BerriAI/litellm - GitHub vs OpenAI Swarm
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. | OpenAI Swarm is an experimental and lightweight framework ideal for building, orchestrating, and deploying multi-agent systems. The framework's primary goal is to facilitate the coordination and execution of multiple AI agents in a manageable and testable manner. With its agent-driven architecture and seamless handoffs, OpenAI Swarm simplifies complex AI interactions and supports a range of applications from customer service to task automation. Built on the OpenAI Chat Completions API, it offers high transparency, fine control over context, and an emphasis on testability, making it a standout choice for developers looking for a flexible multi-agent framework. |
| Category | Developer Tools | If none of these categories are a good fit, create a new category. The new category should be specific to the topic, concise, and avoid generalized adjectives like 'innovative'. Only create a new category if absolutely necessary. |
| Rating | No reviews | No reviews |
| Pricing | Open Source | Custom |
| Starting Price | N/A | N/A |
| Plans | — |
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| Tags | AI gatewayPython SDKLLM routingMCP gatewayvirtual keys | multi-agent systemscoordinationAI agentscustomer servicetask automation |
| 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 | ||
| Lightweight and scalable framework for multi-agent systems | ||
| Highly customizable for specific agent and interaction needs | ||
| Simplifies agent coordination and execution | ||
| Enables agent handoffs for efficient task delegation | ||
| Manages context variables accessible to agents and functions | ||
| Allows agents to execute external functions | ||
| Experimental streaming responses for real-time interaction | ||
| Stateless design for enhanced scalability | ||
| Provides full transparency and control over context, steps, and tool calls | ||
| View BerriAI/litellm - GitHub | View OpenAI Swarm | |
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