What is the difference between a context graph and a knowledge graph?

Knowledge graphs store static relationships between entities (facts), while context graphs capture dynamic decision traces including the reasoning, exceptions, and temporal context behind each decision. Context graphs are optimized for AI agent decision-making and include provenance tracking, confidence levels, and reasoning traces. A knowledge graph tells you "Customer X has a gold tier account." A context graph tells you "Customer X was upgraded to gold tier because VP of Sales approved an exception based on the Q3 incident resolution precedent."

Why do AI agents need context graphs?

Without context graphs, an AI agent is like an extremely smart intern on day one — it can follow written rules but gets tripped up by every unwritten exception. Context graphs provide the institutional memory and decision precedent that AI agents need to handle edge cases and make autonomous decisions. As enterprises shift from "systems of record" to "systems of agents," the context graph becomes the crown jewel — the real source of truth for AI autonomy.

What is context engineering?

Context engineering is the natural evolution from prompt engineering. While prompt engineering focuses on crafting the right instructions for an LLM, context engineering is about curating and maintaining the optimal set of tokens (information) during LLM inference. As Anthropic defines it: "Prompt engineering adjusts instructions; context engineering controls the evidence that the instructions operate on." Context engineering is emerging as the #1 skill for AI builders in 2026.

What are the best open source context graph tools?

The leading open source context graph tools in 2026 include: TrustGraph (Apache 2.0, ontology-driven graph construction with OntologyRAG), Graphiti by Zep (45,000+ GitHub stars, bi-temporal knowledge graph with P95 latency of 300ms), and Neo4j GraphRAG (the industry standard graph database with dedicated RAG pipeline tools). Other notable tools include Graphlit (cloud-native context layer), DataHub Agent Context Kit, and the Agent Trace specification by Cognition AI and Cursor.

What is a decision trace?

A decision trace is a record that captures not just what decision was made, but the specific context, reasoning, exceptions, approvals, and precedents that led to that outcome. For example, instead of recording "customer got a 25% discount," a decision trace captures "customer got a 25% discount because Service Impact Exception was invoked, approved by VP Finance, based on precedent from Q4, due to critical incidents." Decision traces are the core data structure within a context graph.

The Context Graph Ecosystem in 2026: Companies, Tools & the Race to Build AI's Missing Layer

Q: What is a context graph?

A context graph is a living record of decision traces stitched across entities and time, so precedent becomes searchable. Unlike traditional databases that store what happened, context graphs capture why decisions were made — the exceptions, overrides, precedents, and cross-system context that currently live in Slack threads, deal desk conversations, and people's heads. Foundation Capital calls this "AI's trillion-dollar opportunity."

The Context Graph Explosion

In December 2025, Foundation Capital partners Jaya Gupta and Ashu Garg published a thesis that would ignite the AI world: “Context Graphs: AI's Trillion-Dollar Opportunity.” Their argument was deceptively simple but profoundly consequential: the next trillion-dollar platforms won't be systems of record for data—they'll be systems of record for decisions.

The thesis crystallized something many practitioners had felt but couldn't articulate. For years, the AI industry had been obsessed with model capabilities— bigger parameters, longer context windows, faster inference. But as AI agents became increasingly capable, a gap emerged. These agents could reason brilliantly in isolation but lacked the organizational context—the institutional memory, the decision precedent, the tribal knowledge—to operate effectively inside real enterprises.

One month after publication, by January 31, 2026, the Foundation Capital thesis had become one of the most-discussed ideas in AI. It resonated across the industry because it named a problem everyone was experiencing: AI agents are only as good as the context they receive.

“A system of record for decisions, not just data.”
— Dharmesh Shah, CTO & Co-founder of HubSpot

The endorsement from Dharmesh Shah was significant. As the co-founder of one of the most successful SaaS platforms ever built, his recognition signaled that context graphs weren't just a niche infrastructure concept—they were a fundamental shift in how enterprise software would be architected. When Shah describes context graphs as a “system of record for decisions,” he's pointing to a new category of software that sits alongside (or perhaps above) the CRMs, ERPs, and data warehouses that have defined enterprise tech for decades.

“We've entered the era of context.”
— Aaron Levie, CEO of Box

Aaron Levie's declaration was equally consequential. Box has spent two decades managing enterprise content—documents, files, unstructured data. When Levie says we've entered the “era of context,” he's acknowledging that the content his company manages is only valuable insofar as it can be assembled into the right context for AI agents to act on. The raw file is no longer the unit of value—the contextual graph connecting that file to decisions, people, processes, and outcomes is.

