# llm agents

Published articles for llm agents.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Building a RAG Pipeline for Semantic Code Search: A Developer Diary and Field Notes

DevFeed: [Building a RAG Pipeline for Semantic Code Search: A Developer Diary and Field Notes](<https://devfeed.tech/articles/building-a-rag-pipeline-for-semantic-code-search-a-developer-diary-and-field-notes-41302.md>)

Original publisher: [Read original article](<https://blog.jetbrains.com/ai/2026/09/building-a-rag-pipeline-for-semantic-code-search-a-developer-diary-and-field-notes/>)

Author: Adam Malek

Published: 2026-09-17T12:39:40Z

Content type: article

Language: en

Sources: [The JetBrains Blog](<https://devfeed.tech/sources/the-jetbrains-blog.md>)

Topics: [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [code search](<https://devfeed.tech/topics/code-search.md>), [Parsing](<https://devfeed.tech/topics/parsing.md>), [jetbrains](<https://devfeed.tech/topics/jetbrains.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [code-search](<https://devfeed.tech/tags/code-search.md>), [jetbrains](<https://devfeed.tech/tags/jetbrains.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-agents](<https://devfeed.tech/tags/llm-agents.md>), [parsing](<https://devfeed.tech/tags/parsing.md>), [rag](<https://devfeed.tech/tags/rag.md>), [search](<https://devfeed.tech/tags/search.md>), [semantic](<https://devfeed.tech/tags/semantic.md>)

### AI overview

Part 1 of a developer diary explains how JetBrains built a RAG pipeline for semantic code search, covering parsing, chunking, and vectorization. The pipeline is intended to give LLM agents precise, citable evidence from real repositories and retrieve code by meaning rather than exact keywords.

### Source excerpt

Part 1: Parsing, chunking, and vectorization Some time ago, we set out to build the best semantic code search platform we could: a RAG pipeline that gives LLM agents precise, citable evidence from real repositories instead of whatever grep happens to surface. The eventual solution was JetBrains Context. We got it working, we got it [...]

## What I Saw at ICML 2026

DevFeed: [What I Saw at ICML 2026](<https://devfeed.tech/articles/arxiv-icml-2026-24886.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/yandex/articles/1073774/>)

Author: zj-karina (Яндекс)

Published: 2026-08-25T07:01:28Z

Content type: article

Language: ru

Sources: [Яндекс - Как мы делаем Яндекс / Статьи](<https://devfeed.tech/sources/source.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [icml](<https://devfeed.tech/tags/icml.md>), [icml-2026](<https://devfeed.tech/tags/icml-2026.md>), [llm-agents](<https://devfeed.tech/tags/llm-agents.md>), [ml](<https://devfeed.tech/tags/ml.md>), [rlhf](<https://devfeed.tech/tags/rlhf.md>), [tag-316edb31b5b3](<https://devfeed.tech/tags/tag-316edb31b5b3.md>), [tag-44d9110ce940](<https://devfeed.tech/tags/tag-44d9110ce940.md>)

### AI overview

A Yandex developer reports from ICML 2026 in Seoul, describing the conference format, its scale, Yandex research presented there, and discussions about AI agents.

### Source excerpt

Зачем тратить сутки на перелёты, мчаться на другой конец света и жить неделю в режиме нон-стоп на одной из главных ML-конференций планеты, когда пейпер уже на arXiv, код -- на GitHub, а краткие выжимки из выступлений -- мгновенно в соцсетях? Меня зовут Карина Романова, я разработчик в Яндексе и занимаюсь LLM-агентами в Алисе. В июле мы с командой прилетели в Сеул на ICML 2026, и я ответила себе на вопрос "зачем?". Для нас офлайн-конференции -- это единственный способ за несколько дней прочувствовать реальный фокус сообщества, встретиться с авторами работ и узнать детали, которых нет в опубликованных текстах. В этой статье расскажу, как устроена ICML изнутри, чем запомнилась программа этого года, какие наши исследования вызвали наибольший ажиотаж и почему заметная часть разговоров на конференции снова вращалась вокруг AI-агентов. Читать далее

