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agentscope

agentscope

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

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

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:

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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