[
  {
    "id": "pydantic-ai-docs-1",
    "tier": "claimed-docs",
    "url": "https://ai.pydantic.dev",
    "excerpt": "a typed, extensible agent loop with every model a string swap away",
    "fetchedAt": "2026-09-04T00:16:17.936Z"
  },
  {
    "id": "pydantic-ai-docs-2",
    "tier": "claimed-docs",
    "url": "https://ai.pydantic.dev",
    "excerpt": "The same agent runs everywhere you need it: behind a web frontend, in the terminal, on a voice call, on a durable background queue",
    "fetchedAt": "2026-09-04T00:16:17.936Z"
  },
  {
    "id": "pydantic-ai-docs-3",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/overview",
    "excerpt": "A complete coding agent in your terminal: workspace-rooted file access, allowlisted shell, repo orientation, planning, and context management that survives long sessions.",
    "fetchedAt": "2026-09-04T00:16:17.936Z"
  },
  {
    "id": "pydantic-ai-docs-4",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/core-concepts/agent/",
    "excerpt": "an agent which required dependencies of type `Foobar` and produced outputs of type `list[str]` would have type `Agent[Foobar, list[str]]`",
    "fetchedAt": "2026-09-04T00:16:17.936Z"
  },
  {
    "id": "pydantic-ai-docs-5",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/tools-toolsets/tools/",
    "excerpt": "Function tools provide a mechanism for models to perform actions and retrieve extra information to help them generate a response.",
    "fetchedAt": "2026-09-04T00:16:17.936Z"
  },
  {
    "id": "pydantic-ai-docs-6",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/core-concepts/output/",
    "excerpt": "Here's an example using a Pydantic model as the `output_type`, forcing the model to respond with data matching our specification",
    "fetchedAt": "2026-09-04T00:16:17.936Z"
  },
  {
    "id": "pydantic-ai-docs-7",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/core-concepts/message-history/",
    "excerpt": "Pydantic AI provides access to messages exchanged during an agent run. These messages can be used both to continue a coherent conversation, and to understand how an agent performed.",
    "fetchedAt": "2026-09-04T00:16:17.936Z"
  },
  {
    "id": "pydantic-ai-docs-8",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/mcp/client/",
    "excerpt": "Pydantic AI can act as an MCP client, connecting to MCP servers to use their tools as part of an agent run.",
    "fetchedAt": "2026-09-04T00:16:17.936Z"
  },
  {
    "id": "pydantic-ai-docs-9",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/guides/multi-agent-applications/",
    "excerpt": "\"Agent delegation\" refers to the scenario where an agent delegates work to another agent, then takes back control when the delegate agent ... finishes.",
    "fetchedAt": "2026-09-04T00:16:17.936Z"
  },
  {
    "id": "pydantic-ai-docs-10",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/capabilities/durable_execution/overview/",
    "excerpt": "Pydantic AI allows you to build durable agents that can preserve their progress across transient API failures and application errors or restarts",
    "fetchedAt": "2026-09-04T00:16:17.936Z"
  },
  {
    "id": "pydantic-ai-docs-11",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/tools-toolsets/deferred-tools/",
    "excerpt": "it may need to be approved by the user first",
    "fetchedAt": "2026-09-04T00:16:17.936Z"
  },
  {
    "id": "pydantic-ai-docs-12",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/models/overview/",
    "excerpt": "Pydantic AI is model-agnostic and has built-in support for multiple model providers",
    "fetchedAt": "2026-09-04T00:16:17.936Z"
  },
  {
    "id": "pydantic-ai-docs-13",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/evals/evals/",
    "excerpt": "Pydantic Evals follows a code-first philosophy where all evaluation components are defined in Python.",
    "fetchedAt": "2026-09-04T00:16:17.936Z"
  },
  {
