Most enterprises are already running AI agents in production. However, agents pulling answers from spreadsheets, internal knowledge bases, and half-updated glossaries lack a governance layer that tells them which number is current, which definition is approved, and which data they can touch.
When something breaks, the failure rarely traces back to the model. It traces back to context. Context graph tools exist to close that gap, but the category is broad.
Open-source memory frameworks and full enterprise governance platforms both call themselves context graph tools, even though they solve different problems for different teams.
This guide covers both categories, evaluates eleven tools across three dimensions that matter to enterprise buyers, and helps you match the right tool to the use case you actually have.
Whether a context graph tool delivers on its claims depends on three dimensions enterprise buyers care about:
Here is how all eleven tools compare, split across two tables: enterprise context platforms first, then developer-layer frameworks.
| Enterprise context platforms | Connectivity & context depth | Governance | AI agent delivery |
| OvalEdge | 170+ connectors (modern + legacy). Full stack: glossary, ontology, lineage, quality, certifications | Native: column-level RBAC, classification, policy enforcement, access workflows, audit trails | MCP server + open context API + agentic analytics. Cloud / on-prem / hybrid |
| Atlan | 80+ (modern stack). Metadata-driven | Limited (relies on integrations) | Growing MCP. Cloud SaaS |
| Collibra | 100+. Glossary-driven | Strong (policy-native) | Emerging. Cloud / on-prem (6-12+ month implementation) |
| IBM watsonx | IBM ecosystem + select. Knowledge Catalog | Strong (policy-native) | Emerging. Cloud / on-prem (complex licensing) |
| DataHub | 50+ (modern stack). Community-driven | Requires customization | Agent Context Kit (MCP). Self-hosted / Acryl Cloud |
| Developer-layer graph frameworks | Connectivity & context depth | Governance | AI agent delivery |
| TrustGraph | Limited connectivity. Strong ontology (OntologyRAG) | None | Emerging MCP. OSS, DIY |
| Graphiti | Limited connectivity. Bi-temporal memory | App-level only | Full MCP, LangChain. Self-hosted, DIY |
| Neo4j GraphRAG | Moderate connectivity. Schema-driven | None | Community MCP. Self-hosted / AuraDB |
| Mem0 | Minimal connectivity. Basic entity extraction | None | Managed MCP. Cloud managed |
| Microsoft GraphRAG | N/A. Community clustering | None | No MCP. OSS, heavy infra |
| Graphwise | Moderate connectivity. Automated graph build | None | No MCP. Cloud |
Developer frameworks cluster around strong retrieval and memory mechanics, but none touch governance or enterprise connectivity natively. That stays the implementing team's responsibility.
Enterprise platforms provide those layers natively, but the depth of governance and AI agent delivery maturity varies more than most vendor pages let on.
OvalEdge expert insight: The gap for most enterprises is not missing governance. It is disconnected governance. Connecting existing investments into a single queryable layer is what makes governed AI practical.
The best context graph tools for AI agents in 2026 include OvalEdge, Atlan, Collibra, IBM watsonx, and DataHub on the enterprise platform side, and TrustGraph, Graphiti, Neo4j GraphRAG, Mem0, Microsoft GraphRAG, and Graphwise on the developer framework side. The right starting point depends on which problem matches yours.
Let us start with the enterprise context platforms, then move to the developer frameworks.
These five platforms deliver the context graph as part of broader governance and metadata infrastructure, handling connectivity, policy enforcement, and maintenance as part of the product itself.
OvalEdge expert insight: The evaluation question for this tier is not whether a platform can build a graph. It is whether it connects to your actual systems, including legacy ones, enforces the policies you already have, and serves trusted context to your agents without requiring you to rebuild what is already in place.
Disclaimer: OvalEdge is evaluated against the same criteria as every other tool in this guide.
OvalEdge is a unified data governance platform that connects and activates an enterprise's existing governance foundation through its Enterprise Context Graph, making catalog, glossary, lineage, quality, and access policies queryable by AI agents and people.
How does it deliver context to AI agents?
