Memory Continue
Resume previous work by reconstructing context from the Basic Memory knowledge graph, so the assistant can pick up across sessions instead of starting cold.
When to Use
- Starting a new session and you need to pick up where you left off
- The user references earlier work: "continue with...", "back to...", "where were we on...?"
- You need context about an ongoing project or spec
- The user asks about something discussed in a previous conversation
- You're working on a task that spans multiple sessions
Building Context
1. Identify What to Continue
If it's unclear, ask:
- What topic or project should you resume?
- What timeframe matters?
- Any specific aspect to focus on?
2. Gather Context with MCP Tools
Known topic — use . Navigate the graph from a starting point, following relations outward:
python
build_context(
url="memory://topic-or-note-name",
depth=2, # how many relation hops to follow
timeframe="7d", # bias toward recent changes
)
No clear starting point — use . See what's changed and let it surface the thread:
python
recent_activity(timeframe="3d", depth=1)
Looking for something specific — use . Find candidate notes by keyword:
python
search_notes(query="async client refactor", page_size=10)
3. Read the Key Notes
Once you've identified the relevant notes, read them in full:
python
read_note(identifier="note-title-or-permalink")
4. Present Context to the User
Summarize what you found, incrementally:
- Current state of the work
- Recent changes or progress
- Open items and next steps
- Related context that might help
Memory URL Reference
and
both accept
URLs, which address notes by permalink and support wildcards for gathering groups of notes.
memory://note-title # a single note by permalink
memory://folder/* # all notes in a folder
memory://specs/SPEC-24* # pattern / prefix match
memory://project/*/requirements # path wildcards
Use a specific note URL to anchor on one starting point; use a wildcard to pull in a whole folder or family of related notes at once.
Timeframe Reference
and
accept natural-language timeframes:
| Timeframe | Meaning |
|---|
| Current day |
| Previous day |
| or | Last 3 days |
| or | Last week |
| Last 2 weeks |
| Last month |
Scenario Playbooks
Resuming a Spec or Project
python
# 1. Read the spec / project note
read_note(identifier="SPEC-24: Postgres Database Migration")
# 2. Pull in related context and recent changes via the graph
build_context(url="memory://SPEC-24*", timeframe="7d")
Then summarize: the goals, what's completed, what's pending, and any blockers or open decisions.
Continuing General Work
python
# 1. Check recent activity
recent_activity(timeframe="3d")
# 2. Read notes from the recent sessions it surfaces
read_note(identifier="relevant-note")
Then list the modified notes with brief descriptions and ask which thread to dive into.
Following Up on a Topic
python
# 1. Find the topic
search_notes(query="topic keywords")
# 2. Build context from the best match, following its relations
build_context(url="memory://found-note-permalink", depth=2)
Then present the full picture — the note plus its connected context.
Project Discovery
Project names are user-specific. To discover what's available before scoping a search or
URL:
In multi-project setups, prefix a
URL with the project name (e.g.
memory://research/papers/crdt
) to scope it.
Guidelines
- Start broad, then narrow. Get an overview with or a wildcard , then drill into specific notes.
- Present incrementally. Share what you find as you go rather than holding everything until the end.
- Follow relations. The graph's connections are the point — with surfaces context you wouldn't find by reading one note.
- Check multiple projects. Specs may live separately from implementation notes; discover projects with .
- Confirm understanding. Verify the reconstructed context is what the user actually needs before acting on it.
- Capture new progress. As the resumed work advances, write it back to the graph (see the memory-notes skill) so the next session can continue too.