Home/Compare/autogen vs llama-hub

Comparison

autogen vs llama-hub

Verdict

Pick autogen when autogen is primarily Python; llama-hub is Jupyter Notebook; pick llama-hub when llama-hub is primarily Jupyter Notebook; autogen is Python.

Markdown twin · autogen alternatives · llama-hub alternatives

GraphCanon updated today

autogen logo

autogen

microsoft/autogen

60kpushed Apr 15, 2026
vs
llama-hub logo

llama-hub

run-llama/llama-hub

3.5kpushed Mar 1, 2024

Trust & integrity

Signalautogenllama-hub
Maintenance
Steady (87d since push)
As of today · github_public_v1
Archived (861d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
No lockfile
As of today · none
121 low (121 low)
As of today · osv@v1

Tagline

autogen
A programming framework for agentic AI
llama-hub
A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain

Stars

autogen
60k
llama-hub
3.5k

Forks

autogen
9.0k
llama-hub
719

Open issues

autogen
945
llama-hub
96

Language

autogen
Python
llama-hub
Jupyter Notebook

Adopt for

autogen
AutoGen is a Python-based framework for developing and managing agentic AI systems. It includes the AutoGen Studio for no-code GUI setup, integrating with various models.
llama-hub
-

Persona

autogen
-
llama-hub
-

Runtime

autogen
-
llama-hub
-

License

autogen
CC-BY-4.0
llama-hub
MIT

Last pushed

autogen
Apr 15, 2026
llama-hub
Mar 1, 2024

Categories

autogen
LLM Frameworks, AI Agents
llama-hub
Data & Retrieval, LLM Frameworks, Evaluation & Observability

Trust and health

Maintenance

autogen
Steady (60%)
llama-hub
Archived (8%)

Days since push

autogen
87d
llama-hub
861d

Archived on GitHub

autogen
No
llama-hub
Yes

Open issues (now)

autogen
945
llama-hub
96

Security scan

autogen
No lockfile
llama-hub
121 low (121 low)

Full report

llama-hub
Trust report

Shared compatibility

  • Python · autogen: Python runtime · llama-hub: Python runtime

Choose autogen if…

  • autogen is primarily Python; llama-hub is Jupyter Notebook.
  • License: autogen is CC-BY-4.0, llama-hub is MIT.
  • Requirements: Min 4 GB RAM; AutoGen requires Python 3.10 or later.; Ensure security when connecting to MCP servers due to the potential for local command execution and sensitive information exposure..
  • Tags unique to autogen: llm-framework, autogen, agents, ai.
  • Also covers AI Agents.
  • You need a framework that supports integration with multiple AI models via OpenAI's chat completion client.

When NOT to use autogen

  • If you require tools supporting multiple programming languages beyond Python, as AutoGen is strictly a Python-based framework.
  • When deploying in environments where connecting to external servers (like those used by MCP) could pose security risks or is prohibited.
  • You need solutions which do not involve additional installations for server components such as `playwright/mcp`, as AutoGen requires this setup for certain functionalities.

Choose llama-hub if…

  • llama-hub is primarily Jupyter Notebook; autogen is Python.
  • License: llama-hub is MIT, autogen is CC-BY-4.0.
  • Tags unique to llama-hub: jupyter notebook.
  • Also covers Data & Retrieval, Evaluation & Observability.

When NOT to use llama-hub

  • llama-hub is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency.
  • Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
  • Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.

Explore

Sources

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

GitHub stars on cards: autogen 60k · llama-hub 3.5k (synced Jul 11, 2026).

Common questions

What is the difference between autogen and llama-hub?
autogen: A programming framework for agentic AI. llama-hub: A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain. See the comparison table for live GitHub stats and shared categories.
When should I choose autogen over llama-hub?
Choose autogen over llama-hub when autogen is primarily Python; llama-hub is Jupyter Notebook; License: autogen is CC-BY-4.0, llama-hub is MIT; Requirements: Min 4 GB RAM; AutoGen requires Python 3.10 or later.; Ensure security when connecting to MCP servers due to the potential for local command execution and sensitive information exposure.; Tags unique to autogen: llm-framework, autogen, agents, ai; Also covers AI Agents; You need a framework that supports integration with multiple AI models via OpenAI's chat completion client.
When should I choose llama-hub over autogen?
Choose llama-hub over autogen when llama-hub is primarily Jupyter Notebook; autogen is Python; License: llama-hub is MIT, autogen is CC-BY-4.0; Tags unique to llama-hub: jupyter notebook; Also covers Data & Retrieval, Evaluation & Observability.
When should I avoid autogen?
If you require tools supporting multiple programming languages beyond Python, as AutoGen is strictly a Python-based framework. When deploying in environments where connecting to external servers (like those used by MCP) could pose security risks or is prohibited. You need solutions which do not involve additional installations for server components such as playwright/mcp, as AutoGen requires this setup for certain functionalities.
When should I avoid llama-hub?
llama-hub is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
Is autogen or llama-hub more popular on GitHub?
autogen has more GitHub stars (59,658 vs 3,473). Stars measure visibility, not whether either tool fits your constraints.
Are autogen and llama-hub open source?
Yes - both are open-source projects on GitHub (autogen: CC-BY-4.0, llama-hub: MIT).
Where can I find alternatives to autogen or llama-hub?
GraphCanon lists graph-backed alternatives at autogen alternatives and llama-hub alternatives (autogen markdown twin, llama-hub 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, autogen or llama-hub?
autogen: Steady. llama-hub: Archived. 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 autogen and llama-hub?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: autogen trust report; llama-hub trust report.