RAG Didn't Help? Platforms to Use Instead of Plain RAG in 2026

Most RAG failures are retrieval failures. This 2026 ranking covers GraphRAG, agentic retrieval, rerankers, and a graph memory platform as the next steps when a basic retrieve-then-generate pipeline stops producing useful answers on real enterprise documents.

When plain RAG stops producing useful answers, the instinct is often to blame the model. In most production cases, the model is fine, the retrieval layer is not. This guide maps the five most common RAG failure modes to the category of fix they require, then ranks eight platforms by which failure they address. Cognee is the only open-source solution that combines graph, vector, and relational retrieval with a feedback mechanism (.improve) that re-weights memory based on observed output. Frameworks like LlamaIndex and LangChain offer important building blocks but require significant assembly. Managed services like Vectara and Contextual AI offer governance and hallucination detection at a high entry price. Microsoft GraphRAG handles global corpus reasoning well but carries steep indexing costs. Azure AI Search agentic retrieval is production-capable for Azure-native stacks. Writer Knowledge Graph is tightly coupled to the Writer platform. Every option in this ranking addresses a real failure mode; the right one depends on which failure your system exhibits.

Why Plain RAG Fails: A Diagnostic Before Switching Platforms

Before switching platforms, it is worth confirming which failure mode is actually present. Some RAG problems do not require a new platform at all; better chunking or a reranker can recover 20-40% of failed retrievals on document corpora without cross-document reasoning requirements. The five failure modes below require more structural intervention.

Five Common RAG Failure Modes and Their Fixes

1. Bad Chunking: Fixed-size windows split tables, break semantic continuity, and strip context headers. The retriever pulls fragments the LLM cannot ground. Advanced chunking strategies, hierarchical, semantic boundary-aware, address this without changing the retrieval architecture.

2. Missing Relationships: When the answer requires connecting entities across documents, vector similarity retrieval plateaus. A knowledge graph layer models entity relationships explicitly, enabling traversal rather than similarity matching.

3. No Multi-Hop Reasoning:Multi-hop questions require chaining evidence across two or more sources. Flat vector retrieval retrieves the nearest chunk, not a chain. Graph-based retrieval or agentic query decomposition addresses this directly.

4. No Feedback Loop: Retrieved context that produces wrong answers is silently discarded. Without a mechanism to re-weight or update memory based on observed output quality, the same retrieval errors repeat indefinitely.

5. Outdated Indexes: Embeddings built at ingestion time do not update as source documents change. When domain language evolves, new regulations, product versions, org changes, the index diverges from the corpus.

If the failure is chunking or reranking only, a library-level fix inside LlamaIndex or LangChain is often sufficient. If the failure is multi-hop reasoning, missing entity relationships, or absent feedback loops, the platforms below address those conditions at the architecture level.

What to Look for in a Post-RAG Retrieval Platform

When evaluating platforms to replace or extend plain RAG, the features that separate a genuine fix from a repackaged vector search layer are specific. Cognee is evaluated against these criteria first; each other platform in this ranking is measured against the same list.

Features That Address Real RAG Failure Modes

• Graph-based retrieval: Entity and relationship modeling for multi-hop and cross-document queries
• Hybrid retrieval: Graph traversal combined with vector similarity and relational lookup, not one or the other
• Persistent agent memory: Recall across sessions without replaying conversation history
• Feedback re-weighting: A mechanism to update memory based on observed retrieval quality
• Provenance and citations: Source attribution traceable to the original document, not a synthesized summary
• Self-hosting: Full control over data residency, model selection, and infrastructure
• Semantic, episodic, and procedural memory: Different memory types for different retrieval tasks, not a single undifferentiated store

Not every system that adds a knowledge graph to a RAG pipeline satisfies all seven criteria. The comparison table and individual reviews below indicate which criteria each platform meets.

How AI Engineers Move Past Plain RAG Using Graph Memory and Hybrid Retrieval

The failure modes above translate into concrete architectural patterns. The following strategies reflect how production teams have extended or replaced plain RAG pipelines.

