SYSTEM ACTIVE
Home›Agents›Haystack

Haystack

deepset

82·Strong

Overall Trust Score

Open-source NLP framework for building production-ready LLM applications, RAG pipelines, and semantic search systems. Modular architecture with pre-built components for document processing, retrieval, and generation.

rag
search
open-source
Version: 2.x
Last Evaluated: November 9, 2025
Official Website →

Trust Vector

Performance & Reliability

84
rag accuracy
88
Methodology
RAG pipeline benchmarking
Evidence
Haystack RAG
Optimized for retrieval-augmented generation workflows
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
document retrieval
90
Methodology
Retrieval accuracy testing
Evidence
Retriever Components
Multiple retriever types: BM25, embedding-based, hybrid
Date: 2024-10-15
Confidence: highLast verified: 2025-11-09
pipeline flexibility
92
Methodology
Architecture assessment
Evidence
Pipeline Architecture
Composable pipelines with custom components
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
llm integration
86
Methodology
LLM integration testing
Evidence
Generator Components
Supports OpenAI, Anthropic, Cohere, HuggingFace models
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
document processing
82
Methodology
Document processing testing
Evidence
Preprocessors
Supports PDF, DOCX, HTML, TXT with text chunking
Date: 2024-09-20
Confidence: highLast verified: 2025-11-09
latency
Value: Varies by pipeline (500ms-5s)
Methodology
Performance benchmarking
Evidence
Performance
Latency depends on retrieval, LLM calls, and document count
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Security

76
self hosted
90
Methodology
Deployment security assessment
Evidence
Deployment Options
Full self-hosting with Docker, Kubernetes support
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
api security
72
Methodology
API security review
Evidence
REST API
Basic auth available, additional security user-implemented
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
data privacy
85
Methodology
Data flow analysis
Evidence
Open Source
Data stays local when self-hosted, no telemetry
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
open source transparency
94
Methodology
Open source assessment
Evidence
GitHub
Apache 2.0 license, 17k+ stars, active development
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
document storage security
68
Methodology
Storage security assessment
Evidence
Document Stores
Security depends on chosen document store (Elasticsearch, etc.)
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Privacy & Compliance

83
data retention
88
Methodology
Privacy architecture review
Evidence
Document Store Control
Full control over document storage and retention policies
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
gdpr compliance
84
Methodology
Compliance capabilities assessment
Evidence
Self-Hosted Option
GDPR compliance possible with self-hosted deployment
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
local deployment
92
Methodology
Deployment options assessment
Evidence
Deployment
Complete local deployment with local LLMs possible
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
llm data sharing
75
Methodology
Data flow analysis
Evidence
LLM Integration
Data sent to LLM provider unless using local models
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
no telemetry
90
Methodology
Telemetry assessment
Evidence
Open Source
No telemetry in open-source version
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09

Trust & Transparency

88
documentation quality
92
Methodology
Documentation completeness review
Evidence
Haystack Docs
Excellent documentation with tutorials and examples
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
open source
95
Methodology
Open source assessment
Evidence
GitHub
Apache 2.0, 17k+ stars, transparent development
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
pipeline traceability
84
Methodology
Traceability features assessment
Evidence
Pipeline Debugging
Debug mode with step-by-step pipeline execution tracking
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
community support
87
Methodology
Community engagement analysis
Evidence
Community
Active Discord, GitHub discussions, and forum
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09

Operational Excellence

80
ease of integration
85
Methodology
Integration complexity assessment
Evidence
Integrations
100+ integrations with document stores, LLMs, embedders
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
scalability
78
Methodology
Scalability testing
Evidence
Scaling Guide
Horizontal scaling with Kubernetes and load balancing
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
cost predictability
92
Methodology
Pricing model analysis
Evidence
Open Source
Free framework, costs for infrastructure and LLM APIs
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
monitoring
75
Methodology
Monitoring features assessment
Evidence
Monitoring
Basic logging, requires external monitoring tools
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
production readiness
79
Methodology
Production readiness assessment
Evidence
Production Deployment
Production-ready with REST API and Docker support
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
modular architecture
91
Methodology
Architecture assessment
Evidence
Components
Highly modular with composable components
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09

✨ Strengths

  • •Open-source (Apache 2.0) specialized for RAG and semantic search
  • •Modular architecture with 100+ pre-built integrations
  • •Excellent documentation and active community (17k+ stars)
  • •Supports multiple LLM providers and local models
  • •Production-ready with REST API and container deployment
  • •Strong document retrieval and processing capabilities

⚠️ Limitations

  • •Requires ML/NLP expertise for optimal pipeline configuration
  • •Limited built-in monitoring and observability features
  • •Setup complexity higher than managed services
  • •Performance tuning requires deep understanding
  • •Limited agent-like autonomous behavior capabilities
  • •Document store choice affects performance and cost significantly

📊 Metadata

license: Apache 2.0
supported models:
0: OpenAI
1: Anthropic
2: Cohere
3: HuggingFace
4: Local LLMs
programming languages:
0: Python
deployment type: Self-hosted (Docker, Kubernetes) or deepset Cloud
tool support:
0: Document stores
1: Vector DBs
2: Embedding models
3: LLMs
pricing model: Free open source (deepset Cloud managed service available)
github stars: 21400+
first release: 2019
supported document stores: Elasticsearch, OpenSearch, Weaviate, Pinecone, Qdrant, Milvus
use case focus: RAG, semantic search, question answering
version: 2.x
eol notice: Haystack 1.x reached end-of-life March 11, 2025

Use Case Ratings

customer support

82

Good for knowledge base-powered support with RAG

code generation

74

Can integrate code-focused LLMs but not specialized

research assistant

92

Excellent for document analysis and research synthesis

data analysis

79

Good for text analytics, limited for numerical data

content creation

76

Can support with RAG-based content generation

education

85

Excellent for building educational Q&A systems

healthcare

84

Good for medical literature search and synthesis

financial analysis

81

Self-hosted option suitable for compliance

legal compliance

88

Excellent for legal document search and analysis

creative writing

71

Limited creative capabilities, better for research