semantic-memory-mcp
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
SemanticMemory
简体中文 | English
SemanticMemory is an evidence-first, local-first store for the facts and tasks an AI agent should treat as current. It keeps claims and tasks in readable Markdown and exposes them through a small CLI and an MCP stdio server.
It is designed to pair with ReMe:
Layer | Answers | Storage |
ReMe | What happened, what was discussed, where is the evidence? | Sessions, daily notes, digests, resources |
SemanticMemory | What is true now, and what still needs action? | Claims, tasks, source quotes |
SemanticMemory does not import ReMe core code. The optional reme_ref field points
back to ReMe evidence without making ReMe a runtime dependency.
This is a community companion, not an official ReMe package. The PyPI distribution
name is semantic-memory-reme; the Python import namespace is
semantic_memory_reme.
Why
Agent memory usually mixes three different things:
original evidence;
a current fact;
an action that still needs to happen.
SemanticMemory keeps the second and third as deterministic files with review states, lifecycle history, and verifiable source quotes. Extracted indexes are not the source of truth.
Related MCP server: aafp-commons
Install
Python 3.11 or newer is required.
PyPI publication is intentionally deferred. Install the current wheel directly from the GitHub Release:
python -m pip install \
https://github.com/star-nebula/SemanticMemory/releases/download/v0.1.1/semantic_memory_reme-0.1.1-py3-none-any.whlFor development, install from source:
git clone https://github.com/star-nebula/SemanticMemory.git
cd SemanticMemory
python -m pip install -e ".[dev]"If pip is unavailable, the source tree can be run with Python 3.11+ and PyYAML already installed:
PYTHONPATH=src python -m semantic_memory_reme demoQuick Start
See the complete flow without creating persistent files:
semantic-memory demoThe demo creates a temporary claim, source quote, related task, and search result.
Create a private workspace:
semantic-memory init --root .semantic-memoryCreate a source file and a claim candidate:
mkdir -p sources .semantic-memory/inbox
printf '%s\n' 'I prefer concise explanations in English.' > sources/profile.md
cp templates/Memory-Claim.md .semantic-memory/inbox/pref-concise-explanations.mdEdit the candidate to use a unique id, set status: active, and point
sources[].path at ../sources/profile.md. Then promote it:
semantic-memory add .semantic-memory/inbox/pref-concise-explanations.md \
--root .semantic-memorySearch current claims:
semantic-memory search "concise" --root .semantic-memory --jsonManage tasks:
semantic-memory task add \
--root .semantic-memory \
--project demo \
--title "Validate my first claim"
semantic-memory task list --root .semantic-memory --jsonWorkspace Layout
.semantic-memory/
claims/<kind>/<id>.md
tasks/open/
tasks/done/
inbox/
archive/The root is resolved in this order:
--rootSEMANTIC_MEMORY_ROOT./.semantic-memory
Relative source paths are resolved from the workspace root. Absolute source paths are supported for private local use.
CLI
semantic-memory init
semantic-memory demo
semantic-memory validate
semantic-memory search QUERY
semantic-memory get CLAIM_ID
semantic-memory trace CLAIM_ID
semantic-memory changes --since YYYY-MM-DD
semantic-memory add FILE
semantic-memory transition CLAIM_ID --status STATUS
semantic-memory review CLAIM_ID --status STATUS
semantic-memory set-validity CLAIM_ID --valid-to YYYY-MM-DD
semantic-memory maintenance
semantic-memory task validate|list|add|complete|cancelAdd --json to commands that support machine-readable output.
MCP
Read-only mode is the recommended default:
{
"mcpServers": {
"semantic-memory": {
"command": "semantic-memory-mcp",
"args": ["--root", ".semantic-memory", "--read-only"]
}
}
}Remove --read-only only when the client should be able to add, transition,
review, or update claims and tasks.
Read-only tools:
semantic_statussemantic_searchsemantic_getsemantic_tracesemantic_changessemantic_validatesemantic_maintenancetask_listtask_validate
Evidence Rules
Every claim requires at least one source path and an exact quote.
human_review: pendingis not accepted truth.Superseding a claim preserves the old file and records the relationship on both sides.
Only one active claim can occupy a
singlesubject + predicate + scopeslot.valid_tomarks time-bound facts and is checked by validation and maintenance.
See Schema.md and architecture.md.
ReMe Integration
ReMe remains responsible for raw sessions, timelines, Markdown memory, and evidence retrieval. SemanticMemory stores only the current conclusion and a pointer such as:
reme_ref: reme/session/example.jsonlSee docs/reme-integration.md for the recommended workflow.
Privacy
SemanticMemory is not encrypted and may contain sensitive personal or business
information. Keep the workspace private, back it up, and apply filesystem
permissions appropriate to your environment. The default workspace name is in
.gitignore, and semantic-memory init adds a nested ignore file to the workspace
as a second safeguard.
Run the release audit before publishing changes:
python scripts/audit_release.py .Development
python -m pip install -e ".[dev]"
python -m unittest discover -s tests -v
python -m build
python scripts/audit_release.py .Validate the synthetic example:
semantic-memory validate --root examples/minimal --json
semantic-memory task validate --root examples/minimal --jsonLicense
This server cannot be deployed
Maintenance
Related MCP Connectors
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Persistent memory, hybrid search and a goal graph for AI agents, over stdio or remote HTTP.
Agent-native MCP server over the public saagarpatel.dev corpus. Read-only, stateless.
Markdown workspace for AI agents: read, write, organize, and share markdown documents.
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