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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... show more

LlamaIndex 🦙

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29,181 次观看 • 17 天前 •via X (Twitter)

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RLM is the most import foundation of my Pi Harness (other than Pi of course). It's seeded with late interaction retrieval results (thanks to @lightonai for pylate). The Agent initiates it with query then.. 𝐒𝐞𝐭𝐮𝐩 A python REPL is created and seeded with: 1. Late interaction search to pre-filter. Instead of doing top 3/5/10, it's top hundreds of documents. This is set into a `context` variable. 2. Python functions are loaded in to do more searches if `context` variable isn't enough. And to make llm calls with cheaper models in parallel batches. 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐨𝐨𝐩 From there, an LLM iterates in the REPL based on the query. It's just like exploring in a jupyter notebook. The LLM writes prose (like a markdown cell) and code to be run in the REPL each turn. This allows the LLM to sort, filter, and synthesize information. It can fan out and ask smaller models to summarize, combine, contrast, or do anything else to documents to help it understand the data. After several turns the LLM reponds with the final answer. Either because it found the answer, or hit the budget limit. Context as a Python variable, LLM as the programmer, REPL as the runtime. 𝐖𝐡𝐲 𝐃𝐨𝐞𝐬 𝐓𝐡𝐢𝐬 𝐖𝐨𝐫𝐤 1. Richer Shell. Agents (and subagents) work by intermixing code and prose/thinking. But they use static scripts or bash that run and exit and start over each tool call. That's not ideal for exploration and synthesis of data. For that, state is useful to continue building and exploring the data as you learn more. There's a reason jupyter notebooks have been popular with data scientists. 2. Keeps main agent context clean. The better context you have the better the agent will perform (duh!). This means three thing: better human input, less missing search results, and less incorrect search results. Letting the agent iterate allows it to synthesize just what is needed and nothing else. All bad paths or peeks at something that turns out to be irrelevant stays out of main agent context. 3. Stack the good ideas! People often compare late interaction search vs RLM. Or static vs dynamic languages. Or agentic search vs semantic search. But...You can just use them all together for what they're each good at. Use them all for the area they're really great for. Read the full post which has more detail about how and why.

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Clara Shih

49,620 次观看 • 2 年前

𝗚𝗿𝗲𝗽 𝗶𝘀 𝗲𝘅𝗽𝗮𝗻𝗱𝗶𝗻𝗴 𝗯𝗲𝘆𝗼𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗱𝘂𝗲 𝗱𝗶𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝘁𝗼 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝗴𝗲𝘁 𝘀𝗲𝗿𝗶𝗼𝘂𝘀 𝘄𝗼𝗿𝗸 𝗱𝗼𝗻𝗲 𝘄𝗶𝘁𝗵 𝟮𝟬 𝗔𝗜 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝟭𝟲 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀 In December, we launched Grep, an AI agent for business due diligence, as a research preview. Within two weeks, hundreds of people were using it in underwriting, maritime law, logistics, oil and gas, crypto, sales and marketing and much more. Today, we're launching the next version with 20 AI experts across 16 industries to help you carry out deep research and get serious work done. 𝗪𝗵𝘆 𝘄𝗲'𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗚𝗿𝗲𝗽 Most people are still stuck with AI that doesn't help them get work done. Chatbots that aren't specialized in their domain or vertical agents built for 2024's models—rigid workflows that break easily. Meanwhile, software engineers using Claude Code or Cursor are shipping 10x faster because those tools figured out what actually works. Modern coding agents load up the right context. Then get to work on your objective. That's what Grep does too. You give it an objective—"research this company to verify it is legitimate," "analyze this market and identify potential leads," "tell me who previously owned this vessel," "evaluate this vendor's compliance risk," "carry out a background check on this employee." Grep picks the right Expert and starts investigating across 50+ specialized sources, including corporate registries, court records, financial filings, sanctions databases, shipping data, academic journals and much more. This deep research provides the context to help you complete your task. Then you dig deeper. Ask follow-ups. Probe specific angles. The expert already has all the skills and integrations to help you get work done. But work doesn't end with research. Grep helps you turn that research into docs, decks, slides, reports, and even code. The context carries through, so the AI Expert can help you execute, not just inform. We're pretty excited about Grep and would love your feedback, especially if you are an early adopter of AI in your domain but aren't satisfied with your existing workflows. If you want to try Grep, join the waitlist at - we'll be starting with a small group of beta users and expanding as we make our AI Experts even better with user feedback. P.S. Huge thanks to Awesomic for the launch video 🎬