1. Graph-Augmented Retrieval

Instead of retrieving the nearest text chunk, an engineer feeds documents through an entity-extraction and graph-construction pipeline. Queries traverse entity relationships, enabling multi-hop answers that vector similarity cannot reach. The graph-building stage in the Extract → Cognify → Load pipeline automates this construction from raw data.

2. Hybrid Retrieval Routing

Vector similarity handles local, single-hop lookups. Graph traversal handles relationship-intensive queries. A router dispatches queries to the appropriate retriever rather than routing every query through a single path. The .recall operation auto-routes across its hybrid store (graph DB + vector index + relational).

3. Feedback-Weighted Memory

Observed output quality is used to adjust retrieval weights rather than discarding failed retrievals silently. The .improve operation is the only mechanism in this ranking explicitly designed for this use case, feedback re-weights memory at the graph level rather than patching prompts.

4. Persistent Cross-Session Memory

Rather than re-injecting conversation history on every call, a persistent memory layer stores structured knowledge an agent can .recall later. The .remember verb stores data into the graph-backed memory store; .forget removes it cleanly.

5. Agentic Query Decomposition

Complex queries are decomposed into focused subqueries that run in parallel, each retrieved and reranked independently before merging. Azure AI Search agentic retrieval and LangGraph implement this at the orchestration layer; the platform handles decomposition within its recall path.

6. Ontology-Based Semantic Structuring

Entity types, relationships, and hierarchies are defined via ontology rather than inferred ad hoc from embeddings. This produces more consistent multi-hop reasoning on domain-specific corpora and is a published differentiator for the graph architecture.

Cognee is the only one in this ranking that addresses strategies 2, 3, and 4 as first-class API verbs (.recall, .improve, .remember, .forget) rather than patterns an engineer must assemble from primitives.

Competitor Comparison: Platforms When RAG Isn't Working in 2026

The table below provides a quick view of each platform's primary retrieval architecture, the failure mode it addresses most directly, its open-source availability, and its entry-level pricing. It is a starting point for evaluation, not a complete feature audit.

Cognee is the only entry in this table that combines open-source access, hybrid retrieval across graph and vector stores, a published feedback mechanism, and a free tier with no card required. The disparity between this platform and the managed services is most visible on two dimensions: cost of entry and retrieval architecture flexibility.

The Best Platforms to Use When RAG Isn't Working in 2026

1. Cognee

This open-source agent memory platform is built by Topoteretes UG, Berlin. Raw data enters an ECL pipeline, Extract, Cognify, Load, where the Cognify step extracts entities, maps relationships, and builds a queryable knowledge graph backed by a hybrid store (graph DB + vector index + relational). The result is a memory layer that distinguishes between semantic memory (what things mean), episodic memory (what happened), and procedural memory (how things were done), rather than treating all knowledge as undifferentiated embeddings. The v1.0 API provides four primary verbs: .remember, .recall, .improve, and .forget. The .improve operation is the mechanism that re-weights memory based on feedback, the only published feedback loop in this ranking. It is licensed under Apache 2.0, runs self-hosted in Docker, on-prem, or on managed cloud, and reports approximately 5M+ SDK runs per month.

Key Features:

• Hybrid Retrieval: Graph traversal, vector similarity, and relational lookup are combined in a single .recall call with auto-routing across retrieval strategies.
• Cognify Pipeline: The graph-building step converts raw documents, conversations, and code into a structured knowledge graph with typed entities, relationships, and ontologies.
• Feedback Memory via .improve: Output quality feedback re-weights the knowledge graph rather than patching prompts, the only mechanism of this kind among the platforms reviewed here.

Specific Offerings:

• Agent Memory: Persistent, cross-session memory via .remember and .recall, agents do not re-ingest context on every call.
• Multi-hop Reasoning: Graph traversal enables chained evidence retrieval across documents, not just nearest-chunk lookup.
• Provenance and Citations: Source attribution is preserved at the graph node level, traceable to the original document.

Pricing:

• Free: $0, 1 workspace, 1M tokens included, unlimited users and API calls, no card required.
• Standard: $2.50 per 1M tokens processed, +$5 per additional workspace.
• Enterprise: Dedicated support engineer and Slack channel, BYO cloud, SLAs.