OvalEdge exposes governed context to Claude, ChatGPT, and custom agents through an MCP server, alongside an open context API for any framework. 170+ native connectors cover both modern cloud platforms and legacy systems, including SAP, Oracle, and mainframes, all built and maintained in-house.
How does it handle governance?
OvalEdge handles governance natively: column-level RBAC, AI-powered sensitive data classification across PII, PHI, and PCI, automated access workflows with Jira and ServiceNow, data masking, self-building audit trails, and Source Code Intelligence that parses SQL and ETL jobs for exact lineage from production code.
Case study: Hallmark had consumer data spread across Salesforce, Akamai, Redshift, Teradata, and multiple MySQL databases with no unified way to locate PII or route privacy requests. OvalEdge connected those systems through a single catalog with PII classification, glossary, and governed query, turning disconnected data into a privacy-ready, queryable foundation. Read the full case study here.
Atlan is an active metadata platform built around what it calls an Enterprise Data Graph, with 80+ connectors concentrated on the modern data stack: Snowflake, dbt, Databricks, and similar cloud-native tools.
How does it deliver context to AI agents?
Atlan serves context through a growing MCP ecosystem and strong metadata activation capabilities. Connectivity is deep for cloud-native environments but thinner for legacy systems like SAP, Oracle, and mainframes.
How does it handle governance?
Atlan relies on integrations with external tools for fine-grained access control and policy enforcement. Its primary strength is metadata collaboration and activation, not native governance infrastructure.
Collibra is an enterprise data governance platform with catalog, glossary, and policy management capabilities, increasingly positioning itself for AI governance use cases.
How does it deliver context to AI agents?
Collibra's MCP and AI agent delivery capabilities are emerging. Its strengths sit in traditional governance workflows rather than real-time context serving to agents. Implementation timelines typically run 6 to 12 months or longer for full enterprise rollouts.
How does it handle governance?
Collibra provides deep, policy-native governance with a strong track record in regulated industries. Access controls, classification, and compliance features are mature. Built for large enterprises with dedicated governance programs and implementation budgets to match.
IBM pairs Knowledge Catalog for metadata, data quality, and policy enforcement with watsonx.governance for AI lifecycle management and factsheet-based traceability across the model lifecycle.
How does it deliver context to AI agents?
IBM’s AI agent delivery is emerging. Context serving is strongest within its own ecosystem. Connectors cover IBM platforms and select third-party systems, but breadth outside the IBM stack is narrower than cloud-native competitors.
How does it handle governance?
IBM brings deep governance heritage with strong policy and classification capabilities, purpose-built compliance features for banking, healthcare, and government, and factsheet-based AI traceability. Licensing and deployment can be complex, especially for organizations not already in IBM’s ecosystem.
DataHub is an open-source metadata platform with an Agent Context Kit purpose-built for exposing catalog metadata to AI agents through MCP-compatible interfaces.
How does it deliver context to AI agents?
DataHub’s Agent Context Kit is MCP-compatible and designed for agent delivery. 50+ connectors cover the modern data stack. Available self-hosted or managed through Acryl Cloud. Connectivity is solid for modern systems, thinner for legacy infrastructure.
How does it handle governance?
DataHub requires customization for governance. The open-source core provides metadata infrastructure, but fine-grained access controls, classification, and policy enforcement need to be built or integrated by the implementing team.
These six tools give engineering teams the building blocks for constructing a context graph inside a single AI agent application. Each is open-source or open-core, tuned for retrieval mechanics and agent memory, and leaves governance, enterprise connectivity, and long-term maintenance to the team that implements it.
If the goal is giving one agent reliable memory and retrieval, start here. If the goal is delivering governed context to production agents across an organization, the enterprise platforms above are the right starting point.
TrustGraph is an Apache 2.0 licensed framework built around ontology-driven knowledge extraction, branded OntologyRAG, supporting more than 40 LLM providers out of the box.
How does it deliver context to AI agents?
