stroblme
1b5a16f636
ai/client.py streams the completion and puts the chunks back together (Assembler), covering OpenAI's piecewise tool-call arguments, Ollama's whole calls, OpenRouter's comment lines, unnumbered calls and in-stream errors. The turn emits a delta event per piece and a message per round as before, and the chat grows the last answer as deltas arrive.
3.3 KiB
Agents over MCP
Fluksio exposes flow tools over MCP. An agent can inspect, edit, publish, and run flows with the permissions of the user who authorizes it.
Connect an agent
Set MCP_ENABLED=true in the engine's environment and restart it. The Python
package defaults to off; the Docker template enables it.
Configure your MCP client with an HTTP endpoint:
- Local Docker stack:
http://api.localhost/mcp - Deployment:
https://api.<your-domain>/mcp
The client registers, opens browser authorization, and receives an OAuth
token after you approve access. The issuer and browser redirects use
DOMAIN, ENVIRONMENT, and FRONTEND_HOST, so configure those for the
addresses your client and browser can reach.
Manage registered clients under Admin → Agents. Revoking a client
invalidates its access. MCP uses a separate signing key at
OAUTH_PRIVATE_KEY_FILE; rotating that key invalidates all MCP tokens.
An instance connected to a portal also answers
through it, at https://hub.fluksio.com/i/<instance-id>/mcp (the portal's
own hub. host for a custom one). The portal forwards the endpoint and its
OAuth, and the instance checks the agent's token as it does locally. The
consent page opens in the instance as the portal serves it, so open the
instance from the portal first if this browser has not recently.
Available tools
| Task | Tools |
|---|---|
| Inspect flows | list_flows, get_flow, get_node_source, list_node_types, get_graph, list_shared_nodes |
| Inspect values | get_flow_state, get_message_history |
| Inspect the instance | list_secrets (names only), get_modules, get_health, get_metrics, list_failures, list_runs (live cascades) |
| Edit flows | save_flow, save_node_source, validate_flow, publish_flow, discard_draft, delete_flow |
| Operate flows | run_flow, trigger_node, start_flow, stop_flow, pause_flow, resume_flow, cancel_node |
| Install node packages | apply_modules |
Tools call the same API as the browser. Drafts, validation, version checks, and superuser restrictions apply. Secrets cannot be read back.
A useful workflow: ask the agent to inspect the flow, save a draft, and validate it. Review the draft before asking it to publish. The agent has a publish tool, so make your intent explicit.
The assistant
A superuser configures an OpenAI-compatible provider under Settings → Assistant, with a base URL, API key, and model. This enables the dashboard's Assistant entry for signed-in users.
The assistant uses the same tools with your own permissions. Draft edits
appear in the editor. Each answer streams in as the model writes it and
renders as markdown with a tool trace.
Conversations are stored per user on the instance; the bin button clears
yours.
MCP_ENABLED is only needed for external clients, so the built-in assistant
works without it.
Control
A live Wren node with both prompt and
actions wired adds a Control tab to the chat. It uses the node's local
model and needs no assistant provider.
Choose a node, type a command, and inspect the reported actions: target
message, value, confidence, and whether each action was applied.
The node's apply and min_confidence settings decide whether it writes
to devices. Control conversations stay in the browser and clear on reload.