Problem
Fragmented access patterns across RLM:
- Memory:
rlm recall, rlm remember, MCP tools
- Analysis:
rlm scan, rlm chunk, rlm extract
- Hooks: direct Python imports
- TUI: direct database access
- Strategies: file I/O
- Archives: exports
Each resource has custom access code. Hard to compose operations across resources.
Proposal (from AIGNE paper analysis)
Unified virtual file system (VFS) abstraction:
from rlm.vfs import mount, list_dir, read, write, search, exec
# Unified namespace
mount('/rlm/memory/', backend=SQLiteMemory)
mount('/rlm/sessions/', backend=LocalFileSystem)
mount('/rlm/strategies/', backend=LocalFileSystem)
mount('/rlm/archive/', backend=ImmutableHistory)
mount('/rlm/scratchpad/', backend=EphemeralWorkspace)
# Uniform access
results = search('/rlm/memory/', query='security')
content = read('/rlm/sessions/abc123/manifest.json')
exec('/rlm/strategies/expand_query.py', args=['Iraq war'])
Implementation
- Design VFS API (
mount, list_dir, read, write, search, exec)
- Create backend adapters (SQLite, filesystem, exports)
- Incrementally migrate existing code
- Update MCP server to expose VFS operations
Impact
- Composability (pipe operations across resources)
- Extensibility (mount new backends without core changes)
- Cleaner architecture (uniform interface)
- Future-proof (easy to add vector DBs, APIs, knowledge graphs)
Effort
5-7 days (design, implementation, migration, testing)
Related
- File system abstraction from 'Everything is Context' paper (arxiv 2512.05470)
- AIGNE's AFS module
Problem
Fragmented access patterns across RLM:
rlm recall,rlm remember, MCP toolsrlm scan,rlm chunk,rlm extractEach resource has custom access code. Hard to compose operations across resources.
Proposal (from AIGNE paper analysis)
Unified virtual file system (VFS) abstraction:
Implementation
mount,list_dir,read,write,search,exec)Impact
Effort
5-7 days (design, implementation, migration, testing)
Related