comet-llm

Log, Track, and Visualize your LLM Prompts and Chains

Stars: 486

Visit

CometLLM is a tool to log and visualize your LLM prompts and chains. Use CometLLM to identify effective prompt strategies, streamline your troubleshooting, and ensure reproducible workflows!

README:

CometLLM is a tool to log and visualize your LLM prompts and chains. Use CometLLM to identify effective prompt strategies, streamline your troubleshooting, and ensure reproducible workflows!

⚡️ Quickstart

Install comet_llm Python library with pip:

pip install comet_llm

If you don't have already, create your free Comet account and grab your API Key from the account settings page.

Now you are all set to log your first prompt and response:

import comet_llm

comet_llm.log_prompt(
    prompt="What is your name?",
    output=" My name is Alex.",
    api_key="<YOUR_COMET_API_KEY>",
)

🎯 Features

[x] Log your prompts and responses, including prompt template, variables, timestamps and duration and any metadata that you need.
[x] Visualize your prompts and responses in the UI.
[x] Log your chain execution down to the level of granularity that you need.
[x] Visualize your chain execution in the UI.
[x] Automatically tracks your prompts when using the OpenAI chat models.
[x] Track and analyze user feedback.
[ ] Diff your prompts and chain execution in the UI.

👀 Examples

To log a single LLM call as an individual prompt, use comet_llm.log_prompt. If you require more granularity, you can log a chain of executions that may include more than one LLM call, context retrieval, or data pre- or post-processing with comet_llm.start_chain.

Log a full prompt and response

import comet_llm

comet_llm.log_prompt(
    prompt="Answer the question and if the question can't be answered, say \"I don't know\"\n\n---\n\nQuestion: What is your name?\nAnswer:",
    prompt_template="Answer the question and if the question can't be answered, say \"I don't know\"\n\n---\n\nQuestion: {{question}}?\nAnswer:",
    prompt_template_variables={"question": "What is your name?"},
    metadata= {
        "usage.prompt_tokens": 7,
        "usage.completion_tokens": 5,
        "usage.total_tokens": 12,
    },
    output=" My name is Alex.",
    duration=16.598,
)

Read the full documentation for more details about logging a prompt.

Log a LLM chain

from comet_llm import Span, end_chain, start_chain
import datetime
from time import sleep


def retrieve_context(user_question):
    if "open" in user_question:
        return "Opening hours: 08:00 to 17:00 all days"


def llm_answering(user_question, current_time, context):
    prompt_template = """You are a helpful chatbot. You have access to the following context:
    {context}
    The current time is: {current_time}
    Analyze the following user question and decide if you can answer it, if the question can't be answered, say \"I don't know\":
    {user_question}
    """

    prompt = prompt_template.format(
        user_question=user_question, current_time=current_time, context=context
    )

    with Span(
        category="llm-call",
        inputs={"prompt_template": prompt_template, "prompt": prompt},
    ) as span:
        # Call your LLM model here
        sleep(0.1)
        result = "Yes we are currently open"
        usage = {"prompt_tokens": 52, "completion_tokens": 12, "total_tokens": 64}

        span.set_outputs(outputs={"result": result}, metadata={"usage": usage})

    return result


def main(user_question, current_time):
    start_chain(inputs={"user_question": user_question, "current_time": current_time})

    with Span(
        category="context-retrieval",
        name="Retrieve Context",
        inputs={"user_question": user_question},
    ) as span:
        context = retrieve_context(user_question)

        span.set_outputs(outputs={"context": context})

    with Span(
        category="llm-reasoning",
        inputs={
            "user_question": user_question,
            "current_time": current_time,
            "context": context,
        },
    ) as span:
        result = llm_answering(user_question, current_time, context)

        span.set_outputs(outputs={"result": result})

    end_chain(outputs={"result": result})


main("Are you open?", str(datetime.datetime.now().time()))

Read the full documentation for more details about logging a chain.

