Persistent semantic memory for MCP-compatible agents, with recall, conflict handling, expiry, and export.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
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π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Inspect callable tools, capabilities, and parameters exposed to AI agents by Memanto.
The Memanto MCP server provides managed semantic memory for MCP-compatible agents. It is designed to sit beside an agent fleet as a memory-focused companion, rather than acting as a passive transcript archive. Memanto extracts durable knowledge such as decisions, preferences, facts, and failures from agent conversations, then maintains that knowledge over time.
Its memory estate can be shared across agents so that one agent can recall information learned by another. The system distinguishes current knowledge from earlier beliefs, keeps provenance and timestamps, and supports confidence tracking. Agent namespaces provide separation between different agents, such as production operations and experimentation.
Memanto belongs in the Knowledge & Memory category for workflows where agents need continuity across sessions, users, or tools. It is especially relevant when duplicated memories, contradictory information, stale preferences, or repeated work make an unmanaged memory store difficult to use.
Memanto observes interaction streams and extracts selected durable knowledge instead of retaining every conversation as memory. Scheduled processing consolidates duplicates, joins related fragments, and increases confidence when observations repeat. When new information conflicts with an older belief, the newer knowledge can supersede it while preserving the historical record.
Memory lifecycle policies control decay and expiry. Expired memories remain available for recall with an expiration marker and can be restored; explicit deletion is separate. The memanto conflicts command supports reviewing contradictions, while memanto forget handles forgetting operations.
Before an agent acts, memanto agent bootstrap supplies a relevant slice of the estate. Agents can also use commands such as memanto recall for searches and memanto answer for grounded responses. Queries can be evaluated as of a date or against changes since a named release, where those options are needed for historical review.
Install the package with Python, npm, or the Go SDK:
pip install memantonpm install @moorcheh-ai/memantogo get github.com/moorcheh-ai/memanto/sdks/goRunning memanto starts the setup path for either an on-premises deployment or the cloud option. The on-premises mode uses Docker and Ollama and is described as requiring no account or API key. memanto config backend switches between configured backends.
Connect supported agents with memanto connect, followed by a client name such as claude-code, cursor, codex, windsurf, or cline. The repository also documents connections for Goose and Copilot, although those are not represented in the directory's supported-client list. memanto ui opens a local dashboard for browsing, searching, and auditing the estate.
The Memanto MCP server includes capabilities for:
memanto remember.memanto answer.memanto schedule enable.memanto conflicts.memanto memory export --okf.The README describes on-premises operation with Docker and Ollama, plus a cloud deployment, but does not specify detailed hardware, model, or hosting requirements. Client connection support is shown through the Memanto CLI rather than a complete transport or configuration reference in the supplied material.
The Memanto MCP server manages memory but does not eliminate the need to define retention policies and review conflicts. Expiry is policy-driven, and expired records remain visible in recall until deleted or restored. Open Knowledge Format export is intended to keep the estate portable as readable, diffable Markdown rather than a proprietary archive.
Memanto is released under the MIT License.
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