Search Results for "ace-step"

Build Your Own OpenClaw is a step-by-step educational framework that teaches developers how to construct a fully functional AI agent system from scratch, gradually evolving from a simple chat loop into a multi-agent, production-ready architecture. The project is structured into 18 progressive stages, each introducing a new concept such as tool usage, memory persistence, event-driven design, and multi-agent coordination, with each step including both explanatory documentation and runnable code. ...

...It also provides practical frameworks for increasing visibility, improving content performance, and leveraging trends effectively. The content is organized to support both beginners and experienced operators, offering step-by-step strategies as well as advanced growth tactics.

...By providing formal descriptions of patterns, conventions, and workflows common to Obsidian users, the skills empower AI tools to give more relevant suggestions, generate content that adheres to user conventions, or execute complex multi-step operations that respect the knowledge graph and file relationships.

OpenClaw-RL is an open-source reinforcement learning framework designed to train and personalize AI agents built on the OpenClaw ecosystem. The project focuses on enabling agents to improve their behavior through interactive learning rather than relying solely on static prompts or predefined skills. One of its key ideas is allowing users to train an AI agent simply by interacting with it conversationally, using natural language feedback to guide the learning process. The system incorporates...

...Each time an AI agent performs an action—such as calling a large language model, invoking a tool, accessing memory, or delegating to a sub-agent—the plugin records the full interaction and sends it to Opik for analysis and visualization. This allows developers to inspect inputs, outputs, token usage, latency, and execution flow across complex multi-step agent workflows. The goal of the project is to provide transparency into the internal reasoning and operational pipeline of agent systems so developers can diagnose failures, control costs, and improve reliability.

clawchief is an agent orchestration and management layer designed to coordinate and control multiple AI agents within structured workflows, acting as a central authority that assigns tasks, monitors execution, and ensures coherence across complex operations. The system is built around the idea of hierarchical control, where a “chief” agent oversees subordinate agents and directs their activities based on high-level objectives. This approach allows for more predictable and organized...

...The project includes mechanisms for semantic memory, reasoning pipelines, and integration points with external data sources and language models so that agents can interpret natural language instructions and produce coherent multi-step outputs. Rather than being a simple chatbot, NagaAgent emphasizes persistent thought cycles, context retention, and the ability to decompose complex tasks into smaller executable units, earning it a place in research explorations of agent design. Its architecture facilitates extensibility, allowing developers to plug in different reasoning modules or knowledge sources depending on the domain of use.