OpenMemBrain is the intelligent membrane for AI coding memory. It autonomously reads and learns from your coding sessions — you never have to tell it
OpenMemBrain is the intelligent membrane for AI coding memory. It autonomously reads and learns from your coding sessions — you never have to tell it what to save. It selectively absorbs project knowledge, blocks secrets, filters noise. Exposed over MCP by the openmembrain mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
Everything the assistant can do here goes through one of these:
remember — save structured memory directly. Provide content, type, and optional scope/tags. No API key needed. Supports single and batch modepropose_memory_from_session — submit a session transcript or summary for server-side LLM extraction. Requires a configured extractor. Useful for automation adaptersget_project_rules — retrieve project rules and conventions for the current scopeget_relevant_context — find memories relevant to a natural language querysearch_memory — search saved memories by query, scope, type, or tagslist_memory_candidates — list pending memory candidates awaiting approvalapprove_memory_candidate — approve a pending candidate to save it as memoryapprove_all_candidates — approve all pending candidates at oncereject_memory_candidate — reject a pending candidate with an optional reasonreject_all_candidates — reject all pending candidates at onceupdate_memory — update the content, type, scope, or tags of a saved memorysupersede_memory — mark a memory as superseded, optionally linking a replacementYou will need one environment variable: OPENMEMBRANE_PROJECT_ID. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Being a remote server, there is no local install. You register the endpoint with your client, authorise it once, and the tools appear.
This sits in the knowledge and memory group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. OpenMemBrain's toolset — remember, propose_memory_from_session, get_project_rules and 11 more — is a fair guide to whether it matches your workflow.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| remember | save structured memory directly. Provide content, type, and optional scope/tags. No API key needed. Supports single and batch mode. |
| propose_memory_from_session | submit a session transcript or summary for server-side LLM extraction. Requires a configured extractor. Useful for automation adapters. |
| get_project_rules | retrieve project rules and conventions for the current scope. |
| get_relevant_context | find memories relevant to a natural language query. |
| search_memory | search saved memories by query, scope, type, or tags. |
| list_memory_candidates | list pending memory candidates awaiting approval. |
| approve_memory_candidate | approve a pending candidate to save it as memory. |
| approve_all_candidates | approve all pending candidates at once. |
| reject_memory_candidate | reject a pending candidate with an optional reason. |
| reject_all_candidates | reject all pending candidates at once. |
| update_memory | update the content, type, scope, or tags of a saved memory. |
| supersede_memory | mark a memory as superseded, optionally linking a replacement. |
| review_stale_memories | list memories older than a threshold (default: 6 months). |
| export_static_memory_files | generate static instruction files (AGENTS.md, CLAUDE.md, etc.). |
{
"mcpServers": {
"openmembrane": {
"command": "npx",
"args": ["openmembrane"]
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| OPENMEMBRANE_PROJECT_ID | Configuration value read at startup. | Optional |
A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.