io.github.marerem/longmem
PYPI · LONGMEM · SCANNED SEP 20
Persistent cross-project memory for Cursor and Claude Code using local semantic search.
Available components
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security49
- Malware scan not yet available for this package.Unverified
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs hatchling.build at install time, a recognised native-build step with no shell scripting around it. View diagnostics → Pass
- 6 of 36 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 156 days ago).Pass
- Security-disclosure policy not yet verified: we couldn't inspect the source repository.Unverified
Schema Quality & AI Usability76
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 1479 tokens (~134/item across 11 items; 11 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management83
- Stability observed for 25 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety75
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- 0 of 1 tool(s) whose name or description implies an irreversible operation declare an MCP destructiveHint annotation; "delete_solution" implies "delete" and declares no destructiveHint at all, which the MCP spec reads as destructive by default. See how to fix → Fail
- An AI judge read all 12 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
How do I install the io.github.marerem/longmem MCP server?
io.github.marerem/longmem runs locally as a PyPI package, launched with uvx longmem. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · longmem
claude mcp add marerem-longmem -- uvx longmem
{
"mcpServers": {
"marerem-longmem": {
"command": "uvx",
"args": [
"longmem"
]
}
}
} {
"servers": {
"marerem-longmem": {
"command": "uvx",
"args": [
"longmem"
]
}
}
} codex mcp add marerem-longmem -- uvx longmem
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"marerem-longmem": {
"type": "local",
"command": [
"uvx",
"longmem"
],
"enabled": true
}
}
} openclaw mcp add marerem-longmem --command uvx --arg longmem
mcp_servers:
marerem-longmem:
command: "uvx"
args: ["longmem"] {
"McpServers": {
"marerem-longmem": {
"Transport": "stdio",
"Command": "uvx",
"Arguments": [
"longmem"
]
}
}
} assistant mcp add marerem-longmem -t stdio -c uvx -a longmem
{
"mcpServers": {
"marerem-longmem": {
"command": "uvx",
"args": [
"longmem"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 20 Sept 26 +1
- Security disclosure: fail → unverified ▼ functional
- 18 Sept 26 −18
- Malware scan: pass → unverified ▼ security
- Stability: pass → 0.77 functional
- 17 Sept 26 +1
- Stability: 0.97 → pass security
- 16 Sept 26 +15
- Malware scan: unverified → pass ▲ security
- 15 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 90 to 93. That category is still filling its 30-day observation window: 27 days of observed history at the previous scan, 28 at this one. The score rises as the window fills, whether or not the server changes.
- 14 Sept 26 −15
- Malware scan: pass → unverified ▼ security
- Security disclosure: unverified → fail ▼ functional
- 13 Sept 26 0
- Security disclosure: fail → unverified ▼ functional
- 12 Sept 26 −2
- Stability: pass → 0.83 functional
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 20 Sept 2026 · Analysed pypi/longmem@0.2.1
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 36 packages
| Packages resolved | 36 |
|---|---|
| Stale | 6 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
add_edge_case ~119
Record a context where a cached solution didn't work as-is. Call this when search_similar returned a match but it needed modification to work in the current project. The edge case is appended to the entry so future suggestions include the caveat.
| Name | Type | Req | Description |
|---|---|---|---|
| edge_case | string | yes | Describe exactly why the solution didn't work in this context and what had to be done differently. Be specific: include versions, OS, config values, or environment details that matter. |
| entry_id | string | yes | The id returned by save_solution or search_similar. |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
confirm_solution ~142
Auto-save a confirmed solution using context from the last search_similar call. Call this after solving a problem instead of save_solution — you only need to provide the solution text. Problem description, category, tags, and language are taken automatically from the last search_similar call. If save_solution was already called manually this session, this is a no-op (no duplicate will be created).
| Name | Type | Req | Description |
|---|---|---|---|
| project | string | – | Repository or workspace name this was solved in. |
| solution | string | yes | The solution that worked. Include code, commands, or steps. Problem metadata (category, tags, language) are filled in automatically from the last search_similar call. |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
correct_solution ~143
Fix a specific piece of text in an already-saved solution. Call this when the user corrects a name, term, or detail that was saved incorrectly — for example 'it's not called Paperless-NGX, it's Papertagging'. Replaces all occurrences of `find` with `replace` in the solution text. Use enrich_solution to add new context. Use correct_solution to fix wrong text.
| Name | Type | Req | Description |
|---|---|---|---|
| entry_id | string | yes | The id returned by save_solution, confirm_solution, or search_similar. |
| find | string | yes | The exact text to find in the saved solution. |
| replace | string | yes | The text to replace it with. |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
delete_solution ~65
Permanently delete a saved entry. Use this to remove entries that were saved incorrectly, contain wrong information that can't be fixed with correct_solution, or are no longer relevant. This cannot be undone.
