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LlamaIndex
Open-source framework plus LlamaCloud for knowledge agents, document workflows, and multi-step agentic apps over enterprise data.
Teams building knowledge agents and multi-step workflows over messy enterprise documents who want OSS plus a managed parse/index cloud.
Buyers that only need classic RPA bots, or a no-code crew demo without document infrastructure.
Co-founded by Jerry Liu.
Verdict
LlamaIndex is a great fit for teams that want knowledge agents and document-heavy workflows with an open framework and a real cloud path. Pricing on LlamaCloud is public: Free at $0, Starter at $50 per month, Pro at $500 per month, and Enterprise custom, with credits at $1.25 per 1,000. Builders like the parse-to-agent path; it is a clear peer to CrewAI and deepset in the multi-agent tooling field.
Score Breakdown
How LlamaIndex scores in the categories that matter to its buyers.
Pricing
LlamaIndex publishes LlamaCloud / LlamaParse plans on llamaindex.ai/pricing. Credits meter parse, extract, index, and related jobs. USD as of September 2026.
| Plan / SKU | Meter | Price (USD) | What stands out |
|---|---|---|---|
| Free | 10K credits / mo | $0 | Community support; limited pay-as-you-go |
| Starter | 40K credits / mo | $50 / mo | Email support; pay-as-you-go up to $500 / mo |
| Pro | 400K credits / mo | $500 / mo | Priority Slack; pay-as-you-go up to $5,000 / mo |
| Enterprise | Custom credits | Sales quote | VPC, SSO, dedicated support |
| Credit overage | Per 1,000 credits | $1.25 | North America / Europe rate card |
The Field at a Glance
How LlamaIndex compares with other Multi-agent / swarm tooling vendors we reviewed, by Overall Score and relative typical engagement cost.
LlamaIndex scores 8.1 in Multi-agent / swarm tooling, ahead of Agno (8.0), Dify (7.5), deepset (7.1), and CrewAI (6.9). Relative cost lands mid-band for this subcategory.
Use-case matrix
| Use case | Fit | Notes |
|---|---|---|
| Document-grounded knowledge agents | Strong | Parse + index + workflows. |
| Multi-step agent apps over private data | Strong | This is the core product. |
| Role-based crew prototypes | Mixed | Possible; CrewAI is more crew-native. |
| Production NLP pipelines (Haystack-style) | Mixed | deepset may fit pipeline-first teams. |
| Classic RPA desktop bots | Poor | LlamaIndex does not build desktop RPA bots. |
| No-code ops automation only | Weak | Engineering framework + cloud. |
Review Excerpts
Excerpts from public reviews and forum discussions of LlamaIndex, plus representative buyer views drawn from public information. Paid reviews and pay-for-play sites were excluded.
“We use LlamaIndex very sparingly. Specifically the context aware document chunking functionality is via LlamaIndex. We couldn't use the more involved pipelines because we needed significant custom logic to enforce permissions, filters ... and other complexities.”
“LlamaParse saved us from hand-tuning PDF chunking, and the agentic parse modes were worth the credits on messy filings.”
Representative buyer view
“We prototype agents in the open-source framework, then move steady document volume onto LlamaCloud credits once pipelines settle.”
Representative buyer view
Where customer reviews couldn't be found, our analysts attempted to give a voice to buyer sentiment, drawing on real information from review sites, forums, and product documentation.
Based on: LlamaIndex’s website
Company Facts
- Founded
- 2023 (CB Insights)
- Headquarters
- San Francisco, California, United States (CB Insights)
- Total funding raised
- $27.5M (CB Insights)
- Key investors
- KPMG, Databricks, Greylock Partners, Norwest (CB Insights)
- Customers
- Cemex and Rakuten (CB Insights)
- Website
- llamaindex.ai
Key Leadership
Jerry Liu
Co-founder and CEO
Methodology
This page is an independent evaluation of LlamaIndex for buyers comparing options in multi-agent & swarm tooling. AI Industry Reviews accepts no sponsorships, advertising, or pay-for-placement fees. LlamaIndex did not pay for this review.
What we scored
The headline number is an Overall Score on a 0-10 scale. Eight criteria fall under it in two groups.
- Buyer outcomes (for multi-agent & swarm tooling)
- Knowledge agent / RAG workflows
- Document parse & extract quality
- Multi-step agent orchestration
- Cloud ops & team features
- Company & commercial
- Innovation & product leadership
- Project management & communication
- Pricing
- Contract fairness
Pricing measures whether the price looks fair for the value delivered, including packaging and renewal friction that show up in real buying cycles.
Score Composition
| Input | Weight | What it covers |
|---|---|---|
| Reviews | 40% | A proprietary read of what practitioners say about likes, complaints, and day-to-day use, including public review sites, forums, and private chat rooms. Paid reviews and pay-for-play sites are out of scope. |
| Product | 35% | Hands-on look at screens and workflows. |
| Pricing | 15% | Whether the price looks fair for what you get. |
| Docs & training | 10% | Docs, tutorials, and training material. |
How we balanced the evidence
The Overall Score is the simple average of the eight criteria. Recommendation language follows that score and the use-case matrix above.
Scope
LlamaIndex is graded here as multi-agent & swarm tooling. Criteria scores can move as more review volume and product checks are added.