Question 1

What is a context graph?

Accepted Answer

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."

Question 2

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

Accepted Answer

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."

Question 3

Why do AI agents need context graphs?

Accepted Answer

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.

Question 4

What is context engineering?

Accepted Answer

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.

Question 5

What are the best open source context graph tools?

Accepted Answer

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.

Question 6

What is a decision trace?

Accepted Answer

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.

Master Context Graphs

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What is a Context Graph?

The Context Graph Ecosystem in 2026

Context Engineering: The New Discipline for AI Agents

The Era of Context

How to Build a Context Graph

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Context Graph vs Knowledge Graph

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Tribal Knowledge

Institutional Intelligence

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Ontologies

Agent Trace

Open Source Context Graph Tools

Context Graph Glossary

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