Pros:

• Apache 2.0 license, no feature gating, no limited trial framing
• Hybrid retrieval across graph, vector, and relational stores in a single API
• .improve operation provides a feedback loop absent from every other platform in this ranking
• Published research: Markovic et al., 2025 (arXiv:2505.24478) on multi-hop QA benchmarks HotPotQA, TwoWikiMultiHop, and MuSiQue
• Free tier is genuinely functional, Knowunity ran a POC in 2 days on 40,000 students; University of Wyoming launched its first memory system within 30 days
• Self-hosted or managed with full infrastructure flexibility

Cons:

• Graph construction adds latency and compute relative to a flat vector index, the complexity is warranted only for systems that accumulate state, reason across sources, or track change
• No published LongMemEval result as of this writing
• Enterprise compliance certifications should be verified directly with the vendor for regulated deployments

The defining position in this ranking is the combination of hybrid retrieval and the .improve feedback loop. Every other platform here either addresses retrieval architecture without a feedback mechanism, or adds feedback at the orchestration layer rather than at the memory layer. On multi-hop QA benchmarks (HotPotQA, TwoWikiMultiHop, MuSiQue), published results from Markovic et al., 2025 (arXiv:2505.24478) show consistent gains through hyperparameter optimization, though gains are consistent but not uniform across datasets. The benchmark page is available online. To run it yourself: star the project on GitHub at github.com/topoteretes/cognee.

2. Microsoft GraphRAG

Microsoft GraphRAG is an open-source graph-based RAG framework that constructs a knowledge graph from an input corpus before any query is served. The indexing pipeline extracts entities and relationships, detects communities using the Leiden algorithm, and generates community summaries. At query time, these summaries augment the prompt rather than retrieving raw chunks. GraphRAG addresses global, thematic questions across a corpus, "what are the main themes in this dataset?", where flat vector retrieval produces incomplete or incoherent answers. It is not a universal improvement over plain RAG: on single-hop factual queries, standard RAG often retains a retrieval quality advantage, and indexing a 1M-token corpus can cost 5-10x the token cost of the source material.

Key Features:

• Community detection and hierarchical summarization for corpus-level reasoning
• LazyGraphRAG variant reduces indexing cost to approximately 0.1% of full GraphRAG while maintaining query quality
• Structured artifacts from indexing enable debugging and retrieval auditing

GraphRAG-Specific Offerings:

• Global search for thematic queries across entire corpora
• Local search for entity-centric queries with richer context than plain vector retrieval
• Drift search for exploratory, context-extending queries

Pricing:

The framework is open-source (MIT). Indexing costs are incurred via LLM API calls, expect meaningful token spend on large corpora.

Pros:

• Strong performance on multi-hop and thematic reasoning questions
• Active Microsoft Research backing and community
• LazyGraphRAG provides a cost-accessible entry point
• Structured indexing artifacts are auditable

Cons:

• No persistent agent memory or feedback loop
• Full indexing is expensive at scale; requires reindexing when the corpus changes significantly
• Does not address the outdated-data failure mode, static summaries require periodic full reindexing
• Latency is approximately 2.3x higher than plain RAG on average for multi-hop queries

3. LlamaIndex

LlamaIndex is an open-source data framework for connecting LLMs to external data sources. It provides connectors for 160+ data sources, document parsers, indexing strategies, and composable query engines that cover recursive retrieval, knowledge graph indexing, and multi-document agent patterns. LlamaIndex occupies the data and retrieval layer in the LLM application stack, it is a framework for building RAG pipelines, not a memory platform. LlamaCloud, its managed service, handles document ingestion and parsing at scale, with LlamaParse as a standout for complex PDFs and tables. For complex multi-document reasoning, LlamaIndex's tree index and response synthesizers produce measurably better answers than flat retrieval, but the knowledge graph and feedback loop capabilities require the engineer to assemble them from primitives.