    "id": "pydantic-ai-docs-14",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/integrations/logfire/",
    "excerpt": "A trace is generated for the agent run, and spans are emitted for each model request and tool call.",
    "fetchedAt": "2026-09-04T00:16:17.936Z"
  },
  {
    "id": "pydantic-ai-docs-15",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/integrations/cli/",
    "excerpt": "Pydantic AI comes with a CLI, `clai` ... You can use it to chat with various LLMs and quickly get answers, right from the command line",
    "fetchedAt": "2026-09-04T00:16:17.936Z"
  },
  {
    "id": "pydantic-ai-docs-16",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/integrations/cli/",
    "excerpt": "you can directly launch CLI mode from an `Agent` instance using `Agent.to_cli_sync()`",
    "fetchedAt": "2026-09-04T00:16:17.936Z"
  },
  {
    "id": "pydantic-ai-docs-17",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/overview",
    "excerpt": "A complete coding agent in your terminal: workspace-rooted file access, allowlisted shell, repo orientation, planning, and context management that survives long sessions",
    "fetchedAt": "2026-09-04T00:18:52.475Z"
  },
  {
    "id": "pydantic-ai-docs-18",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/core-concepts/output/",
    "excerpt": "Here’s an example using a Pydantic model as the `output_type`, forcing the model to respond with data matching our specification",
    "fetchedAt": "2026-09-04T00:18:52.475Z"
  },
  {
    "id": "pydantic-ai-docs-19",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/guides/multi-agent-applications/",
    "excerpt": "“Agent delegation” refers to the scenario where an agent delegates work to another agent, then takes back control when the delegate agent...finishes.",
    "fetchedAt": "2026-09-04T00:18:52.475Z"
  },
  {
    "id": "pydantic-ai-docs-20",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/tools-toolsets/deferred-tools/",
    "excerpt": "There are a few scenarios where the model should be able to call a tool that should not or cannot be executed during the same agent run inside the same Python process",
    "fetchedAt": "2026-09-04T00:18:52.475Z"
  },
  {
    "id": "pydantic-ai-docs-21",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/evals/evals/",
    "excerpt": "Pydantic Evals is a powerful evaluation framework for systematically testing and evaluating AI systems, from simple LLM calls to complex multi-agent applications.",
    "fetchedAt": "2026-09-04T00:18:52.475Z"
  },
  {
    "id": "pydantic-ai-docs-22",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/integrations/logfire/",
    "excerpt": "Pydantic AI has built-in (but optional) support for Logfire...detailed information about agent runs is sent to Logfire.",
    "fetchedAt": "2026-09-04T00:18:52.475Z"
  },
  {
    "id": "pydantic-ai-docs-23",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/integrations/cli/",
    "excerpt": "Pydantic AI comes with a CLI, clai...You can use it to chat with various LLMs and quickly get answers, right from the command line",
    "fetchedAt": "2026-09-04T00:18:52.475Z"
  },
  {
    "id": "pydantic-ai-docs-24",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/core-concepts/agent/",
    "excerpt": "In typing terms, agents are generic in their dependency and output types...your IDE can tell you when you have the right type",
    "fetchedAt": "2026-09-04T00:18:52.475Z"
  },
  {
    "id": "pydantic-ai-docs-25",
    "tier": "claimed-docs",
    "url": "https://ai.pydantic.dev",
    "excerpt": "The same agent runs everywhere you need it: behind a web frontend, in the terminal, on a voice call, on a durable background queue, or as a plain object you call run() on.",
    "fetchedAt": "2026-09-04T00:20:35.256Z"
  },
  {
    "id": "pydantic-ai-docs-26",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/core-concepts/agent/",
