Literal AI is built by the team behind Chainlit
Ship
LLM applications
Empowering Developers, Product Managers and Domain Experts to Collaboratively Build, Evaluate and Monitor LLM apps.
Empowering Developers, Product Managers and Domain Experts to Collaboratively Build, Evaluate and Monitor LLM apps.
Trusted by
Continuous improvement loop
Continuous improvement loop
Monitor your LLM calls
Log & monitor Agent runs, LLM generations and conversation threads.
Monitor your LLM calls
Log & monitor Agent runs, LLM generations and conversation threads.
Create Dataset
Mix hand written examples with production data.
Create Dataset
Mix hand written examples with production data.
Evaluate, iterate and deploy
Reliably iterate on your Prompts and code with online/offline evals and deploy to prod.
Replay LLM sessions
By debugging a monitored LLM call, you have access to the conversation context - prompt templates, prompt variables and provider settings - and leverage those to improve your prompt template or provider settings.
Manage & version your prompts
Prompt Templates, managed in your code or on Literal, are versioned and stored on Literal.
Replay sessions in prompt playground
Objects are linked in Literal. Debugging in context is extremely useful and time-saving.
Evaluate new prompt versions
Leverage online evaluation to score a Dataset on the new challenger prompt. Once validated, deploy the new prompt to production.
Replay LLM sessions
By debugging a monitored LLM call, you have access to the conversation context - prompt templates, prompt variables and provider settings - and leverage those to improve your prompt template or provider settings.
Manage & version your prompts
Prompt Templates, managed in your code or on Literal, are versioned and stored on Literal.
Replay sessions in prompt playground
Objects are linked in Literal. Debugging in context is extremely useful and time-saving.
Evaluate new prompt versions
Leverage online evaluation to score a Dataset on the new challenger prompt. Once validated, deploy the new prompt to production.
Replay LLM sessions
By debugging a monitored LLM call, you have access to the conversation context - prompt templates, prompt variables and provider settings - and leverage those to improve your prompt template or provider settings.
Manage & version your prompts
Prompt Templates, managed in your code or on Literal, are versioned and stored on Literal.
Replay sessions in prompt playground
Objects are linked in Literal. Debugging in context is extremely useful and time-saving.
Evaluate new prompt versions
Leverage online evaluation to score a Dataset on the new challenger prompt. Once validated, deploy the new prompt to production.
LLM Integrations
Seamlessly integrate Literal in your application by leveraging its integrations with the entire LLM ecosystem.
Python & TypeScript SDK
Seamlessly integrate in your application code
Python & TypeScript SDK
Seamlessly integrate in your application code
Multi-modal
Literal supports multi-modal inputs and outputs!
Multi-modal
Literal supports multi-modal inputs and outputs!
Threads, Agents, LLMs
A powerful data model for both conversational and non-conversational AI
Threads, Agents, LLMs
A powerful data model for both conversational and non-conversational AI
Evaluation
Seamlessly incorporate images, videos, and other multimedia into your content.
Offline Evaluation
Leverage popular open-source frameworks such as Ragas or OpenAI Evals to evaluate your LLM system and upload the experiment's results
Offline Evaluation
Leverage popular open-source frameworks such as Ragas or OpenAI Evals to evaluate your LLM system and upload the experiment's results
Offline Evaluation
Leverage popular open-source frameworks such as Ragas or OpenAI Evals to evaluate your LLM system and upload the experiment's results
Online Evaluation
Define LLM-based or code-based evaluators on Literal AI and continuously monitor your LLM application
Online Evaluation
Define LLM-based or code-based evaluators on Literal AI and continuously monitor your LLM application
Online Evaluation
Define LLM-based or code-based evaluators on Literal AI and continuously monitor your LLM application
A/B testing
Compare both pre-production and post-production configurations to improve your LLM application
A/B testing
Compare both pre-production and post-production configurations to improve your LLM application
A/B testing
Compare both pre-production and post-production configurations to improve your LLM application
From Prototype to Production
Literal AI is a comprehensive solution, offering a developer-friendly platform that enables the product team to safely iterate on prompts and effectively monitor the KPIs of your LLM app.
Prompt Management
Both your product and engineering teams can seamlessly manage the full lifecycle of your prompts, from creation to deployment.
Prompt Management
Both your product and engineering teams can seamlessly manage the full lifecycle of your prompts, from creation to deployment.
Prompt Management
Both your product and engineering teams can seamlessly manage the full lifecycle of your prompts, from creation to deployment.
Observability
Log all data from your app including user messages, LLM calls, agents and chain runs, latency, token count, and human feedback.
