Context graph tools give AI agents governed, relationship-aware access to enterprise knowledge. Eight tools take fundamentally different architectural paths to get there.
How we evaluated them:
- Every claim traces to the vendor’s own documentation, linked inline and listed in Sources.
- We scored five axes (MCP support, temporal awareness, lineage, enterprise scale, governance readiness), plus governance scoring and portability, which most comparisons skip.
- Where a vendor documents nothing, we say so rather than claim the capability is missing.
Current as of October 2026. These products ship fast, and pricing and benchmark figures are vendor-reported unless noted. Check the source before relying on any single line.
The honest summary before you read anything else: Neo4j wins on developer ecosystem. Atlan wins on enterprise governance. Everything else is context.
A context graph tool builds and serves a graph-structured knowledge layer that AI agents query at runtime, connecting entities, relationships, history, and policy in a single traversable structure. Unlike vector databases, which retrieve semantically similar chunks, context graph tools preserve the relational depth and temporal history that agents need to reason, not just retrieve.
The tools in this category range from open-source developer libraries (Graphiti, TrustGraph, Cognee) to enterprise-grade governed platforms (Atlan, Glean) to research-origin systems (Microsoft GraphRAG, OriginTrail DKG). They do not all replace each other; they serve different points in the agent stack.
Quick Facts at a glance:
| TrustGraph | Graphiti | Neo4j | MS GraphRAG | Cognee | OriginTrail DKG | Glean | Atlan | |
|---|---|---|---|---|---|---|---|---|
| License | Apache 2.0 | Apache 2.0 | Community/Enterprise | Apache 2.0 | OSS + paid | OSS (DKG v10) | Proprietary | Enterprise |
| GitHub stars (Oct 2026) | — | 32.2K+ | 17.3K+ | 36.4K+ | 32.8K+ | — | — | — |
| MCP support | Yes (native) | Yes (native) | Yes (official) | No | Yes (native) | Yes (official) | Yes (official) | Yes (native) |
| Best retrieval | OntologyRAG + vector | Hybrid (semantic + BM25 + graph) | Multi-hop Cypher + vector | Community summarization | Ontology + vector | Decentralized graph | Hybrid enterprise | MCP-exposed metadata graph |
| Starting cost | Free (self-host) | Free (self-host) | Free (community) | Free (plus LLM indexing cost) | Free / usage-based | Free (blockchain overhead) | Enterprise only | Enterprise only |
What makes the best context graph tool?
The best context graph tools do more than retrieve related chunks; they preserve temporal history, enforce governance policies, and expose context to agents through standard interfaces like MCP. Evaluating them only on latency or open-source status misses the dimension that determines whether agent outputs are auditable in production: governance readiness.
We evaluated all 8 tools against six criteria.
Criterion 1: MCP support and agent interoperability
Model Context Protocol (MCP) is how agents consume external context. We looked for an official, maintained MCP server rather than community integrations, with STDIO and HTTP transport.
Criterion 2: Temporal awareness and incremental updates
Facts change, and a graph that cannot mark a relationship invalid will serve stale context. We looked for bi-temporal data models, incremental updates without full recomputation, and fact invalidation with validity windows.
Criterion 3: Lineage and governance
Agents need to know where context came from, whether it is certified, and what changed downstream when a source was deprecated. We looked for column-level or asset-level lineage, certification propagation, PII and sensitivity classification, and audit trails for agent-consumed context.
Criterion 4: Enterprise scale and multi-tenant architecture
Tools that work for one developer’s data often buckle under 50 concurrent agents querying shared context. We looked for multi-tenant isolation with RBAC, SSO, horizontal scalability, and a 50+ connector ecosystem.
Criterion 5: Multi-agent coordination design
When agents share a graph, consistency guarantees and concurrent write handling matter. We looked for shared context layer design, concurrent read/write handling, and conflict resolution.
Criterion 6: Open source vs. enterprise tradeoff
Open source buys flexibility and cost control; enterprise-first buys managed governance, SLAs and connectors. This criterion captures license type, self-hosting options, and the support model.
The 8 context graph tools at a glance
| Tool | Best For | Key Differentiator | License / Pricing | MCP Support |
|---|---|---|---|---|
| Atlan Enterprise Data Graph | Enterprise AI context delivery: certified, explainable context for any agent fleet | 4-product platform: Enterprise Data Graph + Context Agents + Context Engineering Studio + Context Lakehouse; 5x accuracy improvement; MCP-native to Claude, ChatGPT, Codex, Cortex | Enterprise (custom) | Yes (native server) |
| Graphiti (Zep) | Temporal agent memory; continuously-changing facts | Bi-temporal data model; powers Zep, which reports 94.8% on the DMR benchmark | Apache 2.0 / free | Yes (native server) |
| TrustGraph | Ontology-driven knowledge extraction; multi-cloud deploy | OntologyRAG; modular Context Cores; 40+ LLM providers | Apache 2.0 / free | Yes (native) |
| Neo4j GraphRAG | Multi-hop reasoning on structured relational data | ACID graph DB; explainable multi-hop chains; enterprise clustering | Community free / Enterprise | Yes (official + community) |
| Cognee | Self-improving agent memory; skill routing | ECL pipeline; self-improving feedback loop; connectors for warehouses, databases, and SaaS apps | Free / usage-based ($1 per 1M tokens) | Yes (native) |
| Microsoft GraphRAG | Global synthesis across large static document corpora | Hierarchical community detection; Microsoft reports gains over baseline RAG on comprehensiveness and diversity | Apache 2.0 / free (plus LLM indexing cost) | No (community only) |
| OriginTrail DKG | Cryptographically verifiable multi-agent memory | Blockchain-backed knowledge assets; OriginTrail’s own benchmark reports up to 60% faster multi-agent coordination | OSS (DKG v10) | Yes (official server) |
| Glean | Enterprise knowledge workers; org-wide context without dev setup | 275+ app connectors; ML-inferred entity + personal graphs; Glean-hosted or Cloud-Prem | Proprietary (sales-led) | Yes (official server) |
Best context graph tool for enterprise AI context delivery: Atlan
Atlan is not a governance tool with a context feature bolted on. It is a four-product context engineering platform — Enterprise Data Graph, Context Agents, Context Engineering Studio, and Context Lakehouse — purpose-built to give AI agents certified, explainable, enterprise-accurate context at runtime. The 5x accuracy improvement Atlan AI Labs measured is not a governance outcome; it is a context engineering outcome. Agents powered by Atlan stop hallucinating because they stop guessing.
