--- title: "hello-agents vs future-agi" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-future-agi-future-agi" tools: ["datawhalechina-hello-agents", "future-agi-future-agi"] --- # hello-agents vs future-agi *GraphCanon updated Jul 11, 2026* ## Verdict Pick hello-agents when license: hello-agents is Other, future-agi is Apache-2.0; pick future-agi when license: future-agi is Apache-2.0, hello-agents is Other. [hello-agents](https://hello-agents.datawhale.cc) reports 65k GitHub stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. [future-agi](https://futureagi.com) has 1.4k stars, 368 forks, and 437 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [future-agi's repository](https://github.com/future-agi/future-agi). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [future-agi](/tools/future-agi-future-agi.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. Tracing · Evals · Simulations · Datasets · Gateway · Guardrails. Self-hostable. Apache 2.0. | | Stars | 65,432 | 1,373 | | Forks | 8,109 | 368 | | Open issues | 144 | 437 | | Language | Python | Python | | Adopt for | hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods. | - | | Persona | - | - | | Runtime | - | - | | License | hello-agents is covered under an unconventional license which may require further review before usage. | Apache-2.0 | | Categories | AI Agents, LLM Frameworks | AI Agents, Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [future-agi](/tools/future-agi-future-agi.md) | | --- | --- | --- | | Open issues (now) | 144 | 437 | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/future-agi-future-agi/trust.md) | ## Decision facts: hello-agents - **Requirements:** Min 4 GB RAM; Python knowledge assumed - **Adopt for:** hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods. - **License detail:** hello-agents is covered under an unconventional license which may require further review before usage. ## Choose when ### Choose hello-agents if… - License: hello-agents is Other, future-agi is Apache-2.0. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: agent, rag, tutorial. - You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up. ### Choose future-agi if… - License: future-agi is Apache-2.0, hello-agents is Other. - Tags unique to future-agi: ai, ai-gateway, evals, observability. - Also covers Inference & Serving. - future-agi ships Docker support for self-hosted deployment. ## When NOT to use hello-agents - Avoid using hello-agents if you are looking for a quick, superficial introduction to AI agents; this tool focuses heavily on in-depth learning and practical application. - Do not opt for hello-agents if you want a more general AI development resource; unlike some competitors, it has a narrower focus specifically on agent creation with advanced methods like LLMs and RAG. ## When NOT to use future-agi - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between hello-agents and future-agi? hello-agents: Course on building intelligent agents from scratch. future-agi: Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. Tracing · Evals · Simulations · Datasets · Gateway · Guardrails. Self-hostable. Apache 2.0.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over future-agi? Choose hello-agents over future-agi when License: hello-agents is Other, future-agi is Apache-2.0; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: agent, rag, tutorial; You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up. ### When should I choose future-agi over hello-agents? Choose future-agi over hello-agents when License: future-agi is Apache-2.0, hello-agents is Other; Tags unique to future-agi: ai, ai-gateway, evals, observability; Also covers Inference & Serving; future-agi ships Docker support for self-hosted deployment. ### When should I avoid hello-agents? Avoid using hello-agents if you are looking for a quick, superficial introduction to AI agents; this tool focuses heavily on in-depth learning and practical application. Do not opt for hello-agents if you want a more general AI development resource; unlike some competitors, it has a narrower focus specifically on agent creation with advanced methods like LLMs and RAG. ### When should I avoid future-agi? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is hello-agents or future-agi more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 1,373). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and future-agi open source? Yes - both are open-source projects on GitHub (hello-agents: Other, future-agi: Apache-2.0). ### Where can I find alternatives to hello-agents or future-agi? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [future-agi alternatives](/tools/future-agi-future-agi/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [future-agi markdown twin](/tools/future-agi-future-agi/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/datawhalechina-hello-agents-vs-future-agi-future-agi.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or future-agi? hello-agents: Very active. future-agi: Very active. 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 hello-agents and future-agi? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [future-agi trust report](/tools/future-agi-future-agi/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=datawhalechina-hello-agents`](/api/graphcanon/graph?tool=datawhalechina-hello-agents) - 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/_