LLM Cascade

HYRE enriches every endpoint through a tiered multi-model cascade. Each endpoint picks a tier based on what its output is — a cheap data listing, a heavier analysis, or a genuine analytical decision. Within a tier, models are tried in priority order: if one fails (timeout, rate limit, content policy), the next is tried automatically. If all models fail, raw data is returned with insight: null and HTTP 206 status.

Tiers

Tier	First model	Typical latency	Used for
`fast`	Gemini 2.5 Flash-Lite	~1–1.5s	High-volume listings & formatting (new tokens, pools, prices, TVL)
`quality`	Gemini 2.5 Flash	~1.5–2.5s	Heavier analysis (Smart Money / Nansen)
`reasoning`	OpenServ SERV	~3.5–5s	Analytical decisions — verdicts, recommendations, cross-chain math

Every tier shares the same fallback chain, so a failure in the primary model degrades gracefully to the next provider rather than failing the request.

Reasoning Tier — OpenServ SERV

Decision endpoints use OpenServ SERV Reasoning as their primary model. SERV is an OpenAI-compatible gateway that runs a bounded reasoning pass (BRAID) over an underlying model before answering, which improves multi-factor judgments and arithmetic-heavy comparisons.

Setting	Value
Endpoint	`https://inference-api.openserv.ai/v1/chat/completions`
Model	`gemini-flash-latest`
`reasoning_effort`	`low`
Timeout	12s
`max_tokens`	2500

Reasoning tokens share the completion budget, so max_tokens is raised to 2500 on this tier (vs 800 on fast/quality) to avoid truncating the answer. The 12s timeout gives the reasoning pass headroom above the 8s used elsewhere.

Endpoints on the reasoning tier:

Endpoint	Decision
`POST /ask`	Natural-language DeFi reasoning
`GET /trenches/token/{mint}/verdict`	`snipe` / `watch` / `avoid`
`GET /debridge/quote`	`execute` / `wait` / `avoid`
`GET /debridge/yield-migrate`	`migrate` / `stay` / `wait` (break-even math)
`GET /lp/meteora/pools/recommend`	Pool recommendation
`GET /lp/meteora/pools/strategy`	LP strategy
`GET /lp/positions/{id}/rebalance`	`rebalance` / `hold`

If SERV_API_KEY is not configured, these endpoints fall back to the Gemini cascade automatically — no request fails.

Cascade Order

The fallback chain, in order, after the tier’s primary model:

Priority	Provider / Model	Timeout	Notes
1	SERV (reasoning tier only)	12s	Bounded reasoning over Gemini Flash.
2	Gemini 2.5 Flash-Lite	8s	Primary for `fast`. Cheap + fast JSON.
3	Gemini 2.5 Flash	8s	Primary for `quality`. Stronger analysis.
4	OpenRouter cascade	8–10s	Cross-provider resilience (DeepSeek → GLM → Claude Haiku).
5	Venice AI	8–10s	Last-resort fallback (DeepSeek V3.2, GLM Flash).

fast starts at Flash-Lite then escalates to Flash; quality and reasoning fall back through Flash first. All tiers end with OpenRouter then Venice.

Chat Agent (Playground)

The Playground chat agent uses a separate model:

Model	Provider	Use Case
Gemini 2.5 Flash-Lite	Google AI	Conversation flow, tool selection, response summarization

Failure Modes

Failure	Behavior
Timeout (>8–12s)	Abort and try next model
HTTP 429 (rate limit)	Skip and try next model
HTTP 5xx (server error)	Skip and try next model
Content policy block	Skip and try next model
Empty response	Skip and try next model
All models fail	Return raw data, `insight: null`, HTTP 206

HTTP 206 (Partial Content) indicates the data was fetched successfully but the LLM enrichment failed. The data field contains the full upstream data. The signal field falls back to neutral with confidence: 0.

LLM Call Configuration

Every LLM call uses these parameters:

{
  "temperature": 0.3,
  "max_tokens": 800,
  "response_format": { "type": "json_object" }
}

Low temperature (0.3) — Prioritizes consistent, factual output over creative variation.
JSON mode — Forces the model to return valid JSON, parsed into the response envelope.
800 token limit — Keeps insights concise (1–2 sentences) and response times fast.

On the reasoning tier, max_tokens is raised to 2500 and reasoning_effort: "low" is added, since reasoning tokens share the completion budget.

System Prompts

Each endpoint segment has a dedicated system prompt that instructs the LLM:

Segment	Signal Vocabulary	Prompt Focus
Trenches	`snipe`, `watch`, `avoid`	Token risk assessment, sniper detection, dev behavior
Traders	`follow`, `ignore`	Wallet profitability, copy-trade worthiness
LPs	`add_liquidity`, `rebalance`, `hold`, `remove`	Pool APR sustainability, IL risk, range optimization
DeFi	`high_yield`, `medium_yield`, `low_yield`, `risky`	TVL trends, yield opportunity assessment
deBridge	`execute`, `wait`, `avoid` (quote) / `migrate`, `stay`, `wait` (yield)	Bridge cost efficiency, cross-chain yield comparison
Nansen	`follow`, `ignore`, `accumulate`, `distribute`	Smart money flow interpretation, wallet classification

Response Format

The LLM returns JSON matching this structure:

{
  "insight": "Solana TVL surged 12% this week, driven by...",
  "signal": "high_yield",
  "confidence": 0.87
}

The enrich() function merges this with the raw data. model_used reflects the model that actually answered — e.g. serv/google/gemini-3.5-flash on the reasoning tier, or gemini-2.5-flash-lite on the fast tier:

{
  "data": { ... },
  "insight": "Solana TVL surged 12% this week, driven by...",
  "signal": "high_yield",
  "confidence": 0.87,
  "sources": ["defillama"],
  "model_used": "gemini-2.5-flash-lite",
  "latency_ms": 342,
  "timestamp": "2026-06-10T10:30:00.000Z"
}

Getting Started

LLM Gateway

Agent Playground

Payment

Architecture

ME Protocol

API Reference

LLM Cascade

LLM Cascade

Tiers

Reasoning Tier — OpenServ SERV

Cascade Order

Chat Agent (Playground)

Failure Modes

LLM Call Configuration

System Prompts

Response Format

​LLM Cascade

​Tiers

​Reasoning Tier — OpenServ SERV

​Cascade Order

​Chat Agent (Playground)

​Failure Modes

​LLM Call Configuration

​System Prompts

​Response Format

LLM Cascade

Tiers

Reasoning Tier — OpenServ SERV

Cascade Order

Chat Agent (Playground)

Failure Modes

LLM Call Configuration

System Prompts

Response Format