Pros:
- 4-product context platform: Enterprise Data Graph (80+ connectors, live knowledge graph), Context Agents (auto-bootstrap ontology + descriptions + metrics), Context Engineering Studio (build, test, and ship context), Context Lakehouse (Iceberg-native context store built for AI consumption)
- Context Agents eliminate the cold-start problem: they auto-generate descriptions, glossary definitions, metrics, quality scores, and ontology from the Enterprise Data Graph, removing most manual documentation effort before an agent is useful
- Atlan MCP server delivers certified context to any agent: Claude, ChatGPT, Codex, Snowflake Cortex, and Genie all query the same source of truth through one interface — one server, any agent in your fleet
- Context Engineering Studio: build versioned context repos, simulate agent queries against golden datasets with pass/fail evaluation before production, version with git controls, observe every production query as a feedback signal for improvement
- Explainable AI by design: column-level lineage means every agent answer traces back to a certified source asset through named, auditable relationships — not a black-box retrieval
- 5x accuracy improvement in agents grounded in Atlan context, reported by Atlan AI Labs from work with teams including Workday and Fox. A first-party figure, like the vendor benchmarks elsewhere on this page; Atlan has not published the test design behind it
- Gartner Leader in the 2025 Metadata Management and 2026 Data and Analytics Governance Magic Quadrants; Forrester Wave leader in the 2024 Enterprise Data Catalogs and 2025 Data Governance Solutions reports
- Active metadata keeps context live: continuous sync from 80+ sources means agents never reason from stale documentation snapshots
Cons:
- Enterprise procurement process: not a weekend self-hosted setup; requires an existing data estate to connect and a team to manage context lifecycle
- Full value compounds with adoption — Context Agents bootstrap fast, but ontology quality and certification depth deepen through collaborative human review cycles
- Not the right tool for bespoke per-agent memory graph development; designed for enterprise-wide context delivery across an agent fleet, not custom graph engineering
- Higher entry cost compared to open-source alternatives
Key capabilities:
- Enterprise Data Graph: Connects 80+ data sources — warehouses, BI tools, pipelines, business systems — into one live knowledge graph with continuous event-stream updates. Every node carries business meaning, lineage, quality, and ownership in a single traversable structure.
- Context Agents: 9 specialized AI agents auto-generate descriptions, glossary terms, metrics, quality scores, and ontology at scale — solving the cold-start problem that blocks most enterprise context engineering deployments from getting off the ground.
- Context Engineering Studio: Build versioned context repos from existing data assets. Simulate agent queries against golden datasets with pass/fail evaluation before any agent touches production. A/B test context changes. Observe every production query and turn corrections into context updates automatically.
- Context Lakehouse: The world’s first context store engineered natively for AI — Iceberg-native, open formats, graph+file architecture, vector-native search. Serves certified context to any agent via the Atlan MCP server at inference time.
- Multi-agent coordination: All agents — Claude, ChatGPT, Codex, Snowflake Cortex, Genie — query the same certified context layer through one interface. No siloed per-agent memory stores, no conflicting definitions across the fleet.
- Real proof at scale: Workday used Atlan’s MCP server to expose a shared business language to AI agents, resolving definitional ambiguity that had blocked production deployment. Mastercard, with 100M+ data assets, runs under a “context by design” philosophy — context embedded at asset-creation time so agents reason at transaction speed.
Standout features:
- Context Agents auto-bootstrap: ontology, descriptions, and metric definitions are generated from the graph rather than written by hand — no other tool in this comparison addresses cold-start this directly
- Context Engineering Studio simulation testing: evaluate context against real business questions before any agent reaches production — a QA gate unique in this comparison
- Explainable by lineage: every agent answer traces through named, column-level relationships to certified source assets
- Context Lakehouse: the first context store in the market engineered natively for AI — Iceberg-native, open formats, vector-native search
Best for: Enterprise teams deploying AI agents across complex, multi-system data estates where accuracy and explainability are non-negotiable. Organizations with 50+ agents, 80+ data sources, and a need for one certified version of truth across the entire fleet. Any context deployment where “why did the agent say that?” will be asked by a business user, an analyst, or an auditor.
Not ideal for: Solo developers building a custom per-agent memory graph; early-stage teams without an existing data estate to connect.
| Feature | Capability |
|---|---|
| MCP support | Yes (native server — Claude, ChatGPT, Codex, Cortex, Genie) |
| Temporal awareness | Yes: active metadata event streams, continuously synced |
| Lineage & explainability | Yes (full): column-level lineage, certification propagation, audit trail |
| Certified context | Yes: domain-reviewed, with downstream propagation on certification change |
| Cold-start bootstrapping | Yes: Context Agents auto-generate ontology, descriptions and metric definitions |
| Simulation testing | Yes: Context Engineering Studio pass/fail eval before production |
| Enterprise scale | Yes: 80+ connectors, RBAC, SSO, multi-agent fleet support |
| Multi-agent support | Yes: all agents share one certified context layer via MCP |
| Open source | No (Enterprise SaaS) |
Pricing: Enterprise (custom). No self-serve pricing tier.
Official URL: atlan.com
Best context graph tool for temporal agent memory: Graphiti (Zep)
Graphiti is the strongest open-source option for agents that need to track how facts evolve over time. Its bi-temporal data model, tracking both when events happened and when the system learned about them, underpins Zep, which reports 94.8% on the DMR benchmark, with zero data deletion: facts are invalidated, not erased.
Pros:
- Bi-temporal data model: event time and ingestion time tracked for every fact, unique in open-source context graph tools
- Graphiti is the engine inside Zep, which reports 94.8% on the DMR benchmark against MemGPT’s 93.4%, and up to 18.5% higher accuracy on LongMemEval with 90% lower response latency. These are Zep’s own published figures for Zep, not independently reproduced results for Graphiti on its own.
- 32,000+ GitHub stars (as of October 2026): one of the most widely adopted context graph frameworks in open source
- Hybrid search: Graphiti describes combining “semantic embeddings, keyword (BM25), and graph traversal” with “typically sub-second latency”
- Ships a reference MCP server: Claude, Cursor and other agents query temporal graphs over the standard protocol
- Multiple graph backends: Neo4j, FalkorDB and Amazon Neptune; Kuzu support is marked deprecated upstream
- Architecture published as a preprint on arXiv (2501.13956). arXiv hosts preprints and does not peer-review them
Cons:
- Memory/context layer rather than an enterprise data governance system: episode provenance traces a fact to its source, but there is no asset certification or data-estate lineage
- Graphiti’s documentation lists no pre-built enterprise data connectors; ingestion runs through its own API
- Self-improving governance requires Zep (enterprise tier), which adds dashboards but remains developer-first
- Not designed for “what’s certified and trustworthy?”: designed for “what’s current and relevant?”
Key capabilities:
Graphiti builds a continuously-evolving knowledge graph where every fact carries two timestamps: when the event happened in the world (event time) and when Graphiti’s system learned about it (ingestion time). Facts are never deleted; they receive validity windows, making every historical state queryable. The graph updates incrementally without full recomputation, supporting real-time streaming of new information. Entity resolution tracks the same entity across unstructured conversations and structured business records. Episode provenance links every derived fact to its raw source.
Standout features:
- Bi-temporal model that preserves full history without data deletion: no other open-source tool in this comparison matches this
- Episode provenance: every fact traces to its raw source document or conversation turn
- Incremental updates: new information streams in without rebuilding the graph
Best for: Developers building agent memory systems where facts evolve rapidly: CRM agents, compliance monitoring, healthcare record tracking. Teams that need production-grade context graphs on an open-source budget.
