--- title: "litellm vs rtk" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/berriai-litellm-vs-rtk-ai-rtk" tools: ["berriai-litellm", "rtk-ai-rtk"] --- # litellm vs rtk Neutral, constraint-first comparison with live GitHub stats. | | [litellm](/tools/berriai-litellm.md) | [rtk](/tools/rtk-ai-rtk.md) | | --- | --- | --- | | Tagline | LiteLLM AI Gateway: Open Source AI Gateway for 100+ LLMs | High-performance CLI proxy that reduces LLM token consumption by 60-90% | | Stars | 52,933 | 69,390 | | Forks | 9,554 | 4,309 | | Open issues | 3,724 | 1,517 | | Language | Python | Rust | | Adopt for | LiteLLM provides an open-source AI gateway for over 100 LLMs, integrating them via OpenAI format and offering features like cost tracking, guardrails, load balancing, and logging. | Compresses CLI command outputs to reduce LLM token consumption by 60-90%, enhancing cost efficiency. | | Persona | - | - | | Runtime | - | - | | License | The specific license details are categorized as 'Other', indicating it's neither a standard open-source license like MIT, Apache 2.0 nor GPL. It’s crucial to check the precise licensing terms directly | Apache-2.0 | | Categories | Inference & Serving | Inference & Serving, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [litellm](/tools/berriai-litellm.md) | [rtk](/tools/rtk-ai-rtk.md) | | --- | --- | --- | | Open issues (now) | 3.7k | 1.5k | | Security scan | 2 low (2 low) | No lockfile | | Full report | [trust report](/tools/berriai-litellm/trust.md) | [trust report](/tools/rtk-ai-rtk/trust.md) | **Typed relationship:** litellm _(alternative)_ rtk Both RTK and LiteLLM provide proxy services for LLM calls with optimizations, but they approach the problem differently and can serve as alternatives to each other. ## Decision facts: litellm - **Requirements:** Min 4 GB RAM; - Requires Python for installation and operation.; - Supports deployment as a standalone application or integrated into larger applications via library calls. - **Adopt for:** LiteLLM provides an open-source AI gateway for over 100 LLMs, integrating them via OpenAI format and offering features like cost tracking, guardrails, load balancing, and logging. - **License detail:** The specific license details are categorized as 'Other', indicating it's neither a standard open-source license like MIT, Apache 2.0 nor GPL. It’s crucial to check the precise licensing terms directly ## Decision facts: rtk - **Adopt for:** Compresses CLI command outputs to reduce LLM token consumption by 60-90%, enhancing cost efficiency. ## Choose when ### Choose litellm if… - litellm is primarily Python; rtk is Rust. - License: litellm is Other, rtk is Apache-2.0. - Requirements: Min 4 GB RAM; - Requires Python for installation and operation.; - Supports deployment as a standalone application or integrated into larger applications via library calls.. - Both RTK and LiteLLM provide proxy services for LLM calls with optimizations, but they approach the problem differently and can serve as alternatives to each other. - Tags unique to litellm: llmops, rust, bedrock, ai-gateway. - litellm ships Docker support for self-hosted deployment. - - You require a unified interface to integrate multiple LLM providers (OpenAI, Anthropic, Bedrock, Azure, etc.) using the OpenAI format. ### Choose rtk if… - rtk is primarily Rust; litellm is Python. - License: rtk is Apache-2.0, litellm is Other. - Both RTK and LiteLLM provide proxy services for LLM calls with optimizations, but they approach the problem differently and can serve as alternatives to each other. - Tags unique to rtk: command-line-tool, ai-coding, agentic-coding, claude-code. - Also covers Developer Tools. - - When working on projects where the usage of tokens for Language Models (LLMs) significantly impacts costs, rtk can drastically cut down on these expenses. ## When NOT to use litellm - - If your application only needs to integrate with a single LLM provider that does not require the OpenAI standard interface. - - Your project prioritizes proprietary or closed-source solutions; LiteLLM is open source and may lack some commercial support or enterprise-grade add-ons available in paid alternatives. ## When NOT to use rtk - - If your project or work environment does not involve substantial interaction with LLMs, the benefits of using rtk in terms of token and cost reduction will be negligible. - - For projects where CLI output integrity needs to remain unchanged (e.g., for debugging purposes), rtk's compression and filtering might interfere with essential outputs needed for analysis. ## Common questions ### What is the difference between litellm and rtk? litellm: LiteLLM AI Gateway: Open Source AI Gateway for 100+ LLMs. rtk: High-performance CLI proxy that reduces LLM token consumption by 60-90%. See the comparison table for live GitHub stats and shared categories. ### When should I choose litellm over rtk? Choose litellm over rtk when litellm is primarily Python; rtk is Rust; License: litellm is Other, rtk is Apache-2.0; Requirements: Min 4 GB RAM; - Requires Python for installation and operation.; - Supports deployment as a standalone application or integrated into larger applications via library calls.; Both RTK and LiteLLM provide proxy services for LLM calls with optimizations, but they approach the problem differently and can serve as alternatives to each other; Tags unique to litellm: llmops, rust, bedrock, ai-gateway; litellm ships Docker support for self-hosted deployment; - You require a unified interface to integrate multiple LLM providers (OpenAI, Anthropic, Bedrock, Azure, etc.) using the OpenAI format. ### When should I choose rtk over litellm? Choose rtk over litellm when rtk is primarily Rust; litellm is Python; License: rtk is Apache-2.0, litellm is Other; Both RTK and LiteLLM provide proxy services for LLM calls with optimizations, but they approach the problem differently and can serve as alternatives to each other; Tags unique to rtk: command-line-tool, ai-coding, agentic-coding, claude-code; Also covers Developer Tools; - When working on projects where the usage of tokens for Language Models (LLMs) significantly impacts costs, rtk can drastically cut down on these expenses. ### When should I avoid litellm? - If your application only needs to integrate with a single LLM provider that does not require the OpenAI standard interface. - Your project prioritizes proprietary or closed-source solutions; LiteLLM is open source and may lack some commercial support or enterprise-grade add-ons available in paid alternatives. ### When should I avoid rtk? - If your project or work environment does not involve substantial interaction with LLMs, the benefits of using rtk in terms of token and cost reduction will be negligible. - For projects where CLI output integrity needs to remain unchanged (e.g., for debugging purposes), rtk's compression and filtering might interfere with essential outputs needed for analysis. ### Is litellm or rtk more popular on GitHub? rtk has more GitHub stars (69,390 vs 52,933). Stars measure visibility, not whether either tool fits your constraints. ### Are litellm and rtk open source? Yes - both are open-source projects on GitHub (litellm: Other, rtk: Apache-2.0). ### Where can I find alternatives to litellm or rtk? GraphCanon lists graph-backed alternatives at /tools/berriai-litellm/alternatives and /tools/rtk-ai-rtk/alternatives (/tools/berriai-litellm/alternatives.md, /tools/rtk-ai-rtk/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 /compare/berriai-litellm-vs-rtk-ai-rtk.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, litellm or rtk? litellm: Very active. rtk: Very active. 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 rtk? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: litellm: /tools/berriai-litellm/trust; rtk: /tools/rtk-ai-rtk/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/_