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agentscope

AgentScope repo skill for building agents, provider connectors, RAG and memory workflows, service deployments, and local or sandboxed workspaces.

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来源信息

仓库
VectorSpaceLab/AREX-Skill
最近来源活动
2026年8月26日 16:31
检测到的 SKILL.md 语言
英语
星标
12
分支
2

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SKILL.md
来源说明 · 只读预览
name
agentscope
description
AgentScope repo skill for building agents, provider connectors, RAG and memory workflows, service deployments, and local or sandboxed workspaces.
disable-model-invocation
true
metadata
{"disco-role":"operating"}
license
Apache 2.0
# AgentScope Use this skill for AgentScope agent workflows, provider setup, retrieval and memory, FastAPI service deployment, or workspace/sandbox execution. ## Quick start - Install the broad runtime set with `uv pip install "agentscope[full]"` or install only the extras needed for one sub-skill. - Target Python 3.11 or newer. - After install, run `python scripts/check_env.py --show-backends` from the skill tree to confirm imports and local backend availability. - Read `references/repo-provenance.md` when you need to check whether this skill matches the current checkout. - Read `references/troubleshooting.md` when install or import behavior is off. ## Route map ### `sub-skills/agent-core/` Use for Agent, Toolkit, built-in tools, permissions, message/event handling, task tools, MCP tool wiring, and local skill loading. Typical triggers: - "How do I build an AgentScope agent?" - "Why is this tool blocked or inactive?" - "How do local skills load into a toolkit?" ### `sub-skills/provider-connectors/` Use for chat, embedding, formatter, and TTS provider classes, provider credentials, and provider-specific extras or environment variables. Typical triggers: - "Which extra do I need for Gemini/Ollama/XAI?" - "How do I configure embeddings or TTS?" - "Why does a provider import fail?" ### `sub-skills/rag-memory/` Use for `KnowledgeBase`, RAG middleware, vector stores, filesystem memory, mem0, ReMe, and the RAG or memory examples. Typical triggers: - "How do I index documents and search them?" - "How do I attach RAG to an agent?" - "How do mem0 or ReMe memory modes work?" ### `sub-skills/service-platform/` Use for `create_app`, storage, message buses, channels, hubs, MCP, schedules, knowledge-base service, and service bootstrap/deployment. Typical triggers: - "How do I start the AgentScope service?" - "How do I wire Redis or SQLite storage?" - "How do channels, hubs, or MCP fit into the service?" ### `sub-skills/workspace-sandboxes/` Use for local workspaces and sandboxed backends such as Docker, Bubblewrap, Apple Container, E2B, Daytona, K8s, and OpenSandbox. Typical triggers: - "How do I create a workspace?" - "Which backend do I need for this sandbox?" - "Why did archive import or backend initialization fail?" ## How to choose - If the task is about agent orchestration, start with `agent-core`. - If the task is about provider credentials or model classes, start with `provider-connectors`. - If the task is about retrieval or long-term memory, start with `rag-memory`. - If the task is about HTTP service deployment or platform routes, start with `service-platform`. - If the task is about the execution sandbox or workspace backend, start with `workspace-sandboxes`. If a task spans more than one area, read the most operationally specific sub-skill first, then cross-link to the others. ## Shared reference and diagnostics - `references/repo-provenance.md` — current repo snapshot and refresh baseline. - `references/repo-routing-metadata.json` — machine-readable routing metadata for repo-skill discovery. - `references/troubleshooting.md` — cross-cutting install/import and package-selection problems. - `scripts/check_env.py` — import and backend availability check for this skill tree. ## Minimal import check After installation, a quick check should succeed: ```bash python -c "import agentscope; from agentscope.agent import Agent; from agentscope.tool import Toolkit" ``` If that import fails, fix the environment before using any sub-skill.
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