diff --git a/python/src/aiconfig/Config.py b/python/src/aiconfig/Config.py
index a78f98ea1..40ce861bb 100644
--- a/python/src/aiconfig/Config.py
+++ b/python/src/aiconfig/Config.py
@@ -53,7 +53,9 @@
 ModelParserRegistry.register_model_parser(
     DefaultAnyscaleEndpointParser("AnyscaleEndpoint")
 )
-ModelParserRegistry.register_model_parser(GeminiModelParser("gemini-pro"), ["gemini-pro"])
+ModelParserRegistry.register_model_parser(
+    GeminiModelParser("gemini-pro"), ["gemini-pro"]
+)
 ModelParserRegistry.register_model_parser(ClaudeBedrockModelParser())
 ModelParserRegistry.register_model_parser(HuggingFaceTextGenerationParser())
 for model in gpt_models_extra:
@@ -61,6 +63,7 @@
 ModelParserRegistry.register_model_parser(PaLMChatParser())
 ModelParserRegistry.register_model_parser(PaLMTextParser())
 
+
 class AIConfigRuntime(AIConfig):
     # A mapping of model names to their respective parsers
 
@@ -116,14 +119,37 @@ def load(cls, config_filepath: str) -> "AIConfigRuntime":
             else:
                 data = file.read()
 
-        # load the file as bytes and let pydantic handle the parsing
-        # validated_data =  AIConfig.model_validate_json(file.read())
-        aiconfigruntime = cls.model_validate_json(data)
-        update_model_parser_registry_with_config_runtime(aiconfigruntime)
-
+        config_runtime = cls.load_json(data)
         # set the file path. This is used when saving the config
-        aiconfigruntime.file_path = config_filepath
-        return aiconfigruntime
+        config_runtime.file_path = config_filepath
+        return config_runtime
+
+    @classmethod
+    def load_json(cls, config_json: str) -> "AIConfigRuntime":
+        """
+        Constructs AIConfigRuntime from provided JSON and returns it.
+
+        Args:
+            config_json (str): The JSON representing the AIConfig.
+        """
+
+        config_runtime = cls.model_validate_json(config_json)
+        update_model_parser_registry_with_config_runtime(config_runtime)
+
+        return config_runtime
+
+    @classmethod
+    def load_yaml(cls, config_yaml: str) -> "AIConfigRuntime":
+        """
+        Constructs AIConfigRuntime from provided YAML and returns it.
+
+        Args:
+            config_yaml (str): The YAML representing the AIConfig.
+        """
+
+        yaml_data = yaml.safe_load(config_yaml)
+        config_json = json.dumps(yaml_data)
+        return cls.load_json(config_json)
 
     @classmethod
     def load_from_workbook(cls, workbook_id: str) -> "AIConfigRuntime":
diff --git a/typescript/lib/config.ts b/typescript/lib/config.ts
index 2ecde4f9e..7289b8218 100644
--- a/typescript/lib/config.ts
+++ b/typescript/lib/config.ts
@@ -114,11 +114,25 @@ export class AIConfigRuntime implements AIConfig {
     return config;
   }
 
+  /**
+   * Loads an AIConfig from a YAML string.
+   * @param aiConfigYAML YAML string to load the AIConfig from.
+   */
+  public static loadYAML(aiConfigYAML: string) {
+    const aiConfigObj = yaml.load(aiConfigYAML);
+    return this.loadJSON(aiConfigObj);
+  }
+
   /**
    * Loads an AIConfig from a JSON object.
    * @param aiConfigObj JSON object to load the AIConfig from.
    */
   public static loadJSON(aiConfigObj: any) {
+    if (typeof aiConfigObj === "string") {
+      // Parse the string as JSON
+      aiConfigObj = JSON.parse(aiConfigObj);
+    }
+
     // TODO: saqadri - validate that the type satisfies AIConfig interface
     const aiConfig = new AIConfigRuntime(
       aiConfigObj.name,