MCP & Pemanggilan Tool

AgentScope 2.0

agentscope-ai/agentscope

A production-ready, easy-to-use agent framework designed to leverage the reasoning and tool-use abilities of increasingly agentic LLMs without constraining them with strict prompts or opinionated orchestrations.

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Tangkapan layar proyek

Tangkapan layar AgentScope 2.0 Tangkapan layar AgentScope 2.0

Ringkasan

AgentScope 2.0 is a Python-based multi-agent framework that provides essential abstractions for building, running, and scaling LLM agents. It includes an SDK layer for composing agents from building blocks like ReAct loops, toolkits, and context management, alongside a batteries-included agent service featuring a FastAPI backend and pre-built Web UI for multi-tenant, multi-session applications.

Fitur utama

  • ReAct reasoning-acting loop with structured output and realtime interruption
  • Agentic tool management over Python tools, MCP servers, and skills
  • Automatic context compaction and tool-result offload via middleware
  • Unified event bus streaming multimodal content
  • Fine-grained permission control and Human-in-the-Loop (HITL)
  • Agentic memory with switchable backends (ReMe, Mem0)
  • Isolated workspace and sandbox execution (Docker, E2B, OpenSandbox, Daytona, K8s, etc.)
  • Multi-tenancy and multi-session isolation with FastAPI backend
  • Integration with GitHub MCP Registry and ClawHub
  • Background task offloading and scheduled agent wakeup

Persyaratan, instalasi, dan mulai cepat

From PyPI: `uv pip install agentscope`. From source: `git clone -b main https://github.com/agentscope-ai/agentscope.git`, `cd agentscope`, `uv pip install -e .`.

Penggunaan

To start the agent service backend: clone the repository, navigate to `examples/agent_service`, and run `python main.py`. To start the web UI, open another terminal, navigate to `examples/web_ui`, and run `pnpm install` followed by `pnpm dev`. For SDK usage, developers can define an agent with a system prompt, model, and toolkit, then stream replies asynchronously using the `agent.reply_stream` method.

Kompatibilitas model dan kasus penggunaan

Supports LLM, embedding, and TTS across major providers including OpenAI, Anthropic, Gemini, DashScope, DeepSeek, Moonshot, xAI, and Ollama.

Catatan lisensi dan risiko

Released under the Apache License 2.0.

Editorial verification 2026-08-09: repository URL, owner, description, license and repository statistics were reviewed. License metadata: Apache-2.0. README was fetched for the channel draft; re-check repository dependencies, releases and model terms before production use.

Rilis dan pemeliharaan

Recent updates include support for Feishu and Discord channels, MCP & Skill Hub integration, Daytona and K8s-based workspace/sandbox, ReMe long-term memory, Agentic Memory, and distributed, multi-tenancy RAG service.

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