LlamaIndex
A Python framework for building retrieval, indexing, and document-intelligence applications over private data.
- Source repo
- run-llama/llama_index
- Stars
- ★ 53k
- Last updated
- 2d ago
- License
- MIT
- Primary language
- Python
- FA score
- 48/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platformOpenAI API
- Cost
- Free software; you pay for model usage
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- A Python developer building a question-answering feature over a local document directory can load files with
SimpleDirectoryReaderand query aVectorStoreIndex. - Not a fit if
- Users who want a ready-made document parsing product without writing Python
- Teams needing a framework whose README keeps pace with the docs
- Source review
- 48/100 · Major gaps 2 safety controls not found
What does this agent do, and when should you use it?
LlamaIndex OSS is an open-source Python framework for building agentic applications that augment LLMs with private data. Its llama-index-core package is extended through separate integration packages for LLMs, embedding models, and vector stores; the README states that more than 300 integration packages are available. The framework supplies data connectors, index and graph structures, and retrieval/query interfaces that produce knowledge-augmented output. The documented flow loads a local directory with SimpleDirectoryReader, creates a VectorStoreIndex, and queries it through index.as_query_engine().query(). LlamaParse is a separate platform for document agents, offering Parse, Extract, Index, Split, and Agents, and may be used with the framework or on its own.
An application installs llama-index-core plus selected integrations such as llama-index-llms-openai, llama-index-llms-ollama, and llama-index-embeddings-huggingface. SimpleDirectoryReader("YOUR_DATA_DIRECTORY").load_data() loads documents from a chosen local directory, and VectorStoreIndex.from_documents(documents) creates a vector index. index.as_query_engine() creates a query interface, whose .query("YOUR_QUESTION") call returns a query result. Data is in memory by default; index.storage_context.persist() writes storage under ./storage, which can later be reloaded with StorageContext.from_defaults(persist_dir="./storage") and load_index_from_storage(storage_context). The OpenAI example reads OPENAI_API_KEY; the Ollama example configures Settings.llm, Settings.tokenizer, and Settings.embed_model.
- A Python developer building a question-answering feature over a local document directory can load files with
SimpleDirectoryReaderand query aVectorStoreIndex. - A team using OpenAI for generation can install
llama-index-llms-openaiand supplyOPENAI_API_KEYwhile using the core indexing interface. - A developer running models through Ollama can configure
Ollama, a Hugging Face embedding model, and anAutoTokenizerinSettings. - An application that must survive restarts can persist its index to
./storageand reload it withload_index_from_storage. - An organization needing document parsing, OCR, structured extraction, indexing, or document-agent capabilities can use LlamaParse with the framework or separately.
How do you install or deploy this agent?
A Python environment is required. Install the core package and the integrations used by the documented examples:
pip install llama-index-core
pip install llama-index-llms-openai
pip install llama-index-llms-ollama
pip install llama-index-embeddings-huggingfaceFor the OpenAI example, set OPENAI_API_KEY in the runtime environment first. No local LlamaParse installation command is documented; its documented entry point is account signup and API-key creation.
How do you use this agent?
A minimal documented flow is:
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader("YOUR_DATA_DIRECTORY").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("YOUR_QUESTION")The OpenAI path requires OPENAI_API_KEY and the OpenAI integration package. Index data is memory-only by default; for reuse after restart, call index.storage_context.persist() and reload with StorageContext.from_defaults(persist_dir="./storage") and load_index_from_storage(...).
What are this agent's strengths and limitations?
- The core-and-integration package split lets teams install
llama-index-coreand choose only the LLM, embedding, and vector-store integrations they need. - The README documents an end-to-end path from reading a local directory through
VectorStoreIndexcreation, querying, persistence, and reload. - Documented examples cover OpenAI as well as Ollama-hosted non-OpenAI models, with a Hugging Face embedding configuration.
- LlamaParse can operate separately or alongside the framework for parsing, OCR, extraction, indexing, and document-agent-oriented workflows.
- The OpenAI example requires
OPENAI_API_KEY; selecting different model or embedding providers requires their corresponding integration packages and configuration. - Indexes are memory-resident by default, so applications that need restart durability must explicitly persist and reload them.
- The README says it is updated less frequently than the documentation, creating a version-alignment consideration for adopters.
- LlamaParse signup and API-key acquisition are documented, but the README does not document pricing, quotas, or a local deployment path.
How does this agent compare with similar options?
