by Zleap-AI · Codex Skill · ★ 68
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Workspace Is All Agents Need English | 简体中文 Preview status Zleap-Agent is an early preview. The public repository is intended for source review, local development, and feedback. APIs, UI details, packaging, and release flows may change before a stable release. What Is Zleap-Agent? Zleap-Agent is a workspace-first agent harness for local and OpenAI-compatible models. It is built around one practical idea: An agent should not see every tool, memory, rule, and previous message on every step. It should first know which workspace it is in, then receive only the context needed for that workspace. Instead of treating context as one large prompt, Zleap-Agent separates the agent runtime into workspaces. Each workspace can have its own prompt, tools, skills, memory, model, and execution history. This makes the system easier to reason about when running smaller local models, enterprise-local deployments, or workflows where permissions and data boundaries matter. Highlights Workspace isolation for prompts, tools, skills, models, memory, and history. Web UI for chat, workspaces, assistants, models, tools, MCP servers, skills, memory, tasks, gateway configuration, and artifacts.
| Stars | 68 |
| Forks | 11 |
| Language | TypeScript |
| Category | Codex Skill |
| Quality Score | 76.7248292652108/100 |
| Open Issues | 1 |
| Last Updated | 2026-07-01 |
| Created | 2026-05-31 |
| Platforms | node |
| Est. Tokens | ~15k |
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Zleap-Agent is Agent Harness developed specifically for local small-parameter models.. It is categorized as a Codex Skill with 68 GitHub stars.
Zleap-Agent is primarily written in TypeScript. It covers topics such as agent, agent-harness, hermes-agent.
You can find installation instructions and usage details in the Zleap-Agent GitHub repository at github.com/Zleap-AI/Zleap-Agent. The project has 68 stars and 11 forks, indicating an active community.
The top alternatives to Zleap-Agent on Agent Skills Hub include Network-AI, omnicoreagent, bitrouter. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.