Montycat AI Memory
Montycat AI Memory: Private, multi-language agent memory with hybrid retrieval and MCP connectivity. Ranked #23 of 34 in AI Agent Memory Tools by our editors (5.9/10); pricing: Pricing on request; best for private deployments needing multi-language SDKs.
At a glance
- Editor score5.9 / 10
- PricingPricing on request
- Best forPrivate deployments needing multi-language SDKs
- Semantic memoryYes
- Facts checked13 Oct 2026
Where it wins
- On-device embeddings keep semantic search local.
- Hybrid BM25 and vector search supports flexible retrieval.
- Four SDKs plus MCP connect agents and clients.
Where it doesn't
- Graph-memory capability is not included.
- Pricing is handled through contact-sales inquiries.
- Self-hosted deployments put hosting operations on your team.
Our verdict on Montycat AI Memory
Montycat AI Memory is a self-hosted memory layer for AI agents and LLM applications. It stores persistent records and embeddings in one NoSQL engine, supporting semantic vector, keyword, or hybrid retrieval. Episodic interaction logs, per-user profiles, shared memory across agents and clients, and keyspaces that survive restarts and crashes cover common continuity needs. On-device embeddings suit teams that need private deployments, while structured clients for Python, TypeScript, Dart, and Rust broaden its fit across small, mid-market, and enterprise environments.
Pricing follows a contact-sales model: the contact page invites pricing inquiries, and support describes tailored enterprise plans. Montycat offers cloud and self-hosted deployment options, with platforms spanning Linux, macOS, Windows, API access, and self-hosting. Its official MCP server connects Claude Desktop, Claude Code, Codex, Cursor, and other MCP-compatible clients to memory tools. LangChain and LlamaIndex integrations, alongside the four language SDKs, make it practical for teams assembling agent workflows across different runtimes.
The standout capability is a layered retrieval and retention design. HNSW approximate nearest-neighbor indexing supports vector recall, BM25 adds keyword matching, and hybrid search combines both approaches. Configurable expiration controls let teams shape retention, while persistent keyspaces preserve data across restarts. The feature set centers on semantic, episodic, and profile memory rather than graph relationships, so teams that need graph-memory modeling should consider another tool. Choose Montycat for private, durable memory shared across multiple agents; choose an alternative when graph structures or a publicly itemized pricing model are central requirements.
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Montycat AI Memory pricing
Montycat AI Memory fact sheet
| Free plan | Not verified |
|---|---|
| Paid from | Not verified |
| Semantic memory | Yes |
| Episodic memory | Yes |
| User/profile memory | Yes |
| Graph-based memory | No |
| Deployment options | Self_hosted |
| SDK languages | 4 |
| Retention policy | Configurable |
| Deployment | Cloud, Self-hosted |
| Platforms | Linux, macOS, Windows |
| Support | Docs, Tickets |
| Built for | Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 7 integrations: Claude Desktop, Claude Code, Codex, Cursor, MCP-compatible clients, LangChain … |
| Pricing | Pricing on request |
| Website | montygovernance.com |
| Facts checked | 13 Oct 2026 |
Montycat AI Memory integrations
Montycat AI Memory lists 7 integrations on its own site.
- Claude Desktop
- Claude Code
- Codex
- Cursor
- MCP-compatible clients
- LangChain
- LlamaIndex
Alternatives to Montycat AI Memory
- Cloudflare Agent MemoryA cloud API for scoped, searchable memory across AI agent profiles and conversations.9.2
- Mem0A flexible memory layer with semantic, graph, and self-hosted deployment options.8.8
- MCP Memory ServiceA broad open-source memory layer for teams that can manage deployment and configuration.7.9
See all Montycat AI Memory alternatives →
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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