Model-Agnostic Company Knowledge Bases That Work With Any LLM (2026)

Swap Claude for GPT without rebuilding your knowledge base. Onyx, Glean, Dify, Haystack, Notion AI, and Cognee are ranked for 2026 on model choice, local model support, embedding portability, data export format, and connector coverage, the five dimensions that determine whether a knowledge base is genuinely LLM-agnostic or just marketing-agnostic. Cognee in this evaluation offers an open-source core, verified Ollama support, and a COGX export format that preserves portability across providers.

Why LLM Portability in a Company Knowledge Base Matters

Most knowledge base evaluations focus on retrieval quality, connector count, or UI polish. The question that arises only after deployment is whether the system is coupled to the model it launched with. When a new model outperforms the incumbent on your specific workload, or when a provider raises prices or changes retention terms, rebuilding the retrieval layer incurs significant expense. A knowledge base that stores knowledge as structured graph memory, rather than as embeddings tied to one model family, avoids that dependency. That is the architectural distinction this guide evaluates.

What Breaks When You Switch Models

Switching LLM providers in a knowledge base can trigger several failure modes depending on architecture:

Switching LLM providers can cause embedding mismatches if the knowledge base baked embeddings from one model into its index. Changing to a new embedding model requires re-indexing the entire corpus, which involves downtime, cost, and re-verification of retrieval quality. Systems hard-coded to one provider's instruction format (e.g., Anthropic's human/assistant turn structure) may produce degraded outputs when switched without re-tuning. Some platforms use provider-specific function-calling formats or moderation endpoints that have no equivalent on other APIs. Proprietary export formats mean that switching the underlying knowledge base entirely requires rebuilding from source documents rather than migrating structured memory.

The table in the comparison section maps each tool against these failure modes. The tools that score highest are those where swapping the model key changes inference behavior only, not retrieval architecture, embedding indices, or data residency.

What to Look For in a Model-Agnostic Company Knowledge Base

The following criteria formed the evaluation rubric for this guide. Each differentiates a genuinely portable system from one that only claims portability at the marketing layer.

Supported LLM Providers: Does the system support OpenAI, Anthropic, Google, Mistral, Cohere, and open-weight models through a single configuration interface, or does provider support require separate integration work per provider?

Local Model Support (Ollama/vLLM): Can the system run fully air-gapped with a local model runtime? This matters when data sovereignty, cost, or offline requirements prohibit cloud API calls.

Embeddings: Swappable Without Re-Indexing: When the embedding model changes, does the existing index remain valid, or does the full corpus need to be re-processed? Systems that track embedding provenance per dataset avoid this problem.

Data Export Format: Is the knowledge representation exportable in an open, documented format that another system can ingest? Proprietary binary formats create switching costs at the knowledge layer, not just the UI layer.

Connector Coverage: How many data sources can be ingested as knowledge without custom integration work?

Cognee is evaluated against every criterion above and addresses each through its graph-based knowledge architecture, Apache 2.0 license, and the COGX exchange format for memory portability.

How AI Engineers Use Model-Agnostic Knowledge Bases

The use patterns below reflect what a practitioner building a company knowledge system actually configures, not aspirational demos.

Running the Full Stack Locally Without a Cloud Key:
Cognee's v1.6.0 introduced keyless workflows, enabling text memory to be built and searched with local models and no cloud LLM key. Local model downloads are announced on first use; LLM-dependent pipeline stages skip when no LLM is configured. This makes cold-start evaluation possible without committing to any provider account.

Swapping the Inference Model Mid-Deployment:
Its graph pipeline separates the structured knowledge extraction step from the retrieval step, so the model used for extraction and the model used for generation can be configured independently. Changing the generation model requires only an environment variable update.

Importing Memory Across Systems:
The COGX exchange format allows memory import from Mem0, Letta, Zep, and Graphiti. This enables migration between agent memory systems without rebuilding from source documents.

Grounding Multi-Hop Queries Across Sources:
Hybrid retrieval combines a graph database, a vector index, and a relational store. Multi-hop reasoning across these sources is addressed in Markovic et al., 2025 (arXiv:2505.24478), with benchmarks on HotPotQA, TwoWikiMultiHop, and MuSiQue. Note: gains are consistent but not uniform across datasets.

Building a Company Brain With Provenance:
Documents, code repositories, conversation transcripts, and agent session logs are ingested through .remember, converted to structured graph memory via the extraction step, and recalled with provenance through .recall. The knowledge graph retains entity relationships and citations, not just text chunks.

