# AI Code Reviews Pricing Source: https://docs.matterai.so/ai-code-reviews/pricing Simple, Transparent Pricing for AI Code Reviews # Simple, Transparent Pricing For personal projects and individual developers
Free
[Get started free](https://app.matterai.so) **Includes:** * ✓ 25 Reviews/month * ✓ 50 Summaries/month * ✓ 1 user * ✓ Community support
Not included * ✕ AI Security Scan * ✕ AI Release Notes * ✕ PR Analytics * ✕ Custom Rulesets * ✕ AI Memories * ✕ AI Documentation * ✕ Fine-tuning * ✕ Context Integrations * ✕ SSO
For small teams getting started with AI code reviews
\$49 /month
[Start 14-day free trial](https://app.matterai.so) **Includes:** * ✓ 250 Reviews/month * ✓ 500 Summaries/month * ✓ Up to 10 users * ✓ AI Security Scan * ✓ AI Release Notes * ✓ Discord/Email support
Not included * ✕ Custom Rulesets * ✕ AI Memories * ✕ AI Documentation * ✕ Fine-tuning * ✕ Context Integrations * ✕ SSO
For growing teams that need advanced AI features
\$199 /month
[Start 14-day free trial](https://app.matterai.so) **Includes:** * ✓ 2000 Reviews/month * ✓ 4000 Summaries/month * ✓ Up to 50 users * ✓ AI Security Scan * ✓ AI Release Notes * ✓ PR Analytics * ✓ Custom Rulesets * ✓ AI Memories * ✓ AI Documentation * ✓ Fine-tuning * ✓ Context Integrations * ✓ Dedicated Slack support
Not included * ✕ SSO
For organizations with custom needs and security
Custom
[Contact sales](https://matterai.so/contact) **Includes:** * ✓ Bring Your Own Model * ✓ Unlimited PRs * ✓ Unlimited users * ✓ AI Security Scan * ✓ AI Release Notes * ✓ PR Analytics * ✓ Custom Rulesets * ✓ AI Memories * ✓ AI Documentation * ✓ Fine-tuning * ✓ Context Integrations * ✓ LLM Observability * ✓ SSO * ✓ Dedicated support * ✓ Self-hosting option # Chat Source: https://docs.matterai.so/api-reference/endpoint/completions POST https://api2.matterai.so/v1/web/chat/completions Create a chat completion using the MatterAI API ## Authentication All API requests require authentication using a Bearer token. You can obtain your API key from the [MatterAI Console](https://app.matterai.so). ```bash theme={null} Authorization: Bearer MATTERAI_API_KEY ``` Keep your API key secure and never expose it in client-side code. Get your API key from the MatterAI console. ## Request The model used for the chat completion. Available models: `"axon-2-5-pro"`, `"axon-2-5-mini"`. An array of message objects that make up the conversation. The role of the message author. One of `"system"`, `"user"`, or `"assistant"`. The content of the message. Whether to stream the response as it's generated. A list of tools the model may call. Currently, only functions are supported as a tool type. The type of the tool. Currently, only `"function"` is supported. The function definition. The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64. A description of what the function does, used by the model to choose when and how to call the function. The parameters the function accepts, described as a JSON Schema object. Controls which (if any) tool is called by the model. Options: `"none"` means the model will not call any tool and instead generates a message. `"auto"` means the model can pick between generating a message or calling one or more tools. `"required"` means the model must call one or more tools. Specifying a particular tool via `{"type": "function", "function": {"name": "my_function"}}` forces the model to call that tool. The maximum number of tokens to generate in the completion. Configuration for reasoning capabilities. The level of reasoning effort. Options: `"none"`, `"low"`, `"medium"`, `"high"`. The level of reasoning summary. Options: `"none"`, `"auto"`. The format of the response. The type of response format. Currently supports `"text"` OR `json`. Controls randomness in the output. Higher values make output more random, lower values make it more focused and deterministic. Range: 0.0 to 2.0. Controls diversity via nucleus sampling. Range: 0.0 to 1.0. ## Response A unique identifier for the chat completion. The object type, which is always `"chat.completion"`. The Unix timestamp (in seconds) of when the chat completion was created. The model used for the chat completion. Available models: `"axon-2-5-pro"`, `"axon-2-5-mini"`. A list of chat completion choices. The index of the choice in the list of choices. The message generated by the model. The role of the author of this message. Always `"assistant"`. The contents of the message. The reason the model stopped generating tokens. Possible values: `"stop"`, `"length"`, `"content_filter"`. Usage statistics for the completion request. Number of tokens in the prompt. Number of tokens in the generated completion. Total number of tokens used in the request (prompt + completion). ## Example Request ```bash cURL theme={null} curl --location 'https://api2.matterai.so/v1/web/chat/completions' --header 'Content-Type: application/json' --header 'Authorization: Bearer MATTERAI_API_KEY' --data '{ "model": "axon-2-5-pro", "max_new_tokens": 2048, "temperature": 0.1, "messages": [ { "role": "user", "content": "Hi" } ], "stream": true, "stream_options": { "include_usage": true } }' ``` ```javascript JavaScript theme={null} const response = await fetch('https://api2.matterai.so/v1/web/chat/completions', { method: 'POST', headers: { 'Content-Type': 'application/json', 'Authorization': 'Bearer MATTERAI_API_KEY' }, body: JSON.stringify({ model: 'axon-2-5-pro', messages: [ { role: 'user', content: 'Hi' } ], max_new_tokens: 2048, temperature: 0.1, stream: true, stream_options: { include_usage: true } }) }); const data = await response.json(); console.log(data); ``` ```python Python theme={null} import requests url = "https://api2.matterai.so/v1/web/chat/completions" headers = { "Content-Type": "application/json", "Authorization": "Bearer MATTERAI_API_KEY" } payload = { "model": "axon-2-5-pro", "messages": [ { "role": "user", "content": "Hi" } ], "max_new_tokens": 2048, "temperature": 0.1, "stream": True, "stream_options": { "include_usage": True } } response = requests.post(url, json=payload, headers=headers) print(response.json()) ``` ## Example Response ```json theme={null} { "id": "chatcmpl-cd3ac60c-9746-457b-b4fa-aca53a993249", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "Axon is an Agentic LLM Model for coding by MatterAI, designed as an elite software engineering assistant for architecting mission-critical systems." }, "finish_reason": "stop" } ], "created": 1756372473, "model": "axon-2-5-pro", "object": "chat.completion", "usage": { "prompt_tokens": 47, "completion_tokens": 176, "total_tokens": 223, "completion_tokens_details": { "reasoning_tokens": 97 }, "prompt_tokens_details": { "cached_tokens": 25 } } } ``` ## Streaming When `stream` is set to `true`, the API will return a stream of Server-Sent Events (SSE). Each event contains a JSON object with the partial response: ```json theme={null} data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1677652288,"model":"axon-2-5-pro","choices":[{"index":0,"delta":{"content":"Hello"},"finish_reason":null}]} data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1677652288,"model":"axon-2-5-pro","choices":[{"index":0,"delta":{"content":"!"},"finish_reason":null}]} data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1677652288,"model":"axon-2-5-pro","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]} data: [DONE] ``` ## Error Responses The API returns standard HTTP status codes to indicate success or failure: Invalid request parameters or malformed JSON. Invalid or missing API key. Too many requests. Please slow down. Server error. Please try again later. Example error response: ```json theme={null} { "error": { "message": "Invalid API key provided", "type": "invalid_request_error", "code": "invalid_api_key" } } ``` # Messages Source: https://docs.matterai.so/api-reference/endpoint/messages POST https://api2.matterai.so/v1/messages Create a message completion using the MatterAI API (Anthropic-compatible) ## Authentication All API requests require authentication using a Bearer token. You can obtain your API key from the [MatterAI Console](https://app.matterai.so). ```bash theme={null} Authorization: Bearer MATTERAI_API_KEY ``` Keep your API key secure and never expose it in client-side code. Get your API key from the MatterAI console. ## Request The model used for the completion. Available models: `"axon-2-5-pro"`, `"axon-2-5-mini"`. An array of message objects that make up the conversation. The role of the message author. One of `"user"`, or `"assistant"`. The content of the message, as an array of content blocks. The type of content block. Use `"text"` for text content. The text content. System prompts to provide context or instructions. The type of block. Use `"text"` for text content. The system prompt text. The maximum number of tokens to generate in the completion. Whether to stream the response as it's generated. Configuration for thinking/reasoning capabilities. The type of thinking. Use `"enabled"` to enable thinking. The maximum tokens to use for thinking. Controls randomness in the output. Higher values make output more random, lower values make it more focused and deterministic. Range: 0.0 to 2.0. Controls diversity via nucleus sampling. Range: 0.0 to 1.0. ## Response A unique identifier for the message completion. The object type, which is always `"message"`. The role of the response, always `"assistant"`. The content blocks in the response. The type of content block. Values: `"text"`, `"thinking"`. The text content (for text blocks). The thinking content (for thinking blocks). The latency in milliseconds for the thinking process. The model used for the completion. Available models: `"axon-2-5-pro"`, `"axon-2-5-mini"`. The reason the model stopped generating tokens. Possible values: `"end_turn"`, `"stop_sequence"`, `"max_tokens"`. The stop sequence that triggered the stop, if any. Usage statistics for the completion request. Number of tokens in the input. Number of tokens in the generated completion. Number of tokens used for creating the cache. Number of tokens read from cache. ## Example Request ```bash cURL theme={null} curl --location 'https://api2.matterai.so/v1/messages' \ --header 'content-type: application/json' \ --header 'Authorization: Bearer MATTERAI_API_KEY' \ --data '{ "model": "axon-2-5-pro", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Hi" } ] } ], "system": [ { "type": "text", "text": "You are Axon, helpful assistant" } ], "max_tokens": 512, "thinking": { "type": "enabled", "budget_tokens": 8192 }, "stream": true }' ``` ```javascript JavaScript theme={null} const response = await fetch("https://api2.matterai.so/v1/messages", { method: "POST", headers: { "Content-Type": "application/json", Authorization: "Bearer MATTERAI_API_KEY", }, body: JSON.stringify({ model: "axon-2-5-pro", messages: [ { role: "user", content: [ { type: "text", text: "Hi", }, ], }, ], system: [ { type: "text", text: "You are Axon, helpful assistant", }, ], max_tokens: 512, thinking: { type: "enabled", budget_tokens: 8192, }, stream: true, }), }); const data = await response.json(); console.log(data); ``` ```python Python theme={null} import requests url = "https://api2.matterai.so/v1/messages" headers = { "Content-Type": "application/json", "Authorization": "Bearer MATTERAI_API_KEY" } payload = { "model": "axon-2-5-pro", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Hi" } ] } ], "system": [ { "type": "text", "text": "You are Axon, helpful assistant" } ], "max_tokens": 512, "thinking": { "type": "enabled", "budget_tokens": 8192 }, "stream": True } response = requests.post(url, json=payload, headers=headers) print(response.json()) ``` ## Example Response ```json theme={null} { "id": "msg-abc123", "type": "message", "role": "assistant", "content": [ { "type": "thinking", "thinking": "Let me analyze this request carefully...", "thinking_latency_ms": 450 }, { "type": "text", "text": "Hello! I'm Axon, a helpful assistant. How can I help you today?" } ], "model": "axon-2-5-pro", "stop_reason": "end_turn", "stop_sequence": null, "usage": { "input_tokens": 35, "output_tokens": 89, "cache_creation_input_tokens": 0, "cache_read_input_tokens": 0 } } ``` ## Streaming When `stream` is set to `true`, the API will return a stream of Server-Sent Events (SSE). Each event contains a JSON object with the partial response: ```json theme={null} data: {"type":"message_start","message":{"id":"msg-abc123","type":"message","role":"assistant","content":[],"model":"axon-2-5-pro","stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"output_tokens":0}}} data: {"type":"content_block_start","index":0,"content_block":{"type":"thinking","thinking":""}} data: {"type":"content_block_delta","index":0,"delta":{"type":"thinking_delta","thinking":"Let me"}} data: {"type":"content_block_delta","index":0,"delta":{"type":"thinking_delta","thinking":" think"}} data: {"type":"content_block_stop","index":0} data: {"type":"content_block_start","index":1,"content_block":{"type":"text","text":""}} data: {"type":"content_block_delta","index":1,"delta":{"type":"text_delta","text":"Hello!"