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Comparison

pallms vs Agent-Reach

Verdict

Pick pallms when also covers Model Training; pick Agent-Reach when tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code.

Markdown twin · pallms alternatives · Agent-Reach alternatives

GraphCanon updated today

pallms logo

pallms

mik0w/pallms

★ 141pushed Jan 13, 2026
vs
Agent-Reach logo

Agent-Reach

Panniantong/Agent-Reach

★ 55kpushed Jul 10, 2026

Trust & integrity

SignalpallmsAgent-Reach
Maintenance
Slowing (179d since push)
As of today · github_public_v1
Very active (0d since push)
As of today · github_public_v1
Provenance
Not a fork · Personal account
As of today · github_public_v1
Not a fork · Personal account
As of today · github_public_v1
Security (OSV)
No lockfile
As of today · none
No MCP manifest
As of today · mcp_manifest
pallms trust reportAgent-Reach trust reportTrust methodology

Tagline

pallms
Payloads for Attacking Large Language Models
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.

Stars

pallms
141
Agent-Reach
55k

Forks

pallms
17
Agent-Reach
4.5k

Open issues

pallms
0
Agent-Reach
144

Language

pallms
-
Agent-Reach
Python

Adopt for

pallms
-
Agent-Reach
-

Persona

pallms
-
Agent-Reach
-

Runtime

pallms
-
Agent-Reach
-

License

pallms
MIT
Agent-Reach
MIT

Last pushed

pallms
Jan 13, 2026
Agent-Reach
Jul 10, 2026

Categories

pallms
Model Training, LLM Frameworks
Agent-Reach
AI Agents, LLM Frameworks, Developer Tools

Trust and health

Maintenance

pallms
Slowing (36%)
Agent-Reach
Very active (96%)

Days since push

pallms
179d
Agent-Reach
0d

Open issues (now)

pallms
0
Agent-Reach
144

Security scan

pallms
No lockfile
Agent-Reach
No MCP manifest

Full report

pallms
Trust report
Agent-Reach
Trust report

Choose pallms if…

  • Also covers Model Training.
  • Leaner open-issue backlog (0).

When NOT to use pallms

  • Last GitHub push was 179 days ago (slowing maintenance, Jan 13, 2026). Validate activity before betting a new project on pallms.
  • Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
GitHubDetails

Choose Agent-Reach if…

  • Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code.
  • Also covers AI Agents, Developer Tools.
  • More GitHub stars (55k vs 141) - visibility, not fit.

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.
GitHubDetails

Explore

pallms trust report →Agent-Reach trust report →Model Training category →LLM Frameworks category →AI Agents category →Developer Tools category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: pallms 141 · Agent-Reach 55k (synced Jul 11, 2026).

Common questions

What is the difference between pallms and Agent-Reach?
pallms: Payloads for Attacking Large Language Models. 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 pallms over Agent-Reach?
Choose pallms over Agent-Reach when Also covers Model Training; Leaner open-issue backlog (0).
When should I choose Agent-Reach over pallms?
Choose Agent-Reach over pallms when Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code; Also covers AI Agents, Developer Tools; More GitHub stars (55k vs 141) - visibility, not fit.
When should I avoid pallms?
Last GitHub push was 179 days ago (slowing maintenance, Jan 13, 2026). Validate activity before betting a new project on pallms. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
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 pallms or Agent-Reach more popular on GitHub?
Agent-Reach has more GitHub stars (54,715 vs 141). Stars measure visibility, not whether either tool fits your constraints.
Are pallms and Agent-Reach open source?
Yes - both are open-source projects on GitHub (pallms: MIT, Agent-Reach: MIT).
Where can I find alternatives to pallms or Agent-Reach?
GraphCanon lists graph-backed alternatives at pallms alternatives and Agent-Reach alternatives (pallms markdown twin, Agent-Reach 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, pallms or Agent-Reach?
pallms: Slowing. 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 pallms and Agent-Reach?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: pallms trust report; Agent-Reach trust report.