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Comparison

forge vs llm-python

Verdict

Pick forge if developers working on self-hosted LLM tooling who need flexibility in backend setup and seamless integration of function calling in multi-step workflows might benefit from Forge; pick llm-python if jupyter Notebook tutorials and scripts for working with LangChain, OpenAI API, llamaindex, GPT models, ChromaDB, and Pinecone.

Markdown twin · forge alternatives · llm-python alternatives

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forge logo

forge

antoinezambelli/forge

★ 2.2kpushed Aug 13, 2026
vs
llm-python logo

llm-python

onlyphantom/llm-python

★ 927pushed Feb 20, 2026

Trust & integrity

Signalforgellm-python
Maintenance
Very active (0d since push)
As of 1w · github_public_v1
Slowing (181d since push)
As of today · github_public_v1
Provenance
Not a fork · Personal account
As of 1w · github_public_v1
Not a fork · Personal account
As of today · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
Published findings
As of 1mo · 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
forge trust reportllm-python trust reportTrust methodology

Tagline

forge
A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows
llm-python
LLM tutorials and scripts covering langchain, openai, llamaindex, GPT, ChromaDB, Pinecone

Stars

forge
2.2k
llm-python
927

Forks

forge
173
llm-python
316

Open issues

forge
4
llm-python
0

Language

forge
Python
llm-python
Jupyter Notebook

Adopt for

forge
Developers working on self-hosted LLM tooling who need flexibility in backend setup and seamless integration of function calling in multi-step workflows might benefit from Forge.
llm-python
Jupyter Notebook tutorials and scripts for working with LangChain, OpenAI API, llamaindex, GPT models, ChromaDB, and Pinecone.

Persona

forge
-
llm-python
-

Runtime

forge
-
llm-python
-

License

forge
MIT
llm-python
MIT

Last pushed

forge
Aug 13, 2026
llm-python
Feb 20, 2026

Categories

forge
AI Agents, LLM Frameworks
llm-python
LLM Frameworks, Vector Databases

Trust and health

Maintenance

forge
Very active (96%)
llm-python
Slowing (36%)

Days since push

forge
0d
llm-python
181d

Open issues (now)

forge
4
llm-python
0

Stars delta

forge
Unknown
llm-python
+1 (30d)

Open issues delta

forge
Unknown
llm-python
0 (30d)

OSV dependency advisories

forge
No lockfile (source not queried)
llm-python
Published findings

Full report

forge
Trust report
llm-python
Trust report

Shared compatibility

  • Python · forge: Python runtime · llm-python: Python runtime

Choose forge if…

  • forge is primarily Python; llm-python is Jupyter Notebook.
  • Requirements: Min 4 GB RAM; Requires Docker; Requires Python 3.12+ and a running LLM backend.; Can be set up with local backends (e.g., llama.cpp) or Anthropic via its API, requiring an API key for the latter case..
  • Tags unique to forge: agentic-ai, function-calling, multi-step-workflows, python-framework.
  • Also covers AI Agents.
  • forge ships Docker support for self-hosted deployment.
  • - You require an agnostic backend setup, such as local LLM backends like llama.cpp or cloud-based services with Anthropic.

When NOT to use forge

  • - If your application does not require flexibility in backend selection, and you prefer a single cloud provider like Anthropic without local setup.
  • - For scenarios where simplicity of setup outweighs the need for customization in function calling and workflow management.
  • - When working within environments strictly regulated against self-hosted infrastructure or requiring fully managed services.
GitHubDetails

Choose llm-python if…

  • llm-python is primarily Jupyter Notebook; forge is Python.
  • Tags unique to llm-python: chromadb, gpt-3, langchain, llama-index.
  • Also covers Vector Databases.
  • When you want comprehensive Jupyter-based tutorials on integrating multiple LLM tools including OpenAI and LangChain.

When NOT to use llm-python

  • Avoid if you require a purely code-library without tutorial-like content in Jupyter Notebooks.
  • Not suitable if your project strictly demands proprietary or closed-access LLM tools not covered in the repo, like those beyond OpenAI and LangChain.
GitHubDetails

Explore

forge trust report →llm-python trust report →AI Agents category →LLM Frameworks category →Vector Databases 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: forge 2.2k · llm-python 927 (synced Aug 14, 2026).

Common questions

What is the difference between forge and llm-python?
forge: A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows. llm-python: LLM tutorials and scripts covering langchain, openai, llamaindex, GPT, ChromaDB, Pinecone. See the comparison table for live GitHub stats and shared categories.
When should I choose forge over llm-python?
Choose forge over llm-python when forge is primarily Python; llm-python is Jupyter Notebook; Requirements: Min 4 GB RAM; Requires Docker; Requires Python 3.12+ and a running LLM backend.; Can be set up with local backends (e.g., llama.cpp) or Anthropic via its API, requiring an API key for the latter case.; Tags unique to forge: agentic-ai, function-calling, multi-step-workflows, python-framework; Also covers AI Agents; forge ships Docker support for self-hosted deployment; - You require an agnostic backend setup, such as local LLM backends like llama.cpp or cloud-based services with Anthropic.
When should I choose llm-python over forge?
Choose llm-python over forge when llm-python is primarily Jupyter Notebook; forge is Python; Tags unique to llm-python: chromadb, gpt-3, langchain, llama-index; Also covers Vector Databases; When you want comprehensive Jupyter-based tutorials on integrating multiple LLM tools including OpenAI and LangChain.
When should I avoid forge?
- If your application does not require flexibility in backend selection, and you prefer a single cloud provider like Anthropic without local setup. - For scenarios where simplicity of setup outweighs the need for customization in function calling and workflow management. - When working within environments strictly regulated against self-hosted infrastructure or requiring fully managed services.
When should I avoid llm-python?
Avoid if you require a purely code-library without tutorial-like content in Jupyter Notebooks. Not suitable if your project strictly demands proprietary or closed-access LLM tools not covered in the repo, like those beyond OpenAI and LangChain.
Is forge or llm-python more popular on GitHub?
forge has more GitHub stars (2,217 vs 927). Stars measure visibility, not whether either tool fits your constraints.
Are forge and llm-python open source?
Yes - both are open-source projects on GitHub (forge: MIT, llm-python: MIT).
Where can I find alternatives to forge or llm-python?
GraphCanon lists graph-backed alternatives at forge alternatives and llm-python alternatives (forge markdown twin, llm-python 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, forge or llm-python?
forge: Very active. llm-python: 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 forge and llm-python?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: forge trust report; llm-python trust report.

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