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provider-connectors

provider-connectors

AgentScope provider, embedding, formatter, and TTS configuration workflows.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentación de la Skill

GitHub

name: provider-connectors description: "AgentScope provider, embedding, formatter, and TTS configuration workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

provider-connectors

Use this sub-skill when the task is about provider classes, credentials, provider extras, model names, embeddings, formatters, or TTS.

Read first

  • references/model-overview.md for chat model families and credential shapes.
  • references/embedding-overview.md for embedding model families and dimension notes.
  • references/tts-overview.md for TTS constructors and streaming defaults.
  • references/troubleshooting.md for missing-extra, credential, and provider-specific failures.
  • scripts/provider_matrix.py for a safe availability/import check.

Typical triggers

  • Choose the right extra for OpenAI, Anthropic, DashScope, Gemini, Ollama, Moonshot, DeepSeek, or XAI.
  • Compare chat, embedding, formatter, or TTS constructor defaults.
  • Fix a provider import or credential problem.
  • Check which providers are available in the current environment.

What belongs here

  • agentscope.credential
  • agentscope.model
  • agentscope.embedding
  • agentscope.formatter
  • agentscope.tts
  • provider-specific configuration, env vars, and model names

What does not belong here

  • Agent/tool/permission basics → agent-core
  • RAG and memory workflows → rag-memory
  • Service deployment and API bootstrap → service-platform
  • Workspace or sandbox backend setup → workspace-sandboxes

Use pattern

  1. Identify the provider family first.
  2. Read the matching overview reference for the constructor and default values.
  3. Check whether the provider needs a dedicated extra or only the base package.
  4. Use scripts/provider_matrix.py when you need a safe current-environment status check.
  5. Escalate to rag-memory only if the issue is really an embedding dimension or retrieval workflow.

Shared diagnostics

  • Run ../../scripts/check_env.py if the entire package looks stale.
  • Read references/troubleshooting.md before changing code if the problem is a missing extra, credential class, or model name.

Cross-links

  • If the task needs the agent/tool layer, switch to agent-core.
  • If the task needs RAG or memory, switch to rag-memory.
  • If the task needs service bootstrapping or deployment, switch to service-platform.
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