--- title: "forge vs LLFn" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/antoinezambelli-forge-vs-orgexyz-llfn" tools: ["antoinezambelli-forge", "orgexyz-llfn"] --- # forge vs LLFn *GraphCanon updated Aug 16, 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 LLFn if lightweight, MIT-licensed Python framework for developing with Language Models. [forge](https://github.com/antoinezambelli/forge) reports 2.2k GitHub stars, 173 forks, and 4 open issues, last pushed Aug 13, 2026. [LLFn](https://llfn.orge.xyz/) has 96 stars, 7 forks, and 1 open issues, last pushed Jul 30, 2023. Figures are from public GitHub metadata via [forge's repository](https://github.com/antoinezambelli/forge) and [LLFn's repository](https://github.com/orgexyz/LLFn). | | [forge](/tools/antoinezambelli-forge.md) | [LLFn](/tools/orgexyz-llfn.md) | | --- | --- | --- | | Tagline | A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows | A lightweight framework for creating applications using LLMs | | Stars | 2,217 | 96 | | Forks | 173 | 7 | | Open issues | 4 | 1 | | Language | Python | Python | | 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. | Lightweight, MIT-licensed Python framework for developing with Language Models | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents, LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [forge](/tools/antoinezambelli-forge.md) | [LLFn](/tools/orgexyz-llfn.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1112d | | Open issues (now) | 4 | 1 | | Stars delta | Unknown | 0 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/antoinezambelli-forge/trust.md) | [trust report](/tools/orgexyz-llfn/trust.md) | ## Shared compatibility - **Python**: [forge](/tools/antoinezambelli-forge.md) - Python runtime; [LLFn](/tools/orgexyz-llfn.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: LLFn - **Adopt for:** Lightweight, MIT-licensed Python framework for developing with Language Models ## Choose when ### Choose forge if… - 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 LLFn if… - Tags unique to LLFn: applications with llms, lightweight, python. - Ideal for prototyping and small-scale projects needing quick development cycles. - Leaner open-issue backlog (1). ## 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 LLFn - Avoid if requiring extensive customization or large-scale applications with complex scaling needs. - Not recommended for teams prioritizing enterprise-level support and service features. ## Common questions ### What is the difference between forge and LLFn? forge: A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows. LLFn: A lightweight framework for creating applications using LLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose forge over LLFn? Choose forge over LLFn when 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 LLFn over forge? Choose LLFn over forge when Tags unique to LLFn: applications with llms, lightweight, python; Ideal for prototyping and small-scale projects needing quick development cycles; Leaner open-issue backlog (1). ### 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 LLFn? Avoid if requiring extensive customization or large-scale applications with complex scaling needs. Not recommended for teams prioritizing enterprise-level support and service features. ### Is forge or LLFn more popular on GitHub? forge has more GitHub stars (2,217 vs 96). Stars measure visibility, not whether either tool fits your constraints. ### Are forge and LLFn open source? Yes - both are open-source projects on GitHub (forge: MIT, LLFn: MIT). ### Where can I find alternatives to forge or LLFn? GraphCanon lists graph-backed alternatives at [forge alternatives](/tools/antoinezambelli-forge/alternatives) and [LLFn alternatives](/tools/orgexyz-llfn/alternatives) ([forge markdown twin](/tools/antoinezambelli-forge/alternatives.md), [LLFn markdown twin](/tools/orgexyz-llfn/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-orgexyz-llfn.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, forge or LLFn? forge: Very active. LLFn: 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 LLFn? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [forge trust report](/tools/antoinezambelli-forge/trust); [LLFn trust report](/tools/orgexyz-llfn/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/_