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System diagram

Mako system architecture

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Request lifecycle

1

Authentication

The gateway validates the x-wallet-address header and checks the Authorization bearer token against the API key database. If credits are enabled, it verifies the user has sufficient balance.
2

Model routing

The gateway routes the request to the appropriate inference backend based on the model field. The default production model is the 32B Conductor.
3

Knowledge injection

Before inference, the gateway scans the user’s message against 85+ keyword triggers. If a match is found (e.g., the user mentions “aerodrome” or “virtuals”), relevant project context is injected as a system message — up to 2 projects, ~500 tokens.
4

Agentic tool loop

The model’s response is checked for tool calls. If found, the gateway executes the tool, feeds the result back to the model, and loops — up to 8 rounds. The gateway also detects when the model is “stalling” (saying “let me check…” without actually calling a tool) and nudges it.
5

Auto-chaining

When a web_search returns results, the gateway automatically reads any tweet URLs via read_tweet and extracts the top non-tweet page via web_extract — all without additional model round-trips.
6

Response + billing

The final text response is returned in OpenAI format. Credits are deducted based on total token usage across all tool rounds.

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Tool system

The gateway has 16 built-in tools, all read-only: Tools are defined as a registry with descriptions, parameter schemas, and async execution functions. The gateway generates tool definitions from this registry and sends them with every inference call.

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Knowledge base (RAG)

The gateway includes a curated knowledge base of Base ecosystem projects, loaded at startup. Each entry contains metadata, keywords, and detailed context. Two retrieval modes:
  1. Auto-injection — Keywords in the user’s message trigger automatic context injection before inference. Top 2 matches, ~500 tokens.
  2. Explicit search — The knowledge_search tool lets the model query the knowledge base directly. Returns up to 5 results with fuzzy scoring.

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Inference engine

The gateway manages model routing, cold start handling, and response formatting. It supports multiple backends and handles the translation between different inference APIs transparently. Key features:
  • Cold start management — Automatic polling and retry for models scaling from zero
  • Fallback routing — Graceful degradation if the primary backend is unavailable
  • Format normalization — All responses are returned in OpenAI Chat Completions format regardless of the underlying backend