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Appears in 4 awesome lists
Coding agent whose Agent-Computer Interface (ACI) — purpose-built file viewer, search, and editor tools with explicit state constraints and error feedback — is the reference design for adapting a tool interface to a specific task domain rather than using generic bash.
Section: CTF / Exploit / Bug-Bounty Agents & Benchmarks · 🟢🔬 — EnIGMA offensive-CTF mode; SOTA on NYU CTF, InterCode-CTF, and Cybench (v0.7 branch). · updated 2026-07-16); Related: Cybench · NYU CTF Bench · InterCode
Section: Demo Harnesses · Coding agent whose Agent-Computer Interface (ACI) — purpose-built file viewer, search, and editor tools with explicit state constraints and error feedback — is the reference design for adapting a tool interface to a specific task domain rather than using generic bash.
Section: Other LLM Frameworks · SWE-agent takes a GitHub issue and tries to automatically fix it, using GPT-4, or your LM of choice.
Section: Other · SWE-agent takes a GitHub issue and tries to automatically fix it, using your LM of choice. It can also be employed for offensive cybersecurity or competitive coding challenges. [NeurIPS 2024]
(MIT) provides modules for structured outputs at different levels of abstraction, including output parsers for text completion endpoints, Pydantic programs for mapping prompts to structured outputs using function calling or output parsing, and pre-defined Pydantic programs for specific output types.
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search,…
Microsoft's multi-agent conversation framework with a complete AgentChat layer covering agent loop, tool integration, termination conditions, and human-in-the-loop. The most comprehensive open-source reference for large-scale multi-agent harness design.
(MIT) is a framework for algorithmically optimizing LM prompts and weights. DSPy introduced typed predictor and signatures to leverage Pydantic for enforcing type constraints on inputs and outputs, improving upon string-based fields.
Test your prompts, models, RAGs. Evaluate and compare LLM outputs, catch regressions, and improve prompt quality. LLM evals for OpenAI/Azure GPT, Anthropic Claude, VertexAI Gemini, Ollama, Local & private models like Mistral/Mixtral/Llama with CI/CD
Semantic Kernel is an SDK that integrates Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. Semantic Kernel achieves this by allowing you to define plugins that can be chained together in just a few lines of…
Minimal browser-automation agent harness with clean separation of tool registration, DOM state injection, action loop, and error recovery. Small codebase, clear structure — the best "minimal viable harness" reference for understanding core loop mechanics.