A Python library and runnable Jupyter textbook implementing 35 agentic AI architectures, from Reflexion and ReAct to MemGPT and BrowserAgent, on LangGraph.
framerslab/agentos
TypeScript AI agent framework with persistent cognitive memory, optional HEXACO personality, multi-agent orchestration, and a unified interface across 13 LLM providers.
- 스타
- 681
- 포크
- 100
- 열린 이슈
- 24
- 마지막 커밋
- 2시간 전
어떤 프로젝트인가요?
AgentOS is an open-source TypeScript framework for building AI agents that keep memory across sessions, adapt their behavior, and can create new tools at runtime. A basic agent is created with a single call that picks a provider from configuration, and sessions keep conversation history that later requests replay.
The framework includes a cognitive memory layer with Ebbinghaus-style decay and several memory mechanisms such as reconsolidation and retrieval-induced forgetting. Agents can carry an optional HEXACO personality vector that shapes prompts and memory encoding. Multi-agent teams can run under six coordination strategies, including sequential, parallel, debate, review-loop, hierarchical and graph, and the package also provides guardrail packs, voice pipeline support and RAG retrieval over seven vector backends.
AgentOS is split into a core package plus separate extension, skill and registry packages. The project publishes benchmark results on the LongMemEval dataset with its methodology in a companion repository.
핵심 기능
- 01Persistent cognitive memory with decay and several memory mechanisms.
- 02Optional HEXACO personality traits that shape prompts and memory.
- 03Multi-agent teams with six coordination strategies.
- 04One interface across 13 LLM providers, with API key pooling and fallback.
- 05Runtime tool generation, with generated tools reviewed by an LLM judge before use.
- 06Guardrail packs, voice support and RAG over seven vector backends.
활용 사례
- Building chatbots or tutors that remember users across multiple conversations.
- Orchestrating several specialized agents, such as researcher, writer and reviewer, on one task.
- Prototyping agents that work across different LLM providers without rewriting code.
- Adding voice or chat-channel interfaces to an agent backend.
토픽
자주 묻는 질문
- agentos은(는) 무엇인가요?
- TypeScript AI agent framework with persistent cognitive memory, optional HEXACO personality, multi-agent orchestration, and a unified interface across 13 LLM providers. AgentOS is an open-source TypeScript framework for building AI agents that keep memory across sessions, adapt their behavior, and can create new tools at runtime. A basic agent is created with a single call that picks a provider from configuration, and sessions keep conversation history that later requests replay.
- agentos은(는) 무료로 사용할 수 있나요? 라이선스는 무엇인가요?
- agentos은(는) Apache-2.0 라이선스의 오픈소스입니다. 상업용 제품에 사용하기 전에 라이선스 조건을 확인하세요.
- agentos은(는) 아직 유지보수되고 있나요?
- 활발히 유지보수 중. agentos의 마지막 커밋은 2026년 10월 19일입니다.
- agentos은(는) 어디에 사용할 수 있나요?
- Building chatbots or tutors that remember users across multiple conversations. Orchestrating several specialized agents, such as researcher, writer and reviewer, on one task. Prototyping agents that work across different LLM providers without rewriting code. Adding voice or chat-channel interfaces to an agent backend.
- agentos은(는) 어디서 받을 수 있나요?
- 소스 코드와 설치 방법은 GitHub에 있습니다: https://github.com/framerslab/agentos
요약은 프로젝트 README를 바탕으로 AI가 작성했습니다. GitHub 통계는 매일 갱신됩니다.
관련 프로젝트
A free, open-source Chinese-language book on AI Agent design principles and engineering practice, with 109 companion experiments and downloadable PDF/EPUB.
An agent harness for Claude Code and Codex that adds multi-agent swarms, self-learning memory, vector retrieval and cross-machine agent federation.
Open-source platform that turns any topic or document into an interactive classroom with AI teachers, slides, quizzes and simulations.
An open-source coding agent that reads your project, edits files and runs tests from your terminal, desktop, browser or editor.
A self-hosted Rust knowledge base that builds a bitemporal knowledge graph from documents, with RAG search, ontology-driven reasoning and MCP access for agents.