Comparison
Agent-Reach vs scalene
Verdict
Pick Agent-Reach when license: Agent-Reach is MIT, scalene is Apache-2.0; pick scalene when license: scalene is Apache-2.0, Agent-Reach is MIT.
Markdown twin · Agent-Reach alternatives · scalene alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | Agent-Reach | scalene |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Very active (6d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No MCP manifest As of today · mcp_manifest | 18 low (18 low) As of today · osv@v1 |
Tagline
- 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.
- scalene
- Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
Stars
- Agent-Reach
- 55k
- scalene
- 13k
Forks
- Agent-Reach
- 4.5k
- scalene
- 435
Open issues
- Agent-Reach
- 144
- scalene
- 151
Language
- Agent-Reach
- Python
- scalene
- Python
Adopt for
- Agent-Reach
- -
- scalene
- -
Persona
- Agent-Reach
- -
- scalene
- -
Runtime
- Agent-Reach
- -
- scalene
- -
License
- Agent-Reach
- MIT
- scalene
- Apache-2.0
Last pushed
- Agent-Reach
- Jul 10, 2026
- scalene
- Jul 5, 2026
Categories
- Agent-Reach
- LLM Frameworks, AI Agents, Developer Tools
- scalene
- Developer Tools
Trust and health
Days since push
- Agent-Reach
- 0d
- scalene
- 6d
Open issues (now)
- Agent-Reach
- 144
- scalene
- 151
Owner type
- Agent-Reach
- User
- scalene
- Organization
Security scan
- Agent-Reach
- No MCP manifest
- scalene
- 18 low (18 low)
Full report
- Agent-Reach
- Trust report
- scalene
- Trust report
Choose Agent-Reach if…
- License: Agent-Reach is MIT, scalene is Apache-2.0.
- Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code.
- Also covers LLM Frameworks, AI Agents.
When NOT to use Agent-Reach
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- Developer Tools: A gateway is overkill when you're pinned to a single provider and model.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Panniantong/Agent-Reach) · observed Jul 11, 2026
- GitHub forks (Panniantong/Agent-Reach) · observed Jul 11, 2026
- Last push (Panniantong/Agent-Reach) · observed Jul 10, 2026
- License file (MIT) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (plasma-umass/scalene) · observed Jul 11, 2026
- GitHub forks (plasma-umass/scalene) · observed Jul 11, 2026
- Last push (plasma-umass/scalene) · observed Jul 5, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Agent-Reach 55k · scalene 13k (synced Jul 11, 2026).
Common questions
- What is the difference between Agent-Reach and scalene?
- 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.. scalene: Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals. See the comparison table for live GitHub stats and shared categories.
- When should I choose Agent-Reach over scalene?
- Choose Agent-Reach over scalene when License: Agent-Reach is MIT, scalene is Apache-2.0; Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code; Also covers LLM Frameworks, AI Agents.
- When should I choose scalene over Agent-Reach?
- Choose scalene over Agent-Reach when License: scalene is Apache-2.0, Agent-Reach is MIT; Tags unique to scalene: cpu-profiling, gpu, gpu-programming, memory-allocation.
- When should I avoid Agent-Reach?
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Developer Tools: A gateway is overkill when you're pinned to a single provider and model.
- When should I avoid scalene?
- Developer Tools: A gateway is overkill when you're pinned to a single provider and model.
- Is Agent-Reach or scalene more popular on GitHub?
- Agent-Reach has more GitHub stars (54,715 vs 13,467). Stars measure visibility, not whether either tool fits your constraints.
- Are Agent-Reach and scalene open source?
- Yes - both are open-source projects on GitHub (Agent-Reach: MIT, scalene: Apache-2.0).
- Where can I find alternatives to Agent-Reach or scalene?
- GraphCanon lists graph-backed alternatives at Agent-Reach alternatives and scalene alternatives (Agent-Reach markdown twin, scalene markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, Agent-Reach or scalene?
- Agent-Reach: Very active. scalene: 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 Agent-Reach and scalene?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Agent-Reach trust report; scalene trust report.