Agenten stärken
Der eigene Befehl des Herausgebers. Kein PlugMCP-Endpunkt.
Host a copy with our keys and logs.
mcpb https://github.com/PeterPirog/mcp-light-memory/releases/download/v1.8.1/mcp-light-memory-1.8.1.mcpbClaude Code hat keinen Installationslink für einen lokalen Befehl. Kopieren Sie den Befehl unten und führen Sie ihn in einem Terminal aus.
claude mcp add io.github.peterpirog-mcp-light-memory -- mcpb https://github.com/PeterPirog/mcp-light-memory/releases/download/v1.8.1/mcp-light-memory-1.8.1.mcpbIn einem Terminal ausführen.
{ "mcpServers": { "io.github.peterpirog-mcp-light-memory": { "command": "mcpb", "args": [ "https://github.com/PeterPirog/mcp-light-memory/releases/download/v1.8.1/mcp-light-memory-1.8.1.mcpb" ] } }}claude_desktop_config.json
Remote-URL oder ein Befehl auf Ihrem Rechner? Worin sich die beiden Installationen unterscheiden
Auf PlugMCP ausführen
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
StartenVom Herausgeber
MCP Light Memory is a lightweight, local-first, persistent memory system for coding agents and MCP clients (Warp, OpenCode, JetBrains AI Assistant / PyCharm, Claude Code, Cursor). It acts as a checkpoint + retrieval layer — it stores the minimum durable state needed to resume complex work across sessions, without keeping the full conversation in the model's context window.
Local-first persistent memory for coding agents and MCP clients.
When your agent starts a task, it calls context and gets back relevant past decisions, gotchas, constraints, and hypotheses — ranked, deduplicated, and trust-bounded. When it finishes, it checkpoints the working state. Next session, even after a restart, the memory is there.
.md file with YAML frontmatter (id, type, status, tags, sources, links, valid_from, valid_to, supersedes). Human-readable, diffable, durable.remember → update → supersede (links both directions, never deletes history) → forget (archives, never deletes) → timeline (temporal view). search --at YYYY-MM-DD for historical queries.=== BEGIN/END INTERNAL_RAG MEMORY === with a SECURITY NOTICE header. Structured JSON/MCP carries trust: untrusted + optional security_flags: ["instruction_like_content"].evidence_state (present/missing/unverifiable) for local path-like evidence — derived at retrieval time, never persisted.write:false blocks mutating tools before spawning a child. Per-call subprocess isolation (no shared state).py launcher, python, or python3 — the installer auto-detects the real interpreter and rejects the WindowsApps stub)pip install sentence-transformers numpy for better semantic retrievalThe current version is defined by the VERSION file — check it (or run mlm.py --version) instead of hard-coding an expected number.
Clone this repo once, then install into any project:
# Windows (PowerShell)
git clone https://github.com/PeterPirog/mcp-light-memory.git ~/mcp-light-memory
python ~/mcp-light-memory/install.py . --client warp
# Linux/macOS
git clone https://github.com/PeterPirog/mcp-light-memory.git ~/mcp-light-memory
python3 ~/mcp-light-memory/install.py . --client warp
The installer:
INTERNAL_RAG/ + AGENTS.mdinit + checkpoint + validate (so guard is OK immediately)MANUAL_REQUIRED / prints JetBrains instructions)python .agents\skills\internal-rag\mlm.py --version # reports the installed version
python .agents\skills\internal-rag\mlm.py status # expect: INTERNAL_RAG ready
python .agents\skills\internal-rag\mlm.py guard # expect: GUARD OK
One installer, four clients, two config scopes. Full guide: docs/INSTALLATION.md.
--global changes the scope of the CLIENT CONFIG (~/.warp/.mcp.json vs {repo}/.warp/.mcp.json, ~/.config/opencode/opencode.json vs project opencode.json). The server still points at the target project you installed into.You can paste one of these directly into the client agent. Replace C:\Projects\App with the real target repository path.