}} data: {"type":"content_block_stop","index":1} data: {"type":"message_delta","delta":{"stop_reason":"end_turn"},"usage":{"output_tokens":45}} data: [DONE] ``` ## Error Responses The API returns standard HTTP status codes to indicate success or failure: Invalid request parameters or malformed JSON. Invalid or missing API key. Too many requests. Please slow down. Server error. Please try again later. Example error response: ```json theme={null} { "error": { "type": "error", "error": { "type": "authentication_error", "message": "Invalid API key provided" } } } ``` # Responses Source: https://docs.matterai.so/api-reference/endpoint/responses POST https://api2.matterai.so/v1/responses Create a model response using the MatterAI API (OpenAI-compatible) ## Authentication All API requests require authentication using a Bearer token. You can obtain your API key from the [MatterAI Console](https://app.matterai.so). ```bash theme={null} Authorization: Bearer MATTERAI_API_KEY ``` Keep your API key secure and never expose it in client-side code. Get your API key from the MatterAI console. ## Request The model used for the response. Available models: `"axon-2-5-pro"`, `"axon-2-5-mini"`. Text or array of input items to the model, used to generate a response. Accepts a plain string (equivalent to a `"user"` message) or an array of input items. The role of the message. One of `"user"`, `"assistant"`, `"system"`, or `"developer"`. Text content or an array of content blocks. The type of content. Use `"input_text"` for text content, `"input_image"` for image URLs. The text content (for `input_text` blocks). The URL of the image (for `input_image` blocks). A system (or developer) message inserted into the model's context. When used with `previous_response_id`, instructions from a previous response are not carried over to the next response. Equivalent to the `"system"` role in chat completions. An upper bound for the number of tokens that can be generated for a response, including visible output tokens and reasoning tokens. Whether to stream the response as it's generated using server-sent events. Configuration for reasoning capabilities. The level of reasoning effort. Options: `"none"`, `"low"`, `"medium"`, `"high"`. The level of reasoning summary. Options: `"auto"`, `"concise"`, `"detailed"`. Controls randomness in the output. Higher values make output more random, lower values make it more focused and deterministic. Range: 0.0 to 2.0. Controls diversity via nucleus sampling. Range: 0.0 to 1.0. Configuration options for a text response from the model. An object specifying the format that the model must output. The type of response format. Options: `"text"`, `"json_schema"`, `"json_object"`. The name of the response format (for `json_schema`). The JSON Schema for the response format (for `json_schema`). Whether to enable strict schema adherence (for `json_schema`). Constrains the verbosity of the model's response. Options: `"low"`, `"medium"`, `"high"`. Whether to store the generated model response for later retrieval via API. Set of up to 16 key-value pairs that can be attached to the response. Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters. ## Response Unique identifier for this response. The object type, which is always `"response"`. The status of the response generation. One of `"completed"`, `"failed"`, `"in_progress"`, `"cancelled"`, or `"incomplete"`. Unix timestamp (in seconds) of when this response was created. The model used to generate the response. Available models: `"axon-2-5-pro"`, `"axon-2-5-mini"`. An array of content items generated by the model. The unique ID of the output message. The type of the output item. Always `"message"`. The role of the output message. Always `"assistant"`. The status of the message. One of `"in_progress"`, `"completed"`, `"incomplete"`. The content of the output message. The type of content. Values: `"output_text"`, `"refusal"`. The text output from the model (for `output_text` blocks). Annotations of the text output, such as citations. SDK-only convenience property containing the aggregated text output from all `output_text` items in the `output` array. Usage statistics for the response request. Number of tokens in the input. Number of tokens in the generated output. Total number of tokens used (input + output). A detailed breakdown of the output tokens. Number of tokens used for reasoning. A detailed breakdown of the input tokens. Number of tokens retrieved from cache. An error object returned when the model fails to generate a response. The error code. Possible values: `"server_error"`, `"rate_limit_exceeded"`, `"invalid_prompt"`, etc. A human-readable description of the error. ## Example Request ```bash cURL theme={null} curl --location 'https://api2.matterai.so/v1/responses' \ --header 'content-type: application/json' \ --header 'Authorization: Bearer MATTERAI_API_KEY' \ --data '{ "model": "axon-2-5-pro", "input": "Tell me a short story about a curious robot." }' ``` ```javascript JavaScript theme={null} const response = await fetch("https://api2.matterai.so/v1/responses", { method: "POST", headers: { "Content-Type": "application/json", Authorization: "Bearer MATTERAI_API_KEY", }, body: JSON.stringify({ model: "axon-2-5-pro", input: "Tell me a short story about a curious robot.", }), }); const data = await response.json(); console.log(data); ``` ```python Python theme={null} import requests url = "https://api2.matterai.so/v1/responses" headers = { "Content-Type": "application/json", "Authorization": "Bearer MATTERAI_API_KEY" } payload = { "model": "axon-2-5-pro", "input": "Tell me a short story about a curious robot." } response = requests.post(url, json=payload, headers=headers) print(response.json()) ``` ## Example Response ```json theme={null} { "id": "resp_abc123def456", "object": "response", "created_at": 1741476542, "status": "completed", "model": "axon-2-5-pro", "output": [ { "id": "msg_abc123def456", "type": "message", "status": "completed", "role": "assistant", "content": [ { "type": "output_text", "text": "In a gleaming city of tomorrow, a small robot named Bolt was built to sort packages.", "annotations": [] } ] } ], "usage": { "input_tokens": 27, "output_tokens": 94, "total_tokens": 121, "output_tokens_details": { "reasoning_tokens": 0 }, "input_tokens_details": { "cached_tokens": 0 } } } ``` ## Example: Multi-turn Conversation To continue a conversation, pass the `previous_response_id` from the previous response: ```bash cURL theme={null} curl --location 'https://api2.matterai.so/v1/responses' \ --header 'content-type: application/json' \ --header 'Authorization: Bearer MATTERAI_API_KEY' \ --data '{ "model": "axon-2-5-pro", "input": "What happened next?", "previous_response_id": "resp_abc123def456" }' ``` ```javascript JavaScript theme={null} const response = await fetch("https://api2.matterai.so/v1/responses", { method: "POST", headers: { "Content-Type": "application/json", Authorization: "Bearer MATTERAI_API_KEY", }, body: JSON.stringify({ model: "axon-2-5-pro", input: "What happened next?", previous_response_id: "resp_abc123def456", }), }); const data = await response.json(); console.log(data); ``` ```python Python theme={null} import requests url = "https://api2.matterai.so/v1/responses" headers = { "Content-Type": "application/json", "Authorization": "Bearer MATTERAI_API_KEY" } payload = { "model": "axon-2-5-pro", "input": "What happened next?", "previous_response_id": "resp_abc123def456" } response = requests.post(url, json=payload, headers=headers) print(response.json()) ``` ## Example: With Reasoning ```bash cURL theme={null} curl --location 'https://api2.matterai.so/v1/responses' \ --header 'content-type: application/json' \ --header 'Authorization: Bearer MATTERAI_API_KEY' \ --data '{ "model": "axon-2-5-pro", "instructions": "You are a helpful assistant that explains complex topics simply.", "input": "Explain quantum entanglement in one paragraph.", "reasoning": { "effort": "medium" } }' ``` ```javascript JavaScript theme={null} const response = await fetch("https://api2.matterai.so/v1/responses", { method: "POST", headers: { "Content-Type": "application/json", Authorization: "Bearer MATTERAI_API_KEY", }, body: JSON.stringify({ model: "axon-2-5-pro", instructions: "You are a helpful assistant that explains complex topics simply.", input: "Explain quantum entanglement in one paragraph.", reasoning: { effort: "medium", }, }), }); const data = await response.json(); console.log(data); ``` ```python Python theme={null} import requests url = "https://api2.matterai.so/v1/responses" headers = { "Content-Type": "application/json", "Authorization": "Bearer MATTERAI_API_KEY" } payload = { "model": "axon-2-5-pro", "instructions": "You are a helpful assistant that explains complex topics simply.", "input": "Explain quantum entanglement in one paragraph.", "reasoning": { "effort": "medium" } } response = requests.post(url, json=payload, headers=headers) print(response.json()) ``` ## Streaming When `stream` is set to `true`, the API returns a stream of Server-Sent Events (SSE). The streaming events use the OpenAI Responses API format: ```json theme={null} data: {"type":"response.created","response":{"id":"resp_abc123","object":"response","created_at":1741476542,"status":"in_progress","model":"axon-2-5-pro","output":[],"usage":null}} data: {"type":"response.in_progress","response":{"id":"resp_abc123","object":"response","created_at":1741476542,"status":"in_progress","model":"axon-2-5-pro","output":[],"usage":null}} data: {"type":"response.output_item.added","output_index":0,"item":{"id":"msg_abc123","type":"message","status":"in_progress","role":"assistant","content":[]}} data: {"type":"response.content_part.added","item_id":"msg_abc123","output_index":0,"content_index":0,"part":{"type":"output_text","text":"","annotations":[]}} data: {"type":"response.output_text.delta","item_id":"msg_abc123","output_index":0,"content_index":0,"delta":"Hello"} data: {"type":"response.output_text.done","item_id":"msg_abc123","output_index":0,"content_index":0,"text":"Hello world!"} data: {"type":"response.output_item.done","output_index":0,"item":{"id":"msg_abc123","type":"message","status":"completed","role":"assistant","content":[{"type":"output_text","text":"Hello world!","annotations":[]}]}} data: {"type":"response.completed","response":{"id":"resp_abc123","object":"response","created_at":1741476542,"status":"completed","model":"axon-2-5-pro","output":[...],"usage":{"input_tokens":10,"output_tokens":12,"total_tokens":22}}} ``` ## Migrating from Chat Completions The Responses API provides a cleaner interface for text generation. Key differences: | Chat Completions | Responses | | ---------------------------- | ------------------------------------------ | | `POST /v1/chat/completions` | `POST /v1/responses` | | `messages` array | `input` (string or array) | | `system` message role | `instructions` string parameter | | `choices[0].message.content` | `output[].content[].text` or `output_text` | | `max_tokens` | `max_output_tokens` | | `finish_reason` | `status` field on response | ## Error Responses The API returns standard HTTP status codes to indicate success or failure: Invalid request parameters or malformed JSON. Invalid or missing API key. Too many requests. Please slow down. Server error. Please try again later. Example error response: ```json theme={null} { "error": { "message": "Invalid API key provided", "type": "invalid_request_error", "code": "invalid_api_key" } } ``` # Axon 2.5 Mini Source: https://docs.matterai.so/axon-2-5-mini Generate a new API key View model pricing ## Model Details | Property | Value | | -------------- | ------------------ | | Model ID | `axon-2-5-mini` | | Context Window | 1,000,000 tokens | | Max Output | 128,000 tokens | | Input Price | \$0.50 / 1M tokens | | Output Price | \$2.00 / 1M tokens | | Cached Input | \$0.25 / 1M tokens | ## Capabilities * Tool Calling * Structured Outputs * Web Search * Text + Image Input * Text Output ## Quick Start ```bash cURL theme={null} curl --location 'https://api2.matterai.so/v1/web/chat/completions' \ --header 'Content-Type: application/json' \ --header 'Authorization: Bearer MATTERAI_API_KEY' \ --data '{ "model": "axon-2-5-mini", "max_new_tokens": 2048, "temperature": 0.1, "messages": [ { "role": "user", "content": "Hi" } ], "stream": false }' ``` ```javascript JavaScript theme={null} import OpenAI from "openai"; const client = new OpenAI({ apiKey: "MATTERAI_API_KEY", baseURL: "https://api2.matterai.so/v1", }); const response = await client.chat.completions.create({ model: "axon-2-5-mini", messages: [{ role: "user", content: "Hi" }], max_tokens: 2048, temperature: 0.1, }); console.log(response.choices[0].message.content); ``` ```python Python theme={null} from openai import OpenAI client = OpenAI( api_key="MATTERAI_API_KEY", base_url="https://api2.matterai.so/v1" ) response = client.chat.completions.create( model="axon-2-5-mini", messages=[ {"role": "user", "content": "Hi"} ], max_tokens=2048, temperature=0.1 ) print(response.choices[0].message.content) ``` Compare all available models View full API documentation # Axon 2.5 Pro Source: https://docs.matterai.so/axon-2-5-pro Generate a new API key View model pricing ## Model Details | Property | Value | | -------------- | ------------------ | | Model ID | `axon-2-5-pro` | | Context Window | 262,000 tokens | | Max Output | 64,000 tokens | | Input Price | \$2.00 / 1M tokens | | Output Price | \$8.00 / 1M tokens | | Cached Input | \$1.00 / 1M tokens | ## Capabilities * Tool Calling * Structured Outputs * Web Search * Text + Image Input * Text Output ## Quick Start ```bash cURL theme={null} curl --location 'https://api2.matterai.so/v1/web/chat/completions' \ --header 'Content-Type: application/json' \ --header 'Authorization: Bearer MATTERAI_API_KEY' \ --data '{ "model": "axon-2-5-pro", "max_new_tokens": 2048, "temperature": 0.1, "messages": [ { "role": "user", "content": "Hi" } ], "stream": false }' ``` ```javascript JavaScript theme={null} import OpenAI from "openai"; const client = new OpenAI({ apiKey: "MATTERAI_API_KEY", baseURL: "https://api2.matterai.so/v1", }); const response = await client.chat.completions.create({ model: "axon-2-5-pro", messages: [{ role: "user", content: "Hi" }], max_tokens: 2048, temperature: 0.1, }); console.log(response.choices[0].message.content); ``` ```python Python theme={null} from openai import OpenAI client = OpenAI( api_key="MATTERAI_API_KEY", base_url="https://api2.matterai.so/v1" ) response = client.chat.completions.create( model="axon-2-5-pro", messages=[ {"role": "user", "content": "Hi"} ], max_tokens=2048, temperature=0.1 ) print(response.choices[0].message.content) ``` Compare all available models View full API documentation # API Keys Source: https://docs.matterai.so/axon-ai/api-keys Manage API keys for your MatterAI account API keys are used to authenticate requests to the MatterAI Axon inferencing API. You can manage your API keys in the MatterAI console. ## Generate a new API key Go to the [MatterAI API Key Management](https://app.matterai.so/api-keys) and click `Create Key` button. MatterAI API Key Management Enter a name for the API key. MatterAI API Key Management Click on the `Copy Key` button to copy the API key. MatterAI API Key Management # Model Pricing Source: https://docs.matterai.so/axon-ai/pricing Pricing for Axon AI models ## Pricing and Comparison | Model | Input Price (per 1M tokens) | Output Price (per 1M tokens) | Context Window Size | Max Output Tokens | | ------------- | --------------------------- | ---------------------------- | ------------------- | ----------------- | | Axon 2.5 Pro | \$2.00 | \$8.00 | 200K tokens | 128K | | Axon 2.5 Mini | \$0.50 | \$2.00 | 1M tokens | 128K | ## Prompt Caching Cached input tokens are automatically discounted at 50% off the standard input price. Caching kicks in automatically for repeated prompt prefixes. | Model | Cached Input Price (per 1M tokens) | | ------------- | ---------------------------------- | | Axon 2.5 Pro | \$1.00 | | Axon 2.5 Mini | \$0.25 | All models support tool calling, structured outputs, web search, and text + image input with text output. # Prompt Caching Source: https://docs.matterai.so/axon-ai/prompt-caching Learn how prompt caching works with Axon models to optimize token usage and reduce costs ## Overview Input Prompt token caching is now live for all Axon models! This feature significantly reduces costs and improves response times by caching frequently used prompt tokens. ## Key Features * **Default TTL**: 5 minutes * **Supported Modes**: Both streaming and non-streaming responses * **Automatic**: Works transparently with all Axon models * **Cost Reduction**: Cached tokens are billed at a reduced rate ## How It Works When you send a request with similar or identical prompt content, the system automatically caches the tokenized prompt. Subsequent requests within the 5-minute TTL period will reuse the cached tokens, resulting in: * Faster response times * Lower token costs * Improved API performance ## Usage Example The cached tokens are reported in the API response under the `usage.prompt_tokens_details` field: ```json theme={null} { "id": "chatcmpl-954ab54b-ee3f-4199-b0a7-06457c426dc8", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "matched_stop": 151645, "message": { "content": "Hello. How can I assist you today?", "role": "assistant", "tool_calls": null } } ], "created": 1766466952, "model": "axon-mini", "object": "chat.completion", "usage": { "prompt_tokens": 25, "completion_tokens": 74, "total_tokens": 99, "completion_tokens_details": { "reasoning_tokens": 64 }, "prompt_tokens_details": { "cached_tokens": 25 } } } ``` ## Best Practices To maximize the benefits of prompt caching: 1. **Reuse System Prompts**: Keep system messages consistent across requests 2. **Batch Similar Requests**: Send related requests within the 5-minute window 3. **Cache-Friendly Content**: Use stable, reusable prompt components 4. **Monitor Usage**: Track cached token metrics to optimize your integration ## Benefits * **Cost Savings**: Up to 70% reduction in prompt token costs for cached content * **Performance**: Faster response times for cached prompts * **Scalability**: Better API performance under high load * **Transparency**: Clear visibility into cached token usage via API responses # Rate Limits Source: https://docs.matterai.so/axon-ai/rate-limits Understand Axon AI's rate limiting policies and tier-based quotas Axon AI enforces rate limits to ensure fair usage and maintain system stability. These limits are applied per API key and are measured across a one-minute window. ## Rate Limit Tiers Different service tiers offer varying rate limits to accommodate different usage patterns and requirements. Below are the rate limits for each tier: | Tier | Requests per Minute | Input Tokens per Minute | Output Tokens per Minute | Credits to Purchase | Max Monthly Spend | | ------- | ------------------- | ----------------------- | ------------------------ | ------------------- | ----------------- | | Default | 20 | 30,000 | 8,000 | \$5 | \$100 | | Tier 2 | 200 | 300,000 | 80,000 | \$40 | \$500 | | Tier 3 | 400 | 600,000 | 160,000 | \$200 | \$1,000 | ### Default Tier The default tier provides basic rate limits suitable for most development and testing scenarios. This tier is unlocked with `$5` credits and has a maximum monthly spend of `$100`. ### Tier 2 Tier 2 offers 10x the default limits, ideal for production applications with moderate usage. This tier is unlocked with `$40` credits and has a maximum monthly spend of `$500`. ### Tier 3 Tier 3 provides the highest limits at 20x the default, designed for high-volume production workloads. This tier is unlocked with `$200` credits and has a maximum monthly spend of `$1,000`. ## Understanding Rate Limits * **Requests per Minute**: The maximum number of API calls allowed per minute * **Input Tokens per Minute**: The maximum number of tokens that can be sent as input per minute * **Output Tokens per Minute**: The maximum number of tokens that can be generated as output per minute **When a rate limit is exceeded, the API will return a 429 (Too Many Requests) status code.** Your application should implement appropriate retry logic with exponential backoff to handle rate limit errors gracefully. # Axon Eido 3 Flash Source: https://docs.matterai.so/axon-eido-3-flash Generate a new API key View model pricing Axon Eido 3 Flash is a fast, low-latency model suitable for low-complexity, day-to-day coding tasks and general use — with the same 1M-token context window and native multimodal understanding. ## Model Details | Property | Value | | -------------- | ------------------- | | Model ID | `axon-eido-3-flash` | | Context Window | 1,000,000 tokens | | Max Output | 128,000 tokens | | Input Price | \$0.30 / 1M tokens | | Output Price | \$0.90 / 1M tokens | | Cached Input | \$0.15 / 1M tokens | ## Capabilities * Tool Calling * Structured Outputs * Web Search * Text + Image Input * Text Output ## Quick Start ```bash cURL theme={null} curl --location 'https://api2.matterai.so/v1/web/chat/completions' \ --header 'Content-Type: application/json' \ --header 'Authorization: Bearer MATTERAI_API_KEY' \ --data '{ "model": "axon-eido-3-flash", "max_new_tokens": 2048, "temperature": 0.1, "messages": [ { "role": "user", "content": "Hi" } ], "stream": false }' ``` ```javascript JavaScript theme={null} import OpenAI from "openai"; const client = new OpenAI({ apiKey: "MATTERAI_API_KEY", baseURL: "https://api2.matterai.so/v1", }); const response = await client.chat.completions.create({ model: "axon-eido-3-flash", messages: [{ role: "user", content: "Hi" }], max_tokens: 2048, temperature: 0.1, }); console.log(response.choices[0].message.content); ``` ```python Python theme={null} from openai import OpenAI client = OpenAI( api_key="MATTERAI_API_KEY", base_url="https://api2.matterai.so/v1" ) response = client.chat.completions.create( model="axon-eido-3-flash", messages=[ {"role": "user", "content": "Hi"} ], max_tokens=2048, temperature=0.1 ) print(response.choices[0].message.content) ``` Compare all available models View full API documentation # Axon Eido 3 Mini Source: https://docs.matterai.so/axon-eido-3-mini Generate a new API key View model pricing Axon Eido 3 Mini is a versatile, balanced model built to carry the bulk of day-to-day workloads — chat, extraction, summarization, RAG and agents — with the same 1M-token context and native multimodal understanding, at a fraction of Pro pricing. ## Model Details | Property | Value | | -------------- | ------------------ | | Model ID | `axon-eido-3-mini` | | Context Window | 1,000,000 tokens | | Max Output | 128,000 tokens | | Input Price | \$1.50 / 1M tokens | | Output Price | \$4.50 / 1M tokens | | Cached Input | \$0.75 / 1M tokens | ## Capabilities * Tool Calling * Structured Outputs * Web Search * Text + Image Input * Text Output ## Quick Start ```bash cURL theme={null} curl --location 'https://api2.matterai.so/v1/web/chat/completions' \ --header 'Content-Type: application/json' \ --header 'Authorization: Bearer MATTERAI_API_KEY' \ --data '{ "model": "axon-eido-3-mini", "max_new_tokens": 2048, "temperature": 0.1, "messages": [ { "role": "user", "content": "Hi" } ], "stream": false }' ``` ```javascript JavaScript theme={null} import OpenAI from "openai"; const client = new OpenAI({ apiKey: "MATTERAI_API_KEY", baseURL: "https://api2.matterai.so/v1", }); const response = await client.chat.completions.create({ model: "axon-eido-3-mini", messages: [{ role: "user", content: "Hi" }], max_tokens: 2048, temperature: 0.1, }); console.log(response.choices[0].message.content); ``` ```python Python theme={null} from openai import OpenAI client = OpenAI( api_key="MATTERAI_API_KEY", base_url="https://api2.matterai.so/v1" ) response = client.chat.completions.create( model="axon-eido-3-mini", messages=[ {"role": "user", "content": "Hi"} ], max_tokens=2048, temperature=0.1 ) print(response.choices[0].message.content) ``` Compare all available models View full API documentation # Axon Eido 3 Pro Source: https://docs.matterai.so/axon-eido-3-pro Generate a new API key View model pricing Axon Eido 3 Pro is our flagship model. It matches frontier intelligence across coding, tool-use and agentic workflows — with a 1M-token context window and native multimodal understanding, at a fraction of frontier pricing. ## Model Details | Property | Value | | -------------- | ------------------ | | Model ID | `axon-eido-3-pro` | | Context Window | 1,000,000 tokens | | Max Output | 128,000 tokens | | Input Price | \$3.00 / 1M tokens | | Output Price | \$9.00 / 1M tokens | | Cached Input | \$1.50 / 1M tokens | ## Capabilities * Tool Calling * Structured Outputs * Web Search * Agentic Workflows * Text + Image Input * Text Output ## Benchmarks Axon Eido 3 Pro benchmarks vs Claude Opus 4.8, GPT-5.5, Gemini 3.1 Pro ## Quick Start ```bash cURL theme={null} curl --location 'https://api2.matterai.so/v1/web/chat/completions' \ --header 'Content-Type: application/json' \ --header 'Authorization: Bearer MATTERAI_API_KEY' \ --data '{ "model": "axon-eido-3-pro", "max_new_tokens": 2048, "temperature": 0.1, "messages": [ { "role": "user", "content": "Hi" } ], "stream": false }' ``` ```javascript JavaScript theme={null} import OpenAI from "openai"; const client = new OpenAI({ apiKey: "MATTERAI_API_KEY", baseURL: "https://api2.matterai.so/v1", }); const response = await client.chat.completions.create({ model: "axon-eido-3-pro", messages: [{ role: "user", content: "Hi" }], max_tokens: 2048, temperature: 0.1, }); console.log(response.choices[0].message.content); ``` ```python Python theme={null} from openai import OpenAI client = OpenAI( api_key="MATTERAI_API_KEY", base_url="https://api2.matterai.so/v1" ) response = client.chat.completions.create( model="axon-eido-3-pro", messages=[ {"role": "user", "content": "Hi"} ], max_tokens=2048, temperature=0.1 ) print(response.choices[0].message.content) ``` Compare all available models View full API documentation # Models Overview Source: https://docs.matterai.so/axon-overview Overview of Axon AI Models - secure, production-ready AI models for general-purpose tasks, coding and deep-research. **Axon offers secure, production-ready AI models for both code generation and general-purpose tasks, leveraging State-of-the-Art Deep Reasoning powered by Interleaved Reasoning [https://arxiv.org/abs/2505.19640](https://arxiv.org/abs/2505.19640) and State Machines** ## Benchmarks ## Architecture Axon Models are based on open source models from Kimi and Deepseek, fine tuned on our proprietary dataset and upgraded with deep reasoning and state machine capabilities. ### Mixture of Experts (MoE) architecture MoE is a technique that allows the model to dynamically select the expert model to use for a given input. This architecture enables engineering teams to build more scalable and efficient systems by routing tasks to specialized experts, reducing computational overhead while maintaining high performance across diverse workloads. ## What makes Axon different? * **Deep Reasoning**: Our SOTA Deep Reasoner generates a detailed reasoning process for your requests, detects what needs to be done and how to do it, ensuring all the context is considered and the best possible solution is provided. * **State Machines**: Our SOTA State Machine uses temporal memories to remember your continued flow of usage on what has accomplished and what needs to be completed next. ## Deep Reasoner Our State-of-the-Art Deep Reasoner generates a detailed reasoning process for your requests, detects what needs to be done and how to do it, ensuring all the context is considered and the best possible solution is provided. * **Multi-sources causal graph traversal** for inferencing across heterogeneous data sources, enabling root-cause analysis and counterfactual reasoning. * **Dynamic symbolic grounding** via contextual ontologies to map abstract concepts into actionable knowledge representations in real time. * **Probabilistic logic synthesis** with uncertainty quantification to evaluate solution optimality under incomplete or ambiguous input conditions. * **Hierarchical attention over structured memory** to maintain long-range dependencies during complex, multi-step problem decomposition. * **Meta-cognitive feedback loops** that refine internal heuristics based on outcome validation, improving future reasoning trajectories. * **Real-time web search integration** with federated query optimization across multiple search providers for comprehensive knowledge retrieval. * **Adaptive web content parsing** using semantic-aware scrapers that extract structured data from dynamic web sources while respecting rate limits and ToS. ## State Machine Our State-of-the-Art State Machine Engine uses temporal memories to remember your continued flow of usage on what has accomplished and what needs to be completed next. * **Hierarchical semi-Markov decision processes** (HSMDPs) for modeling variable-duration states and adaptive task sequencing. * **Distributed state persistence with vector-clock reconciliation** to ensure consistency across asynchronous, concurrent user sessions. * **Reinforcement learning-driven transition policies** that optimize long-term user goal completion over immediate action rewards. * **Temporal difference learning over latent state embeddings** to predict and pre-fetch likely next states for zero-latency transitions. * **Context-sensitive state compression** using learned subroutines to reduce combinatorial state explosion while preserving semantic fidelity. ## Model Family General Purpose Model for high-effort day to day tasks General Purpose Model for low-effort day to day tasks Code Generation Model for high-effort coding tasks ## Getting Started ### Get API Key Generate a new API key ### API & SDK Integration ```bash cURL theme={null} curl --request POST \ --url https://api.matterai.so/v1/chat/completions \ --header 'Content-Type: application/json' \ --header 'Authorization: Bearer MATTER_API_KEY' \ --data '{ "model": "{{MODEL_ID}}", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "What is Rust?" } ], "stream": false, "max_tokens": 1000, "reasoning": { "effort": "high", "summary": "none" }, "response_format": { "type": "text" }, "temperature": 0, "top_p": 1 }' ``` ```javascript OpenAI NodeJS SDK theme={null} import OpenAI from "openai"; const openai = new OpenAI({ apiKey: "MATTER_API_KEY", baseURL: "https://api.matterai.so/v1", }); async function main() { const response = await openai.chat.completions.create({ model: "{{MODEL_ID}}", messages: [ { role: "system", content: "You are a helpful assistant.", }, { role: "user", content: "What is Rust?", }, ], stream: false, max_tokens: 1000, reasoning: { effort: "high", summary: "none", }, response_format: { type: "text", }, temperature: 0, top_p: 1, }); console.log(response.choices[0].message.content); } main(); ``` ```python OpenAI Python SDK theme={null} from openai import OpenAI client = OpenAI( api_key='MATTER_API_KEY', base_url='https://api.matterai.so/v1' ) response = client.chat.completions.create( model='{{MODEL_ID}}', messages=[ { 'role': 'system', 'content': 'You are a helpful assistant.' }, { 'role': 'user', 'content': 'What is Rust?' } ], stream=False, max_tokens=1000, reasoning={ 'effort': 'high', 'summary': 'none' }, response_format={ 'type': 'text' }, temperature=0, top_p=1 ) print(response.choices[0].message.content) ``` # Data Privacy MatterAI never trains on your codebase, all data is temporary and deleted automatically. # AI Code Reviews Changelog Source: https://docs.matterai.so/changelog/ai-code-reviews-changelog Product updates and announcements for MatterAI Code Reviews ## v4.205.0 ### Added * **New AI Code Review Features:** * `/matter review-max` - Advanced AI Code Review with tool-use enabled analysis * `/matter fix` - Automated Code Fix Generation for review comments * Enhanced documentation for new AI-powered code review capabilities * Confidence scoring system for review comments * Automated fix PR generation with separate branch creation ### Changed * Updated command list documentation with detailed feature descriptions * Enhanced code review workflow with deeper analysis capabilities * Improved developer experience with automated fix application ### Benefits * **Time saved**: 15-30 minutes per PR on fix application * **Review depth**: 3x more comprehensive with tool-use * **Accuracy**: Confidence scoring reduces false positives * **Developer satisfaction**: Zero manual copy-pasting ## Formatting & Config * **Enhanced Readability**: Improved AI-generated PR review formatting for a better developer experience. * **Optimized Configuration**: Fine-tuned AI model settings specifically for code review workflows. ## Reliability & Reasoning * **Robust Fallbacks**: Intelligent model fallback mechanisms for uninterrupted reviews. * **"Thinking" Models**: Added support for reasoning tool calls before review generation. ## Fix in Cursor Button MatterAI review comments now have a `Fix in Cursor` button to open Cursor with a ready to use AI Prompt to fix the issue! Fix in Cursor Button ## Faster Review Cycles With priority setting, MatterAI now adds `Low Priority` comments in the PR review body with diff patch instead of review comments. Allowing you to merge PRs faster while still getting all the code reviews. Clear PR review body with diff patch ## Hallicination Evals MatterAI now detects and removes 99% of hallucinations in code reviews with new eval engine, powered by Axon Code model. ## Login via Microsoft You can now login via Microsoft to access your MatterAI account. ## Enable PR Review Skip Alerts Enable or Disable PR review skip alerts to reduce noise in your code reviews. ## PR Review Priority Settings Set the priority level for what PR review comments are added in your PRs/MRs. Options are: All, Medium and above, Only HIGH. ## Gitlab V16 Support MatterAI now supports Self-Hosted Gitlab V16 to along with v17 and v18. ## Major AI Code Review Engine Upgrade Smarter, faster and more accurate AI code reviews with our latest engine upgrade. ## Dependabot PR Reviews Skip Configurable to skip Dependabot PR reviews to reduce noise in your code reviews. ## Migration to Axon-Code Model All MatterAI Customers are now using the default `axon-code` model for all code reviews. ## Codebase Learnings File Patterns Update Minor updates to skip heavy and non-essential files from the learning map. ## Codebase Learnings in GA MatterAI Codebase learnings creates a learning map of all your code for maximum content in code reviews. Learn more: [Codebase Learnings](/features/codebase-learnings) ## Mermaid Diagram Bugfix Fixed an issue where mermaid diagrams were not rendering correctly in the AI code review summary. ## Auto-Resolve Conversations When MatterAI detects an existing review suggestion is resolved, it will automatically mark the conversation as resolved alon with a ✅ Resolved comment. Auto-Resolve Conversations ## MATTER.md Rules Now you can generate `MATTER.md` file in your repositories to define rules for AI code reviews. Get Started with MatterAI Rulesets ### Example ```markdown MATTER.md lines icon="markdown" theme={null} # CODE_REVIEW_RULES ## 1. Code Format & Style - Use camelCase for variables and functions - Use PascalCase for constructors and classes - Use const for immutable values, let for variables - Prefer arrow functions for callbacks and short functions ## 2. Modern JavaScript Patterns - Use destructuring for object/array extraction - Prefer template literals over string concatenation - Use optional chaining (?.) for safe property access - Use nullish coalescing (??) for default values ## 3. Async Operations - Prefer async/await over .then() chains - Always handle promise rejections with try/catch - Use Promise.all() for parallel async operations - Avoid mixing async/await with .then() ## 4. Error Handling - Validate function parameters at entry - Use meaningful error messages - Handle edge cases (null, undefined, empty arrays) - Log errors with sufficient context ## 5. Performance & Memory - Avoid creating functions inside render loops - Use array methods (map, filter, reduce) appropriately - Clean up event listeners and timers - Minimize DOM manipulation in loops ``` ## Low Verbosity Summary Verbosity in summaries are reduced to provide a more concise and focused summary. ## Bugfix - AI Summary generation Minor bugfix on AI Summary generation JSON formatting that in somecases caused undefined to show ## Review Engine v6 The latest version of our AI code review engine is now available, featuring: * **Improved Accuracy**: Enhanced understanding of code context and structure. * **Faster Processing**: Optimized algorithms for quicker review generation. * **Better Language Support**: Expanded support for more programming languages and frameworks. * **Enhanced Security Checks**: More robust detection of security vulnerabilities and code smells. ## Improvements * Stronger code reviews with our updated review engine * Updates to on-prem enterprise with better scaling and lite-reviews for cost savings * Bug fixes in summary generations and mermaid diagrams * Tanka poem in Summary: Similar to Haiku but expressing deeper emotions or nature themes. These upgrades mean faster, sharper, and more efficient reviews, try it out and let us know what you think! ### Slack Alerts 🔔 Get alerts on Slack channel when any PR is merged below your configured Code Quality threshold or with an unresolved security vulnerability! Configure Slack Alerts ### Auto Resolve Comments ✅ MatterAI automatically marks resolved issues in new commits with a ✅ Resolved comment, including a resolution reason. Benefits: Saves engineers time by eliminating manual comments and issue resolution. Validates fixes in new commits, exclusive to MatterAI-generated AI code reviews. Learn More about Auto Resolve Comments ### Similarity Search 🔍 Similarity Search automatically reviews new or modified function calls in pull requests, comparing them to existing repository definitions. It verifies alignment with the function's contract (parameters, return value, side-effects). If a mismatch is found, it provides an inline suggestion detailing: The issue: What's misused and why. The fix: Minimal code changes needed. The impact: Risks if the mismatch is deployed. Learn More about Similarity Search ## Integrations ### Gitlab Setup is GA MatterAI seamlessly integrates with GitLab to enhance your development workflow! Setting up MatterAI service account Get Started with Gitlab AI Code Reviews ### Atlassian Bitbucket Setup is GA MatterAI seamlessly integrates with Atlassian Bitbucket to enhance your