The Marketplace for
Context Graphs
Turn tribal knowledge into searchable precedent. Download enterprise-grade context graphs or contribute your own to the community.
Join 500+ early adopters shaping the future of AI memory.
What is a Context Graph?
A context graph is a living record of decision traces stitched across entities and time, so precedent becomes searchable and AI agents can learn from institutional memory.
Decision Traces
Captures the "why" behind every decision. Not just rules, but the specific context, exceptions, and reasoning that led to each outcome.
Tribal Knowledge
Exception logic that lives in people's heads. The unwritten rules, edge cases, and institutional memory that AI agents need to operate.
Searchable Precedent
Turn scattered decisions into reusable patterns. Every automated decision adds another trace to the graph, creating compound intelligence.
“Rules tell an agent what should happen in general. Decision traces capture what happened in this specific case.
AI's Trillion-Dollar Platform
While existing systems of record own canonical data, new platforms will own the reasoning behind decisions. This is the next trillion-dollar opportunity.
Systems of Record
Salesforce, Workday, SAP
Systems of Decision
Context graphs that capture the why
Siloed Data
Context lost in ETL
Agent Orchestration
Real-time decision lineage
Manual Workflows
High headcount, slow decisions
Autonomous Agents
Powered by context graphs
Why Incumbents Can't Build This
Exception Logic
Tribal knowledge stuck in people's heads, unavailable to AI systems.
Lost Precedent
Past decisions unlinked from current context, forcing repeated manual review.
Cross-System Gaps
Reasoning happening in Slack, Zoom, and email—never captured.
Approval Chains
Decision authority scattered outside formal systems.
Understanding Context Graphs
Essential terminology and concepts for navigating the world of context graphs, AI agent memory, and enterprise intelligence.
Related Concepts
Context Engineering
The practice of designing systems to capture and deliver the right context to AI agents.
Agentic AI
AI systems that can take autonomous actions based on reasoning and context.
World Models
Learned representations of how an environment works, enabling simulation and prediction.
Temporal Validity
The time period during which a fact or relationship is considered true.
MCP (Model Context Protocol)
An open standard for connecting AI agents to external data and tools.
RAG (Retrieval-Augmented Generation)
A technique that enhances AI responses by retrieving relevant context from external sources.
The Marketplace
Browse enterprise-grade context graphs from leading institutions or contribute your own to the community.
Enterprise Sales Qualification
by SalesOps Team
Code Review Decision Tree
by DevEx Community
Customer Escalation Handler
by CX Institute
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Frequently Asked Questions
Everything you need to know about context graphs, decision traces, and AI agent memory
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.
Learn more →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. A knowledge graph tells you "Customer X has a gold tier." A context graph tells you "Customer X was upgraded to gold tier because VP Sales approved an exception based on precedent from Q4."
Learn more →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.
Learn more →What is context engineering?
Context engineering is the natural evolution from prompt engineering. While prompt engineering focuses on crafting instructions for LLMs, context engineering is about curating the optimal set of tokens during inference. Anthropic defines it as: "Prompt engineering adjusts instructions; context engineering controls the evidence."
Learn more →What are the best open source context graph tools?
Leading tools in 2026 include TrustGraph (Apache 2.0, OntologyRAG), Graphiti by Zep (45k+ GitHub stars, bi-temporal knowledge graph), and Neo4j GraphRAG.
Learn more →What is a decision trace?
A decision trace captures not just what decision was made, but the specific context, reasoning, exceptions, and approvals that led to that outcome. Decision traces are the core data structure within a context graph.
Learn more →Build the Future of AI Memory
Context Graph is more than a marketplace—it's a community of pioneers shaping how AI agents learn from institutional knowledge.
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Upload your institutional context graphs to help others solve similar problems. Earn recognition and credits.
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Create connectors for popular AI agent frameworks. Help the ecosystem grow with your technical expertise.
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