LlamaIndex Framework
Connect data sources and model providers to build indexing, retrieval and agent applications in Python.
- Starting price
- $0 software licence
- Free access
- Self-managed edition
- Billing unit
- Compute / provider usage separate
- Platform
- Python framework
- Verified
- 17 Oct 2026
Overview
What you provide
- You provide data connectors, documents, embeddings and questions.
What you get
- You receive indexes, retrieval responses and application/agent results.
Choosing this tool
Choose the LlamaIndex framework for its modular data and model integrations when you can own application code. Distinguish it from the company’s separately offered LlamaParse platform. The README states that the company’s main focus has shifted to parsing; consider Haystack when explicit pipeline maintenance is your priority.
A decision worked through
Install core plus only the integrations your app uses rather than assuming the starter bundle defines your final cost. List the parser, embedding model, vector store and LLM separately; a free framework does not price those four services.
LlamaIndex Framework alternatives: choose an implementation route
For LlamaIndex, separate connector/indexing requirements from parsing-service access, then compare code-defined pipelines with a visual workflow route.
| Product | Deployment requirements | Free allowance | Input → output | Key limit |
|---|---|---|---|---|
| LlamaIndex FrameworkRead profileOfficial source ↗ | Install Python core and selected integrations; configure parsing, embedding, vector storage and the LLM independently. LlamaParse service credits are separate. | Self-managed edition | Data connectors, documents, embeddings and questions → Indexes, retrieval responses and application/agent results | See the product-specific review limits below. |
| HaystackRead profileOfficial source ↗ | Python library, not a desktop app: wire retrieval/model components and build the hosting and permissions layer. Hardware follows the chosen components. | Self-managed edition | Documents, component graph, model connections and queries → Pipeline results, retrieval answers and configured agent actions | The Apache-2.0 framework does not include hosted inference or managed enterprise services. You must build deployment, data permissions and operational monitoring. |
| LangflowRead profileOfficial source ↗ | Python 3.10–3.14 package or Windows/macOS Desktop; connect a model provider. A local GPU is not a requirement for a cloud-model workflow. | Self-managed edition | Workflow graph, documents, model connection and tool settings → Runnable flows, API responses and MCP tools | Running a local canvas does not make a connected cloud model local. Configure authentication before exposing its API; hosted model and database fees are separate. |
Setup & workflow
- Choose core and integrations.
- Configure providers.
- Index one small corpus.
- Verify retrieved evidence.
- Pin package versions for the app.
Requirements & installation
- Python framework
Limits & review
- MIT framework availability does not include LlamaParse service credits. Model/provider charges and integration versions need separate review; the stated company focus shift affects long-term selection.
Your part
- Pin integration versions and confirm that parser outputs preserve the fields your application needs.
Sources & verification6
This profile is based on official sources, not a hands-on product test.
Vendor descriptions and demos document advertised features. Editorial guidance is based on these sources.
- LlamaIndex Framework official READMEgithub.com
- LlamaIndex Framework software licencegithub.com
- Haystack official READMEgithub.com
- Haystack software licencegithub.com
- Langflow official READMEgithub.com
- Langflow software licencegithub.com
