Memory and retrieval tools for agents
Store, retrieve, and rank durable context for agents across sessions.
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32 agent-ready tools mapped to this capability.
ActiveloopCLI
Deep Lake database for multimodal AI and agent memory
AirbyteMCPCLI
Data and context layer for production AI agents
AirweaveMCPCLI
Open-source context retrieval for AI agents
DatrisMCP
Agent-native open-source data platform with built-in MCP tools
GraphlitMCP
Knowledge graph RAG platform with 30+ live-sync connectors for AI agents
HelixDBCLI
Graph-vector database and knowledge infrastructure for agents
LanceDBCLI
Open-source multimodal lakehouse and vector database
Lantern
Open-source Postgres vector search for AI applications
LettaMCPCLI
Stateful agents with persistent memory and portable identity
Mem0MCPCLI
Universal memory layer for AI agents — persistent, personalized recall across sessions
MidplaneMCP
A safe MCP layer between AI agents and PostgreSQL
MindsDBCLI
Federated AI data layer for agents across enterprise sources
MovingLakeCLI
Real-time event-driven data connectors for agent context
NeonCLI
Serverless Postgres with branching and scale-to-zero
OutlitMCP
Customer context layer for AI agents — every interaction, structured and queryable
PineconeMCP
Managed vector database for agent knowledge, retrieval, and memory
QdrantMCP
Open-source vector database and semantic memory for AI agents
RailwayCLI
All-in-one intelligent cloud provider — deploy anything without the complexity
SupabaseMCPCLI
Open-source Firebase alternative: Postgres, auth, storage, and realtime
SupermemoryMCP
Context and persistent memory infrastructure for AI agents
TinybirdMCPCLI
Managed ClickHouse for real-time analytics APIs — every endpoint becomes an MCP tool
TursoMCPCLI
Edge SQLite database that scales to millions of agents
Upstash RedisCLI
Serverless Redis with REST API and global low-latency replication
Val TownMCPCLI
Collaborative serverless JavaScript runtime for APIs, crons, and data
Weaviate CloudMCP
Managed vector database with hybrid search and native MCP
ZepMCP
Context engineering platform with temporal knowledge graphs for agent memory
ZeroEntropy
High-accuracy retrieval, reranking, and search APIs for agents
Zilliz CloudMCP
Managed Milvus vector database for scalable agent retrieval
EasypanelMCP
Deploy and manage self-hosted applications through a server control panel and built-in MCP
AI for Database
Ask your database. Build dashboards. Trigger workflows.
Artifact Council
Councils of AI agents propose, vote on and keep shared documents, recorded on Solana mainnet.
VectoreeMCPCLI
Give your coding agent a backend
How to evaluate Memory and retrieval tools
Memory and retrieval preserve useful context beyond a single model call. A complete design separates what is stored, how it is indexed, who may retrieve it, and when it should be corrected or deleted.
Evaluate retrieval quality with your own conversations and documents. Measure relevance, latency, filtering, tenant isolation, deletion guarantees, embedding portability, and the ability to inspect why an item was returned. Storage volume alone does not indicate useful memory.
The 32 providers listed on this page are candidates, not a universal ranking. Shortlist tools from the requirements below, confirm the claims in each provider's current documentation, and run a small proof of concept with production-like inputs.
Evaluation checklist
- Every memory has an owner, provenance, retention rule, and deletion path.
- Retrieval supports metadata filters and prevents cross-user or cross-tenant leakage.
- Quality tests measure whether recalled context improves the target task.
Sources and verification
Use these primary sources to verify current behavior before making an implementation or purchasing decision. Product capabilities, limits, and prices can change after a 4agent listing is reviewed.