Add Enum for agent types (#2321)

This pull request adds an enum class for the various types of agents
used in the project, located in the `agent_types.py` file. Currently,
the project is using hardcoded strings for the initialization of these
agents, which can lead to errors and make the code harder to maintain.
With the introduction of the new enums, the code will be more readable
and less error-prone.

The new enum members include:

- ZERO_SHOT_REACT_DESCRIPTION
- REACT_DOCSTORE
- SELF_ASK_WITH_SEARCH
- CONVERSATIONAL_REACT_DESCRIPTION
- CHAT_ZERO_SHOT_REACT_DESCRIPTION
- CHAT_CONVERSATIONAL_REACT_DESCRIPTION

In this PR, I have also replaced the hardcoded strings with the
appropriate enum members throughout the codebase, ensuring a smooth
transition to the new approach.

docs/ecosystem/aim_tracking.ipynb

@@ -205,7 +205,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.agents import initialize_agent, load_tools"
+    "from langchain.agents import initialize_agent, load_tools\n",
+    "from langchain.agents.agent_types import AgentType"
    ]
   },
   {
@@ -252,7 +253,7 @@
     "agent = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
-    "    agent=\"zero-shot-react-description\",\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
     "    callback_manager=manager,\n",
     "    verbose=True,\n",
     ")\n",

docs/ecosystem/clearml_tracking.ipynb

@@ -520,13 +520,14 @@
    ],
    "source": [
     "from langchain.agents import initialize_agent, load_tools\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "\n",
     "# SCENARIO 2 - Agent with Tools\n",
     "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
     "agent = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
-    "    agent=\"zero-shot-react-description\",\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
     "    callback_manager=manager,\n",
     "    verbose=True,\n",
     ")\n",

docs/ecosystem/google_serper.md

@@ -23,6 +23,7 @@ You can use it as part of a Self Ask chain:
 from langchain.utilities import GoogleSerperAPIWrapper
 from langchain.llms.openai import OpenAI
 from langchain.agents import initialize_agent, Tool
+from langchain.agents.agent_types import AgentType
 
 import os
 
@@ -39,7 +40,7 @@ tools = [
     )
 ]
 
-self_ask_with_search = initialize_agent(tools, llm, agent="self-ask-with-search", verbose=True)
+self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)
 self_ask_with_search.run("What is the hometown of the reigning men's U.S. Open champion?")
 ```
 

docs/ecosystem/wandb_tracking.ipynb

@@ -505,7 +505,8 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.agents import initialize_agent, load_tools"
+    "from langchain.agents import initialize_agent, load_tools\n",
+    "from langchain.agents.agent_types import AgentType"
    ]
   },
   {
@@ -580,7 +581,7 @@
     "agent = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
-    "    agent=\"zero-shot-react-description\",\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
     "    callback_manager=manager,\n",
     "    verbose=True,\n",
     ")\n",

docs/getting_started/getting_started.md

@@ -197,6 +197,7 @@ Now we can get started!
 ```python
 from langchain.agents import load_tools
 from langchain.agents import initialize_agent
+from langchain.agents.agent_types import AgentType
 from langchain.llms import OpenAI
 
 # First, let's load the language model we're going to use to control the agent.
@@ -207,7 +208,7 @@ tools = load_tools(["serpapi", "llm-math"], llm=llm)
 
 
 # Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
-agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
+agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
 
 # Now let's test it out!
 agent.run("What was the high temperature in SF yesterday in Fahrenheit? What is that number raised to the .023 power?")
@@ -404,11 +405,12 @@ chain.run(input_language="English", output_language="French", text="I love progr
 `````
 