development workflow! Setting up MatterAI service account Get Started with Bitbucket AI Code Reviews ### Developer Workflow Integration * **Keyboard Shortcut Customization**: Personalize your MatterAI interaction experience. ### Code Analysis Upgrades * **Cross-Repository Analysis**: Identify patterns and issues across multiple repositories. * **Legacy Code Support**: Improved analysis for older codebases and deprecated languages. ### Review Engine Improvements * **End Line Character Fixes**: Intelligent handling of line ending inconsistencies in GitHub. * **Committable Code Generation**: Enhanced code suggestions that are ready to commit without manual fixes. * **Reduced Verbosity**: More concise code and text outputs without sacrificing clarity. ### LLM Cost Metrics * **Token Usage**: Tools to monitor and optimize token consumption. * **Model Performance**: Side-by-side comparison of different LLM providers. ### Enterprise AI Integration * **Vertex AI Support**: Native integration with Google's Vertex AI platform for enhanced model capabilities. * **Multi-Model Orchestration**: Intelligent routing between different AI providers based on task requirements. ### Enterprise Security Enhancements * **Role-Based Access Control**: Granular permission management for teams of all sizes. ### New Features & Improvements * ✅ **Similarity Search (GA)**: Now generally available with AST grep functionality (no AI required). * 🔍 **Enhanced Code Reviews**: Deeper analysis, better accuracy, and improved string literals handling. * 📝 **Code Suggestion Formatting**: Fixed edge-case issues. * 🤖 **Vertex API Support**: Added Service Account authentication. * ✨ **Gemini Models**: Updated to latest stable versions. ### Fixes & Tweaks * 🔧 **GitHub New-Line Character Parsing**: Fixed edge cases. * 📊 **Updated PR Review & Summary Formats**: Improved presentation and clarity. * 📈 **Added LLM Cost/Usage Tracking Metrics**: Better visibility into AI consumption. * 🐛 **Command Pattern Recognition**: Bug fixes for improved command detection. ### AI Summaries v2 * **Advanced Detail Summary**: Comprehensive PR summaries with deeper context understanding * **Structured Information**: Automatically splits summaries into logical sections for better readability * **Security Visualization**: Tables for package vulnerabilities, CVEs, and detailed explanations ### AI Code Review Engine V2 * **Precise Code Suggestions**: Deterministic commit positioning ensures feedback exactly where you need it * **Duplicate Prevention**: Smarter review intelligence avoids re-analyzing unchanged code sections * **Cost Optimization**: Auto-model swapping selects the best AI for each use-case, balancing performance and efficiency ### Command Improvements * **/matter review**: Reviews only new commits for faster and more efficient reviews * **/matter review-full**: Triggers a comprehensive re-review of the entire codebase when needed ### Agentic Chat * **Code-Aware Conversations**: Parses PR diffs, file context, and repo structure for relevant answers * **Technical Explanations**: Explains implementation decisions with precise technical details * **Impact Analysis**: Identifies potential side effects and integration points of code changes *** For detailed release notes and API changes, please refer to our [GitHub repository](https://github.com/MatterAIOrg/matter-ai). ### Knowledge Retention * **Codebase Familiarity**: Develops understanding of project-specific patterns and conventions *** ### Continuous Improvement * **Adaptive Reviews**: Refines review approach based on team interactions and preferences * **Team Alignment**: Becomes increasingly aligned with team-specific needs and standards ### AI Memories * **Learning & Adaptation**: Observes developer interactions to capture team preferences and coding styles * **Pattern Recognition**: Identifies recurring patterns in code reviews and discussions for better context * **Contextual Understanding**: Builds deeper understanding of your codebase and project architecture # Axon Changelog Source: https://docs.matterai.so/changelog/axon-models-changelog Product updates and announcements for MatterAI Axon LLM ## Axon Eido 3 model family Three new flagship models, all with a 1M-token context window, native multimodal input, and tool calling / structured outputs / web search. ### Axon Eido 3 Pro — `axon-eido-3-pro` The most intelligent model in the family. Built for long-running agents, tool calling, coding, and deep research. Frontier reasoning with native multimodal understanding. * Context: 1M tokens * Max output: 128k tokens * Input: \$3.00 / 1M tokens * Output: \$9.00 / 1M tokens * Cached input: \$1.50 / 1M tokens ### Axon Eido 3 Mini — `axon-eido-3-mini` The everyday general-purpose workhorse for day-to-day tasks — chat, extraction, RAG, and agents — with the same 1M context, fast and cost-efficient. * Context: 1M tokens * Max output: 128k tokens * Input: \$1.50 / 1M tokens * Output: \$4.50 / 1M tokens * Cached input: \$0.75 / 1M tokens ### Axon Eido 3 Flash — `axon-eido-3-flash` Fast, low-latency model suitable for low-complexity, day-to-day coding tasks and general use — with a 1M-token context and native multimodal input. * Context: 1M tokens * Max output: 128k tokens * Input: \$0.30 / 1M tokens * Output: \$0.90 / 1M tokens * Cached input: \$0.15 / 1M tokens ### Caching Cached input tokens are automatically discounted 50% off the standard input price. Caching kicks in automatically for repeated prompt prefixes across all five Axon models. ## Axon Model 2.0 Major performance upgrade with reduced pricing and state-of-the-art benchmarks. * **2x Faster Speed**: Optimized inference latency for rapid responses. * **89% LiveCodeBench**: Achieving top-tier accuracy in code generation benchmarks. * **New Pricing**: Reduced to $1/1M input (was $2) and $4/1M output (was $6). ## Core Improvements * **Parallel Processing**: Enabled parallel client functionality for improved concurrent request handling. * **Rate Limiting**: Refined strategies and fixed context propagation in API responses. ## Cortex Intelligence Upgrade Updated Cortex modules with advanced planning capabilities. * **Enhanced Reasoning**: New planning prompts and task memory helper. * **Better Execution**: Significantly improved agent reliability in complex tasks. ## Axon Playground Axon Playground is now available to test and explore the capabilities of Axon models in the dashboard. ## New Axon-Mini model released Axon Mini 1 is now available for everyday low-effort tasks with deep reasoning. Read more [here](/axon-mini). ## Deep Reasoner v2.0 Deep Reasoner v2.0 is now available with massive boost in speed, reasoning and accuracy. ## Global Rate Limits Global API Rate Limits are not applicable based on your Tier. More details [here](/axon-ai/rate-limits). ## Tool call usage response format fixed Tool call usage response format is now fixed for AI editors and tools to consume. ## Fix reasoning summary in response Breaking reasoning summary in response format is not fixed. ## Support for `v1/messages` API for Claude Code Deep Reasoner now outputs less tokens to reduce the cost of the model. ## Reduced tokens for Deep Reasoner Deep Reasoner now outputs less tokens to reduce the cost of the model. ## Improved Response Speed MatterAI Axon now supports improved response speed to provide a high level overview of the reasoning process. This allows you to balance between accuracy and performance. ## Web Search & Web Fetch Tool Calling MatterAI Axon now supports web search and web fetch tool calling to provide a high level overview of the reasoning process. This allows you to balance between accuracy and performance. * `web_search` - LLM will use web search to find relevant information * `web_fetch` - LLM will scrape the provided URL to find relevant information Tool Calling is always available and enabled by default. ## Reasoning Summary in Response MatterAI Axon now supports reasoning summary in response to provide a high level overview of the reasoning process. This allows you to balance between accuracy and performance. * `auto` - AI will decide whether to provide a summary or not * `none` - AI will never provide a summary ```json theme={null} { "reasoning": { "summary": "auto" } } ``` Reasoning summary is always enclosed in a `` REASONING `` tag. ## Reasoning Effort Options MatterAI Axon now supports reasoning effort options to control the level of reasoning and analysis performed by the AI model. This allows you to balance between accuracy and performance. * `None` - No reasoning, fastest * `Low` - Fastest, least accurate * `Medium` - Good balance * `High` - Most accurate, slowest ```json theme={null} { "reasoning": { "effort": "low | medium | high | none" } } ``` ## Private Beta Release MatterAI Axon private beta release # v1.3.0 Source: https://docs.matterai.so/changelog/matterai-enterprise-changelog Product updates and announcements for MatterAI Enterprise Self-Hosted **Release Date:** January 7, 2026 Transform your development workflow with Matter AI's new asynchronous review engine designed for IDE integration. **Benefits:** * Instant feedback submission without waiting for analysis * IDE stays responsive during complex code reviews * Perfect for large diffs and complex codebases * Built-in retry logic and error handling **Release Date:** December 23, 2024 Introducing a revolutionary upgrade to the code review experience. Matter AI can now actively explore your codebase during reviews using intelligent tool calling. **What's New:** * **Repository Cloning & Exploration**: The AI now clones your repository and generates a directory listing to understand the full codebase structure * **`read_file` Tool**: AI can read files with line ranges to understand context, verify imports, and trace function calls * **`grep_search` Tool**: AI can search for patterns across the codebase using grep, enabling comprehensive code analysis * **Multi-Provider Support**: Advanced reviews work with both **Google Gemini** and **Anthropic Claude** providers * **Configurable Iterations**: Control how many tool calls the AI can make during analysis **Benefits:** * Deeper, more contextual code reviews * AI understands how changes affect other parts of your codebase * Catches cross-file issues and dependency problems Advanced AI Code Review Example **Configuration:** Enable advanced reviews per repository via the admin dashboard under "Advanced Reviews" settings. **Release Date:** December 23, 2024 Automatically generate code fixes from review comments with a single command. **Usage:** ```bash theme={null} /matter fix ``` **How It Works:** 1. Collects all unresolved review comments on the PR 2. Reads the current file contents for affected files 3. Uses AI to generate targeted code fixes 4. Creates a new PR with the fix branch automatically **Supported Models:** * Claude Sonnet 4.5 (preferred) * Gemini 3 Pro Preview * Axon Code (MatterAI) Automated Code Fix Example Automated Code Fix Example **Release Date:** December 23, 2024 Leverage stored repository knowledge for context-aware code reviews. **Features:** * **File-Based Memory Matching**: Memories are matched based on files changed in the PR * **Usage Tracking**: Automatically tracks which memories are used and how often * **PR Summary Display**: Shows which repository memories influenced the review * **Prompt Injection**: Relevant memories are injected into AI prompts for contextual reviews **Memory Analytics:** * `usage_count`: Track how often each memory is used * `last_used_at`: See when memories were last referenced **PR Summary Example:** ```markdown theme={null} ### 📚 Repository Memories Used **1. Authentication Guidelines** - *Tags:* security, auth - *Files:* src/auth/*.ts - *Content:* Always validate JWT tokens... ``` Repository Memory Integration **Release Date:** December 23, 2024 The LLM Logs dashboard now includes comprehensive filtering capabilities for better observability: * **Repository Filter** — Filter logs by specific repositories or by MCP Server requests * **Provider Filter** — Filter by LLM provider (OpenAI, Anthropic, Google/Vertex, MatterAI) * **Model Filter** — Filter logs by specific AI models * **Prompt ID Filter** — Filter by prompt identifiers for tracing specific request types All filters work together to provide granular insights into your LLM usage patterns and costs. LLM Logs Enhanced Filtering LLM Logs Enhanced Filtering **Release Date:** December 23, 2024 LLM log entries now display recognizable provider logos for: * OpenAI * Anthropic * Google / Vertex AI * MatterAI This visual enhancement makes it easier to quickly identify which providers are being used across your organization. **Release Date:** December 23, 2024 Enterprise administrators can now configure which repositories receive advanced AI code reviews: * New **Advanced Reviews** settings section in the dashboard * Multi-repository selection via autocomplete * Save/update controls