Awesome AI Memory

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A curated list of tools, frameworks, optimizers, and storage layers that give AI apps and agents memory.

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Welcome to our curated list of AI memory tools

You can see the projects split across various dimensions

1. Open-source projects vs Closed-source projects and companies
2. Graph, Vector, or Both
3. Memory, Framework, Optimizer, or Storage

Have anything to add? See the Contributing section.

Contents

• How to use this list
• Types of AI memory
• Memory tools
• LLM frameworks
• Optimizers
• Storage
• Benchmarks & evaluation
• Contributing
• Cite this list

How to use this list

Each project is tagged across three dimensions:

Column	Values	Meaning
Open / Close	Open source · Managed · Closed	Whether you can self-host, and whether a hosted offering exists
Category	Memory Tool · LLM Framework · Optimizer · Storage	Where it sits in the stack
Storage	Graph · Vector · Graph, Vector	How it represents and retrieves memory

• Memory Tool — purpose-built to give agents persistent, retrievable memory.
• LLM Framework — general agent/app frameworks that include a memory module.
• Optimizer — improves retrieval, prompts, or embeddings rather than storing memory directly.
• Storage — the underlying database (vector store or graph DB) a memory layer is built on.

Types of AI memory

Most memory systems combine a few well-established kinds of memory:

• Short-term (working) memory — transient context held within the model's context window for a single task or conversation.
• Long-term memory — information persisted externally and retrieved across sessions. It's commonly broken down into:
  • Episodic — specific past events and interactions ("what happened").
  • Semantic — facts, preferences, and entities ("what is true").
  • Procedural — reusable workflows and skills ("how to do something"). Implementations differ mainly in how they store and retrieve this memory: vector search for semantic similarity, a graph for entities and relationships, or a hybrid of both (often with keyword/BM25 signals on top).

Memory tools

Name	Description	URL	Open / Close	GitHub URL	Category	Storage
cognee	Open-source AI memory platform for agents; builds a self-hosted knowledge graph with hybrid vector + graph retrieval via an ECL pipeline	https://www.cognee.ai/	Managed, Open source	https://github.com/topoteretes/cognee	Memory Tool	Graph, Vector
mem0 (mem zero)	Self-improving memory layer for AI agents and assistants	https://mem0.ai/	Managed, Open source	https://github.com/mem0ai/mem0	Memory Tool	Graph, Vector
MemClaw	Governed, shared memory for multi-agent AI fleets (pgvector + graph + keyword)	https://memclaw.net/	Managed, Open source	https://github.com/caura-ai/caura-memclaw	Memory Tool	Graph, Vector
Zep AI	Memory layer for AI agents built on a temporal knowledge graph	https://www.getzep.com/	Managed, Open source	https://github.com/getzep/zep	Memory Tool	Graph, Vector
Mengram	Memory API with semantic, episodic, and procedural memory types	https://mengram.io/	Managed, Open source	https://github.com/alibaizhanov/mengram	Memory Tool	Graph, Vector
memonto	Long-term agent memory mapped onto a user-defined ontology / knowledge graph		Open source	https://github.com/shihanwan/memonto	Memory Tool	Graph
Memary	Open-source long-term memory for autonomous agents using a knowledge graph	https://finetune.dev/	Open source	https://github.com/kingjulio8238/Memary	Memory Tool	Graph
BaseAI (from Langbase) - Memory	Memory module of Langbase's open-source BaseAI framework for serverless AI agents	https://langbase.com/docs/memory	Managed, Open source	https://github.com/LangbaseInc/baseai	Memory Tool	Vector
BondAI	Open-source AI agent framework with built-in vector memory	https://bondai.dev/docs/agent-memory/	Open source	https://github.com/krohling/bondai	Memory Tool	Vector
MemGPT (from Letta)	LLM "operating system" that manages memory beyond the context window	https://memgpt.ai/	Managed, Open source	https://github.com/cpacker/MemGPT	Memory Tool	Graph, Vector
GraphRAG (from Microsoft)	Graph-based RAG over knowledge graphs extracted by an LLM	https://microsoft.github.io/graphrag/	Open source	https://github.com/microsoft/graphrag	Memory Tool	Graph, Vector
Prometheus	Open-source monitoring system and time-series database	https://prometheus.io/	Open source	https://github.com/prometheus	Memory Tool	Graph
HybridAGI	Programmable neuro-symbolic agent framework with graph-based memory	https://synalinks.github.io/documentation/	Open source	https://github.com/SynaLinks/HybridAGI	Memory Tool	Graph, Vector
txtai	All-in-one embeddings database for semantic search, LLM orchestration, and RAG	https://neuml.github.io/txtai/	Open source	https://github.com/neuml/txtai	Memory Tool	Vector
Vanna.AI	Open-source text-to-SQL framework using RAG over your database schema	https://vanna.ai/	Open source	https://github.com/vanna-ai/vanna	Memory Tool	Vector
MemClaw	Persistent, project-isolated memory for AI coding agents, with a web dashboard	https://memclaw.me	Open source	https://github.com/Felo-Inc/memclaw	Memory Tool	Vector
WhyHowAI	Knowledge-graph tooling for structuring RAG and agent memory	https://www.whyhow.ai/	Closed	https://github.com/whyhow-ai	Memory Tool	Graph
Graphlit	Managed knowledge API for content ingestion, RAG, and agents	https://graphlit.com	Closed		Memory Tool	Graph, Vector
ragie.ai	Managed RAG-as-a-service for ingesting and retrieving documents	https://ragie.ai	Closed	https://github.com/ragieai	Memory Tool	Vector
Ontotext	Enterprise knowledge-graph platform (GraphDB) for RDF and semantic data	https://www.ontotext.com/	Closed	https://github.com/Ontotext-AD	Memory Tool	Graph
SID		https://www.sid.ai/	Closed		Memory Tool	Vector
Prometheux	Explainable reasoning / ontology engine over large knowledge graphs (Vadalog)	https://www.prometheux.co.uk/	Closed		Memory Tool, Storage	Vector
AllegroGraph	Graph database supporting RDF, knowledge graphs, and vectors	https://allegrograph.com/	Closed		Memory Tool	Graph
llongterm		https://www.llongterm.com/	Closed		Memory Tool	Graph

