--- title: "forge vs agentflow" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/antoinezambelli-forge-vs-simonmesmith-agentflow" tools: ["antoinezambelli-forge", "simonmesmith-agentflow"] --- # forge vs agentflow *GraphCanon updated Sep 20, 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 agentflow if agentflow simplifies the creation of complex workflows for large language models through simple JSON configurations. [forge](https://github.com/antoinezambelli/forge) reports 2.2k GitHub stars, 173 forks, and 3 open issues, last pushed Sep 1, 2026. [agentflow](https://github.com/simonmesmith/agentflow) has 320 stars, 27 forks, and 13 open issues, last pushed Aug 11, 2023. Figures are from public GitHub metadata via [forge's repository](https://github.com/antoinezambelli/forge) and [agentflow's repository](https://github.com/simonmesmith/agentflow). | | [forge](/tools/antoinezambelli-forge.md) | [agentflow](/tools/simonmesmith-agentflow.md) | | --- | --- | --- | | Tagline | A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows | Complex LLM Workflows from Simple JSON | | Stars | 2,248 | 320 | | Forks | 173 | 27 | | Open issues | 3 | 13 | | 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. | Agentflow simplifies the creation of complex workflows for large language models through simple JSON configurations. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [forge](/tools/antoinezambelli-forge.md) | [agentflow](/tools/simonmesmith-agentflow.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 19d | 1100d | | Open issues (now) | 3 | 13 | | Stars delta | +31 (30d) | -1 (30d) | | Open issues delta | -1 (30d) | 0 (30d) | | Full report | [trust report](/tools/antoinezambelli-forge/trust.md) | [trust report](/tools/simonmesmith-agentflow/trust.md) | ## Shared compatibility - **Python**: [forge](/tools/antoinezambelli-forge.md) - Python runtime; [agentflow](/tools/simonmesmith-agentflow.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: agentflow - **Adopt for:** Agentflow simplifies the creation of complex workflows for large language models through simple JSON configurations. ## 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. - 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 agentflow if… - Tags unique to agentflow: json, large-language-models, python, workflow-management. - When you need to rapidly prototype LLM workflows with minimal coding via JSON configs ## 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 agentflow - Avoid if requiring advanced customization that goes beyond basic JSON configurations - Not suitable for scenarios needing real-time dynamic changes in workflow setup during execution ## Common questions ### What is the difference between forge and agentflow? forge: A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows. agentflow: Complex LLM Workflows from Simple JSON. See the comparison table for live GitHub stats and shared categories. ### When should I choose forge over agentflow? Choose forge over agentflow 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; 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 agentflow over forge? Choose agentflow over forge when Tags unique to agentflow: json, large-language-models, python, workflow-management; When you need to rapidly prototype LLM workflows with minimal coding via JSON configs. ### 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 agentflow? Avoid if requiring advanced customization that goes beyond basic JSON configurations Not suitable for scenarios needing real-time dynamic changes in workflow setup during execution ### Is forge or agentflow more popular on GitHub? forge has more GitHub stars (2,248 vs 320). Stars measure visibility, not whether either tool fits your constraints. ### Are forge and agentflow open source? Yes - both are open-source projects on GitHub (forge: MIT, agentflow: MIT). ### Where can I find alternatives to forge or agentflow? GraphCanon lists graph-backed alternatives at [forge alternatives](/tools/antoinezambelli-forge/alternatives) and [agentflow alternatives](/tools/simonmesmith-agentflow/alternatives) ([forge markdown twin](/tools/antoinezambelli-forge/alternatives.md), [agentflow markdown twin](/tools/simonmesmith-agentflow/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-simonmesmith-agentflow.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, forge or agentflow? forge: Active. agentflow: 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 agentflow? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [forge trust report](/tools/antoinezambelli-forge/trust); [agentflow trust report](/tools/simonmesmith-agentflow/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/_