Compare tools
Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
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supermemory™
✓ verifiedFreemium
Developer API that gives AI agents persistent memory, retrieval, and connectors, usable both as infrastructure and a personal app.
174K visits/mo
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Relevance AI
✓ verifiedPaid
Enterprise platform for building and deploying customized AI agent teams across sales, support, marketing, and HR workflows.
325K visits/mo
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qdrant.io
✓ verifiedFreemium
High-performance open-source vector database for production AI retrieval and RAG, for teams needing scale, hybrid search, or self-hosting.
166K visits/mo
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AnythingLLM
✓ verifiedFree
Free all-in-one desktop AI app to chat with your documents and run RAG and AI agents fully local and private.
682K visits/mo
Pricing
Free: $0/mo (~$5/mo of usage included)
Pro: $19/mo (~$20/mo of usage, unlimited storage, 2 teammates)
Max: $100/mo (~$130/mo of usage, 6x Pro headroom)
Scale: $399/mo (~$600/mo of usage, up to 10 teammates)
No public pricing
No public pricing
No public pricing
Core features
- ✦Persistent, structured memory built as a knowledge graph
- ✦Sub-300ms hybrid retrieval (RAG) with reranking
- ✦Native filesystem mount for agent memory access
- ✦Connectors to Slack, Notion, Drive, Gmail, GitHub, S3
- ✦Automatic extraction from PDFs, images, and audio
- ✦User profile and behavior tracking across sessions
- ✦Custom AI agent builder for specific tasks
- ✦Support for multiple LLMs (GPT, Gemini, Claude, etc.)
- ✦Agent evaluation and performance tracking
- ✦2,000+ integrations
- ✦Embedded deployment and training program
- ✦SSO, role-based access, and audit logs for enterprise governance
- ✦Hybrid dense and sparse vector search (BM25, SPLADE, miniCOIL)
- ✦Advanced metadata filtering applied during search traversal
- ✦Multivector support for multimodal retrieval
- ✦Reranking with score boosting and late-interaction models (ColBERT, MMR)
- ✦Flexible deployment: cloud, hybrid, private, or edge
- ✦Rust-based engine optimized for low-latency, high-scale search
- ✦Chat with your documents (RAG)
- ✦Runs locally and offline for privacy
- ✦Supports any LLM (local or cloud)
- ✦Built-in AI agents
- ✦Handles PDFs, Word, CSV, codebases
- ✦No-code setup
Use cases
- →Developers adding long-term memory to AI agents
- →Teams building agents that need to sync with existing tools
- →Individuals wanting one memory layer shared across multiple AI assistants
- →Automating sales prospecting, CRM enrichment, and meeting prep
- →Automating post-call follow-ups and pipeline forecasting
- →Building customer support and HR task agents
- →Scaling an internal AI workforce across departments
- →Building retrieval-augmented generation (RAG) pipelines
- →Powering AI recommendation and semantic search systems
- →Enterprises needing on-prem or hybrid deployment for compliance
- →AI agent platforms needing fast contextual retrieval at scale
- →Privately querying your own documents
- →Running local AI without the cloud
- →Building AI agents over your data
- →Using multiple LLM providers in one app
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