How does chain-of-thought tracing work?

For each conversation turn, the trace viewer shows the agent's step-by-step reasoning: which tool it decided to call, the tool input and output, and the reasoning that led to the final response. This is invaluable for debugging.

How is long-term memory implemented?

User preferences and key facts from past conversations are extracted and stored as embeddings in a vector database. At each new session, relevant memories are retrieved and injected into the agent's context.

What is human-in-the-loop escalation?

When the agent's confidence score falls below a threshold, or a user explicitly requests a human, the conversation is flagged and queued for a human operator. The operator sees the full conversation history and resumes on behalf of the user.

Technology & SaaS

Build a AI Agent Builder Platform with AI - in Hours

ProjectCode generates a complete, production-ready AI Agent Builder Platform from a single natural language prompt. In hours instead of months, you get a React frontend, NestJS REST API, PostgreSQL database, and authentication - fully integrated, live-previewed, and deployed to a URL you own.

Estimated build time: 2–4 hours

Build my AI Agent now View pricing

Free to start · No credit card required

The Challenge

Why building a AI Agent from scratch is hard

Building a AI Agent Builder Platform from scratch requires months of development time and significant engineering resources. Teams struggle to coordinate frontend, backend, database, and authentication while managing ai agent-specific business logic, integrations, and role-based access rules.

What's Included

Everything your AI Agent needs

ProjectCode generates a production-ready AI Agent with all the modules your team needs from day one.

Agent creation with system prompt, tools, and knowledge base

Multi-step reasoning with chain-of-thought tracing

Tool integration: web search, database query, API calls, code execution

Conversation memory: short-term and long-term user context

Human-in-the-loop escalation workflow

Agent evaluation suite with test case runner

Usage and cost analytics per agent and user

Embeddable widget and REST API for deployment

Architecture

How it's built

The generated AI Agent follows production-grade architecture patterns.

React 18 + TypeScript frontend with role-based dashboards tailored for ai agent workflows

NestJS REST API with dedicated ai agent modules, DTOs, validators, and service layer

PostgreSQL relational schema with normalized tables for all ai agent entities

JWT + Google OAuth authentication with granular permission gates per role

Real-time Socket.IO events for live ai agent status updates and notifications

Managed hosting with one-click deploy, custom domain, and auto-SSL

Database

PostgreSQL schema designed for AI Agent

ProjectCode generates a normalized, indexed PostgreSQL schema with the right tables and relationships for your use case. You own the schema and can evolve it freely.

agents (id, workspace_id, name, model, system_prompt, tools jsonb, knowledge_base_id, max_steps, created_by)
conversations (id, agent_id, user_id, session_id, messages jsonb, total_tokens, cost, started_at, ended_at)
tool_calls (id, conversation_id, tool_name, input jsonb, output jsonb, duration_ms, status, created_at)
agent_evals (id, agent_id, test_case_id, input, expected, actual, score, passed, run_at)

schema.sql (preview)

-- Generated by ProjectCode
-- AI Agent Builder Platform schema

CREATE TABLE agents__id (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  created_at TIMESTAMPTZ DEFAULT NOW(),
  updated_at TIMESTAMPTZ DEFAULT NOW()
  -- ... auto-generated columns
);

CREATE TABLE conversations__id (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  created_at TIMESTAMPTZ DEFAULT NOW(),
  updated_at TIMESTAMPTZ DEFAULT NOW()
  -- ... auto-generated columns
);

CREATE TABLE tool_calls__id (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  created_at TIMESTAMPTZ DEFAULT NOW(),
  updated_at TIMESTAMPTZ DEFAULT NOW()
  -- ... auto-generated columns
);

CREATE TABLE agent_evals__id (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  created_at TIMESTAMPTZ DEFAULT NOW(),
  updated_at TIMESTAMPTZ DEFAULT NOW()
  -- ... auto-generated columns
);

Tech Stack

Production-ready from day one

Every generated app uses a battle-tested stack you can deploy anywhere and own completely.

Frontend

React 18 + TypeScript + Tailwind CSS

Backend

NestJS 10 + Node.js + TypeScript

Database

PostgreSQL (managed or Supabase)

Auth

Email OTP · Google OAuth · GitHub OAuth

Deployment

Managed hosting · Custom domain · Auto-SSL

AI Models

Claude Sonnet · Claude Opus · Kimi

Try It Now

Example prompt to get started

Copy this prompt into ProjectCode and get a working app in minutes.

ProjectCode prompt

Build an AI agent builder platform where teams can create custom AI agents for internal use. Include an agent configuration editor (system prompt, tool selection, knowledge base upload), multi-step reasoning with chain-of-thought trace viewer, pre-built tool connectors (web search, company database query, Slack send, code interpreter), human escalation when confidence is low, an evaluation suite for testing agents on predefined test cases, per-agent usage and cost dashboards, and a deployment option via embeddable widget or REST API.

Build this now - it's free

Deployment

Deploy your AI Agent anywhere

Managed hosting

One-click deploy to a live URL managed by ProjectCode.

Custom domain

Connect your own domain with auto-provisioned SSL via Cloudflare.

GitHub export

Push to your GitHub repo and self-host or deploy via Vercel, Railway, or AWS.

Docker ready

Export a Dockerfile and docker-compose for any containerized environment.

Pricing

Start free, scale as you grow

No per-seat fees. No vendor lock-in. Token-based pricing that scales with your usage.

Free

Start building your first app. Live preview, managed hosting, and full code export included.

Token Packs

From $5

Purchase token packs that roll over every month. Build more, pay only for what you use.

Enterprise

Custom

Custom models, SLA, SSO, and private cloud deployment for large teams.

See full pricing details

FAQ

Common questions about building a AI Agent

The platform supports Claude (Sonnet, Opus), GPT-4o, and configurable OpenRouter fallbacks. Each agent selects its model at creation. Model routing rules can be added to optimize cost vs quality per task type.

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Build your AI Agent in hours, not months

Start free. No credit card. Your first app is live in minutes.

Get started free