agent-inspector

Agent Inspector

Analyze and debug AI agents in real-time. Scan your code for vulnerabilities (both statically and dynamically), trace LLM calls, and evaluate runtime behavior—all from a single command.

Install through the IDE

IDE integration provides MCP query tools for inspecting sessions, risk metrics, and security findings directly in your editor. It also enables static analysis to scan your agent code for vulnerabilities before runtime.

Claude Code

Run these commands to register the marketplace and install the plugin:

/plugin marketplace add cylestio/agent-inspector
/plugin install agent-inspector@cylestio
/agent-inspector:setup

After installation, restart Claude Code for the MCP connection to activate.

Cursor

Copy this command to Cursor and it will set everything up for you:

Fetch and follow instructions from https://raw.githubusercontent.com/cylestio/agent-inspector/main/integrations/AGENT_INSPECTOR_SETUP.md

After setup, restart Cursor and approve the MCP server when prompted.

Install without IDE Integration

Run directly with uvx:

uvx agent-inspector openai   # or: anthropic

Install via pipx or pip:

pipx install agent-inspector
agent-inspector openai   # or: anthropic

This starts:

• A proxy server on port 4000 (configurable)
• A live trace dashboard on port 7100 (configurable)

CLI Options

Flag	Description
--port, -p	Override the proxy server port (default: 4000)
--ui-port	Override the dashboard port (default: 7100)
--base-url	Override the LLM provider base URL
--use-local-storage	Enable persistent SQLite storage for traces
--local-storage-path	Custom database path (requires --use-local-storage)
--log-level	Set logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
--no-presidio	Disable Presidio PII detection (enabled by default)

Point your agent to the proxy:

# OpenAI
client = OpenAI(base_url=f"http://localhost:4000/agent-workflow/{AGENT_WORKFLOW_ID}")

# Anthropic
client = Anthropic(base_url=f"http://localhost:4000/agent-workflow/{AGENT_WORKFLOW_ID}")

Replace AGENT_WORKFLOW_ID with your project identifier (e.g., derived from your git repo name, package name, or folder name).

Open http://localhost:7100 to view the live dashboard.

Identifying and Grouping LLM Calls

The proxy automatically detects and groups most identifiers. All headers below are optional and only needed when you want to override the automatic behavior. Add headers via your SDK's extra_headers or default_headers parameter.

Workflow ID (required in URL)

An agentic workflow composed of multiple LLM calls with different prompts should be identified using the workflow ID in the base URL:

http://localhost:4000/agent-workflow/{AGENT_WORKFLOW_ID}

This groups all calls from the same agent or application together, regardless of prompt type or conversation.

Session Grouping (optional)

When an agent executes a series of different LLM conversations as part of a single run or task, you can group them into one session:

x-cylestio-session-id: request-f1a1b2a8

This is useful for multi-step workflows where a classifier, retriever, and generator each make separate calls but belong to the same execution. No automatic detection - must be provided explicitly.

Conversation Type (auto-detected)

The proxy automatically identifies different conversation types based on the system prompt hash. Each unique system prompt creates a distinct conversation type in the dashboard.

To assign a meaningful name instead of an auto-generated hash, optionally use:

x-cylestio-prompt-id: tool-decision-making

Conversation ID (auto-detected)

Multi-turn conversations with message history are automatically inferred from the prompt content and conversation structure.

To explicitly track a conversation across API calls, optionally generate your own identifier:

x-cylestio-conversation-id: conv-uuid-here

Custom Tags (optional)

Attach arbitrary metadata to any LLM call for filtering and analysis. Use comma-separated key:value pairs:

x-cylestio-tags: user:alice@example.com,env:production,team:backend

Tags appear in the dashboard and can be used to filter sessions by user, environment, feature flag, or any custom dimension.

Features

Security Scanning & Fixes

• Scan your agent code for OWASP LLM Top 10 vulnerabilities
• Get AI-powered, context-aware fixes for security issues
• Track remediation progress with recommendation lifecycle
• Check production deployment readiness with gate status

Live Tracing & Debugging

• Stream live traces of sessions, tool executions, and messages
• Real-time token usage and duration tracking
• Debug agent sessions with full event replay and timeline
• Health badges and status indicators