Observability
Log all data from your app including user messages, LLM calls, agents and chain runs, latency, token count, and human feedback.
Observability
Log all data from your app including user messages, LLM calls, agents and chain runs, latency, token count, and human feedback.
Dataset
Create Datasets to evaluate prompt templates directly on Literal. Mix hand written input/output pairs with production data.
Dataset
Create Datasets to evaluate prompt templates directly on Literal. Mix hand written input/output pairs with production data.
Dataset
Create Datasets to evaluate prompt templates directly on Literal. Mix hand written input/output pairs with production data.
Evaluation
Track your prompt performances, iterate, and ensure no regression occurs before deploying the new prompt version.
Evaluation
Track your prompt performances, iterate, and ensure no regression occurs before deploying the new prompt version.
Evaluation
Track your prompt performances, iterate, and ensure no regression occurs before deploying the new prompt version.
Analytics
Monitor your application usage through a blend of traditional product metrics and advanced LLM-powered analytics.
Analytics
Monitor your application usage through a blend of traditional product metrics and advanced LLM-powered analytics.
Analytics
Monitor your application usage through a blend of traditional product metrics and advanced LLM-powered analytics.
Multi Modal
Designed to support multi-modal content as LLMs continue to evolve and expand their capabilities.
Multi Modal
Designed to support multi-modal content as LLMs continue to evolve and expand their capabilities.
Multi Modal
Designed to support multi-modal content as LLMs continue to evolve and expand their capabilities.
What our Users Say
Hear directly from those who build with Literal.
Managing and understanding the performance of our chatbot is crucial. Literal has been an invaluable tool in this process. It has allowed us to log every conversation, collect user feedback, and leverage analytics to gain a deeper understanding of our chatbot's usage.
Managing and understanding the performance of our chatbot is crucial. Literal has been an invaluable tool in this process. It has allowed us to log every conversation, collect user feedback, and leverage analytics to gain a deeper understanding of our chatbot's usage.
Managing and understanding the performance of our chatbot is crucial. Literal has been an invaluable tool in this process. It has allowed us to log every conversation, collect user feedback, and leverage analytics to gain a deeper understanding of our chatbot's usage.
Developing and monitoring all of our GenAI projects is a critical part of my role. Literal has been an absolute game-changer. It not only allows us to track the Chain of Thought of our agents/chains but also enables prompt collaboration with different teams.
Developing and monitoring all of our GenAI projects is a critical part of my role. Literal has been an absolute game-changer. It not only allows us to track the Chain of Thought of our agents/chains but also enables prompt collaboration with different teams.
Developing and monitoring all of our GenAI projects is a critical part of my role. Literal has been an absolute game-changer. It not only allows us to track the Chain of Thought of our agents/chains but also enables prompt collaboration with different teams.
Building an effective chatbot for Evertz's internal operations was a daunting task but working with Literal has made the process significantly easier. It has allowed us to analyze each step of our users interactions and to more quickly converge on the desired behaviour.
Building an effective chatbot for Evertz's internal operations was a daunting task but working with Literal has made the process significantly easier. It has allowed us to analyze each step of our users interactions and to more quickly converge on the desired behaviour.
Building an effective chatbot for Evertz's internal operations was a daunting task but working with Literal has made the process significantly easier. It has allowed us to analyze each step of our users interactions and to more quickly converge on the desired behaviour.
Pricing
Starter
Free
/month
1,000 Threads / 30,000 Steps
Threads, Runs, Steps Observability
Human Feedback Collection
Python / TypeScript SDK
Prompt Playground
LLM Usage Analytics
Custom
Business
Prompt Evaluation
Prompt A/B Testing
Volume-based pricing
Automated Evaluation
LLM powered user analytics
Self-Hosting / Dedicated Infrastructure
1,000 Threads / 30,000 Steps
Threads, Runs, Steps Observability
Human Feedback Collection
Python / TypeScript SDK
Prompt Playground
LLM Usage Analytics
Prompt Evaluation
Prompt A/B Testing
Volume-based pricing
Automated Evaluation
LLM powered user analytics
Self-Hosting / Dedicated Infrastructure
1,000 Threads / 30,000 Steps
Threads, Runs, Steps Observability
Human Feedback Collection
Python / TypeScript SDK
Prompt Playground
LLM Usage Analytics
Prompt Evaluation
Prompt A/B Testing
Volume-based pricing
Automated Evaluation
LLM powered user analytics
Self-Hosting / Dedicated Infrastructure
Ship
LLM applications
Empowering Developers, Product Managers and Domain Experts to Collaboratively Build, Evaluate and Monitor LLM apps.
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