The speed of adoption has been remarkable. In barely two months, “context graph” went from a specialized infrastructure term to a boardroom-level strategic concept that enterprise leaders are actively investing in.

Foundation Capital estimates the total addressable market for context graph infrastructure could exceed $1 trillion as AI agents become the primary interface for enterprise operations.

Enterprise Platform Players

The largest and most established companies in the context graph ecosystem are enterprise platforms that have been building the connective tissue between AI agents and organizational knowledge. These players bring existing customer bases, deep integrations, and the enterprise credibility needed to sell into Fortune 500 organizations.

GGlean — The Enterprise Graph

Glean has emerged as one of the most prominent players in the context graph ecosystem, building what they call the “Enterprise Graph”—a fusion of personal graphs (who knows what, who works with whom) and knowledge graphs (what information exists and how it connects). This dual-graph approach means Glean doesn't just index documents; it understands the organizational context in which those documents are created, shared, and acted upon.

“Everyone is suddenly talking about context graphs. At Glean, we're excited—because it finally has a name.”
— Arvind Jain, CEO of Glean

Jain's comment reveals something important: Glean has been building context graph infrastructure for years, even before the term gained mainstream traction. Their enterprise search product was always, at its core, a context graph that connected people, documents, conversations, and organizational structure.

In early 2026, Glean shipped MCP servers for Cursor and Claude Code, allowing developers to query the Enterprise Graph directly from their coding environments. This is significant because it demonstrates the composability of context graphs—organizational knowledge flowing seamlessly into the tools where decisions are actually made.

Key event: “Context in Action” conference scheduled for February 17, 2026—a sign that Glean is positioning itself as the convener of the context graph movement.

AAtlan — Data & Analytics Governance Leader

Atlan was named a Leader in the 2026 Gartner Magic Quadrant for Data & Analytics Governance—a significant milestone that positions the company at the intersection of data governance and AI context. While many context graph players are startups or pure-play AI companies, Atlan brings enterprise data governance credibility that is critical for regulated industries.

Co-founder Prukalpa Sankar has emerged as one of the most thoughtful voices in the context graph debate. Her core argument is that context graphs are fundamentally a platform problem, not a point-solution problem. The reasoning: enterprise decisions don't happen in isolation. A single business decision might pull context from 6 to 10 or more systems—the CRM, the data warehouse, Slack conversations, email threads, internal wikis, previous decision records, and compliance policies.

This multi-system reality means that no single vertical tool can build a complete context graph. You need a platform that can federate context across heterogeneous systems—which is precisely what Atlan's metadata and governance platform is designed to do.

DDataHub — Context Management Pioneer

DataHub has made a strategic bet on defining and owning a new category: “context management.” Where traditional data management focuses on storing and governing structured data, context management is an organization-wide capability to deliver the right data to AI context windows at the right time.

The distinction matters. Context management isn't just about having a knowledge graph or a vector database. It's about the full pipeline: knowing what context exists across the organization, understanding its relevance to a given agent task, assembling it within token limits, and delivering it with appropriate provenance and governance metadata.

DataHub has shipped two key products to support this vision: the Agent Context Kit (a developer toolkit for building context-aware agents) and an MCP Server that exposes organizational metadata to any MCP-compatible AI tool.

Notable customers: Block, Apple, and Netflix—demonstrating that context management is already a requirement for the most sophisticated technology organizations.

SSquirro — Financial Services Context

Squirro has carved out a distinctive position in the context graph ecosystem by focusing on the financial services vertical. Their acquisition of Synaptica, a leading taxonomy and ontology management platform, signals a clear strategic direction: combining structured ontological knowledge with AI-driven context retrieval to serve the most demanding enterprise customers.

Their customer base speaks to the kind of organizations that require the deepest context infrastructure: the European Central Bank, the Bank of England, and Standard Chartered. These are institutions where decisions carry enormous regulatory weight and where AI agents need complete contextual provenance to operate—exactly the kind of environment where context graphs provide the most value.

Context Graph Startups & Innovators

While the enterprise platforms bring scale and existing customer relationships, the startup ecosystem is where the most radical innovation in context graph architecture is happening. These companies are rethinking the fundamentals of how context is captured, structured, and served to AI agents.

