We've created a comprehensive Retrieval Harness for modern agentic retrieval in 2026.
The harness provides a persistent data pipeline that can connect to a data source, index and update a large knowledge base, and expose a broad set of tools akin to filesystem operations (semantic/keyword search, regex grep, file search, read).
You can plug this into any of your agents to let them autonomously crawl an arbitrary knowledge base to solve a task with any complexity.
Check out our reference implementation: https://github.com/run-llama/legal-kb
LlamaParse: https://cloud.llamaindex.ai/
Agentic retrieval is changing the way retrieval-augmented applications are built, especially in domains like legal and fintech, where agents need to autonomously navigate large, evolving knowledge bases.
That鈥檚 exactly the use case we designed Index v2 for.
To demonstrate what鈥檚 possible, we built legal-kb, a reference application that integrates Index v2 into an agentic knowledge automation workflow. It uses Index v2 as the underlying knowledge base and exposes its retrieve, read, grep, and find APIs as tools that an AI agent can use to autonomously explore and reason over your documents.
With legal-kb, you get:
馃摎 Project-scoped knowledge bases for agent-powered chat 馃摉 Visual citations directly in agent responses 馃尶 Version control for your knowledge base 馃摝 Data export capabilities
Try it out: https://legal-kb.dev Explore the code: https://github.com/run-llama/legal-kb Get started with LlamaParse: https://cloud.llamaindex.ai/signup
