Skip to content
Alternatives
Software like Mem Deep Research
What else does this job. Matched on what each project does, not on who links to whom.
Closest first
- projectmemprojectmem.devprojectmem is an open-source tool that provides a local-first memory and judgment layer for AI coding agents. It helps agents avoid repeating failed fixes by tracking past actions and outcomes, supporting more efficient and reliable AI-driven coding workflows.
- DeepMemdeepmem.devDeepMem is a hosted, Mem0-compatible memory-as-a-service that provides persistent memory for AI agents through a drop-in HTTP API. It offers sub-50ms p95 latency, multi-tenant isolation, GDPR controls, and Stripe-billed subscriptions for developers and teams.
- MemMachinememmachine.aiMemMachine is an open-source memory layer built to enhance advanced AI agents by enabling them to learn, store, and recall data and user preferences across sessions. Its primary function is to transform AI-powered applications, such as chatbots and assistants, into context-aware and personalized agents capable of delivering more precise and meaningful interactions. By persisting memory across multiple sessions, agents, and large language models, MemMachine helps applications build evolving user profiles that inform future responses and actions. The platform is designed to support sophisticated personalization and context retention. It features two distinct types of memory: Episodic Memory, which captures conversational context, and Profile Memory, which stores long-term user facts and preferences. These memory types allow agents to recall relevant information, enabling them to provide tailored responses and manage complex, long-running workflows. For example, MemMachine can be used in healthcare AI assistants to remember patient preferences and history, or in team collaboration tools to deliver proactive, context-aware insights that improve with each interaction. MemMachine is accessed through a RESTful API, a Python SDK, or an MCP Server, providing flexibility in how developers integrate memory capabilities into their AI agents. The memory data is persisted to databases, supporting robust and reliable storage of user and interaction data. The platform is suitable for engineering teams and developers building AI agents that require persistent, context-rich memory to support personalized and intelligent behavior. As an open-source solution, MemMachine is available for integration into a variety of AI-powered applications. Its architecture and features are designed to abstract complexity while allowing flexibility for developers to use components independently. The tool is positioned within the class of memory infrastructure solutions for AI agents, focusing on enabling context-aware, personalized, and sophisticated automation in AI-driven systems.
- memdpypi.orgmemd is a Python package that acts as an agent-driven project-memory curator. It helps maintain context, memory, and knowledge across AI agent sessions, particularly those using Claude and other LLMs. The tool is designed for developers building complex, long-running AI agent workflows that require persistent and intelligent memory management.
- supermempypi.orgsupermem is an open-source command-line tool that provides persistent AI memory using a four-tier retrieval system, integrating SQLite FTS5, graph, vector, and LLM agent layers. It is designed for AI developers and researchers seeking advanced memory and retrieval capabilities for language models.
- AgentMemagentmem.devAgentMem is a memory layer for AI agents. It is built for developers who want agents to keep context instead of starting from zero, and it describes its purpose as giving persistent, searchable memory that can sync across frameworks including LangChain, CrewAI, OpenAI, Claude, AutoGPT, and LlamaIndex. The product stores and retrieves memories through an API. Its listed capabilities include semantic search, which finds memories by meaning rather than keywords and is powered by vector embeddings, and cross-agent sync for sharing context between agents. The page also says memories are persistent and survive restarts, that the API is simple to use with an API key, and that memories are auto-embedded when stored. A short code example shows calls for storing a memory, searching memories, and syncing across multiple agents. AgentMem’s production-oriented features include fast API response times under 100 ms, edge caching, API key authentication, tenant isolation, encrypted-at-rest storage, usage analytics for memory usage, search patterns, and agent activity, and a global CDN setup. It is deployed on Railway with a Cloudflare proxy. The page also includes a worked example of registering an agent through a POST endpoint and then using POST endpoints to store memories and search them. Pricing is presented in three plans. A free plan costs $0 per month and includes 10,000 memories, semantic search, support for three agents, and community support. Pro costs $29 per month and adds unlimited memories, unlimited agents, cross-agent sync, priority support, and usage analytics. Enterprise costs $199 per month and adds a self-hosted option, custom integrations, an SLA guarantee, and dedicated support. The page identifies AgentMem as being built by Gritza and says it is in public beta.
- memmangithub.commemman is an open-source Python package that provides LLM-supervised persistent memory for AI agents, supporting intent-aware graph recall, retrieval-augmented generation (RAG), and pluggable embeddings. It is designed for developers building advanced agentic systems that require long-term memory and semantic search capabilities. The package integrates with Claude Code, OpenClaw, and NanoClaw.
