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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
Where LlamaIndex ranks among Multi-agent & swarm tooling vendors we reviewed, by Overall Score and relative typical engagement cost.
LlamaIndex leads this swarm/tooling peer set at 8.1 versus deepset (7.1) and CrewAI (6.9). Relative cost on Starter/Pro credit plans is lower than CrewAI's hosted control path when volume stays on published tiers.
Use-case matrix
| Use case | Fit | Notes |
|---|---|---|
| Document-grounded knowledge agents | Strong | Parse + index + workflows. |
| Multi-step agent apps over private data | Strong | Core product story. |
| 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 | Wrong category. |
| No-code ops automation only | Weak | Engineering framework + cloud. |
Who it’s for
Good fit
- Teams drowning in PDFs and needing agentic extraction
- Builders who want OSS locally and cloud when ready
- Buyers who need published credit pricing
Poor fit
- RPA license replacements
- Non-technical crew demo buyers only
- Teams unwilling to manage prompt/eval quality
Review Excerpts
Below are excerpts from public reviews. Paid reviews and pay-for-play sites such as Clutch were excluded.
“LlamaParse saved us from hand-tuning PDF chunking; agentic parse modes were worth the credits on messy filings.”
“We prototype agents in the open-source framework, then move steady document volume onto LlamaCloud credits once pipelines stabilize.”
“LlamaIndex premium parse tiers burned credits until we turned on Autopick cheaper modes for clean pages.”
“Having parse, extract, and index under one credit system beat gluing three vendors for a knowledge agent MVP.”
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 such as Clutch 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 fit pattern described above.
Scope
LlamaIndex is graded here as multi-agent & swarm tooling. Criteria scores can move as more review volume and product checks are added.