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Software like Remembra
What else does this job. Matched on what each project does, not on who links to whom.
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- rememorirememori.devrememori is an embedded memory engine designed for AI agents, implemented in pure TypeScript with zero dependencies, zero servers, and zero native bindings. It addresses the challenge of providing persistent, in-process memory for agents that would otherwise lose context, user preferences, and prior interactions at the end of each session. The tool offers a lightweight alternative to external vector databases and retrieval services, enabling agents to remember and recall information efficiently without additional infrastructure. The core API revolves around four main operations: remembering, recalling, forgetting, and reinforcing memories. Text can be stored along with its embedding, tags, and automatically extracted entities. Recall operations rank stored memories using a combination of similarity, shared entities, user feedback, importance, and time decay, while the forget function removes specific memories. The reinforce function strengthens memories only when there is verifiable evidence of their use, and demote provides a mechanism to reduce the prominence of incorrect memories. rememori automatically constructs a bipartite knowledge graph by linking memories to their mentioned entities, enhancing recall accuracy even when queries do not directly match stored text. Developers can use any embedding model, including local options like Ollama, OpenAI-compatible endpoints, or custom functions. Storage is handled locally: on Node and Bun, data is kept in a single append-only file; in browsers, IndexedDB is used; and on edge platforms, a key-value adapter is available. The tool operates wherever JavaScript runs, including Electron and edge workers, and it is suitable for a range of applications such as chatbots, coding agents, local-first apps with semantic search, compliance-sensitive assistants, home automation, and game NPCs. rememori is distributed as a single npm package, with a core size of approximately 8 kB gzipped, and is licensed under the MIT license. It is not intended as a multi-user server, cloud solution, or document-ingestion platform, but rather as a memory engine embedded directly within an agent's process for rapid deployment and minimal operational overhead.
- Remind AIrecallmemory.ioRemind AI is an open-source desktop tool that uses AI to capture and organize your digital activity as a personal memory system. It runs locally on your device, ensuring privacy, and provides an API for integration. Ideal for users seeking to optimize workflow and recall tasks without cloud dependency.
- remembgithub.comPersistent memory standard for AI agents — local, portable, zero config
- 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.
- Recallrecallmcp.comRecall is a memory-as-a-service system for Claude Code and other Model Context Protocol clients. It is built to give Claude a permanent memory store that survives session restarts and context compaction, so earlier decisions, patterns, preferences, and tasks can be retrieved in later sessions. The product’s core behavior is automatic memory capture and restoration through Claude Code hooks. According to the documentation, install the plugin once and four hooks handle context capture and restore on every session with no prompting required. Recall also supports cross-session workflows with named threads, automatic consolidation of similar memories while keeping originals as history, persistent to-do lists with priority levels, and semantic search based on vector embeddings and cosine similarity. It organizes memories by types such as decisions, patterns, preferences, and errors, and it includes real-time knowledge sharing so one Claude can learn something and other instances can know it immediately. Recall is described for developers who want AI agents to remember conversations, learn patterns, and maintain context across sessions, projects, and teams. The page gives examples that include multi-session continuity, deployment monitoring, cross-session bug hunts, automatic decision records, multi-project switching, and sprint tracking. It also states that webhook ingestion can receive events from GitHub, Stripe, Sentry, Slack, Linear, PagerDuty, Discord, or any custom system and route them into Claude sessions, with HMAC signature verification, event history, replay, and audit trails. Delivery is through a Claude Code plugin, MCP support for Claude Desktop and other MCP clients, and a REST API with CRUD operations, search, and batch processing. The MCP configuration shown uses the URL https://recallmcp.com/mcp. Pricing is shown as free on the site, and the documentation mentions a Get Started Free option. The page also states that it is SOC 2 ready, uses AES-256-GCM encryption at rest with per-tenant keys derived via HKDF, supports complete data isolation per API key, and offers seven embedding providers: Voyage AI, Cohere, OpenAI, Deepseek, Grok, Anthropic, and self-hosted Ollama.