- MemDatamemdata.aiMemData is a decentralized memory infrastructure designed for autonomous AI agents and developers seeking persistent, searchable storage of unstructured data. The platform enables agents and applications to ingest files—including PDFs, images, screenshots, audio, and text—automatically handling optical character recognition (OCR) for images and PDFs, as well as audio transcription. Uploaded content is chunked, embedded, and indexed without the need for manual configuration or tuning, allowing users to query their data in natural language and receive relevant context with source citations. MemData is accessible via a REST API and supports integration with AI tools such as Claude, ChatGPT, Gemini, Cursor, and automation platforms like n8n and Make. Developers can interact with the service using API calls or the MCP server, and file ingestion and querying are demonstrated through simple command-line examples. The platform supports a variety of file types, including PDFs, PNG, JPG, MP3, WAV, M4A, and text files, and provides persistent, account-isolated storage. 3 and at rest with AES-256. Data is stored on US-based infrastructure using SOC 2 compliant providers, and users retain ownership of their data, which is not used for model training or sold. Data can be deleted at any time through the API or dashboard, with purging completed within 24 hours. MemData offers a tiered pricing model. The Free plan includes 100 MB of storage, 250 queries per month, a 10 MB file size limit, and access to OCR, audio transcription, and both API and MCP interfaces. The Pro plan, at $29 per month, expands storage to 10 GB, allows 10,000 queries per month, and increases the file size limit to 100 MB, adding priority support. The Scale plan, at $99 per month, provides 100 GB of storage, 50,000 queries per month, a 500 MB file size limit, priority support, and custom integrations. No credit card is required to start, and users can upgrade as needed. Positioned as a complete memory pipeline rather than just a vector database, MemData is built for AI builders and agents requiring long-term, semantic memory and context retrieval capabilities.
- agent-framework-mem0pypi.orgagent-framework-mem0 is an open-source Python package that integrates Mem0 memory management with the Microsoft Agent Framework. It enables developers to add memory capabilities to their agent-based applications using a simple interface.
- AutoMemautomem.aiAutoMem is a persistent memory layer designed for AI agents, enabling them to recall both facts and their context rather than starting each session without memory. The tool addresses the challenge of agents forgetting previous interactions by capturing and organizing relevant information as users work, allowing for more effective and context-aware recall in subsequent sessions. AutoMem integrates into agent workflows by providing a memory architecture that combines a knowledge graph for relationships and a vector index for semantic meaning. The platform stores every memory in a graph structure, mapping out entities, relationships, and temporal data using FalkorDB, while also leveraging Qdrant for vector-based semantic search. This hybrid approach allows agents to retrieve not only semantically similar information but also the specific threads or contexts to which that information belongs. AutoMem consolidates new memories in the background, clustering related ideas, strengthening frequently accessed connections, and allowing irrelevant data to decay over time, resulting in increasingly relevant and refined recall. AutoMem is compatible with a range of agent clients and platforms, including Claude, Cursor, ChatGPT, Codex, and any client supporting the Model Context Protocol (MCP). It can be deployed locally via Docker, as a managed cloud service through Railway, or self-hosted within a user's own infrastructure, including Kubernetes environments. All deployment options expose the same MCP endpoint, ensuring consistent integration regardless of setup. The tool supports macOS, Linux, Windows (WSL2), and is accessible from both desktop and mobile clients that are MCP-compatible. The software is open source and distributed under the MIT License. AutoMem has been benchmarked using the neutral Agent Memory Benchmark (BEAM), where it achieved a high accuracy rate and was ranked second among competitors. Its design is informed by research focused on enhancing recall for AI agents, ensuring that memory compounds and becomes more useful over time.
- MemBrainmem-brain.ioMemBrain is a persistent memory infrastructure designed for AI agents, offering a self-evolving knowledge graph that enables long-term storage, traversal, and recall of reasoning paths and semantic relationships. The platform addresses the challenge of maintaining continuity and context for AI agents over extended periods, allowing them to recall information and reasoning across weeks or months without session resets. Rather than simply storing documents, MemBrain parses incoming events, calls, and state changes into a neural graph composed of semantic nodes, edges, and type definitions, supporting autonomous learning, linking, and pruning of context based on actual reasoning patterns. Key features include an event observation and parsing engine that transforms raw data and tool calls into typed nodes, a causal reasoning engine that identifies connections and traces causal paths between temporal events, and a graph traversal system for memory recall that returns logical paths through the memory graph. The platform supports interactive exploration of stored memories as a graph, with capabilities such as Graph Search and Regex Scoping for finding and highlighting nodes. MemBrain's architecture is engineered for agentic speed and reliability, with features like pre-cached logic for rapid response times, surgical retrieval to prevent context window bloat, and temporal accuracy that distinguishes structural shifts from temporary anomalies in agent behavior. MemBrain integrates natively via the Model Context Protocol (MCP), enabling compatibility with any large language model (LLM). Users can connect through exposed interfaces such as search_narrative, observe_state, and evolve_memory for querying, reading, and writing to the memory graph. The platform is accessible via API and command-line interface (CLI), and offers a read-only demo graph for interactive exploration without an API key. Pricing is provisioned in Indian Rupees (INR) and is structured into four tiers: a Free plan for individuals with 1,000 total memories and API/MCP access, a Pro plan for developers with 10,000 total memories, a Scale plan for high-volume users with unlimited total memories and increased weekly creation limits, and an Enterprise plan with custom limits, dedicated support, security audits, and white-labeling options. The product is developed by Alphanimble.
- memantogithub.commemanto is an open-source companion memory agent designed to help AI agents focus and improve by managing and retaining knowledge. It supports retrieval-augmented generation (RAG) and semantic memory, giving users ownership over learned information.
- Memrausememra.comMemra is a developer API and CLI tool that offers persistent, privacy-first memory for AI agents and LLM applications. It provides long-term semantic recall, PII masking, and is EU-hosted for compliance. Memra is designed for developers building advanced agentic systems requiring reliable memory infrastructure.
Ranked by how close each one sits to Mem Deep Research in the index, not by popularity. Back to Mem Deep Research →