--- title: "raft vs Agent-Reach" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-raft-vs-panniantong-agent-reach" tools: ["nvidia-raft", "panniantong-agent-reach"] --- # raft vs Agent-Reach *GraphCanon updated Jul 11, 2026* ## Verdict Pick raft when raft is primarily Cuda; Agent-Reach is Python; pick Agent-Reach when agent-Reach is primarily Python; raft is Cuda. [raft](https://docs.rapids.ai/api/raft/stable/) reports 1.0k GitHub stars, 240 forks, and 448 open issues, last pushed Jul 11, 2026. [Agent-Reach](https://github.com/Panniantong/Agent-Reach) has 55k stars, 4.5k forks, and 144 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [raft's repository](https://github.com/NVIDIA/raft) and [Agent-Reach's repository](https://github.com/Panniantong/Agent-Reach). | | [raft](/tools/nvidia-raft.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Tagline | RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing hig | Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees. | | Stars | 1,026 | 54,715 | | Forks | 240 | 4,509 | | Open issues | 448 | 144 | | Language | Cuda | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | LLM Frameworks, Vector Databases, Data & Retrieval | AI Agents, LLM Frameworks, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [raft](/tools/nvidia-raft.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Open issues (now) | 448 | 144 | | Owner type | Organization | User | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/nvidia-raft/trust.md) | [trust report](/tools/panniantong-agent-reach/trust.md) | ## Choose when ### Choose raft if… - raft is primarily Cuda; Agent-Reach is Python. - License: raft is Apache-2.0, Agent-Reach is MIT. - Tags unique to raft: clustering, anns, gpu, building-blocks. - Also covers Vector Databases, Data & Retrieval. ### Choose Agent-Reach if… - Agent-Reach is primarily Python; raft is Cuda. - License: Agent-Reach is MIT, raft is Apache-2.0. - Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code. - Also covers AI Agents, Developer Tools. ## When NOT to use raft - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. ## When NOT to use Agent-Reach - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ## Common questions ### What is the difference between raft and Agent-Reach? raft: RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing hig. Agent-Reach: Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.. See the comparison table for live GitHub stats and shared categories. ### When should I choose raft over Agent-Reach? Choose raft over Agent-Reach when raft is primarily Cuda; Agent-Reach is Python; License: raft is Apache-2.0, Agent-Reach is MIT; Tags unique to raft: clustering, anns, gpu, building-blocks; Also covers Vector Databases, Data & Retrieval. ### When should I choose Agent-Reach over raft? Choose Agent-Reach over raft when Agent-Reach is primarily Python; raft is Cuda; License: Agent-Reach is MIT, raft is Apache-2.0; Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code; Also covers AI Agents, Developer Tools. ### When should I avoid raft? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. ### When should I avoid Agent-Reach? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ### Is raft or Agent-Reach more popular on GitHub? Agent-Reach has more GitHub stars (54,715 vs 1,026). Stars measure visibility, not whether either tool fits your constraints. ### Are raft and Agent-Reach open source? Yes - both are open-source projects on GitHub (raft: Apache-2.0, Agent-Reach: MIT). ### Where can I find alternatives to raft or Agent-Reach? GraphCanon lists graph-backed alternatives at [raft alternatives](/tools/nvidia-raft/alternatives) and [Agent-Reach alternatives](/tools/panniantong-agent-reach/alternatives) ([raft markdown twin](/tools/nvidia-raft/alternatives.md), [Agent-Reach markdown twin](/tools/panniantong-agent-reach/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/nvidia-raft-vs-panniantong-agent-reach.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, raft or Agent-Reach? raft: Very active. Agent-Reach: 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 raft and Agent-Reach? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [raft trust report](/tools/nvidia-raft/trust); [Agent-Reach trust report](/tools/panniantong-agent-reach/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nvidia-raft`](/api/graphcanon/graph?tool=nvidia-raft) - 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/_