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Evaluation record · llamaindex-agent

LlamaIndex Agent

v0.14.x

LlamaIndex

Agentragretrievalopen-source
82
Strong
About This Agent

Data framework optimized for building LLM applications with advanced RAG (Retrieval-Augmented Generation) capabilities. Agents can reason over complex data sources using sophisticated query engines and retrieval strategies; the event-driven Workflows engine (llama-index-workflows 2.x) has matured agent orchestration well beyond the earlier ReAct-only story. Managed cloud offerings (LlamaCloud parse/extract/index, LlamaAgents) are available alongside the MIT-licensed framework.

Last Evaluated: July 9, 2026
Official Website

Trust Vector Analysis

Dimension Breakdown

🚀Performance & Reliability
+
task completion accuracy

Based on RAG performance benchmarks

Evidence
LlamaIndex Documentation — High accuracy for data-heavy tasks with RAG optimization
highVerified: 2026-07-09
tool use reliability

Tool integration testing

Evidence
LlamaIndex Tools — Function calling tools and query engines as tools
highVerified: 2026-07-09
multi step planning

Complex task testing

Evidence
ReAct Agent — ReAct-style reasoning for multi-step task execution
mediumVerified: 2026-07-09
memory persistence

Memory system evaluation

Evidence
Vector Stores — Extensive vector store integrations for persistent memory
highVerified: 2026-07-09
error recovery

Error handling testing

Evidence
Community Reports — Basic error handling, improving with agent iterations
mediumVerified: 2026-07-09
rag performance

RAG benchmark testing

Evidence
RAG Capabilities — Industry-leading RAG performance with advanced retrieval strategies
highVerified: 2026-07-09
🛡️Security
+
tool sandboxing

Security architecture review

Evidence
Framework Architecture — Sandboxing depends on implementation, not built into framework
mediumVerified: 2026-07-09
access control

Access control assessment

Evidence
Self-Hosted Framework — Access control is developer's responsibility
mediumVerified: 2026-07-09
prompt injection defense

Injection attack testing

Evidence
Prompt Engineering — Prompt templates provide some separation but require careful design
mediumVerified: 2026-07-09
data isolation

Data architecture review

Evidence
Index Isolation — Separate indices provide data isolation
highVerified: 2026-07-09
open source transparency

Source code review plus advisory history analysis

Evidence
GitHub Repository — MIT licensed, 35k+ stars, very active development
GitHub Repository — Now ~50,700 stars, still very active (pushed 2026-07-08); a series of 2025 CVEs in reader/CLI components (CVE-2025-1753 CLI command injection fixed in 0.12.21, CVE-2025-5302 JSONReader DoS, CVE-2025-7647/CVE-2025-7707 insecure temp/cache file handling) were disclosed and patched via the public advisory process; current 0.14.x releases are past all published fixes
highVerified: 2026-07-09
🔒Privacy & Compliance
+
data retention

Privacy architecture review

Evidence
Self-Hosted Architecture — Full control over data retention when self-hosted
highVerified: 2026-07-09
gdpr compliance

Compliance capabilities assessment

Evidence
Open Source Framework — GDPR compliance achievable with proper configuration
mediumVerified: 2026-07-09
third party data sharing

Data flow analysis

Evidence
LLM Integration — Data sent to configured LLM provider and embedding services
mediumVerified: 2026-07-09
local deployment option

Deployment options assessment

Evidence
Local Model Support — Excellent support for local LLMs via Ollama, HuggingFace, etc.
highVerified: 2026-07-09
👁️Trust & Transparency
+
documentation quality

Documentation completeness review

Evidence
LlamaIndex Docs — Excellent documentation with comprehensive guides and examples
highVerified: 2026-07-09
execution traceability

Logging capabilities assessment

Evidence
Observability — Built-in callbacks and integration with observability platforms
mediumVerified: 2026-07-09
decision explainability

Explainability features assessment

Evidence
Response Synthesis — Source nodes and retrieval context visible for RAG queries
highVerified: 2026-07-09
open source code

Open source assessment

Evidence
GitHub Repository — MIT licensed, 35k+ stars, backed by venture funding
highVerified: 2026-07-09
community activity

Community engagement analysis

Evidence
Community Engagement — Very active community with frequent releases and contributions
highVerified: 2026-07-09
⚙️Operational Excellence
+
ease of integration

Integration complexity assessment

Evidence
Quickstart Guide — Straightforward for RAG use cases, more complex for agents
highVerified: 2026-07-09
scalability

Scalability testing

Evidence
Production Guide — Good scalability with proper vector database configuration
highVerified: 2026-07-09
cost predictability

Pricing model analysis

Evidence
Open Source Framework — Free framework, costs from LLM API and embedding services
highVerified: 2026-07-09
monitoring capabilities

Monitoring features assessment

Evidence
Observability Integrations — Integration with external observability platforms (Arize, W&B, etc.)
mediumVerified: 2026-07-09
rag optimization

RAG capabilities assessment

Evidence
RAG Optimization — Extensive tools for RAG optimization and evaluation
highVerified: 2026-07-09
Strengths
  • +Industry-leading RAG capabilities and retrieval strategies
  • +Extensive vector database and data connector integrations
  • +Outstanding documentation with comprehensive examples
  • +Open source with very active development community
  • +Flexible data indexing and querying strategies
  • +Excellent for knowledge-intensive applications
Limitations
  • !Agent orchestration improved substantially with the Workflows engine, but the framework remains RAG-first compared to dedicated agent frameworks
  • !Requires understanding of RAG concepts for optimal use
  • !Security and sandboxing must be implemented separately
  • !Repeated 2025 CVEs in reader/CLI components (command injection, DoS, insecure temp files) mean data connectors should be treated as an attack surface and dependencies kept current
  • !Can have high latency with complex retrieval strategies
  • !Embedding costs can accumulate with large datasets
  • !Less suitable for tasks not requiring data retrieval
Metadata
license: MIT
supported models
0: OpenAI
1: Anthropic
2: Cohere
3: HuggingFace
4: Local LLMs
programming languages
0: Python
1: TypeScript (LlamaIndex.TS)
deployment type: Self-hosted
tool support
0: Query engines
1: Function tools
2: Custom tools
github stars: 50700+
first release: 2022
current version: 0.14.23 (2026-06-24); llama-index-workflows 2.22.2 (2026-06-30)
pricing: Framework free (MIT) - Costs from LLM API and embedding services. LlamaCloud managed platform: credit-based, Free tier 10,000 credits/month, Starter $50/month, Pro $500/month, custom Enterprise
pricing last verified: 2026-07-09
security advisories: 2025 CVEs patched: CVE-2025-1753 (CLI command injection, fixed 0.12.21), CVE-2025-1752/CVE-2025-5302 (reader DoS), CVE-2025-7647/CVE-2025-7707 (insecure temp/cache files); current 0.14.x is past all published fixes

Use Case Ratings

customer support

Excellent when support requires knowledge base retrieval

code generation

Works but not specifically optimized for code tasks

research assistant

Outstanding for research with advanced document retrieval

data analysis

Strong for analyzing structured and unstructured data sources

content creation

Good for content that requires grounding in source material

education

Excellent for tutoring grounded in educational materials

healthcare

Strong for medical literature retrieval, needs security hardening

financial analysis

Good for policy and regulation retrieval with proper setup

legal compliance

Outstanding for legal document analysis and retrieval

creative writing

Can help with research-based ideation but not optimized for creativity