Search Results for "weight"

...The system can improve either underlying model weights or the surrounding agent harness, including prompts, skills, and rules. Its core loop follows four stages: serve requests, observe feedback, grow candidate improvements, and commit accepted updates. Weight-training workflows can integrate with systems such as Slime and SGLang, while harness optimization can run without local training GPUs. Versioned artifacts let deployments stay operational while new candidates are evaluated and released. Reef also supports test-time learning scenarios where measurable task outcomes can drive iterative improvement.

...It provides an opinionated agent setup while allowing developers to override or replace individual pieces without forking the project. The framework is model-agnostic and works with tool-calling models from hosted providers, open-weight deployments, or local runtimes. Built on LangGraph, it includes persistence, checkpointing, streaming, and production-oriented orchestration. Agents can delegate to sub-agents, work with files, run shell commands, manage long contexts, and retain memory across sessions. It also supports human approval of tool calls, reusable skills, custom tools, and MCP servers.

...The agent breaks requested outcomes into steps and works across local files, the terminal, desktop resources, and connected applications. Users can bring API keys for multiple commercial or open-weight model providers, or run locally through Ollama. More than 25 integrations connect services such as GitHub, Slack, Jira, Notion, Gmail, Outlook, and Google Calendar. OpenWorker requests approval before consequential actions such as sending messages, changing events, or running commands. Scheduled automations and Slack-triggered sessions extend it to recurring and team-based workflows.