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VERSION

@@ -1 +1 @@
-d41f44e05d8d4b3e36c81f2091e9f279a0ec4af5
+10b5c8926a4be3ed9767f412aa1b3a4f105ab56b

gemini/gemini_litellm.py

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+import os
+from smolagents import LiteLLMModel, CodeAgent
+
+gemini_api_key = os.getenv("GEMINI_API_KEY")
+
+# LiteLLM uses the 'gemini/' prefix for Gemini models
+model = LiteLLMModel(
+    model_id="gemini-2.0-flash", # Use a valid Gemini model ID
+    api_base="https://generativelanguage.googleapis.com/v1beta/openai/",
+    api_key=gemini_api_key,
+)
+
+agent = CodeAgent(tools=[], model=model)
+agent.run("Provide a brief history of the Eiffel Tower.")

gemini/gemini_openai.py

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+import os
+from smolagents import CodeAgent, OpenAIModel
+
+# Get API key from Windows Environment Variable
+gemini_api_key = os.getenv("GEMINI_API_KEY")
+
+# Initialize Gemini Model via smolagents
+model = OpenAIModel(
+    model_id="gemini-2.0-flash", # Use a valid Gemini model ID
+    api_base="https://generativelanguage.googleapis.com/v1beta/openai/",
+    api_key=gemini_api_key,
+)
+
+# Initialize Agent
+agent = CodeAgent(tools=[], model=model)
+
+# Run Agent
+agent.run("Explain how AI agents work.")

ollama/hello_ollama.py

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+from smolagents import LiteLLMModel, ToolCallingAgent
+
+
+def main() -> None:
+    model = LiteLLMModel(
+        model_id="ollama_chat/qwen2:7b",
+        api_base="http://127.0.0.1:11434",
+        temperature=0.3,
+    )
+
+    # ToolCallingAgent is more reliable than CodeAgent for general chat-like prompts
+    # with local Ollama models, because it avoids strict code-block parsing.
+    agent = ToolCallingAgent(
+        tools=[],
+        model=model,
+        max_steps=3,
+    )
+
+    prompt = "Explain how AI agents work in 3 bullet points."
+
+    try:
+        answer = agent.run(prompt)
+        print(answer)
+    except Exception:
+        # Some Ollama models intermittently skip tool-call JSON.
+        # Fallback to direct model generation while still using smolagents.
+        response = model.generate(
+            messages=[
+                {"role": "system", "content": "You are a concise and helpful assistant."},
+                {"role": "user", "content": prompt},
+            ]
+        )
+        print(response.content)
+
+
+if __name__ == "__main__":
+    main()

