diff --git a/README.md b/README.md
index a9165e95..c9a5a57d 100644
--- a/README.md
+++ b/README.md
@@ -200,4 +200,7 @@ In the context of swarm LLMs, one could consider an **Omni-Vector Embedding Data
 
 * Add task completion logic with meta prompting, task evaluation as a state from 0.0 to 1.0, and critiquing for meta prompting.
 
-* Integrate meta prompting for every agent boss and worker
\ No newline at end of file
+* Integrate meta prompting for every agent boss and worker
+
+* Get baby agi set up with the autogpt instance as a tool
+
diff --git a/swarms/agents/boss/babyagi_agent.py b/swarms/agents/boss/babyagi_agent.py
index e6f3f8b4..47f8931d 100644
--- a/swarms/agents/boss/babyagi_agent.py
+++ b/swarms/agents/boss/babyagi_agent.py
@@ -17,6 +17,8 @@ from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
 from langchain import OpenAI, SerpAPIWrapper, LLMChain
 
 
+from swarms.agents.workers.auto_agent import agent
+
 # Define your embedding model
 embeddings_model = OpenAIEmbeddings()
 # Initialize the vectorstore as empty
@@ -43,6 +45,11 @@ tools = [
         func=todo_chain.run,
         description="useful for when you need to come up with todo lists. Input: an objective to create a todo list for. Output: a todo list for that objective. Please be very clear what the objective is!",
     ),
+    Tool(
+        name="AUTONOMOUS AGENT",
+        func=agent.run,
+        description="Useful for when you need to spawn an autonomous agent instance as a worker to accomplish complex tasks"
+    )
 ]