 `````{dropdown} Agents with Chat Models
-Agents can also be used with chat models, you can initialize one using `"chat-zero-shot-react-description"` as the agent type.
+Agents can also be used with chat models, you can initialize one using `AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION` as the agent type.
 
 ```python
 from langchain.agents import load_tools
 from langchain.agents import initialize_agent
+from langchain.agents.agent_types import AgentType
 from langchain.chat_models import ChatOpenAI
 from langchain.llms import OpenAI
 
@@ -421,7 +423,7 @@ tools = load_tools(["serpapi", "llm-math"], llm=llm)
 
 
 # Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
-agent = initialize_agent(tools, chat, agent="chat-zero-shot-react-description", verbose=True)
+agent = initialize_agent(tools, chat, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
 
 # Now let's test it out!
 agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?")

docs/modules/agents/agent_executors/examples/agent_vectorstore.ipynb

@@ -154,6 +154,7 @@
    "source": [
     "# Import things that are needed generically\n",
     "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "from langchain.tools import BaseTool\n",
     "from langchain.llms import OpenAI\n",
     "from langchain import LLMMathChain, SerpAPIWrapper"
@@ -189,7 +190,7 @@
    "source": [
     "# Construct the agent. We will use the default agent type here.\n",
     "# See documentation for a full list of options.\n",
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -316,7 +317,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -433,7 +434,7 @@
    "source": [
     "# Construct the agent. We will use the default agent type here.\n",
     "# See documentation for a full list of options.\n",
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {

docs/modules/agents/agent_executors/examples/async_agent.ipynb

@@ -39,6 +39,7 @@
     "import time\n",
     "\n",
     "from langchain.agents import initialize_agent, load_tools\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "from langchain.llms import OpenAI\n",
     "from langchain.callbacks.stdout import StdOutCallbackHandler\n",
     "from langchain.callbacks.base import CallbackManager\n",
@@ -175,7 +176,7 @@
     "        llm = OpenAI(temperature=0)\n",
     "        tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm)\n",
     "        agent = initialize_agent(\n",
-    "            tools, llm, agent=\"zero-shot-react-description\", verbose=True\n",
+    "            tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION verbose=True\n",
     "        )\n",
     "        agent.run(q)\n",
     "\n",
@@ -311,7 +312,7 @@
     "        llm = OpenAI(temperature=0, callback_manager=manager)\n",
     "        async_tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm, aiosession=aiosession, callback_manager=manager)\n",
     "        agents.append(\n",
-    "            initialize_agent(async_tools, llm, agent=\"zero-shot-react-description\", verbose=True, callback_manager=manager)\n",
+    "            initialize_agent(async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, callback_manager=manager)\n",
     "        )\n",
     "    tasks = [async_agent.arun(q) for async_agent, q in zip(agents, questions)]\n",
     "    await asyncio.gather(*tasks)\n",
@@ -381,7 +382,7 @@
     "llm = OpenAI(temperature=0, callback_manager=manager)\n",
     "\n",
     "async_tools = load_tools([\"llm-math\", \"serpapi\"], llm=llm, aiosession=aiosession)\n",
-    "async_agent = initialize_agent(async_tools, llm, agent=\"zero-shot-react-description\", verbose=True, callback_manager=manager)\n",
+    "async_agent = initialize_agent(async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, callback_manager=manager)\n",
     "await async_agent.arun(questions[0])\n",
     "await aiosession.close()"
    ]

docs/modules/agents/agent_executors/examples/intermediate_steps.ipynb

@@ -19,6 +19,7 @@
    "source": [
     "from langchain.agents import load_tools\n",
     "from langchain.agents import initialize_agent\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "from langchain.llms import OpenAI"
    ]
   },
@@ -56,7 +57,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True, return_intermediate_steps=True)"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, return_intermediate_steps=True)"
    ]
   },
   {

docs/modules/agents/agent_executors/examples/max_iterations.ipynb

@@ -19,6 +19,7 @@
    "source": [
     "from langchain.agents import load_tools\n",