Warp — install for one project:
Install and configure MCP Light Memory (mcp-light-memory) as an MCP server for project C:\Projects\App in Warp, using project scope. Use the repository https://github.com/PeterPirog/mcp-light-memory. If the tool is not cloned yet, clone it to a stable location outside the project; if it already exists, update it with git pull --ff-only. Apply the canonical installation contract from the repository and run install.py with TARGET_PROJECT=C:\Projects\App and --client warp without --global. Do not force-overwrite an existing configuration. After installation, verify from cwd=C:\Projects\App: mlm.py --version, mlm.py status, and mlm.py guard, and confirm that the Warp configuration contains mcp-light-memory and the C:\Projects\App path. Report success only after MCP REGISTRATION: REGISTERED and successful verification. If Warp requires an additional project activation/toggle/approval, state the exact client-side step and do not claim the server is active before it is completed.
Warp — global client config for one project:
Install and configure MCP Light Memory (mcp-light-memory) in Warp globally for project C:\Projects\App. Use the repository https://github.com/PeterPirog/mcp-light-memory. If the tool is not cloned yet, clone it to a stable location outside the project; if it already exists, run git pull --ff-only. Apply the canonical installation contract and run install.py with TARGET_PROJECT=C:\Projects\App, --client warp, and --global. Remember: --global means the global Warp client configuration, while the server must still be bound to C:\Projects\App; do not use the multi-project router. After installation, verify from cwd=C:\Projects\App: mlm.py --version, mlm.py status, and mlm.py guard, and confirm that the global Warp configuration contains mcp-light-memory and the C:\Projects\App path. Report success only after MCP REGISTRATION: REGISTERED and successful verification.
OpenCode — install for one project (stable/V1):
Install and configure MCP Light Memory (mcp-light-memory) as an MCP server for project C:\Projects\App in OpenCode. By "OpenCode" I mean stable/V1, so use --client opencode, not opencode2. Use the repository https://github.com/PeterPirog/mcp-light-memory. If the tool is not cloned yet, clone it to a stable location outside the project; if it already exists, run git pull --ff-only. Run install.py with TARGET_PROJECT=C:\Projects\App and --client opencode without --global. Do not force-overwrite an existing configuration. If the installer returns MCP REGISTRATION: MANUAL_REQUIRED (for example because opencode.jsonc exists), do not report success: safely edit the JSONC while preserving comments and unrelated settings if you have appropriate file-editing tools; otherwise report the exact manual action required. After real registration, verify from cwd=C:\Projects\App: mlm.py --version, mlm.py status, and mlm.py guard, and confirm that the OpenCode configuration contains mcp-light-memory and C:\Projects\App.
OpenCode — global client config for one project (stable/V1):
Install and configure MCP Light Memory (mcp-light-memory) globally in OpenCode for project C:\Projects\App. By "OpenCode" I mean stable/V1, so use --client opencode. Use the repository https://github.com/PeterPirog/mcp-light-memory. If the tool is not cloned yet, clone it to a stable location outside the project; if it already exists, run git pull --ff-only. Run install.py with TARGET_PROJECT=C:\Projects\App, --client opencode, and --global. --global means the global OpenCode client configuration, while the server must still be bound only to C:\Projects\App; do not use the multi-project router. If the installer returns MCP REGISTRATION: MANUAL_REQUIRED, do not report success and follow the safe JSONC instructions. After real registration, verify from cwd=C:\Projects\App: mlm.py --version, mlm.py status, and mlm.py guard, and confirm that the global OpenCode configuration contains mcp-light-memory and the C:\Projects\App path.
For OpenCode 2 / V2, use the same prompts but explicitly say OpenCode 2 / V2 and require --client opencode2. More variants: docs/ZERO-SHOT-SETUP-PROMPTS.md.
Warp reads MCP server configs from ~/.warp/.mcp.json (global, auto-spawns) or
{repo}/.warp/.mcp.json (project, requires a manual toggle per Warp docs).
