowflake",
+    "deepinfra",
 ]
 
 
@@ -431,6 +432,13 @@ def __new__(cls, model: str, is_litellm: bool = False, **kwargs: Any) -> LLM:
             provider = explicit_provider
             use_native = True
             model_string = model
+            if provider == "deepinfra" and not cls._matches_provider_pattern(
+                model, provider
+            ):
+                raise ValueError(
+                    "DeepInfra model ids are org/model, for example "
+                    f"'deepseek-ai/DeepSeek-V4-Flash-0731'; got '{model}'"
+                )
         elif "/" in model:
             prefix, _, model_part = model.partition("/")
 
@@ -452,6 +460,7 @@ def __new__(cls, model: str, is_litellm: bool = False, **kwargs: Any) -> LLM:
                 "cerebras": "cerebras",
                 "dashscope": "dashscope",
                 "snowflake": "snowflake",
+                "deepinfra": "deepinfra",
             }
 
             canonical_provider = provider_mapping.get(prefix.lower())
@@ -582,6 +591,12 @@ def _matches_provider_pattern(cls, model: str, provider: str) -> bool:
             # OpenRouter uses org/model format but accepts anything
             return True
 
+        if provider == "deepinfra":
+            # DeepInfra ids are exactly org/model, so a full reference is
+            # deepinfra/<org>/<model>: one slash with both parts non-empty.
+            parts = model_lower.split("/")
+            return len(parts) == 2 and all(parts)
+
         if provider == "snowflake":
             return True
 
@@ -723,6 +738,7 @@ def _get_native_provider(cls, provider: str) -> type | None:
             "hosted_vllm",
             "cerebras",
             "dashscope",
+            "deepinfra",
         }
         if provider in openai_compatible_providers:
             from crewai.llms.providers.openai_compatible.completion import (

lib/crewai/src/crewai/llms/providers/openai_compatible/completion.py

@@ -90,6 +90,12 @@ class ProviderConfig:
         base_url_env="DASHSCOPE_BASE_URL",
         api_key_required=True,
     ),
+    "deepinfra": ProviderConfig(
+        base_url="https://api.deepinfra.com/v1/openai",
+        api_key_env="DEEPINFRA_API_KEY",
+        base_url_env="DEEPINFRA_BASE_URL",
+        api_key_required=True,
+    ),
 }
 
 _OLLAMA_DEFAULT_PORT = 11434
@@ -142,6 +148,7 @@ class OpenAICompatibleCompletion(OpenAICompletion):
         - hosted_vllm: vLLM server (https://github.com/vllm-project/vllm)
         - cerebras: Cerebras (https://cerebras.ai)
         - dashscope: Alibaba Dashscope/Qwen (https://dashscope.aliyun.com)
+        - deepinfra: DeepInfra (https://deepinfra.com)
 
     Example:
         # Using provider prefix

lib/crewai/tests/llms/openai_compatible/test_openai_compatible.py

@@ -95,6 +95,14 @@ def test_dashscope_config(self):
         assert config.api_key_env == "DASHSCOPE_API_KEY"
         assert config.api_key_required is True
 
+    def test_deepinfra_config(self):
+        """Test DeepInfra provider configuration."""
+        config = OPENAI_COMPATIBLE_PROVIDERS["deepinfra"]
+        assert config.base_url == "https://api.deepinfra.com/v1/openai"
+        assert config.api_key_env == "DEEPINFRA_API_KEY"
+        assert config.base_url_env == "DEEPINFRA_BASE_URL"
+        assert config.api_key_required is True
+
 
 class TestNormalizeOllamaBaseUrl:
     """Tests for _normalize_ollama_base_url helper."""
@@ -162,6 +170,15 @@ def test_missing_required_api_key_raises_error(self):
             if original is not None:
                 os.environ[env_key] = original
 