Not ideal for: Enterprises that need governance, certification, and lineage baked in; teams without infrastructure capacity to self-host.
| Feature | Capability |
|---|---|
| MCP support | Yes (native server) |
| Temporal awareness | Yes (bi-temporal): event time + ingestion time; validity windows |
| Lineage & governance | Partial: episode provenance links every fact to its source; no asset certification or data-estate lineage |
| Enterprise scale | Partial: Zep adds enterprise tier; core library is developer-first |
| Multi-agent support | Yes: shared graph across agents |
| Open source | Yes (Apache 2.0) |
Pricing: Free (self-hosted open source). Zep enterprise: contact for pricing.
Official URL: getzep.com | GitHub: github.com/getzep/graphiti (32.2K+ stars)
Best context graph tool for ontology-driven knowledge extraction: TrustGraph
TrustGraph bills itself as the “Context Operating System for AI” and delivers on it with OntologyRAG, a methodology that uses formal ontologies to guide knowledge extraction rather than pulling unstructured chunks. Open source (Apache 2.0), MCP-native, and deployable on-premise, it is the strongest choice for teams with domain-specific ontology requirements.
Pros:
- OntologyRAG methodology: domain-specific ontologies guide extraction, not generic LLM chunking
- Context Cores: modular, versioned, reusable context packages that load and remove dynamically at runtime
- Broad LLM provider support: TrustGraph’s repository lists Anthropic, AWS Bedrock, AzureAI, AzureOpenAI, Cohere, Google AI Studio, Google VertexAI, Mistral and OpenAI as LLM APIs, plus LM Studio, Llamafiles, Ollama, TGI and vLLM for self-hosted models (TrustGraph on GitHub)
- Native MCP integration: TrustGraph can launch an MCP server as part of the deployment so external agents connect to it, and can itself act as an MCP client
- Multiple graph store backends: Apache Cassandra (recommended), Neo4j, Memgraph and FalkorDB
- Workspaces: programmatic data isolation for users and agents, with RBAC tied to SSO
- Data sovereignty: on-premise and private cloud deployment options
Cons:
- Ontology engineering prerequisite: teams without ontology expertise face a steep initial setup curve
- Smaller community than Graphiti or Neo4j, meaning fewer community resources and integrations
- Built as a context operating system rather than a data governance layer: provenance is “built-in, standards-based (W3C PROV-O)”, with extraction lineage, query traces and agent behaviour stored as queryable triples, but there is no asset certification or column-level lineage across a data estate
- No temporal model documented at the data layer
Key capabilities:
TrustGraph automates context graph construction from PDFs, databases, APIs, and unstructured data using OntologyRAG, where formal ontologies define the entity types and relationship patterns the LLM extracts, rather than leaving structure to chance. The result is a knowledge graph that is domain-consistent by construction. Context Cores package this graph into versioned, reusable modules that agents can dynamically load or discard at runtime, enabling contextual switching without rebuilding the entire graph. TrustGraph ships with 3D GraphViz visualization, Prometheus/Grafana observability, Docker single-command setup, and vector store support via Qdrant (default), Pinecone, or Milvus.
Standout features:
- Context Cores architecture: context as modular, versioned packages; the only tool in this comparison with this pattern
- 3D interactive graph visualization for exploring knowledge structure
- Data sovereignty controls for private cloud and on-premise deployment
Best for: Teams that need domain-specific ontology modeling (legal, life sciences, financial compliance). Organizations with private cloud requirements. Multi-cloud deployments where data sovereignty is non-negotiable.
Not ideal for: Teams without ontology expertise; organizations that need a documented temporal model or data-estate lineage out of the box.
| Feature | Capability |
|---|---|
| MCP support | Yes (native) |
| Temporal awareness | Not documented at the data-model layer |
| Lineage & governance | Partial: W3C PROV-O provenance covering extraction lineage, query traces and agent behaviour; no asset certification or data-estate lineage |
| Enterprise scale | Yes: workspaces, RBAC tied to SSO, observability, on-prem |
| Multi-agent support | Yes: Context Cores enable modular agent context switching |
| Open source | Yes (Apache 2.0) |
Pricing: Free (open source, self-hosted).
Official URL: trustgraph.ai | GitHub: github.com/trustgraph-ai/trustgraph
Best context graph tool for multi-hop relational reasoning: Neo4j GraphRAG
Neo4j GraphRAG extends the market-leading enterprise graph database with AI-powered retrieval, combining ACID transactions, multi-hop Cypher reasoning, and native vector search in one system. With an official MCP server and a GraphRAG toolkit actively maintained on GitHub, it is the most mature choice for teams that need explainable multi-hop relational reasoning at enterprise scale.
Pros:
- Market-leading graph database infrastructure: ACID transactions, production-scale support for nodes and relationships at enterprise volumes
- Multi-hop reasoning follows chains of named entity relationships, making every answer traceable
- Official Neo4j MCP server, plus hosted MCP built into every Aura instance; the community mcp-neo4j servers add vector search, fulltext search, and Cypher tools
- Native vector index (since v5.11): hybrid graph and vector search in one query
- Enterprise features: clustering, RBAC, audit logging, multi-datacenter replication
- GraphAcademy: free educational certifications and courses
- Integration with LangChain, LlamaIndex, Google MCP Toolbox for Databases
Cons:
- Indexing is expensive for large corpora and requires schema design investment upfront
- Not purpose-built for agent memory: temporal tracking requires custom implementation
- Database-level governance (RBAC, audit logging) is strong, but Neo4j is a graph database rather than a data governance layer, so asset certification and column-level lineage across a data estate sit outside its scope
- Requires Cypher query expertise; steeper learning curve than developer-first tools
Key capabilities:
Neo4j GraphRAG layers AI-powered retrieval over the Neo4j graph database, the industry standard for relational graph storage. The pipeline extracts entities and relationships from source documents, maps them to graph nodes and edges, and then supports both local search (specific entity neighborhood traversal) and global search (community-based summarization across the whole graph). Every answer can be traced through named entity relationships; this explainability is Neo4j’s strongest differentiator. The enterprise edition adds clustering, role-based access, audit logging, and multi-datacenter replication. The official MCP server makes Neo4j graph queries accessible to any MCP-compatible agent.
Standout features:
- Full graph database infrastructure: not a wrapper, an actual enterprise graph database at the core
- Explainable multi-hop reasoning: every agent answer can trace its path through named relationships via Cypher
- GraphAcademy certifications provide team-level organizational learning
Best for: Teams already operating in the Neo4j ecosystem; applications where multi-hop relational reasoning is the core value (fraud detection, supply chain, knowledge management). Structured data with known schemas.
