Open-source IBM watsonx alternatives

Dify is a platform for building applications on top of large language models. Instead of wiring together prompts, retrieval code and model APIs by hand, teams assemble AI workflows, knowledge pipelines and agents in a shared visual workspace, then move them from prototype to production without changing stacks.

The product covers a workflow studio for agentic flows, a knowledge pipeline that prepares searchable knowledge bases for retrieval-augmented generation, model management across many providers, and a marketplace of tools, models and integrations. Observability integrations such as Opik, Langfuse and Arize Phoenix help trace and debug runs once an app is live.

Dify comes in a few editions: a community edition you deploy yourself with Docker, a hosted cloud service, and an enterprise edition for private deployments. The repository carries a custom license rather than a standard one, so check its terms before offering Dify as a service to others.

MLflow is an open-source AI engineering platform for agents, LLM applications and ML models. It helps teams debug, evaluate, monitor and optimize AI applications in production while keeping control of costs and access to models and data. It is written in Python, released under the Apache-2.0 license, and has been developed since 2018, with topics covering MLOps, model management and LLMOps.

For LLM and agent work it offers production observability with traces, plus evaluation, prompt management and prompt optimization, and an AI Gateway that governs costs and model access. It works with Python, TypeScript and JavaScript, Java and other languages, and integrates with OpenTelemetry and MCP. Getting started means starting an MLflow server, enabling logging in your code and running it, then exploring traces and metrics in the web UI on port 5000. A setup wizard can connect to an MLflow server or a Databricks workspace and add tracing with a coding agent.