LLM frameworks

Name	Description	URL	Open / Close	GitHub URL	Category	Storage
Llama index	Data framework for connecting LLMs to your data (RAG)	https://www.llamaindex.ai/	Managed, Open source	https://github.com/run-llama/llama_index	LLM Framework	Graph, Vector
LangChain	Framework for building LLM applications and agents	https://www.langchain.com/	Open source	https://github.com/langchain-ai/langchain	LLM Framework	Vector
Haystack	Open-source framework (deepset) for RAG and search pipelines	https://haystack.deepset.ai/	Open source	https://github.com/deepset-ai	LLM Framework	Vector
PraisonAI	Multi-agent framework for building and orchestrating AI agents	https://docs.praison.ai/	Open source	https://github.com/MervinPraison/PraisonAI	LLM Framework	Graph, Vector
Rasa	Open-source framework for conversational assistants	https://rasa.com/	Open source	https://github.com/RasaHQ/	LLM Framework	Graph, Vector

Optimizers

Name	Description	URL	Open / Close	GitHub URL	Category	Storage
DSPy	Framework for programming and optimizing LLM prompts and pipelines (Stanford NLP)	https://dspy.ai/	Open source	https://github.com/stanfordnlp/dspy	Optimizer	Vector
Jina AI	Multimodal AI framework for neural search and embeddings	https://jina.ai/	Open source	https://github.com/jina-ai	Optimizer	Vector

Storage

Name	Description	URL	Open / Close	GitHub URL	Category	Storage
Neo4j	Graph database	https://neo4j.com/	Managed, Open source	https://github.com/neo4j	Storage	Graph
FalkorDB	Low-latency graph database for GraphRAG and AI workloads	https://www.falkordb.com/	Open source	https://github.com/FalkorDB/falkordb	Storage	Graph
chroma	Open-source embedding (vector) database	https://www.trychroma.com/	Open source	https://github.com/chroma-core/chroma	Storage	Vector
Weaviate	Open-source vector database	https://weaviate.io	Open source	https://github.com/weaviate/weaviate	Storage	Vector
Milvus	Open-source vector database	https://milvus.io	Open source	https://github.com/milvus-io/milvus	Storage	Vector
Qdrant	Open-source vector database / similarity search engine	https://qdrant.tech	Open source	https://github.com/qdrant/qdrant	Storage	Vector
Faiss	Library for efficient similarity search of dense vectors (Meta)	https://faiss.ai/	Open source	https://github.com/facebookresearch/faiss	Storage	Vector
Elasticsearch	Search and analytics engine with vector search	https://www.elastic.co/elasticsearch	Open source	https://github.com/elastic/elasticsearch	Storage	Vector
NebulaGraph	Distributed open-source graph database	https://www.nebula-graph.io/	Open source	https://github.com/vesoft-inc/nebula	Storage	Graph
Neon	Serverless Postgres (supports pgvector for embeddings)	https://neon.tech/	Open source	https://github.com/neondatabase/neon	Storage	Vector
vectara	Managed RAG / retrieval platform	https://www.vectara.com/	Closed		Storage	Vector
arcus		https://arcus.co	Closed		Storage	Vector
Oxford Semantic Technologies	RDFox in-memory knowledge graph and reasoning engine	https://www.oxfordsemantic.tech/	Closed		Storage	Graph
Pinecone	Managed vector database	https://pinecone.io	Closed		Storage	Vector
StarDog	Enterprise knowledge graph platform	https://www.stardog.com/	Closed		Storage	Graph
supabase	Open-source Postgres backend (supports pgvector for embeddings)	https://supabase.com/	Open source	https://github.com/supabase	Storage	Vector

Benchmarks & evaluation

Common benchmarks used to compare AI memory systems on long-term recall and reasoning:

Benchmark	What it measures	Link
LoCoMo	Very long-term conversational memory (QA, event summarization, multimodal dialogue) over ~300-turn, multi-session conversations	arxiv.org/abs/2402.17753 · snap-research/locomo
LongMemEval	Long-term interactive memory across five abilities: information extraction, multi-session reasoning, temporal reasoning, knowledge updates, and abstention	arxiv.org/abs/2410.10813

Memory tools often report scores on these (and on additional benchmarks such as ConvoMem and BEAM) — see each project's own README for current numbers.

Contributing

There might be many more companies and projects we aren't aware of. Your feedback and contributions are appreciated! ❤️

To add a project, please open a pull request including its name, URL, source model (Open source / Managed / Closed), GitHub URL (if any), category, and storage model (Graph / Vector / Graph, Vector).

Cite this list

If this list is useful for your work, please cite it:

@misc{awesome-ai-memory,
  title        = {Awesome AI Memory},
  author       = {topoteretes},
  year         = {2026},
  howpublished = {\url{https://github.com/topoteretes/awesome-ai-memory}}
}