    "excerpt": "agents are generic in their dependency and output types, e.g., an agent which required dependencies of type Foobar and produced outputs of type list[str] would have type Agent[Foobar, list[str]]",
    "fetchedAt": "2026-09-04T00:20:35.256Z"
  },
  {
    "id": "pydantic-ai-docs-27",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/tools-toolsets/tools/",
    "excerpt": "@agent.tool is considered the default decorator since in the majority of cases tools will need access to the agent context.",
    "fetchedAt": "2026-09-04T00:20:35.256Z"
  },
  {
    "id": "pydantic-ai-docs-28",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/core-concepts/output/",
    "excerpt": "Here’s an example using a Pydantic model as the output_type, forcing the model to respond with data matching our specification",
    "fetchedAt": "2026-09-04T00:20:35.256Z"
  },
  {
    "id": "pydantic-ai-docs-29",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/guides/multi-agent-applications/",
    "excerpt": "“Agent delegation” refers to the scenario where an agent delegates work to another agent, then takes back control when the delegate agent ... finishes.",
    "fetchedAt": "2026-09-04T00:20:35.256Z"
  },
  {
    "id": "pydantic-ai-docs-30",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/models/overview/",
    "excerpt": "Pydantic AI also comes with TestModel and FunctionModel for testing and development.",
    "fetchedAt": "2026-09-04T00:20:35.256Z"
  },
  {
    "id": "pydantic-ai-docs-31",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/integrations/logfire/",
    "excerpt": "Pydantic AI has built-in (but optional) support for Logfire. That means if the logfire package is installed and configured and agent instrumentation is enabled then detailed information about agent runs is sent to Logfire.",
    "fetchedAt": "2026-09-04T00:20:35.256Z"
  },
  {
    "id": "pydantic-ai-docs-32",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/integrations/cli/",
    "excerpt": "Pydantic AI comes with a CLI, clai (pronounced “clay”). You can use it to chat with various LLMs and quickly get answers, right from the command line",
    "fetchedAt": "2026-09-04T00:20:35.256Z"
  },
  {
    "id": "pydantic-ai-docs-33",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/integrations/cli/",
    "excerpt": "you can directly launch CLI mode from an Agent instance using Agent.to_cli_sync().",
    "fetchedAt": "2026-09-04T00:20:35.256Z"
  },
  {
    "id": "pydantic-ai-docs-34",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/models/overview/",
    "excerpt": "many providers are compatible with the OpenAI API, and can be used with OpenAIChatModel in Pydantic AI",
    "fetchedAt": "2026-09-04T00:20:35.256Z"
  },
  {
    "id": "pydantic-ai-docs-35",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/core-concepts/agent/",
    "excerpt": "conceptually you can think of an agent as a container for: Instructions... Function tool(s) and toolsets... Structured output type",
    "fetchedAt": "2026-09-16T00:15:03.105Z"
  },
  {
    "id": "pydantic-ai-docs-36",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/guides/multi-agent-applications/",
    "excerpt": "\"Agent delegation\" refers to the scenario where an agent delegates work to another agent, then takes back control when the delegate agent... finishes.",
    "fetchedAt": "2026-09-16T00:15:03.105Z"
  },
  {
    "id": "pydantic-ai-docs-37",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/capabilities/durable_execution/overview/",
    "excerpt": "Pydantic AI allows you to build durable agents that can preserve their progress across transient API failures and application errors or restarts, and handle long-running, asynchronous, and human-in-the-loop workflows with production-grade reliability.",
    "fetchedAt": "2026-09-16T00:15:03.105Z"
  },
  {
    "id": "pydantic-ai-docs-38",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/tools-toolsets/deferred-tools/",
