From 7fb1374238ba7152e24fc1ad6e38ef06551b628d Mon Sep 17 00:00:00 2001
From: Kye <kye@apacmediasolutions.com>
Date: Mon, 26 Jun 2023 18:58:01 -0400
Subject: [PATCH] baby agi agent

---
 swarms/agents/README.MD     |  7 ++++
 swarms/agents/auto_agent.py |  1 -
 swarms/agents/babyagi.py    | 84 +++++++++++++++++++++++++++++++++++++
 3 files changed, 91 insertions(+), 1 deletion(-)
 create mode 100644 swarms/agents/babyagi.py

diff --git a/swarms/agents/README.MD b/swarms/agents/README.MD
index 527a46ce..2283e91e 100644
--- a/swarms/agents/README.MD
+++ b/swarms/agents/README.MD
@@ -117,3 +117,10 @@ def evaluate_results(CompAs):
 ```
 
 Note: In the real world, the complexity of the architecture and requirements will significantly exceed what is presented here. These examples provide a basic starting point but should be expanded upon based on the specifics of the task or problem you're trying to solve.
+
+
+
+# Swarms
+
+BabyAGI -> Autogpt's -> tools -> other agents
+ 
\ No newline at end of file
diff --git a/swarms/agents/auto_agent.py b/swarms/agents/auto_agent.py
index 0c036c75..7c56bb2b 100644
--- a/swarms/agents/auto_agent.py
+++ b/swarms/agents/auto_agent.py
@@ -188,7 +188,6 @@ agent = AutoGPT.from_llm_and_tools(
     tools=tools,
     llm=llm,
     memory=vectorstore.as_retriever(search_kwargs={"k": 8}),
-    # prompt=""
     human_in_the_loop=True, # Set to True if you want to add feedback at each step.
 )
 
diff --git a/swarms/agents/babyagi.py b/swarms/agents/babyagi.py
new file mode 100644
index 00000000..e6f3f8b4
--- /dev/null
+++ b/swarms/agents/babyagi.py
@@ -0,0 +1,84 @@
+import os
+from collections import deque
+from typing import Dict, List, Optional, Any
+
+from langchain import LLMChain, OpenAI, PromptTemplate
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.llms import BaseLLM
+from langchain.vectorstores.base import VectorStore
+from pydantic import BaseModel, Field
+from langchain.chains.base import Chain
+from langchain.experimental import BabyAGI
+
+from langchain.vectorstores import FAISS
+from langchain.docstore import InMemoryDocstore
+
+from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
+from langchain import OpenAI, SerpAPIWrapper, LLMChain
+
+
+# Define your embedding model
+embeddings_model = OpenAIEmbeddings()
+# Initialize the vectorstore as empty
+import faiss
+
+embedding_size = 1536
+index = faiss.IndexFlatL2(embedding_size)
+vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
+
+
+todo_prompt = PromptTemplate.from_template(
+    "You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}"
+)
+todo_chain = LLMChain(llm=OpenAI(temperature=0), prompt=todo_prompt)
+search = SerpAPIWrapper()
+tools = [
+    Tool(
+        name="Search",
+        func=search.run,
+        description="useful for when you need to answer questions about current events",
+    ),
+    Tool(
+        name="TODO",
+        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!",
+    ),
+]
+
+
+prefix = """You are an AI who performs one task based on the following objective: {objective}. Take into account these previously completed tasks: {context}."""
+suffix = """Question: {task}
+{agent_scratchpad}"""
+prompt = ZeroShotAgent.create_prompt(
+    tools,
+    prefix=prefix,
+    suffix=suffix,
+    input_variables=["objective", "task", "context", "agent_scratchpad"],
+)
+
+llm = OpenAI(temperature=0)
+llm_chain = LLMChain(llm=llm, prompt=prompt)
+tool_names = [tool.name for tool in tools]
+
+agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
+agent_executor = AgentExecutor.from_agent_and_tools(
+    agent=agent, tools=tools, verbose=True
+)
+
+OBJECTIVE = "Write a weather report for SF today"
+
+
+# Logging of LLMChains
+verbose = False
+# If None, will keep on going forever
+max_iterations: Optional[int] = 3
+baby_agi = BabyAGI.from_llm(
+    llm=llm,
+    vectorstore=vectorstore,
+    task_execution_chain=agent_executor,
+    verbose=verbose,
+    max_iterations=max_iterations,
+)
+
+baby_agi({"objective": OBJECTIVE})
+