Deploying at Scale With a Managed Option:
Managed cloud deployment provides the same graph memory architecture under a managed deployment, with dedicated support engineering, BYO cloud, and SLAs for production workloads.

Competitor Comparison: Model-Agnostic Company Knowledge Bases

The table below summarizes each tool across the five evaluation dimensions. "Partial" indicates a feature exists but with architectural limitations described in the individual tool sections.

Cognee is the only tool in this comparison that combines true local model support, per-dataset embedding tracking (preventing mismatch after model changes), and an open exchange format (COGX) designed for cross-system memory portability, within an Apache 2.0 licensed open-source core. Tools like Glean and Notion AI score high on connector coverage but impose architectural constraints that limit provider independence in practice.

Best Model-Agnostic Company Knowledge Bases in 2026

1. Cognee

This platform is Cognee for AI agents, designed so that memory records what was said while knowledge represents its meaning. Raw data passes through an Extract → Cognify → Load pipeline into a hybrid store comprising a graph database, vector index, and relational layer. This architecture separates knowledge representation from the model used to generate or retrieve it, which enables provider substitution without disrupting retrieval.

Key Features:

• Keyless Local Operation: v1.6.0 introduced the ability to build and search text memory with local models and no cloud LLM key. Datasets track their embedding model to prevent mismatches when the embedding configuration changes.
• COGX Exchange Format: Memory is exportable and importable using COGX, an open format compatible with Mem0, Letta, Zep, and Graphiti. This is the only tool in this comparison with a documented open memory exchange format.
• GraphRAG with Provenance: The extraction step builds a knowledge graph with typed entities, relationships, and source citations. Multi-hop retrieval traverses this graph rather than relying solely on vector similarity, which is relevant when answering questions that span multiple documents or knowledge domains.

Model-Agnostic Offerings:

• Any OpenAI-compatible endpoint configures as the inference provider, including Ollama local runtimes
• Embedding model is configured independently from the generation model; pipeline recovery preserves completed documents after crashes
• Separation of extraction and generation means different models can serve each function

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, Slack channel, BYO cloud, SLAs

Pros:

• Apache 2.0 license, free for personal and commercial use, not a limited trial
• Fully local operation available without any cloud API key (v1.6.0+)
• COGX export format enables migration across agent memory systems
• Knowledge graph architecture keeps retrieval quality independent of generation model
• Benchmark-backed multi-hop retrieval (Markovic et al., 2025, arXiv:2505.24478); gains are consistent but not uniform across datasets
• Token-based pricing scales with usage, not headcount

Cons:

• Very small local models may produce malformed JSON during graph extraction; stronger models required for dense domain-specific corpora
• Graph complexity adds engineering overhead for teams that only need flat document retrieval
• The complexity of this architecture is justified for systems that accumulate state, reason across sources, or track change; flat Q&A over a small static corpus may not require it

Proofs of concept include Knowunity completing a project in 2 days on 40,000 students and the University of Wyoming launching its first memory system within 30 days. The platform's GitHub repository has approximately 29k stars and 5M+ SDK runs per month, reflecting practitioner adoption at meaningful scale.

Star the platform on GitHub at github.com/topoteretes/cognee or sign up for the managed cloud at platform.cognee.ai.

2. Onyx (Formerly Danswer)

Onyx is an MIT-licensed open-source enterprise search and AI assistant platform that connects large language models to internal documents, tickets, and wikis. It operates as a chat interface with a permission-aware retrieval layer built over 40+ connectors. Self-hosting is free; Onyx Cloud runs at $20 per user per month.

Key Features:

• Broad LLM Provider Support: Onyx works with OpenAI, Anthropic Claude, Google Gemini, and self-hosted models via Ollama or vLLM, configurable without rewriting the retrieval stack.
• Enterprise-Grade Permission Mirroring: Source-system permissions from Slack, Google Drive, and Confluence are mirrored and enforced at retrieval time, a capability that matters when access control must reflect the source system rather than a separately maintained ACL.
• Deployment Flexibility: Runs on a laptop, on-premise, or in cloud environments across AWS, GCP, and Azure.

