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Comparison

go-streams vs awesome-llm-apps

Verdict

Pick go-streams if go-streams is a lightweight stream processing library for Go that simplifies constructing and executing data pipelines and streaming applications; pick awesome-llm-apps if awesome-llm-apps is a collection of over 100 AI Agent and Retrieval Augmented Generation (RAG) applications that enable users to quickly implement, customize, and deploy practical use cases in Python.

Markdown twin · go-streams alternatives · awesome-llm-apps alternatives

GraphCanon updated Sep 20, 2026

14views this month

go-streams logo

go-streams

reugn/go-streams

★ 2.2kpushed Jan 14, 2026
vs
awesome-llm-apps logo

awesome-llm-apps

Shubhamsaboo/awesome-llm-apps

★ 136kpushed Sep 2, 2026

Trust & integrity

Signalgo-streamsawesome-llm-apps
Maintenance
Slowing (245d since push)
As of Sep 17, 2026 · github_public_v1
Very active (4d since push)
As of Sep 7, 2026 · github_public_v1
Provenance
Not a fork · Personal account
As of Sep 17, 2026 · github_public_v1
Not a fork · Personal account
As of Sep 7, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 15, 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
go-streams trust reportawesome-llm-apps trust reportTrust methodology

Tagline

go-streams
lightweight stream processing library for Go
awesome-llm-apps
Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy.

Stars

go-streams
2.2k
awesome-llm-apps
136k

Forks

go-streams
174
awesome-llm-apps
20k

Open issues

go-streams
12
awesome-llm-apps
10

Language

go-streams
Go
awesome-llm-apps
Python

Adopt for

go-streams
go-streams is a lightweight stream processing library for Go that simplifies constructing and executing data pipelines and streaming applications.
awesome-llm-apps
awesome-llm-apps is a collection of over 100 AI Agent and Retrieval Augmented Generation (RAG) applications that enable users to quickly implement, customize, and deploy practical use cases in Python.

Persona

go-streams
-
awesome-llm-apps
-

Runtime

go-streams
-
awesome-llm-apps
-

License

go-streams
MIT
awesome-llm-apps
The Apache-2.0 license allows users to freely use, modify, and distribute the projects found in awesome-llm-apps under specific conditions outlined by the license.

Last pushed

go-streams
Jan 14, 2026
awesome-llm-apps
Sep 2, 2026

Categories

go-streams
Data & Retrieval, Developer Tools
awesome-llm-apps
AI Agents, Data & Retrieval

Trust and health

Maintenance

go-streams
Slowing (36%)
awesome-llm-apps
Very active (96%)

Days since push

go-streams
245d
awesome-llm-apps
4d

Open issues (now)

go-streams
12
awesome-llm-apps
10

Stars delta

go-streams
0 (30d)
awesome-llm-apps
+5.2k (30d)

Open issues delta

go-streams
+1 (30d)
awesome-llm-apps
-3 (30d)

Full report

go-streams
Trust report
awesome-llm-apps
Trust report

Choose go-streams if…

  • go-streams is primarily Go; awesome-llm-apps is Python.
  • License: go-streams is MIT, awesome-llm-apps is Apache-2.0.
  • Tags unique to go-streams: aerospike, data-pipeline, kafka, low-code.
  • Also covers Developer Tools.
  • When you need a lightweight solution for handling streams in Go projects.

When NOT to use go-streams

  • In scenarios where a more robust framework with extended features beyond lightweight streams is required.
  • For projects preferring languages other than Go, as go-streams is exclusively for Go applications and does not offer similar functionality out-of-the-box compared to language-agnostic tools.
GitHubDetails

Choose awesome-llm-apps if…

  • awesome-llm-apps is primarily Python; go-streams is Go.
  • License: awesome-llm-apps is Apache-2.0, go-streams is MIT.
  • Pricing: Free with open-source licensing, but commercial exploitation is allowed..
  • Tags unique to awesome-llm-apps: agents, applications, customizable, deployable.
  • Also covers AI Agents.
  • When you need quick implementations of various real-world use cases for AI Agents and RAG.

When NOT to use awesome-llm-apps

  • If your project requires highly specialized customization beyond what the provided apps can offer out-of-the-box, as deep integration might be required from scratch.
  • When you are looking for a fully managed service or support directly from developers; this repository is more about self-service and community interaction.
GitHubDetails

Explore

go-streams trust report →awesome-llm-apps trust report →Data & Retrieval category →Developer Tools category →AI Agents category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: go-streams 2.2k · awesome-llm-apps 136k (synced Sep 20, 2026).

Common questions

What is the difference between go-streams and awesome-llm-apps?
go-streams: lightweight stream processing library for Go. awesome-llm-apps: Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy.. See the comparison table for live GitHub stats and shared categories.
When should I choose go-streams over awesome-llm-apps?
Choose go-streams over awesome-llm-apps when go-streams is primarily Go; awesome-llm-apps is Python; License: go-streams is MIT, awesome-llm-apps is Apache-2.0; Tags unique to go-streams: aerospike, data-pipeline, kafka, low-code; Also covers Developer Tools; When you need a lightweight solution for handling streams in Go projects.
When should I choose awesome-llm-apps over go-streams?
Choose awesome-llm-apps over go-streams when awesome-llm-apps is primarily Python; go-streams is Go; License: awesome-llm-apps is Apache-2.0, go-streams is MIT; Pricing: Free with open-source licensing, but commercial exploitation is allowed.; Tags unique to awesome-llm-apps: agents, applications, customizable, deployable; Also covers AI Agents; When you need quick implementations of various real-world use cases for AI Agents and RAG.
When should I avoid go-streams?
In scenarios where a more robust framework with extended features beyond lightweight streams is required. For projects preferring languages other than Go, as go-streams is exclusively for Go applications and does not offer similar functionality out-of-the-box compared to language-agnostic tools.
When should I avoid awesome-llm-apps?
If your project requires highly specialized customization beyond what the provided apps can offer out-of-the-box, as deep integration might be required from scratch. When you are looking for a fully managed service or support directly from developers; this repository is more about self-service and community interaction.
Is go-streams or awesome-llm-apps more popular on GitHub?
awesome-llm-apps has more GitHub stars (136,444 vs 2,172). Stars measure visibility, not whether either tool fits your constraints.
Are go-streams and awesome-llm-apps open source?
Yes - both are open-source projects on GitHub (go-streams: MIT, awesome-llm-apps: Apache-2.0).
Where can I find alternatives to go-streams or awesome-llm-apps?
GraphCanon lists graph-backed alternatives at go-streams alternatives and awesome-llm-apps alternatives (go-streams markdown twin, awesome-llm-apps 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, go-streams or awesome-llm-apps?
go-streams: Slowing. awesome-llm-apps: 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 go-streams and awesome-llm-apps?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: go-streams trust report; awesome-llm-apps trust report.

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