     "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "from langchain.llms import OpenAI"
    ]
   },
@@ -59,7 +60,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -139,7 +140,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True, max_iterations=2)"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, max_iterations=2)"
    ]
   },
   {
@@ -198,7 +199,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True, max_iterations=2, early_stopping_method=\"generate\")"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, max_iterations=2, early_stopping_method=\"generate\")"
    ]
   },
   {

docs/modules/agents/agents/examples/chat_conversation_agent.ipynb

@@ -34,7 +34,8 @@
     "from langchain.memory import ConversationBufferMemory\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.utilities import SerpAPIWrapper\n",
-    "from langchain.agents import initialize_agent"
+    "from langchain.agents import initialize_agent\n",
+    "from langchain.agents.agent_types import AgentType"
    ]
   },
   {
@@ -72,7 +73,7 @@
    "outputs": [],
    "source": [
     "llm=ChatOpenAI(temperature=0)\n",
-    "agent_chain = initialize_agent(tools, llm, agent=\"chat-conversational-react-description\", verbose=True, memory=memory)"
+    "agent_chain = initialize_agent(tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)"
    ]
   },
   {

docs/modules/agents/agents/examples/conversational_agent.ipynb

@@ -20,6 +20,7 @@
    "outputs": [],
    "source": [
     "from langchain.agents import Tool\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "from langchain.memory import ConversationBufferMemory\n",
     "from langchain import OpenAI\n",
     "from langchain.utilities import GoogleSearchAPIWrapper\n",
@@ -61,7 +62,7 @@
    "outputs": [],
    "source": [
     "llm=OpenAI(temperature=0)\n",
-    "agent_chain = initialize_agent(tools, llm, agent=\"conversational-react-description\", verbose=True, memory=memory)"
+    "agent_chain = initialize_agent(tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)"
    ]
   },
   {

docs/modules/agents/agents/examples/mrkl.ipynb

@@ -27,7 +27,8 @@
    "outputs": [],
    "source": [
     "from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain\n",
-    "from langchain.agents import initialize_agent, Tool"
+    "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.agents.agent_types import AgentType"
    ]
   },
   {
@@ -68,7 +69,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mrkl = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "mrkl = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {

docs/modules/agents/agents/examples/mrkl_chat.ipynb

@@ -28,6 +28,7 @@
    "source": [
     "from langchain import OpenAI, LLMMathChain, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain\n",
     "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "from langchain.chat_models import ChatOpenAI"
    ]
   },
@@ -70,7 +71,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mrkl = initialize_agent(tools, llm, agent=\"chat-zero-shot-react-description\", verbose=True)"
+    "mrkl = initialize_agent(tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {

docs/modules/agents/agents/examples/react.ipynb

@@ -19,6 +19,7 @@
    "source": [
     "from langchain import OpenAI, Wikipedia\n",
     "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "from langchain.agents.react.base import DocstoreExplorer\n",
     "docstore=DocstoreExplorer(Wikipedia())\n",
     "tools = [\n",
@@ -35,7 +36,7 @@
     "]\n",
     "\n",
     "llm = OpenAI(temperature=0, model_name=\"text-davinci-002\")\n",
-    "react = initialize_agent(tools, llm, agent=\"react-docstore\", verbose=True)"
+    "react = initialize_agent(tools, llm, agent=AgentType.REACT_DOCSTORE, verbose=True)"
    ]
   },
   {

docs/modules/agents/agents/examples/self_ask_with_search.ipynb

@@ -46,6 +46,7 @@
    "source": [
     "from langchain import OpenAI, SerpAPIWrapper\n",
     "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "search = SerpAPIWrapper()\n",
@@ -57,7 +58,7 @@
     "    )\n",
     "]\n",
     "\n",
-    "self_ask_with_search = initialize_agent(tools, llm, agent=\"self-ask-with-search\", verbose=True)\n",
+    "self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)\n",
     "self_ask_with_search.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
    ]
   }

docs/modules/agents/getting_started.ipynb

@@ -92,7 +92,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {

docs/modules/agents/tools/custom_tools.ipynb

@@ -27,6 +27,7 @@