TrustGraph runs hybrid retrieval across both graph structure and semantic search. MCP support is emerging. It works with multiple graph backends and vector stores, giving engineering teams flexibility in how they assemble the pipeline.
How does it handle governance?
TrustGraph has no native governance. Access controls, classification, and audit trails are the implementing team’s responsibility. Strong ontology modeling provides structural accuracy, but policy enforcement is not part of the framework.
Graphiti is Zep’s bi-temporal context graph engine, built around episode-based memory that tracks both when a fact was true in the real world and when the system learned it.
How does it deliver context to AI agents?
Graphiti offers a full MCP implementation with LangChain integration. Zep’s published benchmarks show 94.8% accuracy on the Deep Memory Retrieval benchmark. Real-time incremental updates run without batch recomputation.
How does it handle governance?
Graphiti handles governance at the app level only. The framework manages temporal accuracy well, knowing which fact is current, but enterprise access controls, data classification, and audit trails are outside its scope.
OvalEdge expert insight: When evaluating a developer-layer framework, ask the vendor directly how it handles access control today, not on its roadmap. Most of these frameworks are genuinely strong at retrieval and memory mechanics, but governance is rarely more than a placeholder in year one.
Neo4j pairs its established graph database with a GraphRAG pipeline supporting three memory types: episodic, semantic, and procedural. Published case studies cite deployments at organizations including UBS and NASA.
How does it deliver context to AI agents?
Neo4j provides community MCP support and integrates with LangChain, LlamaIndex, CrewAI, and OpenAI Agents. A managed AuraDB service reduces infrastructure overhead. One of the largest communities in the graph database space.
How does it handle governance?
Neo4j has no native governance layer. It provides mature graph infrastructure, but access controls, classification, and policy enforcement sit outside the GraphRAG framework. Governance must be built or sourced separately.
Mem0 combines vector and graph memory with automatic entity extraction, giving AI agents persistent memory across sessions through a managed cloud service.
How does it deliver context to AI agents?
Mem0 provides a managed MCP service with simple integration, designed for fast deployment in product-facing AI applications. Entity extraction is automatic, reducing setup overhead for development teams.
How does it handle governance?
Mem0 has no enterprise governance. It is built for product teams adding persistent memory to customer-facing agents. Access controls, data classification, lineage, and audit trails are not part of the product’s scope.
Microsoft’s MIT-licensed GraphRAG uses graph-based clustering to summarize entire document corpora at scale, surfacing topics that simple keyword search misses.
How does it deliver context to AI agents?
Microsoft GraphRAG has no MCP support. It is built for offline document indexing and summarization rather than real-time context delivery. Requires significant infrastructure investment.
How does it handle governance?
Microsoft GraphRAG has no governance layer. The framework handles document clustering and summarization. Access controls, policy enforcement, classification, and audit trails are outside its scope entirely.
Graphwise automates knowledge graph construction from unstructured and semi-structured data through a no-code interface, offering a fast path from raw data to a working graph.
How does it deliver context to AI agents?
Graphwise has no MCP support. It builds the graph but does not serve it to AI agents natively. Agent delivery and real-time context serving need to be layered on separately through other tools or custom integrations.
How does it handle governance?
Graphwise has no native governance. The platform handles graph construction. Access controls, classification, lineage, policy enforcement, and audit trails need to be sourced from a separate governance layer.
Picking the right context graph tool comes down to matching it to your actual problem. If you need single-agent memory and retrieval, developer frameworks like Graphiti or Neo4j GraphRAG will get you started.
If you need governed, trusted context across your data estate for production AI, the deciding factor is not retrieval speed. It is whether your agents only touch authorized data, use the approved definition, and trace every answer back to a governed source.
Most enterprises already have significant governance foundations in place: catalogs, glossaries, lineage, quality rules, access policies. The opportunity is not rebuilding that work inside a new system. It is connecting what already exists into a single queryable layer and making it available to analytical AI. That is what OvalEdge's Enterprise Context Graph is designed to do.
Book a demo to see how OvalEdge connects your existing governance foundation into a single queryable layer for AI agents.