LIVE
Google Introduces "Gemini 4 Carbon" as Next Step in Its Model Lineup28/10/26 · Google|DeepMind unveils Genie 4, a new generation of world model27/10/26 · Google DeepMind|Anthropic files IPO prospectus, with risk disclosures taking center stage26/10/26 · Anthropic|Robot policy trained only in simulation matches top real-world systems26/10/26|OpenAI unveils GPT-6.1 Terra, a model built for computer use26/10/26 · OpenAI|NVIDIA introduces Nemotron 3.5 Transcribe, a promptable multilingual transcription model28/10/26 · NVIDIA|Failed startups' data finds a second life training AI models28/10/26|Anthropic shares early results on using Claude to accelerate drug development27/10/26 · Anthropic|GEPA-Curator: Filtering Failures to Better Train LLM Agents27/10/26|New Benchmark Tests Whether LLMs Can Uncover Hidden Physical Laws27/10/26|Liquid AI unveils LFM3, a base for specialized multimodal models27/10/26 · Liquid AI|US Police Departments Use AI to Flag 'Suspicious' Driving Behavior27/10/26|Google Introduces "Gemini 4 Carbon" as Next Step in Its Model Lineup28/10/26 · Google|DeepMind unveils Genie 4, a new generation of world model27/10/26 · Google DeepMind|Anthropic files IPO prospectus, with risk disclosures taking center stage26/10/26 · Anthropic|Robot policy trained only in simulation matches top real-world systems26/10/26|OpenAI unveils GPT-6.1 Terra, a model built for computer use26/10/26 · OpenAI|NVIDIA introduces Nemotron 3.5 Transcribe, a promptable multilingual transcription model28/10/26 · NVIDIA|Failed startups' data finds a second life training AI models28/10/26|Anthropic shares early results on using Claude to accelerate drug development27/10/26 · Anthropic|GEPA-Curator: Filtering Failures to Better Train LLM Agents27/10/26|New Benchmark Tests Whether LLMs Can Uncover Hidden Physical Laws27/10/26|Liquid AI unveils LFM3, a base for specialized multimodal models27/10/26 · Liquid AI|US Police Departments Use AI to Flag 'Suspicious' Driving Behavior27/10/26|
Toolofficial publisher

Tools LlamaIndex

Tool : A precise action the AI can call · requires llamaindex

LlamaHub tools and loaders for agents and RAG.

The most comprehensive option for data and document handling.

Docs
Overall score
6.5 /10
Security: one caveat, see the card
Utility
7.0
Reliability
6.0
Security
One caveat
Ease
5 min · Technical

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

Building an application that answers questions about your own data is complex: language models do not know your documents, databases, or internal APIs. LlamaIndex solves this by providing a complete framework to ingest, index, and query your data with an LLM.

In practice, you connect your data sources (PDFs, SQL, APIs, file directories) through connectors, index them in a format optimised for retrieval, then query them via query engines or agents capable of reasoning and chaining actions. Everything is orchestrated in Python in just a few lines of code.

LlamaIndex is aimed at Python developers who want to build RAG applications, document chatbots, autonomous agents, or structured data extraction pipelines. A high-level API allows for a quick start, while a low-level API offers deep customisation for advanced users.

However, this tool is not suitable for people without programming experience: there is no graphical interface, everything happens in code. Data security also depends on each connector or tool used, which requires case-by-case vigilance.

What it offers

  • ·Data connectors to ingest local files, APIs, SQL databases, and many other sources via LlamaHub.
  • ·Vector indexing of documents to enable performant semantic search at inference time.
  • ·Query engines to answer questions in RAG mode over your indexed data.
  • ·Chat engines for multi-turn conversational interactions with your data.
  • ·LLM agents capable of using tools, calling APIs, and chaining actions to complete complex tasks.
  • ·Event-driven workflows to combine multiple agents, connectors, and tools into production pipelines.
  • ·Observability and evaluation integrations to measure and monitor application performance.
  • ·Advanced parsing of complex documents via LlamaParse, including nested tables and images (paid).

In practice

Price
Free
Commercial use
Commercial use: yes
Available on
sdk-python
Host
llamaindex
Licence
MIT
FR interface
No

Security and data

Rating
Security: one caveat, see the card
Capabilities
readswritesexecutesnetwork
Your data
Local or cloud depending on use
Maintenance
Active

Links and repository checked on 25 October 2026 · added on 1 January 2023

How to install Tools LlamaIndex

1. Make sure Python is installed on your machine. 2. Set an environment variable called OPENAI_API_KEY with your OpenAI API key (or the key for your chosen LLM). 3. Install the library via pip: pip install llama-index 4. Place your documents in a folder named data. 5. Use the quickstart code to ingest and query your data in 5 lines. 6. Check the permissions and privacy conditions of each third-party connector before using it in production.
pip install llama-index

Official documentation ↗

In the same category

Whole category Orchestrate →