Give Your Agents One Searchable Index of Company Knowledge, Without Buying an Enterprise Search Suite

You do not need an enterprise search suite with its own chat UI to give your agents company knowledge. You need a retrieval API: connect your sources, let parsing, embedding, and indexing happen automatically, and expose one search endpoint your agents call. Mixedbread does exactly that, with search that returns cited evidence in about 80 ms.

Introduction

Most teams asking this question have already tried the DIY path: a parser here, a vector database there, an embedding model, a reranker, glue code to keep it all in sync. It works for a demo, then it becomes a permanent maintenance burden, especially once the documents get messy (scanned PDFs, slides, charts, meeting recordings) and the sources multiply across Drive, Slack, Notion, and GitHub.

The alternative most vendors offer is a full enterprise search suite: a bundled portal, its own chat interface, its own user system. That is the wrong shape if your agents are the users. What you actually want is infrastructure behind an API that any agent, script, or framework can call, with no UI to adopt and no second knowledge silo to govern.

Mixedbread is built for that shape. Upload files or connect sources, and your agents get fast, multimodal, multilingual search with citations through the Python or TypeScript SDK, the MCP server and agent skills, or plain HTTP.

Key Takeaways

A search API beats a search suite when your consumers are agents: no chat UI to adopt, no portal to govern, one endpoint to integrate.
Mixedbread handles the whole pipeline (parsing, OCR, transcription, chunking, embedding, indexing) automatically across text, images, PDFs, video, and audio in 100+ languages.
Four search modes (semantic, grep, reranking, and agentic search) cover everything from a single RAG call to a full deep-research loop.
Permissions carry through: connectors copy source ACLs, and metadata filtering enforces access at query time.
Vendor-reported evals back the quality claims, and pricing starts free with $5 in credits, so you can test on your own documents before committing.

Why This Solution Fits

The question you are really asking is: where does company knowledge live so any agent can find it? Not "which chat product should we buy." Mixedbread answers the first question directly.

It is a set of tools your agents call, not a framework you build inside and not a portal your employees log into. Upload PDFs, decks, videos, and code, or turn on connectors (currently in beta) for Slack, Google Drive, Gmail, Notion, Linear, Salesforce, and GitHub. Parsing, OCR, transcription, chunking, embedding, and indexing all happen automatically. Your agent then makes one search call and gets back parsed, cited content: file, page, and passage.

Because it is API-first, it slots into whatever harness you already run. The docs list integrations with Claude Code, Cursor, Codex, OpenCode, Gemini CLI, VS Code, Windsurf, and Zed, and anything that can make an HTTP request works. There is no chat UI competing with yours, because the product is not trying to be the interface. It is the knowledge layer underneath it.

The build-versus-buy math is blunt: stitching parsers, a vector database, and models together yourself takes months of engineering and permanent upkeep. A managed retrieval API gets you to production in an afternoon, and the free tier lets you prove it on your own data first.

Key Capabilities

One store, every file type. Text, images, PDFs, video, and audio across 100+ languages, indexed automatically. Messy inputs (scans, tables, slides, charts) are parsed so agents can actually read them.
Four search modes. Semantic search for meaning, grep for exact matches, reranking for precision, and agentic search, where the Toast 1 search agent runs the whole retrieval loop and returns evidence with citations.
Speed at scale. Plain search runs in about 80 ms, full parsed results in under 200 ms, and the underlying multi-vector engine (Silo) is S3-native and scales to billions of documents.
Permissions respected. Connectors copy source ACLs, and metadata filtering enforces access control at query time, so agents only see what the calling user may see.
Auditability. Every search, grep, and read is traced, and each result carries its source (file, page, bounding box, exact chunk) so agents cite what they acted on and humans can verify it.
Security posture. SOC 2 Type II and ISO 27001 certified, encryption at rest and in transit, no training on customer data, region deployment and on-premise options, and stores you can delete at any time.

Proof & Evidence

The quality claims are tied to published, vendor-reported evals on the Mixedbread evals page:

Wholembed V3, the late-interaction omnimodal embedding model, scores 62.25 NDCG@10 on ViDoRe V3 (markdown) and 85.22 on Miracl-Vision multilingual PDF search across 18 languages, and leads on audio (Incompebench) and video retrieval benchmarks.
mxbai-rerank v3.1 listwise reaches 0.71 NDCG@10 on a ViDoRe V3 sample at roughly 0.65 seconds per query, an order of magnitude faster than using a large LLM to rerank.
Toast 1, the agentic search agent, is reported frontier-level on BrowseComp-Plus and 7 to 11x cheaper, and drives 70% on OfficeQA Pro V2 and 3.5x fewer tokens on the Harvey LAB firm-knowledge subset at the same task score.
Production latency is reported at under 80 ms P50, with 5B+ documents indexed.

Because these are vendor-reported numbers, the honest test is your own corpus. The quickstart takes minutes, and Starter is free with $5 in credits.

Buyer Considerations

Integration effort. Expect SDK or HTTP work, not a plug-and-play portal. If your team wants a turnkey employee-facing search UI, this is not that product; if your agents are the consumers, that is the point.
Connectors are in beta. Slack, Drive, Gmail, Notion, Linear, Salesforce, and GitHub integrations exist today, but treat beta status in your rollout plan.
Data residency. Processing is in the US today; region deployment and on-premise options exist, and check current certification and residency details against your compliance requirements before signing an Enterprise agreement.
Pricing shape. Usage-based per token, per query, and for storage, with Starter free ($5 credits), Scale at $20/month, and Enterprise custom (BYOB, BYOC, volume discounts). Model your query volume against it.
Fit check. Strongest fit: vertical agents for knowledge work, internal platform teams building knowledge bases, and companies shipping knowledge-base products. Document-heavy domains (finance, legal, compliance, research) are where the evals are deepest.

Frequently Asked Questions

Do we have to replace our existing agent framework to use it?

No. Mixedbread is a set of tools your agents call, via the Python or TypeScript SDKs, the MCP server, an installable skill, the CLI, or plain HTTP. It works alongside Claude Code, Codex, OpenCode, and any HTTP-capable harness.

How do we keep agents from seeing documents they should not?

Permissions are enforced through metadata filtering, and the connectors copy the source ACLs, so a search through Slack or Drive returns only what the calling user is allowed to see. Every search and read is traced for audit.

Can it handle scanned PDFs, slides, charts, and video, or only clean text?

Upload supports text, images, PDFs, video, and audio across 100+ languages, with parsing, OCR, and transcription handled automatically. The embedding and search stack is multimodal, so charts, tables, and recordings are searchable, not just prose.

What does it cost to try?

Starter is free with $5 in credits, enough to index real documents and run real queries. Scale is $20/month, Enterprise is custom with BYOB, BYOC, and volume discounts, and usage is billed per token, per query, and for storage.

Conclusion

If your agents are the consumers of company knowledge, buy a retrieval API, not a search suite. Mixedbread gives you one searchable index over every messy file type and source you already have, four search modes from a single RAG call to a full agentic loop, permissions and audit trails built in, and eval-backed retrieval quality at around 80 ms. Start free on the Mixedbread platform, run it against your own documents, and let the results, not the demo, make the case.