Not ideal for: Real-time streaming updates with no batch indexing time; teams without Cypher expertise; use cases where governance lineage is a first-class requirement.
| Feature | Capability |
|---|---|
| MCP support | Yes (official + community) |
| Temporal awareness | Not built in: temporal modelling is done in your own schema |
| Lineage & governance | Limited (graph-level): RBAC and audit logging at DB level |
| Enterprise scale | Yes: enterprise edition: clustering, replication, RBAC |
| Multi-agent support | Yes: shared graph database; concurrent agent access |
| Open source | Yes (community Apache 2.0); enterprise is paid |
Pricing: Community edition free. Enterprise edition: contact Neo4j for pricing.
Official URL: neo4j.com | GitHub: neo4j/neo4j (17.3K+ stars, the core database), neo4j/neo4j-graphrag-python (the GraphRAG toolkit), neo4j/mcp (the official MCP server)
Best context graph tool for self-improving agent memory: Cognee
Cognee is the only context graph tool in this comparison where the graph actively improves itself; its feedback loop refines edge weights based on rated responses, making memory sharper with production use. With native MCP support, 32.8K GitHub stars, and a usage-metered free tier, it is the most accessible self-improving option in the market.
Pros:
- Self-improving memory: feedback loops refine edge weights based on rated agent responses, meaning memory improves with use
- ECL pipeline (Extract, Cognify, Load): connectors for Snowflake, PostgreSQL, PDFs, Slack, and APIs
- Skill routing: agent skills become knowledge graph nodes routed by semantic similarity and historical success rate
- Native MCP server: Claude Code, LangGraph, Cursor, CrewAI, and others connect natively
- Cross-session persistence
- Real-world enterprise deployments in scientific research (Bayer) and education (Knowunity) per Cognee’s case studies
Cons:
- Usage-based cloud billing: cost scales with tokens processed, so large corpora need volume modelling before you commit
- Self-improving feedback loop requires production volume before meaningful improvement; it does not sharpen on day one
- Built as an agent memory layer rather than a data governance system: asset certification and data-estate lineage sit outside its documented scope
- Smaller community than Graphiti or Neo4j
Key capabilities:
Cognee’s six-stage Cognify pipeline classifies incoming data, checks permissions, extracts chunks, runs LLM-based entity and relationship extraction, summarizes, and then embeds and commits to the knowledge graph. Auto-extracted ontologies and managed world models mean teams do not define schema upfront; structure emerges from data. The feedback loop is the distinctive mechanic: as agents use the graph and responses get rated, edge weights between nodes shift, making frequently-correct context paths stronger over time. Skill routing extends this by treating agent skills as graph nodes, routing by semantic similarity and historical success when an agent asks which tool to use for a task.
Standout features:
- Self-improving edge weight feedback loop: unique in this comparison
- Skill routing architecture: agent capabilities embedded in the knowledge graph
- Managed world models that auto-extract ontologies without upfront schema design
Best for: Developer teams building research or domain-specific agent memory that improves with use. Organizations with structured skill-routing requirements. Startups and SMBs that want to start free and pay by usage.
Not ideal for: Enterprises needing data-estate lineage or certification governance; organizations with very large corpora that have not modelled usage-based cost.
| Feature | Capability |
|---|---|
| MCP support | Yes (native) |
| Temporal awareness | Limited: the feedback loop refines weights; no bi-temporal model documented |
| Lineage & governance | Not part of its documented scope: built as an agent memory layer |
| Enterprise scale | Limited: usage-metered cloud; enterprise BYOC available |
| Multi-agent support | Yes: skill routing; cross-session persistence |
| Open source | Yes (OSS + paid) |
Pricing: Free (local OSS). Cognee Cloud free tier: 1M tokens, 1 workspace. Standard: $1.00 per 1M tokens plus $5 per additional workspace. Enterprise: custom (BYOC).
Official URL: cognee.ai | GitHub: github.com/topoteretes/cognee (32.8K+ stars)
Best context graph tool for large-document synthesis: Microsoft GraphRAG
Microsoft GraphRAG’s hierarchical community detection enables global synthesis queries, asking “what are the themes across our entire regulatory library?”, that no other tool in this comparison can answer at scale. Microsoft reports that GraphRAG consistently outperforms a baseline vector RAG approach on comprehensiveness and diversity. Microsoft has not published a headline accuracy percentage, nor a cost figure for standard indexing.
Pros:
- Hierarchical community detection (Leiden algorithm): groups related entities and pre-generates summaries at multiple hierarchy levels
- Global synthesis queries: the only tool here that synthesizes themes across an entire document corpus in one query
- Microsoft reports that GraphRAG consistently outperforms baseline RAG on comprehensiveness and diversity. Microsoft has not published a headline accuracy percentage for this comparison.
- LazyGraphRAG variant: Microsoft reports indexing costs “identical to vector RAG and 0.1% of the costs of full GraphRAG” and “more than 700 times lower query cost” than GraphRAG global search
- 36.4K+ GitHub stars as of October 2026; used in research and scientific knowledge management
Cons:
- High indexing cost: standard GraphRAG runs LLM-based entity extraction and community summarization across every document, so the bill scales with corpus size and model price. Microsoft publishes no figure, but its own framing is that LazyGraphRAG indexing costs the same as vector RAG and 0.1% of full GraphRAG, which puts the full pipeline three orders of magnitude above it by Microsoft’s own account
- Microsoft calls it “a research project” and states the code “serves as a demonstration and is not an officially supported Microsoft offering”. As of late 2026 the repository is “largely in maintenance mode, and won’t be accepting new PRs or implementing new features”, with bug and CVE fixes continuing
- Index updates are append-oriented: the CLI’s
graphrag updatecommand “update[s] an existing knowledge graph index” with standard-update and fast-update methods, but the workflow is built around batch corpora rather than continuously-changing data - No native MCP server: community integrations only
- Best reserved for global synthesis workloads; the design optimises for themes across a corpus rather than direct single-fact lookup
Key capabilities:
Microsoft GraphRAG runs a full pipeline: text extraction, LLM-based entity and relationship extraction, graph construction, Leiden community detection, multi-level community summarization, and search index. The community hierarchy is what makes global synthesis possible; rather than retrieving a chunk that is semantically similar, GraphRAG retrieves the pre-generated summary of the entire community of related entities and their relationships. Two search modes exist: local (specific entity neighborhood) and global (aggregates across community summaries). LazyGraphRAG (released late 2024) reduces indexing cost to 0.1% of full GraphRAG by deferring community summarization until query time. Available as a component in Microsoft Azure AI Foundry.
Standout features:
- Global synthesis: the only tool that pre-generates hierarchical community summaries for organization-wide queries
- LazyGraphRAG: Microsoft reports more than 700x lower query cost than GraphRAG global search
- Azure AI Foundry integration for teams already in the Microsoft ecosystem
Best for: Regulatory libraries, technical manuals, research paper collections where “what are the themes?” is more important than “what does this specific document say?” Teams in the Microsoft Azure ecosystem. Organizations willing to pay the indexing cost for periodically-updated static corpora.
