- Builders evaluating this category
Agents
LlamaIndex
Data framework for connecting LLMs to external data with RAG, agents, and structured retrieval.
46K Stars
MIT License
run-llama Maintainer
2026-06-16 Verified
Overview
What is LlamaIndex?
Data framework for connecting LLMs to external data with RAG, agents, and structured retrieval.
FAQ
Frequently asked questions
What should I check before using LlamaIndex?
Build one two-step agent flow with a tool call, a state transition, and an observable failure path.
Is LlamaIndex open source?
LlamaIndex is listed on OpenAgent.bot with MIT based on the current resource metadata. Re-check the official repository, docs, and license before production use.
Decision brief
JSON
Should you use LlamaIndex?
- Production adoption without checking source links, permissions, and maintenance.
- Verified 2026-06-16
- License: MIT
- Repo: run-llama/llama_index
- Open-source signal
Deployment
Check source
Permission surface
memory, external services
Decision signals
Local first, API, CLI
Agent packet
Structured decision data for LlamaIndex
This packet is the compact machine-readable view agents should use before following source links or taking action.
Capabilities
rag, tool calling, connectors, context retrieval
Constraints
open source
Deployment
Check source
Permission surface
memory, external services
Recommended workflows
Memory or RAG workflow
Overview
What LlamaIndex does
What it is
LlamaIndex is a agent in the agents category. Data framework for connecting LLMs to external data with RAG, agents, and structured retrieval.
Why it matters
LlamaIndex matters when builders need a clearer way to choose tools by workflow fit, constraints, source quality, and operational risk rather than by category labels alone.
How to evaluate it
Evaluate LlamaIndex by starting from the official sources, checking its interface surface, and running one narrow workflow before expanding scope.
Facts
Known metadata and operating surface
These fields are separated from editorial interpretation so agents can reason over facts and missing checks.
Resource type agent
Category Agents
Maturity active
Difficulty Unknown
License MIT
Pricing open source
Verified 2026-06-16
Source confidence medium
Risk level moderate
Fit matrix
Where LlamaIndex fits in an agent stack
strong
Memory or RAG workflow
LlamaIndex has multiple signals for memory or rag workflow, including matching tags, capabilities, category, or positioning.
- Create, update, retrieve, correct, and delete memory or retrieval objects with real data.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Coding agent workflow
LlamaIndex has at least one signal for coding agent workflow, but should be checked against a real task before adoption.
- Run a small repository change and inspect the diff, tests, and rollback path.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Connector or protocol layer
LlamaIndex has at least one signal for connector or protocol layer, but should be checked against a real task before adoption.
- Connect one low-risk service, then inspect schemas, auth scope, errors, and logs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Evaluation and observability
LlamaIndex has at least one signal for evaluation and observability, but should be checked against a real task before adoption.
- Add one repeatable test case and confirm results can run again in review or CI.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Reusable skill workflow
LlamaIndex has at least one signal for reusable skill workflow, but should be checked against a real task before adoption.
- Run one skill end to end and check whether it produces evidence or structured output.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak
Browser automation
LlamaIndex is not primarily positioned for browser automation in the current metadata.
- Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
Inputs and outputs
What an agent should inspect
Likely inputs
- Documents, user facts, entities, context, or retrieval queries
- Official setup instructions and a small real workflow
Likely outputs
- Retrieved context, memory updates, graph relations, or citations
- Scores, traces, regression results, dashboards, or failure cases
- A decision on whether this resource fits the target workflow
Evidence
Sources, claims, and missing checks
Claims are marked separately from source links so future crawlers and reviewers can update them without rewriting the page.
LlamaIndex is listed as open source.
License metadata: MITLlamaIndex has a recorded GitHub repository: run-llama/llama_index.
Resource facts and GitHub source link.- Dedicated docs link is missing.
- Repository freshness has not been recorded.
Next action
How to start evaluating LlamaIndex
Inspect repository
Check license, recent activity, issues, examples, and security-sensitive code paths.
Open source Compare
Alternatives and nearby resources
Use related resources to compare category fit, license, deployment model, and first-workflow behavior.
FAQ
Common questions about LlamaIndex
What is LlamaIndex used for?
LlamaIndex is used as a agent for agents workflows. The most relevant recorded capabilities are rag, tool calling, connectors, context retrieval.
Is LlamaIndex open source?
LlamaIndex is listed as open source with MIT license metadata. Re-check the official repository or source link before production use.
Can agents use LlamaIndex directly?
LlamaIndex has recorded interfaces such as official source links. Agents should prefer the JSON or Markdown profile first, then follow official docs for real execution.
What should I check before production use?
Check source confidence (medium), risk level (moderate), license, maintenance freshness, permission surface, required credentials, and whether the first workflow succeeds in a sandbox.