--- title: "open-multi-agent vs agents-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/open-multi-agent-open-multi-agent-vs-pguso-agents-from-scratch" tools: ["open-multi-agent-open-multi-agent", "pguso-agents-from-scratch"] --- # open-multi-agent vs agents-from-scratch *GraphCanon updated Sep 20, 2026* ## Verdict Pick open-multi-agent if provides TypeScript-based orchestration for dynamic AI workflows with support for multiple language models including open platforms; pick agents-from-scratch if agents-from-scratch is for those who want absolute control over their AI agent development using only local resources and Python, focusing on deep learning without relying on external frameworks or cloud dependencies. [open-multi-agent](https://open-multi-agent.com/go/repo) reports 6.9k GitHub stars, 2.4k forks, and 4 open issues, last pushed Sep 18, 2026. [agents-from-scratch](https://github.com/pguso/agents-from-scratch) has 1.0k stars, 251 forks, and 4 open issues, last pushed Jul 25, 2026. Figures are from public GitHub metadata via [open-multi-agent's repository](https://github.com/open-multi-agent/open-multi-agent) and [agents-from-scratch's repository](https://github.com/pguso/agents-from-scratch). | | [open-multi-agent](/tools/open-multi-agent-open-multi-agent.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Tagline | Orchestrates AI agents with dynamic workflows via runtime task planning. | Build AI agents locally without relying on frameworks or cloud APIs. | | Stars | 6,943 | 1,017 | | Forks | 2,433 | 251 | | Open issues | 4 | 4 | | Language | TypeScript | Python | | Adopt for | Provides TypeScript-based orchestration for dynamic AI workflows with support for multiple language models including open platforms. | agents-from-scratch is for those who want absolute control over their AI agent development using only local resources and Python, focusing on deep learning without relying on external frameworks or cloud dependencies. | | Persona | - | - | | Runtime | - | - | | License | MIT License | MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes. | | Categories | AI Agents, Developer Tools | AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [open-multi-agent](/tools/open-multi-agent-open-multi-agent.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 1d | 56d | | Stars delta | +175 (30d) | +63 (30d) | | Open issues delta | -7 (30d) | +1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/open-multi-agent-open-multi-agent/trust.md) | [trust report](/tools/pguso-agents-from-scratch/trust.md) | ## Decision facts: open-multi-agent - **Hosting:** self hosted - **Adopt for:** Provides TypeScript-based orchestration for dynamic AI workflows with support for multiple language models including open platforms. - **License detail:** MIT License ## Decision facts: agents-from-scratch - **Requirements:** Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs. - **Adopt for:** agents-from-scratch is for those who want absolute control over their AI agent development using only local resources and Python, focusing on deep learning without relying on external frameworks or cloud dependencies. - **License detail:** MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes. ## Choose when ### Choose open-multi-agent if… - open-multi-agent is primarily TypeScript; agents-from-scratch is Python. - Tags unique to open-multi-agent: agent-framework, agent-orchestration, agentic-ai, anthropic. - - When you need to dynamically plan task DAGs (Directed Acyclic Graphs) at runtime and execute them across different LLMs, such as Claude or ChatGPT. ### Choose agents-from-scratch if… - agents-from-scratch is primarily Python; open-multi-agent is TypeScript. - Requirements: Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs.. - Tags unique to agents-from-scratch: agent-architecture, llm, no-framework, prompt-engineering. - You plan to teach yourself or others about the fundamentals of creating AI agents from ground zero with complete transparency into each layer of architecture. ## When NOT to use open-multi-agent - - Avoid using open-multi-agent if you are looking for a ready-to-use solution for simple scripts or single-model tasks where runtime planning is not required. - - Do not use this framework if your application strictly requires support for proprietary or custom language models not listed as supported options. ## When NOT to use agents-from-scratch - You are working on an application that needs to be deployed quickly. The tool's approach from first principles can be time-consuming compared to using established frameworks. - If you need scalability or cloud capabilities such as easy scaling with demand, this tool will not provide these features since it strictly operates on local infrastructure. ## Common questions ### What is the difference between open-multi-agent and agents-from-scratch? open-multi-agent: Orchestrates AI agents with dynamic workflows via runtime task planning.. agents-from-scratch: Build AI agents locally without relying on frameworks or cloud APIs.. See the comparison table for live GitHub stats and shared categories. ### When should I choose open-multi-agent over agents-from-scratch? Choose open-multi-agent over agents-from-scratch when open-multi-agent is primarily TypeScript; agents-from-scratch is Python; Tags unique to open-multi-agent: agent-framework, agent-orchestration, agentic-ai, anthropic; - When you need to dynamically plan task DAGs (Directed Acyclic Graphs) at runtime and execute them across different LLMs, such as Claude or ChatGPT. ### When should I choose agents-from-scratch over open-multi-agent? Choose agents-from-scratch over open-multi-agent when agents-from-scratch is primarily Python; open-multi-agent is TypeScript; Requirements: Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs.; Tags unique to agents-from-scratch: agent-architecture, llm, no-framework, prompt-engineering; You plan to teach yourself or others about the fundamentals of creating AI agents from ground zero with complete transparency into each layer of architecture. ### When should I avoid open-multi-agent? - Avoid using open-multi-agent if you are looking for a ready-to-use solution for simple scripts or single-model tasks where runtime planning is not required. - Do not use this framework if your application strictly requires support for proprietary or custom language models not listed as supported options. ### When should I avoid agents-from-scratch? You are working on an application that needs to be deployed quickly. The tool's approach from first principles can be time-consuming compared to using established frameworks. If you need scalability or cloud capabilities such as easy scaling with demand, this tool will not provide these features since it strictly operates on local infrastructure. ### Is open-multi-agent or agents-from-scratch more popular on GitHub? open-multi-agent has more GitHub stars (6,943 vs 1,017). Stars measure visibility, not whether either tool fits your constraints. ### Are open-multi-agent and agents-from-scratch open source? Yes - both are open-source projects on GitHub (open-multi-agent: MIT, agents-from-scratch: MIT). ### Where can I find alternatives to open-multi-agent or agents-from-scratch? GraphCanon lists graph-backed alternatives at [open-multi-agent alternatives](/tools/open-multi-agent-open-multi-agent/alternatives) and [agents-from-scratch alternatives](/tools/pguso-agents-from-scratch/alternatives) ([open-multi-agent markdown twin](/tools/open-multi-agent-open-multi-agent/alternatives.md), [agents-from-scratch markdown twin](/tools/pguso-agents-from-scratch/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/open-multi-agent-open-multi-agent-vs-pguso-agents-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, open-multi-agent or agents-from-scratch? open-multi-agent: Very active. agents-from-scratch: Steady. 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 open-multi-agent and agents-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [open-multi-agent trust report](/tools/open-multi-agent-open-multi-agent/trust); [agents-from-scratch trust report](/tools/pguso-agents-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=open-multi-agent-open-multi-agent`](/api/graphcanon/graph?tool=open-multi-agent-open-multi-agent) - 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/_