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OpenAI's Assistants API

OpenAI's Assistants API simplifies AI integration for developers, eliminating the need for managing conversation histories and providing access to tools like Code Interpreter and Retrieval. The API also allows developers to integrate their own tools, making it a versatile platform for AI assistant development.

General
AuthorOpenAI
DocumentationLink
TypeAI Assistant

Model Overview

The Assistants API enables developers to create AI assistants using OpenAI models and tools. It supports various functionalities such as managing conversation threads, triggering responses, and integrating customized tools.

Assistants API Tutorials

Technology Resources

The Assistants API allows developers to construct AI assistants within their applications. An assistant can leverage models, tools, and knowledge to respond to user queries effectively. Presently supporting Code Interpreter, Retrieval, and Function calling, the API aims to introduce more tools developed by OpenAI while also allowing user-provided tools on the platform.

To explore its capabilities, developers can use the Assistants Playground or follow the integration guide in the official documentation. The integration process involves defining an Assistant, enabling tools, managing conversation threads, and triggering responses.

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OpenAI Assistants API AI technology Hackathon projects

Discover innovative solutions crafted with OpenAI Assistants API AI technology, developed by our community members during our engaging hackathons.

The-Agnets-Worksation

The-Agnets-Worksation

The Agents Workstation is a production-grade, autonomous software engineering agency designed to solve the critical hallucination and execution gaps inherent in traditional AI code generation. Built as a highly concurrent Python orchestration engine, the system decentralizes intelligence across a specialized Band of Agents—including an Architect (Planner), Domain Builders (Frontend/Backend), a deterministic Executor (Terminal), and QA Specialists (Supervisor/Repair). Operating as a native node on the Band AI network, these agents dynamically spin up programmatic chat rooms to plan, coordinate, and hand off tasks using Directed Acyclic Graphs (DAGs) with complete, observable transparency. Unlike standard code assistants that leave execution and debugging to the human developer, the workstation features an indestructible, headless Execution Sandbox. The Terminal Agent handles virtual environments, bypasses interactive prompts in "CI Mode," and actively pings local network ports to guarantee server stability. If an application throws an error on startup, the Supervisor Agent catches the runtime traceback, calculates a project stability score, and triggers a surgical, self-healing Repair Loop to patch the codebase without human intervention. To guarantee zero downtime, the architecture is shielded by a Universal LLM Gateway featuring multi-provider failover routing, dynamically shifting loads between Tier-1 models like Gemini, Claude, and GPT-4o if rate limits are hit. Operators monitor this entire hive mind through a premium, zero-simulation Cyberpunk Dashboard. Powered by real-time WebSockets, this command center streams deterministic telemetry, agent state updates, and system logs with millisecond precision, proving that the AI is not just writing code—it is autonomously orchestrating an entire software factory.

Adversarial AI Recruiting Council

Adversarial AI Recruiting Council

The Adversarial AI Recruiting Council is a multi-agent hiring intelligence system built on Band that simulates a real-world recruiting committee through structured AI collaboration. Instead of relying on a single AI model to evaluate candidates, the system creates a transparent decision-making process where multiple specialized agents debate a candidate's CV before reaching a final hiring recommendation. When a CV is submitted, the Skeptic agent performs a critical review, identifying weaknesses, risks, and reasons not to hire the candidate. The Investigator agent then validates claims, analyzes inconsistencies, and uncovers missing information or potential red flags. Next, the Devil's Advocate agent challenges previous criticisms, highlights strengths, and presents arguments in favor of the candidate. Finally, the Recruiter agent reviews the entire discussion and delivers a final Hire/No Hire verdict supported by clear reasoning. Band serves as the core collaboration layer throughout the workflow. Agents communicate through Band, exchange structured context, receive task handoffs, and build upon each other's analyses in real time. Rather than operating independently, every decision is shaped by the collective reasoning of multiple agents, creating a more balanced and explainable outcome. This approach addresses a major limitation of traditional AI recruiting systems: opaque and potentially biased one-shot decisions. By introducing verification, disagreement, and structured debate into the evaluation process, the system produces recommendations that are more transparent, defensible, and aligned with how real hiring committees operate. Built using Band SDK, Google Gemini 2.5 Flash, and Python, the Adversarial AI Recruiting Council demonstrates how collaborative multi-agent systems can support enterprise hiring workflows where accountability, traceability, and decision quality are critical.

