--- title: "forge vs llm-python" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/antoinezambelli-forge-vs-onlyphantom-llm-python" tools: ["antoinezambelli-forge", "onlyphantom-llm-python"] --- # forge vs llm-python *GraphCanon updated Aug 21, 2026* ## 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. [forge](https://github.com/antoinezambelli/forge) reports 2.2k GitHub stars, 173 forks, and 4 open issues, last pushed Aug 13, 2026. [llm-python](https://www.youtube.com/playlist?list=PLXsFtK46HZxUQERRbOmuGoqbMD-KWLkOS) has 927 stars, 316 forks, and 0 open issues, last pushed Feb 20, 2026. Figures are from public GitHub metadata via [forge's repository](https://github.com/antoinezambelli/forge) and [llm-python's repository](https://github.com/onlyphantom/llm-python). | | [forge](/tools/antoinezambelli-forge.md) | [llm-python](/tools/onlyphantom-llm-python.md) | | --- | --- | --- | | Tagline | A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows | LLM tutorials and scripts covering langchain, openai, llamaindex, GPT, ChromaDB, Pinecone | | Stars | 2,217 | 927 | | Forks | 173 | 316 | | Open issues | 4 | 0 | | Language | Python | Jupyter Notebook | | Adopt for | 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. | Jupyter Notebook tutorials and scripts for working with LangChain, OpenAI API, llamaindex, GPT models, ChromaDB, and Pinecone. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents, LLM Frameworks | LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [forge](/tools/antoinezambelli-forge.md) | [llm-python](/tools/onlyphantom-llm-python.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 181d | | Open issues (now) | 4 | 0 | | Stars delta | Unknown | +1 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/antoinezambelli-forge/trust.md) | [trust report](/tools/onlyphantom-llm-python/trust.md) | ## Shared compatibility - **Python**: [forge](/tools/antoinezambelli-forge.md) - Python runtime; [llm-python](/tools/onlyphantom-llm-python.md) - Python runtime ## Decision facts: forge - **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. - **Adopt for:** 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. ## Decision facts: llm-python - **Adopt for:** Jupyter Notebook tutorials and scripts for working with LangChain, OpenAI API, llamaindex, GPT models, ChromaDB, and Pinecone. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/antoinezambelli-forge/alternatives) and [llm-python alternatives](/tools/onlyphantom-llm-python/alternatives) ([forge markdown twin](/tools/antoinezambelli-forge/alternatives.md), [llm-python markdown twin](/tools/onlyphantom-llm-python/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/antoinezambelli-forge-vs-onlyphantom-llm-python.md) 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](/tools/antoinezambelli-forge/trust); [llm-python trust report](/tools/onlyphantom-llm-python/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=antoinezambelli-forge`](/api/graphcanon/graph?tool=antoinezambelli-forge) - 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/_