Comparison
langserve vs Awesome-LLMOps
Verdict
Pick langserve if langServe offers tools to deploy and serve models using LangChain with FastAPI; pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Markdown twin · langserve alternatives · Awesome-LLMOps alternatives
GraphCanon updated Sep 20, 2026
11views this month
vs
Trust & integrity
| Signal | langserve | Awesome-LLMOps |
|---|---|---|
| Maintenance | Archived (124d since push) As of Sep 7, 2026 · github_public_v1 | Slowing (121d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 7, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- langserve
- LangServe 🦜️🏓
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- langserve
- 2.3k
- Awesome-LLMOps
- 5.9k
Forks
- langserve
- 270
- Awesome-LLMOps
- 1.1k
Open issues
- langserve
- 139
- Awesome-LLMOps
- 317
Language
- langserve
- JavaScript
- Awesome-LLMOps
- Shell
Adopt for
- langserve
- LangServe offers tools to deploy and serve models using LangChain with FastAPI.
- Awesome-LLMOps
- Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Persona
- langserve
- -
- Awesome-LLMOps
- -
Runtime
- langserve
- -
- Awesome-LLMOps
- -
License
- langserve
- Other
- Awesome-LLMOps
- CC0-1.0
Last pushed
- langserve
- May 5, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- langserve
- Inference & Serving
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- langserve
- Archived (8%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- langserve
- 124d
- Awesome-LLMOps
- 121d
Archived on GitHub
- langserve
- Yes
- Awesome-LLMOps
- No
Open issues (now)
- langserve
- 139
- Awesome-LLMOps
- 317
Stars delta
- langserve
- -4 (30d)
- Awesome-LLMOps
- +26 (30d)
Open issues delta
- langserve
- 0 (30d)
- Awesome-LLMOps
- +70 (30d)
Full report
- langserve
- Trust report
- Awesome-LLMOps
- Trust report
Choose langserve if…
- langserve is primarily JavaScript; Awesome-LLMOps is Shell.
- License: langserve is Other, Awesome-LLMOps is CC0-1.0.
- Tags unique to langserve: deployment, fastapi, langchain, llm.
- When you are working in an environment where models need to be served efficiently and require the capabilities of both LangChain and FastAPI.
When NOT to use langserve
- When you prefer using frameworks or tools that are not built around Python's ecosystem and require languages like JavaScript or Java.
- If your project specifically requires a non-FastAPI backend for serving models because of specific performance criteria, constraints, or compatibility issues with FastAPI.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; langserve is JavaScript.
- License: Awesome-LLMOps is CC0-1.0, langserve is Other.
- Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training, Speech & Audio.
- - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
When NOT to use Awesome-LLMOps
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list.
- - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (langchain-ai/langserve) · observed Sep 20, 2026
- GitHub forks (langchain-ai/langserve) · observed Sep 20, 2026
- Last push (langchain-ai/langserve) · observed May 5, 2026
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: langserve 2.3k · Awesome-LLMOps 5.9k (synced Sep 20, 2026).
Common questions
- What is the difference between langserve and Awesome-LLMOps?
- langserve: LangServe 🦜️🏓. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories.
- When should I choose langserve over Awesome-LLMOps?
- Choose langserve over Awesome-LLMOps when langserve is primarily JavaScript; Awesome-LLMOps is Shell; License: langserve is Other, Awesome-LLMOps is CC0-1.0; Tags unique to langserve: deployment, fastapi, langchain, llm; When you are working in an environment where models need to be served efficiently and require the capabilities of both LangChain and FastAPI.
- When should I choose Awesome-LLMOps over langserve?
- Choose Awesome-LLMOps over langserve when Awesome-LLMOps is primarily Shell; langserve is JavaScript; License: Awesome-LLMOps is CC0-1.0, langserve is Other; Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- When should I avoid langserve?
- When you prefer using frameworks or tools that are not built around Python's ecosystem and require languages like JavaScript or Java. If your project specifically requires a non-FastAPI backend for serving models because of specific performance criteria, constraints, or compatibility issues with FastAPI.
- When should I avoid Awesome-LLMOps?
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
- Is langserve or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,941 vs 2,328). Stars measure visibility, not whether either tool fits your constraints.
- Are langserve and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (langserve: Other, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to langserve or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at langserve alternatives and Awesome-LLMOps alternatives (langserve markdown twin, Awesome-LLMOps 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, langserve or Awesome-LLMOps?
- langserve: Archived. Awesome-LLMOps: 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 langserve and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: langserve trust report; Awesome-LLMOps trust report.