Chroma

Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

Chroma

Chroma is building the database that learns. It is an open-source AI-native embedding database. Chroma makes it easy to build LLM apps by making knowledge, facts, and skills pluggable for LLMs. The fastest way to build Python or JavaScript LLM apps with memory

General
Relese date	2023
Author	Chroma
Type	embedding database

Tutorials

Great tutorials on how to build with Chroma

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Chroma - Helpful Resources

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Chroma Chroma website
Chroma Docs Documentation of Chroma
Chroma Repo Open-source Chroma repository

Chroma - Clients

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Chroma Python Python Client for Chroma
Chroma JavaScript High-Performance Chroma Client for JavaScript

Chroma - integration

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Chroma AI technology page Hackathon projects

Discover innovative solutions crafted with Chroma AI technology page, developed by our community members during our engaging hackathons.

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

ClaimGuard: Multi-Agent Insurance Claims System

ClaimGuard is a multi-agent auto-insurance claims system that treats data governance as a first-class collaboration problem, not an afterthought. Four agents — Intake, Assessment, Payout, and a Compliance Firewall — communicate entirely through Band. Most multi-agent demos pass full records down a fixed pipeline, leaking sensitive data (SSNs, medical details, bank accounts) to agents that never needed it. ClaimGuard inverts this: the Compliance Firewall is a queryable Band participant, not a preprocessing step. Before Assessment or Payout act on any claim data, they actively request clearance over Band — "I am Assessment, clear me for my role" — and the Firewall responds with only the fields that agent is permitted to see, plus a logged justification for every decision. The firewall itself is a deterministic policy engine, not an LLM—compliance decisions must be auditable and immune to prompt injection, not probabilistic. Intake extracts structured claims from free text and validates policy status; Assessment runs a RAG-powered Four-Point Coverage Analysis against real policy documents to approve, deny, or escalate; Payout simulates disbursement. Every firewall decision is logged with a field-level reason, producing a complete data-minimization audit trail

Sangam : Polypharmacy Safety Council

Sangam is a multi-agent pharmaceutical safety system built on Band AI's infrastructure. Six specialist agents — Intake, PatientProfile, StructuralBio, PKPD, EvidenceRAG, and ComplianceGuard — coordinate via @mention routing inside a shared Band room to screen any combination of allopathic drugs and Ayurvedic herbs for dangerous interactions. The problem it solves is real and largely ignored. India has the world's highest rate of concurrent allopathic and traditional medicine use. Up to 70% of patients never disclose herbal supplement use to their doctor. No clinical tool exists to screen these combinations systematically — every major drug interaction checker screens against the Western pharmacopoeia only, with zero Ayurvedic herb coverage. Each agent handles a distinct analytical layer. The Intake Agent parses free-text patient queries and fetches compound data from PubChem. PatientProfile computes a personalized clearance modifier based on CYP2C9/3A4 genotype, renal function, and age. StructuralBio queries a curated molecular docking database of 26 drug-herb pairs across six enzyme targets — CYP1A2, CYP2C9, CYP2C19, CYP3A4, P-gp, and OCT — returning binding affinity in ΔG kcal/mol. The PKPD Agent runs a one-compartment pharmacokinetic model and computes AUC percentage change with a full 48-hour concentration curve. EvidenceRAG retrieves supporting findings from a curated corpus of 70 peer-reviewed studies and traditional pharmacology texts. ComplianceGuard synthesizes all five upstream reports and issues a RED, YELLOW, or GREEN verdict with confidence score, clinical action, and regulatory disclaimer. The system also includes a fast deterministic combination screener at /api/interactions/screen that returns pairwise risk verdicts in milliseconds without an LLM call — built for point-of-care use. The stack is FastAPI backend, React + Vite frontend with WebSocket streaming, ChromaDB vector index, DeepSeek LLM, Docker Compose deployment, and GitHub Actions CI.

