Skip to main content

Open-source AI agent security middleware for policy-enforced tool calls, prompt injection defense, and PII masking

Project description

███╗   ███╗███████╗ ██████╗ ███████╗███╗   ██╗████████╗
████╗ ████║██╔════╝██╔════╝ ██╔════╝████╗  ██║╚══██╔══╝
██╔████╔██║█████╗  ██║  ███╗█████╗  ██╔██╗ ██║   ██║   
██║╚██╔╝██║██╔══╝  ██║   ██║██╔══╝  ██║╚██╗██║   ██║   
██║ ╚═╝ ██║███████╗╚██████╔╝███████╗██║ ╚████║   ██║   
╚═╝     ╚═╝╚══════╝ ╚═════╝ ╚══════╝╚═╝  ╚═══╝   ╚═╝   

A zero-trust safety layer for AI agents. Wrap tools fast. Ship with confidence.

Open-source AI agent security middleware for policy-enforced tool calls, prompt injection resistance, and PII masking.

License: Apache 2.0 Python PyPI Status

Docs · Policies


Megent: Open-Source AI Agent Security Middleware

Megent helps secure AI agents by enforcing allow/deny tool policies, masking sensitive data, and logging every tool decision.


The Problem

AI agents are calling tools. Most of those calls look harmless. But sequences don't lie.

agent.read_file("/etc/passwd")          ← looks fine
agent.web_search("paste.bin upload")    ← looks fine  
agent.http_post("https://...")          ← looks fine

# combined? that's data exfiltration.

Traditional security tools inspect calls one by one. Megent enforces policy at execution time.

Built for teams that want speed without security debt.


How It Works

Megent sits between your agent and its tools, running every call through three primitives:

┌─────────────────────────────────────────────────────┐
│                    AGENT RUNTIME                    │
│                                                     │
│  tool_call() ──► [ INTERCEPT ] ──► [ CONTEXT ]      │
│                                          │          │
│                                     [ JUDGE ]       │
│                                          │          │
│                              allow / deny / modify  │
└─────────────────────────────────────────────────────┘
Primitive Role
Intercept Hooks into every tool invocation before execution
Context Maintains a behavioral window — the sequence of recent calls
Judge Evaluates the sequence against your policy rules

Install

pip install megent

Super Simple Setup (3 Steps)

No framework migration. No plugin boilerplate. Just one policy file and one decorator.

  1. Create a megent.yaml file in your project root:
version: "1"
default_action: deny

tools:
  send_email:
    allow: true
    pii_mask: [email, phone]
  1. Add Megent to your function:
import megent as mg

mg.configure(policy_path="megent.yaml")

@mg.guard
def send_email(to: str, body: str) -> str:
    return "sent"

send_email("ops@example.com", "Call me at +1 555 111 2222")
  1. Run your app. Calls are now policy-checked, and sensitive fields are masked automatically.

Quickstart

Drop-in decorator

import megent as mg

mg.configure(policy_path="policies/agent.yaml")

@mg.guard
def send_email(to: str, subject: str, body: str) -> str:
  # your tool implementation
  return "sent"

send_email(
  to="ops@example.com",
  subject="Daily summary",
  body="Contact me at jane.doe@example.com",
)

Wrap an existing agent

import megent as mg

runtime = mg.Runtime(policy_path="policies/agent.yaml")

safe_execute = mg.wrap(
  third_party_agent.execute,
  runtime=runtime,
  tool_name="agent_execute",
)

safe_execute(task="Summarize latest reports")

That's it. Megent intercepts every tool call, evaluates it against your policy, and either allows, denies, or modifies it — all without changing your agent code.


Policy Language

Policies are plain YAML. No DSL to learn.

# policies/agent.yaml
version: "1"
default_action: deny
pii_mask: [email]

tools:
  read_file:
    allow: true

  send_email:
    allow: true
    pii_mask: [email, phone, ssn]

  delete_all_data:
    allow: false

Agent Identity (JWT)

Megent can attribute calls to an agent identity using a JWT (HS256). Set MEGENT_JWT_SECRET (or pass secret= to verify_agent_token) and include agent_id (or sub) in the token claims.

import megent as mg

runtime = mg.Runtime(policy_path="policies/agent.yaml")
token = "<jwt-from-your-auth-system>"

safe_send = mg.wrap(send_email, runtime=runtime, tool_name="send_email", agent_token=token)
safe_send(to="ops@example.com", subject="Ping", body="hello")

Audit Log

Every decision is logged in structured JSON.