| Name | Type | Req | Description |
|---|---|---|---|
| entry_id | string | yes | The id of the entry to delete. |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
enrich_solution ~165
Append new context to an already-saved solution. Call this when a conversation reveals additional details AFTER a solution was already saved — for example, a follow-up clarification that makes the solution more reusable across projects. This is NOT for failures (use add_edge_case for those). This is for enrichment: new facts, patterns, or context that improve the answer.
| Name | Type | Req | Description |
|---|---|---|---|
| context | string | yes | New information that refines or extends the saved solution. Write as a reusable insight: state the general pattern first, then give specific details. E.g.: 'Port 4181 is used when 4180 is already tak… |
| entry_id | string | yes | The id returned by save_solution, confirm_solution, or search_similar. |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
list_recent ~66
List the most recently saved memory entries. Use this to audit what has been saved — for example, to find a recently saved entry whose id is not in context. Results are ordered newest-first.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Number of recent entries to return. Default 10. |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
rebuild_index ~78
Rebuild the vector search index for faster similarity search. LanceDB falls back to brute-force scan when the table has fewer than 256 rows. Once you have 256+ entries, call this once to build an ANN index — subsequent searches will be significantly faster. Safe to call at any time; existing data is not modified.
Input schema present but exposes no named parameters.
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
save_solution ~239
Save a problem/solution pair to the cross-project memory. Call this after successfully solving a problem so future sessions — in any project — can find and reuse the solution. Returns the entry ID which can be passed to add_edge_case later.
| Name | Type | Req | Description |
|---|---|---|---|
| category | string | yes | Problem domain. One of: ci_cd, containers, infrastructure, cloud, networking, observability, auth_security, data_pipeline, ml_training, model_serving, experiment_tracking, llm_rag, llm_api, vector_db… |
| language | string | – | Programming language, e.g. 'python'. |
| problem | string | yes | Clear description of the problem that was solved. |
| project | string | – | Repository or workspace name this was solved in. |
| solution | string | yes | The solution, including code snippets, commands, or steps. Be specific — this will be reused verbatim in future projects. |
| tags | – | – | Keywords for filtering: library names, tools, error types. E.g. ['airflow', 'dag', 'python', 'skip']. |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
search_by_project ~134
List saved entries for a specific project. Use this at the start of a new conversation when you need to find a project-specific entry to correct or enrich but no entry_id is in context. Returns entry ids, problems, and solutions so you can pick the right one and pass its id to correct_solution or enrich_solution.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Maximum number of entries to return. Default 20. |
| project | string | yes | Repository or workspace name to look up. |
| query | string | – | Optional keyword to filter results — searches problem and solution text. Leave empty to list all entries for the project. |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
search_similar ~249
Search the cross-project memory for solutions similar to the current problem. Call this FIRST before reasoning about a problem from scratch. If similarity ≥ threshold a cached solution is returned — check edge_cases to see if any known limitations apply to the current context. If no match is found, solve normally and then call confirm_solution.
| Name | Type | Req | Description |
|---|---|---|---|
| category | string | – | Problem domain. One of: ci_cd, containers, infrastructure, cloud, networking, observability, auth_security, data_pipeline, ml_training, model_serving, experiment_tracking, llm_rag, llm_api, vector_db… |
| language | string | – | Programming language if relevant, e.g. 'python', 'typescript'. |
| problem | string | yes | Describe the problem you are trying to solve. |
| tags | – | – | Optional keywords to narrow the search — library names, framework, error type, tool name. E.g. ['kubernetes','oom','python']. |
| threshold | – | – | Minimum similarity (0–1). Defaults to similarity_threshold in config.toml (default 0.85). |
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
stats ~50
Return database statistics: total entries, breakdown by category, and date range. Useful for understanding the size and composition of the memory store, and for deciding when to call rebuild_index (threshold: 256+ entries).
Input schema present but exposes no named parameters.
| Name | Type | Req | Description |
|---|---|---|---|
| result | string | yes | – |
No examples provided.
What is the io.github.marerem/longmem MCP server?
io.github.marerem/longmem is an MCP server listed in the public MCP registry as io.github.marerem/longmem. Persistent cross-project memory for Cursor and Claude Code using local semantic search. This page covers its PyPI package (longmem).
Is the io.github.marerem/longmem MCP server safe to use?
io.github.marerem/longmem scores 66 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 20 September 2026. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the io.github.marerem/longmem MCP server expose?
io.github.marerem/longmem exposes 11 tools: search_similar, save_solution, confirm_solution, correct_solution, enrich_solution, and 6 more. Their descriptions and schemas cost roughly 1,450 tokens of context every time the server is loaded.
Is the io.github.marerem/longmem MCP server still maintained?
io.github.marerem/longmem is still listed as active in the MCP registry. We last reached this channel on 20 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the io.github.marerem/longmem MCP server under?
io.github.marerem/longmem declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.