Skip to main content

Documentation Index

Fetch the complete documentation index at: https://mintlify.com/SanMuzZzZz/LuaN1aoAgent/llms.txt

Use this file to discover all available pages before exploring further.

The LuaN1aoAgent Web UI is a standalone FastAPI application that provides a persistent, database-backed dashboard for monitoring and managing agent tasks. It runs independently from the agent worker and stays alive between runs.

​
Architecture

The web server is built on FastAPI and persists all state to a SQLite database (luan1ao.db). The frontend communicates with the backend over a combination of REST API calls and Server-Sent Events (SSE) for real-time updates. 
Browser  ──────►  FastAPI (web/server.py)  ──────►  luan1ao.db (SQLite)
                        │
                        ▼
               SSE stream per op_id
               (polls DB every 1 second)
All task graphs, logs, and intervention requests survive web server restarts because they are stored in the database, not in memory.

​
Starting the web server

python -m web.server
The server starts on http://localhost:8088 by default. Configure the host and port with environment variables: 
# .env
WEB_HOST=0.0.0.0   # bind to all interfaces (e.g., for remote access)
WEB_PORT=8088
If you set WEB_HOST=0.0.0.0, the dashboard will be accessible on your network. Do not expose it to untrusted networks without authentication.

​
Dashboard features

Real-time task graph

Watch the DAG evolve as the Planner adds, deprecates, and completes nodes live.

Live log streaming

Every P-E-R event is streamed to the log panel as it is written to the database.

Node detail panel

Click any node to view its full execution log, tool outputs, artifacts, and state transition history.

Task management

Create new tasks, abort running tasks, delete historical records, and reorder the task sidebar.

​
Creating a task from the UI

1

Open the dashboard

Navigate to http://localhost:8088 in your browser.
2

Click New Task

Click the New Task button in the sidebar. A dialog will appear.
3

Fill in task details

Enter the Goal (the penetration testing objective) and an optional Task Name for display purposes.You can also configure:
  • Output mode: simple, default, or debug
  • Human-in-the-Loop: toggle HITL mode for this task
  • LLM models: override planner, executor, and reflector models per task
4

Submit

Click Start. The web server calls agent.py in a detached subprocess, passing the generated op_id via --op-id. The task appears in the sidebar immediately.
The web server creates the task database record before spawning the agent subprocess, so the task appears in the UI instantly even before the agent initializes.

​
Understanding node states and colors

The task graph visualizes nodes with colors corresponding to their state machine status:
StateColorMeaning
pendingGrayAwaiting dependency completion
in_progressBlueCurrently being executed by the Executor
completedGreenSuccessfully finished; Reflector confirmed goal achieved
failedRedExecutor exhausted steps without success
deprecatedFadedPlanner pruned this node (replaced by a better path)
stalled_orphanOrangeDependency was removed without a replacement
Node types have distinct visual styles:
  • Root node — the overall task goal
  • Task nodes — subtasks created by the Planner (displayed with description labels)
  • Action nodes — individual tool invocations (execution steps)

​
Viewing the causal graph

Switch to the Causal Graph tab to see the evidence-hypothesis-vulnerability-exploit chain the agent is building. Each node represents a causal inference: 
Evidence (port 3306 open)
  ↓ SUPPORTS (confidence 0.8)
Hypothesis (MySQL service running)
  ↓ VALIDATES
Vulnerability (weak credentials)
  ↓ EXPLOITS
Exploit (mysql -h target -u root)
Click any causal node to see its title, description, confidence score, severity rating, and supporting evidence.

​
Viewing node details

Click any node in the execution graph to open the detail panel on the right. The panel shows:
  • Description — the subtask objective
  • Status — current state with timestamp
  • Execution log — all tool calls and observations for that subtask
  • Artifacts — structured findings produced (vulnerabilities, credentials, flags)
  • Thought — the Executor’s internal reasoning chain

​
Managing tasks

​
Aborting a running task

Click a task in the sidebar, then click Abort. The web server sends SIGKILL to the entire process group of the agent subprocess (using os.killpg), which also terminates any MCP tool child processes.

​
Deleting a historical task

Click the task in the sidebar, then click Delete. This removes the SessionModel record and all associated graph nodes, edges, and event logs from the SQLite database via cascading delete.

​
Reordering tasks

Drag tasks in the sidebar to reorder them. The order is persisted to the database (sort_index column on SessionModel) and restored on the next page load.

​
Active intervention: injecting subtasks

During a running task, you can inject new subtasks directly into the agent’s task graph without waiting for the next Planner cycle:
1

Open the task

Select the running task in the sidebar.
2

Click Add Task

Click the Add Task button in the task detail view.
3

Describe the subtask

Enter the subtask description and optionally specify node IDs this subtask depends on.
4

Submit

The web server creates an intervention_request record of type task_injection. The agent picks this up during its next planning cycle and adds the node to the task graph.

​
HITL approval modal

When a task is started with Human-in-the-Loop enabled (HUMAN_IN_THE_LOOP=true), the agent pauses after generating each plan and waits for human approval before execution begins. The Web UI detects pending approval requests via the SSE stream (intervention.required event) and automatically displays a modal:
  • Approve — execute the plan as generated
  • Reject — discard the plan; the agent will re-plan
  • Modify — edit the raw plan JSON in the modal’s editor before approving
For full details on HITL mode, see the Human-in-the-Loop guide.

​
Data persistence

All task data is stored in luan1ao.db (SQLite) in the project root. The database is created automatically on first startup. The following tables are used:
TableContents
SessionModelOne row per task: goal, name, status, config
GraphNodeModelTask graph and causal graph nodes
GraphEdgeModelDependency and causal edges
EventLogModelAll P-E-R log events (streamed to the UI)
InterventionModelPending and resolved HITL/injection requests
Back up luan1ao.db to preserve task history. The file can be copied while the web server is not running.

Build docs developers (and LLMs) love

Get started for freeTalk to us