AI Agents Directory

Agent-RL / ReCall

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Deepgram Voice Agent

A library of function calling you can use with the Voice Agent API - deepgram-devs/voice-agent-function-calling-examples

OpenDevin

🙌 OpenHands: AI-Driven Development. Contribute to OpenHands/OpenHands development by creating an account on GitHub.

Plasma AI

Plasma AI builds infrastructure for intelligence at scale.

Code Review Agent Benchmark

Software engineering agents have shown significant promise in writing code. As AI agents permeate code writing, and generate huge volumes of code automatically -- the matter of code quality comes front and centre. As the automatically generated code gets integrated into huge code-bases -- the issue of code review and broadly quality assurance becomes important. In this paper, we take a fresh look at the problem and curate a code review dataset for AI agents to work with. Our dataset called c-CRAB (pronounced see-crab) can evaluate agents for code review tasks. Specifically given a pull-request (which could be coming from code generation agents or humans), if a code review agent produces a review, our evaluation framework can asses the reviewing capability of the code review agents. Our evaluation framework is used to evaluate the state of the art today -- the open-source PR-agent, as well as commercial code review agents from Devin, Claude Code, and Codex. Our c-CRAB dataset is systematically constructed from human reviews -- given a human review of a pull request instance we generate corresponding tests to evaluate the code review agent generated reviews. Such a benchmark construction gives us several insights. Firstly, the existing review agents taken together can solve only around 40% of the c-CRAB tasks, indicating the potential to close this gap by future research. Secondly, we observe that the agent reviews often consider different aspects from the human reviews -- indicating the potential for human-agent collaboration for code review that could be deployed in future software teams. Last but not the least, the agent generated tests from our data-set act as a held out test-suite and hence quality gate for agent generated reviews. What this will mean for future collaboration of code generation agents, test generation agents and code review agents -- remains to be investigated.

Agent Test-Time Scaling

There is a popular assumption baked into many agentic AI systems: if an agent doesn't succeed on the first attempt, just let it try again. Give it more turns. Sample more trajectories. Add a reflection step. More compute at inference time should mean...

Blotato

Blotato is the #1 social media API and MCP server designed for AI agents, ChatGPT, and Claude. Schedule posts, manage comments and DMs, create content, and track analytics.

Runcell

Runcell is an AI agent for Jupyter notebooks that automates writing Python code, executing cells, debugging, and explaining data analysis results in real time.

Sprocket

An open-source AI agent for hardware and software development that can autonomously purchase items online based on user commands, streamlining the procurement process.

WebArena

Contribute to Agent-Tools/awesome-autonomous-web development by creating an account on GitHub.

OpenAgent

🔓 The open-source autonomous agent LLM initiative 🔓 - OpenAgentLLM/OpenAgent

ReactAgent

The open-source React.js Autonomous LLM Agent. Contribute to eylonmiz/react-agent development by creating an account on GitHub.