8 Best TencentDB-Agent-Memory Alternatives in 2026 (Open Source)
TencentDB-Agent-Memory — TencentDB Agent Memory is a team-level memory hub for AI Agents — turning conversations, docs, and code into four reusable memory assets (Chat Memory, Skill, LL. Provides a team-level memory hub that transforms diverse inputs into four reusable asset types shared across multiple agent frameworks without code changes.
These 8 open-source tools do the same job. They are ordered by how closely they match TencentDB-Agent-Memory, with live GitHub data so you can see which projects are actively maintained.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| TencentDB-Agent-Memory(original) | 27.6k | +2,299 | 2026-09-29 |
| Mem0 | 66.4k | +2,429 | 2026-09-25 |
| MemOS | 11.7k | +972 | 2026-09-22 |
| Memary | 2.7k | +12 | 2024-10-18 |
| Cognee | 31.2k | +2,656 | 2026-09-29 |
| Memori | 17.0k | +1,419 | 2026-09-18 |
| Supermemory | 31.0k | +2,587 | 2026-09-30 |
| EverOS | 13.3k | +1,109 | 2026-09-30 |
| memvid | 16.6k | +1,381 | 2026-07-14 |
1. Mem0
Universal memory layer for AI Agents
What sets it apart: Unlike Zep (session-focused memory) or ChatGPT's built-in memory (closed, limited), Mem0 provides a standalone, open-source memory layer with proven +26% accuracy gains over OpenAI Memory, multi-level (user/session/agent) state management, and 90% token reduction via intelligent memory retrieval.
Best for: AI assistant developers who need persistent, personalized memory across conversations without building custom infrastructure; Customer support chatbots that need to recall past tickets and user preferences
2. MemOS
Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse, with 35.24% token savings and DeepSeek Harne
What sets it apart: Provides a unified memory operating system with graph-structured memory that's inspectable and editable, not just a black-box embedding store.
Best for: AI agents needing long-term memory; multi-agent collaboration systems; developers building context-aware agents
3. Memary
The Open Source Memory Layer For Autonomous Agents
What sets it apart: vs LangChain Memory / Mem0: graph-database-backed memory system emulating human memory (breadth + depth tracking) — agents automatically build and query knowledge graphs rather than flat conversation history
Best for: Building persistent, context-aware AI agents with evolving memory; User preference tracking and personalization across sessions; Multi-user agent management with separate knowledge contexts
4. Cognee
Knowledge Engine for AI Agent Memory in 6 lines of code
What sets it apart: Unlike Mem0 (conversation memory) or Chroma (pure vector search), Cognee builds an evolving knowledge graph from documents, combining vector + graph search with cognitive science approaches, ontology grounding, and cross-agent knowledge sharing — making it AI memory infrastructure rather than just a vector database.
Best for: AI agent developers who need persistent, learning memory that combines vector search with knowledge graph relationships; Enterprise use cases requiring tenant isolation, audit trails, and cross-agent knowledge sharing
5. Memori
Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production
What sets it apart: Provides agent-native memory infrastructure that works with existing data systems without requiring rip-and-replace.
Best for: enterprise agent systems; production AI applications; long-conversation memory management
6. Supermemory
Memory and context engine + app that is extremely fast, scalable, and can be run fully locally. The Memory API for the AI era.
What sets it apart: Claims #1 performance on three major AI memory benchmarks with 95% recall and 99.4% context reduction.
Best for: Adding persistent memory to AI agents; Building AI products with memory capabilities; Running memory systems locally
7. EverOS
One portable memory layer for every AI agent: local-first, Markdown-native, user-owned, and self-evolving across apps, tools, and workflows.
What sets it apart: Uses readable Markdown files as the canonical source of truth for agent memory instead of API-only or database-centric approaches.
Best for: Building agents with persistent memory; Local-first agent development; Markdown-based knowledge management
8. memvid
Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.
What sets it apart: Packages complete memory system into a single portable file without requiring databases or complex infrastructure.
Best for: Long-running AI agents; Offline-first AI systems; Auditable AI workflows
FAQ
- What are the best alternatives to TencentDB-Agent-Memory?
- The closest open-source alternatives to TencentDB-Agent-Memory are Mem0, MemOS and Memary, followed by Cognee, Memori and Supermemory. They are ranked by how closely they match what TencentDB-Agent-Memory does.
- Which TencentDB-Agent-Memory alternative is the most popular?
- Mem0 has the most GitHub stars among TencentDB-Agent-Memory alternatives, with 66,380 stars.
- Which TencentDB-Agent-Memory alternative is the most actively maintained?
- By recent activity, Cognee (2,437 commits in the last 90 days) is the most actively developed alternative.