
An open-source LLMOps platform offering prompt engineering, evaluation, and observability tools for building robust LLM applications.
Agenta is an open-source platform designed to assist developers and product teams in building robust AI applications powered by Large Language Models (LLMs). It provides integrated tools for prompt engineering, versioning, evaluation, and observability, all in one place. With Agenta, users can rapidly experiment with prompts, manage prompt versions, evaluate application performance, and monitor usage and quality. The platform supports various LLM workflows, including chain-of-prompts and Retrieval Augmented Generation (RAG), and is compatible with frameworks like Langchain and LlamaIndex. Agenta aims to accelerate the development cycle of LLM applications by facilitating collaboration between developers and non-developers, such as product managers or domain experts.
Product overview
Agenta is an open-source workspace for building, sharing, running, and improving specialized agents and automations. It combines interactive chat with background schedules and event triggers, connected tools, per-tool permissions, tracing, configuration history, evaluations, and team access.
Information as of August 9, 2026
Agents can work with users in chat, be shared with a team, and run on schedules or events from connected applications.[3]
Workspaces support files, tool permissions and approvals, MCP servers, reusable skills, and integrations with external applications.[3]
Agenta records model and tool calls, token use, estimated costs, errors, and agent configuration versions, with evaluation and side-by-side comparison features.[2], [3]
Agenta Cloud Hobby is free for two team members and 5,000 monthly runs. Pro is $29 per month, Business is $299 per month, and Enterprise is custom; the self-hosted open-source edition is free, while model usage is billed separately by model providers.[2]
Platforms: Agenta Cloud web workspace, Open-source web workspace[1], [2], [3]
Access: Vendor-hosted cloud, Self-hosted with Docker Compose[1], [2], [3]
38/100
Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.
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For llm evaluation consultancies
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