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LlamaIndex Agent

LlamaIndex

82·Strong

Overall Trust Score

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.

rag
retrieval
open-source
Version: 0.11.x
Last Evaluated: November 9, 2025
Official Website →

Trust Vector

Performance & Reliability

83
task completion accuracy
86
Methodology
Based on RAG performance benchmarks
Evidence
LlamaIndex Documentation
High accuracy for data-heavy tasks with RAG optimization
Date: 2024-10-15
Confidence: highLast verified: 2025-11-09
tool use reliability
84
Methodology
Tool integration testing
Evidence
LlamaIndex Tools
Function calling tools and query engines as tools
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
multi step planning
82
Methodology
Complex task testing
Evidence
ReAct Agent
ReAct-style reasoning for multi-step task execution
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
memory persistence
88
Methodology
Memory system evaluation
Evidence
Vector Stores
Extensive vector store integrations for persistent memory
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
error recovery
78
Methodology
Error handling testing
Evidence
Community Reports
Basic error handling, improving with agent iterations
Date: 2024-09-15
Confidence: mediumLast verified: 2025-11-09
rag performance
92
Methodology
RAG benchmark testing
Evidence
RAG Capabilities
Industry-leading RAG performance with advanced retrieval strategies
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09

Security

77
tool sandboxing
70
Methodology
Security architecture review
Evidence
Framework Architecture
Sandboxing depends on implementation, not built into framework
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
access control
75
Methodology
Access control assessment
Evidence
Self-Hosted Framework
Access control is developer's responsibility
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
prompt injection defense
78
Methodology
Injection attack testing
Evidence
Prompt Engineering
Prompt templates provide some separation but require careful design
Date: 2024-09-20
Confidence: mediumLast verified: 2025-11-09
data isolation
82
Methodology
Data architecture review
Evidence
Index Isolation
Separate indices provide data isolation
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
open source transparency
94
Methodology
Source code review
Evidence
GitHub Repository
MIT licensed, 35k+ stars, very active development
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09

Privacy & Compliance

81
data retention
86
Methodology
Privacy architecture review
Evidence
Self-Hosted Architecture
Full control over data retention when self-hosted
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
gdpr compliance
80
Methodology
Compliance capabilities assessment
Evidence
Open Source Framework
GDPR compliance achievable with proper configuration
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
third party data sharing
75
Methodology
Data flow analysis
Evidence
LLM Integration
Data sent to configured LLM provider and embedding services
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
local deployment option
93
Methodology
Deployment options assessment
Evidence
Local Model Support
Excellent support for local LLMs via Ollama, HuggingFace, etc.
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09

Trust & Transparency

87
documentation quality
90
Methodology
Documentation completeness review
Evidence
LlamaIndex Docs
Excellent documentation with comprehensive guides and examples
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
execution traceability
83
Methodology
Logging capabilities assessment
Evidence
Observability
Built-in callbacks and integration with observability platforms
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
decision explainability
85
Methodology
Explainability features assessment
Evidence
Response Synthesis
Source nodes and retrieval context visible for RAG queries
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
open source code
94
Methodology
Open source assessment
Evidence
GitHub Repository
MIT licensed, 35k+ stars, backed by venture funding
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
community activity
90
Methodology
Community engagement analysis
Evidence
Community Engagement
Very active community with frequent releases and contributions
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09

Operational Excellence

82
ease of integration
85
Methodology
Integration complexity assessment
Evidence
Quickstart Guide
Straightforward for RAG use cases, more complex for agents
Date: 2024-10-15
Confidence: highLast verified: 2025-11-09
scalability
83
Methodology
Scalability testing
Evidence
Production Guide
Good scalability with proper vector database configuration
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
cost predictability
88
Methodology
Pricing model analysis
Evidence
Open Source Framework
Free framework, costs from LLM API and embedding services
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
monitoring capabilities
80
Methodology
Monitoring features assessment
Evidence
Observability Integrations
Integration with external observability platforms (Arize, W&B, etc.)
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
rag optimization
91
Methodology
RAG capabilities assessment
Evidence
RAG Optimization
Extensive tools for RAG optimization and evaluation
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09

✨ 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 capabilities less mature than core RAG features
  • •Requires understanding of RAG concepts for optimal use
  • •Security and sandboxing must be implemented separately
  • •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: 35000+
first release: 2022

Use Case Ratings

customer support

86

Excellent when support requires knowledge base retrieval

code generation

80

Works but not specifically optimized for code tasks

research assistant

95

Outstanding for research with advanced document retrieval

data analysis

89

Strong for analyzing structured and unstructured data sources

content creation

83

Good for content that requires grounding in source material

education

90

Excellent for tutoring grounded in educational materials

healthcare

84

Strong for medical literature retrieval, needs security hardening

financial analysis

82

Good for policy and regulation retrieval with proper setup

legal compliance

92

Outstanding for legal document analysis and retrieval

creative writing

78

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