--- title: "litellm vs openlm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/berriai-litellm-vs-r2d4-openlm" tools: ["berriai-litellm", "r2d4-openlm"] --- # litellm vs openlm *GraphCanon updated Aug 15, 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 openlm if openLM is a flexible Python library designed to interface with various Language Model frameworks similarly to the OpenAI API. [litellm](https://docs.litellm.ai/docs/) reports 55k GitHub stars, 10k forks, and 4.6k open issues, last pushed Aug 1, 2026. [openlm](https://github.com/r2d4/openlm) has 368 stars, 22 forks, and 1 open issues, last pushed May 19, 2023. Figures are from public GitHub metadata via [litellm's repository](https://github.com/BerriAI/litellm) and [openlm's repository](https://github.com/r2d4/openlm). | | [litellm](/tools/berriai-litellm.md) | [openlm](/tools/r2d4-openlm.md) | | --- | --- | --- | | Tagline | Python SDK and Proxy Server for calling multiple LLM APIs | OpenAI-compatible Python client for calling any LLM | | Stars | 55,221 | 368 | | Forks | 10,231 | 22 | | Open issues | 4,634 | 1 | | 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. | OpenLM is a flexible Python library designed to interface with various Language Model frameworks similarly to the OpenAI API. | | 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. | MIT License | | Categories | Inference & Serving, LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [litellm](/tools/berriai-litellm.md) | [openlm](/tools/r2d4-openlm.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1184d | | Open issues (now) | 4.6k | 1 | | Stars delta | Unknown | -1 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/berriai-litellm/trust.md) | [trust report](/tools/r2d4-openlm/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: openlm - **Requirements:** Installation is as simple as using pip to install the openlm package. - **Adopt for:** OpenLM is a flexible Python library designed to interface with various Language Model frameworks similarly to the OpenAI API. - **License detail:** MIT License ## Choose when ### Choose litellm if… - License: litellm is Other, openlm is MIT. - 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, vertex-ai. - Also covers Inference & Serving. - 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 openlm if… - License: openlm is MIT, litellm is Other. - Requirements: Installation is as simple as using pip to install the openlm package.. - Tags unique to openlm: cohere, huggingface. - Use when you want to seamlessly integrate different LLM frameworks, like Hugging Face or Cohere, under a uniform interface similar to OpenAI’s. ## 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 openlm - Avoid when you require maximum performance optimization specific to one LLM framework, as using an intermediary library like OpenLM might introduce additional overhead. - Not suitable if you are strictly working with models that do not have Python support or don't fit within the frameworks supported by OpenLM. ## Common questions ### What is the difference between litellm and openlm? litellm: Python SDK and Proxy Server for calling multiple LLM APIs. openlm: OpenAI-compatible Python client for calling any LLM. See the comparison table for live GitHub stats and shared categories. ### When should I choose litellm over openlm? Choose litellm over openlm when License: litellm is Other, openlm is MIT; 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, vertex-ai; Also covers Inference & Serving; 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 openlm over litellm? Choose openlm over litellm when License: openlm is MIT, litellm is Other; Requirements: Installation is as simple as using pip to install the openlm package.; Tags unique to openlm: cohere, huggingface; Use when you want to seamlessly integrate different LLM frameworks, like Hugging Face or Cohere, under a uniform interface similar to OpenAI’s. ### 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 openlm? Avoid when you require maximum performance optimization specific to one LLM framework, as using an intermediary library like OpenLM might introduce additional overhead. Not suitable if you are strictly working with models that do not have Python support or don't fit within the frameworks supported by OpenLM. ### Is litellm or openlm more popular on GitHub? litellm has more GitHub stars (55,221 vs 368). Stars measure visibility, not whether either tool fits your constraints. ### Are litellm and openlm open source? Yes - both are open-source projects on GitHub (litellm: Other, openlm: MIT). ### Where can I find alternatives to litellm or openlm? GraphCanon lists graph-backed alternatives at [litellm alternatives](/tools/berriai-litellm/alternatives) and [openlm alternatives](/tools/r2d4-openlm/alternatives) ([litellm markdown twin](/tools/berriai-litellm/alternatives.md), [openlm markdown twin](/tools/r2d4-openlm/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-r2d4-openlm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, litellm or openlm? litellm: Very active. openlm: Dormant. 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 openlm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [litellm trust report](/tools/berriai-litellm/trust); [openlm trust report](/tools/r2d4-openlm/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/_