--- title: "litellm vs MOSS" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/berriai-litellm-vs-openmoss-moss" tools: ["berriai-litellm", "openmoss-moss"] --- # litellm vs MOSS *GraphCanon updated Aug 17, 2026* ## Verdict Pick litellm if litellm is a Python SDK and Proxy Server that facilitates the interaction with over 100 LLM APIs, offering features such as cost tracking, guardrails, load balancing, and logging; pick MOSS if an open-source conversational language model from Fudan University providing pre-trained and fine-tuned models for various applications. [litellm](https://docs.litellm.ai/docs/) reports 55k GitHub stars, 10k forks, and 4.6k open issues, last pushed Aug 1, 2026. [MOSS](https://txsun1997.github.io/blogs/moss.html) has 12k stars, 1.1k forks, and 243 open issues, last pushed May 27, 2026. Figures are from public GitHub metadata via [litellm's repository](https://github.com/BerriAI/litellm) and [MOSS's repository](https://github.com/OpenMOSS/MOSS). | | [litellm](/tools/berriai-litellm.md) | [MOSS](/tools/openmoss-moss.md) | | --- | --- | --- | | Tagline | Python SDK and Proxy Server for calling multiple LLM APIs | An open-source conversational language model | | Stars | 55,221 | 12,214 | | Forks | 10,231 | 1,128 | | Open issues | 4,634 | 243 | | Language | Python | Python | | Adopt for | litellm is a Python SDK and Proxy Server that facilitates the interaction with over 100 LLM APIs, offering features such as cost tracking, guardrails, load balancing, and logging. | An open-source conversational language model from Fudan University providing pre-trained and fine-tuned models for various applications. | | Persona | - | - | | Runtime | - | - | | License | The licensing terms for LiteLLM are provided under a license type categorized as 'Other'; details of the exact license should be referenced directly from its source. | Apache-2.0 | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [litellm](/tools/berriai-litellm.md) | [MOSS](/tools/openmoss-moss.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 81d | | Open issues (now) | 4.6k | 243 | | Stars delta | Unknown | +56 (30d) | | Open issues delta | Unknown | +1 (30d) | | Full report | [trust report](/tools/berriai-litellm/trust.md) | [trust report](/tools/openmoss-moss/trust.md) | ## Decision facts: litellm - **Pricing:** freemium - While the core functionality is provided free, specific extended features might require a paid plan. - **Requirements:** Requires Docker - **Adopt for:** litellm is a Python SDK and Proxy Server that facilitates the interaction with over 100 LLM APIs, offering features such as cost tracking, guardrails, load balancing, and logging. - **License detail:** The licensing terms for LiteLLM are provided under a license type categorized as 'Other'; details of the exact license should be referenced directly from its source. ## Decision facts: MOSS - **Requirements:** Min 16 GB RAM; Requires substantial GPU memory, ranging from around 12GB to 24GB depending on the model version.; Hardware must support high-performance matrix operations for effective inference. - **Adopt for:** An open-source conversational language model from Fudan University providing pre-trained and fine-tuned models for various applications. - **License detail:** Apache-2.0 ## Choose when ### Choose litellm if… - License: litellm is Other, MOSS is Apache-2.0. - Pricing: While the core functionality is provided free, specific extended features might require a paid plan.. - Requirements: Requires Docker. - Tags unique to litellm: ai-gateway, azure-openai, bedrock, llm. - litellm ships Docker support for self-hosted deployment. - When you need to integrate multiple LLM (Language Learning Modelling) APIs into your application across different providers like Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, Hugging ### Choose MOSS if… - License: MOSS is Apache-2.0, litellm is Other. - Requirements: Min 16 GB RAM; Requires substantial GPU memory, ranging from around 12GB to 24GB depending on the model version.; Hardware must support high-performance matrix operations for effective inference.. - Tags unique to MOSS: chatgpt, deep-learning, dialogue-systems, large language models. - - MOSS is ideal for use in scenarios that require detailed multi-turn dialogues with advanced plugin capabilities, such as customer support services where context preservation and the ability to call ## When NOT to use litellm - If your project only requires interaction with a single LLM API and basic functionalities, litellm may be overkill. ## When NOT to use MOSS - - Avoid using MOSS in situations where you require models without integrated plugin support, as its advanced feature set might introduce unnecessary complexity. - - MOSS may not be the optimal choice for applications that prioritize extremely low resource consumption because of its demand for significant computational power even with the lower quantized models. ## Common questions ### What is the difference between litellm and MOSS? litellm: Python SDK and Proxy Server for calling multiple LLM APIs. MOSS: An open-source conversational language model. See the comparison table for live GitHub stats and shared categories. ### When should I choose litellm over MOSS? Choose litellm over MOSS when License: litellm is Other, MOSS is Apache-2.0; Pricing: While the core functionality is provided free, specific extended features might require a paid plan.; Requirements: Requires Docker; Tags unique to litellm: ai-gateway, azure-openai, bedrock, llm; litellm ships Docker support for self-hosted deployment; When you need to integrate multiple LLM (Language Learning Modelling) APIs into your application across different providers like Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, Hugging. ### When should I choose MOSS over litellm? Choose MOSS over litellm when License: MOSS is Apache-2.0, litellm is Other; Requirements: Min 16 GB RAM; Requires substantial GPU memory, ranging from around 12GB to 24GB depending on the model version.; Hardware must support high-performance matrix operations for effective inference.; Tags unique to MOSS: chatgpt, deep-learning, dialogue-systems, large language models; - MOSS is ideal for use in scenarios that require detailed multi-turn dialogues with advanced plugin capabilities, such as customer support services where context preservation and the ability to call. ### When should I avoid litellm? If your project only requires interaction with a single LLM API and basic functionalities, litellm may be overkill. ### When should I avoid MOSS? - Avoid using MOSS in situations where you require models without integrated plugin support, as its advanced feature set might introduce unnecessary complexity. - MOSS may not be the optimal choice for applications that prioritize extremely low resource consumption because of its demand for significant computational power even with the lower quantized models. ### Is litellm or MOSS more popular on GitHub? litellm has more GitHub stars (55,221 vs 12,214). Stars measure visibility, not whether either tool fits your constraints. ### Are litellm and MOSS open source? Yes - both are open-source projects on GitHub (litellm: Other, MOSS: Apache-2.0). ### Where can I find alternatives to litellm or MOSS? GraphCanon lists graph-backed alternatives at [litellm alternatives](/tools/berriai-litellm/alternatives) and [MOSS alternatives](/tools/openmoss-moss/alternatives) ([litellm markdown twin](/tools/berriai-litellm/alternatives.md), [MOSS markdown twin](/tools/openmoss-moss/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/berriai-litellm-vs-openmoss-moss.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, litellm or MOSS? litellm: Very active. MOSS: Steady. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for litellm and MOSS? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [litellm trust report](/tools/berriai-litellm/trust); [MOSS trust report](/tools/openmoss-moss/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=berriai-litellm`](/api/graphcanon/graph?tool=berriai-litellm) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_