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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... show more

Jerry Liu

85,798 subscribers

57,248 次观看 • 3 个月前 •via X (Twitter)

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24 条评论

Adnan Qidwai 的头像

Adnan Qidwai3 个月前

btw the GitHub doesn't seem public yet

Jerry Liu 的头像

Jerry Liu3 个月前

sorry about the above - just made it public!

Ira Rothken 的头像

Ira Rothken3 个月前

This looks very promising for AI legal apps and ESI analysis.

Kevin Madura 的头像

Kevin Madura3 个月前

Have you tested it on OBLIQ-Bench?

Pinkman 的头像

Pinkman3 个月前

hybrid search across the whole index with read, grep, and find is basically giving an agent the same tools a developer would use on a local filesystem

Eugene Yaroslavtsev 的头像

Eugene Yaroslavtsev3 个月前

@jerryjliu0 i haven't been able to find a bug bounty / security program link on gh/llama's official site - got a link?

AW 的头像

AW3 个月前

jerry,dropped you a DM about a paid collab

KYD 玩 ai 的头像

KYD 玩 ai3 个月前

Treating retrieval as a persistent tool surface, with search, grep, file search, and read operations, feels much closer to how agents actually investigate large knowledge bases than one-shot RAG calls.

Dmytro Palaniichuk 🇺🇦 的头像

Dmytro Palaniichuk 🇺🇦3 个月前

The filesystem metaphor is the right abstraction. Moving from "query the index" to "navigate the knowledge base" made agents far more predictable — they scope reads before writing, like a good engineer. Partial-update primitives, or is re-index always full corpus?

Christian Müller 的头像

Christian Müller3 个月前

Persistent pipeline plus a bounded, filesystem-like tool surface is a strong combo here. The reliability jump comes when each tool call returns a typed result the next step can rely on, not free-form text the agent has to re-parse every run.

srijanarya 的头像

srijanarya3 个月前

The filesystem-style tools were never my bottleneck. Index freshness was. "Index and update a large KB" buries the ugly part: an agent retrieving mid-reindex and answering off a stale chunk. Does the harness guarantee read-during-update consistency?

Sean Kerr — AI, minus the hype 的头像

Sean Kerr — AI, minus the hype3 个月前

The persistent pipeline is the right call. Most "agentic retrieval" pain isn't recall — it's the agent re-indexing the world every run because nothing survives between calls. The harder half: teaching it when it has enough to stop retrieving.

Nahid 的头像

Nahid3 个月前

persistent data pipeline opens up new possibilities

Shashank Nidhi 的头像

Shashank Nidhi3 个月前

Your harness builds a fresh index, but it never flags when a source document drifts. Staleness is a harder problem than retrieval. A vector store can answer a query, yet it has no idea the underlying truth moved two weeks ago. If you attach a provenance check on each update, the agent can decide whether to trust the result.

Nhost 的头像

Nhost3 个月前

The quiet shift here is that retrieval starts looking more like an agent exploring a filesystem: search, grep, read, follow the thread. That's much closer to how people actually research a case

Oleksandr 的头像

Oleksandr3 个月前

agentic retrieval feels like giving an agent a local filesystem watching legal agents actually crawl PDFs is next‑level

Anshuman Singh 的头像

Anshuman Singh3 个月前

The persistent-pipeline framing matches what we hit with travel data: static snapshots rot fast, so continuous dedup plus per-row source tags are what keep agent citations honest. Curious how the harness handles provenance when two indexed sources conflict, do you keep both with lineage or resolve to one?

Jabir | AI Systems 的头像

Jabir | AI Systems3 个月前

👍🏻

Sebastian Buzdugan 的头像

Sebastian Buzdugan3 个月前

index v2 will live or die on permission drift, not tool breadth

Pawzard 的头像

Pawzard3 个月前

agents out here getting a whole filing cabinet meanwhile i still cant find my keys 😭

The Guy With A Hat 的头像

The Guy With A Hat3 个月前

Another OCR. Sweet, is there a paper attached?

egesea 的头像

egesea3 个月前

Retrieval is becoming less about finding information. It's becoming the workspace where agents think and act.

Makaroni 的头像

Makaroni3 个月前

curious how it handles drift tho like the knowledge base updates mid-task and the agent already cached stale context

Brosko 的头像

Brosko3 个月前

ngl 'filesystem operations but for vibes' is actually the unlock agents been fumbling retrieval tasks for too long fr 🔥

相关视频

We're excited to introduce the Retrieval Harness in LlamaParse - which is the 2026 version of RAG over documents Generalized agents need the right set of tools to scalably search and read through an arbitrary corpus of data (from 10 docs to 1m+ docs). They can already demonstrate great retrieval performance over a local filesystem, need a proper backend for a large collection of managed data. The Retrieval Harness exposes a diverse set of tools for various needs: 1. Hybrid Retrieval: Combine vector search with keyword search, let the agent set the alpha value to toggle between the two 2. List Files: a scalable version of `ls` to list files within an index 3. File Grep: enable regex search within a given file 4. File Read: Allow agents to read a subsection from an existing document. The agent can choose to interleave any sequence of these tools in order to complete a variety of tasks, from simple to hard. Come check it out! Blog: Sign up to LlamaParse:

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18,615 次观看 • 3 个月前

Semantic search alone doesn't cut it. Neither does brute-force grep. Agents need both. Today we're shipping the Retrieval Harness in LlamaParse Index: semantic search, server-side grep, and file-level navigation working together in a single agent reasoning loop. 🦙🌤️ Grep a file, list what's in an index, read past a chunk boundary, run hybrid search with reranking — all as native agent tools. Now in beta across all paid tiers. Full breakdown in the blog 👇 Learn More:

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29,181 次观看 • 3 个月前

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77,373 次观看 • 1 年前

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Santiago

126,565 次观看 • 1 年前

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Arlan

28,737 次观看 • 6 个月前

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40,643 次观看 • 2 年前

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13,028 次观看 • 6 个月前

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Razvan Roman

75,737 次观看 • 26 天前

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Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

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244,593 次观看 • 2 年前

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57,745 次观看 • 1 年前

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Nodepay

183,637 次观看 • 1 年前

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We built a neat tool that lets you convert a directory of Powerpoint files into clean, structured markdown - that Claude Code / agent SDK / any generalized agent wrapper can easily understand. The pptx skill in Claude Code is quite basic and doesn’t have high-fidelity understanding over graphics/charts/tables. Our project Surreal Slides uses LlamaParse to convert presentations into clean structured data that you can put into a db (SurrealDB) for simple retrieval, without having to take screenshots of the data on the fly. Thanks to Clelia Bertelli (🦙/acc) for this project, check it out:

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39,872 次观看 • 7 个月前

Knowledge graphs are infinitely better than vector search for building the memory of AI agents. With five lines of code, you can build a knowledge graph with your data. When you see the results, you'll never go back to vector-mediocrity-land. Here is a quick video:

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Santiago

398,365 次观看 • 1 年前

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Avi Patel

91,697 次观看 • 1 年前

This is probably the biggest news yet in software going headless, and will bring knowledge work agents to the masses. The new ChatGPT agents have access to any of the tools and data you want to work with, with complete coding and tool use available to them. Here's an example of a custom sales assistant agent uses Box as a knowledge source for accessing enterprise content securely to answer questions and generate new content on the fly. The workflows can obviously vastly far more complex as the agent can use any of the tools within Box available via MCP and CLI. This precisely what agents will start to look like for knowledge work. You'll be able to spin them up in the foreground or background to help augment work. Big opportunity right now for headless platforms, and for all the new builders and designers of these agents in the enterprise.

This is probably the biggest news yet in software going headless, and will bring knowledge work agents to the masses. The new ChatGPT agents have access to any of the tools and data you want to work with, with complete coding and tool use available to them. Here's an example of a custom sales assistant agent uses Box as a knowledge source for accessing enterprise content securely to answer questions and generate new content on the fly. The workflows can obviously vastly far more complex as the agent can use any of the tools within Box available via MCP and CLI. This precisely what agents will start to look like for knowledge work. You'll be able to spin them up in the foreground or background to help augment work. Big opportunity right now for headless platforms, and for all the new builders and designers of these agents in the enterprise.

Aaron Levie

420,133 次观看 • 5 个月前

Today we announced the new Einstein Copilot Search and Salesforce Vector Database in Data Cloud to power semantic search and retrieval augmented generation. This will enhance Einstein Copilot's ability to understand, generate outputs, and automate actions across a wide variety of use cases, contexts, and data/content types. LLMs can't be relied on to recall specific data they were trained on, so the way to make them work in the enterprise, where accuracy is paramount, is to feed them with the right data using hybrid keyword and vector search-powered RAG. By bringing all this seamlessly into our apps and Einstein Copilot UX as well as Hyperforce cloud infrastructure, we can offer high performance, low latency, addressing data privacy, security, and residency requirements🌟

Today we announced the new Einstein Copilot Search and Salesforce Vector Database in Data Cloud to power semantic search and retrieval augmented generation. This will enhance Einstein Copilot's ability to understand, generate outputs, and automate actions across a wide variety of use cases, contexts, and data/content types. LLMs can't be relied on to recall specific data they were trained on, so the way to make them work in the enterprise, where accuracy is paramount, is to feed them with the right data using hybrid keyword and vector search-powered RAG. By bringing all this seamlessly into our apps and Einstein Copilot UX as well as Hyperforce cloud infrastructure, we can offer high performance, low latency, addressing data privacy, security, and residency requirements🌟

Clara Shih

49,651 次观看 • 2 年前

Another example of the power of headless software with agents. With Claude Tag, you can give Claude access to any corporate files in Box that you can interact with from Slack. Now all of your enterprise content becomes a portable knowledge base.

Another example of the power of headless software with agents. With Claude Tag, you can give Claude access to any corporate files in Box that you can interact with from Slack. Now all of your enterprise content becomes a portable knowledge base.

Aaron Levie

74,286 次观看 • 3 个月前