    "source": [
     "# Import things that are needed generically\n",
     "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "from langchain.tools import BaseTool\n",
     "from langchain.llms import OpenAI\n",
     "from langchain import LLMMathChain, SerpAPIWrapper"
@@ -102,7 +103,7 @@
    "source": [
     "# Construct the agent. We will use the default agent type here.\n",
     "# See documentation for a full list of options.\n",
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -217,7 +218,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -410,7 +411,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -484,6 +485,7 @@
    "source": [
     "# Import things that are needed generically\n",
     "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "from langchain.llms import OpenAI\n",
     "from langchain import LLMMathChain, SerpAPIWrapper\n",
     "search = SerpAPIWrapper()\n",
@@ -500,7 +502,7 @@
     "    )\n",
     "]\n",
     "\n",
-    "agent = initialize_agent(tools, OpenAI(temperature=0), agent=\"zero-shot-react-description\", verbose=True)"
+    "agent = initialize_agent(tools, OpenAI(temperature=0), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -576,7 +578,7 @@
    "outputs": [],
    "source": [
     "llm = OpenAI(temperature=0)\n",
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {

docs/modules/agents/tools/examples/chatgpt_plugins.ipynb

@@ -23,6 +23,7 @@
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.agents import load_tools, initialize_agent\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "from langchain.tools import AIPluginTool"
    ]
   },
@@ -83,7 +84,7 @@
     "tools = load_tools([\"requests\"] )\n",
     "tools += [tool]\n",
     "\n",
-    "agent_chain = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)\n",
+    "agent_chain = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION verbose=True)\n",
     "agent_chain.run(\"what t shirts are available in klarna?\")"
    ]
   },

docs/modules/agents/tools/examples/google_serper.ipynb

@@ -115,6 +115,7 @@
     "from langchain.utilities import GoogleSerperAPIWrapper\n",
     "from langchain.llms.openai import OpenAI\n",
     "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "search = GoogleSerperAPIWrapper()\n",
@@ -126,7 +127,7 @@
     "    )\n",
     "]\n",
     "\n",
-    "self_ask_with_search = initialize_agent(tools, llm, agent=\"self-ask-with-search\", verbose=True)\n",
+    "self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)\n",
     "self_ask_with_search.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
    ],
    "metadata": {

docs/modules/agents/tools/examples/human_tools.ipynb

@@ -20,6 +20,7 @@
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.llms import OpenAI\n",
     "from langchain.agents import load_tools, initialize_agent\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "\n",
     "llm = ChatOpenAI(temperature=0.0)\n",
     "math_llm = OpenAI(temperature=0.0)\n",
@@ -31,7 +32,7 @@
     "agent_chain = initialize_agent(\n",
     "    tools,\n",
     "    llm,\n",
-    "    agent=\"zero-shot-react-description\",\n",
+    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
     "    verbose=True,\n",
     ")"
    ]

docs/modules/agents/tools/examples/search_tools.ipynb

@@ -23,6 +23,7 @@
    "source": [
     "from langchain.agents import load_tools\n",
     "from langchain.agents import initialize_agent\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "from langchain.llms import OpenAI"
    ]
   },
@@ -63,7 +64,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -131,7 +132,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -199,7 +200,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {
@@ -266,7 +267,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {

docs/modules/agents/tools/examples/zapier.ipynb

@@ -77,6 +77,7 @@
     "from langchain.llms import OpenAI\n",
     "from langchain.agents import initialize_agent\n",
     "from langchain.agents.agent_toolkits import ZapierToolkit\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "from langchain.utilities.zapier import ZapierNLAWrapper"
    ]
   },
@@ -105,7 +106,7 @@
     "llm = OpenAI(temperature=0)\n",
     "zapier = ZapierNLAWrapper()\n",
     "toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)\n",
-    "agent = initialize_agent(toolkit.get_tools(), llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "agent = initialize_agent(toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {

docs/modules/agents/tools/multi_input_tool.ipynb