TTrustGraph — Open-Source Context Operating System

TrustGraph is building what they call the “Context Operating System for AI”—an ambitious, fully open-source framework released under the Apache 2.0 license. Their approach is distinctive in the ecosystem because it is ontology-driven: rather than building context graphs through brute-force ingestion of documents and data, TrustGraph uses formal ontologies to guide how knowledge is extracted, structured, and connected.

This ontological approach led TrustGraph to coin the term “OntologyRAG”—a retrieval-augmented generation pattern where the ontology itself serves as the organizing schema for what gets retrieved. Instead of naively chunking documents and performing vector similarity search, OntologyRAG uses the ontology to understand what types of entities and relationships are relevant to a given query, then traverses the graph to assemble precisely targeted context.

The open-source strategy is strategically important for the broader ecosystem. By releasing under Apache 2.0, TrustGraph is betting that context graph infrastructure will follow the same path as databases and operating systems—where open-source foundations become the standard that commercial products build on top of.

GGraphlit — The Context Layer for AI Agents

Founded by Kirk Marple, Graphlit positions itself as the “Context Layer for AI Agents”—a managed infrastructure platform that handles the full lifecycle of context: ingestion, extraction, graph construction, and retrieval. Where TrustGraph is open-source and ontology-first, Graphlit is a managed service and developer-experience-first.

“Memory is knowledge under the constraint of time and identity.”
— Kirk Marple, Founder of Graphlit

Marple's framing is philosophically rich and technically precise. Memory isn't just stored knowledge—it's knowledge filtered through time (when was it relevant?) and identity (relevant to whom?). This distinction drives Graphlit's product architecture, which separates into two complementary offerings:

●Zine — Human memory. A personal knowledge management tool that captures how individuals accumulate and organize contextual knowledge over time.
●Graphlit — Agent memory. The infrastructure layer that provides AI agents with persistent, queryable context across sessions and tasks.

The dual-product approach reflects a deeper insight: human context and agent context are different but complementary. Humans accumulate context through experience and narrative; agents accumulate context through structured traces and graph queries. Bridging these two forms of memory is one of the key unsolved problems in the context graph ecosystem.

ZZep / Graphiti — Temporal Knowledge Graphs

Zep has built one of the most technically impressive context graph implementations in the ecosystem with Graphiti, a temporal knowledge graph that has amassed over 45,000 GitHub stars—making it one of the most popular open-source projects in the AI infrastructure space.

What sets Graphiti apart is its bi-temporal data model. Most knowledge graphs capture the current state of the world. Graphiti captures two dimensions of time: when something was true in the real world (valid time) and when the system learned about it (transaction time). This bi-temporal approach is essential for enterprise AI because it enables agents to reason not just about what is true now, but about what was believed to be true at any point in the past.

In benchmarks, Graphiti has been shown to outperform MemGPT and similar memory-augmented approaches on tasks that require temporal reasoning and context retrieval. The advantage comes from having explicit temporal structure rather than relying on LLM-driven memory management.

The commercial traction validates the approach: enterprises need context infrastructure that understands time, because decisions always happen in a temporal context—what was the market doing when this pricing decision was made? What policy was in effect when this exception was granted?

WWayfound — Guardian AI Agent

Wayfound takes a fundamentally different approach to the context graph problem by focusing on company culture as context. Founded by former Salesforce and AWS leaders, Wayfound builds what they call a “Guardian AI Agent”—an agent that ensures other AI agents operate within the cultural and operational norms of the organization.

The Guardian Agent concept addresses one of the most challenging aspects of enterprise AI deployment: alignment. It's not enough for an AI agent to make technically correct decisions. Those decisions need to align with the company's values, communication style, risk tolerance, and operational norms. Wayfound's context graph encodes these cultural dimensions, creating a contextual guardrail that other agents operate within.

Their approach features supervised self-improvement—a feedback loop where the Guardian Agent learns from human corrections and gradually expands its understanding of organizational norms. This creates a continuously evolving context graph that reflects the living culture of the organization, not just a static policy document.

CCognition AI — Agent Trace Standard

Cognition AI, the company behind the Devin AI coding agent, has made a strategic move into context graph standards with Agent Trace—an open standard for code context graphs. Agent Trace captures the full context of how code is written, reviewed, deployed, and maintained, creating a queryable graph of software development decisions.

