memoripy vs TencentDB-Agent-Memory
Local agent memory and shared team memory compared
memoripy provides local, evidence-based memory with versioned records, scoped recall, and explanations for why results appear. TencentDB-Agent-Memory is a self-hostable hub for turning conversations, documents, and code into reusable assets shared across agents; its focus is team collaboration rather than an individual agent’s auditable memory store.
memoripy: Evidence-Tracked Memory for AI Agents
Memoripy is a local Python memory runtime for AI agents that screens incoming evidence, tracks changing facts over time, and explains recall with citations. It suits teams that need auditable, scoped memory rather than a simple vector store.
TencentDB-Agent-Memory: Share Reusable Memory Across AI Agents
TencentDB Agent Memory is a team memory hub that turns conversations, documents, and code into reusable assets for AI agents. It suits teams that want agents to share curated context and skills across sessions and frameworks.
| memoripy | TencentDB-Agent-Memory | |
|---|---|---|
| Language | Python | TypeScript |
| License | Apache-2.0 | NOASSERTION |
| Stars | 694 | 27.7k |
| Forks | 59 | 2.7k |
| Last analyzed | Oct 9, 2026 | Oct 3, 2026 |
Key differences
- memoripy emphasizes evidence, temporal validity, admission policies, and recall receipts; TencentDB-Agent-Memory emphasizes shared Chat Memory, reusable Skills, Wiki content, and CodeGraph.
- memoripy is a Python library and runtime with optional service and gateway tooling; TencentDB-Agent-Memory is a TypeScript hub and proxy for agent teams.
- memoripy scopes records across identities and contexts such as users, agents, runs, and projects; TencentDB-Agent-Memory manages asset owners, visibility, and user, role, and agent access controls.
- memoripy is Apache-2.0 licensed; TencentDB-Agent-Memory reports NOASSERTION on GitHub while its README displays an MIT badge, so its terms should be verified.
- memoripy’s core works without a required third-party model provider, though broader extraction needs a supplied model or extractor; TencentDB-Agent-Memory deployment requires configuring LLM parameters for its memory and proxy groups.
- The supplied project data lists 695 stars for memoripy and 27.7k for TencentDB-Agent-Memory.
Choose memoripy if you…
- need sourced, versioned records and explanations for recalled results.
- want control over memory scope, admission rules, and audit history.
- are building a Python agent and prefer a local core that does not require a third-party model provider.
Choose TencentDB-Agent-Memory if you…
- want a team hub to share curated context and skills across agents.
- need to manage asset ownership, visibility, and agent access centrally.
- want to import repositories, documents, or conversation sessions to seed shared project memory.
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.