---
title: "in-context-ralm vs LLMSurvey"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/ai21labs-in-context-ralm-vs-rucaibox-llmsurvey"
tools: ["ai21labs-in-context-ralm", "rucaibox-llmsurvey"]
---

# in-context-ralm vs LLMSurvey

*GraphCanon updated Jul 12, 2026*

## Verdict

Pick in-context-ralm if a Python implementation for reproducing WikiText-103 experiments using AI21 Labs' RALM method, focusing on retrieval-enhanced language models; pick LLMSurvey if lLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训.

[in-context-ralm](https://github.com/AI21Labs/in-context-ralm) reports 295 GitHub stars, 28 forks, and 4 open issues, last pushed Dec 20, 2023. [LLMSurvey](https://arxiv.org/abs/2303.18223) has 12k stars, 935 forks, and 30 open issues, last pushed Mar 11, 2025. Figures are from public GitHub metadata via [in-context-ralm's repository](https://github.com/AI21Labs/in-context-ralm) and [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey).

| | [in-context-ralm](/tools/ai21labs-in-context-ralm.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) |
| --- | --- | --- |
| Tagline | In-Context Retrieval-Augmented Language Models Experiment Reproduction | A comprehensive collection of papers and resources related to Large Language Models. |
| Stars | 295 | 12,187 |
| Forks | 28 | 935 |
| Open issues | 4 | 30 |
| Language | Python | Python |
| Adopt for | A Python implementation for reproducing WikiText-103 experiments using AI21 Labs' RALM method, focusing on retrieval-enhanced language models. | LLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训 |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | The license for LLMSurvey is unknown based on the provided repository information. |
| Categories | Evaluation & Observability, Model Training | Evaluation & Observability, LLM Frameworks |

## Trust and health

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

| | [in-context-ralm](/tools/ai21labs-in-context-ralm.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) |
| --- | --- | --- |
| Maintenance | Archived (8%) | Dormant (18%) |
| Days since push | 934d | 487d |
| Archived on GitHub | Yes | No |
| Open issues (now) | 4 | 30 |
| Security scan | 75 low (75 low) | No lockfile |
| Full report | [trust report](/tools/ai21labs-in-context-ralm/trust.md) | [trust report](/tools/rucaibox-llmsurvey/trust.md) |

## Decision facts: in-context-ralm

- **Adopt for:** A Python implementation for reproducing WikiText-103 experiments using AI21 Labs' RALM method, focusing on retrieval-enhanced language models.

## Decision facts: LLMSurvey

- **Pricing:** freemium - Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage
- **Adopt for:** LLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训
- **License detail:** The license for LLMSurvey is unknown based on the provided repository information.

## Choose when

### Choose in-context-ralm if…

- Tags unique to in-context-ralm: language-models, retrieval-augmentation, wikitext-103.
- Also covers Model Training.
- When aiming to reproduce WikiText-103 results with retrieval-augmented language models as specified in the AI21 Labs paper.

### Choose LLMSurvey if…

- Pricing: Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage.
- Tags unique to LLMSurvey: chain-of-thought, in-context-learning, instruction-tuning, large-language-models.
- Also covers LLM Frameworks.
- You should use LLMSurvey if you are seeking deep insights into specific advancements such as long chain-of-thought (CoT) reasoning approaches used by DeepSeek-R1 or OpenAI's o-series models.

## When NOT to use in-context-ralm

- If working strictly on general-purpose language modeling without utilizing retrieval mechanisms for augmenting contextual information.
- When Python 3.8 compatibility and specific library versions (Transformers, Pyserini) are not alignable with the project environment.

## When NOT to use LLMSurvey

- You might not want to use LLMSurvey if you prefer tools that offer practical implementation details over a survey-style summary and organization of research papers.
- Consider other resources if your focus is on hands-on development rather than deep academic exploration, as LLMSurvey provides extensive academic coverage but fewer direct coding or implementation how

## Common questions

### What is the difference between in-context-ralm and LLMSurvey?

in-context-ralm: In-Context Retrieval-Augmented Language Models Experiment Reproduction. LLMSurvey: A comprehensive collection of papers and resources related to Large Language Models.. See the comparison table for live GitHub stats and shared categories.

### When should I choose in-context-ralm over LLMSurvey?

Choose in-context-ralm over LLMSurvey when Tags unique to in-context-ralm: language-models, retrieval-augmentation, wikitext-103; Also covers Model Training; When aiming to reproduce WikiText-103 results with retrieval-augmented language models as specified in the AI21 Labs paper.

### When should I choose LLMSurvey over in-context-ralm?

Choose LLMSurvey over in-context-ralm when Pricing: Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage; Tags unique to LLMSurvey: chain-of-thought, in-context-learning, instruction-tuning, large-language-models; Also covers LLM Frameworks; You should use LLMSurvey if you are seeking deep insights into specific advancements such as long chain-of-thought (CoT) reasoning approaches used by DeepSeek-R1 or OpenAI's o-series models.

### When should I avoid in-context-ralm?

If working strictly on general-purpose language modeling without utilizing retrieval mechanisms for augmenting contextual information. When Python 3.8 compatibility and specific library versions (Transformers, Pyserini) are not alignable with the project environment.

### When should I avoid LLMSurvey?

You might not want to use LLMSurvey if you prefer tools that offer practical implementation details over a survey-style summary and organization of research papers. Consider other resources if your focus is on hands-on development rather than deep academic exploration, as LLMSurvey provides extensive academic coverage but fewer direct coding or implementation how

### Is in-context-ralm or LLMSurvey more popular on GitHub?

LLMSurvey has more GitHub stars (12,187 vs 295). Stars measure visibility, not whether either tool fits your constraints.

### Are in-context-ralm and LLMSurvey open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to in-context-ralm or LLMSurvey?

GraphCanon lists graph-backed alternatives at [in-context-ralm alternatives](/tools/ai21labs-in-context-ralm/alternatives) and [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) ([in-context-ralm markdown twin](/tools/ai21labs-in-context-ralm/alternatives.md), [LLMSurvey markdown twin](/tools/rucaibox-llmsurvey/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/ai21labs-in-context-ralm-vs-rucaibox-llmsurvey.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, in-context-ralm or LLMSurvey?

in-context-ralm: Archived. LLMSurvey: 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 in-context-ralm and LLMSurvey?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [in-context-ralm trust report](/tools/ai21labs-in-context-ralm/trust); [LLMSurvey trust report](/tools/rucaibox-llmsurvey/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=ai21labs-in-context-ralm`](/api/graphcanon/graph?tool=ai21labs-in-context-ralm)
- 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/_