LlamaBot: A Pythonic bot interface to LLMs

LlamaBot implements a Pythonic interface to LLMs, making it much easier to experiment with LLMs in a Jupyter notebook and build Python apps that utilize LLMs. All models supported by LiteLLM are supported by LlamaBot.

Install LlamaBot

To install LlamaBot:

pip install llamabot==0.17.11

This will give you the minimum set of dependencies for running LlamaBot.

To install all of the optional dependencies, run:

pip install "llamabot[all]"

Get access to LLMs

Option 1: Using local models with Ollama

LlamaBot supports using local models through Ollama. To do so, head over to the Ollama website and install Ollama. Then follow the instructions below.

Option 2: Use an API provider

OpenAI

If you have an OpenAI API key, then configure LlamaBot to use the API key by running:

export OPENAI_API_KEY="sk-your1api2key3goes4here"

Mistral

If you have a Mistral API key, then configure LlamaBot to use the API key by running:

export MISTRAL_API_KEY="your-api-key-goes-here"

Other API providers

Other API providers will usually specify an environment variable to set. If you have an API key, then set the environment variable accordingly.

Option 3: Using local models with LMStudio

LlamaBot supports using local models through LMStudio via LiteLLM. To use LMStudio with LlamaBot:

1. Install and set up LMStudio
2. Load your desired model in LMStudio
3. Start the local server in LMStudio (usually runs on http://localhost:1234)
4. Set the environment variable for LMStudio's API base:

export LM_STUDIO_API_BASE="http://localhost:1234"

5. Use the model with LlamaBot using the lm_studio/ prefix:

import llamabot as lmb

system_prompt = "You are a helpful assistant."
bot = lmb.SimpleBot(
    system_prompt,
    model_name="lm_studio/your-model-name"  # Use lm_studio/ prefix
)

Replace your-model-name with the actual name of the model you've loaded in LMStudio. LlamaBot can use any model provider that LiteLLM supports, and LMStudio is one of the many supported providers.

How to use

!!! tip "Not sure which bot to use?" Check out the Which Bot Should I Use? guide to help you choose the right bot for your needs.

SimpleBot

The simplest use case of LlamaBot is to create a SimpleBot that keeps no record of chat history. This is effectively the same as a stateless function that you program with natural language instructions rather than code. This is useful for prompt experimentation, or for creating simple bots that are preconditioned on an instruction to handle texts and are then called upon repeatedly with different texts.

Using SimpleBot with an API provider

For example, to create a Bot that explains a given chunk of text like Richard Feynman would:

import llamabot as lmb

system_prompt = "You are Richard Feynman. You will be given a difficult concept, and your task is to explain it back."
feynman = lmb.SimpleBot(
  system_prompt,
  model_name="gpt-4.1-mini"
)

For using GPT, you need to have the OPENAI_API_KEY environment variable configured. If you want to use SimpleBot with a local Ollama model, check out this example

Now, feynman is callable on any arbitrary chunk of text and will return a rephrasing of that text in Richard Feynman's style (or more accurately, according to the style prescribed by the system_prompt). For example:

prompt = """
Enzyme function annotation is a fundamental challenge, and numerous computational tools have been developed.
However, most of these tools cannot accurately predict functional annotations,
such as enzyme commission (EC) number,
for less-studied proteins or those with previously uncharacterized functions or multiple activities.
We present a machine learning algorithm named CLEAN (contrastive learning–enabled enzyme annotation)
to assign EC numbers to enzymes with better accuracy, reliability,
and sensitivity compared with the state-of-the-art tool BLASTp.
The contrastive learning framework empowers CLEAN to confidently (i) annotate understudied enzymes,
(ii) correct mislabeled enzymes, and (iii) identify promiscuous enzymes with two or more EC numbers—functions
that we demonstrate by systematic in silico and in vitro experiments.
We anticipate that this tool will be widely used for predicting the functions of uncharacterized enzymes,
thereby advancing many fields, such as genomics, synthetic biology, and biocatalysis.
"""
feynman(prompt)

This will return something that looks like:

Alright, let's break this down.

Enzymes are like little biological machines that help speed up chemical reactions in our
bodies. Each enzyme has a specific job, or function, and we use something called an
Enzyme Commission (EC) number to categorize these functions.