Not ideal for: Continuously-changing data; direct single-fact lookup; teams outside Azure; any use case requiring a production support SLA.
| Feature | Capability |
|---|---|
| MCP support | No (community only) |
| Temporal awareness | No temporal model; graphrag update appends new content to an existing index |
| Lineage & governance | No |
| Enterprise scale | Limited: research project; not production-supported |
| Multi-agent support | Limited: static index; not designed for concurrent agent writes |
| Open source | Yes (Apache 2.0) |
Pricing: Free (open source, self-hosted). Indexing is the real cost and Microsoft publishes no figure for it; it scales with corpus size and model price. Microsoft does state that LazyGraphRAG indexing costs 0.1% of full GraphRAG.
Official URL: microsoft.github.io/graphrag | GitHub: github.com/microsoft/graphrag (36K+ stars)
Best context graph tool for cryptographic verifiability: OriginTrail DKG
OriginTrail’s Decentralized Knowledge Graph is the only tool here where trust is cryptographically enforced; every knowledge asset carries a blockchain-backed proof of origin, integrity, and approval history. In OriginTrail’s own published benchmark, DKG-based coordination reached up to 60% faster multi-agent task completion and up to 40% lower token cost versus siloed agent memory on the most complex interdependent tasks. No independent reproduction is available.
Pros:
- Cryptographically verifiable knowledge assets: every fact carries a blockchain-backed proof of origin and integrity
- Decision lineage: captures not just what happened but who approved it, under what policy, with what precedent
- Tiered memory architecture: Working Memory, Shared Working Memory, Long-term Memory, and Verified Memory (consensus-confirmed)
- Up to 60% faster wall-clock multi-agent task completion and up to 40% lower token cost on the most complex interdependent tasks, per OriginTrail’s own published benchmark. No independent reproduction is available.
- Sub-graphs for specialization: Code, Decisions, Sessions, Tasks, GitHub graphs
- Official MCP server served over stdio: OriginTrail documents tools that let an agent query relationships, inspect sources and contribute knowledge, with Cursor, Claude Code, Claude Desktop, Windsurf, VS Code with Copilot, Cline and Codex CLI as supported clients
- dRAG: agents query verified data through staked nodes
Cons:
- Significant architectural complexity: blockchain substrate adds latency and operational overhead
- Requires crypto/Web3 familiarity, which is unusual for standard enterprise IT procurement
- Not built as a connector into warehouses or BI tools; ingestion is via its own publishing API
- Procurement friction: blockchain infrastructure sits outside most standard enterprise technology evaluation processes
Key capabilities:
OriginTrail DKG operates a three-layer architecture: a trust layer (blockchain consensus for provenance and integrity), a knowledge base layer (peer-to-peer knowledge graph network of DKG Core Nodes), and a verifiable AI layer (agents and systems using both). Agents publish knowledge assets to the DKG, structured knowledge objects anchored to the blockchain with cryptographic proof. When multiple agents collaborate, they share a common queryable knowledge base with tiered memory: working memory for active tasks, shared working memory for agent swarms, long-term memory for persistent facts, and verified memory for consensus-confirmed decisions. The decision lineage capability records every approval, policy reference, and precedent, making agent behavior auditable at a cryptographic level.
Standout features:
- Cryptographic audit trail: every fact’s origin, integrity, and approval chain is provably verifiable; not just logged, but cryptographically enforced
- Swarm coordination: purpose-built for multi-agent architectures where multiple agents must share and trust a common context
- dRAG: decentralized retrieval-augmented generation through staked, verifiable nodes
Best for: Multi-agent coordination scenarios where context must be cryptographically verified. Organizations in regulated industries where “trust but verify” is not sufficient. Decentralized multi-organization knowledge sharing.
Not ideal for: Standard enterprise IT procurement; teams without blockchain/Web3 infrastructure experience; use cases requiring warehouse or BI tool connectors.
| Feature | Capability |
|---|---|
| MCP support | Yes: official MCP server over stdio; documented clients include Cursor, Claude Code, Claude Desktop, Windsurf, VS Code with Copilot, Cline and Codex CLI |
| Temporal awareness | Yes: event and ingestion time tracked via blockchain consensus |
| Lineage & governance | Partial (cryptographic): provenance and decision lineage; not traditional data governance |
| Enterprise scale | Limited: blockchain overhead; decentralized infrastructure |
| Multi-agent support | Yes (core design): purpose-built for swarm coordination |
| Open source | Yes (OSS, DKG v10) |
Pricing: Free (open source). Blockchain infrastructure and node operation costs apply.
Official URL: origintrail.io | GitHub: github.com/OriginTrail/dkg
Best context graph tool for enterprise knowledge workers: Glean
Glean’s Enterprise Graph does not require a developer. Glean states that it “builds these graphs entirely using machine learning by understanding the data structures of enterprise apps and automatically inferring entities” across its connected apps, and layers personal graphs on top that infer “the broader tasks and projects behind” what each person does. Glean’s enterprise context layer is where it describes delivering that context to assistants and agents, including through Glean’s own MCP server.
Every capability below is sourced to Glean’s own product documentation, linked inline.
Pros:
- 275+ out-of-the-box connectors, where Glean states “all data permissions are inherited and strictly enforced, so users only see what they’re allowed to”
- ML-inferred entity graph: entities and relationships are inferred from each application’s data structures automatically, with no manual modelling
- Personal graphs: infers the tasks and projects behind a person’s activity across apps, without manual tagging
- Official MCP server, which Glean documents as “fully managed and built into your Glean instance” and enables “for customers by default with OAuth”. Glean’s developer docs title it a Remote MCP Server and describe it as “tenant-specific and permission-aware”. It exposes Search, Chat, Read Document, Code Search, People, Artifacts and Image generation as platform tools, plus Glean agents that any MCP host can invoke
- Admin audit logs with keyword, user and time-range search plus CSV export, retained 30 days by default and extendable on request, and a Governance API for access policies and compliance reporting
- Hybrid retrieval: Glean describes “hybrid” methods that “take the best of classical information retrieval techniques and modern neural-network based semantic embedders”, combining “dozens different ranking signals (including semantic similarity, keyword match, document freshness, personalization features, and more)”
- Deployment choice: Glean-hosted, or customer-hosted in your own GCP or AWS account, which Glean says carries “full data residency guarantees - data doesn’t leave your organizational boundaries”
- Single-tenant: Glean states processing happens “in each customer’s single-tenant environment”
Tradeoffs:
- Proprietary platform: Glean’s Terms of Service state that “Glean and its licensors retain all right, title, and interest in all intellectual property rights” in the Service “and the underlying technology thereof”, and bar customers from seeking to “obtain or derive the source code or non-public APIs or algorithms of the Service”. Context reaches agents through Glean’s own APIs, MCP server, and agent framework rather than as a library you embed in your own stack
- No published seat price: Glean documents the licensing model and its credit rates, but the per-user figure comes from sales
Key capabilities:
Glean’s Enterprise Graph is built automatically from the data Glean indexes across its connected applications. On its Enterprise Context page Glean describes it as capturing “not just content but the relationships between people, data, and activity across your organization,” and states “1000+ Signals in the Enterprise Graph mapping people, content, and relationships.” Personal graphs layer individual work-pattern inference on top, inferring task context per user without manual tagging. Glean’s enterprise context layer is where it describes delivering that context to assistants and agents, stating that “Glean MCP feeds governed enterprise context into Claude Code, Codex, Gemini, Cursor, Copilot, and more.”
