Breadcrumbs

AI Agent Frameworks

Note:

  • This feature is in open beta.

  • This feature requires a Mend AI Core or Premium subscription.

Overview

Mend AI scans your source code to automatically detect and inventory AI agent components built with popular agent frameworks. The scan identifies:

  • Agents — distinct agent instances constructed in your code

  • Models — LLM model references (e.g. gpt-4o-mini, claude-3-sonnet)

  • System prompts — instruction strings assigned to agents

  • Tools — callable functions or plugins attached to agents

  • MCP servers — Model Context Protocol server references

  • Relations — connections between agents such as handoffs, delegation, and group membership

All detected components appear in their respective inventory tables in the Mend Platform UI, giving you a unified view of your AI agent architecture.

Prerequisites

AI Agents detection is available only with an active Mend AI Core or Premium subscription.

Getting it done

  1. Navigate to the desired application or project in the Mend Platform.

  2. On the left pane, click on AI Agents. This will take you to the AI Agents table.

image-20260604-035326.png

The AI Agents Table

The AI Agents table contains the following columns:

  • Name

  • Framework

  • Models

  • System Prompts

  • Tools

  • Severity

  • MCPs

  • Violations

  • Aggregated Findings

  • Language (e.g., “Python”)

  • Code Location

image-20260604-035646.png

Use the searchbox and filters at the top to display agents that meet your criteria.

Export the data currently visible in the table by clicking the CSV button at the top right.

Click the Reset button at the top to cancel the filters and restore the original table.

The AI Agent Side-Panel

Click anywhere on a row to display that AI Agent’s side-panel.

The side-panel lists all the information about the agent in two tabs: Overview and Findings.

The Overview Tab

Contains the agent’s description and the same information listed in the AI Agents table (Framework, Language, etc.)

image-20260604-040950.png

The Findings Tab

Switch over to the Findings tab to list the AI Findings applicable to the agent in question.

The findings table lists the following columns:

  • AI Finding (the name and description of the finding)

  • Type (e.g., “System Prompt”)

  • Entity Name

  • Severity

  • Finding Id

  • Violations

image-20260604-041547.png

Supported Frameworks

Framework

Language

File Extensions

Agents

Models

System Prompts

Tools

MCP Servers

AgentChat

Python

.py

✅

✅

✅

✅

✅

Agno

Python

.py

✅

✅

✅

✅

✅

AutoGen

Python

.py

✅

✅

✅

✅

✅

Azure OpenAI Plugins

JSON / YAML

.json, .yaml, .yml

—

—

✅

✅

—

CAMEL

Python

.py

✅

✅

✅

✅

✅

CrewAI

Python

.py

✅

✅

✅

✅

✅

Dify

JSON / YAML

.json, .yaml, .yml

✅

✅

✅

✅

✅

Google ADK

Python

.py

✅

✅

✅

✅

✅

LangGraph

Python

.py

✅

✅

✅

✅

✅

Letta

Python

.py

✅

✅

✅

✅

✅

LlamaIndex

Python

.py

✅

✅

✅

✅

✅

Mastra

TypeScript / JS

.ts, .tsx, .mts, .cts, .js, .mjs, .cjs

✅

✅

✅

✅

✅

n8n

JSON

.json

✅

✅

✅

✅

✅

OpenAI Agents SDK

Python

.py

✅

✅

✅

✅

✅

Pydantic AI

Python

.py

✅

✅

✅

✅

✅

Semantic Kernel

Python / C#

.py, .cs

✅

✅

✅

✅

✅

Smolagents

Python

.py

✅

✅

✅

✅

✅

Swarm

Python

.py

✅

✅

✅

✅

—

How Detection Works

Step 1 — File Discovery

During a scan, Mend identifies source files that match the supported file extensions above. Files in excluded paths (vendor directories, build output, etc.) and any paths matching your custom path exclusion globs are skipped.

Step 2 — Framework Identification

Each file is checked for framework-specific markers — typically import statements (Python) or structural patterns (JSON/YAML workflows). Only files that match a known framework proceed to full analysis.

Step 3 — Entity Extraction

Mend parses the source code to identify agent constructions, model references, prompt strings, tool declarations, and MCP server configurations. Detected entities are attributed to their parent agents where applicable.

Step 4 — Inventory Merge

All detected entities are merged into the global inventory tables. For example, a model referenced inside an agent definition appears both on the agent's detail view and in the overall Models table.

Agent Relations

Mend detects relationships between agents in multi-agent systems:

Relation Type

Description

Group membership

Agent belongs to a multi-agent group (team, swarm, group chat)

Delegation / Handoff

One agent delegates work to another (sub-agents, handoffs, conditional routing)

Resource usage

Agent uses a non-agent resource (tool executor, sub-graph, human input node)

Relations are detected for: AgentChat, AutoGen, Agno, CrewAI, Dify, Google ADK, LangGraph, LlamaIndex, Mastra, n8n, OpenAI Agents SDK, and Swarm.

Known Limitations

  • Same-file resolution only — Models, prompts, and tools are resolved within the file where they are used. Cross-file imports are captured as raw reference text rather than fully resolved.

  • String literals only for prompts — Prompts built dynamically at runtime (f-strings, template engines, file reads) are captured as raw source text, not rendered output.

  • Language coverage — Each framework is supported in its primary language(s) as listed above. Additional SDKs (e.g., TypeScript for LangGraph, Java for Google ADK) are not yet supported.

  • Workflow JSON heuristics — For JSON/YAML-based frameworks (Dify, n8n), detection uses structural patterns in the workflow definition. Highly customized workflow shapes may not be recognized.

  • YAML line numbers — For Dify workflows in .yaml/.yml format, reported line numbers refer to the internally processed representation, not the original YAML layout.