AltHub

memvid/memvid Alternatives

AI agent memory / retrieval-augmented generation

Memvid is a single-file, serverless memory layer for AI agents that embeds retrieval and long-term memory without databases, replacing complex RAG pipelines.

16.4kRustApache-2.0active · last push 3mo agoGitHub

Same job, different approach

Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.

deepset-ai/haystackGitHub

Choose when you need a full AI orchestration framework with composable pipelines and many integrations, not a single-file memory layer.

Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.

26.3k
Python
Apache-2.0
active2mo ago
llmware-ai/llmwareGitHub

Choose when you want an enterprise-grade RAG framework with small specialized models and unified tooling.

Unified framework for building enterprise RAG pipelines with small, specialized models

14.8k
Python
Apache-2.0
active5mo ago
neuml/txtaiGitHub

Choose when you need an all-in-one AI framework with semantic search and workflow orchestration, not a file-based memory.

💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows

12.9k
Python
Apache-2.0
active2mo ago
StarTrail-org/LEANNGitHub

Choose when you want a highly storage-efficient, private on-device RAG system instead of a simple embedded memory layer.

[MLsys2026]: RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.

12.8k
Python
MIT
active2mo ago
HKUDS/LightRAGGitHub

Choose when you want a simple, fast, and easy-to-integrate RAG implementation with academic backing.

[EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation

39.1k
Python
MIT
active2mo ago
microsoft/graphragGitHub

Choose when you need graph-based RAG for entity-centric knowledge discovery with global query features.

A modular graph-based Retrieval-Augmented Generation (RAG) system

35.6k
Python
MIT
active2mo ago
HKUDS/RAG-AnythingGitHub

Choose when you want an all-in-one RAG framework covering multiple retrieval strategies and document types.

"RAG-Anything: All-in-One RAG Framework"

23.0k
Python
MIT
active2mo ago
OpenBMB/UltraRAGGitHub

Choose when you want a low-code MCP-based framework to build complex RAG pipelines quickly.

A Low-Code MCP Framework for Building Complex and Innovative RAG Pipelines

5.7k
Python
Apache-2.0
active2mo ago
neo4j/neo4j-graphrag-pythonGitHub

Choose when you're already using Neo4j and want a graph-native RAG integration with its knowledge graph engine.

Neo4j GraphRAG for Python

1.3k
Python
NOASSERTION
active2mo ago
VectifyAI/PageIndexGitHub

Choose when you want a vectorless, reasoning-based RAG approach that avoids embeddings and vector stores.

📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG

35.3k
Python
MIT
active2mo ago
SciPhi-AI/R2RGitHub

Choose when you want a production-ready retrieval system with a REST API and agentic features instead of an embedded file.

SoTA production-ready AI retrieval system. Agentic Retrieval-Augmented Generation (RAG) with a RESTful API.

8.0k
Python
MIT
slowing11mo ago
airweave-ai/airweaveGitHub

Choose when you want a context retrieval layer for AI agents with a different deployment model and more flexibility than a single file.

Open-source context retrieval layer for AI agents

6.6k
Python
MIT
active4mo ago
mem0ai/mem0GitHub

Choose when you want a widely adopted memory layer with multiple storage backends and a larger ecosystem, unlike the single-file approach.

Universal memory layer for AI Agents

63.8k
Python
Apache-2.0
active2mo ago

Older generation, or narrower

An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.

Future-House/paper-qaGitHub

Choose when you need accurate, citation-grounded answers from scientific documents specifically, not a general memory layer.

High accuracy RAG for answering questions from scientific documents with citations

9.1k
Python
Apache-2.0
active2mo ago
memodb-io/memobaseGitHub

Choose when you need user-profile-based long-term memory for chatbot applications, which is narrower than a general memory layer.

User Profile-Based Long-Term Memory for AI Chatbot Applications.

2.9k
Python
Apache-2.0
slowing9mo ago

End-user tools

Does the same job, but ships as an app. Useful to a person, not swappable into a codebase.

infiniflow/ragflowGitHub

Choose when you want a full-featured RAG engine with a web UI for document management and human-in-the-loop workflows.

RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs

89.1k
Go
Apache-2.0
active2mo ago
zylon-ai/private-gptGitHub

Choose when you want a self-hosted API layer for private AI apps with RAG and local models, not an embedded library.

Complete API layer for private AI applications on local models: RAG, skills, tools, MCP, text-to-sql, and more. Works with any OpenAI-compatible inference server.

57.5k
Python
Apache-2.0
active2mo ago
Tencent/WeKnoraGitHub

Choose when you want an open-source knowledge platform with RAG, agents, and a wiki UI for teams.

Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.

