Configuration Reference¶

LLMFS works with zero configuration -- llmfs init is all you need. To tune behavior, create .llmfs/config.json:

{
  "embedder": "local",
  "embedder_model": "all-MiniLM-L6-v2",
  "chunk_size_tokens": 256,
  "chunk_overlap_tokens": 50,
  "search_cache_ttl_seconds": 300,
  "auto_relate_threshold": 0.85,
  "context_manager": {
    "max_tokens": 128000,
    "evict_at": 0.70,
    "target_after_evict": 0.50
  },
  "layers": {
    "short_term": { "ttl_minutes": 60 },
    "session":    { "ttl_minutes": null },
    "knowledge":  { "ttl_minutes": null },
    "events":     { "ttl_minutes": null }
  }
}

Options¶

Key	Default	Description
`embedder`	`"local"`	`"local"` (sentence-transformers) or `"openai"`
`embedder_model`	`"all-MiniLM-L6-v2"`	Model name for local embedder
`chunk_size_tokens`	`256`	Target chunk size in tokens (prose); `512` for code
`chunk_overlap_tokens`	`50`	Overlap between adjacent chunks
`search_cache_ttl_seconds`	`300`	How long to cache search results (0 = disabled)
`auto_relate_threshold`	`0.85`	Auto-create `related_to` edge when similarity exceeds this
`context_manager.max_tokens`	`128000`	Total context window size (tokens)
`context_manager.evict_at`	`0.70`	Fraction of max_tokens at which eviction starts
`context_manager.target_after_evict`	`0.50`	Fraction of max_tokens to reach after eviction
`layers.short_term.ttl_minutes`	`60`	TTL for `short_term` memories

Using OpenAI Embeddings¶

{
  "embedder": "openai",
  "embedder_model": "text-embedding-3-small"
}

export OPENAI_API_KEY=sk-...

Note

OpenAI embeddings are higher quality for some domains but add latency and cost. The local model (22 MB, CPU-only) handles 1,000+ queries/second and is the default.

Environment Variables¶

Variable	Description
`LLMFS_PATH`	Override the storage directory (same as `--llmfs-path`)
`OPENAI_API_KEY`	Required when using `"embedder": "openai"`