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LlamaIndex: a Data Framework for LLM Applications

LlamaIndex is an open source data framework that allows you to connect custom data sources to large language models (LLMs) like GPT-4, Claude, Cohere LLMs or AI21 Studio. It provides tools for ingesting, indexing, and querying data to build powerful AI applications augmented by your own knowledge.

General
AuthorLlamaIndex
Repositoryhttps://github.com/jerryjliu/llama_index
TypeData framework for LLM applications

Key Features of LlamaIndex

  • Data Ingestion: Easily connect to existing data sources like APIs, documents, databases, etc. and ingest data in various formats.
  • Data Indexing: Store and structure ingested data for optimized retrieval and usage with LLMs. Integrate with vector stores and databases.
  • Query Interface: LlamaIndex provides a simple prompt-based interface to query your indexed data. Ask a question in natural language and get an LLM-powered response augmented with your data.
  • Flexible & Customizable: LlamaIndex is designed to be highly flexible. You can customize data connectors, indices, retrieval, and other components to fit your use case.

How to Get Started with LlamaIndex

LlamaIndex is open source and available on GitHub. Visit the repo to install the Python package, access documentation, guides, examples, and join the community:

AI Tutorials

LlamaIndex Libraries

A curated list of libraries and technologies to help you build great projects with LlamaIndex.

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LlamaIndex AI technology page Hackathon projects

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

Parallax

Parallax

PARALLAX is a GenAI-native cyber-fraud investigation platform for banks, fraud teams, SOC analysts, and cyber cells responding to APK-led financial crime. It analyzes suspicious Android applications and related fraud evidence to identify banking-malware behavior such as accessibility abuse, overlay attacks, SMS or notification interception, suspicious permissions, network indicators, and runtime compromise signals. PARALLAX converts these findings into structured evidence, confidence-scored claims, risk assessments, and investigation artifacts. The system is designed around an evidence-first workflow: ingest a suspicious APK, run static and dynamic analysis, extract IOCs and observations, snapshot the evidence bundle, coordinate specialist AI agents, surface challenges or disagreements, and produce a final human-reviewable action packet. Instead of treating fraud review as a static report, PARALLAX models it as a live investigation. Specialist agents can assess device compromise, transaction traces, mule-account patterns, evidence quality, liability context, legal evidence requirements, and final recommendations. The result is an auditable reasoning trail that helps teams understand not just what the system concluded, but why. PARALLAX keeps humans in control. Its outputs are designed to support analysts and bank officers with clear evidence, unresolved questions, recommended next steps, and exportable documentation for escalation or cyber-cell reporting.

SATRIA: AI Triage & Queue for Indonesia Healthcare

SATRIA: AI Triage & Queue for Indonesia Healthcare

SATRIA (AI-Powered Patient Triage and Queue Routing) is a digital transformation solution designed for Indonesian primary healthcare (Puskesmas and clinics). Utilizing a three-agent cooperative orchestration powered by the Band framework and AIML API (Claude-3.5-Sonnet & Gemini-2.5-Pro), SATRIA automates the intake and clinical evaluation pipeline: 1. Anamnesis Agent (@anamnesis): Guides patients in natural language (Indonesian/English) to collect demographic data (via NIK parsing) and clinical history. 2. Clinical Expert Agent (@clinical-expert): Leverages ChromaDB vector search (RAG) over official Kemenkes clinical guidelines (PNPK) to calculate patient triage priority (P1/P2/P3) and automatically book queues at the nearest facility. 3. Compliance Auditor Agent (@compliance-auditor): Conducts clinical safety checks (drug interactions, allergies) and compiles structured SOAP Electronic Medical Records (EMR) according to PMK No. 24/2022. The system connects an Astro/Tailwind frontend, a high-concurrency FastAPI gateway, and Redis caching. It reduces patient queue times, prevents clinical errors, and relieves administrative burdens from doctors.