Standout features:
- Personal graph inference: no other tool in this comparison builds per-person context graphs from observed work patterns
- 275+ connectors with permission enforcement: the widest breadth of organizational data sources in this comparison
- Deployment flexibility without developer setup: fully managed, with a customer-hosted option for data residency
Best for: Enterprise knowledge workers who need AI that understands organizational context without developer setup. Organizations where “who works on what” matters as much as what sits in the database. Enterprise search and agent use cases on a managed platform.
Best paired with: a data governance layer when the requirement is column-level lineage and certification on data assets, which is the axis Atlan is built for. The two are complementary rather than competing.
| Feature | Capability (per Glean’s documentation) |
|---|---|
| MCP support | Yes: official Glean MCP server, “fully managed and built into your Glean instance”, enabled by default with OAuth |
| Temporal awareness | Document freshness is one of the documented ranking signals |
| Retrieval | Hybrid: classical information retrieval plus neural semantic embedders |
| Governance | Query-time permission enforcement against mirrored source ACLs, admin audit logs (30-day default retention, extendable on request), and a Governance API |
| Enterprise scale | 275+ connectors; single-tenant; Glean-hosted or customer-hosted (GCP, AWS) |
| Multi-agent support | Glean agents built on the Enterprise Graph and personal graphs, invocable as MCP tools |
| Open source | No: Glean’s Terms of Service reserve “all rights in the Service not expressly granted herein” |
Pricing: Sales-led. Glean publishes its Enterprise Flex licensing and FlexCredit rate card and Core Suite per-million-token model rates, both “Licensed on a per-user, per-month basis”, but not the seat price itself.
Official URL: glean.com
The honest verdict: two clear winners by architectural role
These tools do not compete on a single axis, so ranking them on one would be dishonest.
- Neo4j wins on developer ecosystem. 17.3K+ stars on the core database, GraphAcademy certifications, deep LangChain and LlamaIndex integrations, ACID transactions, and a decade of production graph deployments. It is the graph database much of this list runs on.
- Atlan wins on certified, explainable context delivery at enterprise scale. Four products built for one outcome: accurate, traceable enterprise context at runtime. Context Agents handle cold start, the Context Engineering Studio lets you test before shipping, and column-level lineage makes every answer traceable.
- Graphiti, TrustGraph and Cognee compose rather than compete. Purpose-built for temporal tracking, ontology-driven extraction and self-improving memory, and designed to run alongside Neo4j or Atlan rather than instead of them.
5-tool focused comparison: governance score, MCP, and portability
The full 8-tool reference directory is at the top of this page. This focused matrix covers the five tools most teams will shortlist. Three columns that most comparisons omit: governance score (4-point scale), portability (lock-in risk), and MCP delivery type.
| Feature | Graphiti (Zep) | Neo4j GraphRAG | TrustGraph | Cognee | Atlan |
|---|---|---|---|---|---|
| Open source | Yes (Apache 2.0) | Yes (community) | Yes (Apache 2.0) | Yes (OSS + paid) | No (Enterprise SaaS) |
| MCP support | Yes (native server) | Yes (official + community) | Yes (native) | Yes (native) | Yes (native server) |
| MCP delivery type | Self-hosted server | Self-hosted, plus hosted on Aura | Self-hosted | Self-hosted or cloud | Managed (HTTPS) |
| Temporal awareness | ✅ Bi-temporal (event + ingestion time) | ⚠️ Modelled in your own schema | ⚠️ Not documented | ⚠️ Feedback loop only | ✅ Active metadata event streams |
| Lineage depth | ⚠️ Episode provenance to source | ⚠️ DB-level only (RBAC, audit log) | ⚠️ Event-level provenance | ❌ Not in scope | ✅ Column-level, full |
| Certification propagation | ❌ | ❌ | ❌ | ❌ | ✅ Downstream auto-propagation |
| PII / data classification | ❌ | ❌ | ❌ | ❌ | ✅ Native |
| Audit trail (agent-consumed context) | ❌ | ⚠️ DB audit log only | ⚠️ Audit logging documented | ❌ | ✅ Structured, queryable |
| Governance score | 1 / 4 | 1 / 4 | 2 / 4 | 0 / 4 | 4 / 4 |
| Portability | High | Medium | High | Medium | Low |
| Portability detail | 3 supported graph backends; Apache 2.0; full self-host | Community free; Cypher proprietary; Enterprise lock-in | 4 graph backends; Apache 2.0; on-prem options | OSS core; cloud is usage-metered | Enterprise SaaS; no self-host option |
| Enterprise scale | Partial (Zep adds enterprise tier) | Yes (enterprise edition) | Yes (workspaces, RBAC tied to SSO) | Limited (usage-metered cloud) | Yes (80+ connectors, enterprise-grade) |
| Multi-agent support | Yes (shared graph) | Yes (shared graph DB) | Yes (Context Cores) | Yes (skill routing) | Yes (shared governed context) |
| Starting cost | Free (self-host) | Free (community) | Free (self-host) | Free / usage-based | Enterprise custom |
Governance score: 4 dimensions — lineage depth, certification propagation, data classification (PII), queryable audit trail for agent-consumed context. Score = number of dimensions present natively without custom build.
Portability: High = Apache 2.0, multiple graph backends, full self-host. Medium = community free tier but proprietary query language or cloud doc-limits. Low = SaaS-only, no self-host option.
TCO breakdown: what these tools actually cost at scale
“Free” is not “zero cost.” TrustGraph and Graphiti are free to download; operating them at production scale requires infrastructure, an ops team, and in TrustGraph’s case, ontology engineering expertise. Cognee’s cloud is usage-metered, so cost tracks tokens processed rather than a document cap. The honest three-row TCO for the focused five:
| Cost dimension | Graphiti (Zep) | Neo4j GraphRAG | TrustGraph | Cognee | Atlan |
|---|---|---|---|---|---|
| Setup / indexing | $0 — incremental updates, no batch indexing | $0 community; schema + Cypher design investment | $0 self-hosted; ontology engineering is the real setup cost | $0 OSS; usage-metered cloud entry | Enterprise procurement + integration sprint |
| Ongoing (per month) | $0 self-hosted; Zep enterprise: custom | $0 community; Neo4j Enterprise: custom contract | $0 self-hosted + infra cost | Usage-metered cloud ($1 per 1M tokens); custom enterprise | Enterprise custom contract |
| At enterprise scale | Zep enterprise + infra team to operate | Neo4j Enterprise license + DBA expertise + clustering | Infra cost + dedicated ontology engineer | Per-doc caps trigger custom tier; pricing scales | All-in contract; governance value scales with connector count |
| Hidden cost | Graph infra ops to run self-hosted | Cypher expertise + schema governance investment | Ontology engineering is a prerequisite, not optional | Usage-metered cloud cost scales with corpus size | Procurement timeline; requires catalog maturity to fully realize value |
How should you choose a context graph tool?
