Google Code Assist, now powered by Gemini 2.5, shows significant improvement in coding capabilities and introduces AI agents to assist across the software development lifecycle. The article details the features available in the free, standard, and enterprise tiers, and raises questions about agent availability and practical implementation.
GitHub Copilot has introduced several new models including Anthropic Claude 3.7 Sonnet, Claude 3.5 Sonnet, OpenAI o3-mini, and Google Gemini Flash 2.0. These models are now available in Copilot Chat and agent mode, offering enhanced capabilities and performance.
This repository provides reference OpenAPI Tool Server implementations, making it easy and secure for developers to integrate external tooling and data sources into LLM agents and workflows.
- Filesystem Access (servers/filesystem) - Manage local file operations safely with configurable restrictions.
- Git Server (servers/git) - Expose Git repositories for searching, reading, and possibly writing via controlled API endpoints.
- WIP: Database Server (servers/database) - Query and inspect database schemas across common DB engines like PostgreSQL, MySQL, and SQLite.
- Memory & Knowledge Graph (servers/memory) - Persistent memory management and semantic knowledge querying using popular and reliable storage techniques.
- WIP: Web Search & Fetch (servers/web-search) - Retrieve and convert web-based content securely into structured API results usable by LLMs.
This article details the Model Context Protocol (MCP), a new approach to integrating Large Language Models (LLMs) like Azure OpenAI with tools. MCP focuses on structured data exchange to improve reliability, observability, and functionality, moving beyond simple text-in, text-out interactions. It aims to standardize how LLMs interact with tools, enhancing their ability to utilize those tools effectively.
This article explores the Model Context Protocol (MCP), an open protocol designed to standardize AI interaction with tools and data, addressing the fragmentation in AI agent ecosystems. It details current use cases, future possibilities, and challenges in adopting MCP.
This tutorial demonstrates how to build a powerful document search engine using Hugging Face embeddings, Chroma DB, and Langchain for semantic search capabilities.
The article discusses the OVON agentic framework for mitigating hallucinations in Large Language Models (LLMs). It explains the structured, collaborative pipeline involving front-end and reviewer agents, the use of 'Conversation Envelopes' and 'Whispers' for efficient data exchange, and novel KPIs for measuring success. The article also addresses future directions and the importance of trust in AI systems.
Model Context Protocol (MCP) is a bridging technology for AI agents and APIs. It standardizes API access for AI agents, making it a universal method for AI agents to trigger external actions.
Browser Use is a library that enables AI agents to interact with web browsers, making websites accessible for automated tasks. It includes features for browser automation, agent memory, and various demos showcasing its capabilities.
AGNTCY is building the Internet of Agents to be accessible for all, focusing on innovation, development, and maintenance of software components and services for agentic workflows and multi-agent applications.
Discover:
1. Agent directory
- Registry for agent publishing and discovery
- Tracks reputation and quality
2. Open agent schema framework
- Standard metadata format for agent capabilities
- Verification for agent providers
- Specification at github.com/agntcy/oasf
Compose:
1. Agent connect protocol and SDK
- Standardized agent communication across frameworks
- Manages message passing, state, and context
- Specification at github.com/agntcy/acp-spec
What could these look like in action? A developer can find suitable agents in the directory (using OASF) and enable their communication with the agent connect protocol, regardless of frameworks.
