✓Truth API

Your agent is confident. And wrong.

What thousands of builders already learned - inside your agent. Updated daily.

What shipped. What broke. What works. What's contested.

36,595 builders614 use cases6,813 methods9,750 products & models2,917 problems

Claude Code CLI Example

55K GitHub PRs32K issues5.5K releases19K Discord threads7.3K YT videos3.1K arXiv papers2.2K Reddit posts2.2K podcasts1.7K HuggingFace models
~/acme/backend-review
acme/backend-review main $ claude
Claude Code
cwd: /Users/maya/acme/backend-review
mcp: truthapi connected
>
We need a multi-agent code review workflow for backend PRs. I was going to build a reviewer from scratch with 5 subagents. Before we commit, check the field: what has already worked, what broke, and what should we copy?
Claude I will check current tools, user reports, and failure modes before designing the workflow.
tool use
mcp__truthapi__getSummaries
{
  "query": "multi-agent code review consensus Claude Code Codex Gemini token burn confidence filters",
  "sources": ["github", "docs", "reddit_post", "youtube_video", "discord_thread"],
  "total": 8
}
tool result - 8 summaries in 142ms
  • [repo] postmortemthis: multi-model PR review CLI using Claude Code, Codex, Gemini, Qwen, Vibe.
  • [docs] Claude Code review plugin: 4 parallel reviewers, confidence threshold at 80, final synthesis.
  • [docs] Cline workflow: model-orchestration consensus across Gemini, Codex, and Opus.
  • [problem] runaway subagents: user reports severe token burn and rate-limit exhaustion.
  • [method] ultracost-style gate: estimate spend before expensive agent fan-out.
Claude I found a likely dead end: unbounded subagents. I need the source records before changing the plan.
tool use
mcp__truthapi__getSet
{
  "ids": [
    "postmortemthis",
    "claude-code-review-plugin",
    "model-orchestration-consensus",
    "ultracost",
    "runaway-subagents"
  ]
}
tool result - decisive records
  • postmortemthis already implements the same product shape: multi-model review, aggregate findings, final verdict.
  • The strongest working pattern is bounded fan-out: 3-4 reviewers, not open-ended subagents.
  • Findings need confidence, source file, line range, and evidence text, or users reject the review.
  • Cost controls are not optional: preflight estimate, per-stage routing, hard stop on budget.
  • Failure reports cluster around vague prompts, duplicate findings, and hidden token spend.
Claude
plan changed
  1. Do not build a reviewer from scratch.
  2. Clone the bounded consensus architecture.
  3. Use 3 reviewers: correctness, security, maintainability.
  4. Drop findings below confidence 80 unless two reviewers agree.
  5. Add budget preflight and stop before spawning expensive agents.
  6. Return only source-backed findings with file, line, evidence, and fix.
outcome skipped the five-subagent dead end, copied the field-tested pattern, and avoided the token-burn failure before writing code.

Avoid the silent trap.

That feature in the docs that compiles clean and 400s on every call — because the fix was quietly rejected weeks ago. Truth API catches it before you ship.

See the contradictions.

A launch claims a number the filing doesn't support. Two labs claim the same capability. Truth API holds both at once and shows which the sources back.

Tell signal from noise.

Seven accounts hyping one launch isn't traction. Eighteen people swarming one fix is. Truth API counts who, not just how many.

Stop guessing.
Start knowing.

Wire your AI to the present. Ship with confidence.