Your

All-In-One

LLM Application Platform

LLM App Platform

Your

All-In-One

Quickly get LLM Apps to production with tools for
prompt-engineering, evaluation and monitoring.

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How it works

Best practices for LLM app Development

Build

Robust

AI Applications

Large Language Models are revolutionizing industries. However, controlling them is hard. Agenta is a comprehensive platform that enable teams to quickly build robust LLM apps.

Collaborative Playground for Prompt Engineering

Evaluation Framework

Interface to to Gather Human Feedback

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Developers

Developer-First

Platform

All the tools you need to build LLM apps with your team and streamline your workflow are available in one place.

Create stunning, professional-quality websites in record time with our powerful UI kit.

Playground

Create a Playground connected to your code wher the whole team can experiment and collaborate.

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Automatic Evaluation

Systematically compare different prompts, models, and embeddings before going to production.

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Human Feedback

Share a link to gather human feedback from the rest of the team

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Integrations

Model and Framework Agnostic

Agenta works out of the box with all frameworks (Langchain, Lama Index, etc.) and model providers (OpenAI, Cohere, Huggingface, self-hosted models, etc.).

Coming Soon

Monitor and Debug

Gain visibility into your LLM apps costs, latency and chain of calls.

Get to know

Frequently

asked

questions

Create robust LLM apps in record time. Focus on your core business logic and leave the rest to us.

Star Us On Github

Do I need to know how to code to create LLM applications with agenta?

Can I use agenta with a self-hosted fine-tuned model such as Llama or Falcon?

How can I limit hallucinations and improve the accuracy of my LLM apps?

Is it possible to use vector embeddings and retrieval-augmented generation with Agenta?

How it works

Frequently

asked

questions

Create robust LLM apps in record time. Focus on your core business logic and leave the rest to us.

Star Us On Github

Do I need to know how to code to create LLM applications with agenta?

Can I use agenta with a self-hosted fine-tuned model such as Llama or Falcon?

How can I limit hallucinations and improve the accuracy of my LLM apps?

Is it possible to use vector embeddings and retrieval-augmented generation with Agenta?

How it works

Frequently

asked

questions

Create robust LLM apps in record time. Focus on your core business logic and leave the rest to us.

Star Us On Github

Do I need to know how to code to create LLM applications with agenta?

Can I use agenta with a self-hosted fine-tuned model such as Llama or Falcon?

How can I limit hallucinations and improve the accuracy of my LLM apps?

Is it possible to use vector embeddings and retrieval-augmented generation with Agenta?