The standard has gained rapid adoption, with Cursor, Cloudflare, and Vercel among the early supporters. This coalition is significant because it spans the full stack of modern software development: the IDE (Cursor), the infrastructure (Cloudflare), and the deployment platform (Vercel).

Agent Trace demonstrates a key pattern in the context graph ecosystem: standards emerge from specific verticals. Code context graphs have different requirements than financial context graphs or healthcare context graphs. The question is whether these vertical standards will eventually converge into a universal context graph protocol.

The Great Debate: Who Owns Context?

Perhaps the most consequential strategic question in the context graph ecosystem is deceptively simple: who will own the context layer? The answer will determine which companies capture the majority of value in the AI infrastructure stack.

The Startup Thesis

Foundation Capital argues that vertical agent startups will own context. The logic: context is domain-specific. A legal context graph requires deep understanding of case law, regulatory frameworks, and legal reasoning patterns. A healthcare context graph requires knowledge of clinical protocols, drug interactions, and patient history structures. No horizontal platform can build this depth across every vertical.

✓Deep domain expertise
✓Industry-specific ontologies
✓Specialized agent workflows
✓Faster iteration cycles

The Platform Thesis

Atlan's Prukalpa Sankar argues that context is fundamentally a platform problem. Enterprise decisions pull from 6 to 10 or more systems. No single vertical agent can span the full breadth of organizational context needed for complex decisions. You need a connective platform that federates context across heterogeneous systems.

✓Cross-system context federation
✓Enterprise-grade governance
✓Unified metadata model
✓Network effects from breadth

The heterogeneity problem adds another dimension to this debate. In practice, enterprises don't use a single AI agent. They deploy dozens of agents from different vendors—a coding agent from one company, a customer support agent from another, a sales agent from a third. Each of these agents is currently building its own context silo, creating fragmented islands of organizational knowledge.

This fragmentation is the context graph equivalent of the data silo problem that plagued enterprise IT for decades. And just as data warehouses and data lakes emerged to consolidate fragmented data, a context layer will need to emerge to consolidate fragmented agent context.

The likely resolution is that both approaches will coexist. Horizontal context platforms will provide the connective tissue—the shared infrastructure for federating, governing, and routing context. Vertical agents will provide domain-specific depth—the specialized ontologies, reasoning patterns, and decision templates that make context actionable in specific industries. The winners will be the companies that best bridge these two layers.

Industry Skeptics

Not everyone is convinced that 2026 will be the year context graphs break through. A vocal contingent of industry observers warns that the space is plagued by fundamental challenges that hype alone cannot overcome.

“Context is very slippery.”

Some predict the industry will “squander the year” debating context graphs rather than building them. The concern is not unfounded. As of early 2026, there is:

✗No accepted standard for what a context graph actually is or how it should be implemented. Every company has its own definition, its own schema, its own approach.
✗No consistent implementation across vendors. A “context graph” from Glean looks fundamentally different from a “context graph” from TrustGraph or Zep.
✗Most companies aren't ready for semi-autonomous agents, let alone the sophisticated context infrastructure needed to make them work reliably in production.
✗The “context” concept is amorphous—it can mean anything from RAG pipelines to knowledge graphs to agent memory to decision traces, making it difficult to evaluate solutions or compare approaches.

The skeptics raise a legitimate point about market timing. The progression from concept to category to product typically takes years, not months. The data mesh movement, for example, generated enormous hype in 2021-2022 but took several more years to produce mature, production-ready implementations. Context graphs may follow a similar trajectory.

However, proponents counter that the context graph movement has a crucial accelerant that data mesh lacked: immediate, painful demand. Every organization deploying AI agents is hitting the context wall right now. They don't need to be convinced that context matters—they experience the absence of it in every failed agent interaction, every hallucinated response, every decision that ignores institutional precedent.

Key Trends for 2026

Despite the skeptics, several clear trends are emerging that will shape the context graph ecosystem through the rest of 2026 and beyond.

1Context Management as a New Category

DataHub's framing of “context management” as an organization-wide capability is gaining traction. Expect Gartner, Forrester, and other analyst firms to formally define a “context management” or “context infrastructure” category by late 2026. This will create a recognized market that enterprises can budget for and vendors can sell into—a critical step in the maturation of any technology category.