20.4k
Go
NOASSERTION
active2mo ago

Listed as an alternative to

These projects were analysed and named memvid among their alternatives. The relationship is not symmetric — how memvid rates them is a separate judgement, made when memvid is analysed in its own right.

supermemoryai/supermemoryGitHub

calls memvid “Use it if you want a Rust serverless single-file memory layer that avoids a separate RAG stack.”

Memory and context engine + app that is extremely fast, scalable, and can be run fully locally. The Memory API for the AI era.

29.1kTypeScriptactive
TencentCloud/TencentDB-Agent-MemoryGitHub

calls memvid “if you need a single-file Rust memory layer that runs serverless with no external database.”

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, LLM-Wiki, Code-Graph) that are governed, shared, and equipped across agents and frameworks.

23.8kTypeScriptactive
MemoriLabs/MemoriGitHub

calls memvid “Choose if you want a Rust-based, serverless single-file memory layer for low operational overhead instead of an enterprise infrastructure.”

Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production systems. Built for enterprise, Memori works with the data infrastructure you already run, no rip-and-replace, and deploys across managed cloud, single-tenant cloud, VPC, and on-premises.

16.2kPythonactive
mem0ai/mem0GitHub

calls memvid “Choose when you want a serverless, single-file memory layer in Rust instead of a Python SDK.”

Universal memory layer for AI Agents

63.8kPythonactive
pathwaycom/llm-appGitHub

calls memvid “when you want a memory layer to replace RAG pipelines”

Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.

59.0kJupyter Notebookactive
MemPalace/mempalaceGitHub

calls memvid “Use when you want a Rust single-file memory layer with no separate database instead of a Python library with pluggable backends.”

The best-benchmarked open-source AI memory system. And it's free.

58.6kPythonactive
HKUDS/LightRAGGitHub

calls memvid “Choose when you prefer a lightweight memory layer to replace full RAG for agent retrieval and long-term memory.”

[EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation

39.1kPythonactive
microsoft/graphragGitHub

calls memvid “Choose it when you want a serverless memory layer for agents instead of an explicit graph-RAG index.”

A modular graph-based Retrieval-Augmented Generation (RAG) system

35.6kPythonactive
VectifyAI/PageIndexGitHub

calls memvid “Use when you want a serverless, single-file memory layer for agents that replaces RAG pipelines; it's a different abstraction than PageIndex's document index.”

📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG

35.3kPythonactive
volcengine/OpenVikingGitHub

calls memvid “choose for a serverless, Rust-based single-file memory layer that replaces RAG pipelines.”

Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills.

34.5kPythonactive
topoteretes/cogneeGitHub

calls memvid “Choose it for a serverless, Rust-based memory layer to replace RAG, if you want a single-file deployment without an external graph DB.”

Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory across sessions with a self-hosted knowledge graph engine.

30.2kPythonactive
rohitg00/agentmemoryGitHub

calls memvid “Use if you want a Rust, serverless single-file memory layer that avoids complex RAG pipelines.”

#1 Persistent memory for AI coding agents based on real-world benchmarks

27.3kTypeScriptactive
HKUDS/RAG-AnythingGitHub

calls memvid “Serverless single-file memory layer; choose when you want to replace RAG pipelines with a lightweight retrieval memory.”

"RAG-Anything: All-in-One RAG Framework"

23.0kPythonactive
vectorize-io/hindsightGitHub

calls memvid “Choose if you want a lightweight Rust serverless, single-file memory layer with instant retrieval instead of Hindsight's Python server stack.”

Hindsight: Agent Memory That Learns

21.0kPythonactive
EverMind-AI/EverOSGitHub

calls memvid “choose it when you prefer a single-file Rust serverless memory layer over a Python local stack.”

One portable memory layer for every AI agent: local-first, Markdown-native, user-owned, and self-evolving across apps, tools, and workflows.

12.4kPythonactive
MemTensor/MemOSGitHub

calls memvid “Lightweight Rust serverless memory layer when you don't need the full graph/multi-modal feature set.”

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 Harness support.

10.9kTypeScriptactive
SciPhi-AI/R2RGitHub

calls memvid “Choose when you need a memory layer for agents instead of traditional RAG.”

SoTA production-ready AI retrieval system. Agentic Retrieval-Augmented Generation (RAG) with a RESTful API.

8.0kPythonslowing
plastic-labs/honchoGitHub

calls memvid “Choose when you need a lightweight Rust/WASM serverless memory layer instead of a Python FastAPI service.”

Memory library for building stateful agents

6.8kPythonactive
airweave-ai/airweaveGitHub

calls memvid “Choose it when you want embedded single-file agent memory instead of running connector sync and a search service.”

Open-source context retrieval layer for AI agents

6.6kPythonactive
neuml/txtaiGitHub

calls memvid “Choose when you only need a lightweight serverless memory layer for agents instead of a full RAG framework.”

💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows

12.9kPythonactive