with loading states * Fetches and displays currently enabled advanced review repositories Advanced Reviews Configuration **Release Date:** December 23, 2024 New analytics metrics to quantify the value of AI-assisted code reviews: * Track estimated developer time saved through automated reviews * Integration with dashboard overview metrics * Helps justify ROI for enterprise AI tooling investments **Release Date:** December 23, 2024 Complete redesign of the enterprise signup page: * **Product Showcase Cards** — Visual cards highlighting key products: * AI Code Reviews * Axon Code AI Agent * Model Inference Gateway * **New Hero Background** — Modern, premium visual design * **Version Display** — Application version now shown during signup * **Enhanced Footer** — Updated branding and product information **Release Date:** December 23, 2024 * Updated base color palette for improved contrast and readability * Refined card component styling * Enhanced input and select component borders * Improved sidenav visual hierarchy * Consistent gradient border styling across components * Updated dashboard navbar styling **Release Date:** December 23, 2024 * Updated statistics cards with new iconography * Improved date range filter presentation * Enhanced PR data visualization * Cleaner table layouts across all views **Release Date:** December 23, 2024 The LLM Metrics tab now supports filtered views: * Cost by Provider chart respects all active filters * Requests by Model chart respects all active filters * Daily Cost Trend reflects filtered data * Daily Request Volume reflects filtered data **Release Date:** December 23, 2024 * Updated to **Gemini 3 Pro Preview** for advanced reviews * Updated to **Gemini 3 Flash Preview** for standard reviews * Improved model routing based on task complexity **Release Date:** December 23, 2024 * Fixed accurate token counting for all providers * Improved token storage for billing and analytics * Corrected input/output token separation **Release Date:** December 23, 2024 * Updated multiple core dependencies for security and performance * React ecosystem packages updated to latest stable versions * Added `@google/genai` - Google Generative AI SDK for advanced Gemini integration * Build configuration optimizations **Release Date:** December 23, 2024 * Optimized LLM logs data filtering with memoized computations * Improved chart rendering performance in metrics views * Reduced re-renders in settings panels **Release Date:** December 23, 2024 * Fixed filter state persistence across tab navigation * Resolved chart rendering issues with empty data sets * Fixed select component dropdown arrow alignment * Corrected toast notification z-index layering * Standardized check run names to "Matter AI Code Review" * Removed duplicate check runs issue **Release Date:** December 23, 2024 ### Feature Flags No new feature flags introduced in this release. ### Audit Logging LLM logs now capture `prompt_id` for enhanced audit trail capabilities. ### Multi-tenant Considerations Advanced review configurations are scoped to organization level. ### Environment Variables No new environment variables required for this release. **Release Date:** December 23, 2024 This release includes routine dependency updates that address known vulnerabilities in third-party packages. SOC 2 Type II compliance maintained. *For questions or support, contact your Matter AI account representative.* # OrbCode CLI Changelog Source: https://docs.matterai.so/changelog/orbcode-cli-changelog Latest updates and improvements for the OrbCode CLI ## v0.5.5 ### Added * **`/create-skill` slash command.** Describe a repository-specific workflow in plain language and OrbCode creates or updates a reusable skill under `.orb/skills//`, keeping any supporting scripts, references, and assets inside the same skill directory. * **Figma design context (`figma_fetch` tool).** A new native tool fetches the full node tree, components, styles, and rendered image URLs for any `figma.com/design/`, `/file/`, or `/proto/` URL. Requests are authenticated against the MatterAI backend (`/axoncode/figma`), which uses the org's configured Figma access token so callers don't need to handle credentials. * **Auto-fetch Figma URLs from user messages.** When a user pastes a Figma link into a prompt, the agent scans the message for Figma URLs and pre-fetches each one before the first model completion. The design data is injected as a tool result, so the model has visual + structural context from step 0 without having to discover and call `figma_fetch` itself. URLs are deduped and trailing punctuation is stripped. * **Load skills from explicit paths.** `use_skill` now accepts an absolute, workspace-relative, or `~/` path to a skill directory or `SKILL.md` file in addition to discovered names. Prompts and `AGENTS.md` files can reference shared skills stored outside the user/project/plugin catalogs. * **`/weekly-reset` slash command.** Eligible paid users (Pro and above) can reset their weekly usage tier once per monthly billing cycle. Calls `/axoncode/weekly-reset`, refreshes tiered usage, and reports the next available time. ### Changed * **External Figma MCP servers are blocked.** The native `figma_fetch` tool authenticates against the Figma API and returns the full node tree, components, styles, and image URLs. External Figma MCP servers (the desktop dev server, the hosted `mcp.figma.com` endpoint, and `figma-developer-mcp` via npx) bypass that integration and can conflict with it, so they are now rejected on connect, config load, config addition, and migration (both live and dry-run). Figma-specific tools are also filtered out of general servers' tool listings. ### Fixed * **Streaming responses stay visible above the input box.** The transcript's live edge is now anchored using its rendered layout instead of relying on approximate text-height calculations, so word-wrapped final lines are not clipped behind the composer. ## v0.5.4 ### Added * **Helpful slash-command tips under the spinner.** After an operation has been running for 2 seconds, a random tip (e.g. "Use `/help` to see every available slash command.") appears beneath the loading spinner. Shown for the `Thinking` and `Working` states. ## v0.5.3 ### Added * **Auto-fetch Figma URLs from user messages.** When a user pastes a Figma link into a prompt, the agent scans the message for Figma URLs and pre-fetches each one before the first model completion, injecting the design data as a tool result so the model has visual + structural context from step 0. * **`figma_fetch` native tool.** Fetches the full node tree, components, styles, and rendered image URLs for any Figma design URL, authenticated through the MatterAI backend. ### Fixed * **Transcript live edge anchoring.** Anchor streaming transcript to the bottom so the live edge stays visible above the input box. ## v0.5.1 ### Fixed * **Allow empty MCP settings objects in project config.** Project `.mcp.json` files with an empty or missing `mcpServers` object no longer error. ## v0.5.0 ### Added * **Accepted code metrics reporting.** Successful `file_edit`, `file_write`, and `multi_file_edit` calls now POST line counters (added/deleted, language) to `/axoncode/meta//lines`, matching the extension's behavior. Works for both user-approved and auto-approved edits; reporting is best-effort and never blocks the session. ## v0.4.8 ### Added * **Plugin marketplace browser (`/plugins`).** A new tabbed UI lists installed plugins and browses the complete Anthropic `claude-plugins-official` marketplace. Installation downloads the pinned plugin source as one bundle into `.orb/plugins//`, preserving skills, commands, agents, MCP config, hooks, scripts, rules, and other supporting files. Search filters by name, description, or author. `/plugin` and the former `/skills` command remain as aliases. * **Non-interactive plugin management.** `orbcode plugin install clickhouse@claude-plugins-official`, `orbcode plugin list`, and `orbcode plugin uninstall ` provide the same install flow outside the TUI. ### Fixed * **Marketplace installation no longer depends on GitHub tree scans.** The previous per-repository `SKILL.md` scan quickly exhausted GitHub's anonymous API limit and incorrectly reported that plugins such as ClickHouse had no skills. OrbCode now installs `git-subdir`, `url`, `github`, and official relative-path sources through git at the marketplace-pinned revision. * **Plugin components are loaded from the installed bundle.** Skills and legacy commands are exposed as `:`, bundled reference files remain available, and `.mcp.json`/manifest MCP servers are namespaced and discovered as project-scoped servers with `${CLAUDE_PLUGIN_ROOT}` substitution. * **Standalone project skills use `.orb/skills/`.** The skill loader discovers that directory alongside the legacy `.orbcode/skills/` location. ### Changed * **All popovers are now centered on screen.** Pickers, prompts, and managers (model picker, session picker, link manager, plugin manager, MCP picker, approval prompts, follow-up prompts, hook trust, MCP approval, migration picker) render as an absolutely-positioned overlay centered vertically and horizontally instead of flowing inline with the transcript. * **The TUI now preserves the terminal's configured colors.** Startup and cleanup no longer emit OSC 10/11 or OSC 110/111 sequences that override or reset the terminal's default foreground and background colors. * **The built-in UI palette follows both light and dark terminal themes.** Neutral text inherits the terminal foreground, semantic accents use the terminal's named ANSI palette, and prompts and popups no longer apply hardcoded background colors. Diff rows retain their original 5% alpha-blended backgrounds, with OrbCode green (`#3FA266`) and red (`#E34671`) used consistently across themes. * **The OrbCode company logo is isolated from terminal theming.** Its outer cyan (`#06E1E7`), inner cyan (`#8BF4F7`), and white core (`#ffffff`) remain identical in every terminal theme. ## v0.4.3 ### Changed * **Diff background colors are now 50% transparent.** Added/removed line backgrounds in the diff view are alpha-blended against the terminal background (`#1a1a1a`), producing a muted green (`#2C5E40`) and muted red (`#7E3045`) that are less visually aggressive while preserving the red/green semantic. * **Viewport layout adapts to the real input box height.** The input box now reports its rendered height to the parent viewport (including multiline prompt wrapping, slash-command popups, and file-completion popups), so the bottom controls stack never overlaps the live response. The previous hardcoded 4-row estimation caused overflow on multiline input or when autocomplete was open. * **Streaming output is truncated to the viewport tail.** Instead of accumulating the entire streaming response in the live area (which pushed the input box off-screen in long generations), only lines fitting the available height are rendered. The complete response is committed to the transcript on completion. * **Diff line-height estimates in the virtualized transcript now account for the number/type gutter.** The diff gutter occupies \~8 columns, so each content line wraps at a narrower width. Hunk headers are structural and skipped. This prevents underestimation that could overflow the viewport. * **Task list height measurement uses proper word-wrap calculations.** Task items are now wrapped at the available terminal width instead of counting raw lines, so long task descriptions don't push controls off-screen. * **Reasoning row height estimates account for the gutter.** Collapsed reasoning rows are measured at `width - 2` to match the actual render, preventing underestimation that clips the fold header. * **Result preview and completion text use correct line widths.** Both are now wrapped at `width - 2` and `width - 4` respectively, matching the indentation they render at. ### Fixed * **Resumed sessions now restore reasoning and tool history.** New sessions persist the exact visible transcript, including thinking durations, tool summaries, result previews, errors, and diffs. Older sessions reconstruct tool calls, results, and edit fragments from their stored model messages when possible. * **`@`-file autocomplete now shows files created during the session.** The file list was computed once on mount; changed to re-scan the workspace each time the `@` popup opens. ## v0.4.2 ### Changed * **Default theme now uses a neutral base with consistent semantic accents.** The explicit palette uses `#ffffff` primary, `#d0d0d0` accent, `#a8a8a8` thinking, `#7a7a7a` dim, and `#1a1a1a` background, while errors and removals use `#E34671`, successes and additions use `#3FA266`, and approval warnings use `#E2CE76`. All `` usages were replaced with explicit `color={COLORS.dim}`. Code blocks follow `COLORS.accent`, and edit-tool diff backgrounds now use the shared error/success colors. * **TUI now runs in the alternate screen buffer as an independent surface.