- Recall™recall.worksRecall™ is an open reference architecture for durable memory, audit, and continuity in AI coding agents. It is designed around the problem that modern agents are stateless: each conversation starts cold, and memory, audit, and long-term context depend on what the host platform keeps or a chat transcript preserves. The site describes it as a way to turn an agent into something that reads a project log before acting, rather than relying on its own short session history. Its documented components are memory, local compute, vault, guardrails, brain backup, and continuity. The architecture uses hot, warm, and cold context tiers and a networked-brain pattern. The brain is described as a vector database that can scale to tens of thousands of chunks of knowledge, and the site says the log lives on disk, in a vector brain, and in immutable cloud storage. It also mentions a cold-start protocol, overnight backup, and a small set of conventions that work with about 600 lines of glue. The reference implementation is shown with VS Code, GitHub Copilot Chat, an object-storage backend, and a local open-weights model running on a consumer GPU. The site also says it is not bound to those components and can be swapped freely. A quickstart is provided with three commands, along with links to the whitepaper, GitHub, and comparison material that references Cursor, Cline, Aider, Continue.dev, LangChain, and AutoGen. Recall™ is free to read, free to use, and free to adapt. The whitepaper is shared freely, and the reference implementation is permissively licensed under MIT. The site also notes that attribution is appreciated and that the trademark application is pending.
- Remnicremnic.aiRemnic is an open-source, local-first memory store that lets AI agents share context across sessions and tools. It stores memories as plain Markdown files on the user's machine, with recall and extraction workflows for Claude Code, Codex CLI, Cursor, ChatGPT, and MCP clients.
- Memori Labsmemorilabs.aiMemori Labs provides an agent-native memory infrastructure designed for production AI systems. The platform offers a layer that is agnostic to large language models (LLMs), enabling agent execution and conversations to be transformed into structured, persistent state. This infrastructure is intended to help AI agents and their developers capture, organize, and recall information from interactions and documents efficiently, without the need for additional external services. A core feature of Memori is its ability to automatically capture each turn in a chat and classify the information into facts, preferences, rules, and summaries. Users retain control over what data is stored, its retention duration, and storage location. When context is needed for prompts, the system retrieves only the most relevant information across conversations and documents. Memori enhances search accuracy through selective semantic search, enriching queries with semantic context to improve results and reduce token costs. Every recall provides an explanation of why specific information was included, offering traceability by entity, time, and source. 95% accuracy rate on the LoCoMo benchmark and a 95% reduction in token usage compared to full-context retrieval. Developers can integrate Memori with a single line of code using its SDK, which manages model calls and callbacks with zero configuration. Memori Cloud allows instant storage and search of memories, requiring no additional setup. The tool also features an interactive memory graph to visualize relationships and analytics to monitor memory creation, recall usage, and cache performance. Memori is positioned to help enterprises reduce costs by over 95% through tokenless recall and structured memory, and aims to deliver fast responses by caching concise snippets. The platform supports secure memory for payments and sensitive information, with PCI and SOC 2 compliance. It is designed for developers and teams building AI agents, and has been noted for potential integration with ecosystems such as MongoDB. The service emphasizes explainable results, intelligent routing, and instant context from historical content, catering to the needs of production-scale AI applications.
- Remanentiaremanentia.comRemanentia is an evidence memory layer for local AI systems that need to remember operational context, retrieve sources, and verify answers before they leave the system. It is described as the memory layer of the Anulum agent runtime and is aimed at operators who want AI systems to use memory without turning internal records into public text. The system turns scattered project knowledge into controlled evidence. It indexes files, documents, handovers, session logs, project facts, and selected corpora into a refreshable memory layer. For retrieval, it combines keyword search, embeddings, reranking, compiled facts, and direct recall paths to produce source-grounded answers. A factual guard verifies generated answers against retrieved evidence and blocks unsupported public output. It also has a public-safety boundary in which public APIs expose allowlisted corpora and redacted snippets while private operational memory remains internal by default. The page also says it ingests findings and decisions from the SYNAPSE coordination feed through an admission gate. Remanentia is offered as a self-hosted memory service that can run beside existing agents and applications, with local-first inference and explicit hosted fallback modes. It also supports a controlled public API for approved corpora, verified snippets, and factual-control decisions. The page says it is part of the Anulum agent runtime, alongside Director-AI, Synapse, and HushLine, but Remanentia itself is the memory component. For commercial use, the verification layer referred to as Director Class AI is licensed for closed-source products or SaaS systems.
- AgentRecallagentrecall.cloudAgentRecall is an open-source SDK that equips AI agents with persistent, intelligent memory, allowing them to store, search, and recall information across sessions. It supports graph relationships, semantic search, and multi-agent scenarios, making it ideal for developers building advanced AI agent systems.
- Memoraememorae.aiMemorae acts as a memory layer above all your apps, consolidating reminders, lists, and briefings into a single, organized system. It helps users manage information overload by unifying context and providing actionable insights across platforms. Suitable for professionals seeking better productivity and organization.
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