Agents.json is an OpenAPI specification designed to standardize interactions with Large Language Models (LLMs). It provides a structured, API-driven approach to defining and executing agent workflows, including tool usage, function calls, and chain-of-thought reasoning. This allows developers to build interoperable agents that can be easily integrated with different LLMs and platforms, simplifying the development and deployment of complex AI-driven applications. The specification aims to foster a collaborative ecosystem around LLM agent development, promoting reusability and reducing the need for bespoke integrations.
mdq is a command-line tool, inspired by jq, that allows users to process and manipulate Markdown files using CSS-like selectors. It can extract specific elements from Markdown, such as headings, paragraphs, or code blocks, and output them in various formats, including Markdown, HTML, and text. This facilitates tasks like extracting specific sections of a document, reformatting content, and generating summaries, offering a powerful way to automate Markdown workflows.
Hacker News users generally praised mdq for its potential usefulness, comparing it favorably to jq for JSON. Several commenters expressed interest in using it for tasks like extracting links or reformatting Markdown files. Some suggested improvements, such as adding support for YAML frontmatter and improving error handling. Others highlighted the complexities of parsing Markdown reliably due to its flexible nature and the potential challenges of handling variations and edge cases. One user pointed out the limitations of existing markdown parsers and the difficulties in accurately representing markdown as a data structure, while another cautioned against over-engineering the tool for simple tasks that could be accomplished with grep, sed, or awk.
Latacora's blog post "How (not) to sign a JSON object" cautions against signing JSON by stringifying it before applying a signature. This approach is vulnerable to attacks that modify whitespace or key ordering, which changes the string representation without altering the JSON's semantic meaning. The correct method involves canonicalizing the JSON object first – transforming it into a standardized, consistent byte representation – before signing. This ensures the signature validates only identical JSON objects, regardless of superficial formatting differences. The post uses examples to demonstrate the vulnerabilities of naive stringification and advocates using established JSON Canonicalization Schemes (JCS) for robust and secure signing.
HN commenters largely agree with the author's points about the complexities and pitfalls of signing JSON objects. Several highlighted the importance of canonicalization before signing, with some mentioning specific libraries like JWS and json-canonicalize to ensure consistent formatting. The discussion also touches upon alternatives like JWT (JSON Web Tokens) and COSE (CBOR Object Signing and Encryption) as potentially better solutions, particularly JWT for its ease of use in web contexts. Some commenters delve into the nuances of JSON's flexibility, which can make secure signing difficult, such as varying key order and whitespace handling. A few also caution against rolling your own cryptographic solutions and advocate for using established libraries where possible.
Summary of Comments ( 60 )
https://news.ycombinator.com/item?id=43243893
Hacker News users discussed the potential of Agents.json to standardize agent communication and simplify development. Some expressed skepticism about the need for such a standard, arguing existing tools like LangChain already address similar problems or that the JSON format might be too limiting. Others questioned the focus on LLMs specifically, suggesting a broader approach encompassing various agent types could be more beneficial. However, several commenters saw value in a standardized schema, especially for interoperability and tooling, envisioning its use in areas like agent marketplaces and benchmarking. The maintainability of a community-driven standard and the potential for fragmentation due to competing standards were also raised as concerns.
The Hacker News post titled "Show HN: Agents.json – OpenAPI Specification for LLMs" has generated a moderate amount of discussion, with several commenters exploring various aspects and implications of the proposed specification.
One commenter expressed skepticism about the value of standardizing agent behavior, arguing that the rapid evolution of the field makes any current standard likely to become quickly outdated. They suggested that focusing on standardizing the "plumbing" around LLMs would be more beneficial in the long run.
Another commenter raised a concern about the potential for malicious agents to be created using such a standard. They highlighted the need for careful consideration of security implications, suggesting that perhaps standardization efforts should be delayed until these issues can be more thoroughly addressed.
A different user focused on the practical limitations of relying solely on JSON Schema for defining agent capabilities. They argued that the complexity of agent interactions often requires more expressive tools. They suggested exploring alternative approaches, possibly drawing inspiration from existing standards like OpenAPI.
Another commenter questioned the readiness of the LLM ecosystem for standardization, given the still-nascent nature of the technology. They drew a parallel to premature standardization attempts in other fields, cautioning against stifling innovation by locking in potentially suboptimal approaches too early.
One commenter expressed interest in the potential of the proposed standard to facilitate the creation of more complex and sophisticated agent interactions. They envisioned a future where agents could seamlessly interact with each other, forming dynamic and collaborative systems.
A user discussed the challenges of effectively managing prompts within the context of a standardized agent framework. They pointed out the complexities of prompt engineering and the need for robust mechanisms to handle prompt variations and evolution.
One comment explored the relationship between the Agents.json specification and other related standards like OpenAPI. They inquired about the potential for integration or overlap between these different approaches.
Finally, one commenter expressed excitement about the potential of Agents.json to drive innovation and collaboration in the LLM agent space. They viewed the project as a positive step towards building a more robust and interoperable ecosystem for agent development.