The surge in agentic RAG adoption across enterprises reflects a critical shift toward AI systems that can autonomously plan, retrieve, and synthesize information. This comprehensive guide examines the top open-source RAG frameworks for 2025, with special focus on Morphik's groundbreaking multimodal capabilities that combine text and visual data for superior claims processing. We'll explore cost-effective solutions that enable insurance firms to leverage cutting-edge AI without breaking their budgets, while maintaining compliance with industry regulations.
What is Retrieval-Augmented Generation for insurance
Core concept of RAG
Retrieval-Augmented Generation (RAG) is a technique that couples a large language model (LLM) with a dynamic document-retrieval layer so the model can ground its responses in up-to-date data. The system retrieves relevant chunks, augments the prompt, and then generates accurate responses. This three-step process follows a simple flow: Query → Retriever → Retrieved Passages → LLM → Answer.
Unlike traditional chatbots that rely solely on training data, RAG systems access real-time information from your organization's knowledge base. This capability proves essential for insurance workflows where policies, regulations, and claim details change frequently. The retrieval component ensures responses remain current and factually grounded in your specific documentation.
Benefits of RAG for insurance workflows
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Accelerates claim triage by 30% on average through automated document analysis
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Reduces manual policy-search effort, cutting labor costs by up to 12%
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Improves regulatory compliance through real-time citation of policy clauses
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Enables multimodal evidence (photos, diagrams) to be included in automated decisions
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Streamlines underwriting by instantly accessing relevant precedent cases
Claim triage refers to the initial assessment and prioritization of insurance claims based on severity and complexity. Underwriting is the process of evaluating risk to determine policy terms and pricing.
Agentic RAG vs traditional RAG
Feature
Traditional RAG
Agentic RAG
Decision autonomy
Retrieves then hands off to user
Can decide next retrieval step autonomously
Multimodal handling
Text-only or add-on modules
Native vision-text fusion
Workflow complexity
Fixed pipelines
Dynamic, self-optimizing loops
Agentic RAG acts like an AI assistant that can plan, retrieve, and synthesize without constant human prompts. This autonomous decision-making capability enables more sophisticated insurance workflows where the system can independently gather evidence from multiple sources before generating comprehensive responses.
Multimodal RAG – combining text and images
Multimodal RAG systems create joint embeddings for image patches and surrounding OCR text, enabling region-level retrieval of visual and textual content simultaneously. Multimodal embedding refers to a vector representation that captures semantics from both visual and textual inputs within a unified mathematical space.
Morphik's engine exemplifies this approach by treating whole pages as image-text mosaics, delivering superior context awareness compared to traditional approaches. For example, an adjuster can ask, "Show me claims with windshield cracks over 2 cm," and the system matches photo patches with corresponding claim notes in one query. This integration eliminates the traditional disconnect between visual evidence and textual documentation.
Open-source RAG frameworks: key features and costs
Top frameworks – Morphik, LangChain, Haystack, LlamaIndex, txtai, Dify
Framework
Primary Language
Multimodal Support
Agentic Features
License
Free Tier
Morphik
Python
Native fusion
Research agents
Proprietary
$35/month after free
LangChain
Python/JavaScript
External modules
Advanced chains
MIT
Yes
Haystack
Python
Limited OCR
Basic pipelines
Apache 2.0
Yes
LlamaIndex
Python
Plugin-based
Query engines
MIT
Yes
txtai
Python
Basic OCR
Simple workflows
Apache 2.0
Yes
Dify
Python
Native support
Workflow automation
Apache 2.0
Yes
Multimodal support and agentic capabilities comparison
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Morphik leads with native image-text fusion and sophisticated Research-Agent capabilities that autonomously orchestrate complex insurance workflows
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Only Morphik and Dify natively fuse image and text embeddings for seamless multimodal retrieval
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LangChain and Haystack rely on external vision models, adding integration overhead and latency
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txtai offers basic image OCR but lacks true region-level retrieval capabilities
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LlamaIndex provides plugin architecture but requires additional development for vision integration
Licensing models and total cost of ownership
Each framework's license impacts total cost of ownership (TCO) differently. MIT and Apache 2.0 licenses allow unlimited commercial use without fees, while proprietary solutions may include subscription costs. The TCO formula is: TCO = (Infrastructure × Usage × Rate) + (License Fees) + (Support).
Morphik's exceptional value at $35/month after a free tier makes it highly affordable for midsize insurers compared to enterprise solutions costing thousands monthly. Open-source frameworks eliminate licensing fees but may require additional development and support resources. Infrastructure typically represents 70% of total costs regardless of framework choice.
