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Adaptive AI Memory Platform — Self-hosted, open source.
Give your AI assistants persistent memory that gets smarter as you use it.
Works with Claude, ChatGPT, Gemini, and any MCP-compatible client.
Python SDK (KaguraClient / KaguraAgent)
Why Kagura Memory Cloud?
Your AI forgets everything after each conversation. Kagura fixes that — and gets smarter every time you search.
Most AI memory tools are just vector databases with a chat wrapper. Kagura is different:
| Feature | Description |
|---|
| Adaptive Memory | Every search automatically strengthens connections between related memories. The more you use it, the better explore() discovers hidden relationships. |
| Hybrid Search | Semantic (OpenAI/Ollama) + BM25 keyword — 96% top-1 accuracy |
| AI Reranking | Ollama (local, free), Voyage AI, or Cohere — cross-encoder reranking for precision |
| Neural Memory Graph | Hebbian learning builds a knowledge graph in the background. explore() traverses it for serendipitous discovery. |
| 14 MCP Tools | remember, recall, forget, reference, explore, merge_contexts, and 8 more |
| Multi-Provider | OpenAI or Ollama (local, private, zero cost) for embeddings |
| Team Ready | Workspaces, RBAC, context isolation, shared memory |
| Web UI | Next.js dashboard — contexts, search settings, member management |
| 5-Minute Setup | ./setup.sh and you're done |
Architecture
Workspace (team/org)
├── Context A ("my-project") ← like a folder
│ ├── Memory 1 ← 3-layer: summary / context / content
│ ├── Memory 2
│ └── Neural edges (Hebbian) ← automatic connections
├── Context B ("learning-notes")
│ └── ...
└── Members (Owner/Admin/Member/Viewer)
Adaptive Memory: Two Search Paths
Kagura separates precision search and discovery into two independent paths, each optimized for its purpose:
recall() ──→ Hybrid Search (semantic + BM25) ──→ [Reranker] ──→ Precise results
│
└──→ Hebbian Learning (background) ──→ Graph edges grow
│
explore() ──→ Graph Traversal (Neural Memory) ←─────────────────┘ Related discoveries
recall() — Precision search. Hybrid (semantic 60% + BM25 40%) with optional AI reranking. Returns the most relevant memories.explore() — Discovery. Traverses the Neural Memory graph to find related memories that keyword search would miss.- Hebbian learning — Every
recall() silently strengthens edges between co-retrieved memories. No explicit training needed — the graph grows organically as you use the system.
This separation is intentional: mixing graph signals into recall degrades precision (validated via benchmarks). Instead, each path does what it's best at.
Data isolation: All data is filtered by workspace_id → context_id → user_id. Memories never leak across boundaries. Single Qdrant collection with payload filtering.
Tech stack: FastAPI (async) · PostgreSQL · Qdrant · Redis · Next.js 16 · OAuth2 · MCP over Streamable HTTP
Quick Start
System Requirements
| Minimum | Recommended |
|---|
| CPU | 2 cores | 4+ cores |
| RAM | 4 GB | 8+ GB |
| Disk | 10 GB free | 20+ GB free |
Prerequisites
- Docker & Docker Compose
- Python 3.11+
- Node.js 20+
- OpenAI API key (for embeddings) — or Ollama for local embeddings
- OAuth2 credentials (optional — password + MFA login available without OAuth)