InvestiGuard AI

InvestiGuard AI

InvestiGuard AI is a multi-agent fraud investigation platform built for regulated and high-stakes financial workflows. Traditional fraud detection systems often provide risk scores with limited transparency, making investigations difficult to audit and explain. My solution uses Band to coordinate five specialized AI agents that collaborate throughout an investigation. The Intake Agent triages suspicious cases, the Transaction Analyst examines transaction patterns, the Behaviour Analyst reviews customer activity, the Risk Assessor evaluates evidence and calculates risk, and the Lead Investigator generates the final recommendation. Rather than relying on a single AI system, agents exchange messages, delegate tasks, share findings, and build upon each other’s analysis through a structured collaboration workflow. The platform visualizes this process through an Agent Collaboration Network, Band activity feed, evidence board, investigation timeline, and collaboration metrics such as handoffs, messages exchanged, active agents, and confidence scores. To support accountability, InvestiGuard AI includes a human-in-the-loop review stage where investigators can inspect evidence, review recommendations, approve or override decisions, and generate a complete audit report. The demo showcases multiple fraud scenarios including transaction structuring, suspicious international wire transfers, and legitimate high-value transactions. By combining multi-agent collaboration, explainable reasoning, human oversight, and auditable decision-making, InvestiGuard AI demonstrates how Band can power trustworthy AI systems for fraud detection, risk assessment, and financial compliance workflows.

Oner: The Continuous-Shot Developer Swarm

Oner: The Continuous-Shot Developer Swarm

Oner is an autonomous, cross-framework developer swarm packaged as a full-stack Next.js web application. Designed to execute software engineering from planning to visual QA in one seamless workflow, it provides users with a premium, split-screen GUI to monitor AI agents in real time. Our absolute standout feature and the core of our business value is the Dynamic Model Routing engine. Traditional multi-agent systems burn through expensive API credits blindly. Oner solves this natively. When a user submits a prompt, our LangChain-based dispatcher, The Librarian, leverages the high-tier reasoning of the AI/ML API to analyze the project's complexity. It then dynamically selects and routes the most efficient LLMs to the rest of the swarm based on task requirements. This allows us to seamlessly swap between cost-saving models for brute-force execution and premium models for complex reasoning, proving a highly scalable architecture. This dynamic routing dictates a roster of 6 specialized agents built across diverse frameworks (LangChain, LlamaIndex, AutoGen, and Native Node.js). To prevent context window bloat, these disparate frameworks are united entirely by the Band collaboration layer, which acts as the definitive shared state and communication bus for all handoffs. Finally, Oner crowns its pipeline with Glassion, a multimodal Anti-Slop QA agent that evaluates headless browser screenshots against high-taste design principles to enforce perfect spatial harmony. Combined with a Band-powered Human-in-the-Loop (HitL) escalation protocol, Oner is the ultimate enterprise-ready builder.

ClaimsChain

ClaimsChain

Insurance Claims Coordination is a Band-powered multi-agent platform designed to modernize insurance claim processing by reducing fraud, minimizing manual review effort, accelerating settlements, and improving auditability. Traditional insurance workflows are fragmented, slow, and heavily dependent on manual verification. Fraudulent claims, policy interpretation disputes, and a lack of transparent decision trails often lead to delays, increased operational costs, and regulatory challenges. Our solution introduces a collaborative network of specialized AI agents that work together throughout the claim lifecycle. The Intake Agent extracts and validates claim information from submitted documents, the Fraud Agent analyzes risk using rules, graph-based relationships, and collaborative signals, the Policy Agent evaluates coverage eligibility and policy constraints, and the Payout Agent calculates settlements using deterministic business rules. Additional Specialist and Compliance Agents can be dynamically recruited through Band when complex medical reviews, disputes, or regulatory checks are required. Band serves as the core collaboration layer, enabling agents to communicate, share structured context, recruit expertise, exchange opinions, and participate in consensus-based decision making. This transforms the workflow from a simple sequential pipeline into a collaborative enterprise-grade agent ecosystem. To ensure trust and accountability, every major decision is cryptographically hashed and anchored to a blockchain-backed audit trail. This creates an immutable record of claim processing activities, allowing insurers, auditors, and regulators to verify that decisions have not been altered after approval. The result is a scalable, transparent, and auditable insurance processing platform capable of reducing fraud, accelerating claim resolution, improving operational efficiency, and increasing trust across insurers, hospitals, regulators, and policyholders.