⚙️ Configuration

You can configure your Comet credentials and where you are logging data to:

Name	Python parameter name	Environment variable name
Comet API KEY	api_key	COMET_API_KEY
Comet Workspace name	workspace	COMET_WORKSPACE
Comet Project name	project	COMET_PROJECT_NAME

📝 License

For Tasks:

Click tags to check more tools for each tasks

log prompts visualize prompts track chains analyze feedback diff prompts

For Jobs:

research scientist data scientist machine learning engineer ai engineer software engineer

Alternative AI tools for comet-llm

Similar Open Source Tools

comet-llm

CometLLM is a tool to log and visualize your LLM prompts and chains. Use CometLLM to identify effective prompt strategies, streamline your troubleshooting, and ensure reproducible workflows!

github

: 486

memobase

Memobase is a user profile-based memory system designed to enhance Generative AI applications by enabling them to remember, understand, and evolve with users. It provides structured user profiles, scalable profiling, easy integration with existing LLM stacks, batch processing for speed, and is production-ready. Users can manage users, insert data, get memory profiles, and track user preferences and behaviors. Memobase is ideal for applications that require user analysis, tracking, and personalized interactions.

github

: 230

parea-sdk-py

Parea AI provides a SDK to evaluate & monitor AI applications. It allows users to test, evaluate, and monitor their AI models by defining and running experiments. The SDK also enables logging and observability for AI applications, as well as deploying prompts to facilitate collaboration between engineers and subject-matter experts. Users can automatically log calls to OpenAI and Anthropic, create hierarchical traces of their applications, and deploy prompts for integration into their applications.

github

: 59

redis-vl-python

The Python Redis Vector Library (RedisVL) is a tailor-made client for AI applications leveraging Redis. It enhances applications with Redis' speed, flexibility, and reliability, incorporating capabilities like vector-based semantic search, full-text search, and geo-spatial search. The library bridges the gap between the emerging AI-native developer ecosystem and the capabilities of Redis by providing a lightweight, elegant, and intuitive interface. It abstracts the features of Redis into a grammar that is more aligned to the needs of today's AI/ML Engineers or Data Scientists.

github

: 253

superagent-py

Superagent is an open-source framework that enables developers to integrate production-ready AI assistants into any application quickly and easily. It provides a Python SDK for interacting with the Superagent API, allowing developers to create, manage, and invoke AI agents. The SDK simplifies the process of building AI-powered applications, making it accessible to developers of all skill levels.

github

: 99

CredSweeper

CredSweeper is a tool designed to detect credentials like tokens, passwords, and API keys in directories or files. It helps users identify potential exposure of sensitive information by scanning lines, filtering, and utilizing an AI model. The tool reports lines containing possible credentials, their location, and the expected type of credential.

github

: 112

llm-web-api

LLM Web API is a tool that provides a web page to API interface for ChatGPT, allowing users to bypass Cloudflare challenges, switch models, and dynamically display supported models. It uses Playwright to control a fingerprint browser, simulating user operations to send requests to the OpenAI website and converting the responses into API interfaces. The API currently supports the OpenAI-compatible /v1/chat/completions API, accessible using OpenAI or other compatible clients.

github

: 68

langcorn

LangCorn is an API server that enables you to serve LangChain models and pipelines with ease, leveraging the power of FastAPI for a robust and efficient experience. It offers features such as easy deployment of LangChain models and pipelines, ready-to-use authentication functionality, high-performance FastAPI framework for serving requests, scalability and robustness for language processing applications, support for custom pipelines and processing, well-documented RESTful API endpoints, and asynchronous processing for faster response times.

github

: 821

OpenAI

OpenAI is a Swift community-maintained implementation over OpenAI public API. It is a non-profit artificial intelligence research organization founded in San Francisco, California in 2015. OpenAI's mission is to ensure safe and responsible use of AI for civic good, economic growth, and other public benefits. The repository provides functionalities for text completions, chats, image generation, audio processing, edits, embeddings, models, moderations, utilities, and Combine extensions.

github

: 2.2k

modelfusion

ModelFusion is an abstraction layer for integrating AI models into JavaScript and TypeScript applications, unifying the API for common operations such as text streaming, object generation, and tool usage. It provides features to support production environments, including observability hooks, logging, and automatic retries. You can use ModelFusion to build AI applications, chatbots, and agents. ModelFusion is a non-commercial open source project that is community-driven. You can use it with any supported provider. ModelFusion supports a wide range of models including text generation, image generation, vision, text-to-speech, speech-to-text, and embedding models. ModelFusion infers TypeScript types wherever possible and validates model responses. ModelFusion provides an observer framework and logging support. ModelFusion ensures seamless operation through automatic retries, throttling, and error handling mechanisms. ModelFusion is fully tree-shakeable, can be used in serverless environments, and only uses a minimal set of dependencies.