The README presents LlamaIndex as able to integrate with LangChain, but it provides no feature or performance comparison between them.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| LlamaIndex This agent | 48 · Major gaps | Library / SDKFree + model costs | ★ 53k | 2d ago | Python | OpenAI API |
| Agentic RAG for Dummies | 51 · Major gaps | Library / SDKFree + model costs | ★ 4.3k | 20d ago | Jupyter Notebook | — |
| RAGLight | 63 · Some gaps | CLIFree + model costs | ★ 672 | 21d ago | Python | OpenAI API |
| Instructor | 55 · Major gaps | Library / SDKFree + model costs | ★ 14k | today | Python | OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows: README and SECURITY.md clearly state the library should be used in a trusted execution environment and list security boundaries, but no least-privilege or user-confirmation mechanisms are provided. Data flow transparency is limited, only mentioning data connectors and indices without detailing data flow. Sensitive data handling: SECURITY.md mentions debug logging may leak API keys but provides no specific mitigations. Dependency security: pyproject.toml lists dependencies but no vulnerability scanning or lock files. External effects: library makes network requests but not explicitly stated. Rollback: not mentioned. Source attribution: README and pyproject.toml provide author and maintainer info, but publisher is unverified. Deductions: lack of user confirmation, insufficient data flow transparency, inadequate sensitive data handling, missing dependency security measures, and no rollback mechanism.
Evidence shows: README and pyproject.toml provide clear installation and usage instructions, code structure is consistent, but error handling details are not provided. Dependency availability: dependency list is clear, but no version locking or mirrors. Failure messages: llama-dev test tool provides detailed error output, but library's own failure messages are not detailed. Deductions: insufficient error handling details, dependency availability not fully ensured.
Evidence shows: README targets both beginners and advanced users, provides multiple usage scenarios, but capability boundaries are not explicit. Trigger precision: no clear trigger conditions or permission controls. Environment fit: supports Python 3.10+, offers various integration options. Deductions: unclear capability boundaries, insufficient trigger precision.
Evidence shows: README provides clear information architecture, detailed installation notes, stable naming, rich examples, but known limitations are not explicitly listed. License is MIT, version info in pyproject.toml, but no changelog. Maintenance responsibility: maintainers listed in pyproject.toml, but no contribution guide. Deductions: known limitations not explicit, changelog missing.
Evidence shows: README provides rich examples, output usability is high, marginal value is clear, cost-benefit is reasonable. Deductions: no major deductions, but no performance or cost data provided.
Evidence shows: Claims in README are supported by documentation, but no cross-validation. Fact-inference separation: README distinguishes facts and inferences but not clearly. Deductions: insufficient claim traceability, missing cross-validation.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Publisher identity is unverified; proceed with caution.
- Library is intended for trusted environments; users must handle input validation and security controls themselves.
- Sensitive data handling and dependency security measures are insufficient; review recommended.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
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Release notes excerpt
Release Notes [2026-09-21] llama-index-agent-agentmesh [0.3.0] - fix: resolve a ton of security alerts ([#22855](https://github.com/run-llama/llama_index/pull/22855)) llama-index-agent-azure [0.4.0] - fix: resolve a ton of security alerts ([#22855](https://github.com/run-llama/llama_index/pull/22855)) llama-index-callbacks-argilla [0.6.0] - fix: resolve a ton of security alerts ([#22855](https://github.com/run-llama/llama_index/pull/22855)) llama-index-callbacks-arize-phoenix [0.8.0] …View on GitHub ↗ -
Release notes excerpt
Release Notes [2026-08-19] llama-index-core [0.14.24] - fix: close HotpotQA evaluator file handles ([#22113](https://github.com/run-llama/llama_index/pull/22113)) - fix(core): preserve oversized leaf nodes in CodeSplitter ([#22119](https://github.com/run-llama/llama_index/pull/22119)) - fix: preserve multiblock chat history writes ([#22124](https://github.com/run-llama/llama_index/pull/22124)) - fix(core): skip empty nodes in HTMLNodeParser ([#22125](https://github.com/run-llama/llama_index/pull/22125)) …View on GitHub ↗ -
Release notes excerpt
Release Notes [2026-06-24] llama-index-callbacks-argilla [0.5.0] - chore(deps): bump the uv group across 32 directories with 3 updates ([#21664](https://github.com/run-llama/llama_index/pull/21664)) - chore(deps): bump the uv group across 29 directories with 3 updates ([#21665](https://github.com/run-llama/llama_index/pull/21665)) - chore(deps): bump the uv group across 31 directories with 3 updates ([#21668](https://github.com/run-llama/llama_index/pull/21668)) …View on GitHub ↗
FAQ
Is OpenAI the only supported model path?
Are indexes retained automatically?
index.storage_context.persist() and later use StorageContext plus load_index_from_storage to restore them.