    "excerpt": "it may need to be approved by the user first... Pydantic AI provides the concept of deferred tools",
    "fetchedAt": "2026-09-16T00:15:03.105Z"
  },
  {
    "id": "pydantic-ai-docs-39",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/integrations/cli/",
    "excerpt": "You can use it to chat with various LLMs and quickly get answers, right from the command line, or spin up a uvicorn server to chat with your Pydantic AI agents from your browser.",
    "fetchedAt": "2026-09-16T00:15:03.105Z"
  },
  {
    "id": "pydantic-ai-docs-40",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/integrations/logfire/",
    "excerpt": "Pydantic Logfire is an observability platform developed by the team who created and maintain Pydantic Validation and Pydantic AI.",
    "fetchedAt": "2026-09-16T00:15:03.105Z"
  },
  {
    "id": "pydantic-ai-docs-41",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/ai-gateway",
    "excerpt": "You can access multiple models with the same API key.",
    "fetchedAt": "2026-09-16T00:15:03.105Z"
  },
  {
    "id": "pydantic-ai-docs-42",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/ai-gateway",
    "excerpt": "One key for all your models with real-time monitoring and budget control that works.",
    "fetchedAt": "2026-09-16T00:15:03.105Z"
  },
  {
    "id": "pydantic-ai-docs-43",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/core-concepts/agent/",
    "excerpt": "Agents are Pydantic AI’s primary interface for interacting with LLMs.",
    "fetchedAt": "2026-09-16T00:20:48.398Z"
  },
  {
    "id": "pydantic-ai-docs-44",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/core-concepts/output/",
    "excerpt": "This can be either plain text, structured data, an image, or the result of a function called with arguments provided by the model.",
    "fetchedAt": "2026-09-16T00:20:48.398Z"
  },
  {
    "id": "pydantic-ai-docs-45",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/guides/multi-agent-applications/",
    "excerpt": "Agent delegation — agents using another agent via tools... Programmatic agent hand-off — one agent runs, then application code calls another agent... Graph based control flow",
    "fetchedAt": "2026-09-16T00:20:48.398Z"
  },
  {
    "id": "pydantic-ai-docs-46",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/tools-toolsets/deferred-tools/",
    "excerpt": "Pydantic AI provides the concept of deferred tools... tools that require approval... tools that are executed externally",
    "fetchedAt": "2026-09-16T00:20:48.398Z"
  },
  {
    "id": "pydantic-ai-docs-47",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/docs/ai/integrations/logfire/",
    "excerpt": "Applications that use LLMs have some challenges that are well known and understood: LLMs are slow, unreliable and expensive.",
    "fetchedAt": "2026-09-16T00:20:48.398Z"
  },
  {
    "id": "pydantic-ai-docs-48",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/pydantic-ai/llms.txt",
    "excerpt": "Build typed agents, long-running coding and research agents, and realtime voice applications with model-agnostic tools and OpenTelemetry tracing.",
    "fetchedAt": "2026-09-16T00:20:48.398Z"
  },
  {
    "id": "pydantic-ai-docs-49",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/feed.xml",
    "excerpt": "The new ImageGenerator gives you a direct API, without an agent run. The same interface supports OpenAI GPT Image, Google Gemini, and xAI Grok Imagine.",
    "fetchedAt": "2026-09-16T00:20:48.398Z"
  },
  {
    "id": "pydantic-ai-docs-50",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/logfire/llm-observability",
    "excerpt": "Instrument the libraries around an agent to record its model calls, tool calls, HTTP requests and database queries in one trace, then query the result with SQL.",
    "fetchedAt": "2026-09-16T00:20:48.398Z"
  },
  {
    "id": "pydantic-ai-docs-51",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/logfire/evals",
