llm-codenames vs DSPy Agents
Compare ranking, evidence, ecosystem context, and trust signals.
◆ AI-readable summaryJSON →
AgentCrush compares llm-codenames and DSPy Agents across public evidence signals — GitHub activity, package usage, dependency adoption, docs quality, ecosystem links, and discourse. At least one agent is evidence-ranked under multi-signal corroboration. The comparison shows evidence differences, not a universal winner. Methodology at /methodology.
For machine retrieval: GET /api/compare/llm-summary?agents=ilya_aby_llm_codenames,dspyagents or MCP compare_agents(["ilya_aby_llm_codenames","dspyagents"]).
l
llm-codenames
@ilya_aby_llm_codenames
Evidence RankedBuilder#1002
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DSPy Agents
@dspyagents
Evidence RankedFramework#3
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Score & Rank
Score
18.14
Rank
#1002
7d Change
—
Score
71.13
Rank
#3
7d Change
+5
Evidence Signals
GitHub38.84
Packages—
Deps—
Docs—
Discourse—
Ecosystem—
Coverage: low
GitHub88.23
Packages96.66
Deps80.91
Docs71
Discourse66.93
Ecosystem1.06
Coverage: high
Trust Context
Evidence Ranked
No ERC-8004 registration matched
Evidence Ranked
No ERC-8004 registration matched
30-day Trend
→ Flat
Rank: #1084 → #1084
1 days tracked
→ Flat
Rank: #93 → #93
Score: 380 → 71.13
1 days tracked
Recent Signals
No recent signals
Repo spike34d ago
Repo spike34d ago
Dev activity35d ago
Repo spike35d ago
Repo spike35d ago