## Agentic relevance tuning: Letting LLM agents do the search engineering work

DevFeed: [Agentic relevance tuning: Letting LLM agents do the search engineering work](<https://devfeed.tech/articles/agentic-relevance-tuning-letting-llm-agents-do-the-search-engineering-work-12784.md>)

Original publisher: [Read original article](<https://opensearch.org/blog/agentic-relevance-tuning/>)

Author: Kylie Wagar-Dirks

Published: 2026-07-31T15:00:47Z

Content type: article

Language: en

Sources: [OpenSearch](<https://devfeed.tech/sources/opensearch.md>)

Topics: [Amazon OpenSearch Service](<https://devfeed.tech/topics/amazon-opensearch-service.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Framework](<https://devfeed.tech/topics/framework.md>), [data](<https://devfeed.tech/topics/data.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [agents](<https://devfeed.tech/tags/agents.md>), [aws](<https://devfeed.tech/tags/aws.md>), [blog](<https://devfeed.tech/tags/blog.md>), [data](<https://devfeed.tech/tags/data.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [framework](<https://devfeed.tech/tags/framework.md>), [llm-agents](<https://devfeed.tech/tags/llm-agents.md>), [opensearch](<https://devfeed.tech/tags/opensearch.md>), [ranking](<https://devfeed.tech/tags/ranking.md>), [search](<https://devfeed.tech/tags/search.md>)

### AI overview

The article introduces Agentic Relevance Tuning (ART), an end-to-end framework that uses specialized LLM-powered agents to automate search relevance improvement in OpenSearch. ART monitors user behavior, proposes ranking changes, runs offline evaluations, and coordinates deployment when improvements are validated.

### Source excerpt

At OpenSearchCon Europe 2026, Bobby Mohammed (AWS) and Daniel Wrigley (OpenSource Connections) introduced Agentic Relevance Tuning (ART), a framework that uses specialized LLM agents to fully automate the search relevance lifecycle in OpenSearch. The post Agentic relevance tuning: Letting LLM agents do the search engineering work appeared first on OpenSearch.

## LLMs Have Reshaped How We Think About Decompilation and Collaboration

DevFeed: [LLMs Have Reshaped How We Think About Decompilation and Collaboration](<https://devfeed.tech/articles/llms-have-reshaped-how-we-think-about-decompilation-and-collaboration-39684.md>)

Original publisher: [Read original article](<https://mahaloz.re/2026/06/10/hexrays-feature.html>)

Published: 2026-06-10T00:00:00Z

Content type: opinion

Language: en

Sources: [mahaloz.re](<https://devfeed.tech/sources/mahaloz-re.md>)

Topics: [codex](<https://devfeed.tech/topics/codex.md>), [Hacking](<https://devfeed.tech/topics/hacking.md>), [coding](<https://devfeed.tech/topics/coding.md>), [harvestbench](<https://devfeed.tech/topics/harvestbench.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [codex](<https://devfeed.tech/tags/codex.md>), [collaboration](<https://devfeed.tech/tags/collaboration.md>), [hacking](<https://devfeed.tech/tags/hacking.md>), [ida](<https://devfeed.tech/tags/ida.md>), [ida-pro](<https://devfeed.tech/tags/ida-pro.md>), [llm-agents](<https://devfeed.tech/tags/llm-agents.md>), [llms](<https://devfeed.tech/tags/llms.md>), [musing](<https://devfeed.tech/tags/musing.md>), [reversing](<https://devfeed.tech/tags/reversing.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

This think piece examines how LLM agents and Codex are changing software decompilation and hacking workflows. It focuses on the shift from humans directly using reversing tools to humans supervising agents, and on the resulting need to coordinate agents, validate findings, and improve information sharing. It also connects these challenges to the authors' earlier work on BinSync, a decompiler collaboration framework.

### Source excerpt

How LLM agents are changing decompilation workflows, collaboration, and the future of reversing tools.

