MemMachine Memory Layer
Persistent, queryable memory for AI agents and LLM applications across sessions.
Per-dimension scores and reasoning
Insufficient evidence: The repository does not provide explicit information on permission model, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. All criteria scored 0 due to lack of evidence.
Insufficient evidence: No explicit evidence for self-consistency, dependency availability, or failure messages. All criteria scored 0 due to lack of evidence.
Insufficient evidence: No explicit evidence for audience, capability boundaries, trigger precision, or environment fit. All criteria scored 0 due to lack of evidence.
Insufficient evidence: No explicit evidence for information architecture, install notes, naming stability, examples/FAQ, known limitations, license, versioning/changelog, or maintenance responsibility. All criteria scored 0 due to lack of evidence.
Insufficient evidence: No explicit evidence for output usability, marginal value, or cost-benefit. All criteria scored 0 due to lack of evidence.
Insufficient evidence: No explicit evidence for claim traceability, cross-source corroboration, or fact/inference separation. All criteria scored 0 due to lack of evidence.
- The repository only provides README, LICENSE, SECURITY.md, pyproject.toml, and some CI workflows, without source code, tests, or detailed documentation, making a meaningful static assessment impossible.
- All criteria scored 0 due to insufficient evidence, not indicating defects but lack of assessable evidence.
- It is recommended to obtain the full source code and documentation before a deeper evaluation.
What does this agent do, and when should you use it?
MemMachine is an open-source long-term memory layer for AI agents and LLM-powered applications that need state beyond a single chat session. It separates Working Memory, graph-backed Episodic Memory, and SQL-backed Profile Memory. Applications can connect through a Python SDK, REST API, TypeScript SDK, or a native MCP server, with local, Docker self-hosted, and managed-service paths described. Its MCP entry points are memmachine-mcp-stdio and memmachine-mcp-http for MCP-compatible clients such as Claude Desktop and Cursor. Adoption requires a running MemMachine Server and persistence infrastructure spanning a graph database and SQL storage.
An application connects to a running MemMachine Server, then uses MemMachineClient.get_or_create_project(org_id, project_id) to obtain a project. It creates a user-session memory object with project.memory(group_id, agent_id, user_id, session_id), stores content with memory.add(content, metadata=...), and retrieves it with memory.search(query). The example reads recalled text from results.content.episodic_memory.long_term_memory.episodes[0].content. MemMachine persists conversational Episodic Memory in a graph database and Profile Memory in SQL, and exposes MCP server processes through memmachine-mcp-stdio or memmachine-mcp-http.
- A sales team using a CRM Agent needs to recall a client’s prior interactions and deal stage during a later session.
- A healthcare navigation assistant needs to retain medical history and follow treatment progress over time.
- A personal-finance advisor needs to preserve portfolio preferences and risk tolerance between consultations.
- A writing assistant needs to retain a user’s style guide and terminology across separate writing sessions.
- A team using LangChain, LangGraph, CrewAI, LlamaIndex, AWS Strands, n8n, Dify, or FastGPT needs a persistent memory integration.
What are this agent's strengths and limitations?
- It distinguishes working memory, cross-session episodic memory, and user-profile memory instead of treating all memory as one session transcript.
- The documented architecture assigns episodic memory to a graph database and profile facts to SQL storage.
- It exposes the same memory layer through Python, REST, TypeScript, and MCP interfaces.
- It names integrations for LangChain, LangGraph, CrewAI, LlamaIndex, AWS Strands, n8n, Dify, and FastGPT.
- The client requires a running MemMachine Server; installing the Python package alone is insufficient.
- Its persistence architecture involves both a graph database and SQL storage, adding operational and data-governance work.
- The supplied Quick Start Python snippet has a syntactically invalid import line and cannot be copied verbatim.
- Authentication, authorization, tenant isolation, backups, migration, and failure-recovery behavior are not documented.
How do you install or deploy this agent?
Start a MemMachine Server first; it is a prerequisite for the client. Then run:
pip install memmachine-clientLocal server startup, Docker setup, authentication, and database configuration are not provided as copyable commands. The project describes local, Docker self-hosted, and managed-service options; the managed platform offers a free account, but credential configuration is not specified.
How do you use this agent?
Point the client base_url at the running service, for example http://localhost:8080, then call get_or_create_project, project.memory, memory.add, and memory.search. The supplied Python quick-start import line contains a duplicated import and is not executable as written; no corrected copyable import is provided, so confirm the import for the installed memmachine-client version before adoption. For MCP clients, run memmachine-mcp-stdio, or run memmachine-mcp-http for web clients.