    "excerpt": "Evaluate agents and LLM apps against datasets built from production traces, run the suite from your code, and compare a candidate with a baseline case by case.",
    "fetchedAt": "2026-09-16T00:20:48.398Z"
  },
  {
    "id": "pydantic-ai-docs-52",
    "tier": "claimed-docs",
    "url": "https://pydantic.dev/logfire/llm-as-a-judge",
    "excerpt": "Score answers against criteria you can read. Every verdict keeps the judge's reason and links to the trace that produced it",
    "fetchedAt": "2026-09-16T00:20:48.398Z"
  },
  {
    "id": "pydantic-ai-comm-1",
    "tier": "community",
    "url": "https://news.ycombinator.com/item?id=45055439",
    "excerpt": "Pydantic-AI is lovely - I've been working on a coding agent CLI for a year plus now. IMO it does make constructing any given agent very easy, though the lower level APIs are a little painful to use but they seem to be aware of that.",
    "fetchedAt": "2026-09-04T00:29:44.050Z"
  },
  {
    "id": "pydantic-ai-comm-2",
    "tier": "community",
    "url": "https://news.ycombinator.com/item?id=45055439",
    "excerpt": "After maintaining my own agents library for a while, I've switched over to pydantic ai recently. I have some minor nits, but overall it's been working great for me. I've especially liked combining it with langfuse.",
    "fetchedAt": "2026-09-04T00:29:44.050Z"
  },
  {
    "id": "pydantic-ai-comm-3",
    "tier": "community",
    "url": "https://news.ycombinator.com/item?id=45055439",
    "excerpt": "I really wish Pydantic invested in... Pydantic, instead of some AI API wrapper garbage. I've been using it a lot lately and anything beyond basic usage is an absolute chore.",
    "fetchedAt": "2026-09-04T00:29:44.050Z"
  },
  {
    "id": "pydantic-ai-comm-4",
    "tier": "community",
    "url": "https://news.ycombinator.com/item?id=45055439",
    "excerpt": "I tried it out with structured output for azure openai but had to give up since somewhere somewhat was broken and it's difficult to figure out if it's the abstraction or the library of the llm provider which the abstraction uses.",
    "fetchedAt": "2026-09-04T00:29:44.050Z"
  },
  {
    "id": "pydantic-ai-comm-5",
    "tier": "community",
    "url": "https://news.ycombinator.com/item?id=45055439",
    "excerpt": "We have a pretty complex agent running on Pydantic AI. The team is very responsive to bugs / feature requests. If I had to do it over again, I'd pick Pydantic AI again.",
    "fetchedAt": "2026-09-04T00:29:44.050Z"
  },
  {
    "id": "pydantic-ai-comm-6",
    "tier": "community",
    "url": "https://news.ycombinator.com/item?id=45055439",
    "excerpt": "I wanted to love pydantic AI as much as I love pydantic but... with the same LLM models, openai.client.chat.completions + a custom prompt to pass in the pydantic JSON schema creates objects successfully whereas vanilla pydantic-ai rarely does, regardless of the number of retries.",
    "fetchedAt": "2026-09-04T00:29:44.050Z"
  },
  {
    "id": "pydantic-ai-comm-7",
    "tier": "community",
    "url": "https://news.ycombinator.com/item?id=45055439",
    "excerpt": "I've been building an integration with Pydantic AI and the experience has been great. Questions usually get answered within a few hours, and the team is super responsive and supportive for external contributors.",
    "fetchedAt": "2026-09-04T00:29:44.050Z"
  },
  {
    "id": "pydantic-ai-comm-8",
    "tier": "community",
    "url": "https://hn.algolia.com/api/v1/items/45055439",
    "excerpt": "I had the opposite experience. I liked the niceties of Pydantic AI, but had trouble with it that I found difficult to deal with. For example, some of the models wouldn't stream, but the OpenAI models did. It took months to resolve.",
    "fetchedAt": "2026-09-04T00:29:44.050Z"
  },
  {
    "id": "pydantic-ai-comm-9",
    "tier": "community",
    "url": "https://hn.algolia.com/api/v1/items/45055439",
