Context Graph for AI Native SDLC
High-performance open-source in-memory graph database for GraphRAG, AI memory, agentic AI, and real-time graph analytics. Cypher-compatible, built in C++.
The context orchestration layer powered by hypergraphs. Build a unified semantic context layer where agentic outcomes are deterministic and agent behavior is not just traceable, but cryptographically verifiable.
Lakehouse native graph engine with git-style workflows
AI agents with graph based reasoning memory, scaffolded in seconds
A graph-native memory system for AI agents and context graphs. Store conversations, build knowledge graphs, and let your agents learn from their own reasoning — all backed by Neo4j.
Transforms Databricks Unity Catalog tables into a materialized knowledge graph, with ontology design and reasoning exposed as tools via MCP.
Meteor extracts structured context from across your systems and delivers it to power your organization's context graph and AI agents.
A context graph which is based on confidence-aware belief that drives more determinstic reasoning in long-horizon agents.
Deterministic memory layer for your agents
Compiles AI agent traces and truns them into reusable context.
Compass is a context engine that builds a knowledge graph of your organization's metadata, capturing entities, relationships, and lineage across systems and time, making it discoverable and queryable for both humans and AI agents.
An open-source Python SDK for analyzing, evaluating, and curating agent traces stored in BigQuery. Built on top of the BigQuery Agent Analytics, it provides a consumption-layer toolkit for agent observability, analysis, evaluation, and advanced capabilities like context graph at scale.
This is the code for the paper 'RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network'.
privacy-first context graph engine for AI agents and human teams.
Graph native short-term, long-term, and reasoning memory to make your claw more powerful and efficient powered by neo4j-agent-memory
A pure-Python structured memory benchmark for multi-agent LLM systems — context graph vs vector RAG vs raw history dump, five scenarios, 18 graded queries, zero API calls.
AI-native financial intelligence platform for accounting, financial reporting, and investment management. Models your ledger, reports, and portfolios as a knowledge graph that AI agents can query and operate. Fork-ready: GitHub Actions CI/CD deploys the CloudFormation infrastructure to your own AWS account.
A coding agent that builds it's own tools and mines it's own skills and always ships with receipts.
Lightweight graph engine for AI graph context, memory, and agent harness with Rust Core.
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