Model-Agnostic Offerings:

• Any LLM provider configures through the admin interface; local models via Ollama and vLLM are supported
• Embedding swap requires re-indexing the corpus; there is no per-dataset embedding tracking
• No open export format for the knowledge representation; data export is document-level

Pricing:

• Community Edition: Free self-host under MIT license
• Onyx Cloud Business: $20 per user per month
• Enterprise: Custom pricing via sales

Pros:

• MIT license, genuinely permissive for commercial self-hosting
• 40+ connectors with permission-aware retrieval is a meaningful enterprise capability
• Ollama and vLLM support for local model operation
• Strong production references including Netflix, Ramp, and Thales Group

Cons:

• Embedding model changes require full corpus re-indexing
• No open knowledge exchange format; switching the retrieval system means rebuilding from source documents
• Self-hosting is a sustained operational commitment, not a single setup
• Enterprise access control features (RBAC, SSO) are gated to paid tiers

3. Glean

Glean is a proprietary enterprise work AI platform with a model hub that provides access to curated LLMs from OpenAI, Anthropic, Google, and Amazon across Bedrock, Vertex AI, and Azure OpenAI. Its 100+ connectors and permission-aware indexing are strong for large enterprise deployments where search breadth matters more than infrastructure control.

Key Features:

• Model Hub: Glean's Universal Model Key accesses 15+ models across cloud providers; the Bring Your Own Model Key option allows using existing provider keys, though cross-cloud access is restricted by deployment environment.
• Connector Breadth: 100+ connectors index enterprise tools at scale, with permission enforcement at retrieval time.
• Managed Infrastructure: Glean manages contracts, capacity, and upgrades with providers; zero-retention commitments are configurable with supported providers.

Model-Agnostic Offerings:

• 15+ curated models from major cloud providers; local model support (Ollama) is not available
• With Customer Key (BYOK), cross-cloud provider access is restricted to the deployment cloud
• No open data export format; knowledge representation is managed within Glean's infrastructure

Pricing:

• Enterprise only: Custom quotes required; no public pricing. Buyer-reported contracts start around $50-75 per user per month, with minimum annual commitments typically around 100 seats (~$60,000/year ACV minimum).

Pros:

• 100+ connectors with deep permission mirroring
• Model Hub simplifies provider management for enterprises that want curated access without managing API relationships
• Zero-retention provider commitments for data governance
• Named a Gartner Market Shaper in 2026

Cons:

• No local model support; cloud-only deployment limits air-gapped or sovereignty-constrained use cases
• Cross-cloud BYOK restrictions mean that, for example, GCP customers cannot use AWS-hosted models with their own key
• No open knowledge export format
• Pricing is opaque, quote-only, and scales with headcount rather than usage
• Median reported contract of ~$98,890/year reflects enterprise-scale cost floors

4. Dify

Dify is an open-source LLM application development platform with a visual workflow builder, built-in RAG pipelines, and broad model provider support. It is model-agnostic at the application layer, any OpenAI-compatible endpoint can be configured as the inference provider, and supports local models via Ollama. Its primary architecture is workflow orchestration rather than persistent knowledge graph memory.

Key Features:

• Broad Model Support: Dify supports hundreds of proprietary and open-source LLMs from dozens of inference providers, including OpenAI, Anthropic, Google, Meta, Hugging Face, and local runtimes via Ollama.
• Visual Workflow Builder: RAG pipelines, agent workflows, and multi-step LLM chains are assembled visually rather than through code, reducing the engineering time to initial deployment.
• Knowledge Base Ingestion: Documents are uploaded, indexed, and connected to agents or workflows; the RAG pipeline handles chunking, indexing, and retrieval within the Dify environment.

Model-Agnostic Offerings:

• Any OpenAI-compatible API endpoint configures as the LLM provider, including local models via Ollama
• Embedding swap behavior requires verification at the deployment level; metadata filter support for vector search had limitations as of early 2025
• Knowledge is stored in Dify's internal vector store; no open cross-system exchange format

Pricing:

• Self-hosted Community Edition: Free (open-source; multi-tenant SaaS deployment requires a commercial license from LangGenius)
• Cloud Professional: $59/workspace/month
• Cloud Team: $159/workspace/month
• Enterprise: Custom pricing

Pros:

• Hundreds of LLM providers supported including Ollama-hosted local models
• Visual builder accelerates time to initial RAG deployment
• Self-hosted Community Edition is free for single-tenant use
• Large ecosystem with MCP integration and observability tooling

Cons:

• Modified Apache 2.0 license prohibits multi-tenant SaaS deployment without a commercial license
• No open cross-system memory exchange format; switching platforms requires rebuilding knowledge from source documents
• Flat document RAG without graph memory means multi-hop reasoning across complex entity relationships is limited compared to graph-based systems
• At production volume, token costs from provider APIs dominate platform fees

5. Haystack

Haystack, built by deepset, is an open-source Python framework for building production-grade AI pipelines. Its design goal of technology agnosticism is explicit: every component, embedding model, document store, LLM, retriever, is independently swappable without modifying the surrounding pipeline. It is a framework for building knowledge systems rather than a deployed knowledge base product.

