feat: add MiniMax as LLM provider for Planner & Coder agents

README.md

@@ -224,9 +224,10 @@ Here is the [official installation guide](https://docs.manim.community/en/stable
 
 Fill in your **API credentials** in `api_config.json`.
 
-* **LLM API**: 
+* **LLM API**:
   * Required for Planner & Coder.
   * Best Manim code quality achieved with **Claude-4-Opus**.
+  * Also supports [MiniMax](https://www.minimaxi.com/) via OpenAI-compatible API (`MiniMax-M2.7` with 1M context window).
 * **VLM API**:
   * Required for Planner Critic.
   * For layout and aesthetics optimization, provide **Gemini API key**.

src/agent.py

@@ -815,6 +815,7 @@ def get_api_and_output(API_name):
         "gpt-4o": (request_gpt4o_token, "Chatgpt4o"),
         "gpt-o4mini": (request_o4mini_token, "Chatgpto4mini"),
         "Gemini": (request_gemini_token, "Gemini"),
+        "minimax": (request_minimax_token, "MiniMax"),
     }
     try:
         return mapping[API_name]
@@ -828,7 +829,7 @@ def build_and_parse_args():
     parser.add_argument(
         "--API",
         type=str,
-        choices=["gpt-41", "claude", "gpt-5", "gpt-4o", "gpt-o4mini", "Gemini"],
+        choices=["gpt-41", "claude", "gpt-5", "gpt-4o", "gpt-o4mini", "Gemini", "minimax"],
         default="gpt-41",
     )
     parser.add_argument(

src/api_config.json

@@ -33,6 +33,11 @@
         "base_url": "...",
         "api_key": "..."
     },
+    "minimax": {
+        "base_url": "https://api.minimax.io/v1",
+        "api_key": "YOUR_MINIMAX_API_KEY",
+        "model": "MiniMax-M2.7"
+    },
     "iconfinder": {
         "api_key": "YOUR_ICONFINDER_KEY"
     }

src/gpt_request.py

@@ -1035,6 +1035,101 @@ def request_gpt41_img(prompt, image_path=None, log_id=None, max_tokens=1000, max
             time.sleep(delay)
 
 
+def request_minimax(prompt, log_id=None, max_tokens=8000, max_retries=3):
+    """
+    Makes a request to the MiniMax model via OpenAI-compatible API with retry functionality.
+
+    Args:
+        prompt (str): The text prompt to send to the model
+        log_id (str, optional): The log ID for tracking requests, defaults to tkb+timestamp
+        max_tokens (int, optional): Maximum tokens for response, default 8000
+        max_retries (int, optional): Maximum number of retry attempts, default 3
+
+    Returns:
+        str: The model's response content
+    """
+    base_url = cfg("minimax", "base_url")
+    api_key = cfg("minimax", "api_key")
+    model_name = cfg("minimax", "model")
+
+    client = OpenAI(base_url=base_url, api_key=api_key)
+
+    if log_id is None:
+        log_id = generate_log_id()
+
+    retry_count = 0
+    while retry_count < max_retries:
+        try:
+            completion = client.chat.completions.create(
+                model=model_name,
+                messages=[{"role": "user", "content": prompt}],
+                max_tokens=max_tokens,
+            )
+            return completion.choices[0].message.content.strip()
+        except Exception as e:
+            retry_count += 1
+            if retry_count >= max_retries:
+                raise Exception(f"Failed after {max_retries} attempts. Last error: {str(e)}")
+
+            delay = (2**retry_count) * 0.1 + (random.random() * 0.1)
+            print(
+                f"Request failed with error: {str(e)}. Retrying in {delay:.2f} seconds... (Attempt {retry_count}/{max_retries})"
+            )
+            time.sleep(delay)
+
+
+def request_minimax_token(prompt, log_id=None, max_tokens=8000, max_retries=3):
+    """
+    Makes a request to the MiniMax model via OpenAI-compatible API with retry and token tracking.
+
+    Args:
+        prompt (str): The text prompt to send to the model
+        log_id (str, optional): The log ID for tracking requests, defaults to tkb+timestamp
+        max_tokens (int, optional): Maximum tokens for response, default 8000
+        max_retries (int, optional): Maximum number of retry attempts, default 3
+
+    Returns:
+        tuple: (completion, usage_info) where usage_info is a dict with token counts
+    """
+    base_url = cfg("minimax", "base_url")
+    api_key = cfg("minimax", "api_key")
+    model_name = cfg("minimax", "model")
+
+    client = OpenAI(base_url=base_url, api_key=api_key)
+
+    if log_id is None:
+        log_id = generate_log_id()
+
+    usage_info = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
+
+    retry_count = 0
+    while retry_count < max_retries:
+        try:
+            completion = client.chat.completions.create(
+                model=model_name,
+                messages=[{"role": "user", "content": prompt}],
+                max_tokens=max_tokens,
+            )
+
+            if completion.usage:
+                usage_info["prompt_tokens"] = completion.usage.prompt_tokens
+                usage_info["completion_tokens"] = completion.usage.completion_tokens
+                usage_info["total_tokens"] = completion.usage.total_tokens
+            return completion, usage_info
+
+        except Exception as e:
+            retry_count += 1
+            if retry_count >= max_retries:
+                raise Exception(f"Failed after {max_retries} attempts. Last error: {str(e)}")
+
+            delay = (2**retry_count) * 0.1 + (random.random() * 0.1)
+            print(
+                f"Request failed with error: {str(e)}. Retrying in {delay:.2f} seconds... (Attempt {retry_count}/{max_retries})"
+            )
+            time.sleep(delay)
+    return None, usage_info
+
+
 if __name__ == "__main__":
 
     # Gemini