Before evaluating features, ask three orienting questions: What is your team’s capacity to operate infrastructure? What failure mode are you protecting against: stale context, wrong context, or unauditable context? Do your agents need to pass a compliance audit? For a deeper look at how memory architecture choices affect these answers, see agent memory architectures. These questions will eliminate at least half the tools from your shortlist before you open a single README.
Decision framework
| If you need… | Consider… | Why |
|---|---|---|
| Temporal tracking: facts that change over time | Graphiti (Zep) | Only open-source bi-temporal data model; powers Zep, which reports 94.8% on DMR in its own published paper |
| Multi-hop relational reasoning on structured data | Neo4j GraphRAG | ACID graph DB with explainable multi-hop Cypher traversal; enterprise ecosystem |
| Global synthesis across large static document corpora | Microsoft GraphRAG | Hierarchical community detection; Microsoft reports gains on comprehensiveness and diversity; LazyGraphRAG reduces cost |
| Self-improving agent memory with skill routing | Cognee | Feedback loop refines edge weights with use; skill routing baked into graph architecture |
| Cryptographically verifiable multi-agent coordination | OriginTrail DKG | Only tool with blockchain-backed knowledge asset provenance; 60% faster multi-agent coordination |
| Certified, explainable context for an enterprise agent fleet | Atlan | 4-product context platform; 5x accuracy improvement (AI Labs); MCP-native to Claude, ChatGPT, Codex, Cortex; Context Agents auto-bootstrap ontology, descriptions and metric definitions |
| Org-wide agent context without developer setup | Glean | 275+ connectors, ML-inferred entity and personal graphs, no developer setup |
| Ontology-driven extraction with custom domain modeling | TrustGraph | OntologyRAG + Context Cores; on-premise data sovereignty; 40+ LLM providers |
By team profile
Individual developer or small startup: Graphiti (open source, Apache 2.0, 32K+ community), TrustGraph (if you have ontology expertise), or Cognee (if self-improving memory fits your use case). All three are free to self-host and have active communities.
Mid-market team (20–200 engineers): Neo4j GraphRAG if your data is structured and relational. Cognee (usage-metered cloud) if you want managed infrastructure with self-improving memory. Microsoft GraphRAG if you have a large static document corpus and Azure infrastructure.
Enterprise with governance requirements: Atlan for data and AI governance in regulated industries (financial services, healthcare, government). Glean for enterprise-wide organizational knowledge without developer configuration. OriginTrail DKG if your compliance requirement is cryptographic proof, not just an audit log.
The composability pattern: These tools are not mutually exclusive. The common production architecture uses Graphiti or TrustGraph as the agent memory layer (handling session context and temporal facts), Neo4j as the graph database backend, and Atlan as the enterprise governance overlay, certifying, classifying, and auditing what flows through the stack. Choosing one tool does not preclude the others; choosing the wrong governance architecture does.
The pattern across production deployments is clear: teams that choose their context tool based on governance requirements, not benchmark scores, are the ones that avoid a six-month replatforming project when the first compliance audit arrives.
What context graph tools do not replace (the vector database reality)
The production pattern is not “context graphs versus vector databases”; it is “vectors for semantic entry-point retrieval, graphs for relational depth and governance.” The comparison matters because many teams reach for a vector database first, hit its structural limits, and then build graph infrastructure on top.
Where vector databases break down:
1. No temporal awareness. Vector databases retrieve semantically similar chunks regardless of when they were true. A policy document from 2024 that has been superseded in 2026 will still surface in vector retrieval. Graphiti and Atlan invalidate stale facts; pgvector does not.
2. No multi-hop reasoning. Vector search answers “what’s similar?” Context graph tools answer “what’s connected to what, and through which relationship?” Fraud detection that needs to trace a transaction through four intermediary entities requires graph traversal, not cosine similarity.
3. No lineage or certification. Every vector query is a trust-the-embedding operation. There is no concept of certification status, audit trail, or downstream propagation of a data quality event. Atlan’s column-level lineage does this; no vector database does.
4. Coordination failures at scale. Fifty agents querying a vector database with different chunking configurations and different embedding models will return inconsistent context. A shared context graph ensures every agent works from the same governed, current state.
By early 2026, production teams have converged on vector stores as semantic entry points (fast similarity retrieval at low cost) with context graph tools handling the relational, temporal, and governance layer. The choice is not either/or; it is where in your stack each tool lives. For a deeper comparison, see context graph vs. vector database for AI agents.
The teams that treat context graphs and vector databases as competitors end up rebuilding their retrieval layer twice: once when they outgrow vectors, and again when compliance asks for an audit trail the vector store was never designed to produce.
Real stories from real customers: governance at enterprise scale
"We're excited to build the future of AI governance with Atlan. All of the work that we did to get to a shared language at Workday can be leveraged by AI via Atlan's MCP server…as part of Atlan's AI Labs, we're co-building the semantic layer that AI needs with new constructs, like context products."
— Joe DosSantos, VP of Enterprise Data & Analytics, Workday
"Atlan is our context operating system to cover every type of context in every system including our operational systems. For the first time we have a single source of truth for context."
— Sridher Arumugham, Chief Data Analytics Officer, DigiKey
Why governance readiness is the missing criterion in context graph tool selection
None of the developer-first tools here offers a full enterprise governance layer, and that is a design choice rather than a flaw. Graphiti, TrustGraph and Cognee are memory layers meant to be governed externally. Neo4j provides real infrastructure-level governance (RBAC, ACID transactions, audit logging) without being a data governance platform. The gap shows up in regulated production, when someone asks who certified the context an agent consumed, what changed when the source dataset was deprecated, and which agent acted on which version.
What governance readiness means in context graph terms:
- Lineage that answers what depends on this and what breaks when it changes, not only where it came from.
- Certification as a formal signal that propagates when the underlying asset changes, not a static tag.
- Classification of PII and sensitivity that travels through the graph and enforces access at the context level.
- Audit trail a compliance team can query: which agent consumed which context, when, and against what certification status.
Three models appear in this comparison: full enterprise data governance (Atlan), cryptographic provenance (OriginTrail DKG), and infrastructure-level controls (Neo4j). The developer-first tools provide none by design and are governed externally through an AI agent governance layer. For enterprises in financial services, healthcare and government, that difference decides whether the choice is composable-and-build or managed-and-governed.
FAQs about context graph tools
1. What is a context graph tool?
A context graph tool builds and serves a graph-structured knowledge layer that AI agents query at runtime. Unlike a vector database, which retrieves semantically similar chunks, a context graph preserves entity relationships, temporal history, and governance context. The result is agents that can reason about how facts connect and how they have changed, not just which chunk is most similar to a query.
