ContextGraph
Active·★ 21·MIT·Updated 2026-04-18
★ Trending★ RAG / Knowledge Base★ Memory & Context
Shared memory bus for MCP-compatible agents with permissions, subscriptions, and optional payments.
ContextGraph is the knowledge layer in the AI agent infrastructure stack. It turns raw agent memories into searchable, governed, tradeable knowledge. It provides a shared memory bus with provenance, impact classification, and multi-protocol support including MCP, A2A, UCP, and real-time streaming.
#agent-memory#agentic-rag#ai-agents#knowledge-graph#mcp#mcp-server#mcp-servers#model-context-protocol
01
Features
01Provenance Chains with immutable audit trails
02Impact Classification & Quorum Consensus for high-impact claims
03Pattern Subscriptions for graph-native knowledge monitoring
04GitHub-like Dashboard with agent profiles and knowledge browser
05Multi-protocol support (MCP, A2A, UCP) and real-time SSE streaming
02
Compatibility
Python
Python 3.11+
Verified via docs
Neo4j
Backend DB
Verified via docs
Docker
Container Deploy
Verified via docs
03
Quick start
1
$ git clone https://github.com/AllenMaxi/ContextGraph.git
2
$ cd ContextGraph
3
$ python3 -m venv .venv
4
$ source .venv/bin/activate
5
$ pip install -e ".[server,mcp,dev]"
04
Use cases
↳Same-company agent follow with provenance and quorum building
↳Cross-company paid knowledge marketplace with impact-based pricing
↳Entity-aware pattern subscriptions for targeted alerts
05
Alternatives
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Comments
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- Drew LeeMar 22, 2026
Shared memory bus with permissions and subscriptions for multi-agent systems.
- JJesse BrownMar 21, 2026
MCP-compatible design means it works with existing agent frameworks.
- HHarley GarciaMar 14, 2026
Good for multi-agent setups where shared context needs access control.
- JJordan ZhangMar 9, 2026
Subscription model means agents get relevant updates without polling.