AgentIndex icon
AgentIndex
ContextGraph logo
ContextGraph
★ 22
vs
pensyve logo
pensyve
★ 97

ContextGraph vs pensyve

ContextGraph: 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.; pensyve: Pensyve is a universal memory runtime for AI agents that eliminates amnesia across sessions. It offers three memory types (episodic, semantic, procedural), 8-signal fusion retrieval, and FSRS-based forgetting curve. Framework-agnostic with SDKs for Python, TypeScript, Go, and integrations with LangChain, CrewAI, AutoGen, plus native plugins for Claude Code, Cursor, and VS Code.

01

TL;DR

ContextGraph logo
ContextGraphRAG / Knowledge Base · ★ 22

Same-company agent follow with provenance and quorum building

Read ContextGraph full review →
pensyve logo
pensyveRAG / Knowledge Base · ★ 97

Persist agent knowledge across sessions and avoid repetitive questions

Read pensyve full review →
02

Side-by-Side Comparison

Field
ContextGraph logoContextGraph
pensyve logopensyve
Category
RAG / Knowledge Base
RAG / Knowledge Base
Stars
★ 22
★ 97
License
MIT
NOASSERTION
Install Command
git clone https://github.com/AllenMaxi/ContextGraph.git && cd ContextGraph && python3 -m venv .venv && source .venv/bin/activate && pip install -e ".[server,mcp,dev]"
pip install pensyve
Updated
6mo ago
1d ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
agent-memory, agentic-rag, ai-agents
agent-memory, ai, ai-agents
03

Features

ContextGraph logoContextGraph
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
pensyve logopensyve
01Three memory types: episodic, semantic, procedural
028-signal fusion retrieval (vector, BM25, graph, intent, recency, frequency, confidence, type boost)
03FSRS forgetting curve with reinforcement for natural decay
04Offline-first with SQLite and ONNX embeddings, no cloud required
05Framework-agnostic with SDKs for Python, TypeScript, Go, and MCP
04

Use Cases

ContextGraph logoContextGraph
↳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
pensyve logopensyve
↳Persist agent knowledge across sessions and avoid repetitive questions
↳Integrate memory into any AI framework (LangChain, CrewAI, AutoGen) via adapters
↳Enable agents to learn effective strategies through procedural memory
FAQ

FAQ

What is the difference between ContextGraph and pensyve?
Both ContextGraph and pensyve are in the RAG / Knowledge Base category. ContextGraph has 22 stars, while pensyve has 97 stars.
Which is better, ContextGraph or pensyve?
The best choice depends on your use case. Choose ContextGraph if Same-company agent follow with provenance and quorum building, and pensyve if Persist agent knowledge across sessions and avoid repetitive questions.
Is ContextGraph free or open source?
Yes, ContextGraph is open source on GitHub (MIT).
Is pensyve free or open source?
Yes, pensyve is open source on GitHub (NOASSERTION).
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