NeoAI / LiteLLM
LiteLLM unifies your model APIs. NeoAI tunes every step inside your workflows.
LiteLLM provides a fast, lightweight proxy that unifies 100+ LLM APIs behind a single OpenAI-compatible interface with load balancing. NeoAI operates in your VPC behind LiteLLM to decompose tasks into discrete steps, route each sub-step across model tiers, and automate quality gates using recipes learned from your accepted runs.
Last updated 25 Oct 2026 · By NeoAI
LiteLLM connects to models. NeoAI structures the work.
Standard LiteLLM Proxy
LiteLLM + NeoAI In-VPC
Workflow decomposition topology. Illustrative Only complex sub-steps reach frontier models.
Side-by-side technical comparison.
| Capability | LiteLLM Proxy | NeoAI Delegation |
|---|---|---|
| Core Function | API Normalization & Fallbacks | Workflow Recipes & Step Optimization |
| Granularity | Request Level (1 call = 1 model) | Step Level (1 task = multi-tier DAG) |
| Model Routing | Load balancing, latency, RPM/TPM | Complexity scoring & quality checks |
| Evaluation & Evals | Relies on external evaluation tools | Built-in held-out eval harness |
| Reviewer Feedback | Logs completions to database | Synthesizes automated assertions from edits |
| Deployment | Self-hosted container or Cloud | Private VPC inside your cloud perimeter |
LiteLLM is open-source software by BerriAI. Comparison based on documented features as of October 2026.
LiteLLM falls back on error. NeoAI escalates on quality.
Infrastructure Fallback (LiteLLM)
If Anthropic returns a 429 rate limit or 500 error, LiteLLM re-routes the prompt to OpenAI. It handles infrastructure reliability, not output correctness.
Quality Escalation (NeoAI)
If a small model generates an answer that fails a schema check or lacks required evidence quotes, NeoAI re-runs only that step on the next model tier.
Zero Error Propagation
Your downstream systems receive verified outputs rather than bad data or crashes. 95% of tasks finish on cheaper tiers; only the 5% edge cases escalate.
Simulated quality escalation behavior. Illustrative Escalation thresholds are calibrated per recipe.
Questions buyers ask before they book.
Can I use LiteLLM and NeoAI together?
Yes. LiteLLM serves as your OpenAI-compatible interface and connection manager to providers (Bedrock, Vertex, OpenAI, Anthropic). NeoAI sits as the upstream target behind LiteLLM, handling task decomposition and quality gating in your VPC.
Doesn't LiteLLM already have a router?
LiteLLM has a router for request-level load balancing, cooldowns, and fallbacks (e.g. if Azure OpenAI is down, fallback to Anthropic). It routes the entire request to one model. NeoAI decomposes a single task into multiple sub-steps with varying model sizes and reasoning budgets.
How does NeoAI handle model keys when using LiteLLM?
NeoAI can either forward its delegated sub-step calls back through LiteLLM's proxy, or call model endpoints directly using your existing credentials. Your model provider relationships and volume discounts remain untouched.
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