Mixedbread API
59
AI ReadinessFoundational (C+)59/100
Official vendor OpenAPI document · agent-readyAI/MLEmbeddings Searchbearer174 EndpointsREST
For Agents
Generate text embeddings, rerank results against a query, run vector stores for semantic search and question answering, and parse and extract structured data from documents. Authenticates with a Bearer token in the Authorization header.
Use for: Generate embeddings for a batch of text passages, Rerank these search results against the user's query, Create a vector store to index my documents, Search my vector store for the most relevant passages
Not supported: Does not handle model fine-tuning, image generation, or billing administration. Use for embeddings, reranking, vector stores, and document parsing and extraction only.
The Mixedbread API is a retrieval and search platform for AI applications. It generates text embeddings, reranks candidate results against a query, and runs vector stores that index your documents for semantic search and question answering. It also parses documents into clean text and extracts structured fields from them, so an agent can build the full pipeline from raw files to retrievable knowledge. Mixedbread targets teams building retrieval-augmented generation and semantic search over their own content.
Install Jentic One Beta
Connect the Mixedbread API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Mixedbread API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fmixedbread.com%2Fmixedbread" | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fmixedbread.com%2Fmixedbread" | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with Mixedbread API.
Generate embedding vectors for text inputs
Rerank a list of candidate documents against a query
Create and manage vector stores that index documents for retrieval
Run semantic search and question answering over a vector store
Parse documents into clean text and extract structured fields from them
Use Cases
Patterns agents use Mixedbread API for, with concrete tasks.
★ Build a retrieval pipeline for RAG
An AI agent can embed a corpus and index it in a Mixedbread vector store, then retrieve the most relevant passages at query time to ground a language model's answer. It generates embeddings for the text, creates a store, and runs a search when a question arrives. Through Jentic the agent discovers each operation by intent and calls it with the text and query.
Generate embeddings for a set of passages, create a vector store, and return the store identifier
Rerank candidate results for better relevance
When a first-pass search returns many candidates, an agent reranks them against the original query so the most relevant results rise to the top before they reach the model or the user. This improves answer quality without re-indexing the corpus. The reranking call takes the query and the candidate list and returns them ordered by relevance.
Rerank a list of ten candidate passages against a user query and return them ordered by relevance
Turn documents into retrievable knowledge
An agent can parse uploaded documents into clean text and extract structured fields from them, then embed and index the result for search. This lets a team point Mixedbread at raw files and get back a queryable knowledge base. Parsing and extraction run as jobs the agent can submit and read back.
Submit a parsing job for an uploaded document and return the job id so the parsed text can be retrieved
Key Endpoints
174 endpoints — the mixedbread api is a retrieval and search platform for ai applications.
METHOD
PATH
DESCRIPTION
POST
/v1/embeddings
Generate embedding vectors for text inputs
POST
/v1/reranking
Rerank candidate documents against a query
POST
/v1/stores
Create a vector store
POST
/v1/stores/search
Search a vector store for relevant passages
POST
/v1/parsing/jobs
Submit a document parsing job
POST
/v1/extractions/jobs
Submit a structured extraction job
POST
/v1/embeddings
Generate embedding vectors for text inputs
POST
/v1/reranking
Rerank candidate documents against a query
POST
/v1/stores
Create a vector store
POST
/v1/stores/search
Search a vector store for relevant passages
POST
/v1/parsing/jobs
Submit a document parsing job
POST
/v1/extractions/jobs
Submit a structured extraction job
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
M
mxbai-omni
- Foundational (C+)59/100
65
Foundational Compliance63
Developer Experience & Jentic Compatibility52
AI-Readiness & Agent Experience55
Agent Usability60
Security75
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.2
Show dimension breakdown
65
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: BSignals: 4
87%
Lint Results
Aggregated quality score from linter diagnostics, weighted by severity.
100%
Resolution Completeness
Percentage of `$ref` references that resolve successfully.
0%
Specification Validity
Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).
75%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
63
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: B-Signals: 4
5%
Example Density
How richly the API is illustrated with examples.
100%
Example Validity
Percentage of examples that conform to their schemas.
46%
Response Coverage
Percentage of operations with complete response definitions (success, client error, server error).
100%
Tooling Readiness
Health of API ingestion, bundling, and resolution within Jentic pipelines.
52
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: C-Signals: 4
53%
Description Coverage
Coverage of descriptions across API elements.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
99%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
57%
Summary Coverage
Coverage of summaries across operations/tags/info.
55
Agent Usability
Functional utility, complexity comfort, and AI orchestration readiness.
Grade: CSignals: 1
55%
Complexity Comfort
Agent comfort level based on API operational and structural complexity.
60
Security
Trust, risk posture, and security compliance.
Grade: B-Signals: 1
60%
Authentication Strength
Average quality of security schemes based on authentication method strength (weakest link for OAuth2).
75
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: A-Signals: 1
75%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring the Mixedbread API by hand means setting up its Bearer token auth, formatting the embedding, reranking, store, and job calls, and polling parsing and extraction jobs to completion yourself. Through Jentic you install once, import the Mixedbread API from the API Directory, store the key once, and your agent calls it.
Permission scoping
The Mixedbread API addresses each vector store by an identifier in the URL path, so you can scope the agent to the stores and operations it needs: allow it to generate embeddings and search a store and leave store deletion out of the allowed set unless you add it.
Credential isolation
Your Mixedbread API key is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'generate embeddings' or 'search my documents', and Jentic returns the matching Mixedbread operation with its input schema so the agent calls the right endpoint without reading the reference docs.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Mixedbread API through Jentic.
What authentication does the Mixedbread API use?
Mixedbread authenticates with a Bearer token, your Mixedbread API key, sent in the Authorization header of every request. Through Jentic the key is stored encrypted by your own Jentic One instance and supplied to requests at execution time, so the agent never sees the raw value.
Can I generate embeddings and rerank results with the Mixedbread API?
Yes. One operation returns embedding vectors for text inputs, and another reranks a list of candidate documents against a query so the most relevant rise to the top. An agent connected through Jentic can call both to build and refine a retrieval pipeline.
How do vector stores work in the Mixedbread API?
You create a vector store, add documents to it, and then run search or question answering over the store. The store indexes your content so an agent can retrieve the most relevant passages at query time without managing its own vector database.
What are the rate limits for the Mixedbread API?
The OpenAPI spec does not declare rate limits for the Mixedbread API. Apply conservative client-side throttling, back off on HTTP 429 responses, and check the Mixedbread developer documentation for the current published limits.
Can I limit what my agent is allowed to do with the Mixedbread API?
Yes. You choose which operations the agent may call, so it can be allowed to generate embeddings and search a store while store deletion stays out of the allowed set unless you add it. Because each store is addressed by an identifier in the URL path, you can also keep the agent to the stores it needs, and every call it makes is logged.
Is there a Mixedbread API MCP server?
You don't need an MCP server to give your agent Mixedbread. Jentic connects it directly from the API Directory: import it, store the key once, and your agent calls the embedding, reranking, and search operations on demand without a separate server to run.
How do I build a retrieval pipeline with Mixedbread through Jentic?
Search Jentic for an intent like 'generate embeddings' or 'search my documents', which returns the matching Mixedbread operation with its input schema so the agent supplies the text or query and reads the response. To run it on your own infrastructure, install Jentic One from its GitHub repo.
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