--- title: "forge vs llm.ts" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/antoinezambelli-forge-vs-r2d4-llm-ts" tools: ["antoinezambelli-forge", "r2d4-llm-ts"] --- # forge vs llm.ts *GraphCanon updated Aug 15, 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.ts if llm.ts is a TypeScript library for interacting with various Large Language Models via a unified API interface. [forge](https://github.com/antoinezambelli/forge) reports 2.2k GitHub stars, 173 forks, and 4 open issues, last pushed Aug 13, 2026. [llm.ts](https://github.com/r2d4/llm.ts) has 214 stars, 9 forks, and 2 open issues, last pushed May 9, 2023. Figures are from public GitHub metadata via [forge's repository](https://github.com/antoinezambelli/forge) and [llm.ts's repository](https://github.com/r2d4/llm.ts). | | [forge](/tools/antoinezambelli-forge.md) | [llm.ts](/tools/r2d4-llm-ts.md) | | --- | --- | --- | | Tagline | A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows | Call any LLM with a single API. Zero dependencies. | | Stars | 2,217 | 214 | | Forks | 173 | 9 | | Open issues | 4 | 2 | | Language | Python | TypeScript | | 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. | llm.ts is a TypeScript library for interacting with various Large Language Models via a unified API interface. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License - permissive license that is short and simple, allowing you to use the software in any project as long as this licensing information is retained | | Categories | AI Agents, LLM Frameworks | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [forge](/tools/antoinezambelli-forge.md) | [llm.ts](/tools/r2d4-llm-ts.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1193d | | Open issues (now) | 4 | 2 | | Stars delta | Unknown | +1 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/antoinezambelli-forge/trust.md) | [trust report](/tools/r2d4-llm-ts/trust.md) | ## 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.ts - **Hosting:** unknown - Supports multiple providers including OpenAI, Cohere, and HuggingFace. Can be extended by opening a PR. - **Adopt for:** llm.ts is a TypeScript library for interacting with various Large Language Models via a unified API interface. - **License detail:** MIT License - permissive license that is short and simple, allowing you to use the software in any project as long as this licensing information is retained ## Choose when ### Choose forge if… - forge is primarily Python; llm.ts is TypeScript. - 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.ts if… - llm.ts is primarily TypeScript; forge is Python. - Supports multiple providers including OpenAI, Cohere, and HuggingFace. Can be extended by opening a PR. - Tags unique to llm.ts: ai, cohere, huggingface, llm. - Also covers Model Training. - You need to interact with multiple LLM providers like OpenAI, Cohere, and HuggingFace through a single API. ## 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.ts - If you require functionalities that are specific to one provider that are not yet unified or supported within the llm.ts framework. - For projects that aim to minimize dependencies, though llm.ts itself claims zero dependencies, its reliance on external LLM providers could indirectly introduce complexities. ## Common questions ### What is the difference between forge and llm.ts? forge: A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows. llm.ts: Call any LLM with a single API. Zero dependencies.. See the comparison table for live GitHub stats and shared categories. ### When should I choose forge over llm.ts? Choose forge over llm.ts when forge is primarily Python; llm.ts is TypeScript; 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.ts over forge? Choose llm.ts over forge when llm.ts is primarily TypeScript; forge is Python; Supports multiple providers including OpenAI, Cohere, and HuggingFace. Can be extended by opening a PR; Tags unique to llm.ts: ai, cohere, huggingface, llm; Also covers Model Training; You need to interact with multiple LLM providers like OpenAI, Cohere, and HuggingFace through a single API. ### 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.ts? If you require functionalities that are specific to one provider that are not yet unified or supported within the llm.ts framework. For projects that aim to minimize dependencies, though llm.ts itself claims zero dependencies, its reliance on external LLM providers could indirectly introduce complexities. ### Is forge or llm.ts more popular on GitHub? forge has more GitHub stars (2,217 vs 214). Stars measure visibility, not whether either tool fits your constraints. ### Are forge and llm.ts open source? Yes - both are open-source projects on GitHub (forge: MIT, llm.ts: MIT). ### Where can I find alternatives to forge or llm.ts? GraphCanon lists graph-backed alternatives at [forge alternatives](/tools/antoinezambelli-forge/alternatives) and [llm.ts alternatives](/tools/r2d4-llm-ts/alternatives) ([forge markdown twin](/tools/antoinezambelli-forge/alternatives.md), [llm.ts markdown twin](/tools/r2d4-llm-ts/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-r2d4-llm-ts.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, forge or llm.ts? forge: Very active. llm.ts: Dormant. 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.ts? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [forge trust report](/tools/antoinezambelli-forge/trust); [llm.ts trust report](/tools/r2d4-llm-ts/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/_