Run and manage an agent in Anthropic's cloud for long or asynchronous tasks. Create a session with packages and tools, send work, inspect status and activity, continue with saved files and conversation, then terminate the session.
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Run an Anthropic Claude Managed Agent — a cloud agent harness (container + filesystem + tools), the cloud counterpart of the local wasm-agent runtime
安装前可以查看交给 AI 代理的指令内容。
ruflo-agent has two agent runtimes behind one mental model:
| Runtime | Tools | Use it when |
|---|---|---|
WASM (local, rvagent) | wasm_agent_* / wasm_gallery_* | fast, free, ephemeral, offline, untrusted code in a sandbox |
| Managed (Anthropic cloud) | managed_agent_* (this skill) | long-running / async work (minutes–hours), a real cloud container with pre-installed packages + network, persistent filesystem + transcript across turns |
This skill drives the managed runtime — Anthropic's Claude Managed Agents (beta). The model: Agent (model + system + tools + MCP servers + skills) → Environment (container template) → Session (running instance) → Events (turns / tool-use / status, persisted server-side). See docs/adr/0001-wasm-contract.md and project ADR-115.
ANTHROPIC_API_KEY (or CLAUDE_API_KEY) in the environment, with Claude Managed Agents beta access.managed_agent_* tool returns a structured "use wasm_agent_create for a local no-key runtime" error — fall back to the WASM skill.Create — mcp__plugin_ruflo-core_ruflo__managed_agent_create
{ model?, system?, name?, networking?, packages?, initScript?, mcpServers?, skills? }
→ { sessionId, agentId, environmentId, status }. Provisions Agent + Environment + Session. Save the three ids.
mcpServers: [{type:"url", url, name, authorization_token?}] — the cloud agent must be able to reach the URL. A local ruflo mcp start is not reachable from Anthropic's cloud; deploy/tunnel an HTTP ruflo MCP server first if you want the cloud agent to have ruflo's tools.packages: {pip?:[], npm?:[], apt?:[], cargo?:[], gem?:[], go?:[]} — installed in the container.Prompt — mcp__plugin_ruflo-core_ruflo__managed_agent_prompt
{ sessionId, message, maxWaitMs? } → sends a user turn, polls the event log until the session goes idle (default 180s, capped 600s) → { finished, status, stopReason, assistantText, toolUses[], eventCount }. For very long tasks, raise maxWaitMs or follow up with managed_agent_events.
Inspect — mcp__plugin_ruflo-core_ruflo__managed_agent_status { sessionId } (idle/running/error) · mcp__plugin_ruflo-core_ruflo__managed_agent_events { sessionId, raw? } (full transcript: user turns, agent thinking, tool_use, tool_result, status — the cloud counterpart of wasm_agent_files).
List — mcp__plugin_ruflo-core_ruflo__managed_agent_list { limit? } — every session on the org (so you can see which are still running / billing).
Terminate — mcp__plugin_ruflo-core_ruflo__managed_agent_terminate { sessionId, environmentId? } — always do this when done: a cloud session keeps billing container time + tokens until deleted. Pass environmentId to also delete the environment ruflo created.
cost-tracking namespace.managed_agent_list then managed_agent_terminate anything stale.managed-agents-2026-04-01); multiagent / define-outcomes on the agent config are research preview.managed_agent_create { "model": "claude-haiku-4-5-20251001", "system": "Terse. Do exactly what is asked.", "name": "scratch" }
→ { sessionId: "sesn_…", agentId: "agent_…", environmentId: "env_…", status: "idle" }
managed_agent_prompt { "sessionId": "sesn_…", "message": "echo hello > /tmp/x && cat /tmp/x — then stop." , "maxWaitMs": 60000 }
→ { finished: true, status: "idle", stopReason: "end_turn", assistantText: "Done.", toolUses: [{name:"bash", input:{command:"echo hello > /tmp/x && cat /tmp/x"}}] }
managed_agent_terminate { "sessionId": "sesn_…", "environmentId": "env_…" }
→ { sessionDeleted: true, environmentDeleted: true }
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Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.
中文简介正在准备中。当前显示英语简介。
Create an Architecture Decision Record, a document for recording important design choices, with the next sequential number. It generates a Markdown template, registers the record in AgentDB, and links related decisions found through search.
中文简介正在准备中。当前显示英语简介。
Build a searchable index and relationship graph from ADRs, records of architectural decisions. Import two supported document formats, refresh existing metadata without duplicate relationships, and review summaries of statuses, missing references, and inconsistencies.
中文简介正在准备中。当前显示英语简介。
Rebuild the index of ADRs, records of architectural decisions, after files or relationship lines are deleted. It clears stored records and relationships, indexes the files still present, and checks that the final record count matches.
中文简介正在准备中。当前显示英语简介。
Review code changes against accepted Architecture Decision Records, documents that capture agreed design choices. Report violations, outdated references, compliant changes, and areas without ADR coverage, then suggest code fixes or a new decision record where appropriate.
中文简介正在准备中。当前显示英语简介。
Check saved architecture decision records (ADRs) for broken references, circular replacement relationships, and inconsistent statuses. Use the results to verify imported records or stop a CI check when selected integrity rules fail.
中文简介正在准备中。当前显示英语简介。