Give LLM agents persistent memory with hybrid search and project-scoped context.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "openchronicle-mcp" yet — see the docs or source repo.
Write this repository’s architecture notes, key modules, common commands, and the last 20 commits into OpenChronicle under the project scope, then build searchable memory for later coding-agent use.
Creates project-scoped long-term memory entries so the agent can later retrieve code context and development history.
Search OpenChronicle for memory related to “auth refactor” and “permission model changes,” combining semantic similarity, keyword matches, and git commit clustering, then return the most relevant findings.
Returns relevant design decisions, context summaries, and associated commit clusters to explain why changes were made.
I manage three codebases at once. Use OpenChronicle to create separate memory spaces for each project and ensure the agent only retrieves information from the active project scope when answering.
Sets up clear project-level memory isolation, reducing cross-project leakage and improving answer accuracy.
Give AI agents persistent memory with semantic and keyword search across projects.
Give MCP-compatible AI agents persistent local memory across sessions.
Give AI assistants persistent memory for preferences, context, and decisions.
Provide local-first persistent memory for AI agents across projects.
Give AI coding agents persistent semantic memory with embedding-based retrieval.
Lets AI retain conversation memory for more consistent long-term collaboration.