## Safe Yolo Mode: Running LLM Agents in VMs with Libvirt and Virsh

DevFeed: [Safe Yolo Mode: Running LLM Agents in VMs with Libvirt and Virsh](<https://devfeed.tech/articles/safe-yolo-mode-running-llm-agents-in-vms-with-libvirt-and-virsh-31870.md>)

Original publisher: [Read original article](<https://www.metachris.dev/2026/02/safe-yolo-mode-running-llm-agents-in-vms-with-libvirt-and-virsh/>)

Author: Chris Hager

Published: 2026-02-10T00:00:00Z

Content type: tutorial

Language: en

Sources: [Chris Hager](<https://devfeed.tech/sources/chris-hager.md>)

Topics: [LLMs](<https://devfeed.tech/topics/llms.md>), [virtualization](<https://devfeed.tech/topics/virtualization.md>), [Security](<https://devfeed.tech/topics/security.md>), [Linux](<https://devfeed.tech/topics/linux.md>), [qemu](<https://devfeed.tech/topics/qemu.md>), [ssh](<https://devfeed.tech/topics/ssh.md>)

Tags: [linux](<https://devfeed.tech/tags/linux.md>), [llm-agents](<https://devfeed.tech/tags/llm-agents.md>), [llms](<https://devfeed.tech/tags/llms.md>), [security](<https://devfeed.tech/tags/security.md>), [tool-use](<https://devfeed.tech/tags/tool-use.md>), [virtual-machines](<https://devfeed.tech/tags/virtual-machines.md>)

### AI overview

A guide to isolating LLM agents in Linux virtual machines with libvirt and virsh. It explains how VM isolation can reduce risks from destructive operations and unauthorized access when agents receive broad tool-use permissions, and covers setup, cloud-image provisioning, VM creation, and SSH access.

### Source excerpt

This is a guide for isolating LLM agents in virtual machines, using libvirt and virsh on Linux servers. Running LLMs in VMs isolates them from the host system, mitigating numerous security risks such as destructive operations or unauthorized file access (i.e. private keys, secrets, credentials for communication tools). This is particularly important when granting LLM agents broad permissions, like auto-approving tool use ("yolo mode"). It's also useful to keep sessions running for extended periods of time, and to interact with agents from the phone / on the go.

## AI Agent Evaluation

DevFeed: [AI Agent Evaluation](<https://devfeed.tech/articles/ai-agent-evaluation-30448.md>)

Original publisher: [Read original article](<https://booking.ai/ai-agent-evaluation-82e781439d97?source=rss----4d265f07defc---4>)

Author: Antonio Castelli

Published: 2026-01-21T13:11:23Z

Content type: tutorial

Language: en

Sources: [Booking.com Data Science](<https://devfeed.tech/sources/booking-com-data-science.md>)

Topics: [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Tool](<https://devfeed.tech/topics/tool.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [api](<https://devfeed.tech/tags/api.md>), [best-practices](<https://devfeed.tech/tags/best-practices.md>), [blog-posts](<https://devfeed.tech/tags/blog-posts.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [llm-agents](<https://devfeed.tech/tags/llm-agents.md>), [llm-evaluation](<https://devfeed.tech/tags/llm-evaluation.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [sql](<https://devfeed.tech/tags/sql.md>), [tools](<https://devfeed.tech/tags/tools.md>)

### AI overview

Booking.com's article presents practical guidance for evaluating LLM agents. It explains how agents differ from single LLMs because they can use external tools and iterate through thoughts, actions, and observations, then introduces black-box and glass-box evaluation approaches.