+    def test_deepinfra_missing_api_key_names_env_var(self):
+        """DeepInfra requires a key; the error tells the user which env var to set."""
+        with patch.dict(os.environ, {}, clear=True), pytest.raises(
+            ValueError, match="DEEPINFRA_API_KEY"
+        ):
+            OpenAICompatibleCompletion(
+                model="deepseek-ai/DeepSeek-V4-Flash-0731", provider="deepinfra"
+            )
+
     def test_api_key_from_env(self):
         """Test API key is read from environment variable."""
         with patch.dict(os.environ, {"DEEPSEEK_API_KEY": "test-key-from-env"}):
@@ -312,6 +329,71 @@ def test_llm_creates_openai_compatible_for_dashscope_non_qwen(self):
             assert llm.provider == "dashscope"
             assert llm.base_url == "https://my-dashscope.example.com/v1"
 
+    def test_llm_creates_openai_compatible_for_deepinfra(self):
+        """Test LLM factory creates OpenAICompatibleCompletion for DeepInfra."""
+        with patch.dict(os.environ, {"DEEPINFRA_API_KEY": "test-key"}):
+            llm = LLM(model="deepinfra/deepseek-ai/DeepSeek-V4-Flash-0731")
+            assert isinstance(llm, OpenAICompatibleCompletion)
+            assert llm.provider == "deepinfra"
+            # DeepInfra ids are org/model, so the part after the provider
+            # prefix is itself two segments and must reach the API intact.
+            assert llm.model == "deepseek-ai/DeepSeek-V4-Flash-0731"
+            assert llm.base_url == "https://api.deepinfra.com/v1/openai"
+
+    def test_deepinfra_base_url_env_override(self):
+        """DEEPINFRA_BASE_URL redirects DeepInfra to a proxy or private endpoint."""
+        with patch.dict(
+            os.environ,
+            {
+                "DEEPINFRA_API_KEY": "test-key",
+                "DEEPINFRA_BASE_URL": "https://proxy.example.com/v1/openai",
+            },
+        ):
+            llm = LLM(model="deepinfra/meta-llama/Llama-4-Scout-17B-16E-Instruct")
+            assert isinstance(llm, OpenAICompatibleCompletion)
+            assert llm.base_url == "https://proxy.example.com/v1/openai"
+
+    def test_llm_creates_openai_compatible_for_deepinfra_explicit_provider(self):
+        """Explicit provider="deepinfra" routes natively without the model prefix."""
+        with patch.dict(os.environ, {"DEEPINFRA_API_KEY": "test-key"}):
+            llm = LLM(model="deepseek-ai/DeepSeek-V4-Flash-0731", provider="deepinfra")
+            assert isinstance(llm, OpenAICompatibleCompletion)
+            assert llm.provider == "deepinfra"
+            assert llm.model == "deepseek-ai/DeepSeek-V4-Flash-0731"
+
+    @pytest.mark.parametrize(
+        "model",
+        [
+            "deepinfra/deepseek-ai/DeepSeek-V4-Flash-0731",
+            "DeepSeek-V4-Flash-0731",
+            "deepseek-ai/DeepSeek-V4-Flash-0731/",
+        ],
+    )
+    def test_llm_explicit_deepinfra_provider_rejects_malformed_model(self, model):
+        """provider="deepinfra" applies the org/model check before building a client."""
+        with patch.dict(os.environ, {"DEEPINFRA_API_KEY": "test-key"}), pytest.raises(
+            ValueError, match="org/model"
+        ):
+            LLM(model=model, provider="deepinfra")
+
+    @pytest.mark.parametrize(
+        ("model", "expected"),
+        [
+            ("deepseek-ai/DeepSeek-V4-Flash-0731", True),
+            ("Qwen/Qwen3.5-27B", True),
+            ("meta-llama/Llama-4-Scout-17B-16E-Instruct", True),
+            ("", False),
+            ("qwen", False),
+            ("Qwen/", False),
+            ("/Qwen3.5-27B", False),
+            ("deepseek-ai/DeepSeek-V4-Flash-0731/extra", False),
+            ("deepseek-ai/DeepSeek-V4-Flash-0731/", False),
+        ],
+    )
+    def test_deepinfra_requires_an_org_and_a_model(self, model, expected):
+        """DeepInfra references are exactly org/model: one slash, both parts present."""
+        assert LLM._matches_provider_pattern(model, "deepinfra") is expected
+
     def test_llm_with_explicit_provider(self):
         """Test LLM with explicit provider parameter."""
         with patch.dict(os.environ, {"DEEPSEEK_API_KEY": "test-key"}):