Entry
Appears in 4 awesome lists
Minimal terminal coding harness built around "lazy skills": each capability keeps only a one-line description in active context, loading full instructions and tool schemas only when invoked. Keeps the system prompt under 1,000 tokens versus 7,000–10,000 for typical agents, making it a concrete…
Section: Demo Harnesses · Minimal terminal coding harness built around "lazy skills": each capability keeps only a one-line description in active context, loading full instructions and tool schemas only when invoked. Keeps the system prompt under 1,000 tokens versus 7,000–10,000 for typical agents, making it a concrete…
Section: 智能体 Agents · AI agent toolkit: unified LLM API, agent loop, TUI, coding agent CLI.
Section: Agent Frameworks · AI agent toolkit: coding agent CLI, unified LLM API, TUI & web UI libraries, Slack bot, vLLM pods
Section: 4. Agentic AI & Multi-Agent Systems · Modular agent toolkit with terminal-first CLI, unified model/provider layer, and runtime integrations for coding workflows and TUI/web UIs.
Langchain integrates various providers like Anthropic, AWS, and OpenAI, and offers tools for components such as LLMs, chat models, and data analysis, supporting functionalities from Alpha Vantage to YouTube github | docs
(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.
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.
June 2026 harness-first redesign built around the Capability primitive: a single composable unit bundling instructions, tools, lifecycle hooks, and model settings. The split between a small stable core and a fast-moving pydantic-ai-harness lets capabilities graduate as they prove essential, while…
Flowise simplifies the creation of applications leveraging large language models (LLMs) by providing a drag-and-drop interface for customizing AI workflows, offering easy installation, Docker support, development tools, and documentation for integrating various functionalities such as…
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.
Dual-layer harness orchestration: Crew handles autonomous agent delegation, Flow provides event-driven deterministic control (branching + shared Pydantic state). The clearest open-source example of mixing autonomous and scripted execution in the same harness.
OpenAI's official autonomous coding agent CLI — the open-source reference implementation of the Codex harness with sandboxed tool execution, multi-file editing, and a streaming agent loop. Worth studying because it is the most widely adopted terminal-native coding agent harness and exposes the…