Now, the problem is that we don't always know what function an enzyme has, especially if
it's a less-studied or new enzyme. This is where computational tools come in. They try
to predict the function of these enzymes, but they often struggle to do so accurately.

So, the folks here have developed a new tool called CLEAN, which stands for contrastive
learning–enabled enzyme annotation. This tool uses a machine learning algorithm, which
is a type of artificial intelligence that learns from data to make predictions or
decisions.

CLEAN uses a method called contrastive learning. Imagine you have a bunch of pictures of
cats and dogs, and you want to teach a machine to tell the difference. You'd show it
pairs of pictures, some of the same animal (two cats or two dogs) and some of different
animals (a cat and a dog). The machine would learn to tell the difference by contrasting
the features of the two pictures. That's the basic idea behind contrastive learning.

CLEAN uses this method to predict the EC numbers of enzymes more accurately than
previous tools. It can confidently annotate understudied enzymes, correct mislabeled
enzymes, and even identify enzymes that have more than one function.

The creators of CLEAN have tested it with both computer simulations and lab experiments,
and they believe it will be a valuable tool for predicting the functions of unknown
enzymes. This could have big implications for fields like genomics, synthetic biology,
and biocatalysis, which all rely on understanding how enzymes work.

Using SimpleBot with a Local Ollama Model

If you want to use an Ollama model hosted locally, then you would use the following syntax:

import llamabot as lmb

system_prompt = "You are Richard Feynman. You will be given a difficult concept, and your task is to explain it back."
bot = lmb.SimpleBot(
    system_prompt,
    model_name="ollama_chat/llama2:13b"
)

Simply specify the model_name keyword argument following the <provider>/<model name> format. For example:

• ollama_chat/ as the prefix, and
• a model name from the Ollama library of models

All you need to do is make sure Ollama is running locally; see the Ollama documentation for more details. (The same can be done for the QueryBot class below!)

The model_name argument is optional. If you don't provide it, Llamabot will try to use the default model. You can configure that in the DEFAULT_LANGUAGE_MODEL environment variable.

SimpleBot with memory for chat functionality

If you want chat functionality with memory, you can use SimpleBot with ChatMemory. This allows the bot to remember previous conversations:

import llamabot as lmb

# Create a bot with memory
system_prompt = "You are Richard Feynman. You will be given a difficult concept, and your task is to explain it back."

# For simple linear memory (fast, no LLM calls)
memory = lmb.ChatMemory()

# For intelligent threading (uses LLM for smart connections)
# memory = lmb.ChatMemory.threaded(model="gpt-4o-mini")

feynman = lmb.SimpleBot(
    system_prompt,
    memory=memory,
    model_name="gpt-4.1-mini"
)

# Have a conversation
response1 = feynman("Can you explain quantum mechanics?")
print(response1)

# The bot remembers the previous conversation
response2 = feynman("Can you give me a simpler explanation?")
print(response2)

The ChatMemory system provides intelligent conversation memory that can maintain context across multiple interactions. It supports both linear memory (fast, no LLM calls) and graph-based memory with intelligent threading (uses LLM to connect related conversation topics).

Note: For RAG (Retrieval-Augmented Generation) with document stores, use QueryBot with a document store instead of SimpleBot with memory. SimpleBot's memory parameter is specifically for conversational memory, while QueryBot is designed for document retrieval and question answering.

For more details on chat memory, see the Chat Memory component documentation.

ToolBot

ToolBot is a specialized bot designed for single-turn tool execution and function calling. It analyzes user requests and selects the most appropriate tool to execute, making it perfect for automation tasks and data analysis workflows.

import llamabot as lmb
from llamabot.components.tools import write_and_execute_code

# Create a ToolBot with code execution capabilities
bot = lmb.ToolBot(
    system_prompt="You are a data analysis assistant.",
    model_name="gpt-4.1",
    tools=[write_and_execute_code(globals_dict=globals())],
    memory=lmb.ChatMemory(),
)

# Create some data
import pandas as pd
import numpy as np
data = pd.DataFrame({
    'x': np.random.randn(100),
    'y': np.random.randn(100)
})

# Use the bot to analyze the data
response = bot("Calculate the correlation between x and y in the data DataFrame")
print(