diff --git a/pipelines/examples/anthropic_manifold_pipeline.py b/pipelines/examples/anthropic_manifold_pipeline.py
new file mode 100644
index 0000000..a20929a
--- /dev/null
+++ b/pipelines/examples/anthropic_manifold_pipeline.py
@@ -0,0 +1,100 @@
+"""
+title: Anthropic Pipeline
+author: justinh-rahb
+date: 2024-05-27
+version: 1.0
+license: MIT
+description: A pipeline for generating text using the Anthropic API.
+dependencies: requests, anthropic
+environment_variables: ANTHROPIC_API_KEY
+"""
+
+import os
+from anthropic import Anthropic, RateLimitError, APIStatusError, APIConnectionError
+
+from schemas import OpenAIChatMessage
+from typing import List, Union, Generator, Iterator
+from pydantic import BaseModel
+import requests
+
+
+class Pipeline:
+    def __init__(self):
+        self.type = "manifold"
+        self.id = "anthropic"
+        self.name = "Anthropic/"
+
+        class Valves(BaseModel):
+            ANTHROPIC_API_KEY: str
+
+        self.valves = Valves(**{"ANTHROPIC_API_KEY": os.getenv("ANTHROPIC_API_KEY")})
+        self.client = Anthropic(api_key=self.valves.ANTHROPIC_API_KEY)
+
+    def get_anthropic_models(self):
+        # In the future, this could fetch models dynamically from Anthropic
+        return [
+            {"id": "claude-3-haiku-20240307", "name": "claude-3-haiku"},
+            {"id": "claude-3-opus-20240229", "name": "claude-3-opus"},
+            {"id": "claude-3-sonnet-20240229", "name": "claude-3-sonnet"},
+            # Add other Anthropic models here as they become available
+        ]
+
+    async def on_startup(self):
+        print(f"on_startup:{__name__}")
+        pass
+
+    async def on_shutdown(self):
+        print(f"on_shutdown:{__name__}")
+        pass
+
+    async def on_valves_update(self):
+        self.client = Anthropic(api_key=self.valves.ANTHROPIC_API_KEY)
+        pass
+
+    # Pipelines are the models that are available in the manifold.
+    # It can be a list or a function that returns a list.
+    def pipelines(self) -> List[dict]:
+        return self.get_anthropic_models()
+
+    def pipe(
+        self, user_message: str, model_id: str, messages: List[dict], body: dict
+    ) -> Union[str, Generator, Iterator]:
+        try:
+            if body.get("stream", False):
+                return self.stream_response(model_id, messages, body)
+            else:
+                return self.get_completion(model_id, messages, body)
+        except (RateLimitError, APIStatusError, APIConnectionError) as e:
+            return f"Error: {e}"
+
+    def stream_response(
+        self, model_id: str, messages: List[dict], body: dict
+    ) -> Generator:
+        stream = self.client.messages.create(
+            model=model_id,
+            messages=messages,
+            max_tokens=body.get("max_tokens", 1024),
+            temperature=body.get("temperature", 1.0),
+            top_k=body.get("top_k", 40),
+            top_p=body.get("top_p", 0.9),
+            stop_sequences=body.get("stop", []),
+            stream=True,
+        )
+
+        for chunk in stream:
+            if chunk.type == "content_block_start":
+                yield chunk.content_block.text
+            elif chunk.type == "content_block_delta":
+                yield chunk.delta.text
+
+    def get_completion(self, model_id: str, messages: List[dict], body: dict) -> str:
+        response = self.client.messages.create(
+            model=model_id,
+            messages=messages,
+            max_tokens=body.get("max_tokens", 1024),
+            temperature=body.get("temperature", 1.0),
+            top_k=body.get("top_k", 40),
+            top_p=body.get("top_p", 0.9),
+            stop_sequences=body.get("stop", []),
+        )
+        return response.content[0].text