--- title: "forge vs openlm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/antoinezambelli-forge-vs-r2d4-openlm" tools: ["antoinezambelli-forge", "r2d4-openlm"] --- # forge vs openlm *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 openlm if openLM is a flexible Python library designed to interface with various Language Model frameworks similarly to the OpenAI API. [forge](https://github.com/antoinezambelli/forge) reports 2.2k GitHub stars, 173 forks, and 4 open issues, last pushed Aug 13, 2026. [openlm](https://github.com/r2d4/openlm) has 368 stars, 22 forks, and 1 open issues, last pushed May 19, 2023. Figures are from public GitHub metadata via [forge's repository](https://github.com/antoinezambelli/forge) and [openlm's repository](https://github.com/r2d4/openlm). | | [forge](/tools/antoinezambelli-forge.md) | [openlm](/tools/r2d4-openlm.md) | | --- | --- | --- | | Tagline | A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows | OpenAI-compatible Python client for calling any LLM | | Stars | 2,217 | 368 | | Forks | 173 | 22 | | 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. | OpenLM is a flexible Python library designed to interface with various Language Model frameworks similarly to the OpenAI API. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License | | Categories | AI Agents, LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [forge](/tools/antoinezambelli-forge.md) | [openlm](/tools/r2d4-openlm.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1184d | | Open issues (now) | 4 | 1 | | Stars delta | Unknown | -1 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/antoinezambelli-forge/trust.md) | [trust report](/tools/r2d4-openlm/trust.md) | ## Shared compatibility - **Python**: [forge](/tools/antoinezambelli-forge.md) - Python runtime; [openlm](/tools/r2d4-openlm.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: openlm - **Requirements:** Installation is as simple as using pip to install the openlm package. - **Adopt for:** OpenLM is a flexible Python library designed to interface with various Language Model frameworks similarly to the OpenAI API. - **License detail:** MIT License ## 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 openlm if… - Requirements: Installation is as simple as using pip to install the openlm package.. - Tags unique to openlm: cohere, huggingface, llm, openai. - Use when you want to seamlessly integrate different LLM frameworks, like Hugging Face or Cohere, under a uniform interface similar to OpenAI’s. ## 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 openlm - Avoid when you require maximum performance optimization specific to one LLM framework, as using an intermediary library like OpenLM might introduce additional overhead. - Not suitable if you are strictly working with models that do not have Python support or don't fit within the frameworks supported by OpenLM. ## Common questions ### What is the difference between forge and openlm? forge: A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows. openlm: OpenAI-compatible Python client for calling any LLM. See the comparison table for live GitHub stats and shared categories. ### When should I choose forge over openlm? Choose forge over openlm 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 openlm over forge? Choose openlm over forge when Requirements: Installation is as simple as using pip to install the openlm package.; Tags unique to openlm: cohere, huggingface, llm, openai; Use when you want to seamlessly integrate different LLM frameworks, like Hugging Face or Cohere, under a uniform interface similar to OpenAI’s. ### 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 openlm? Avoid when you require maximum performance optimization specific to one LLM framework, as using an intermediary library like OpenLM might introduce additional overhead. Not suitable if you are strictly working with models that do not have Python support or don't fit within the frameworks supported by OpenLM. ### Is forge or openlm more popular on GitHub? forge has more GitHub stars (2,217 vs 368). Stars measure visibility, not whether either tool fits your constraints. ### Are forge and openlm open source? Yes - both are open-source projects on GitHub (forge: MIT, openlm: MIT). ### Where can I find alternatives to forge or openlm? GraphCanon lists graph-backed alternatives at [forge alternatives](/tools/antoinezambelli-forge/alternatives) and [openlm alternatives](/tools/r2d4-openlm/alternatives) ([forge markdown twin](/tools/antoinezambelli-forge/alternatives.md), [openlm markdown twin](/tools/r2d4-openlm/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-openlm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, forge or openlm? forge: Very active. openlm: 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 openlm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [forge trust report](/tools/antoinezambelli-forge/trust); [openlm trust report](/tools/r2d4-openlm/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/_