Memanto
A memory agent that curates, retrieves, and hands off durable context for coding agents across sessions.
What does this agent do, and when should you use it?
Memanto is a memory-management tool for other agents, centered on the remember, recall, and answer operations. Its Python CLI manages agents and sessions, stores and retrieves memories, detects conflicts, and can launch a local REST API and Web UI. Memories can be organized into 13 built-in types such as instruction, fact, decision, goal, and preference, with temporal queries, export, sync, and migration commands. The backend can run locally through Docker with Moorcheh and Ollama, use the Moorcheh cloud service, or be hosted in a private VPC according to the README. It connects to coding tools including Claude Code and Codex through memanto connect, and includes a TypeScript/Node.js SDK.
With memanto remember, users can store an individual memory, ingest JSON in batches, or use --from-conversation to extract facts from chat logs; memanto upload places PDF, DOCX, XLSX, JSON, TXT, CSV, or Markdown files into an agent memory namespace. memanto recall returns relevant memories using a query, type filters, and temporal options such as --as-of and --changed-since, while memanto answer generates a RAG answer from retrieved memory. The memanto agent, memanto session, and memanto status command groups manage agents, sessions, and runtime state; memanto conflicts and memanto daily-summary support contradiction handling and daily summaries. memanto memory export and memanto memory sync produce structured Markdown or OKF bundles, and memanto migrate imports memory from Mem0, Letta, Supermemory, or OKF. For programmatic access, memanto serve starts a local session-based REST API, and @moorcheh-ai/memanto starts a local server through uvx and exposes a Memanto client for TypeScript.
- A developer using Claude Code or Codex who wants a coding assistant to retain project decisions, preferences, and task context after a context reset.
- A team operating several development agents that needs separate agent memories, session activation, and handoffs through the memanto agent and session commands.
- An engineering group that wants to turn chat logs, a project MEMORY.md, or uploaded documents into searchable memory.
- A technical lead who needs to inspect decisions as of a particular time or identify contradictory memories.
- A user migrating established memory from Mem0, Letta, or Supermemory and wanting Markdown or OKF exports for inspection and transfer.
- An organization that wants to run its memory backend locally with Docker or in its own VPC instead of using only a cloud workflow.
What are this agent's strengths and limitations?
- Defines memory work through remember, recall, and answer, with CLI, REST API, and TypeScript/Node.js SDK entry points.
- Provides 13 built-in memory types, temporal retrieval, conflict detection, and version-related capabilities for long-running project context.
- Offers local Docker/Ollama, cloud, and private-VPC deployment choices, plus Markdown/OKF export and migration from several memory sources.
- The README states that stored memories are searchable immediately without separately provisioning a vector database, embedding pipeline, or reranker.
- The documented full local mode depends on Docker and is guided through Ollama setup; users without that runtime need to choose the cloud backend.
- Cloud mode requires a Moorcheh API key, and Moorcheh is the core retrieval engine, so deployment and service choice involve that provider.
- Although the README lists 20+ integrations, the supplied material gives only the command entry point and some names, not configuration or behavioral details for each integration.
- The README reports LongMemEval and LoCoMo results, but the supplied material does not include reproducible evaluation settings or independent validation details.
How do you install or deploy this agent?
For local mode, run pip install memanto, then memanto, and choose “On-Prem.” The flow guides Docker and Ollama setup; the README explicitly requires Docker and says no account or API key is needed. For cloud mode, run pip install memanto and memanto, choose “Cloud,” and paste a free Moorcheh API key from https://console.moorcheh.ai/api-keys. Use memanto config backend to switch between local and cloud backends.
How do you use this agent?
After configuration, store and retrieve a memory with memanto remember "User prefers concise answers" --type preference and memanto recall "user communication style" --type preference. To connect a coding tool, run memanto connect <integration-tool-id>, for example memanto connect claude-code. For a local API or interface, run memanto serve and optionally memanto ui; when the server is running, API documentation is available at http://localhost:8000/docs.
How does this agent compare with similar options?
The README positions Memanto as an active memory agent rather than passive memory infrastructure and claims higher LongMemEval and LoCoMo results than Mem0, Zep, and Letta. Its migration command also imports from Mem0, Letta, Supermemory, or OKF; the supplied material does not provide feature-by-feature comparisons.