MRKL Systems (Modular Neuro-Symbolic)
Route each request through an LLM dispatcher to specialized symbolic or neural expert modules (calculator, knowledge base, code executor) rather than asking one LLM to do everything; integrate the modules' results for the final response.
Problem
Single-LLM 'do it all' wastes the model on tasks symbolic systems do better, and inherits the LLM's failures on those tasks (calculation errors, fabricated DB facts). Yet rejecting the LLM throws out its reasoning value.
Solution
Karpas et al. 2022 — MRKL architecture. (1) Router LLM receives the request, identifies relevant expert modules. (2) Dispatch to each module with structured inputs. (3) Integrate module outputs back into the LLM's reasoning. Expert modules can be calculator (Wolfram Alpha), knowledge base (SQL, vector DB), code executor (Python sandbox), specialist models. Precursor to modern tool-using agents. Pair with tool-use, function-calling, augmented-llm, multi-model-routing, hybrid-symbolic-neural-routing.
When to use
- Tasks mixing reasoning with exact computation or DB lookup.
- Symbolic expert modules available for the relevant operations.
- Router can be trained or prompted reliably.
Open the full interactive page →
Diagram, neighbourhood map, code examples, related patterns and full provenance.