2Guardian Agents Rise

Gartner predicts that guardian agents could represent 15% of the AI agent market. The concept—AI agents that monitor, govern, and align other AI agents—is a natural evolution of the context graph ecosystem. Guardian agents need the richest context of all: not just operational data but cultural norms, risk tolerances, communication styles, and ethical boundaries. Wayfound is the early leader, but expect every major enterprise AI platform to add guardian capabilities.

3MCP as Universal Protocol

The Model Context Protocol (MCP) is rapidly becoming the de facto standard for how AI agents consume contextual information. Originally developed by Anthropic, MCP has been adopted across the ecosystem—Glean ships MCP servers, DataHub ships an MCP server, and dozens of smaller tools now expose their data via MCP. This standardization is critical because it decouples context producers from context consumers, enabling a composable ecosystem where any context source can serve any agent.

4Knowledge Graph-Powered Context Becomes Table Stakes

The era of naive RAG—chunk documents, embed them, do vector similarity search—is drawing to a close. Every serious context graph implementation now incorporates knowledge graph structures for entity resolution, relationship traversal, and structured reasoning. Vector search remains important for semantic similarity, but the graph structure provides the connective tissue that makes context truly useful for complex agent reasoning. Companies that are still relying on pure vector RAG are falling behind.

5Temporal Context as a Differentiator

Zep's success with Graphiti has validated that temporal context is not a nice-to-have—it's a fundamental requirement. Enterprise decisions always happen in time. Understanding not just what is true now but what was true when a decision was made, how knowledge evolved, and how context shifted over time is essential for audit trails, compliance, and agent learning. Expect bi-temporal and multi-temporal data models to become standard features in context graph platforms.

The bottom line: 2026 is shaping up to be the year that context graphs transition from concept to category. The infrastructure is being built, the standards are emerging, and the demand from AI agent deployments is creating urgent pull.

The companies and teams that invest in context infrastructure now will have a significant compound advantage as AI agents become the primary interface for enterprise operations.

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Frequently Asked Questions

What is a context graph and why is it trending in 2026?

A context graph is a structured representation of knowledge optimized for AI agent decision-making. It captures not just data and relationships but the full context—the “why” behind decisions, temporal evolution, and source provenance. It is trending in 2026 because Foundation Capital's December 2025 thesis on “Context Graphs: AI's Trillion-Dollar Opportunity” catalyzed widespread industry attention, and enterprise leaders like Dharmesh Shah (HubSpot) and Aaron Levie (Box) have publicly endorsed the concept.

Which companies are building context graph platforms in 2026?

Major enterprise players include Glean (Enterprise Graph combining personal and knowledge graphs), Atlan (2026 Gartner Magic Quadrant Leader for Data & Analytics Governance), DataHub (context management platform used by Block, Apple, and Netflix), and Squirro (acquired Synaptica, serves central banks). Key startups include TrustGraph (open-source Context Operating System), Graphlit (Context Layer for AI Agents), Zep/Graphiti (temporal knowledge graph with 45k+ GitHub stars), Wayfound (Guardian AI Agent), and Cognition AI (Agent Trace open standard).

What is the difference between context management and traditional data management?

Context management, a term coined by DataHub, is an organization-wide capability to deliver the right data to AI context windows at the right time. Unlike traditional data management which focuses on storing and governing structured data, context management focuses on assembling relevant context from multiple heterogeneous systems—documents, decision traces, metadata, relationships, and temporal information—so that AI agents can make informed, autonomous decisions.

Who owns the context layer — startups or platforms?

This is one of the great debates of 2026. Foundation Capital argues that vertical agent startups will own context because they can build deeply specialized context graphs for specific industries. Atlan's Prukalpa Sankar counters that context is a platform problem because enterprise decisions pull from 6 to 10 or more systems. The likely resolution is that both approaches will coexist, with horizontal platforms providing the connective tissue and vertical agents providing domain-specific depth.

What is MCP and how does it relate to context graphs?

MCP (Model Context Protocol) is a universal protocol originally developed by Anthropic that standardizes how AI models and agents consume contextual information. In 2026, MCP is emerging as the de facto standard for connecting context graph infrastructure to AI agents. Companies like Glean and DataHub have shipped MCP servers that allow tools like Cursor and Claude Code to directly query enterprise context graphs, making organizational knowledge accessible to AI agents across any interface.

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FUNDAMENTALS

The Context Graph Ecosystem in 2026