### Source excerpt

AI Agent Evaluation: practical tips at Booking.com Authors: Zeno Belligoli, Antonio Castelli, George Chouliaras This article is the 2nd part of our GenAI evaluation best practices series. You can read the first part, focusing on single LLM evaluation, here. 1. Overview LLM agents are advanced AI systems that leverage large language models to perform complex tasks autonomously. Unlike single LLMs that respond to direct prompts, agents can break down problems, use external tools, and iterate on solutions through a series of thoughts, actions, and observations. This allows them to achieve goals that require more intricate planning, reasoning, and interaction with various environments. Agents can autonomously determine if tool utilization is necessary to fulfill a user's request. For example, they might employ: a calculator for mathematical operations, a flights API to retrieve available flights for a certain destination, or execute a SQL query to fetch information about an hotel reservation from a database. Figure1: Schematic view of an agent. The user might interact with the agent within a multi-turn conversation, asking it to perform various tasks (recommend vacation destinations, ask questions about a hotel etc.). The agent should try to complete the requested tasks abiding by a set of rules (or constraints) provided by the product specification (e.g. do not recommend properties outside the inventory). In performing the tasks the agent has freedom to use a certain set of tools, and it can interact with them via API requests. Tool examples typically range from general (e.g. calculate travel distance) to use case specific functionalities (e.g. retrieving information from a database). While these enhanced capabilities are fundamental to solve tasks which would be difficult (or even impossible) to solve with text generation only, they require a more complex evaluation process compared to evaluating a single "prompt-response" LLM. The evaluation of an agent's performance

## You Should Write An Agent

DevFeed: [You Should Write An Agent](<https://devfeed.tech/articles/you-should-write-an-agent-1695.md>)

Original publisher: [Read original article](<https://fly.io/blog/everyone-write-an-agent/>)

Published: 2025-11-06T00:00:00Z

Content type: article

Language: en

Sources: [The Fly Blog](<https://devfeed.tech/sources/the-fly-blog.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [API](<https://devfeed.tech/topics/api.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>), [Code](<https://devfeed.tech/topics/code.md>), [context window](<https://devfeed.tech/topics/context-window.md>), [ChatGPT](<https://devfeed.tech/topics/chatgpt.md>), [Tool](<https://devfeed.tech/topics/tool.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [api](<https://devfeed.tech/tags/api.md>), [cdn](<https://devfeed.tech/tags/cdn.md>), [chatgpt](<https://devfeed.tech/tags/chatgpt.md>), [close-to-users](<https://devfeed.tech/tags/close-to-users.md>), [code](<https://devfeed.tech/tags/code.md>), [context-window](<https://devfeed.tech/tags/context-window.md>), [deploy-app-servers](<https://devfeed.tech/tags/deploy-app-servers.md>), [docker](<https://devfeed.tech/tags/docker.md>), [elixir](<https://devfeed.tech/tags/elixir.md>), [fly](<https://devfeed.tech/tags/fly.md>), [fly-io](<https://devfeed.tech/tags/fly-io.md>), [heroku-alternative](<https://devfeed.tech/tags/heroku-alternative.md>), [heroku-competitor](<https://devfeed.tech/tags/heroku-competitor.md>), [hosting](<https://devfeed.tech/tags/hosting.md>), [i](<https://devfeed.tech/tags/i.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-agents](<https://devfeed.tech/tags/llm-agents.md>), [networking](<https://devfeed.tech/tags/networking.md>), [openai](<https://devfeed.tech/tags/openai.md>), [postgresql-clusters](<https://devfeed.tech/tags/postgresql-clusters.md>), [servers](<https://devfeed.tech/tags/servers.md>), [tool](<https://devfeed.tech/tags/tool.md>)

### AI overview

The article argues that developers should write an LLM agent to understand the technology through practice. It presents a small Python engine for an LLM application using the OpenAI Responses API, showing how a terminal conversation can reproduce ChatGPT-like behavior, maintain a context window, and add tool use.

### Source excerpt

Some concepts are easy to grasp in the abstract. Boiling water: apply heat and wait. Others you really need to try. You only think you understand how a bicycle works, until you learn to ride one. There are big ideas in computing that are easy to get your head around. The AWS S3 API. It's the most important storage technology of the last 20 years, and it's like boiling water. Other technologies, you need to get your feet on the pedals first. LLM agents are like that. People have wildly varying opinions about LLMs and agents. But whether or not they're snake oil, they're a big idea. You don't have to like them, but you should want to be right about them. To be the best hater (or stan) you can be. So that's one reason you should write an agent. But there's another reason that's even more persuasive, and that's It's Incredibly Easy Agents are the most surprising programming experience I've had in my career. Not because I'm awed by the magnitude of their powers -- I like them, but I don't like-like them. It's because of how easy it was to get one up on its legs, and how much I learned doing that. I'm about to rob you of a dopaminergic experience, because agents are so simple we might as well just jump into the code. I'm not even going to bother explaining what an agent is. Wrap text Copy to clipboard from openai import OpenAI client = OpenAI() context = [] def call(): return client.responses.create(model="gpt-5", input=context) def process(line): context.append({"role": "user", "content": line}) response = call() context.append({"role": "assistant", "content": response.output_text}) return response.output_text It's an HTTP API with, like, one important endpoint. This is a trivial engine for an LLM app using the OpenAI Responses API. It implements ChatGPT. You'd drive it with the the obvious loop. It'll do what you'd expect: the same thing ChatGPT would, but in your terminal. Wrap text Copy to clipboard def main(): while True: line = input("&gt; ") result = process(line) p