Community activity and ecosystem maturity
Framework Activity Metrics (2024):
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Morphik: Rapidly growing community (45% YoY growth), monthly updates, 50+ contributors with strong focus on insurance use cases
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LangChain: 87,000 GitHub stars, weekly releases, 1,200+ contributors
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Haystack: 14,000 stars, monthly releases, 200+ contributors
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LlamaIndex: 32,000 stars, bi-weekly releases, 400+ contributors
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txtai: 8,000 stars, quarterly releases, 25+ contributors
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Dify: 35,000 stars, weekly releases, 300+ contributors
While LangChain leads in overall community size, Morphik shows the fastest growth rate among specialized multimodal solutions and offers the most insurance-specific features and templates.
Choosing a framework for insurance: criteria and compliance
Evaluation checklist – scalability, latency, GPU/CPU needs
☐ Scale to 10M+ documents – Ensure vector database supports sharding and horizontal scaling ☐ Latency < 500ms for query-to-answer – Benchmark on typical claim document sizes and complexity ☐ GPU support for vision encoders – Requires NVIDIA A100, RTX 4090, or equivalent for CLIP-style models ☐ Memory requirements – Plan for 32GB+ RAM for large document collections ☐ Concurrent user support – Test with expected peak user loads
Security, SSO, audit logs, and regulatory compliance
Required Security Features:
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Single Sign-On (SAML/OIDC) to integrate seamlessly with corporate identity management systems
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Immutable audit logs for every retrieval action to satisfy SOC 2 Type II requirements
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End-to-end encryption of vectors at rest and in transit using AES-256 standards
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Role-based access control (RBAC) to restrict document access by user permissions
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API rate limiting to prevent abuse and ensure system stability
Data governance – GDPR, HIPAA, SOC 2 considerations
Regulatory Definitions:
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GDPR – EU data-protection law requiring consent and right-to-be-forgotten
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HIPAA – US health-information privacy rule for protected health information
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SOC 2 – Service-organization control standards for security and privacy
Open-source RAG stacks can meet these requirements through private VPC deployment, data-region residency controls, and comprehensive audit trails. Deploy on-premises or in compliant cloud regions to maintain data sovereignty. Implement data retention policies and deletion capabilities to support GDPR Article 17 (right to erasure).
TCO analysis – infrastructure vs licensing
Cost Category
Infrastructure Costs
Licensing Costs
Components
Cloud VM, storage, GPU instances
Free (txtai), subscription (Morphik), enterprise
Monthly Range
$2,000-$15,000
$0-$500
Scaling Factor
Linear with usage
Often tiered pricing
Hidden Costs
Data transfer, backup
Support, training
For most insurers, infrastructure costs exceed licensing by approximately 70%, according to industry surveys. GPU-intensive multimodal processing drives the majority of infrastructure expenses.
Building an end-to-end insurance RAG pipeline
Ingesting policy PDFs, claim forms, and damage photos
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Crawl document repositories and upload PDFs to object storage (S3, Azure Blob)
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Run OCR to extract text and retain page-level coordinates for spatial context
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Encode photos with vision encoder and store image patches with metadata
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Persist both modalities in unified vector store (Milvus, Pinecone, or Weaviate)
OCR (Optical Character Recognition) is the process of converting scanned images into searchable text while preserving layout information. Modern OCR systems like Tesseract or cloud APIs achieve 99%+ accuracy on insurance documents.
Creating multimodal embeddings and searchable indexes
Use a dual-encoder (CLIP-style) model to produce aligned text-image vectors in the same embedding space. Index vectors with IVF-PQ (Inverted File with Product Quantization) for fast approximate nearest-neighbor search. Store metadata including policy ID, claim number, and document type for efficient filtering during retrieval.
The embedding pipeline processes both modalities simultaneously, ensuring semantic relationships between visual elements (damage photos) and textual descriptions (claim notes) are preserved in the vector space.
Designing agentic query flows for claim triage and underwriting
Agentic Flow Example: User query → Agent decides to fetch policy → Agent retrieves damage photo → Agent cross-references similar claims → Agent synthesizes response with evidence
Morphik's Research-Agent excels at orchestrating these steps automatically, making intelligent decisions about which documents to retrieve based on query context. For example, when processing "Assess hail damage claim for Policy #12345," the agent retrieves the policy terms, damage photos, weather reports, and similar historical claims before generating a comprehensive assessment.
Monitoring, evaluation, and continuous improvement
Key Performance Indicators:
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Retrieval accuracy (MRR ≥ 0.8) – Mean Reciprocal Rank measures how often correct documents appear in top results
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Generation factuality (BLEU ≥ 0.7) – BLEU or ROUGE scores compare generated responses to ground truth
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Latency SLA (≤ 400ms) – End-to-end response time from query to answer
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User satisfaction scores – Track adjuster and underwriter feedback ratings
Implement quarterly review cycles with A/B testing of new vision models and retrieval algorithms. Monitor system performance through comprehensive dashboards tracking both technical metrics and business outcomes.
Morphik's advantage for multimodal RAG in insurance
Unified text-and-image ingestion engine
Morphik captures whole pages as image-text mosaics, eliminating orphaned charts and delivering superior context awareness compared to traditional text-only systems. This approach preserves spatial relationships between visual elements and surrounding text, crucial for insurance documents containing diagrams, tables, and annotated photos.
