Official vendor OpenAPI document · agent-readyAI/MLLanguage ModelsapiKey3 EndpointsREST
For Agents
Send multimodal chat completions to Reka models with text, images, video, audio, or PDFs and optional tool calls, list available models, and transcribe or translate audio.
Use for: I need to ask a model a question about a video clip, Summarise this PDF with a Reka model, Describe what's in an image URL, Transcribe a podcast audio file to text
Not supported: Does not handle model fine-tuning, embeddings, or image generation. Use for multimodal chat completions and audio transcription or translation only.
The Reka AI API generates chat completions from Reka's multimodal models, accepting messages that mix text with image_url, video_url, audio_url, and pdf_url content. Requests can pass tools with a tool_choice of auto, tool, or none, along with sampling controls such as temperature, top_p, top_k, seed, stop strings, and streaming. A separate speech operation transcribes audio, translates it into one of eight target languages, or returns translated speech audio. A model list shows which model ids your key can use.
Install Jentic One Beta
Connect the Reka AI API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Reka AI 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%2Freka.ai%2Freka" | 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%2Freka.ai%2Freka" | 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 Reka AI API.
Generate a chat completion from messages and a model id via POST /chat/completions, with optional streaming
Ask a Reka model about an image, video, audio clip, or PDF by adding image_url, video_url, audio_url, or pdf_url content to a message
Let the model call your functions by passing tools and setting tool_choice to auto, tool, or none
Make generations repeatable with seed and temperature 0.0, and stop them early with stop strings
Transcribe speech to text, optionally with word-level timestamps, from an audio_url or base64 data URI
Translate speech into french, spanish, japanese, chinese, korean, italian, portuguese, or german, optionally returning translated audio
List the model ids your API key can access with GET /models
Use Cases
Patterns agents use Reka AI API for, with concrete tasks.
★ Multimodal analysis for an agent through Jentic
An agent that needs to look at a screenshot, watch a short video, or read a PDF can delegate that step to a Reka model without a developer wiring the X-Api-Key header and message content types by hand. Through Jentic the agent searches for 'ask a model about a video', loads POST /chat/completions, and sends a user message whose content includes a video_url item alongside a text prompt. The completion returns as structured JSON the agent can parse.
POST /chat/completions with model set to an id from GET /models and a user message containing a video_url item plus the text 'List the products shown in this clip'
Meeting and podcast transcription
Teams that record calls or publish audio need text they can search. POST /v1/transcription_or_translation takes an audio_url, which can be a link or a base64 data URI, and its sampling_rate, default 16000 Hz, and returns a transcript with optional word-level timestamps for building captions or chapter markers.
POST /v1/transcription_or_translation with audio_url 'https://example.com/call.wav' and sampling_rate 16000, then return the transcript
Speech translation for support content
Support and content teams localising voice recordings can translate speech directly. Setting is_translate to true with a target_language such as german or korean returns translated text, and return_translation_audio adds base64-encoded speech in the target language for speech-to-speech output.
POST /v1/transcription_or_translation with audio_url, sampling_rate 16000, is_translate true, target_language 'japanese', and return_translation_audio true
Tool-using assistants
Developers building assistants can let a Reka model decide when to call their functions. The chat request takes a tools array and a tool_choice of auto, tool, or none, and the response carries tool calls the application executes before sending tool output back in the next message.
POST /chat/completions with a tools array defining get_weather, tool_choice 'auto', and the message 'What is the weather in Lisbon?'
Key Endpoints
3 endpoints — the reka ai api generates chat completions from reka's multimodal models, accepting messages that mix text with image_url, video_url, audio_url, and pdf_url content.
METHOD
PATH
DESCRIPTION
POST
/chat/completions
Create a chat completion
GET
/models
List available models
POST
/v1/transcription_or_translation
Transcribe or translate audio
POST
/chat/completions
Create a chat completion
GET
/models
List available models
POST
/v1/transcription_or_translation
Transcribe or translate audio
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.
R
Reka AI API
- Foundational (C+)58/100
80
Foundational Compliance63
Developer Experience & Jentic Compatibility39
AI-Readiness & Agent Experience94
Agent Usability50
Security58
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.2
Show dimension breakdown
80
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: A-Signals: 4
99%
Lint Results
Aggregated quality score from linter diagnostics, weighted by severity.
100%
Resolution Completeness
Percentage of `$ref` references that resolve successfully.
100%
Specification Validity
Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).
21%
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
0%
Example Density
How richly the API is illustrated with examples.
100%
Example Validity
Percentage of examples that conform to their schemas.
50%
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.
39
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: FSignals: 4
46%
Description Coverage
Coverage of descriptions across API elements.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
67%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
43%
Summary Coverage
Coverage of summaries across operations/tags/info.
94
Agent Usability
Functional utility, complexity comfort, and AI orchestration readiness.
Grade: A+Signals: 1
94%
Complexity Comfort
Agent comfort level based on API operational and structural complexity.
50
Security
Trust, risk posture, and security compliance.
Grade: C-Signals: 1
50%
Authentication Strength
Average quality of security schemes based on authentication method strength (weakest link for OAuth2).
58
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: C+Signals: 1
58%
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 Reka by hand means sending the X-Api-Key header, building typed message content for images, video, audio, and PDFs, and handling streamed chunks. Through Jentic you install once, import Reka from the API Directory, store the key once, and your agent calls it.
Permission scoping
Reka carries the model and content inside the request body, so rules bound which operations your agent may call, not which model it picks. Limit it to the operations it needs, such as chat completions; speech transcription and translation are not included unless you add them.
Credential isolation
Your Reka 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 'ask a model about a video' or 'transcribe an audio file', and Jentic returns the matching Reka operation with its input schema so the agent calls the right endpoint without browsing the reference docs.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Reka AI API through Jentic.
What authentication does the Reka AI API use?
Per its OpenAPI spec, the Reka AI API authenticates with an API key sent in the X-Api-Key request header. Through Jentic, the key is stored encrypted by your own Jentic One instance and injected at call time, so it never enters the agent's prompt, logs, or context.
What kinds of input can Reka chat completions take?
Message content can mix text with media items whose type is image_url, video_url, audio_url, or pdf_url, so one request can ask about an image, a clip, a recording, or a document. Set stream to true to receive the response incrementally.
What are the rate limits for the Reka AI API?
The OpenAPI spec does not specify rate limits. Check the Reka documentation at https://docs.reka.ai for current limits on your plan.
Is there a Reka AI MCP server?
You don't need an MCP server to give your agent Reka. Jentic connects it directly from the API Directory: import it, store your API key once, and your agent calls chat, model listing, or transcription as needed, discovered on demand rather than loaded into its context up front.
How do I transcribe audio with Reka through Jentic?
Search Jentic for 'transcribe an audio file', load POST /v1/transcription_or_translation, and send audio_url with the file link or a base64 data URI plus sampling_rate, which defaults to 16000. Add is_translate and target_language to translate instead. To run it on your own infrastructure, install Jentic One from its GitHub repo.
Can I limit what my agent is allowed to do with the Reka AI API?
Yes. Write a rule that allows only POST /chat/completions and GET /models, and the agent can generate completions and check model ids but cannot send audio for transcription or translation. Every call it makes is logged, so you can see what it sent.
GET STARTED