---
title: "litellm vs rag-time"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/berriai-litellm-vs-microsoft-rag-time"
tools: ["berriai-litellm", "microsoft-rag-time"]
---

# litellm vs rag-time

*GraphCanon updated Jul 11, 2026*

## Verdict

Pick litellm when litellm is primarily Python; rag-time is Jupyter Notebook; pick rag-time when rag-time is primarily Jupyter Notebook; litellm is Python.

[litellm](https://docs.litellm.ai/docs/) reports 53k GitHub stars, 9.7k forks, and 3.9k open issues, last pushed Jul 11, 2026. [rag-time](https://github.com/microsoft/rag-time) has 894 stars, 316 forks, and 4 open issues, last pushed Jun 17, 2025. Figures are from public GitHub metadata via [litellm's repository](https://github.com/BerriAI/litellm) and [rag-time's repository](https://github.com/microsoft/rag-time).

| | [litellm](/tools/berriai-litellm.md) | [rag-time](/tools/microsoft-rag-time.md) |
| --- | --- | --- |
| Tagline | Python SDK and Proxy Server for calling multiple LLM APIs | RAG Time: A 5-week Learning Journey to Mastering RAG |
| Stars | 53,271 | 894 |
| Forks | 9,671 | 316 |
| Open issues | 3,915 | 4 |
| Language | Python | Jupyter Notebook |
| 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. | - |
| 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 |
| Categories | Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks, Vector Databases |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [litellm](/tools/berriai-litellm.md) | [rag-time](/tools/microsoft-rag-time.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Dormant (18%) |
| Days since push | 0d | 388d |
| Open issues (now) | 3.9k | 4 |
| Security scan | 2 low (2 low) | No lockfile |
| Full report | [trust report](/tools/berriai-litellm/trust.md) | [trust report](/tools/microsoft-rag-time/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.

## Choose when

### Choose litellm if…

- litellm is primarily Python; rag-time is Jupyter Notebook.
- License: litellm is Other, rag-time 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, 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 rag-time if…

- rag-time is primarily Jupyter Notebook; litellm is Python.
- License: rag-time is MIT, litellm is Other.
- Tags unique to rag-time: ai, azure, binary-quantization, generative-ai.
- Also covers Vector Databases.

## 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 rag-time

- Last GitHub push was 389 days ago (dormant maintenance, Jun 17, 2025). Validate activity before betting a new project on rag-time.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

## Common questions

### What is the difference between litellm and rag-time?

litellm: Python SDK and Proxy Server for calling multiple LLM APIs. rag-time: RAG Time: A 5-week Learning Journey to Mastering RAG. See the comparison table for live GitHub stats and shared categories.

### When should I choose litellm over rag-time?

Choose litellm over rag-time when litellm is primarily Python; rag-time is Jupyter Notebook; License: litellm is Other, rag-time 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, 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 rag-time over litellm?

Choose rag-time over litellm when rag-time is primarily Jupyter Notebook; litellm is Python; License: rag-time is MIT, litellm is Other; Tags unique to rag-time: ai, azure, binary-quantization, generative-ai; Also covers Vector Databases.

### 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 rag-time?

Last GitHub push was 389 days ago (dormant maintenance, Jun 17, 2025). Validate activity before betting a new project on rag-time. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

### Is litellm or rag-time more popular on GitHub?

litellm has more GitHub stars (53,271 vs 894). Stars measure visibility, not whether either tool fits your constraints.

### Are litellm and rag-time open source?

Yes - both are open-source projects on GitHub (litellm: Other, rag-time: MIT).

### Where can I find alternatives to litellm or rag-time?

GraphCanon lists graph-backed alternatives at [litellm alternatives](/tools/berriai-litellm/alternatives) and [rag-time alternatives](/tools/microsoft-rag-time/alternatives) ([litellm markdown twin](/tools/berriai-litellm/alternatives.md), [rag-time markdown twin](/tools/microsoft-rag-time/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-microsoft-rag-time.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, litellm or rag-time?

litellm: Very active. rag-time: 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 rag-time?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [litellm trust report](/tools/berriai-litellm/trust); [rag-time trust report](/tools/microsoft-rag-time/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/_