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Attesta provides first-class integrations for the most popular AI agent frameworks. Every integration follows the same pattern: intercept tool/action calls, evaluate risk through the Attesta pipeline, and only allow approved actions to execute.

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Choosing an Integration

FrameworkLanguageIntegration ClassHook PointInstall Extra
LangChain / LangGraphPythonAttestaToolWrapper, attesta_nodeTool func/coroutine replacement; LangGraph nodeattesta[langchain]
LangChain (TS)TypeScriptgatedTool, createGateNodeProxy on invoke; LangGraph node@kyberon/attesta
OpenAI Agents SDKPythonattesta_approval_handler, AttestaGuardrailRunner.run(approval_handler=...), Agent(tool_guardrails=[...])attesta[openai]
Anthropic ClaudePythonAttestaToolGatetool_use content block evaluationattesta[anthropic]
CrewAIPythonAttestaHumanInputTask(callback=...)attesta[crewai]
MCPPythonattesta_tool_handler, MCPProxycall_tool decorator; stdio proxyattesta (core)
Vercel AI SDKTypeScriptgatedVercelTool, createAttestaMiddlewareTool execute wrapper; experimental_onToolCall@kyberon/attesta
Not sure which integration to use? If you are building a Python agent, start with the framework-specific integration (LangChain, OpenAI, etc.). If you are building a TypeScript agent, use the Vercel AI or LangChain TS integration. If you want to gate any MCP server without code changes, use the MCPProxy.

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How Integrations Work

Every integration follows the same 4-stage pipeline described in How It Works:
1

Intercept

The integration intercepts tool calls before they execute. Each framework has a different hook point (tool wrapper, approval handler, decorator, middleware, etc.), but the result is the same: the call is paused.
2

Build ActionContext

The integration extracts the tool name, arguments, and any available metadata (description, agent ID, session) and builds an ActionContext object for Attesta to evaluate.
3

Evaluate

The ActionContext is passed to attesta.evaluate(), which runs risk scoring, challenge selection, verification, and audit logging.
4

Dispatch

Based on the verdict:
  • Approved / Modified — the original tool call proceeds normally
  • Denied / Timed Out / Escalated — the call is blocked and a denial message is returned (or an exception is raised, depending on the integration)

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Installation

Install Attesta with the extras for your framework: 
# Pick one or more:
pip install attesta[langchain]
pip install attesta[openai]
pip install attesta[anthropic]
pip install attesta[crewai]

# MCP is included in the core package:
pip install attesta

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Common Pattern

Regardless of framework, every integration starts with a configured Attesta instance: 
from attesta import Attesta

# Minimal (all defaults)
attesta = Attesta()

# With config file
attesta = Attesta.from_config("attesta.yaml")
Then pass this instance to the framework-specific integration class. See the individual integration pages for complete examples.

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No-Code Platforms

If you are using a visual workflow builder instead of writing code, see the No-Code Platforms section for drop-in nodes and components:

n8n

AttestaGate node for n8n workflows

Flowise

AttestaGate tool component for chatflows

Langflow

AttestaGate component for Langflow pipelines

Dify

Attesta plugin for the Dify platform