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Mixpeek

The semantic retrieval layer for unstructured and multimodal data.

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For agents: Semantic retrieval layer for unstructured and multimodal data; site hosts guides, comparisons, glossary and changelog for agentic search.

semantic retrievalmultimodal searchunstructured dataagentic searchcolpalivector search

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51 reach 6 · trust 5 · quality 25 · freshness 15
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llms.txt
https://mixpeek.com/llms.txt
llms-full.txt
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Categories
AI and machine learning · Data and analytics
For agents
Interface: a documented API or declared MCP endpoint an agent can call
Audience
developers · agents can: read docs, call api, search content · en
First seen
2026-10-19T10:44:30.299Z
Last checked
2026-10-21T11:49:37.538Z
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Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-21T11:49:29.262Z. Cached copy · Source on mixpeek.com

# Mixpeek

> The semantic retrieval layer for unstructured and multimodal data.

**Last Updated:** 2026-10-21

## What is Mixpeek?

Mixpeek is the semantic retrieval layer for unstructured and multimodal data. It turns video, audio, images, documents and other raw objects into named semantic features, connects them through taxonomies and ontologies, and exposes stable multi-stage retrievers that applications and AI agents query by name, without binding themselves to a particular model or index.

**Core Mission:** Make the semantics inside unstructured data addressable, so an application or an agent asks for what it needs by name and keeps working when the model underneath is replaced.

## When to Use Mixpeek

Reach for Mixpeek when the job involves retrieval over unstructured files that
already live in object storage, and the answer depends on what is INSIDE the
files rather than on their names or metadata.

Good fits, stated as jobs:

- **Find a moment inside a video.** "The scene where the red car turns left" over
  a library of footage, returning a timestamped segment rather than a whole file.
- **Search across modalities at once.** One query over video, images, audio and
  PDFs where the match may come from a transcript, a frame, OCR text or an
  embedding.
- **Make an existing object-storage corpus searchable** without moving it. Point
  a bucket at S3-compatible storage and index in place.
- **Cluster or taxonomize a large media set** by content, and keep the labels
  updated as the corpus grows.
- **Give an agent a retrieval tool** over private media, via MCP or the REST API,
  with permissioning scoped per namespace.

Poor fits, so you do not waste a call:

- Plain text-only document search where a standard text index is sufficient.
- Anything requiring the raw file to leave your storage: source and destination
  are both object storage by design.
- Real-time sub-second indexing of a live stream. Extraction is a pipeline.

How an agent should call it:

1. **MCP** is the shortest path for tool use. `@mixpeek/mcp` on npm, or see
   https://mixpeek.com/docs/agent-integrations/mcp
2. **REST** for everything else, against https://api.mixpeek.com. The full
   machine-readable contract is at https://api.mixpeek.com/docs/openapi.json
3. Outside the public marketplace below, authenticate with
   `Authorization: Bearer <api key>`. Scope work to a
   namespace; buckets hold objects, collections hold documents, retrievers run
   the queries.
4. Start at https://mixpeek.com/docs/overview/quickstart (append `.md` for raw
   markdown).

## Try a hosted retriever with no account

Mixpeek hosts public retrievers over sample datasets in its marketplace. Free
listings run without an API key or a signup.

1. List them:
   `curl "https://api.mixpeek.com/v1/marketplace/catalog?limit=100"`
2. Read one:
…

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