** On startup the app enters the alternate screen and sets the terminal's default foreground/background via OSC 10/11 to the greyscale palette. The terminal scrollback is no longer affected; on exit the original screen and colors are restored. * **All content has horizontal margin.** The root container carries `marginX={2}` so the header, conversation rows, pickers, prompts, input box, and status bar all sit inset from the terminal edge consistently. * **Input box is now a fully-bordered rounded box with quieter chrome.** It draws all four sides with `borderStyle="round"`, using a 50%-brightness border so the input remains visually anchored without dominating the chat. * **The initial OrbCode header is top-anchored with a stable margin.** The borderless intro uses a half-size cyan/white ASCII interpretation of the `orbital.svg` brand mark alongside a metadata column, followed by a compact two-column command grid and shortcut row. * **User messages render as full-width borderless highlighted blocks.** A subtle neutral background spans the transcript width with two-cell horizontal and one-row vertical padding. * **Command and file-completion popups use padded rounded surfaces.** Slash commands and `@file` results now share a solid neutral background with same-color half-cell corner caps and padded content, without a contrasting border. * **Approval modes have semantic highlighting again.** Ask mode is white, edit approval is yellow, and auto-approval is green. ### Fixed * **Diff add/remove lines now render with explicit foreground color.** Previously the text was invisible on some terminals against dark green/red backgrounds. * **Spinner no longer causes the input box to flicker.** The viewport budget now correctly accounts for the spinner's full height. * **Fullscreen TUI no longer leaves duplicate frames in the scrollback.** Full frames use retained, cursor-addressed row replacements inside a synchronized terminal update. Neither linefeeds nor per-frame display clears are emitted. * **Scrolling is smoother and fixed controls no longer flicker.** Wheel input is coalesced, completed transcript rows are memoized, and the terminal adapter keeps a retained row cache. * **The thinking animation remains visible during reasoning streams.** The spinner stays mounted above the live preview until reasoning completes. * **Ctrl+D now requires confirmation.** The first press shows a three-second warning; only a second press exits. * **Live AI output is no longer clipped in resumed or narrow-terminal chats.** Transcript virtualization accounts for every committed row's margins and borders. * **Diff background colors are now 50% transparent.** Added/removed line backgrounds in the diff view are alpha-blended against the terminal background (`#1a1a1a`), producing a muted green (`#2C5E40`) and muted red (`#7E3045`). * **Resumed sessions now restore reasoning and tool history.** New sessions persist the exact visible transcript, including thinking durations, tool summaries, result previews, errors, and diffs. Older sessions reconstruct tool calls, results, and edit fragments from stored model messages when possible. * **@-file autocomplete now shows files created during the session.** The file list was computed once on mount; changed to re-scan the workspace each time the `@` popup opens. ## v0.4.1 ### Fixed * **Input box now stays pinned to the bottom of the terminal.** Removed `` which permanently wrote rows to stdout and caused the whole view to scroll up when a long response completed. Rows are now rendered in the dynamic region with viewport-capped visibility. ## v0.4.0 ### Added * **`/task` slash command to reference a previous task.** Opens a session picker over previous sessions in the same directory. On selection, the prior conversation is wrapped in `` tags and the model is prompted to summarize it as reference. * **`axon-eido-3-flash`** replaces `axon-code-2-5-mini` as the free model in the built-in registry. Offers 200K context, fast responses, and zero cost. ### Changed * **System prompt rewritten for speed and editing discipline.** The `always gather exhaustive context` guidance is replaced with a `gather enough context, then act` principle. New editing discipline block instructs the model to copy `old_string` verbatim and never guess at corrected values. ### Fixed * **Transient model stream failures are now automatically retried.** Connection drops before the first chunk are retried up to 3 times with exponential backoff capped at 8s. * **Reasoning phase timing now reflects only the thinking time.** Reasoning is modeled as open/close segments, matching the on-screen `Thinking` block behavior. * **Mid-stream retry allowed when partial output can be rolled back.** The agent can re-issue the request if it can cleanly undo the partial output. ## v0.3.3 ### Added * **Linked repositories (`/link`).** A new `/link` slash command opens an interactive manager where you point this repo at other repos on your machine (enter a folder path — absolute, `~/path`, or relative to the project). Links are persisted per-project in `.orb/links.json` and injected into the agent's environment details — including each linked repo's `AGENTS.md`, pulled in ahead of time — so a change here is checked for impact on, or propagated to, the linked repos. `.orb/links.json` is shared with the Orbital IDE extension (links written there are honored here, and vice versa), and a linked repo's `AGENTS.md` is read from `.orb/`, `.orbital/`, or `.orbcode/`. ### Changed * **`/init` now writes to `.orb/AGENTS.md` and targets cold-start.** The generated `AGENTS.md` is written to the repo-level `.orb/` directory and now captures project structure, architecture, business-logic mapping, and code patterns/conventions — the context an agent needs to start coding without re-exploring. The cap is now \~150 lines (up from \~60) so it can cover all four sections without being truncated. * **Repo-level agent data lives in `.orb/`.** The folder OrbCode creates in a project for `AGENTS.md` (and now `links.json`) is `.orb/` — a single, tool-neutral name shared by the IDE and the CLI. Machine settings are unchanged (`~/.orbcode` and `/.orbcode/settings.json` stay put); the legacy `.orbcode/AGENTS.md` location is still read for backward compatibility. ## v0.3.2 ### Fixed * **MatterAI inference routed to the wrong backend.** `AxonClient` was building the OpenAI `baseURL` by running `API_GATEWAY_PATH` (`https://api2.matterai.so/v1/web/`) through `getUrlFromToken`, which rehosts *any* `api.matterai.so` target onto the control-plane host resolved from the JWT — so every inference request silently hit `https://api.matterai.so/v1/web/` instead of the gateway at `https://api2.matterai.so/v1/web/`. The gateway URL is now used directly, with the per-model `baseUrl` override still winning for local dev. Profile, task title, balance, and web search/fetch still go through the rehost helper (they intentionally target the control plane). ## v0.3.1 ### Changed * **Release workflow run title now shows the version.** The `Release` GitHub Actions workflow gained a `run-name` (e.g. `Release v0.3.1`) so manual dispatches and tag pushes are easier to tell apart in the Actions list. No functional change to the publish flow. ## v0.3.0 ### Added * **MCP server migration from Claude Code / Claude Desktop.** A new `orbcode mcp migrate` subcommand (with `--all` and `--dry-run` flags) and a `/migrate` slash command in the TUI scan well-known paths for MCP server configs and copy them into `~/.orbcode/settings.json` (user scope). Sources detected: * `~/.claude/settings.json` (Claude Code, user scope) * `~/.claude.json` → root `mcpServers` (Claude Code, user scope — the most common location, written by `claude mcp add -s user …`) * `~/.claude.json` → `projects..mcpServers` (Claude Code, this project) * `claude_desktop_config.json` (Claude Desktop — platform-specific path) When the same server name appears in both layers of `~/.claude.json`, the root entry is shown and the project-layer duplicate is hidden (Claude's own per-project override precedence). The TUI shows a combined checklist across all sources; the CLI prints a preview by default and only writes with `--all`. Servers whose name already exists in the destination are silently skipped and counted in the summary. Codex support (TOML) is intentionally deferred. * **Delete action in the `/mcp` picker.** The interactive server manager gained a "Delete" action (last in the list, red) that permanently removes a server from its config file (whichever scope it lives in) and shows a y/n confirmation before doing it. Unlike Disable, Delete is irreversible — the config entry is gone, and you'll need to re-add the server with `orbcode mcp add` to get it back. * **Styled OAuth callback page.** The local browser page that receives the OAuth redirect now matches the matterai.so look (dark `#0d1117` background, centered card, green/red circular icon, brand footer) for success, error, and not-found paths, replacing the previous plain `

` strings. ## v0.2.4 ### Added * **`-s` / `--system-prompt` flag to override the default system prompt.** Lets power users and CI pipelines inject a custom system prompt for a single run without editing their `settings.json`. The flag accepts a string, so quoted inline prompts and heredoc-style values both work. ## v0.2.3 ### Changed * **`/cost` renamed to `/usage`.** The slash command and any references in the UI now use the clearer "usage" label, matching the API field returned by the backend. `/cost` is no longer recognized. * **`orbcode mcp add --` separator.** The `--` flag is now consumed as the separator between the server name and the stdio command, matching Claude Code's `claude mcp add -- `. Fixes cases where a stdio server's own flags (e.g. `-y`) were mistaken for OrbCode flags. * **Local `AGENTS.md` ignored.** A repo-local `AGENTS.md` placed in the current working directory is no longer auto-loaded; only `~/.orbcode/AGENTS.md` (user) and the parent-directory walk (project) are read, matching the published skill/AGENTS.md docs. ## v0.2.0 ### Added * **MCP server support.** Connect external tools via the Model Context Protocol in three scopes (user, project `.mcp.json`, local). The interactive `/mcp` manager and `orbcode mcp add/remove/list` subcommand handle stdio, http, and sse transports. Project-scope servers require a one-time approval; OAuth 2.0 flows (RFC 9728 / 8414 / PKCE / dynamic client registration) are handled automatically, with tokens persisted under `~/.orbcode/mcp-auth/`. * **Skills system.** Reusable instruction sets the model loads on demand. Drop a `SKILL.md` under `~/.orbcode/skills/` (user) or `.orbcode/skills/` (project); the catalog is injected into the system prompt and `use_skill` loads the full body when a task matches a skill's `when_to_use` condition. * **AGENTS.md memory.** Project memory is discovered by walking the cwd and parent directories for `AGENTS.md` and `.orbcode/AGENTS.md` (closer-to-cwd wins), with `AGENTS.local.md` layered on top for private per-machine notes. User-level `~/.orbcode/AGENTS.md` is always loaded. `@include` directives let a memory file pull in shared snippets. * **Clipboard utility.** A single `clipboard` helper consolidates platform-specific copy logic so the rest of the codebase stays portable. ## v0.1.14 ### Changed * **Env var rename: `ORBCODE_*` → `MATTERAI_*`.** All `ORBCODE_*` environment variables have been renamed to `MATTERAI_*` (e.g. `ORBCODE_CONFIG_DIR` → `MATTERAI_CONFIG_DIR`). Old names are no longer recognized. This unifies the CLI's environment surface with the rest of the MatterAI tooling. ## v0.1.8 ### Fixed * **Status bar layout cleanups.** Fixed overlapping labels and inconsistent padding in the TUI status bar. ## v0.1.5 ### Added * **Initial public release.** OrbCode CLI ships with interactive TUI, headless `-p` mode, `--yolo` auto-approval, `--resume` for saved sessions, browser device-flow login, `~/.orbcode` config, and Axon model selection. # Orbital IDE Changelog Source: https://docs.matterai.so/changelog/orbital-changelog Latest updates and improvements for Orbital IDE ## v6.4.7 ### Added * **Shared `.orb/` convention with OrbCode CLI.** `AGENTS.md` is now loaded from the repo-level `.orb/` directory (alongside the project root and legacy `.orbital/`), so the Orbital IDE extension and the OrbCode CLI read the same project memory. Machine and settings locations are unchanged. * **Linked-repositories service.** A new `src/services/links` module is the single source of truth for the linked-repo feature, sharing `.orb/links.json` and the produced environment context between the IDE extension and the CLI. * **`/link` built-in command.** A new slash command for managing Linked Repositories: reads the current list, drives add/remove via `ask_followup_question`, and writes `.orb/links.json` with verbatim user input. Resolution and validation of the folder path happens at read time so links written by either tool stay portable. ### Changed * **`/init` reworked for cold-start.