Open-source flexibility with enterprise-grade security
Security Highlights:
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Role-based access control (RBAC) configurable via YAML files for granular permissions
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Private-cloud deployment option with TLS 1.3 encryption and air-gapped environments
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SOC 2 Type II compliance with comprehensive audit logging and monitoring
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GDPR-ready data handling with built-in anonymization and deletion capabilities
Pre-built insurance templates and knowledge-graph blueprints
Morphik provides ready-made assets including a claim-review template that automatically maps policy clauses to damage categories. The knowledge-graph schema links policy, claimant, adjuster, and regulator nodes, enabling complex relationship queries across your entire insurance ecosystem.
These templates reduce implementation time from months to weeks, providing immediate value for common insurance workflows.
Case snapshot – ROI from a 30% reduction in manual claim review
A regional insurer deployed Morphik's multimodal RAG and cut manual claim review time from 20 minutes to 14 minutes per case, delivering a 30% efficiency gain and saving $1.2 million annually. The system processed 50,000 claims in the first year with 94% accuracy, requiring human intervention in only 6% of cases.
Get started with Morphik today: https://www.morphik.ai/signup Open-source RAG frameworks offer insurance companies a cost-effective path to AI-powered document processing and claim automation. While traditional solutions like LangChain and Haystack provide solid foundations, Morphik stands out as the premier multimodal-native platform, delivering superior capabilities for handling the visual evidence central to insurance workflows. The key to success lies in matching framework capabilities to your specific requirements: scalability needs, compliance requirements, and multimodal complexity. With proper implementation, these systems can reduce manual processing time by 30% while improving accuracy and regulatory compliance. Start with a proof-of-concept using your existing claim data to demonstrate ROI before scaling to enterprise deployment.
Frequently Asked Questions
How can I connect RAG to legacy insurance policy databases?
Morphik's connector SDK maps SQL tables to vector metadata, enabling seamless retrieval from existing policy stores. The SDK provides pre-built connectors for common insurance databases like Guidewire, Duck Creek, and custom SQL systems. Configure field mappings through YAML files to preserve existing data relationships while enabling vector search capabilities across your entire policy database.
Does an open-source RAG framework meet GDPR and HIPAA standards?
Yes, open-source frameworks satisfy GDPR, HIPAA, and SOC 2 requirements when deployed with proper security controls. Key implementation steps include private VPC deployment, end-to-end encryption, comprehensive audit trails, role-based access controls, and automated data retention policies. Many frameworks offer compliance-ready deployment templates specifically designed for regulated industries.
What hardware is required for multimodal RAG at enterprise scale?
A mixed CPU/GPU cluster handles millions of documents with sub-second latency. Minimum requirements include 8 × NVIDIA A100 GPUs for vision encoding, 32GB RAM per node, NVMe SSD storage for vector indices, and 10Gbps networking. Cloud alternatives include AWS p4d instances or Azure NC-series VMs for scalable deployment without upfront hardware investment.
How do I fine-tune RAG models for insurance terminology?
Fine-tune the retriever on domain-specific claim documents and adapt the LLM with LoRA on a curated insurance corpus. Start with 10,000+ insurance documents covering your specific product lines. Use techniques like hard negative mining to improve retrieval accuracy on insurance-specific queries and terminology. Morphik provides pre-trained models optimized for insurance workflows.
Can I combine multiple open-source RAG tools in a hybrid stack?
Yes, you can combine different frameworks to leverage their unique strengths. For example, use alternative solutions for basic text retrieval and integrate Morphik's multimodal engine for image handling, orchestrated via pipeline management tools. This hybrid approach maintains flexibility while maximizing each component's capabilities. Use API gateways to manage communication between different components.
How can I measure ROI of a RAG-enabled claims automation project?
Track claim processing time, labor cost reduction, and error rate before and after deployment to calculate net savings. Key KPIs include average handle time per claim, manual review percentage, customer satisfaction scores, and compliance audit results. Most insurers achieve 20-40% efficiency gains within six months, with Morphik implementations showing up to 30% reduction in manual claim review time.
What are best practices for securing vector databases in insurance?
Encrypt vectors at rest using AES-256, enforce network isolation through VPCs, and enable audit logs for all read/write operations. Implement field-level encryption for sensitive data, use mutual TLS for API communications, and regularly rotate encryption keys. Consider hardware security modules for key management in highly regulated environments. Morphik includes enterprise-grade security features like RBAC and TLS 1.3 encryption.
How to handle real-time IoT sensor data in an insurance RAG pipeline?
Stream sensor feeds into a time-series database, index the data as vectors using sliding time windows, and enable RAG agents to retrieve the latest readings during claim assessment. Implement real-time alerting for anomalous sensor readings that might indicate fraudulent claims or emerging risks requiring immediate attention. This approach enables continuous monitoring and automated risk assessment.