Comparison
litellm vs Kimi-K2
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 Kimi-K2 if kimi K2, developed by Moonshot AI team, brings a large language model series providing an API compatible with OpenAI and Anthropic interfaces.
Markdown twin · litellm alternatives · Kimi-K2 alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | litellm | Kimi-K2 |
|---|---|---|
| Maintenance | Very active (0d since push) As of 3w · github_public_v1 | Slowing (197d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- litellm
- Python SDK and Proxy Server for calling multiple LLM APIs
- Kimi-K2
- Large language model series developed by Moonshot AI team
Stars
- litellm
- 55k
- Kimi-K2
- 11k
Forks
- litellm
- 10k
- Kimi-K2
- 902
Open issues
- litellm
- 4.6k
- Kimi-K2
- 70
Language
- litellm
- Python
- Kimi-K2
- -
Adopt for
- litellm
- 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.
- Kimi-K2
- Kimi K2, developed by Moonshot AI team, brings a large language model series providing an API compatible with OpenAI and Anthropic interfaces.
Persona
- litellm
- -
- Kimi-K2
- -
Runtime
- litellm
- -
- Kimi-K2
- -
License
- litellm
- 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.
- Kimi-K2
- The code and model weights of Kimi K2 are released under a Modified MIT License.
Last pushed
- litellm
- Aug 1, 2026
- Kimi-K2
- Jan 21, 2026
Categories
- litellm
- Inference & Serving, LLM Frameworks
- Kimi-K2
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- litellm
- Very active (96%)
- Kimi-K2
- Slowing (36%)
Days since push
- litellm
- 0d
- Kimi-K2
- 197d
Open issues (now)
- litellm
- 4.6k
- Kimi-K2
- 70
OSV dependency advisories
- litellm
- Published findings
- Kimi-K2
- No lockfile (source not queried)
Full report
- litellm
- Trust report
- Kimi-K2
- Trust report
Choose litellm if…
- 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 NOT to use litellm
- If your project only requires interaction with a single LLM API and basic functionalities, litellm may be overkill.
Choose Kimi-K2 if…
- Pricing: N/A.
- Requirements: Model deployment examples are available for vLLM and SGLang, aiding in setup and integration..
- Tags unique to Kimi-K2: anthropic-compatibility, api accessible, ktransformers, moonshot ai.
- - When looking to deploy models on specific inference engines like vLLM or SGLang which are well-supported for Kimi K2.
When NOT to use Kimi-K2
- - Avoid using it if your application strictly requires a different model format that isn't supported by Kimi K2 (currently block-fp8).
- - Do not use this tool if you are dependent on running inference outside of the recommended engines, as compatibility and performance may be compromised without specific support.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (BerriAI/litellm) · observed Aug 1, 2026
- GitHub forks (BerriAI/litellm) · observed Aug 1, 2026
- Last push (BerriAI/litellm) · observed Aug 1, 2026
- License file (Other) · observed Aug 1, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (MoonshotAI/Kimi-K2) · observed Aug 6, 2026
- GitHub forks (MoonshotAI/Kimi-K2) · observed Aug 6, 2026
- Last push (MoonshotAI/Kimi-K2) · observed Jan 21, 2026
- License file (Other) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: litellm 55k · Kimi-K2 11k (synced Aug 1, 2026).
Common questions
- What is the difference between litellm and Kimi-K2?
- litellm: Python SDK and Proxy Server for calling multiple LLM APIs. Kimi-K2: Large language model series developed by Moonshot AI team. See the comparison table for live GitHub stats and shared categories.
- When should I choose litellm over Kimi-K2?
- Choose litellm over Kimi-K2 when 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 Kimi-K2 over litellm?
- Choose Kimi-K2 over litellm when Pricing: N/A; Requirements: Model deployment examples are available for vLLM and SGLang, aiding in setup and integration.; Tags unique to Kimi-K2: anthropic-compatibility, api accessible, ktransformers, moonshot ai; - When looking to deploy models on specific inference engines like vLLM or SGLang which are well-supported for Kimi K2.
- 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 Kimi-K2?
- - Avoid using it if your application strictly requires a different model format that isn't supported by Kimi K2 (currently block-fp8). - Do not use this tool if you are dependent on running inference outside of the recommended engines, as compatibility and performance may be compromised without specific support.
- Is litellm or Kimi-K2 more popular on GitHub?
- litellm has more GitHub stars (55,221 vs 11,098). Stars measure visibility, not whether either tool fits your constraints.
- Are litellm and Kimi-K2 open source?
- Yes - both are open-source projects on GitHub (litellm: Other, Kimi-K2: Other).
- Where can I find alternatives to litellm or Kimi-K2?
- GraphCanon lists graph-backed alternatives at litellm alternatives and Kimi-K2 alternatives (litellm markdown twin, Kimi-K2 markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, litellm or Kimi-K2?
- litellm: Very active. Kimi-K2: Slowing. 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 Kimi-K2?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: litellm trust report; Kimi-K2 trust report.