## Aligning to What? Rethinking Agent Generalization in MiniMax M2

DevFeed: [Aligning to What? Rethinking Agent Generalization in MiniMax M2](<https://devfeed.tech/articles/aligning-to-what-rethinking-agent-generalization-in-minimax-m2-7019.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/MiniMax-AI/aligning-to-what>)

Author: MiniMax

Published: 2025-10-30T10:03:45Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Usability](<https://devfeed.tech/topics/usability.md>), [generalization in machine learning](<https://devfeed.tech/topics/generalization-in-machine-learning.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-agents](<https://devfeed.tech/tags/llm-agents.md>), [performance](<https://devfeed.tech/tags/performance.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [tool](<https://devfeed.tech/tags/tool.md>), [usability](<https://devfeed.tech/tags/usability.md>)

### AI overview

The article examines how MiniMax M2 is aligned for both benchmark performance and reliable real-world use. It argues that agents must generalize across unfamiliar tools, IDEs, CLIs, scaffolding, and user setups, and proposes interleaved thinking so models can maintain focus, diagnose errors, and adapt to changing tool outputs during long-horizon tasks.

### Source excerpt

If you've worked with LLM Agents, you've felt this pain: the same model can feel brilliant in one framework and useless in another. An agent might crush a tool-use leaderboard but fail spectacularly at a simple, real-world task. This gap between benchmark performance and practical usability is one of the biggest challenges in the field. When we designed M2, we knew we had to tackle this problem head-on.

## Apollo announces Apollo MCP Server 1.0 and GraphQL platform updates at GraphQL Summit 2025

DevFeed: [Apollo announces Apollo MCP Server 1.0 and GraphQL platform updates at GraphQL Summit 2025](<https://devfeed.tech/articles/graphql-summit-2025-product-highlights-building-the-future-of-ai-and-apps-23340.md>)

Original publisher: [Read original article](<https://www.apollographql.com/blog/graphql-summit-2025-apollo-product-announcements>)

Author: Rob Brazier

Published: 2025-10-07T07:00:24Z

Content type: release

Language: en

Sources: [Apollo Blog](<https://devfeed.tech/sources/apollo-blog.md>)

Topics: [GraphQL](<https://devfeed.tech/topics/graphql.md>), [MCP](<https://devfeed.tech/topics/mcp.md>), [MCP Server](<https://devfeed.tech/topics/mcp-server.md>), [API](<https://devfeed.tech/topics/api.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>), [Security](<https://devfeed.tech/topics/security.md>), [observability](<https://devfeed.tech/topics/observability.md>), [OpenTelemetry](<https://devfeed.tech/topics/opentelemetry.md>), [OAuth](<https://devfeed.tech/topics/oauth.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>)

Tags: [2025](<https://devfeed.tech/tags/2025.md>), [ai](<https://devfeed.tech/tags/ai.md>), [announcement](<https://devfeed.tech/tags/announcement.md>), [api](<https://devfeed.tech/tags/api.md>), [apollo-mcp-server](<https://devfeed.tech/tags/apollo-mcp-server.md>), [authorization](<https://devfeed.tech/tags/authorization.md>), [graphql](<https://devfeed.tech/tags/graphql.md>), [llm-agents](<https://devfeed.tech/tags/llm-agents.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [mcp-server](<https://devfeed.tech/tags/mcp-server.md>), [oauth](<https://devfeed.tech/tags/oauth.md>), [observability](<https://devfeed.tech/tags/observability.md>), [opentelemetry](<https://devfeed.tech/tags/opentelemetry.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>)

### AI overview

Apollo's GraphQL Summit 2025 announcements include the generally available Apollo MCP Server 1.0, which connects LLM agents to GraphQL APIs by turning GraphQL operations into MCP tools. The release also adds optional schema discovery tools, operation guardrails, OpenTelemetry integration, and OAuth 2.1 support.