𝗚𝗿𝗲𝗽 𝗶𝘀 𝗲𝘅𝗽𝗮𝗻𝗱𝗶𝗻𝗴 𝗯𝗲𝘆𝗼𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗱𝘂𝗲 𝗱𝗶𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝘁𝗼 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝗴𝗲𝘁 𝘀𝗲𝗿𝗶𝗼𝘂𝘀 𝘄𝗼𝗿𝗸 𝗱𝗼𝗻𝗲 𝘄𝗶𝘁𝗵 𝟮𝟬 𝗔𝗜 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝟭𝟲 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀 In December, we launched Grep, an AI agent for business due diligence, as a research preview. Within two weeks, hundreds of people were using it in underwriting, maritime law, logistics, oil and gas, crypto, sales and marketing and much more. Today, we're launching the next version with 20 AI experts across 16 industries to help you carry out deep research and get serious work done. 𝗪𝗵𝘆 𝘄𝗲'𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗚𝗿𝗲𝗽 Most people are still stuck with AI that doesn't help them get work done. Chatbots that aren't specialized in their domain or vertical agents built for 2024's models—rigid workflows that break easily. Meanwhile, software engineers using Claude Code or Cursor are shipping 10x faster because those tools figured out what actually works. Modern coding agents load up the right context. Then get to work on your objective. That's what Grep does too. You give it an objective—"research this company to verify it is legitimate," "analyze this market and identify potential leads," "tell me who previously owned this vessel," "evaluate this vendor's compliance risk," "carry out a background check on this employee." Grep picks the right Expert and starts investigating across 50+ specialized sources, including corporate registries, court records, financial filings, sanctions databases, shipping data, academic journals and much more. This deep research provides the context to help you complete your task. Then you dig deeper. Ask follow-ups. Probe specific angles. The expert already has all the skills and integrations to help you get work done. But work doesn't end with research. Grep helps you turn that research into docs, decks, slides, reports, and even code. The context carries through, so the AI Expert can help you execute, not just inform. We're pretty excited about Grep and would love your feedback, especially if you are an early adopter of AI in your domain but aren't satisfied with your existing workflows. If you want to try Grep, join the waitlist at - we'll be starting with a small group of beta users and expanding as we make our AI Experts even better with user feedback. P.S. Huge thanks to Awesomic for the launch video 🎬

AJ Asver

28,077 次观看 • 5 个月前

The founder of LangChain says both models and harnesses have gotten really good between December and now. According to Harrison Chase, the core idea of an agent before Christmas was a model running in a loop and calling tools. This had been the north star for 3 years. - langchain had this when it launched - autogpt was the same idea - openclaw is kind of a future version of it Then about a year ago, they started getting really good. Claude Code, Manus, and Deep Research were all launched around the same time. All of them use the same pattern: running in a loop with harnesses (planning tools, file systems, code execution, etc) Harness engineering became a thing. Then Opus came out in November and really unlocked it. - the harness let the model do more and more - less hardcoded logic - way more control Then everyone went on vacation, played around, and realized that the model and the harness finally worked reliably.

The founder of LangChain says both models and harnesses have gotten really good between December and now. According to Harrison Chase, the core idea of an agent before Christmas was a model running in a loop and calling tools. This had been the north star for 3 years. - langchain had this when it launched - autogpt was the same idea - openclaw is kind of a future version of it Then about a year ago, they started getting really good. Claude Code, Manus, and Deep Research were all launched around the same time. All of them use the same pattern: running in a loop with harnesses (planning tools, file systems, code execution, etc) Harness engineering became a thing. Then Opus came out in November and really unlocked it. - the harness let the model do more and more - less hardcoded logic - way more control Then everyone went on vacation, played around, and realized that the model and the harness finally worked reliably.

Ivan Burazin

33,948 次观看 • 3 个月前

🔥 Your X Agent just got eyes. Watch it think. Built for strict X API compliance. No automated bulk actions. Every engagement is a suggestion you act on manually. Your account stays safe. 💥 New: Live Reasoning Terminal — see every search, thread read, memory recall, and engagement decision across 21 tools as your autonomous agent works. No black box. Here's the full loop in 60 seconds: ↓🎥 1️⃣ Configure Topics, accounts, model tier, run interval, action toggles — full control over what your agent does and how often. 2️⃣ Run Trigger manually or set it on autopilot. Every 30 min to once a day. 3️⃣ Watch it think Live terminal streams every tool call, search, thread read, and decision in real-time. You see exactly what the agent sees. 4️⃣ Review suggestions Replies, likes, follows land in your inbox. Nothing goes live without you. Human-in-the-loop — built for strict X API compliance. 5️⃣ Restyle Don't like the tone? Rewrite any reply — concise, insightful, casual, or your own custom style. One click. 6️⃣ Feedback Hit "Replied" after you post. The agent learns what you approve and adapts next run. The more you use it, the sharper it gets. 🆓 X API costs on us for early users. No extra keys needed. 10 latest models. 7 providers. (OpenAI, Anthropic, Kimi.ai, and more) Try now →

🔥 Your X Agent just got eyes. Watch it think. Built for strict X API compliance. No automated bulk actions. Every engagement is a suggestion you act on manually. Your account stays safe. 💥 New: Live Reasoning Terminal — see every search, thread read, memory recall, and engagement decision across 21 tools as your autonomous agent works. No black box. Here's the full loop in 60 seconds: ↓🎥 1️⃣ Configure Topics, accounts, model tier, run interval, action toggles — full control over what your agent does and how often. 2️⃣ Run Trigger manually or set it on autopilot. Every 30 min to once a day. 3️⃣ Watch it think Live terminal streams every tool call, search, thread read, and decision in real-time. You see exactly what the agent sees. 4️⃣ Review suggestions Replies, likes, follows land in your inbox. Nothing goes live without you. Human-in-the-loop — built for strict X API compliance. 5️⃣ Restyle Don't like the tone? Rewrite any reply — concise, insightful, casual, or your own custom style. One click. 6️⃣ Feedback Hit "Replied" after you post. The agent learns what you approve and adapts next run. The more you use it, the sharper it gets. 🆓 X API costs on us for early users. No extra keys needed. 10 latest models. 7 providers. (OpenAI, Anthropic, Kimi.ai, and more) Try now →

Solaris AI

16,671 次观看 • 4 个月前

Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base

Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base

Malte Ubl

124,713 次观看 • 6 个月前