@@ -23,7 +23,8 @@
    "outputs": [],
    "source": [
     "from langchain.llms import OpenAI\n",
-    "from langchain.agents import initialize_agent, Tool"
+    "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.agents.agent_types import AgentType"
    ]
   },
   {
@@ -64,7 +65,7 @@
     "        description=\"useful for when you need to multiply two numbers together. The input to this tool should be a comma separated list of numbers of length two, representing the two numbers you want to multiply together. For example, `1,2` would be the input if you wanted to multiply 1 by 2.\"\n",
     "    )\n",
     "]\n",
-    "mrkl = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "mrkl = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {

docs/modules/models/llms/examples/fake_llm.ipynb

@@ -31,7 +31,8 @@
    "outputs": [],
    "source": [
     "from langchain.agents import load_tools\n",
-    "from langchain.agents import initialize_agent"
+    "from langchain.agents import initialize_agent\n",
+    "from langchain.agents.agent_types import AgentType"
    ]
   },
   {
@@ -65,7 +66,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {

docs/modules/models/llms/examples/token_usage_tracking.ipynb

@@ -107,11 +107,12 @@
    "source": [
     "from langchain.agents import load_tools\n",
     "from langchain.agents import initialize_agent\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "from langchain.llms import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n",
-    "agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+    "agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
    ]
   },
   {

docs/tracing/agent_with_tracing.ipynb

@@ -35,6 +35,7 @@
     "\n",
     "import langchain\n",
     "from langchain.agents import Tool, initialize_agent, load_tools\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.llms import OpenAI"
    ]
@@ -93,7 +94,7 @@
    ],
    "source": [
     "agent = initialize_agent(\n",
-    "    tools, llm, agent=\"zero-shot-react-description\", verbose=True\n",
+    "    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
     ")\n",
     "\n",
     "agent.run(\"What is 2 raised to .123243 power?\")"
@@ -177,7 +178,7 @@
    "source": [
     "# Agent run with tracing using a chat model\n",
     "agent = initialize_agent(\n",
-    "    tools, ChatOpenAI(temperature=0), agent=\"chat-zero-shot-react-description\", verbose=True\n",
+    "    tools, ChatOpenAI(temperature=0), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
     ")\n",
     "\n",
     "agent.run(\"What is 2 raised to .123243 power?\")"

docs/use_cases/evaluation/agent_benchmarking.ipynb

@@ -85,9 +85,10 @@
     "from langchain.llms import OpenAI\n",
     "from langchain.chains import LLMMathChain\n",
     "from langchain.agents import initialize_agent, Tool, load_tools\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "\n",
     "tools = load_tools(['serpapi', 'llm-math'], llm=OpenAI(temperature=0))\n",
-    "agent = initialize_agent(tools, OpenAI(temperature=0), agent=\"zero-shot-react-description\")\n"
+    "agent = initialize_agent(tools, OpenAI(temperature=0), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)\n"
    ]
   },
   {

docs/use_cases/evaluation/agent_vectordb_sota_pg.ipynb

@@ -255,6 +255,7 @@
    "outputs": [],
    "source": [
     "from langchain.agents import initialize_agent, Tool\n",
+    "from langchain.agents.agent_types import AgentType\n",
     "tools = [\n",
     "    Tool(\n",
     "        name = \"State of Union QA System\",\n",
@@ -276,7 +277,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "agent = initialize_agent(tools, OpenAI(temperature=0), agent=\"zero-shot-react-description\", max_iterations=3)"
+    "agent = initialize_agent(tools, OpenAI(temperature=0), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, max_iterations=3)"
    ]
   },
   {

langchain/agents/agent_types.py

@@ -0,0 +1,10 @@
+from enum import Enum
+
+
+class AgentType(str, Enum):
+    ZERO_SHOT_REACT_DESCRIPTION = "zero-shot-react-description"
+    REACT_DOCSTORE = "react-docstore"
+    SELF_ASK_WITH_SEARCH = "self-ask-with-search"
+    CONVERSATIONAL_REACT_DESCRIPTION = "conversational-react-description"
+    CHAT_ZERO_SHOT_REACT_DESCRIPTION = "chat-zero-shot-react-description"
+    CHAT_CONVERSATIONAL_REACT_DESCRIPTION = "chat-conversational-react-description"

langchain/agents/conversational/base.py

@@ -5,6 +5,7 @@ import re
 from typing import Any, List, Optional, Sequence, Tuple
 
 from langchain.agents.agent import Agent
