Tuana

Thanks! Fixed 🫶

Vibe-Code a Document Agent with LlamaAgents In this video, Senior DevRel Enginner Tuana Celik walks through how to use LlamaAgent Builder to create an end-to-end agent workflow. The example here is an ...

Give it a try and let me know what you build. We're running a contest for the most difficult document workflow you can throw at it. Full video here:
youtu.be/0Zhf5z2Onjs...

the whole point is you're describing the problem, not building the pipeline. the agent builder decides the architecture BUT with the caveat that coding agents aren't perfect and the code is yours to edit and perfect!

The agent builder figured out it needed Split and Extract, configured both, built the workflow, and deployed it: API + UI, code in my GitHub

I revisited my old demo: I took a resume book from NYU (resumes mixed with cover pages and curriculum pages) and just told the agent builder: split this into individual resumes, ignore the rest, extract graduation year, work experience, etc.

I filmed a walkthrough of LlamaAgent Builder, our new tool for building document agents by just describing what you want @llama_index

Announcement: www.llamaindex.ai/blog/llamaa...
Documentation: developers.llamaindex.ai/python/llam...

Note: our first release is a beta release, tuned and optimized for agents that do complex document extractions. We'll be adding more use cases as we test and confirm quality.

If you try it out, please let us know! Drop us feedback in the UI or DM me 💛

But: ultimately the workflow is code, which the builder will create a repository for wherever you want, and deploy to LlamaCloud after. You get the ease of no-code to begin with, but full flexibility to customize (or fully change) the code if you want.

🧠 The Builder will generate the agent workflow code, ask you clarifying questions etc
🎨 While this is happening, you'll see a visualisation of the resulting workflow

We have a new tool to help you build and deploy document agents in LlamaCloud. The LlamaAgents Builder is kiiinda no-code, but not:

🦋 We have a new chat interface: just describe the document processing task you want in natural language

Announcing New LlamaCloud SDKs and Parse API v2 LlamaIndex is a simple, flexible framework for building knowledge assistants using LLMs connected to your enterprise data.

Wrote about it here with Logan and Preston. www.llamaindex.ai/blog/announ...

· processing_options for fine-grained control when you need it.
Plus new llama-cloud SDKs (Python + TypeScript) with way better developer experience.
If you're on v1, you're fine, we're maintaining support. But for new projects, v2 and new SDKs is the way.

We rebuilt LlamaParse's API from the ground up, and also released new SDKs for LlamaCloud in its entirety.

API v2 for LlamaParse simplfies parsing config into structured objects
· input_options for file-specific settings.
· output_options for controlling what you get back.

Latest example in our docs at @llama_index: LlamaSheets (one of our latest products within LlamaCloud) has an example listed on how you can use LlamaSheets alongside coding agents: developers.llamaindex.ai/python/clou...

For that to work, both the developer and the agent needs to understand the context that they're working in.

that makes the most sense.

Something that bothers me: In an ideal world, the purpose isn't to replace the developer, but to get to a place where we have coding agents that can sit alongside us in the development process.

A completely new way of thinking about documentation, oss projects, developer tools we provide has actually been figuring out how to structure them so that without compromising the main audience (the developer) - we can make sure coding agents also get the most relevant context, formatted in the way

I went offline for a couple of days to be with family and it seems like all we talked about on this platform has been coding agents.

And of course, bring along any questions too!

That's a lot, so this week, we want to take the time to hear from you! On Thursday, me, @LoganMarkewich and @itsclelia will be in our Discords voice channel for an hour dedicated to chatting about these two new tools. Drop by for our office hours, we'd love to hear from you.

LlamaSheets: Another addition to LlamaCloud that parses, extracts information and deep context (also hidden in metadata) from tables and spreadsheets, as well as identifying sub-groups from complex sheets

The team at LlamaIndex have been cooking! 🧑‍🍳 🍳
Over the last few weeks, we released:

LlamaAgents: This is agent workflows that come with complete, deployable templates (more coming on this this week!)

LlamaSheets | AI Parsing and Extraction for Spreadsheets LlamaSheets is a new LlamaCloud API that automatically transforms complex, messy spreadsheets into clean, structured, AI-ready data. Using advanced semantic analysis and a multi-stage extraction pipeline, LlamaSheets identifies tables, preserves hierarchical headers, interprets formatting, and outputs typed Parquet datasets for analytics, agents, and automation workflows.

@LoganMarkewich explains the technical approach here: youtu.be/eOp6_vbA5Kc
Try it out: www.llamaindex.ai/blog/announ...

Humans parse this instantly. Agents however, struggle. So LlamaSheets handles the problem by extracting 40+ features per cell, clustering regions, preserving hierarchy, and finally outputting typed parquet files. Your agent gets clean data instead of formatted chaos.

We shipped LlamaSheets today (beta, free).
It approaches spreadsheets as a visual structure: Bold headers, merged cells, color-coded categories.

Announcing LlamaAgents Open Preview: Build, Serve & Deploy Document Agents LlamaIndex is a simple, flexible framework for building knowledge assistants using LLMs connected to your enterprise data.

www.llamaindex.ai/blog/llamaa...

- Initialize a document agent project starting with one of our templates (In the blog, we used the SEC Insights Agent as an example)
- Serve agents locally
- Deploy them to LlamaCloud
- Use all the LlamaCloud tooling like Extract and Classify as inherent components to your agent workflows

Yesterday we announced the open preview for LlamaAgents

So, me and my colleague Adrian wrote this intro blog to help you get started. Learn about all the document agent templates available to you via llamactl and:

For example, here's me extracting the train time table from CalTrain schedule which is 2 large tables in a PDF. I also used the schema generation option for this one. Just prompt -> check schema,.