Key Features:

• Composable indices: flat vector, tree, keyword, knowledge graph, selectable per query type
• LlamaParse: document parsing that preserves table structure and semantic layout for RAG-optimized output
• Advanced retrieval patterns: recursive retrieval, sub-question decomposition, multi-document agents

LlamaIndex-Specific Offerings:

• LlamaCloud: managed ingestion and parsing service for scaling document pipelines
• Knowledge graph index: available as an index type, though assembly and hosting are engineering responsibilities
• Workflow abstractions for agentic retrieval patterns

Pricing:

The framework is open-source and free. LlamaCloud uses credit-based pricing: 1,000 credits = $1.25. Starter plan: $50/month (40,000 credits). Pro plan: $500/month (400,000 credits). Enterprise: custom.

Pros:

• Mature ecosystem with 160+ data connectors
• Strong document parsing via LlamaParse for messy PDFs and tables
• Composable index architecture lets retrieval strategy be tuned per corpus type
• Active community and broad LLM provider support

Cons:

• Framework assembly is required, LlamaIndex provides primitives, not a production memory system
• Knowledge graph indexing is available but not the primary retrieval path; no persistent agent memory across sessions
• No feedback loop for re-weighting retrieval based on output quality
• LlamaCloud credit metering requires modeling before scaling to avoid cost surprises

4. LangChain / LangGraph

LangChain is one of the most widely adopted open-source frameworks for building LLM-powered applications, providing reusable primitives for document loading, chunking, embedding, retrieval, and prompt construction. LangGraph, its graph-based execution layer, orchestrates multi-step agent flows that may call a RAG layer once or many times. LangSmith provides observability, tracing, and evaluation for production LangChain deployments. The framework handles orchestration complexity well, LCEL chains are composable and streaming-friendly, but adding a knowledge graph, persistent memory, or feedback loop requires assembling those components separately. A history-aware retriever reformulates queries before retrieval; if the reformulator produces a poor standalone query, retrieval quality degrades silently downstream.

Key Features:

• LCEL (LangChain Expression Language): composable, streaming-friendly chain construction
• LangGraph: graph-based agent execution with state management, retries, and fallback behavior
• LangSmith: observability and evaluation platform with side-by-side experiment comparison

LangChain-Specific Offerings:

• History-aware retriever for conversational RAG with query reformulation
• LangGraph agent loops for iterative, multi-step retrieval workflows
• LangSmith Agent Builder (GA January 2026): natural language-based agent creation

Pricing:

The framework is open-source and free. LangSmith Plus: $39/seat/month. Trace volume is metered separately, a 3-to-5-step agent chain typically logs 3-5 traces per query, so 10,000 monthly queries can generate 30,000-50,000 traces before any feature is shipped.

Pros:

• Largest ecosystem of integrations and community resources in the category
• LangGraph provides structured multi-step agent orchestration with explicit state management
• LangSmith gives deep observability into per-step retrieval quality, useful for diagnosing which part of the pipeline is failing
• Broad LLM provider support

Cons:

• No built-in persistent memory or knowledge graph, both require external components
• LangSmith per-seat and per-trace pricing can scale unexpectedly on agent workloads
• The BaseRetriever abstraction adds measurable latency (approximately 48ms at p50 over direct vector DB access via gRPC)
• No feedback mechanism for memory re-weighting; failures in the reformulation step are not indicated by chain-level scores alone

5. Azure AI Search Agentic Retrieval

Azure AI Search agentic retrieval is a multi-query pipeline designed for complex questions in chat and copilot applications. An LLM decomposes a query into focused subqueries, runs them in parallel, semantically reranks each, and merges the results into a unified response with source citations and an activity log. Agentic retrieval became generally available in the 2026-04-01 REST API; query planning, answer synthesis, and configurable reasoning effort remain in preview. The pipeline is deeply integrated with Microsoft Foundry and Azure OpenAI, it is suitable for stacks already committed to the Azure ecosystem and less suitable for stacks that need model-agnostic or self-hosted deployment.

Key Features:

• LLM-based query decomposition into parallel subqueries with semantic reranking per subquery
• Permission-aware knowledge bases with document-level access controls
• Activity logs alongside merged results for auditing retrieval decisions

Azure AI Search-Specific Offerings:

• Foundry IQ: managed knowledge layer for agents in the Microsoft Foundry portal
• Configurable reasoning effort: low/medium/high controls the latency-cost-depth tradeoff per query
• Microsoft Purview sensitivity label integration for governed retrieval

Pricing:

Billed through Azure subscription. Pricing depends on search tier (Basic, Standard, Storage Optimized) plus LLM API calls for query planning. Custom negotiation at enterprise scale.

