RAG Frameworks · moorcheh-ai

memanto

Memanto is a Python-based memory system for AI agents that persists context across sessions without requiring external vector databases or API keys. It offers local (Docker-based) or cloud deployment options and integrates with popular agent platforms like Claude Code, Cursor, and CrewAI.

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Source: GitHub — github.com/moorcheh-ai/memanto

1.6k

GitHub stars

461

Forks

Python

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	moorcheh-ai/memanto
Owner	moorcheh-ai
Primary language	Python
License	MIT — OSI-approved
Stars	1.6k
Forks	461
Open issues	245
Latest release	v0.2.5 (2026-07-06)
Last updated	2026-07-07
Source	https://github.com/moorcheh-ai/memanto

What memanto is

A memory agent built on information-theoretic search (Moorcheh engine) providing three primitives—remember, recall, answer—with typed semantic memory across 13 categories. Supports both on-premise Docker deployment and cloud SaaS, with zero indexing latency and single-query retrieval without multi-stage pipelines.

Quickstart

Get the memanto source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/moorcheh-ai/memanto.git2cd memanto3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Long-context agent persistence across sessions

Agents can maintain codebase knowledge, project decisions, and user preferences without re-ingesting documentation or context after context resets. Reduces token spend and improves task continuity.

Local-first AI development workflows

Teams building with Claude Code, Cursor, or similar tools can embed persistent memory entirely on-machine without cloud dependencies, API keys, or backend provisioning. Ideal for privacy-sensitive or air-gapped environments.

Multi-agent coordination and knowledge sharing

CrewAI, LangChain, and other multi-agent frameworks can use Memanto as a shared episodic/semantic store to coordinate decisions, avoid conflicting goals, and track versioned facts across agent boundaries.

Implementation considerations

Docker is required for local (on-prem) mode; cloud mode avoids this but introduces API rate limits (100K free ops documented). Verify quota aligns with agent query frequency.
Integration is via CLI command (`memanto connect <tool-id>`); verify your agent platform is in the documented list (Claude Code, Cursor, Codex, Windsurf, Cline, Continue, Goose, GitHub Copilot listed). Custom integrations require SDK usage.
Memanto manages Moorcheh backend provisioning (Docker or cloud API key) but you retain memory ownership. Test switching backends (`memanto config backend`) in non-prod first to confirm data portability.
245 open issues suggest active development; review high-priority issues for blockers (e.g., type system changes, API breaking changes). Latest release v0.2.5 is recent (2026-07-06); stability is evolving.
On-prem requires managing Docker lifecycle; cloud option offloads ops but adds API dependency. Hybrid approaches documented but require manual config management.

When to avoid it — and what to weigh

Pre-built vector database already deployed — If you have Pinecone, Weaviate, or Qdrant in production, the additional abstraction layer and learning curve may not justify switching. However, no lock-in; both can coexist.
Sub-100ms latency requirements at scale — On-prem Docker deployment and cloud SaaS both introduce network or container overhead. Real-time (<50ms) memory queries may require direct embedding lookups instead.
Mature, battle-tested vendor lock-in preferred — Project is ~4 months old (created 2026-03-23) with 245 open issues. If your org prioritizes vendor maturity and multi-year SLA guarantees, established memory tools (Mem0, Zep, Letta) carry lower risk.
Non-Python agent ecosystems — Memanto is Python-first. Tight integration with Node.js, Go, or Java agent frameworks is not clearly documented. Unknown.

License & commercial use

MIT License. Permissive open-source license allowing commercial use, modification, and redistribution with no restrictions beyond attribution and liability disclaimer.

MIT is a clear permissive OSI license. Commercial use is explicitly allowed. However, if using cloud deployment (SaaS), terms are subject to Moorcheh's cloud service agreement (not provided in data). On-prem Docker usage has no commercial restrictions. For production commercial use, review Moorcheh cloud SaaS terms and ensure API quota covers your load.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Strong
Assessment confidence	High

Security considerations

On-prem Docker deployment keeps all memory on your machine; no cloud data transmission. Cloud mode sends memory queries to Moorcheh API—review data residency and compliance implications. MIT license itself carries no warranty. No security audit data provided. Memanto retains memory ownership but crypto/auth for backend storage unknown. For regulated data (PII, HIPAA, SOC2), require explicit security review of Moorcheh cloud infrastructure before adoption.

Alternatives to consider

Mem0

Established memory layer with multi-LLM support, graph-based retrieval, and commercial backing. Outperforms Memanto on some benchmarks; requires vector DB provisioning and indexing overhead.

Zep

Long-term memory for agents with built-in summarization and hybrid search. Self-hosted or SaaS; more mature integration ecosystem but steeper operational complexity.

Letta

Agent memory framework with explicit episodic/semantic/procedural separation. Heavier than Memanto, more opinionated architecture, but battle-tested in production multi-agent systems.

Software development agency

Build on memanto with DEV.co software developers

Start with `pip install memanto` and choose local (Docker) or cloud (free API key) in 2 minutes. No vector DB, no backend to manage.

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Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

memsearch claude-mem mem0

Related on DEV.co

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memanto FAQ

Does Memanto work offline?

Yes, via on-prem Docker + Ollama setup. No internet or API keys required. Cloud mode requires internet but avoids local Docker overhead.

Can I switch from cloud to local deployment without losing memories?

Memanto CLI supports backend switching via `memanto config backend`. Data portability is documented as a design goal, but migration path details are unclear; test in non-prod first.

What is Moorcheh and do I need to understand it?

Moorcheh is the semantic search engine powering Memanto's retrieval (information-theoretic, zero indexing latency). You don't manage it directly; the CLI abstracts it. On-prem, it runs in Docker. On cloud, it's a managed service.

Are my memories searchable immediately after storage?

Yes. Memanto claims 'zero indexing latency'—memories are queryable at write time. No extraction or graph construction bottleneck like traditional vector DB pipelines.

Software developers & web developers for hire

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If memanto is part of your rag frameworks roadmap, our team can implement, customize, migrate, and maintain it.

Ready to Add Persistent Memory to Your Agent?

Start with `pip install memanto` and choose local (Docker) or cloud (free API key) in 2 minutes. No vector DB, no backend to manage.

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