{
  "event": "allow",
  "tool": "http_post",
  "agent_id": "reports-agent-v2",
  "timestamp": 1767945230.137,
  "args": {
    "body": "[REDACTED]"
  },
  "masked_fields": ["email"]
}

Pipe to any SIEM. Query with any log tool.


Framework-agnostic

Megent is not a plugin for LangChain, CrewAI, or any other framework. It is an independent security layer.

You build your agent on whatever platform you want. Megent wraps it.

┌──────────────────────────────────────┐
│              MEGENT                  │  ← security layer (this is us)
│  ┌────────────────────────────────┐  │
│  │   your agent (LangChain,       │  │  ← built on any framework
│  │   CrewAI, OpenAI Agents SDK,   │  │
│  │   raw Python, anything)        │  │
│  └────────────────────────────────┘  │
└──────────────────────────────────────┘

Megent doesn't know or care what your agent is built on. It intercepts tool calls at the boundary — before execution — regardless of the underlying platform.

import megent as mg

# agent built on LangChain? wrap it.
safe_agent = mg.wrap(langchain_agent.invoke, runtime=mg.Runtime(policy_path="policies/agent.yaml"))

# agent built on CrewAI? wrap it.
safe_agent = mg.wrap(crew.kickoff, runtime=mg.Runtime(policy_path="policies/agent.yaml"))

# raw Python agent? same thing.
safe_agent = mg.wrap(my_agent.run, runtime=mg.Runtime(policy_path="policies/agent.yaml"))

The platforms (LangChain, CrewAI, OpenAI Agents SDK, AutoGen, LlamaIndex) are where agents are built. Megent is where they are secured. These are separate concerns.


Threat Coverage

Attack Megent Defense
Unauthorized tool calls Per-tool allow/deny policy enforcement
Unknown-by-default execution default_action: deny for explicit allowlists
PII leakage in arguments Configurable regex masking (pii_mask)
Unattributed execution Optional JWT-based agent_id attribution
Weak observability Structured audit events via standard logging


Contributing

Megent is Apache 2.0 licensed and open to contributions.

git clone https://github.com/Megents/Megent.git
cd megent
pip install -e ".[dev]"
pytest

See CONTRIBUTING.md for guidelines.


License

Apache 2.0 — free to use, modify, and distribute.


Built for production AI. Designed for developers who ship.

megent.dev

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

megent-0.1.5.tar.gz (27.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

megent-0.1.5-py3-none-any.whl (23.7 kB view details)

Uploaded Python 3

File details

Details for the file megent-0.1.5.tar.gz.

File metadata

  • Download URL: megent-0.1.5.tar.gz
  • Upload date:
  • Size: 27.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.3

File hashes

Hashes for megent-0.1.5.tar.gz
Algorithm Hash digest
SHA256 6ef87c27eecfe0e5bedd6dd241656b6ed5bb8fc19a42bbbc00d61d6df67cd133
MD5 15938d4dccceea909b3f8f3048af98c5
BLAKE2b-256 e823f9356130911c8c301469c667be0d60672c7a7a10ab11730bca26286c6cfe

See more details on using hashes here.

File details

Details for the file megent-0.1.5-py3-none-any.whl.

File metadata

  • Download URL: megent-0.1.5-py3-none-any.whl
  • Upload date:
  • Size: 23.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.3

File hashes

Hashes for megent-0.1.5-py3-none-any.whl
Algorithm Hash digest
SHA256 f52ba6a04470b759eaf8b1fda07be8acc4022d2c89f699e28909d1f4926ae4fe
MD5 8ba7a2ae023ea9c390e7bf5a84b8f03a
BLAKE2b-256 59045c022d1c5510897534cce865099f24502992992eba0debb5e97e4c6a2b25

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page