+from langchain.agents.agent_types import AgentType
 from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains import LLMChain
@@ -21,7 +22,7 @@ class ConversationalAgent(Agent):
     @property
     def _agent_type(self) -> str:
         """Return Identifier of agent type."""
-        return "conversational-react-description"
+        return AgentType.CONVERSATIONAL_REACT_DESCRIPTION
 
     @property
     def observation_prefix(self) -> str:

langchain/agents/initialize.py

@@ -2,6 +2,7 @@
 from typing import Any, Optional, Sequence
 
 from langchain.agents.agent import AgentExecutor
+from langchain.agents.agent_types import AgentType
 from langchain.agents.loading import AGENT_TO_CLASS, load_agent
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.schema import BaseLanguageModel
@@ -11,7 +12,7 @@ from langchain.tools.base import BaseTool
 def initialize_agent(
     tools: Sequence[BaseTool],
     llm: BaseLanguageModel,
-    agent: Optional[str] = None,
+    agent: Optional[AgentType] = None,
     callback_manager: Optional[BaseCallbackManager] = None,
     agent_path: Optional[str] = None,
     agent_kwargs: Optional[dict] = None,
@@ -22,15 +23,8 @@ def initialize_agent(
     Args:
         tools: List of tools this agent has access to.
         llm: Language model to use as the agent.
-        agent: A string that specified the agent type to use. Valid options are:
-            `zero-shot-react-description`
-            `react-docstore`
-            `self-ask-with-search`
-            `conversational-react-description`
-            `chat-zero-shot-react-description`,
-            `chat-conversational-react-description`,
-           If None and agent_path is also None, will default to
-            `zero-shot-react-description`.
+        agent: Agent type to use. If None and agent_path is also None, will default to
+            AgentType.ZERO_SHOT_REACT_DESCRIPTION.
         callback_manager: CallbackManager to use. Global callback manager is used if
             not provided. Defaults to None.
         agent_path: Path to serialized agent to use.
@@ -41,7 +35,7 @@ def initialize_agent(
         An agent executor
     """
     if agent is None and agent_path is None:
-        agent = "zero-shot-react-description"
+        agent = AgentType.ZERO_SHOT_REACT_DESCRIPTION
     if agent is not None and agent_path is not None:
         raise ValueError(
             "Both `agent` and `agent_path` are specified, "

langchain/agents/loading.py

@@ -6,6 +6,7 @@ from typing import Any, List, Optional, Union
 import yaml
 
 from langchain.agents.agent import Agent
+from langchain.agents.agent_types import AgentType
 from langchain.agents.chat.base import ChatAgent
 from langchain.agents.conversational.base import ConversationalAgent
 from langchain.agents.conversational_chat.base import ConversationalChatAgent
@@ -18,12 +19,12 @@ from langchain.llms.base import BaseLLM
 from langchain.utilities.loading import try_load_from_hub
 
 AGENT_TO_CLASS = {
-    "zero-shot-react-description": ZeroShotAgent,
-    "react-docstore": ReActDocstoreAgent,
-    "self-ask-with-search": SelfAskWithSearchAgent,
-    "conversational-react-description": ConversationalAgent,
-    "chat-zero-shot-react-description": ChatAgent,
-    "chat-conversational-react-description": ConversationalChatAgent,
+    AgentType.ZERO_SHOT_REACT_DESCRIPTION: ZeroShotAgent,
+    AgentType.REACT_DOCSTORE: ReActDocstoreAgent,
+    AgentType.SELF_ASK_WITH_SEARCH: SelfAskWithSearchAgent,
+    AgentType.CONVERSATIONAL_REACT_DESCRIPTION: ConversationalAgent,
+    AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION: ChatAgent,
+    AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION: ConversationalChatAgent,
 }
 
 URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/agents/"

langchain/agents/mrkl/base.py

@@ -5,6 +5,7 @@ import re
 from typing import Any, Callable, List, NamedTuple, Optional, Sequence, Tuple
 
 from langchain.agents.agent import Agent, AgentExecutor
+from langchain.agents.agent_types import AgentType
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
 from langchain.agents.tools import Tool
 from langchain.callbacks.base import BaseCallbackManager
@@ -56,7 +57,7 @@ class ZeroShotAgent(Agent):
     @property
     def _agent_type(self) -> str:
         """Return Identifier of agent type."""
-        return "zero-shot-react-description"
+        return AgentType.ZERO_SHOT_REACT_DESCRIPTION
 
     @property
     def observation_prefix(self) -> str:

langchain/agents/react/base.py

