Complete

Complete is an AI-powered collaboration platform designed to transform isolated AI chats into collaborative environments. It enables teams to work together with AI agents in shared, contextual workspaces, ensuring a single source of truth for conversations, context, and outputs. By integrating multiple AI models and specialized agents, Complete empowers teams to work smarter and faster through unified AI teamwork.

General
Author	Complete
Release Date	2024
Website	https://complete.dev/
Documentation	https://docs.complete.dev/
Technology Type	AI Collaboration Platform

Key Features

Collaborative AI Workspaces: Shared chat threads where teams can build on each other's prompts and maintain context across projects.
Multi-Model Integration: Run leading AI models in parallel, allowing teams to choose the best model for specific tasks like coding, reasoning, or creative writing.
Specialized AI Agents: Access role-specific agents for Product Managers, Project Managers, and Developers to automate summaries, monitor progress, and accelerate debugging.
Shared Context through Files: Organize and access documents across projects, providing AI agents with the necessary context to perform accurately.
Built-in Code Editor: An integrated environment for real-time collaboration between human developers and AI agents on codebases.
Governance and Control: Advanced tools for managing AI context, memory, and rules to ensure safe, consistent, and compliant AI usage across the organization.

Start Building with Complete

Complete offers a scalable environment for teams to integrate AI into their daily workflows. From simple shared chats to complex agentic processes, it provides the infrastructure needed for collaborative AI development and operations. Developers can leverage Complete's specialized agents and model flexibility to build more robust AI-powered applications.

Complete Resources

Fresno LUX: A Living City

Fresno LUX is a prototype for a living intelligence layer across an entire city. Fresno already contains enormous amounts of intelligence: residents know where problems exist, nonprofits know where help is needed, businesses know where opportunities exist, educators understand gaps in learning, researchers generate knowledge, and government maintains critical infrastructure and data. The problem is that much of this intelligence is fragmented across disconnected systems. Fresno LUX connects those pieces. The platform creates a shared city graph where people, organizations, resources, needs, projects, observations, experiments, and outcomes become connected nodes. Instead of simply collecting data, LUX continuously asks: What is happening? What do we know? What is missing? What can we test? What changed? A community problem can become an observable signal. That signal can become an experiment. The experiment produces evidence. Evidence changes the system's understanding. That updated understanding informs the next action. Observe → Connect → Understand → Act → Measure → Learn → Converge. The goal is to move Fresno from disconnected information toward collective intelligence—a system where knowledge can travel across boundaries and successful solutions can be discovered, tested, measured, and replicated. LUX is designed to make the invisible connections visible: the relationship between a neighborhood and its resources, between a student and educational opportunity, between a business and workforce capacity, between environmental conditions and community health, and between an idea and measurable real-world impact. This hackathon prototype demonstrates the beginning of a Living Fresno Graph: a city that can observe itself, learn from evidence, and coordinate toward better outcomes.

Veritas-Agent

VERITAS is an execution-aware autonomous AI trading agent built for the Alpaca AI Trading Agents Hackathon. Its goal is not simply to predict market direction, but to determine whether a proposed options trade is trustworthy enough to execute. VERITAS focuses on defined-risk short-DTE credit spreads on SPY and QQQ. A deterministic candidate engine first generates valid options spreads using predefined constraints. An AI reasoning layer then evaluates and ranks those opportunities using market context, technical signals, volatility information, and the strategy's rules. The AI does not have unrestricted control over execution: every proposal must pass deterministic mathematical validation and a multi-layer risk gate before an order can be submitted. The project's core principle is: “LLM proposes, math disposes.” The system independently recalculates option spread width, credit, maximum loss, breakeven, position size, and other critical values instead of trusting the language model's numerical output. It also applies an Execution Reality Layer that evaluates whether a trade remains attractive after conservative assumptions about liquidity, slippage, and paper-trading execution behavior. A key feature of VERITAS is its Execution Confidence Score, which measures the quality and trustworthiness of each candidate based on factors such as quote quality, liquidity, data freshness, spread characteristics, and execution assumptions. Low-confidence opportunities can be rejected or traded at reduced size. The agent also continuously reconciles positions and account activity, polls for options activities that are not delivered through streaming events, and uses a hierarchical risk system with per-trade limits, daily loss protection, position-count limits, portfolio heat, liquidity checks, DTE constraints, and an emergency kill switch. The default strategy avoids holding options into expiration and prioritizes controlled, defined-risk exposure.

RE-EVOLVE ON HGI

RE-EVOLVE ON HGI explores a shift from isolated AI inference to coordinated intelligence. Instead of relying on a single model, the platform orchestrates missions across planning, memory, governance, adaptive provider routing, and runtime execution through a modular architecture. Built using a specification-driven development process, the project combines multi-agent coordination, persistent memory concepts, runtime observability, and developer-focused tooling into a unified Intelligence Operating System. The repository includes production deployments, comprehensive engineering documentation, architecture diagrams, traceability matrices, runtime validation reports, and a complete judge guide. It is designed as an open engineering project that investigates how orchestration can complement heterogeneous AI compute environments while remaining transparent, extensible, and maintainable.

Atlas Arc: Decentralized AI Agentic Economy

`Atlas Arc is an Economic OS for AI Agents. In traditional blockchains, high gas fees make micro-transactions for AI inference impossible. Atlas Arc solves this by integrating Circle’s USDC on the Arc L1 network, enabling settlement at a scale of $0.0001 per action. Our system features a hierarchical multi-agent architecture where "Brain" agents use a Random Forest Regression model to predict market demand and price volatility. "Executor" agents handle high-frequency settlements via Circle Developer Controlled Wallets, while "Guardian" agents maintain security using Z-Score statistical analysis to detect anomalies. The project demonstrates a 99.9% reduction in gas overhead compared to legacy L1s, proving that a machine-to-machine economy is not just possible, but economically viable. By combining Google Gemini for analytical reasoning and Circle’s nano payment infrastructure, Atlas Arc creates a transparent, scalable, and autonomous marketplace for the next generation of AI services.`