LangGraph AgentDevelopment System

Build production-ready AI agents through natural conversation with Claude Code. No boilerplate, no complex setup—just describe what you need.

GitHub

Everything You Need

Professional-grade tools and infrastructure, ready to use.

Agent Architecture

Production-ready LangGraph patterns with built-in state management, error handling, and retry logic.

40+ Built-in Tools

Google Workspace (Gmail, Calendar, Sheets, Drive), Slack (12 tools), HTTP APIs, data processing, file operations, and workflow automation.

MCP Integrations (Beta)

Experimental integrations via Model Context Protocol for extended capabilities.

Memory Systems

Persistent conversations, RAG support, and vector storage for context-aware agents.

Production Deployment

Docker containers, Kubernetes manifests, monitoring, and security configurations included.

Testing Framework

Unit tests, integration tests, and debugging tools for reliable agent development.

A Better Way to Build Agents

Skip the complexity. Focus on what your agent should do, not how to build it.

Traditional No-Code

Manual Coding

Braid + Claude Code

Setup Time

Minutes

Limited scope

Hours/Days

Boilerplate heavy

Minutes

Any complexity

Customization

Limited to presets

Full control, high effort

Full control, AI-assisted

Production Ready

Basic deployment

Complex setup

Enterprise-grade

Debugging

Black box

Manual instrumentation

Built-in observability

Integration

Preset connectors

Build everything

40+ tools & integrations

How It Works

You don't use CLIs or write boilerplate. You work with Claude Code to build agents through natural conversation:

Step 1: Prepare Claude Code

You say:

"Please prepare to create a LangGraph agent by reading your development guide."

Braid + Claude Code:

Reads its instructional guide and understands the full system architecture, available tools, and best practices.

Step 2: Define Your Agent Requirements

You provide:

## Core Tasks and Sequences of Agent
**Your Requirements**:
I need an agent that monitors customer support tickets, analyzes sentiment,
and posts daily summaries to Slack with trend data in Google Sheets.

## Tools & MCPs Required
**Your Required Tools & MCPs**:
[Claude Code will recommend optimal combinations]

Braid + Claude Code:

Analyzes your requirements and recommends the optimal combination of tools and MCPs for your specific use case.

Step 3: Create Your Agent

You say:

"Please read my agent requirements and create the agent."

Braid + Claude Code:

Analyzes your requirements
Selects optimal tools and MCPs
Creates production-ready agent structure
Sets up all configurations and dependencies
Provides testing and deployment instructions

Step 4: Enhance with Pro Pack (Optional)

You say:

"Please add the Braid Pro Pack for enhanced testing and monitoring."

Braid + Claude Code adds:

Advanced unit test suites
Integration test frameworks
Performance monitoring
Error handling enhancements
Development workflows

Step 5: Prepare for Deployment

You say:

"Please prepare this agent for production deployment."

Braid + Claude Code:

Packages with optimized Docker containers
Sets up production monitoring
Creates deployment documentation
Configures security hardening
Generates Kubernetes manifests (if needed)

Comprehensive Tool Library

40+ tools and integrations with experimental MCP support.

Built-in Tools

HTTP APIs
File Ops
Transform
CSV/Data
Scheduling
Python

Built-in Integrations

MCP Integrations (Beta)

Experimental integrations via Model Context Protocol

Case Study

How ProAI built a sophisticated financial forecasting system with Braid

Custom Integrations

Custom Tools

Python

SQL

ProAI Financial Forecasting Agent

Dynamic forecasting with accounting integrations

The Challenge

ProAI needed dynamic financial forecasting with accounting software integration (QuickBooks, Xero), market research inputs, and Python compatibility. Required iterative feedback loops and persistent memory.

Why Traditional Solutions Failed

No-code platforms: Limited chat versatility and edge case handling
Basic integrations: No sequencing enforcement or business structure adaptation

The Braid Solution

Intelligent agent that adapts to unique business structures, enforces proper sequencing, and maintains context.

Intelligent Sequencing: Enforces proper data gathering before forecasting
Contextual Adaptation: Handles varying business types and data structures
Feedback Loops: Iterative refinement with three-statement models

Demo Agents

Agents created using Braid for testing.

Financial Operations Assistant

Automates financial reporting to reduce manual work and improve accuracy.

Key Capabilities:

Smart Triggers: Activates via schedule or Slack.
Xero Integration: Pulls P&Ls, balance sheets, and invoices.
Intelligent Analysis: Identifies trends and adds market context.
Notion Reporting: Generates automated reports with key insights.
Slack Notifications: Delivers summaries and report links.
Adaptable: Configurable with natural language.

Autonomous AR Clerk

Automates the full AR lifecycle, from contract to cash, ensuring timely payments.

Key Capabilities:

Google Drive Trigger: Starts workflow from new contracts.
Automated Xero Invoicing: Creates and schedules invoices.
Payment Reconciliation: Tracks payments and notifies team on Slack.
Automated Collections: Sends email & SMS reminders.
Human Escalation: Flags overdue accounts for team review.

Sales Intelligence Engine

Proactively finds and synthesizes sales opportunities to accelerate outreach.

Key Capabilities:

Web Monitoring: Scans for sales trigger events.
Data Fusion: Combines web data with internal Notion CRM.
Slack Briefings: Delivers synthesized opportunity briefs.
Human-in-the-Loop: Enables one-click actions for sales reps.
Gmail Drafts: Auto-generates personalized outreach emails.

Frequently Asked Questions

Get answers to common questions about Braid's approach and capabilities.

Why use built-in integrations instead of MCP Servers?

MCP Servers offer more flexible tool usage out of the box, but their primary advantage is offering an extensive library of tools for dynamic tasks (E.g. Find a list of influencers and email them). In more sequential tasks, where the range of scenarios are more controlled, they offer fewer advantages (E.g. Process this refund for Order #01A01), and they result in increased latency and issues during debugging process since they need separate deployment.

What are the advantages of just using Claude Code directly without the Braid framework?

When creating agents in Claude code, significant compute is applied to redundant tasks (E.g. creating tools, integrations, ect.), which is better applied thinking through agent architecture and testing. Further, having a pre-defined set of workflow paths tested for systematic agent production allows layered guardrails and bug prevention.

Will Braid zero shot my agents into production?

No, but eventually it will be able to, and the primary goal is to assist with agent scaffolding with predefined tools documented for the LLM so you can focus on more refined testing and integration.