smolagents/codeagent_litellm_template.py

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+import argparse
+import os
+from dataclasses import dataclass
+
+from smolagents import CodeAgent, LiteLLMModel
+
+
+@dataclass(frozen=True)
+class ProviderPreset:
+    name: str
+    api_key_env: str
+    api_base_env: str
+    model_env: str
+    default_api_base: str
+    default_model: str
+
+
+PROVIDERS: dict[str, ProviderPreset] = {
+    # GitHub Models (OpenAI-compatible endpoint)
+    "github": ProviderPreset(
+        name="github",
+        api_key_env="GITHUB_TOKEN",
+        api_base_env="GITHUB_API_BASE",
+        model_env="GITHUB_MODEL_ID",
+        default_api_base="https://models.inference.ai.azure.com",
+        default_model="openai/gpt-4.1-mini",
+    ),
+    # Gemini via Google OpenAI-compatible endpoint
+    "gemini": ProviderPreset(
+        name="gemini",
+        api_key_env="GEMINI_API_KEY",
+        api_base_env="GEMINI_API_BASE",
+        model_env="GEMINI_MODEL_ID",
+        default_api_base="https://generativelanguage.googleapis.com/v1beta/openai/",
+        default_model="gemini-2.0-flash",
+    ),
+    # Z.ai / GLM (BigModel) - set ZAI_API_BASE to your account endpoint if needed
+    "zai": ProviderPreset(
+        name="zai",
+        api_key_env="ZAI_API_KEY",
+        api_base_env="ZAI_API_BASE",
+        model_env="ZAI_MODEL_ID",
+        default_api_base="https://open.bigmodel.cn/api/paas/v4/",
+        default_model="zhipuai/glm-4.5",
+    ),
+    # Hugging Face Inference Router (OpenAI-compatible endpoint)
+    "huggingface": ProviderPreset(
+        name="huggingface",
+        api_key_env="HF_TOKEN",
+        api_base_env="HF_API_BASE",
+        model_env="HF_MODEL_ID",
+        default_api_base="https://router.huggingface.co/v1",
+        default_model="moonshotai/Kimi-K2.5",
+    ),
+    # Fireworks (OpenAI-compatible endpoint)
+    "fireworks": ProviderPreset(
+        name="fireworks",
+        api_key_env="FIREWORKS_API_KEY",
+        api_base_env="FIREWORKS_API_BASE",
+        model_env="FIREWORKS_MODEL_ID",
+        default_api_base="https://api.fireworks.ai/inference/v1",
+        default_model="accounts/fireworks/models/llama-v3p1-8b-instruct",
+    ),
+    # Minimax (OpenAI-compatible endpoint)
+    "minimax": ProviderPreset(
+        name="minimax",
+        api_key_env="MINIMAX_API_KEY",
+        api_base_env="MINIMAX_API_BASE",
+        model_env="MINIMAX_MODEL_ID",
+        default_api_base="https://api.minimax.chat/v1",
+        default_model="MiniMax-Text-01",
+    ),
+}
+
+
+def build_model(provider: str, model_override: str | None, temperature: float) -> LiteLLMModel:
+    preset = PROVIDERS[provider]
+
+    api_key = os.getenv(preset.api_key_env)
+    api_base = os.getenv(preset.api_base_env, preset.default_api_base)
+    model_id = model_override or os.getenv(preset.model_env, preset.default_model)
+
+    if not api_key:
+        raise ValueError(
+            f"Missing API key. Set environment variable {preset.api_key_env} for provider '{provider}'."
+        )
+
+    return LiteLLMModel(
+        model_id=model_id,
+        api_base=api_base,
+        api_key=api_key,
+        temperature=temperature,
+    )
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="smolagents CodeAgent + LiteLLMModel template for multi-provider usage"
+    )
+    parser.add_argument(
+        "--provider",
+        choices=sorted(PROVIDERS.keys()),
+        default="gemini",
+        help="Which provider preset to use",
+    )
+    parser.add_argument(
+        "--model",
+        default=None,
+        help="Optional model override (otherwise uses provider env/default)",
+    )
+    parser.add_argument(
+        "--prompt",
+        default="Explain how AI agents work in 3 concise bullet points.",
+        help="Prompt to run",
+    )
+    parser.add_argument(
+        "--temperature",
+        type=float,
+        default=0.2,
+        help="Sampling temperature",
+    )
+    parser.add_argument(
+        "--max-steps",
+        type=int,
+        default=5,
+        help="Maximum CodeAgent steps",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Print resolved config without calling the model",
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    preset = PROVIDERS[args.provider]
+
+    resolved_api_base = os.getenv(preset.api_base_env, preset.default_api_base)
+    resolved_model = args.model or os.getenv(preset.model_env, preset.default_model)
+
+    if args.dry_run:
+        print("Provider:", args.provider)
+        print("Model:", resolved_model)
+        print("API base:", resolved_api_base)
+        print("Required API key env:", preset.api_key_env)
+        return
+
+    model = build_model(args.provider, args.model, args.temperature)
+
+    # CodeAgent works best when models follow strict structured output behavior.
+    # 'markdown' tags and structured outputs improve cross-provider reliability.
+    agent = CodeAgent(
+        tools=[],
+        model=model,
+        max_steps=args.max_steps,
+        code_block_tags="markdown",
+        use_structured_outputs_internally=True,
+    )
+
+    answer = agent.run(args.prompt)
+    print(answer)
+
+
+if __name__ == "__main__":
+    main()