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Caesar is the search engine for AI, organising the world’s data. Use the API to search the web, read sources in full, and cite the sources used. Sign up in the Caesar app to create an API key. For a limited time, all accounts receive a $1,000 starting credit grant. A surreal animated collage of a hand holding a glowing glass orb while graph-paper cards drift in a seamless loop.

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Start with an agent

For agents that support skills, install Caesar search directly:

Use Caesar to search, read the most relevant result, cite the source URL, and preserve doc_id and search_id.

For agents: use /llms.txt to discover docs, append .md to any page for markdown, and start with /agents/install for client-specific setup.

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Call the API directly

This works as written with curl and CAESAR_API_KEY:
Each result includes a doc_id you can pass to /v1/document to read the page in full. Run the first search and read.

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Search, read, cite

Search

Get ranked documents from the web.

Read

Read the selected doc_id as markdown.

Cite

Cite the returned source URL and capture metadata.

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Search and read everywhere

The same search and read operations appear on every surface: search/read in the CLI and SDKs, web_search/web_fetch over MCP. Feedback is available from REST, the CLI, and SDKs when you need it.

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Citable sources

Search results can drift: URLs change, snippets age, and the source an agent saw may not match what a human opens later. Caesar returns source metadata agents can keep with their answer:
  • doc_id — deterministic identifier for a canonical document that can be reused across searches and recrawls. Read it in full any time with /v1/document.
  • passage_id — identifier for a specific passage in the latest capture, with section context.
  • search_id — identifier for the search itself, used to attribute feedback to the exact ranking that produced a result.
  • capture_id and capture_time — exactly which capture of the document your content came from (on search results with response.verbosity: "full"; always available from /v1/document).
That means an agent can search, read the selected source, and cite where and when the content was captured. See provenance for the full object model.

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Pick a surface

API reference

Three endpoints, an OpenAPI spec, and an interactive playground. Start here for raw HTTP.

How search works

Canonical documents, passages, freshness, ranking, and source metadata.

Pricing

API usage is credit-backed. New accounts get a limited-time $1,000 starting credit grant.

Python SDK

pip install caesar-search — sync and async clients, typed models, automatic retries.

TypeScript SDK

npm install caesar-search — fully typed, ESM and CJS, works in Node, Bun, Deno, and edge runtimes.

AI SDK tools

web_search and web_fetch from the caesar-search/ai subpath.

CLI

caesar-search — search and read from the shell, with a --json contract built for scripts and agents.

MCP server

Remote MCP endpoint at /mcp — generic web-search tools, no local install, one command to connect.

Integrations

Recipes for framework tools, generic tool calling, and Agent Development Kit projects.

Agent Skills

Install the search skill with npx skills add, or use the API installer for the full bundle.

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Next steps

  • Quickstart — sign up, set CAESAR_API_KEY, then search and read with curl.
  • How search works - how Caesar turns public pages into citable documents and passages.
  • Pricing - API usage, credits, and the limited-time starting grant.
  • Authentication — creating, storing, and sending API keys.
  • Agents guide — conventions for building agents on Caesar.