Shape: mcpServers.<name> with command, args, working_directory (always set it — the memory store is resolved from it). See examples/warp.example.json and docs/WARP-SETUP.md.
OpenCode reads opencode.json/.jsonc in the project root, or
~/.config/opencode/opencode.json globally. V1 servers are flat under
mcp.<name> (no servers sub-key) with enabled: true and command as an
array — see examples/opencode-legacy.example.json and docs/OPENCODE.md.
Same config files, different shape: mcp.servers.<name>, command as an
array, and no enabled field (V2 disables via disabled: true) — see
examples/opencode-v2.example.jsonc and docs/OPENCODE.md.
PyCharm does NOT auto-read any MCP config file. The installer prints
ready-to-paste JSON + Working Directory; you add the server in
Settings → Tools → AI Assistant → MCP (STDIO) and choose Server level =
Project or Global. See examples/jetbrains.example.json.
One MCP connection in front of many projects — registry allowlist, write:false hard boundary, per-call subprocess isolation.
projects.json){
"projects": {
"backend": { "root": "/abs/path/backend", "write": true },
"shared-lib": { "root": "/abs/path/shared-lib", "write": false }
}
}
{
"mcpServers": {
"mcp-light-memory-router": {
"command": "python3",
"args": ["/abs/path/mcp-light-memory/.agents/skills/internal-rag/irag_mcp_router.py", "--registry", "/abs/path/projects.json"],
"working_directory": "/abs/path/mcp-light-memory"
}
}
}
See docs/MCP-MULTI-PROJECT.md for details.
context --task "current task"
↓
recovery, if required (RECOVERY REQUIRED)
↓
checkpoint before first change
↓
implementation
↓
checkpoint after each milestone
↓
guard before finishing
Core commands (CLI alias: mlm.py or legacy irag.py):
mlm.py context --task "..."
mlm.py checkpoint --reason "..."
mlm.py search --query "..." --limit 8
mlm.py remember --type decision --title "..." --body "..."
mlm.py show <ref>
mlm.py update <ref> --status superseded
mlm.py status
mlm.py guard
mlm.py validate
mlm.py doctor
The on-disk folder INTERNAL_RAG/ and the skill directory .agents/skills/internal-rag/ are intentionally kept under their legacy names for zero-migration backward compatibility. See docs/MIGRATION-TO-MCP-LIGHT-MEMORY.md.
remember --type decision --title "..." --body "..." --tags "a,b" --evidence "src/x.py:42" --links "decisions/other.md"
show <path-or-id>
show <ref> --section Knowledge
update <ref> --add-tags "new" --append "New evidence: ..."
supersede <ref> --by <new> --reason "..."
forget <ref> # archives, does not delete
link --from <ref> --to <ref>
timeline --limit 20
status
history
Types: decision, knowledge, constraint, gotcha, failure, hypothesis, session.
mlm.py push --task "interrupted work" --reason "user-priority"
mlm.py tasks
mlm.py resume
mlm.py forget-task <id> # drop a specific task
mlm.py forget-task # clear the whole stack
.irag.yml, optional)retrieval:
limit: 10
mmr_lambda: 0.4
min_score: 0.3
embeddings: auto # auto | on | off
profile: english-fast # english-fast (default) | multilingual (PL/EN projects)
embeddings_model: null # explicit model overrides the profile
tokens:
context_budget: 5000
checkpoints:
auto_archive_sessions: true
max_task_stack: 24
mlm.py config shows the effective configuration. mlm.py config --init writes a template.
pip install -r requirements-optional.txt
When the package is available and .irag.yml has embeddings: auto (default), retrieval uses embeddings with fallback to BM25. Override at runtime with --embeddings on|off|auto.
Two retrieval profiles (see docs/EMBEDDINGS.md):
english-fast (default, all-MiniLM-L6-v2)multilingual (intfloat/multilingual-e5-small) — for Polish-English projectspython pack.py --with-embeddings --profile english-fast
# -> internal-rag-offline-1.8.1.zip (name from pack.py; 1.8.1 = VERSION file)
# On the air-gapped machine:
unzip internal-rag-offline-*.zip -d internal-rag-offline
pip install --no-index --find-links wheels/ -r requirements-optional.txt
python install.py "/path/to/project" --client <warp|opencode|opencode2|jetbrains>
See docs/OFFLINE.md for details.