@@ -5,6 +5,7 @@ from typing import Any, List, Optional, Sequence, Tuple
 from pydantic import BaseModel
 
 from langchain.agents.agent import Agent, AgentExecutor
+from langchain.agents.agent_types import AgentType
 from langchain.agents.react.textworld_prompt import TEXTWORLD_PROMPT
 from langchain.agents.react.wiki_prompt import WIKI_PROMPT
 from langchain.agents.tools import Tool
@@ -21,7 +22,7 @@ class ReActDocstoreAgent(Agent, BaseModel):
     @property
     def _agent_type(self) -> str:
         """Return Identifier of agent type."""
-        return "react-docstore"
+        return AgentType.REACT_DOCSTORE
 
     @classmethod
     def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:

langchain/agents/self_ask_with_search/base.py

@@ -2,6 +2,7 @@
 from typing import Any, Optional, Sequence, Tuple, Union
 
 from langchain.agents.agent import Agent, AgentExecutor
+from langchain.agents.agent_types import AgentType
 from langchain.agents.self_ask_with_search.prompt import PROMPT
 from langchain.agents.tools import Tool
 from langchain.llms.base import BaseLLM
@@ -17,7 +18,7 @@ class SelfAskWithSearchAgent(Agent):
     @property
     def _agent_type(self) -> str:
         """Return Identifier of agent type."""
-        return "self-ask-with-search"
+        return AgentType.SELF_ASK_WITH_SEARCH
 
     @classmethod
     def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:

langchain/tools/zapier/tool.py

@@ -65,7 +65,7 @@ toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)
 agent = initialize_agent(
     toolkit.get_tools(),
     llm,
-    agent="zero-shot-react-description",
+    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
     verbose=True
 )
 

tests/unit_tests/agents/test_agent.py

@@ -5,6 +5,7 @@ from typing import Any, List, Mapping, Optional
 from pydantic import BaseModel
 
 from langchain.agents import AgentExecutor, initialize_agent
+from langchain.agents.agent_types import AgentType
 from langchain.agents.tools import Tool
 from langchain.callbacks.base import CallbackManager
 from langchain.llms.base import LLM
@@ -55,7 +56,11 @@ def _get_agent(**kwargs: Any) -> AgentExecutor:
         ),
     ]
     agent = initialize_agent(
-        tools, fake_llm, agent="zero-shot-react-description", verbose=True, **kwargs
+        tools,
+        fake_llm,
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+        verbose=True,
+        **kwargs,
     )
     return agent
 
@@ -98,7 +103,7 @@ def test_agent_with_callbacks_global() -> None:
     agent = initialize_agent(
         tools,
         fake_llm,
-        agent="zero-shot-react-description",
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
         verbose=True,
         callback_manager=manager,
     )
@@ -144,7 +149,7 @@ def test_agent_with_callbacks_local() -> None:
     agent = initialize_agent(
         tools,
         fake_llm,
-        agent="zero-shot-react-description",
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
         verbose=True,
         callback_manager=manager,
     )
@@ -191,7 +196,7 @@ def test_agent_with_callbacks_not_verbose() -> None:
     agent = initialize_agent(
         tools,
         fake_llm,
-        agent="zero-shot-react-description",
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
         callback_manager=manager,
     )
 
@@ -223,7 +228,7 @@ def test_agent_tool_return_direct() -> None:
     agent = initialize_agent(
         tools,
         fake_llm,
-        agent="zero-shot-react-description",
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
     )
 
     output = agent.run("when was langchain made")
@@ -249,7 +254,7 @@ def test_agent_tool_return_direct_in_intermediate_steps() -> None:
     agent = initialize_agent(
         tools,
         fake_llm,
-        agent="zero-shot-react-description",
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
         return_intermediate_steps=True,
     )
 
@@ -280,7 +285,7 @@ def test_agent_with_new_prefix_suffix() -> None:
     agent = initialize_agent(
         tools=tools,
         llm=fake_llm,
-        agent="zero-shot-react-description",
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
         agent_kwargs={"prefix": prefix, "suffix": suffix},
     )
 
@@ -307,7 +312,7 @@ def test_agent_lookup_tool() -> None:
     agent = initialize_agent(
         tools=tools,
         llm=fake_llm,
-        agent="zero-shot-react-description",
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
     )
 
     assert agent.lookup_tool("Search") == tools[0]