Agent and RAG Builders
Build agents and applications that use your documents, knowledge, and connected systems.
Agent and RAG builders connect models to knowledge sources and actions. Retrieval helps an application find relevant material; an agent may also use tools to complete a task. Visual builders and developer frameworks require different implementation skills, so choose the operating model first. Test document updates, source references, unanswered questions, and permissions for connected actions before judging a demonstration successful.
Compare by workflow
- Knowledge ingestion
- Agent actions
- Evaluation
- Deployment
Shortlist by use case
| Tool | Consider it for |
|---|---|
| Dify | Visual apps and agents with license-based self-hosting |
| Flowise | Self-maintained visual AI workflow builder |
| LangChain | Observe, evaluate, and deploy reliable AI agents |
| Langflow | Build AI agents and RAG apps visually |
| Voiceflow | Own every customer interaction |
Explore all Agent and RAG Builders
How to choose
- Test answers against your own documents, including questions without an answer.
- Check how you update knowledge and review agent actions.
Frequently asked questions
- How do visual builders differ from agent frameworks?
- Visual builders expose configuration through an interface. Frameworks provide components for developers to assemble in code. Compare the ongoing maintenance work as well as the first setup.
- What should a RAG evaluation include?
- Use questions with known answers, conflicting documents, outdated material, and missing information. Check retrieval quality and whether the answer accurately reflects the retrieved sources.
Last reviewed:
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