The default install mode is local-only. The installer uses .git/info/exclude, not the project's .gitignore, so local memory and integration files are not accidentally committed.
Before publishing a project:
python .\privacy_check.py "D:\path\to\project"
Expected: RESULT: PASS
python .\uninstall.py "D:\path\to\project"
The uninstaller creates a backup outside the repository, then removes INTERNAL_RAG and its integrations. Use --keep-memory to preserve the memory data.
project/
├── AGENTS.md
├── .irag.yml # optional config
├── INTERNAL_RAG/
│ ├── WORKING_STATE.md
│ ├── INDEX.md
│ ├── .checkpoint.json
│ ├── decisions/ knowledge/ gotchas/ failures/ hypotheses/ sessions/ archive/
│ └── exports/
├── .agents/skills/internal-rag/
│ ├── SKILL.md
│ ├── mlm.py # primary CLI (forwards to irag.py)
│ ├── irag.py # core (legacy alias, still the canonical module)
│ ├── irag_embeddings.py # optional plugin
│ └── irag_hooks.py # optional git hooks
└── .opencode/ # OpenCode integration (optional)
Memory can be stale. Code takes precedence.
MIT.
--client jetbrains no longer writes a fake config file (PyCharm ignores MCP config files). Prints ready-to-paste JSON + IDE menu instructions instead.--unregister --client jetbrains prints a reminder to remove in the IDE UI.working_directory as a hint + prints WARNING with exact path to set in Settings → Tools → AI Assistant → MCP.Memory store: <path> printed in install output for immediate verification.detect_python() rejects the WindowsApps 0-byte stub; prefers py -0p; verifies each candidate with --version.--version immediately after writing the config and reports PASS/FAIL.--unregister deletes empty config files + parent dirs (fixes dead .warp/.mcp.json skeleton → GUARD STALE).internal-rag to MCP Light Memory (mcp-light-memory). New CLI alias mlm (mlm.py). Logo/icon assets. Migration doc. GitHub rebrand checklist.irag.py, INTERNAL_RAG/, old MCP server names preserved as deprecated aliases.trust: untrusted + security_flags. Evidence freshness (ADR-016): evidence_state. Scale benchmark (100/1k/10k). Router security regressions (+12 tests). Docs consistency test. 249 tests pass.2026-07-28 dual-era (server/discover, _meta, structuredContent, outputSchema). Registry strict write. Sources in chunk prefix. Adaptive retrieval. Link-aware context. consolidate --prepare. Router latency benchmark. ADR-010…016.--meta). FTS5 candidate prefilter. Multi-project MCP router. MCP protocol hardening (pure stdout, SDK-verified). 168 tests.valid_from/valid_to/supersedes/--at). consolidate --dry-run.index --vacuum/--embed-missing.Local-first persistent memory for coding agents and MCP clients.
{ "mcpServers": { "io.github.peterpirog-mcp-light-memory": { "command": "mcpb", "args": [ "https://github.com/PeterPirog/mcp-light-memory/releases/download/v1.8.1/mcp-light-memory-1.8.1.mcpb" ] } }}Tools
Noch keine Tool-Liste. Remote-Server erhalten eine nach dem ersten Health-Check.
FAQ
Kopieren Sie auf dieser Seite die stdio-Konfiguration für Cursor, Claude Desktop, Claude Code oder VS Code. Sie nutzt die Installation des Herausgebers, keinen PlugMCP-Proxy.
In diesem Eintrag ist keine erforderliche Umgebungsvariable aufgeführt.
MCP Light Memory läuft lokal über stdio.
Zuletzt geprüft: 2026-11-09. Local-first persistent memory for coding agents and MCP clients.