Model Orchestrator

Route tasks to different LLM providers based on content classification. Each task type maps to a provider chain with automatic fallback. Use the orchestrator to combine local and cloud models — for example, embeddings via Ollama and chat via Claude.

Configuration

[llm]
provider = "orchestrator"

[llm.orchestrator]
default = "claude"
embed = "ollama"

[llm.orchestrator.providers.ollama]
provider_type = "ollama"

[llm.orchestrator.providers.claude]
provider_type = "claude"

[llm.orchestrator.routes]
coding = ["claude", "ollama"]       # try Claude first, fallback to Ollama
creative = ["claude"]               # cloud only
analysis = ["claude", "ollama"]     # prefer cloud
general = ["claude"]                # cloud only

Provider Keys

default — provider for chat when no specific route matches
embed — provider for all embedding operations (skill matching, semantic memory)

Task Classification

Task types are classified via keyword heuristics:

Task Type	Keywords
`coding`	code, function, debug, refactor, implement
`creative`	write, story, poem, creative
`analysis`	analyze, compare, evaluate
`translation`	translate, convert language
`summarization`	summarize, summary, tldr
`general`	everything else

Fallback Chains

Routes define provider preference order. If the first provider fails, the next one in the list is tried automatically.

coding = ["local", "cloud"]  # try local first, fallback to cloud

Hybrid Setup Example

Embeddings via free local Ollama, chat via paid Claude API:

[llm]
provider = "orchestrator"

[llm.orchestrator]
default = "claude"
embed = "ollama"

[llm.orchestrator.providers.ollama]
provider_type = "ollama"

[llm.orchestrator.providers.claude]
provider_type = "claude"

[llm.orchestrator.routes]
general = ["claude"]