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name: agent-workflows description: "Work with RAGFlow agent canvas DSL, components, tools, templates, memory, MCP retrieval, sandbox execution, sessions, traces, and workflow debugging." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Agent Workflows

Use this sub-skill when a task involves RAGFlow agents, canvas/workflow DSL, agent templates, component wiring, tool/sub-agent orchestration, memory-aware agents, MCP retrieval, sandbox/code execution, webhooks, sessions, traces, or component-level debugging.

Start Here

• For DSL shape, execution order, variable references, components, loops, sessions, and debug flows, read references/canvas-components.md.
• For agent tools, plugin-style tools, memory read/write, MCP retrieval, sandbox/code execution, and security defaults, read references/tools-memory-mcp.md.
• For common failures such as broken component references, cycles, async/streaming surprises, credentials, sandbox, webhooks, memory, MCP, and template import/export, read references/troubleshooting.md.
• To statically inspect an exported template or DSL JSON without starting RAGFlow, run python scripts/inspect_agent_template.py --help.

Scope Boundaries

• Treat the canvas DSL as the backend contract: components, path, globals, history, retrieval, memory, optional variables, and optional visual graph metadata.
• Use component IDs exactly as they appear in the DSL; variable references are usually {component_id@output}, {{component_id@output}}, sys.*, or env.*.
• Keep frontend canvas UI concerns out of this sub-skill; only use visual graph fields to preserve layout or diagnose component IDs.
• For public HTTP/SDK request details, cross-link to sdk-http-integration; this sub-skill only summarizes behavior-level agent, session, memory, and MCP surfaces.
• For retrieval ranking, parsing, chunking, and dataset internals, cross-link to dataset-ingestion-retrieval; this sub-skill focuses on retrieval as an agent tool.

Common Workflows

• Inspect a template: validate JSON shape, ensure dsl.components exists, confirm begin or File start nodes, list component IDs/classes, and check references before import.
• Debug a canvas run: map path to component IDs, verify every downstream/upstream target exists, check {component@output} references against declared outputs, then inspect trace events when available.
• Wire tools into an Agent: put tool definitions under the Agent component parameters, keep tool names/descriptions actionable, provide credentials explicitly, and keep downstream Message handling in mind for streaming output.
• Add memory: create/configure memory separately, use Retrieval to read memory IDs, and use Message save-to-memory behavior for durable conversation facts.
• Expose retrieval through MCP: configure MCP server base URL and API key per environment, use ragflow_retrieval, and keep API key transport separate from dataset/document selection.

Guardrails

• Do not publish real API keys, MCP headers, webhook secrets, database passwords, or sandbox host details in examples or logs.
• Do not assume all component imports are available in a source-only environment; optional dependencies can affect deeper imports.
• Do not run code execution, network tools, webhooks, deletes, or live MCP calls unless the user explicitly asks and provides safe targets.
• Prefer static template checks before live import/export; use the bundled inspector to catch missing keys and obvious graph/reference issues.
• Keep generated runtime guidance self-contained; source files, docs, and tests are evidence only.