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Provide local-first multilingual memory for Codex, Claude Code, and MCP clients.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Ariadne" yet — see the docs or source repo.
Please organize this project's key context into searchable memory: the stack is Python, FastAPI, and PostgreSQL; deployment uses Docker; all API responses should use snake_case; prioritize this information in future questions.
A structured project context suitable for storing in memory, so it can be retrieved and reused in later coding conversations.
Turn the following Chinese and English notes into unified memory: Chinese: “User data must not leave the local environment”; English: “All embeddings should be processed locally when possible.” Keep the original text and link them together.
Bilingual entries ready for a multilingual memory system, enabling cross-language retrieval of the same concept.
Please consolidate the shared conclusions from my last three requirement discussions into a long-term context summary for future MCP client sessions.
A concise long-term context summary suitable for storing in a memory layer and reusing in future sessions.
Developers using Codex or Claude Code can store project context, conventions, and prior decisions as local memory to avoid repeating themselves. This helps future sessions continue from earlier context.
When a team records information in both Chinese and English, multilingual memory can preserve it in a unified way. Later, related knowledge is easier to retrieve regardless of the query language.
Users who prefer local processing can connect Ariadne to MCP-compatible clients to add reusable memory to local-first workflows. Its description mentions Qdrant and Ollama.
Ariadne is an MCP tool that provides local-first multilingual memory for Codex, Claude Code, and other MCP clients. Its description also mentions using Qdrant and Ollama.
It is stated to support Codex, Claude Code, and MCP clients. The exact compatibility scope is not provided in the given material; see the source repository.
The description explicitly mentions Qdrant and Ollama, so they appear to be relevant prerequisites. Installation steps, configuration details, and any other dependencies are not provided in the given material; see the source repository.
Search local AI coding chat history and retrieve compact evidence fast.
Provide local-first shared memory, auditing, and sync across AI clients.
Provide durable, local-first memory for AI coding agents across MCP hosts.
Give Claude Code persistent memory, checkpoints, and project sync across sessions.
Give Claude Code long-term memory, context compression, and cross-session semantic search.
Provide categorized long-term memory and retrieval for Claude Code and MCP clients.