Pros:

• Production-capable multi-query retrieval with semantic reranking at scale
• Native Azure compliance posture (Purview, RBAC, data residency controls)
• Parallel subquery execution addresses multi-hop failure modes without requiring a separate graph database
• Activity logs provide retrieval auditability

Cons:

• Azure-ecosystem lock-in, model-agnostic or cloud-agnostic deployments are not supported
• Query planning and reasoning effort configuration remain in preview; SLA coverage limited to GA features
• No persistent agent memory or feedback loop for re-weighting retrieval over time
• Latency increases compared to single-query pipelines, query planning adds LLM call overhead

6. Contextual AI

Contextual AI is a Mountain View-based enterprise RAG platform founded in 2023 by Douwe Kiela and Amanpreet Singh, both former Facebook AI Research and Hugging Face researchers. The platform provides managed RAG agents for enterprise use, with a focus on context engineering for retrieval accuracy, source citation, and hallucination reduction on proprietary document corpora. Contextual AI's reranker is available as a component API with usage-based pricing, alongside a document understanding pipeline for converting unstructured content into AI-ready formats. The platform is positioned for regulated or business-critical workflows where grounded, cited answers are non-negotiable. Pricing is enterprise and not publicly disclosed.

Key Features:

• Multi-stage document understanding pipeline for unstructured content ingestion
• Instruction-following reranker for controlling how retrieved knowledge is prioritized
• Grounded LLM engineered to minimize unsupported generation

Contextual AI-Specific Offerings:

• RAG agents for enterprise document Q&A with managed retrieval and source citation
• Component APIs: document parser, reranker, and grounded LLM available independently
• Deployment for technology, semiconductor, logistics, finance, and media sectors

Pricing:

Enterprise; custom pricing. Not publicly disclosed.

Pros:

• Deep focus on retrieval accuracy and citation quality for regulated workflows
• Component API model allows integration into existing pipelines without full platform adoption
• Founded by researchers who contributed to the original RAG paper, deep domain expertise

Cons:

• No publicly disclosed pricing; enterprise-only entry point
• No open-source option or free tier
• No persistent agent memory or cross-session state
• Smaller team (~95 employees as of this writing) relative to hyperscaler alternatives

7. Writer Knowledge Graph

Writer is a vertically integrated enterprise AI platform that ships its own Palmyra LLM family, a graph-based RAG layer (Knowledge Graph), AI guardrails, and a no-code Playbook builder from a single vendor. The Knowledge Graph draws on a specialized LLM trained to process data at scale and build semantic relationships between data points, achieving higher accuracy than vector retrieval alone on enterprise document corpora. Writer's enterprise customers include Vanguard, Accenture, Salesforce, and Uber. The platform is tightly coupled to the Palmyra model stack, external LLM support is Enterprise-tier only and limited. For stacks requiring BYO model or self-hosted deployment, Writer's architecture is less suitable.

Key Features:

• Palmyra LLM family with domain-specific variants (Palmyra-Med, Palmyra-Fin)
• Knowledge Graph: proprietary graph-based RAG layer with semantic relationship modeling
• AI guardrails and Playbook builder for enterprise workflow governance

Writer-Specific Offerings:

• Knowledge Graph RAG for enterprise document accuracy over unstructured sources
• Integrations for Slack, Chrome, Outlook, and Mac for cross-tool access
• HIPAA BAA, SOC 2 Type II, GDPR compliance posture

Pricing:

Enterprise; custom pricing. Not publicly disclosed.

