Memora
Memora: A self-hosted memory layer for agents that need searchable context across restarts. Ranked #29 of 34 in AI Agent Memory Tools by our editors (5.3/10); pricing: Open source; best for small self-hosted apps needing lightweight memory.
At a glance
- Editor score5.3 / 10
- PricingOpen source
- Best forSmall self-hosted apps needing lightweight memory
- Paid fromNone
- Semantic memoryYes
- Facts checked13 Oct 2026
Where it wins
- Keeps agent memories across restarts and retrieves them with semantic search
- Deduplicates similar memories and decays relevance for unused ones
- Offers encrypted local storage, optional sharing, and HTTP endpoints
Where it doesn't
- Python is the only stated SDK language
- Self-hosted deployment may not suit teams seeking a managed service
- Integrations are focused on LangChain, AutoGPT, and custom agents
Our verdict on Memora
Memora is a self-hosted memory layer for AI agents, aimed at solo developers and small teams building lightweight apps. It stores information outside an agent’s model context window and retrieves relevant items through semantic search, so context can persist across restarts. The product exposes a Python Memory interface for creating an agent-specific memory, saving values, and searching; it can also be accessed through HTTP endpoints for storing, recalling, searching, and forgetting memories.
Its feature set is centered on keeping memory useful over time rather than simply accumulating records. Semantic deduplication handles similar memories, while relevance decay reduces the prominence of items that go unused. Retention is described as indefinite, and optional collective memory sharing supports a shared memory pool. Local encrypted storage is part of the stated design. These capabilities make Memora a fit when an app needs persistent semantic and user-profile memory under its own deployment, but teams seeking managed hosting or a broader SDK selection may prefer another approach.
Memora is installed with pip and is described as compatible with LangChain, AutoGPT, and custom agents. That gives it a direct path into those agent setups, while its self-hosted deployment and Python interface suit developers comfortable integrating a memory component into their application. Its open-source model may appeal to teams that want to operate the component themselves. Choose Memora for a compact, self-hosted memory layer with semantic retrieval and lifecycle controls; look elsewhere if your project depends on a managed service or language interfaces beyond Python.
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Memora pricing
Memora fact sheet
| Free plan | Not verified |
|---|---|
| Paid from | None |
| Semantic memory | Yes |
| Episodic memory | Not verified |
| User/profile memory | Yes |
| Graph-based memory | Not verified |
| Deployment options | Self_hosted |
| SDK languages | 1 |
| Retention policy | Indefinite |
| Deployment | Self-hosted |
| Built for | Solo, Small business (editorial estimate) |
| Integrations | 2 integrations: LangChain, AutoGPT |
| Pricing | Open source |
| Website | memora.sh |
| Facts checked | 13 Oct 2026 |
Memora integrations
Memora lists 2 integrations on its own site.
- LangChain
- AutoGPT
Alternatives to Memora
- 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
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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