2. What is the difference between a context graph and a knowledge graph?
Context graphs and knowledge graphs share the same graph data structure but serve different architectural roles. Knowledge graphs organize facts about a domain for human consumption; they are reference systems. Context graphs serve as the runtime delivery layer for AI agents; they must be current, queryable, and often governed. Every context graph is a knowledge graph; not every knowledge graph is suitable as a production context graph.
3. Which context graph tool has MCP support?
Seven of the eight tools in this comparison ship an official MCP server: Atlan, Graphiti, TrustGraph, Neo4j GraphRAG (official and community), Cognee, OriginTrail DKG, and Glean. Glean’s remote MCP server has been documented since September 2025, and OriginTrail publishes one served over stdio for Cursor, Claude Code, Claude Desktop and other MCP clients. Microsoft GraphRAG is the only tool here with community integrations but no official server.
4. Which context graph tool is the best for open-source teams?
Graphiti (Zep) is the strongest open-source option: 32,000+ GitHub stars, Apache 2.0 license, bi-temporal data model, and full MCP server support. Graphiti is the engine inside Zep, which reports 94.8% on the DMR benchmark in its own published paper. TrustGraph is the best open-source choice for teams with ontology expertise and domain-specific extraction requirements. Cognee’s OSS tier is free but the self-improving feedback loop requires production volume to activate meaningfully.
5. How much does Microsoft GraphRAG cost?
The library is free to download; the real cost is indexing. Standard GraphRAG runs LLM-based entity extraction and community summarization across every document, so the bill scales with corpus size and model price. Microsoft publishes no dollar figure, but states LazyGraphRAG cuts indexing to about 0.1% of full GraphRAG by deferring community summarization until query time.
6. Does Graphiti replace a vector database?
No. Graphiti uses vector embeddings internally as part of its hybrid retrieval: semantic embeddings + BM25 keyword + graph traversal in one query. The vector component handles semantic similarity matching; the graph component handles temporal validity and relationship traversal. Graphiti replaces the need for a standalone vector database in agent memory architectures, but it uses vector search internally. The correct mental model: Graphiti is a context graph that incorporates vector search, not an alternative to it.
7. What context graph tool works best for regulated industries?
Atlan is the designed choice for regulated industries: financial services, healthcare, and government. Column-level lineage, data classification (PII, sensitivity), certifications, audit trails, and active metadata propagation are all native. It is a Gartner Leader in both Metadata Management (2025) and Data and Analytics Governance (2026). OriginTrail DKG is relevant for use cases where cryptographic proof of context provenance is a compliance requirement, but it requires blockchain infrastructure expertise.
8. Can I use multiple context graph tools together?
Yes, and production architectures increasingly do. The common pattern is layered: Graphiti or TrustGraph for agent memory (temporal facts, session context), Neo4j as the graph database backend, and Atlan as the enterprise governance layer that certifies and classifies the context assets. Tools in this space compose rather than compete. The exception is fully-managed platforms (Glean, Atlan enterprise), which are integrated systems designed to be the entire context layer rather than components in a stack.
9. What is the governance gap in context graph tools?
Governance comes in degrees here, and none of the developer-first tools offers a full data governance layer. Graphiti traces each fact to its source episode, TrustGraph stores W3C PROV-O provenance for extraction, queries and agent behaviour, and Neo4j provides database-level RBAC and audit logging. What none of them provides is asset certification, PII classification, and column-level lineage across a data estate. That gap becomes a production blocker when enterprises need to answer: which agent consumed which version of this context, against what certification status? Only Atlan addresses all four natively in this comparison.
10. How do context graph tools differ from context stores?
A context store is any persistence layer that holds context for agents; it can be as simple as a JSON file or a key-value cache. A context graph is a specific, connected implementation: entities and relationships stored in a graph structure that supports traversal, temporal tracking, and (in some tools) governance. Every context graph is a context store, but a context store that lacks relational structure, temporal awareness, and governance is not a context graph. For a detailed comparison, see the context graph vs. context store guide.
Sources
- Zep: A Temporal Knowledge Graph Architecture for Agent Memory, Rasmussen et al., arXiv
- Graphiti GitHub repository (32.2K+ stars), Zep
- Microsoft GraphRAG: Unlocking Global Synthesis, Microsoft Research
- Microsoft GraphRAG GitHub repository (36K+ stars), Microsoft
- Cognee GitHub repository (32.8K+ stars), Topoteretes
- From AI Memory Silos to Multi-Agent Memory, OriginTrail
- The Next Big Shift in AI Agents: Shared Context Graphs, OriginTrail
- TrustGraph: MCP Integration Guide, TrustGraph
- Neo4j MCP Server, Neo4j Developer
- Glean Enterprise Graph, Glean
- Enterprise context for AI, Glean
- About the Glean MCP server, Glean docs
- Set up the Glean MCP server, Glean docs
- Glean connectors (275+), Glean
- Admin audit logs, Glean docs
- Governance API overview, Glean Developer
- Glean deployment models, Glean docs
- Customer-hosted deployment, Glean docs
- Learning lessons from building an enterprise AI assistant, Glean
- Open Source Context Graph Tools Compared, contextgraph.tech
- Atlan Enterprise Data Graph, Atlan
- TrustGraph CLI configuration (graph store backends, MCP server option), TrustGraph docs
- TrustGraph GitHub repository, TrustGraph
- GraphRAG CLI reference (
graphrag update), Microsoft - LazyGraphRAG: setting a new standard for quality and cost, Microsoft Research
- MCP integration, OriginTrail docs
- Neo4j MCP server, Neo4j
- GraphRAG: unlocking LLM discovery on narrative private data, Microsoft Research
- Introducing MCP for Aura: hosted MCP built into every Aura instance, Neo4j
- AI agents with the GenAI Toolbox for Databases, Neo4j
- Neo4j graph database repository, Neo4j
- neo4j-graphrag-python toolkit, Neo4j
- Neo4j, Neo4j
- Cognee integrations, Cognee docs
- Cognee, Cognee
- Cognee pricing ($1.00/1M tokens; free tier 1 workspace, 1M tokens), Cognee
- Zep, Zep
- TrustGraph, TrustGraph
- OriginTrail Decentralized Knowledge Graph, OriginTrail
- OriginTrail DKG repository, OriginTrail
- Graph RAG vs Vector RAG: Agent Memory, Neo4j, pgvector, AgentMarketCap
- Glean Enterprise Context (1000+ signals; people, data and activity), Glean
- Remote MCP Server, Glean Developer
- Glean’s MCP servers bring full company context to where your AI runs, Glean
- Glean Enterprise Flex pricing and FlexCredit rate card, Glean docs
- Glean Core Suite pricing, Glean docs
- Core security principles (query-time permission enforcement), Glean docs
- Terms of Service, section 4.1 Title by Glean, Glean