Key Features:

• Technology-Agnostic Architecture: Haystack integrates with OpenAI, Mistral, Anthropic, Cohere, Hugging Face, Google, Azure OpenAI, AWS Bedrock, and local models. Components implement a common interface that allows any element to be replaced without rewriting the pipeline.
• Component-Level Embedding Swap: Because embeddings are components with defined input/output contracts, swapping the embedding model is an architectural replacement rather than a system-level re-indexing operation, though the document store may still require re-population depending on the store implementation.
• Production-Grade Pipelines: Hayhooks wraps pipelines as REST APIs or MCP servers, supporting OpenAI-compatible chat completion endpoints.

Model-Agnostic Offerings:

• Full model agnosticism at the framework level; any provider or local model can be wired in as a component
• No managed knowledge base product; the pipeline and document store must be built and operated by the engineering team
• No dedicated open cross-system memory exchange format (knowledge is in Python objects and document stores)

Pricing:

• Haystack OSS: Free (Apache 2.0)
• Haystack Enterprise Platform (deepset): Contact sales for pricing (cloud and on-premise options available)

Pros:

• Maximum architectural flexibility, any component can be swapped at the framework level
• Fully open-source core under Apache 2.0
• Local model support via any OpenAI-compatible endpoint
• Production deployments at Airbus, Lufthansa, and Bosch validate enterprise readiness

Cons:

• A framework, not a product, there is no out-of-the-box company knowledge base; the full stack must be engineered and maintained
• No persistent graph memory; knowledge representation depends on the document store chosen
• No built-in cross-system memory exchange format
• Operational burden is higher than product-based alternatives; the team owns the full infrastructure lifecycle

6. Notion AI

Notion AI is a proprietary AI layer embedded in the Notion workspace platform. As of January 2026, users on Business and Enterprise plans can manually select from GPT, Claude, Gemini, and Grok model families through a model picker in the Agent chat interface. Notion AI operates on a RAG framework that retrieves workspace blocks before generating responses.

Key Features:

• Model Picker (Business/Enterprise): Business and Enterprise users can switch between Claude, GPT, Gemini, and Grok families manually or set the interface to "Auto" mode, which selects a model on behalf of the user.
• Workspace-Native Retrieval: Notion AI retrieves content from the workspace, pages, databases, and connected tools like Slack and Google Drive, before generating responses.
• Custom Agents: Autonomous Custom Agents launched in February 2026 and run on a credit system at $10 per 1,000 monthly Notion credits.

Model-Agnostic Offerings:

• Model selection is limited to provider options managed by Notion; custom LLMs and local models cannot be configured
• No self-hosting option; the system is entirely SaaS
• No open data export format for AI-processed knowledge

Pricing:

• Free/Plus: Limited AI trial only
• Business: $20/user/month (billed annually); full Notion AI bundled
• Enterprise: Custom pricing; zero-retention commitment with LLM providers
• Custom Agents: $10 per 1,000 monthly Notion credits (additional, on top of plan)

Pros:

• Zero friction for teams already operating in Notion, no separate deployment
• Model picker (Business/Enterprise) provides access to multiple provider families without engineering work
• Enterprise plan includes zero-retention agreements with LLM providers
• 100M+ users and strong G2 ratings reflect broad adoption and polish

Cons:

• No custom LLM or local model support, a closed SaaS system with no BYOM capability for self-hosted inference
• No open knowledge export format; knowledge is locked to the Notion workspace
• Full AI access requires Business plan at $20/user/month minimum, no standalone AI tier
• Ungoverned or outdated pages in the workspace feed AI retrieval directly; there is no native content certification or freshness enforcement

Evaluation Rubric for Model-Agnostic Company Knowledge Bases

The five criteria below, and their relative weight in this evaluation, reflect what matters when a knowledge system needs to survive provider transitions:

Embedding portability carries the highest weight because it is the failure mode that causes the most operational disruption in practice. A re-indexing event on a large corpus is costly in compute, time, and verification overhead, and it is silent until it degrades retrieval quality.

