Call 100+ LLMs using the OpenAI Input/Output Format
LiteLLM is a unified interface to access multiple LLMs (100+ LLMs). It provides consistent output, retry/fallback logic across multiple deployments, and tools for tracking spend and setting budgets per project. It can be used through a proxy server (LLM Gateway) or a Python SDK. The proxy server offers a central service to access multiple LLMs, track LLM usage and setup guardrails, and customize logging, guardrails, and caching per project. The Python SDK allows developers to use LiteLLM in their python code, providing retry/fallback logic and consistent output.
NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
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Handle multi-step workflows autonomously
Example
Schedule meeting → Find time → Send invite → Confirm attendees
Save 5-10 hours/week on routine coordination tasks
Gather data from multiple sources and summarize
Example
Research competitor pricing across 5 websites, create comparison table
Reduce research time from hours to minutes
Analyze options and recommend actions
Example
Review 20 vendor proposals, score against criteria, rank top 3
Make data-driven decisions faster
AI agents combine large language models with tools, memory, and decision-making logic to autonomously complete multi-step tasks without constant human guidance.
Large language model for reasoning and decision-making
Understand tasks, plan steps, generate responses
APIs, databases, external services the agent can call
Take actions beyond text generation (search, compute, write files)
Short-term (conversation) and long-term (persistent) memory
Maintain context across interactions and learn from past actions
Decision engine for choosing next action
Plan multi-step workflows and handle errors/edge cases
Prerequisites
Steps
✓ Do
✗ Don't
Key Metrics
Optimization Tips
Solid agent profile: LiteLLM links out cleanly and the on-site reviews add signal beyond marketing copy.
I recommend LiteLLM for teams already running multiple AI agents; the listing helped us narrow the short list quickly.
Good discoverability: LiteLLM shows up in the agents directory with enough detail to pre-qualify buyers.
Solid agent profile: LiteLLM links out cleanly and the on-site reviews add signal beyond marketing copy.
Good discoverability: LiteLLM shows up in the agents directory with enough detail to pre-qualify buyers.
LiteLLM reduced evaluation time — saves/upvotes on explainx.ai correlated with fewer surprises in the trial.
LiteLLM reduced evaluation time — saves/upvotes on explainx.ai correlated with fewer surprises in the trial.
I recommend LiteLLM for teams already running multiple AI agents; the listing helped us narrow the short list quickly.
Solid agent profile: LiteLLM links out cleanly and the on-site reviews add signal beyond marketing copy.
We piloted LiteLLM for two weeks; the registry summary and category tag matched what the product actually emphasizes.
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Key Considerations