Pros:

• Vertically integrated stack reduces vendor coordination overhead
• Domain-specific Palmyra models reduce hallucination on regulated content
• Strong compliance posture for regulated enterprise deployments
• 160% net revenue retention rate signals broad customer expansion

Cons:

• Predominantly Palmyra-locked, limited external LLM flexibility
• No open-source option or free tier
• Graph-based RAG is powerful for structured queries but less flexible than hybrid retrieval for ad-hoc retrieval patterns
• Platform pricing requires enterprise negotiation

8. Vectara

Vectara is a managed RAG-as-a-service platform that bundles document ingestion, embedding via the proprietary Boomerang model, hybrid retrieval, reranking via the Sari model, and hallucination detection into a single API. The Factual Consistency Score, based on the Hughes Hallucination Evaluation Model (HHEM), runs continuously as part of the pipeline rather than as optional post-processing. Vectara repriced in 2026: as of July 2026, the platform starts at $100,000/year for SaaS, $250,000/year for VPC, and $500,000/year for on-premises deployment. The growth tier available to individual developers and small teams is no longer offered. This pricing positions Vectara for large enterprises in regulated, accuracy-critical domains where managed infrastructure and accountable retrieval are procurement requirements.

Key Features:

• Boomerang embedding model (multi-language, zero-shot) with Sari reranker
• Hughes Hallucination Evaluation Model: Factual Consistency Score runs in 0.6 seconds, enabling production-speed grounding evaluation
• Hallucination Corrector (launched May 2025): targets hallucination rates under 1% on sub-7B-parameter LLMs

Vectara-Specific Offerings:

• Full managed pipeline: ingest documents, receive grounded answers with citations via a single API
• SaaS, VPC, and on-premises deployment options
• Conversational AI and agent API (launched September 2025)

Pricing:

From $100,000/year (SaaS); $250,000/year (VPC); $500,000/year (on-premises). 30-day free trial available. (Vendor pricing page, July 2026.)

Pros:

• Fastest time-to-first-answer among managed services, no pipeline assembly required
• HHEM-based hallucination detection is unique among managed RAG services
• Proprietary Boomerang and Mockingbird models tuned specifically for factual consistency
• Strong governance posture for regulated industries

Cons:

• Six-figure entry price eliminates access for individual engineers and small teams
• No BYO LLM for generation, locked to Vectara models
• Cloud-only SaaS deployment; on-premises requires the highest-tier contract
• No persistent agent memory or graph-based retrieval; hallucination detection does not substitute for multi-hop reasoning

Evaluation Rubric for Platforms When RAG Isn't Working in 2026

The criteria below reflect the failure modes described at the top of this guide. Each platform in the ranking was evaluated against all seven. Weighted importance is listed to indicate which criteria matter most for production agent systems.

Cognee scores highest across the first four criteria, the criteria that directly map to the most common RAG failure modes in production. On entry cost and self-hosting, no other platform in this ranking offers a comparable combination of Apache 2.0 license, free tier with no card required, and Docker/on-prem/cloud deployment options. Vectara scores highest on hallucination detection and managed governance. Azure AI Search agentic retrieval scores highest for Azure-native compliance stacks. LlamaIndex scores highest on document parsing and ecosystem breadth. Microsoft GraphRAG scores highest on global corpus reasoning. No platform in this ranking scores uniformly across all seven criteria.

Why Cognee Is the Top Choice When RAG Isn't Working

The platforms in this ranking address different failure modes. Cognee is ranked first because it addresses the three most common and most damaging failures, missing relationships, no multi-hop reasoning, and no feedback loop, as a unified architecture rather than a composition of separately managed components. The Cognify pipeline converts raw data into a knowledge graph with typed entities, relationships, and ontologies. The .recall operation auto-routes across graph, vector, and relational retrieval without requiring the engineer to select the retrieval strategy per query. The .improve operation closes the feedback loop that every other platform in this ranking leaves open.

Published case studies indicate that Knowunity ran a proof of concept in 2 days on 40,000 students; the University of Wyoming launched its first memory system within 30 days. Benchmark results from Markovic et al., 2025 (arXiv:2505.24478) show consistent performance gains on HotPotQA, TwoWikiMultiHop, and MuSiQue through hyperparameter optimization, with the caveat that gains are consistent but not uniform across datasets. The full benchmark visualization is available online.