Why Cognee Is the Leading Model-Agnostic Company Knowledge Base for 2026

This platform is the only tool in this comparison that addresses all five evaluation dimensions through its architecture rather than through configuration workarounds. The graph-based knowledge representation is inherently model-independent: the extraction step extracts structured entities and relationships from documents regardless of which LLM performs the extraction, and the hybrid retrieval layer (graph DB + vector index + relational) operates independently of the generation model. Per-dataset embedding tracking in v1.6.0 prevents mismatch when embedding configurations change. COGX provides an open format for migrating memory across systems. Keyless local operation requires no cloud provider account.

For systems that accumulate state, reason across sources, or need to track how knowledge changes over time, this architecture handles the underlying requirements directly. Flat RAG systems, even model-agnostic ones, retrieve chunks; this platform recalls the meaning of knowledge. That distinction is what the evaluation rubric above measures.

The Apache 2.0 license means the OSS package is never a limited trial. Token-based pricing on the managed cloud means cost scales with usage, not with headcount. The proof points are published: Knowunity in 2 days on 40,000 students; University of Wyoming in 30 days.

Start locally for free at github.com/topoteretes/cognee, or sign up for the managed cloud at platform.cognee.ai.

FAQs About Model-Agnostic Company Knowledge Bases

What does it mean for a knowledge base to be model-agnostic?

A model-agnostic knowledge base separates the knowledge representation from the model used to generate or retrieve from it. When the LLM provider changes, because of cost, capability, or data residency requirements, the index, graph, or document store does not need to be rebuilt. This platform achieves this by storing knowledge as a typed graph through the extraction step, so the retrieval architecture operates independently of which LLM is configured for generation. Tools that couple embeddings tightly to a single provider's model family are model-agnostic in name but not in practice.

Which enterprise knowledge base lets me bring my own LLM?

Onyx, Haystack, Dify, and Cognee all support bring-your-own-LLM configurations. Onyx and Dify accept any OpenAI-compatible API endpoint, including local models via Ollama. Haystack is fully provider-agnostic at the framework level. Cognee supports any OpenAI-compatible endpoint and runs entirely locally without a cloud key as of v1.6.0. Glean and Notion AI do not support custom or locally-hosted LLMs; their model selection is bounded by what the platform has pre-integrated and manages internally.

What breaks when I switch LLM providers in a company knowledge base?

The failure modes depend on architecture. If the system stored embeddings from one model family, switching the embedding model requires re-indexing the full corpus, which triggers compute cost, downtime, and retrieval quality re-verification. If the system used provider-specific prompt formats or function-calling schemas, switching providers may degrade output quality without obvious error signals. Cognee tracks the embedding model per dataset (v1.6.0+), which prevents silent mismatch. Haystack's component model makes provider substitution explicit at the architecture level. Glean manages this on behalf of the customer within its curated model set.

Can a company knowledge base run without any cloud LLM provider?

Cognee's v1.6.0 introduced keyless workflows that build and search text memory with local models and no cloud LLM key. Onyx also supports local model operation via Ollama and vLLM. Dify supports Ollama-hosted models in self-hosted deployments. Haystack operates with any endpoint including local runtimes. Glean and Notion AI require cloud provider connectivity and do not support fully air-gapped or offline operation.

What is the COGX format and why does it matter for portability?

COGX is the open memory exchange format of Cognee. It allows memory to be imported from and exported to other agent memory systems, including Mem0, Letta, Zep, and Graphiti, without rebuilding from source documents. This matters because knowledge accumulated over months in one system can be migrated to another without losing the structured graph representation. No other tool in this comparison ships a documented cross-system memory exchange format. COGX addresses the data portability dimension of model agnosticism at the knowledge layer rather than just the inference layer.

What is GraphRAG and how does Cognee use it?

GraphRAG is a retrieval approach that traverses a knowledge graph to answer multi-hop questions rather than retrieving document chunks by vector similarity alone. The extraction step builds a typed graph extracting entities, relationships, and provenance from raw documents, and then uses hybrid retrieval across the graph database, vector index, and relational store to answer complex queries. This is documented in Markovic et al., 2025 (arXiv:2505.24478), with benchmark results on HotPotQA, TwoWikiMultiHop, and MuSiQue. Gains are consistent but not uniform across datasets. The full benchmark results are available on the platform's research page.