Linea simulator

Linea simulator

Hello, this project is with a two-person team in Iran. This project is a drone simulator for detecting natural disasters that have occurred in Iran. We are working on a physical project whose image is in the Documents folder. The simulator codes are in the Simulator folder. A presentation of natural disasters that are mentioned in the simulator, such as the Tabas earthquake, is available in both PowerPoint and PDF formats in the Documents folder + a photo of the physical project. And a video introducing and operating the simulator is available in both Persian and English in the Presentation folder. The English video has a pause in some situations because we did not use artificial intelligence for translation. There is also a teaser made with artificial intelligence for introduction. This simulator uses the Anthropic API in some situations and is web-based. Because our computer system is weak, the graphics of the simulator are weak and low so that we do not have any problems running it. Thanks (: Github Link : https://github.com/LineaWebIR

ResumePilot AI

ResumePilot AI

ResumePilot AI is a comprehensive career engine engineered to dismantle the opaque barriers created by modern corporate Applicant Tracking Systems (ATS). Built entirely using Python and deployed via Streamlit Community Cloud, the platform transforms a traditionally blind job application process into a transparent, data-driven launchpad. The application features a robust text-extraction pipeline capable of processing PDF, DOCX, and TXT files natively. Using advanced semantic matching powered by the Google Gemini API, it evaluates an applicant's resume against up to three target job roles simultaneously. Real-time keyword metrics and skill alignment gaps are dynamically displayed using interactive Plotly Express data charts. Going beyond basic keyword checkers, ResumePilot AI actively generates tailored, high-converting cover letters, highlights weak bullet points with professional phrasing, and builds a custom mock interview preparation simulator to ensure absolute candidate readiness.

TrialSync

TrialSync

Clinical protocol design is an unmonitored $50M+ data-synchronization problem. Qualitative scientific literature is manually translated into text-based protocol prose, which biostatistics teams must interpret into script files. This disconnected process causes silent context decay, unmanaged version divergence, and late-stage regulatory rejection loops right before an FDA Investigational New Drug (IND) submission. Every single day of protocol layout delay compromises $600K to $8M in potential market revenue. TrialSync replaces traditional document-shuffling handoffs with an adversarial, state-driven multi-agent network orchestrated over the Band Protocol. Agents collaborate within a synchronized room environment to continuously cross-verify protocol thresholds against live, authoritative biomedical datasets, compressing design cycles from months to minutes. Unified Orchestration Framework Literature Scout Agent (Gemini 1.5 Pro): Authenticated with real NCBI PubMed API keys to extract structured adverse reactions and safety limits from peer-reviewed literature. Protocol Design Agent: Queries live ClinicalTrials.gov v2 endpoints to map historical execution layouts and inclusion/exclusion variables. Regulatory Reviewer Agent: Cross-references active drafts against official black-box warnings and contraindications pulled from openFDA. Enterprise Infrastructure Adapters Instead of processing raw prose, our backend passes strict, type-safe data schemas: CDISC SDTM Standards Encoder: Converts unstructured clinical variables directly into proper FDA-mandated LBTESTCD data attributes. eCTD Submission Compiler: Groups multi-agent decisions directly into ICH M4 modular blocks ready for automated regulatory filing tools. Tamper-Evident Ledger: Applies a SHA-256 integrity hash across the Band metadata pool to prevent undetected downstream protocol mutations.

FinOps Swarm

FinOps Swarm

Enterprise project finances run across three legacy systems: a mainframe ERP holding committed and actual costs, IBM TM1 holding the budget via a SQL source database, and Cognos BI as the reporting layer. When a change order is approved, all three systems should update. They rarely do. Nobody finds out until Cognos renders a report days later, by which time decisions have already been made on wrong numbers. This is not a data quality problem. It is an event propagation problem. Change orders fall into a manual queue that nobody owns end-to-end. A senior analyst spends 2-4 hours per project per week just to answer one question: what did we actually spend this week? Because the mainframe only stores cumulative running totals, not period-bounded figures. FinOps Swarm replaces manual reconciliation with six Band-coordinated AI agents. Agent 1 detects approved change orders and posts to the Band shared room. Agents 2 and 3 run in parallel: one checks the mainframe ERP, one checks the SQL source feeding TM1 via TurboIntegrator. Agent 4 waits for both findings in the Band room, then calls a local Llama 3.2 model on AMD hardware to generate a CFO-ready narrative. Financial data never leaves the network. Agent 5 surfaces only genuine exceptions to the CFO with a one-click decision card. Agent 6 computes period-bounded weekly spend automatically from mainframe snapshots. Band's shared room is load-bearing, not decorative. Agents 2 and 3 post independent findings. Agent 4 waits for both. Agent 5 reads everything. Built by a systems engineer who works on TM1 and mainframe pipelines daily. Every pain point is real.