BerriAI/litellm - GitHub vs Onri
Side-by-side comparison · Updated October 2026
| Description | LiteLLM is an AI gateway and Python SDK from Berrie AI Incorporated, published in the BerriAI GitHub repository. The SDK provides a common interface for model calls inside Python applications. The proxy gateway centralizes access for a team, with virtual keys, model routing, spend tracking, budgets and an administration interface. The official documentation lists support for more than 100 model providers. Supported endpoints and features vary by integration, so verify your model’s streaming, tool-calling, image, audio or embedding requirements. The router supports retries, fallbacks and load balancing; observability integrations can send request data to tools such as Langfuse, LangSmith and OpenTelemetry. LiteLLM also provides an MCP gateway. It can connect upstream servers using Streamable HTTP, SSE or stdio, expose tools through a fixed gateway endpoint, and scope access by key, team or organization. This requires configuring the upstream servers and authentication; the gateway does not automatically grant access to third-party tools. Agent-to-agent integrations are documented separately. The open-source offering has no software license fee for self-hosting. Code outside the enterprise directory is MIT-licensed, while enterprise code has separate terms. Enterprise pricing is quoted by annual gateway request capacity, deployment architecture and support needs, rather than a per-token license charge. Model-provider charges and infrastructure costs still apply. Enterprise adds controls and support such as SSO, SCIM, audit logs and service-level agreements. Compare New API for another self-hosted gateway with provider-channel management and usage accounting. Evaluate a representative workload, inspect request logging and secret handling, test budget and failure behavior, and decide whether SDK integration or a shared gateway best fits your application. | Onri is an innovative learning platform designed to help users learn efficiently and effectively. It offers personalized learning paths by asking users to select their learning goals and what they already know. Onri then creates a customized plan with curated, bite-sized study materials, ensuring prerequisite knowledge is covered first. Ideal for students, industry analysts, and self-learners, Onri focuses on delivering high-quality content from various sources and maintaining a big-picture perspective. This approach makes learning faster, cheaper, and more accessible. |
| Category | Developer Tools | Education |
| Rating | No reviews | No reviews |
| Pricing | Open Source | Pricing unavailable |
| Starting Price | N/A | N/A |
| Use Cases |
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| Tags | AI gatewayPython SDKLLM routingMCP gatewayvirtual keys | learning platformpersonalized learningcustomized study planstudentsself-learners |
| Features | ||
| Python SDK for direct application integration | ||
| Shared AI proxy gateway and administration UI | ||
| More than 100 documented model-provider integrations | ||
| Virtual keys, users, teams, budgets and rate limits | ||
| Spend tracking and observability integrations | ||
| Router retries, fallbacks and load balancing | ||
| MCP gateway for Streamable HTTP, SSE and stdio upstreams | ||
| Key, team and organization MCP permissions | ||
| Separate enterprise identity, audit and support features | ||
| Personalized learning paths | ||
| Curated, high-quality study materials | ||
| Prerequisite knowledge emphasis | ||
| Bite-sized learning concepts | ||
| Variety of topics for different groups | ||
| Big-picture perspective | ||
| Flexible learning pace | ||
| Cost-effective learning solutions | ||
| Source variety for materials | ||
| Support for lifelong and self-learners | ||
| View BerriAI/litellm - GitHub | View Onri | |
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