Quick Start
Let's walk through the basic workflow: connecting a node, discovering an agent, running recon, and executing an operation.
Prerequisites
You should have:
- Praxis service running (via Docker or native build)
- At least one LLM configured (see Configuration)
- A node running on a system with an AI agent installed
- The
praxisTUI installed (see Installation)
Step 1: Check Your Node
Launch the TUI:
praxis
Open the Nodes window with Ctrl+L. You should see your node in the
node list. Use the arrow keys (or click) to select it. The detail pane
shows:
- Machine name and OS details
- Detected agents — which AI assistants were found
- Status of interception, sessions, etc.
If no agents show up, make sure the target system actually has Claude Code, Codex CLI, Gemini CLI, or another supported agent installed and configured.
Step 2: Select an Agent
In the Nodes window, focus the agent list and select one. This focuses all subsequent operations on that specific agent.
Step 3: Run Reconnaissance
With an agent selected, press r to open the Recon overlay. This
performs static reconnaissance:
- Discovers MCP servers and other tool integrations
- Lists configuration files and their contents
- Shows session history — past conversations and their locations
- Enumerates project paths where the agent has been used
Switch tabs with Tab (or 1 2 3) to browse Config, Tools, and
Sessions. Press r to refresh static recon.
Semantic Recon
For deeper discovery, press d to run semantic recon (requires an LLM
configured for "Semantic Parser"). This uses the LLM to parse
configuration files and extract tool definitions that might not be
obvious from static analysis. It also creates sessions and communicates
directly with the agent to discover its full capabilities, so it takes
longer than static recon.
Step 4: Look Around
With recon data, you can:
View configuration files — In the Config tab, pick any file to see its contents.
Browse sessions — In the Sessions tab, see what conversations the agent has had and which projects it's worked on.
Check tools — In the Tools tab, see what MCP servers, skills, or plugins are available to the agent.
Step 5: Create a Session
In the Nodes window, with an agent selected, start a session chat. You can specify a working directory and toggle YOLO mode.
Working Directory — where the agent should operate. Affects what files it can see and work with.
YOLO Mode — when enabled, the agent auto-approves all tool calls without asking for confirmation. Use this for automation, but be careful — it will execute whatever the agent decides to run.
Once the session is created, send prompts directly from the chat view.
Step 6: Run an Operation
Operations are predefined tasks you can execute through agents. The library starts empty, so let's create a simple one first.
Create Your First Operation
- Open the Operations window (
Ctrl+P) and switch to the Library tab - Create a new operation
- Fill in:
- Name:
hello-world - Category:
test - Description:
A simple test operation - Prompt:
Say hello and tell me what directory you're currently in. - Mode:
one-shot - Timeout:
60
- Name:
- Save
Run It
- Switch to the Executions tab
- Run the operation, selecting your node and agent
- Choose
test::hello-world
The operation executes through your agent. Watch the output in real-time in the Executions tab — you'll see the agent's response appear as it completes.
Operation Modes
- One-shot - sends the prompt directly to the agent and returns the response
- Agent - uses an orchestrating LLM to run multi-turn interactions with the target agent (useful for complex tasks)
For more complex workflows, you can chain multiple operations together. See Semantic Operations for details.
Step 7: Enable Interception (Optional)
To see the traffic between the agent and its LLM backend, open the
Intercept window (Ctrl+I):
- Select your node
- Choose a method:
- Proxy - configures system proxy settings
- VPN - uses a TUN adapter for packet-level routing
- Hosts - modifies the hosts file
- Enable interception
Captured traffic streams into the Log tab. You can see:
- Full request/response bodies
- Prompts and completions
- Tool calls and results
See Interception for details on each method.
What's Next?
- Configure LLM providers for semantic features
- Learn about agent connectors and their capabilities
- Set up traffic interception in detail
- Build operation chains for automation