### Source excerpt

Software delivery isn't slowing down, and neither is Apollo. At GraphQL Summit 2025, we're showcasing a platform that's now even easier to use, enables you to connect your APIs and development workflows to agents, and accelerates your graph development. AI is exerting immense pressure on both businesses and platform teams. Organizations have the opportunity to build better experiences and to get them to customers faster, but platforms need to be ready to support them.

## Five Big Improvements to Gradio MCP Servers

DevFeed: [Five Big Improvements to Gradio MCP Servers](<https://devfeed.tech/articles/five-big-improvements-to-gradio-mcp-servers-7230.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/gradio-mcp-updates>)

Author: Freddy Boulton

Published: 2025-07-17T00:00:00Z

Content type: release

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [gradio](<https://devfeed.tech/topics/gradio.md>), [MCP Server](<https://devfeed.tech/topics/mcp-server.md>), [OpenAPI Specification](<https://devfeed.tech/topics/openapi.md>), [API](<https://devfeed.tech/topics/api.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Authentication](<https://devfeed.tech/topics/authentication.md>)

Tags: [authentication](<https://devfeed.tech/tags/authentication.md>), [community](<https://devfeed.tech/tags/community.md>), [gradio](<https://devfeed.tech/tags/gradio.md>), [improvements](<https://devfeed.tech/tags/improvements.md>), [llm-agents](<https://devfeed.tech/tags/llm-agents.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [mcp-server](<https://devfeed.tech/tags/mcp-server.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [openapi](<https://devfeed.tech/tags/openapi.md>), [real-time](<https://devfeed.tech/tags/real-time.md>)

### AI overview

The article presents five improvements to Gradio MCP servers in version 5.38.0. Updates include a File Upload MCP server for remote file inputs, real-time progress notifications, automatic conversion of OpenAPI schemas into Gradio applications and MCP servers, and support for passing authentication headers to MCP tools.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## ChainGPT: Exploring open source projects with LLM agents

DevFeed: [ChainGPT: Exploring open source projects with LLM agents](<https://devfeed.tech/articles/chaingpt-exploring-open-source-projects-with-llm-agents-12921.md>)

Original publisher: [Read original article](<https://www.chainguard.dev/unchained/chaingpt-exploring-open-source-projects-with-llm-agents>)

Published: 2024-08-29T00:00:00Z

Content type: article

Language: en

Sources: [Chainguard: Unchained](<https://devfeed.tech/sources/chainguard-unchained.md>)

Topics: [Open Source](<https://devfeed.tech/topics/open-source.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [chainguard](<https://devfeed.tech/topics/chainguard.md>), [Code](<https://devfeed.tech/topics/code.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [AI Chat](<https://devfeed.tech/topics/ai-chat.md>), [Command-line interface](<https://devfeed.tech/topics/cli.md>)

Tags: [commands](<https://devfeed.tech/tags/commands.md>), [developers](<https://devfeed.tech/tags/developers.md>), [github](<https://devfeed.tech/tags/github.md>), [go](<https://devfeed.tech/tags/go.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-agents](<https://devfeed.tech/tags/llm-agents.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [software](<https://devfeed.tech/tags/software.md>)

### AI overview

This article presents ChainGPT, a GPT-4-powered chatbot and LLM agent for exploring open source projects through GitHub. It describes capabilities for browsing repository directories, reading files, and executing shell commands in an isolated Wolfi container, with potential applications in understanding projects and automating package discovery for Wolfi.

### Source excerpt

Chainguard's ChainGPT: The LLM agent revolutionizing open source exploration. Browse code, execute commands, and gain insights faster than ever before.