8 Best TencentDB-Agent-Memory Alternatives in 2026 (Open Source)

TencentDB-Agent-Memory — Shared memory server that turns conversations, documents, and code into reusable memory assets for AI agents. 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.

By package downloads Cognee is the most used here (87.9K in the last 30 days), even though Mem0 has the most GitHub stars. See all agent tools by downloads.

1. 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

   ⭐ 66.5kTencentDB-Agent-Memory vs Mem0 →Full profile →

2. 2. MemOS

   Memory OS for LLMs and AI agents with graph-structured, multimodal storage and retrieval

   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

   ⭐ 11.7kTencentDB-Agent-Memory vs MemOS →Full profile →

3. 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

   ⭐ 2.7kTencentDB-Agent-Memory vs Memary →Full profile →

4. 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

   ⭐ 31.3kTencentDB-Agent-Memory vs Cognee →Full profile →

5. 5. Memori

   LLM-agnostic memory infrastructure that turns agent execution and conversations into persistent state

   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

   ⭐ 17.1kTencentDB-Agent-Memory vs Memori →Full profile →

6. 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

   ⭐ 31.1kTencentDB-Agent-Memory vs Supermemory →Full profile →

7. 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

   ⭐ 13.3kTencentDB-Agent-Memory vs EverOS →Full profile →

8. 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

   ⭐ 16.6kTencentDB-Agent-Memory vs memvid →Full profile →