    "excerpt": "Pydantic AI maintainer: as of right now we support streaming against the OpenAI, Claude, Bedrock, Gemini, Groq, HuggingFace, and Mistral APIs, as well as all OpenAI Chat Completions-compatible APIs like DeepSeek, Grok, Perplexity, Ollama and vLLM, and cloud gateways like OpenRouter, Together AI, Fireworks AI, Azure AI Foundry, Vercel, Heroku, GitHub and Cerebras.",
    "fetchedAt": "2026-09-04T00:29:44.050Z"
  },
  {
    "id": "pydantic-ai-comm-10",
    "tier": "community",
    "url": "https://hn.algolia.com/api/v1/items/45055439",
    "excerpt": "My experience is that pretty frequently the LLM just refuses to actually supply json conforming to the model and summarizes the input instead. Even with several retries configured I still couldn't rely on it.",
    "fetchedAt": "2026-09-04T00:29:44.050Z"
  },
  {
    "id": "pydantic-ai-comm-11",
    "tier": "community",
    "url": "https://hn.algolia.com/api/v1/items/45055439",
    "excerpt": "Pydantic AI maintainer: The vast majority of bugs we encounter are not in Pydantic AI itself but rather in having to deal with supposedly OpenAI Chat Completions-compatible APIs that aren't really, and with local models ran through e.g. Ollama or vLLM that tend to not be the best at tool calling.",
    "fetchedAt": "2026-09-04T00:29:44.050Z"
  },
  {
    "id": "pydantic-ai-comm-12",
    "tier": "community",
    "url": "https://news.ycombinator.com/item?id=45243320",
    "excerpt": "I've been very happy with pydantic-ai, it blows the rest of the python ai ecosystem out of the water",
    "fetchedAt": "2026-09-04T00:29:44.050Z"
  },
  {
    "id": "pydantic-ai-comm-13",
    "tier": "community",
    "url": "https://hn.algolia.com/api/v1/items/45243320",
    "excerpt": "I did look at instructor and probably for structured output pydantic-ai and instructor are about the same, but pydantic-ai supports a ton of other stuff that isn't part of instructor's feature set. For me killer apps were the ability to serialize/deserialize conversations as json; frictionless tool-calling; and ability to mock the LLM client for testing.",
    "fetchedAt": "2026-09-04T00:29:44.050Z"
  },
  {
    "id": "pydantic-ai-comm-14",
    "tier": "community",
    "url": "https://news.ycombinator.com/item?id=46544231",
    "excerpt": "We've been building agents with pydantic-ai in production. The framework is great for defining agents, but we kept rewriting the same infrastructure to actually show them to users... pydantic-ai gives you Agent.iter() and streaming primitives, but wiring this up - structured events, reconnection, history across turns - is a lot of glue.",
    "fetchedAt": "2026-09-04T00:29:44.050Z"
  },
  {
    "id": "pydantic-ai-probe-1",
    "tier": "probe",
    "url": "https://pydantic.dev/llms.txt",
    "excerpt": "PROBE llms.txt: HTTP 200 at https://pydantic.dev/llms.txt ## Querying This Documentation\n\n**warning**: agent query parameters (`goal` and `organization`) not included.\n\nInclude t",
    "fetchedAt": "2026-09-16T00:20:49.339Z"
  },
  {
    "id": "pydantic-ai-probe-2",
    "tier": "probe",
    "url": "https://pydantic.dev/docs/ai/overview.md",
    "excerpt": "PROBE docs-md: HTTP 200 at https://pydantic.dev/docs/ai/overview.md > ## Documentation Index\n> Fetch the complete documentation index at: https://pydantic.dev/llms.txt\n> Use this file to d",
    "fetchedAt": "2026-09-16T00:20:49.339Z"
  },
  {
    "id": "pydantic-ai-probe-3",
    "tier": "probe",
    "url": "https://pydantic.dev/openapi.json",
    "excerpt": "PROBE openapi: HTTP 200 at https://pydantic.dev/openapi.json — contains \"openapi\" key",
    "fetchedAt": "2026-09-16T00:20:49.339Z"
  },
  {
    "id": "pydantic-ai-probe-4",
    "tier": "probe",
    "url": "https://pydantic.dev/docs/ai/integrations/cli/",
    "excerpt": "official CLI documented at https://pydantic.dev/docs/ai/integrations/cli/",
    "fetchedAt": "2026-09-16T00:20:49.339Z"
  }
]