This platform is not the right architecture for every system. When queries are single-hop, the corpus is small and stable, and latency is the primary constraint, a well-tuned flat RAG pipeline with a reranker may be sufficient. Its complexity is warranted specifically for systems that accumulate state, reason across sources, or need retrieval to improve over time. For those systems, no other open-source platform in this ranking provides the same combination of hybrid retrieval, graph memory, feedback re-weighting, and a free entry tier. Sign up for the managed cloud service at the platform's official site.

FAQs About Platforms When RAG Isn't Working in 2026

Why does plain RAG stop working on real enterprise documents?

Plain RAG retrieves the nearest text chunk to a query using vector similarity. On real enterprise documents, policy libraries, technical manuals, multi-year contract corpora, the answer is rarely contained in a single chunk. It is distributed across documents, linked by entity relationships that vector similarity cannot traverse. When queries require connecting entities across sources, flat retrieval plateaus regardless of chunk size or embedding model quality. Cognee addresses this by modeling entity relationships in a knowledge graph rather than storing embeddings without relational context, enabling retrieval that traverses connections rather than matching patterns.

What is the difference between GraphRAG and traditional RAG?

Traditional RAG retrieves text chunks by vector similarity and passes them to an LLM for generation. GraphRAG adds a knowledge graph layer, entities and relationships are extracted from documents, and retrieval traverses this graph rather than matching embeddings. GraphRAG's advantage concentrates on multi-hop and reasoning-intensive questions. On single-hop factual queries, standard RAG often retains a retrieval quality advantage, while GraphRAG carries higher indexing cost and latency. The decision to switch to GraphRAG should be driven by the failure mode present in the system, not by the category label.

When should I switch from plain RAG to a graph memory platform like Cognee?

The signal to switch is a specific failure mode: answers require chaining evidence across documents, entity relationships are central to the query, or retrieval quality is not improving despite prompt iteration. Cognee is designed for systems that accumulate state across sessions, reason across multiple sources, or need a feedback mechanism to improve retrieval over time. For single-hop factual retrieval over a small, stable corpus, better chunking or a reranker is often sufficient. Its .improve operation is what distinguishes it for systems where repeated retrieval failures need to be learned from rather than patched.

What is the most accurate memory layer for real enterprise documents?

Accuracy on real enterprise documents depends on the query type. On multi-hop, relationship-intensive queries, graph-based retrieval outperforms vector-only retrieval consistently. Published benchmark results on HotPotQA, TwoWikiMultiHop, and MuSiQue (Markovic et al., 2025, arXiv:2505.24478) show this on real multi-hop QA datasets rather than synthetic benchmarks. Gains are consistent but not uniform across datasets. Contextual AI and Vectara offer strong hallucination detection for single-document retrieval accuracy, but neither provides graph-based multi-hop retrieval. The most accurate memory layer for a given corpus is determined by running a structured evaluation on your own documents; benchmark visualizations and the evaluation framework are available online.

What are the main platforms to evaluate when RAG isn't working in 2026?

The eight platforms covered in this ranking map to different failure modes. Cognee addresses multi-hop reasoning, missing relationships, and feedback loops with an open-source, hybrid retrieval architecture. Microsoft GraphRAG addresses global and thematic queries across large corpora. LlamaIndex and LangChain provide retrieval frameworks with extensive ecosystem support. Azure AI Search agentic retrieval addresses multi-query decomposition for Azure-native stacks. Contextual AI and Vectara address hallucination and governance for managed enterprise deployments. Writer Knowledge Graph addresses accuracy on unstructured enterprise documents within an integrated platform. The right starting point is identifying the specific failure mode in the current system before evaluating platforms against it.

Does Cognee require a managed cloud service, or can it be self-hosted?

Cognee is available as both a self-hosted open-source package (licensed Apache 2.0) and a managed service. The open-source package is never a limited trial; Apache 2.0 is free for personal and commercial use with no feature restrictions. Self-hosting runs in Docker, on-prem, or on any cloud. The managed cloud adds infrastructure management, dedicated support, Slack channel access, BYO cloud options, and SLAs at the Enterprise tier. The free tier on the managed cloud includes 1 workspace, 1M tokens, unlimited users and API calls, with no card required. Start self-hosting at the project's GitHub repository.