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Documentation Index

Fetch the complete documentation index at: https://docs.gildea.ai/llms.txt

Use this file to discover all available pages before exploring further.

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1. Get your API key

Sign up at gildea.ai to get your API key. Keys are prefixed with gld_.

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2. List signals

Filter by entity, theme, or q — or omit all filters to get the most recent signals. 
from gildea_sdk import Gildea

client = Gildea()  # reads GILDEA_API_KEY from env
signals = client.signals.list(entity="NVIDIA", limit=3)
for signal in signals["data"]:
    print(f"{signal['title']} — {signal['verified_text_unit_count']} verified units")
Response — lightweight signal cards with metadata: 
{
  "data": [
    {
      "signal_id": "0001f3a7b9c8d4e5f6a7b8c9d0e1f2a3b4c5d6e7",
      "title": "NVIDIA H200 Shipments Surge as Supply Eases",
      "content_type": "analysis",
      "published_at": "2026-01-15T10:00:00Z",
      "decomposition_text": "NVIDIA's H200 shipments increased significantly in Q4...",
      "tags": {
        "value_chain": ["Infrastructure"],
        "market_force": ["Competitive Dynamics"]
      },
      "entities": [
        {"entity_id": "gld:/a1b2c3d4e5f6", "name": "NVIDIA", "type": "organization"}
      ],
      "verified_text_unit_count": 12
    }
  ],
  "has_more": true,
  "next_cursor": "eyJwdWJsaXNoZWRfYXQiOi..."
}

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3. Get the full decomposition

This is Gildea’s core value — the complete verified reasoning structure of a signal: 
curl -H "X-API-Key: gld_your_key" \
  "https://api.gildea.ai/v1/signals/0001f3a7b9c8d4e5f6a7b8c9d0e1f2a3b4c5d6e7?include=evidence"
Response — expert analysis signals decompose into a thesis, supporting arguments, and verified claims, all returned as siblings under decomposition: 
{
  "signal_id": "0001f3a7b9c8d4e5f6a7b8c9d0e1f2a3b4c5d6e7",
  "content_type": "analysis",
  "decomposition": {
    "thesis": {
      "text": "NVIDIA's H200 shipments increased significantly in Q4...",
      "sentences": [
        {
          "unit": {
            "unit_type": "thesis_sentence",
            "text": "NVIDIA's H200 shipments increased significantly in Q4."
          },
          "verification": {
            "final_verdict": "pass",
            "scoring_mode": "regression",
            "primary_score": 0.8912
          },
          "evidence": {
            "parts": [
              {
                "part_index": 0,
                "preview_prefix": "According to supply chain data, NVIDIA shipped over 400,000 H200 units in Q4, a 40% increase from...",
                "preview_truncated": true,
                "full_part_char_length": 342
              }
            ],
            "combined_full_char_length": 342
          }
        }
      ]
    },
    "arguments": [
      {
        "sentences": [{ "..." : "..." }]
      }
    ],
    "claims": [
      {
        "unit": {
          "unit_type": "analysis_claim",
          "text": "NVIDIA increased H200 shipments by 40% quarter-over-quarter."
        },
        "verification": {
          "final_verdict": "pass",
          "scoring_mode": "nli",
          "primary_score": 0.7421
        }
      }
    ]
  }
}
Event signals have a summary + claims structure (no arguments block). Both content types serve claims as a top-level sibling, not nested under arguments. See Signals for details.

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4. Explore entities and themes

from gildea_sdk import Gildea

client = Gildea()

# Rising entities with statistical significance
rising = client.entities.list(direction="Rising", confidence="Significant", sort="trend", limit=5)

# Entity profile
nvidia = client.entities.get("NVIDIA")

# Themes
themes = client.themes.list(axis="value_chain")

# Semantic search
results = client.search("GPU supply constraints", limit=5)

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Next steps

Python SDK

Install the official client library

Signals

Understand signal decomposition

Themes

Value chain and market force themes

Authentication

API key setup and tiers

MCP Server

Connect via MCP