Define the stop sequence#
This is important because it tells the LLM when to stop generation.
This depends heavily on the prompt and model you are using. Generally, you want this to be whatever token you use in the prompt to denote the start of an Observation (otherwise, the LLM may hallucinate an observation for you).
Set up the Agent#
We can now combine everything to set up our agent
# LLM chain consisting of the LLM and a prompt
llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in tools]
agent = LLMSingleActionAgent(
    llm_chain=llm_chain, 
    output_parser=output_parser,
    stop=["\nObservation:"], 
    allowed_tools=tool_names
)
Use the Agent#
Now we can use it!
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
agent_executor.run("Search for Leo DiCaprio's girlfriend on the internet.")
> Entering new AgentExecutor chain...
Thought: I should use a reliable search engine to get accurate information.
Action: Search
Action Input: "Leo DiCaprio girlfriend"
Observation:He went on to date Gisele Bündchen, Bar Refaeli, Blake Lively, Toni Garrn and Nina Agdal, among others, before finally settling down with current girlfriend Camila Morrone, who is 23 years his junior.
I have found the answer to the question.
Final Answer: Leo DiCaprio's current girlfriend is Camila Morrone.
> Finished chain.
"Leo DiCaprio's current girlfriend is Camila Morrone."
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Custom LLM Agent
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Custom MRKL Agent
 Contents
  
Set up environment
Set up tool
Prompt Template
Output Parser
rtdocs_stable/api.python.langchain.com/en/stable/modules/agents/agents/custom_llm_chat_agent.html