github

: 918

swarmgo

SwarmGo is a Go package designed to create AI agents capable of interacting, coordinating, and executing tasks. It focuses on lightweight agent coordination and execution, offering powerful primitives like Agents and handoffs. SwarmGo enables building scalable solutions with rich dynamics between tools and networks of agents, all while keeping the learning curve low. It supports features like memory management, streaming support, concurrent agent execution, LLM interface, and structured workflows for organizing and coordinating multiple agents.

github

: 164

json-repair

JSON Repair is a toolkit designed to address JSON anomalies that can arise from Large Language Models (LLMs). It offers a comprehensive solution for repairing JSON strings, ensuring accuracy and reliability in your data processing. With its user-friendly interface and extensive capabilities, JSON Repair empowers developers to seamlessly integrate JSON repair into their workflows.

github

: 135

redisvl

Redis Vector Library (RedisVL) is a Python client library for building AI applications on top of Redis. It provides a high-level interface for managing vector indexes, performing vector search, and integrating with popular embedding models and providers. RedisVL is designed to make it easy for developers to build and deploy AI applications that leverage the speed, flexibility, and reliability of Redis.

github

: 158

client-python

The Mistral Python Client is a tool inspired by cohere-python that allows users to interact with the Mistral AI API. It provides functionalities to access and utilize the AI capabilities offered by Mistral. Users can easily install the client using pip and manage dependencies using poetry. The client includes examples demonstrating how to use the API for various tasks, such as chat interactions. To get started, users need to obtain a Mistral API Key and set it as an environment variable. Overall, the Mistral Python Client simplifies the integration of Mistral AI services into Python applications.

github

: 510

promptic

Promptic is a tool designed for LLM app development, providing a productive and pythonic way to build LLM applications. It leverages LiteLLM, allowing flexibility to switch LLM providers easily. Promptic focuses on building features by providing type-safe structured outputs, easy-to-build agents, streaming support, automatic prompt caching, and built-in conversation memory.

github

: 223

SimplerLLM

SimplerLLM is an open-source Python library that simplifies interactions with Large Language Models (LLMs) for researchers and beginners. It provides a unified interface for different LLM providers, tools for enhancing language model capabilities, and easy development of AI-powered tools and apps. The library offers features like unified LLM interface, generic text loader, RapidAPI connector, SERP integration, prompt template builder, and more. Users can easily set up environment variables, create LLM instances, use tools like SERP, generic text loader, calling RapidAPI APIs, and prompt template builder. Additionally, the library includes chunking functions to split texts into manageable chunks based on different criteria. Future updates will bring more tools, interactions with local LLMs, prompt optimization, response evaluation, GPT Trainer, document chunker, advanced document loader, integration with more providers, Simple RAG with SimplerVectors, integration with vector databases, agent builder, and LLM server.

github

: 91

For similar tasks

comet-llm

CometLLM is a tool to log and visualize your LLM prompts and chains. Use CometLLM to identify effective prompt strategies, streamline your troubleshooting, and ensure reproducible workflows!

github

: 486

obsei

Obsei is an open-source, low-code, AI powered automation tool that consists of an Observer to collect unstructured data from various sources, an Analyzer to analyze the collected data with various AI tasks, and an Informer to send analyzed data to various destinations. The tool is suitable for scheduled jobs or serverless applications as all Observers can store their state in databases. Obsei is still in alpha stage, so caution is advised when using it in production. The tool can be used for social listening, alerting/notification, automatic customer issue creation, extraction of deeper insights from feedbacks, market research, dataset creation for various AI tasks, and more based on creativity.

github

: 1.2k

lector

Lector is a text analysis tool that helps users extract insights from unstructured text data. It provides functionalities such as sentiment analysis, keyword extraction, entity recognition, and text summarization. With Lector, users can easily analyze large volumes of text data to uncover patterns, trends, and valuable information. The tool is designed to be user-friendly and efficient, making it suitable for both beginners and experienced users in the field of natural language processing and text mining.

github

: 213

ell

ell is a lightweight, functional prompt engineering framework that treats prompts as programs rather than strings. It provides tools for prompt versioning, monitoring, and visualization, as well as support for multimodal inputs and outputs. The framework aims to simplify the process of prompt engineering for language models.

github

: 4.9k

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 390

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 595

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 953

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 1.2k

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.1k