CrisisNet — Multi-Agent Crisis Coordination

CrisisNet is a multi-agent AI system built for food and energy security decision support. When a crisis hits — drought, energy spike, supply chain collapse, or market shock — analysts across agriculture, logistics, energy, and finance each build their own picture separately. By the time the reports reach a decision-maker, the window for early intervention has closed. CrisisNet solves this by deploying five specialised AI agents simultaneously: a Farmer agent analysing crop yields and climate risk, a Logistics agent mapping supply routes and bottlenecks, an Energy agent tracking grid load and fuel prices, a Market agent monitoring demand signals and speculation, and a Regulator agent that synthesises all four into a final policy recommendation. Each agent queries a domain-specific vector knowledge base (ChromaDB RAG) built from real FAO reports, USDA stock data, and regional agricultural statistics before calling an open-source LLM via Featherless AI. Agents coordinate through Band AI's platform — every finding is published to a shared Band Room with a full audit trail. A coordinator automatically detects conflicts between agent signals — for example, when market price recommendations contradict supply shortage data. The Regulator agent then issues a structured policy decision: emergency imports, reserve releases, subsidy plans, price controls — with a calibrated confidence score. If confidence falls below 70% or critical thresholds are breached, the system escalates to a human with a clear explanation. The output is a professional PDF report ready to share with a minister — crop forecasts by region, logistics route plans with costs, price recommendations per commodity, and subsidy proposals with budget estimates. Built on FastAPI, Band AI, Featherless AI, ChromaDB, and React.

AetherDev Pro

AetherDev Pro is an advanced, production-ready multi-agent software development platform and interactive IDE designed for automated software engineering workflows. Built on a Flask backend and a premium glassmorphic HTML/CSS/JS frontend, the platform integrates Microsoft Monaco Editor (the core engine of VS Code) to allow developers to view, edit, and save generated files in real-time. Key Features & Agent Workflow: 1. **Multi-Model Agent Teams**: Users can customize their AI engineering team by routing specific LLMs (e.g. Google Gemini 1.5 Pro, Llama 3.3 70B, GPT-4o) to specialized roles: - **Planner Agent**: Analyzes prompts and outputs structural design layouts and DAGs. - **Engineer Agent**: Automatically implements code for planned files. - **Reviewer Agent**: Evaluates syntax, error handling, and logical correctness, requesting iterative improvements. - **Tester Agent**: Autonomously writes test suites using python's unittest framework. - **Documenter Agent**: Generates comprehensive README files and code documentation. 2. **Self-Healing Code Compilation (TDD Loop)**: AetherDev Pro executes generated test suites in a secure local sandbox subprocess. If any test fails, the error traceback is dynamically parsed and fed back to the Engineer agent with instructions to repair the codebase. This loop repeats autonomously until all tests pass, ensuring that the final output is verified and functional. 3. **Stateless Persistence (SQLite)**: All sessions, file trees, source contents, run records, and terminal logs are persisted in a local SQLite database. This keeps the application robust, resilient to server restarts (such as on cloud platforms like Render), and allows users to resume past projects seamlessly.

NetOps incident responder platform

NetOps AI is a multi-agent, Retrieval-Augmented Generation (RAG) platform designed to automate network incident investigation and accelerate root cause analysis in enterprise networks. The system combines historical incident reports, troubleshooting playbooks, diagnostic outputs, and networking documentation within a ChromaDB-powered knowledge base. When a network alert is received, a Planner Agent analyzes the incident and determines the required investigation steps. A Knowledge Agent retrieves relevant historical incidents and operational guidance, while a Diagnostic Agent collects and analyzes device telemetry and command outputs. The gathered context is then passed to a Review Agent that performs AI-driven root cause analysis and generates evidence-backed findings. Finally, a Decision Agent recommends remediation actions and operational next steps for network engineers. The platform leverages LangGraph to orchestrate collaboration between specialized agents and uses large language models to reason over network data, historical incidents, and troubleshooting procedures. By reducing Mean Time to Resolution (MTTR), improving operational efficiency, and providing explainable recommendations, NetOps AI enables faster and more reliable incident response. The solution is designed as a scalable prototype for modern Network Operations Centers (NOCs), demonstrating how multi-agent AI systems can transform network monitoring, diagnostics, and automated decision support.