** Replaces the verbose `.roo/rules-*` per-mode layout with a single, concise `.orb/AGENTS.md` (project structure, architecture, business-logic mapping, code patterns/conventions) capped at \~150 lines so it stays cheap to include in every future prompt. Existing AI assistant rules (CLAUDE.md, Cursor, Copilot) are still folded in. Refines an existing `AGENTS.md` rather than overwriting it. * **Built-in commands spec.** The expected command list is now `commit`, `migrate`, `init`, `link`; `init` is asserted to target `.orb/AGENTS.md` with the new concise structure, and `link` is asserted to manage `.orb/links.json` via `ask_followup_question`. ## v6.4.6 ### Added * **Tiered usage support in `OutOfCreditsBanner`.** New `selectResetIso()` helper picks the correct reset time: when a tier is exhausted it surfaces the latest reset among exhausted windows; otherwise it falls back to the soonest upcoming reset, then to the legacy `creditsResetDate`. Banner label shortened from "Pro models limits reset at" to "Limits reset at". * **OrbCode CLI marketing card.** A third rotating marketing card in the welcome view pointing users at the CLI, joining the existing two on the 10-second rotation (`(prev + 1) % 3`). * **New custom icons in `utils/customIcons.tsx`.** `Folder01Icon`, `Folder02Icon`, `ArrowDown01Icon`, `AddCircleHalfDotIcon` and others added to support the refreshed MCP and history views. ### Changed * **Axon Code → Orbital rebrand in copy.** `troubleMessage` string in `chat.json` and the consecutive-mistake-limit description in `settings.json` now read "Orbital" where they previously read "Axon Code". * **Axon marketing copy.** Welcome-view subtitle changed from "Frontier LLMs, fraction of the cost. Save 70% inference cost." to "Cut agent inference costs by 60% using Frontier Axon models". * **History UI refresh (`TaskItem`).** Visual and layout rework of the history list (90 insertions / 52 deletions). * **MCP UI refresh (`McpView`).** Major rework of the MCP view with a new icon set and updated i18n strings (283 insertions / 228 deletions across `McpView.tsx`, `mcp.json`, and `customIcons.tsx`). * **Login UI refresh (`KiloCodeAuth`, `ProfileView`, `WelcomeView`).** Consolidated the auth and welcome flows with refreshed i18n strings. * **Chat agent UI refresh.** Visual updates to `ChatRow`, `ExplorationGroupRow`, and `ReasoningBlock` (63 insertions / 61 deletions). * **Button UI refresh.** Updated button styles in `index.css` for consistency across surfaces (17 insertions / 5 deletions). ### Fixed * **Stream idle timeout bumped to 180s.** `STREAM_IDLE_TIMEOUT_MS` raised from 60s to 180s to reduce false-positive timeouts during long-running streaming responses, especially with slower or more verbose model outputs. ## v6.4.4 ### Added * **Stream idle timeout**: 60s idle window on model stream consumption. If no chunk arrives within the window (network drop, socket close, server stall), a descriptive error is thrown so the catch block can persist the failure and surface a `streaming_failed` row to the UI. * **Stream disconnection UI surfacing**: New `streaming_failed` `ErrorRow` variant with a localized explanation of the likely cause for transport-level errors (ECONNRESET, socket hang up, fetch failed, ETIMEDOUT, ENOTFOUND, etc.), and a retry button matching the existing `Provider error:` behavior. * **`chat:apiRequest.streamDisconnected` i18n key**. ### Fixed * **Ripgrep binary resolution on modern VS Code / Orbital hosts**: `@vscode/ripgrep-universal` nests the binary in a per-platform subfolder (`bin/{os}-{arch}/rg`) that the previous lookup chain did not check, causing `getBinPath` to return undefined and file searches to silently fail. Adds the universal package to the lookup chain with both `node_modules` and `node_modules.asar.unpacked` paths. * **Unified message queue**: Replaced the local `manualMessageQueue` with the shared `messageQueueService` as the single source of truth. Queued messages now stay visible in the UI until they are actually dispatched; `isWaitingForAskResponse` is set before dequeue so a follow-up message resolves the ask rather than being re-routed into the queue. ### Changed * **About footer actions**: Export, import, and reset buttons now sit in a flex container with `gap-1.5` so the icon and label share a single evenly spaced row. Removed ad-hoc `pb-0.5` icon padding. * **Footer support copy**: Removed the dead Discord link; the message now ends at the Reddit mention. ## v6.2.3 ### Fixed * **Chat title display**: Handle JSON-wrapped title strings from server, now supports plain string, JSON object, and stringified JSON responses, with normalized-title fallback at every consumer layer to clean already-persisted malformed titles ### Changed * Updated matterai models, llm router spec tests, exploration group row, out of credits banner, pretty model name, and openrouter provider hooks ## v6.2.0 ### Added * **Tool call result pairing safety net**: New `backfillMissingToolResults` function in OpenAI format transform that backfills placeholder tool messages for unanswered parallel tool calls, preventing provider rejections when tool\_call/tool\_result pairs are mismatched * **Tool call result pairing module**: Pure functions (`reconcileAssistantToolUses`, `toolUseIdsRequiringResults`, `allToolResultsCollected`) to keep assistant tool\_calls and tool\_results paired 1:1, with comprehensive unit test coverage * **Task.ts reconciliation**: Gating API requests until every tool\_call in the assistant message has its matching tool\_result collected, preventing partial result sets from being sent to providers ### Changed * **AssistantMessageParser performance**: Avoided O(n²) string slicing in the streaming hot loop by deferring content updates to end-of-chunk; added 20KB max extract length threshold for large accumulated arguments during streaming * **presentAssistantMessage optimization**: Replaced deep clone with shallow copy for streaming content blocks to reduce overhead during large file streaming * **FileWriteTool partial display**: Truncated large file content during streaming preview (5KB limit) to prevent IPC bottlenecks and UI freezing * **Model list cleanup**: Removed deprecated `axon-code-2-pro` and `axon-code-2-pro-high` model entries from provider configurations ### Fixed * ReasoningBlock arrow icon rotation styling * OutOfCreditsBanner border-radius styling consistency ## v6.0.0 🚀 **Agent Manager Update** ### Highlights This major release introduces the **Agent Manager**, a powerful new feature for managing multiple AI agents across workspaces with a dedicated sidebar and file viewer panel. ### Added #### Agent Manager * **Agent Manager Sidebar**: New collapsible sidebar for managing multiple agent tasks across workspaces * Workspace-based task organization with expandable folders * Quick access to recent tasks with compact timestamps * "New Agent" button for starting fresh conversations * **Agent File Viewer**: Resizable file viewer panel for reviewing diffs and file changes * Drag-to-resize functionality with min/max width constraints * Automatic viewport-aware sizing * Pull request-style diff view integration * **Agent Toggle**: Toggle button to show/hide the agent manager sidebar with smooth animations #### Exploration Groups * **Elapsed Time Display**: Exploration groups now show elapsed time during and after exploration * Live timer updates during active exploration (e.g., "Exploring for 1m30s") * Summary with total time on completion (e.g., "Explored 5 files, 2 searches for 45s") * Auto-collapse when exploration completes #### Model Selection * **Axon Model Tooltips**: Hover tooltips in ModelSelector showing details for Axon models * **Enhanced Model Provider Hook**: New `useOpenRouterModelProviders` hook for better model management #### Image Handling * **ImageAttachment Interface**: Refactored image handling to use a unified `ImageAttachment` interface * **Improved Thumbnails**: Enhanced thumbnail component with better image preview support #### UI Components * **Custom Icons Utility**: New custom SVG icons including `Folder01Icon`, `Folder02Icon`, `ArrowDown01Icon`, `AddCircleHalfDotIcon` * **Copy Button Update**: Updated copy icon styling ### Changed * **Streamlined Chat UI**: Cleaner, more focused chat interface with improved visual hierarchy * **Better Diff View**: Enhanced diff view for the edit tool with improved readability * **Maximize States**: Fixed maximize state handling for better window management * **ChatRow Component**: Enhanced with agent manager mode support and improved rendering * **ChatTextArea**: Refined for better user interaction * **ReasoningBlock**: Minor styling improvements ### Fixed * Maximize state persistence issues * Various UI inconsistencies across components ### Breaking Changes * Minimum VS Code version requirement updated ## v5.7.6 ### Added * Native tool call helpers for kilocode provider * Enhanced assistant message parsing with improved tool handling * LSP tool integration for better code navigation * File write tool with improved content handling * Multi-file search replace strategy for complex edit operations * New tool type definitions in packages/types * Git branch display in bottom API config showing current repository branch * GitBranchIcon custom SVG icon for branch visualization * New message types for git branch request/response (fetchGitBranchRequest, gitBranchResponse) ### Changed * **Major Tool Architecture Refactoring**: Consolidated multiple file editing tools into a unified, simplified tool system * Merged `edit_file`, `insert_content`, `search_and_replace`, `write_to_file`, `apply_diff` tools into streamlined `file_write` tool * Consolidated plan file tools (`list_plan_files`, `read_plan_file`, `plan_file_edit`) into core task management * Removed redundant native tool prompt definitions * Simplified `getAllowedJSONToolsForMode` with cleaner tool selection logic * Refactored system prompts for better tool organization and maintainability * Updated assistant message parsing with new `parseAssistantMessageV2` implementation * Enhanced diff strategies with improved multi-search-replace functionality * Updated task handling with improved checkpoint service integration * Enhanced webview message handler for better task coordination * Improved ChatRow, ChatView, and CommandExecution UI components * Updated i18n translations for en locale * Refactored BottomApiConfig component with improved layout and branch display * Enhanced ProgressIndicator component styling * Updated index.css with improved styling utilities ### Fixed * Shadow checkpoint service stability improvements * Assistant message presentation edge cases * Browser action tool compatibility * Webview message type definitions * File write tool: proper workspace path detection for partial display during streaming * File write tool: ensure tool result is always pushed on user rejection * GitHubDiffView: dynamic margin calculation for proper diff alignment * ToolUseBlock: improved component structure and styling * Git utilities: properly distinguish between current branch and default branch * Added `currentBranch` field to `GitRepositoryInfo` interface * Fixed webviewMessageHandler to use `currentBranch` instead of `defaultBranch` * Updated tests to reflect the correct field name ### Removed * Deprecated individual file editing tools (editFileTool, insertContentTool, searchAndReplaceTool, writeToFileTool, applyDiffTool, multiApplyDiffTool) * Deprecated plan file tools (listPlanFilesTool, readPlanFileTool, planFileEditTool) * Redundant tool prompt files (edit-file.ts, insert-content.ts, search-and-replace.ts, write-to-file.ts) * Native tool prompt definitions (apply\_diff.ts, edit\_file.ts, insert\_content.ts, list\_plan\_files.ts, plan\_file\_edit.ts, read\_plan\_file.ts, search\_and\_replace.ts, write\_to\_file.ts) * Associated test files for removed tools * FastApplyChatDisplay component (functionality consolidated) ## v5.7.3 ### Added * OAuth authentication support for MCP servers * LSP tool for code definition navigation * file\_write tool implementation ### Changed * Modernized settings panel UI with new card-based design * Reduced VSCodeButton size by 5% across all variants for better visual consistency ### Fixed * Allow changing 3p models for existing tasks and fixed model display names ## v5.7.2 ### Added * Third-party provider support with Fireworks, Ollama, and OpenCode integrations ## v5.7.0 ### Changed * Major release with enhanced provider integrations ## v5.6.5 ### Changed * Updated git repository URL across all localization files * Minor stability improvements ## v5.6.4 ### Changed * Name cleanup and branding consistency updates * Improved CSS styling for better UI consistency ## v5.6.3 ### Changed * Improved reasoning flow with enhanced time tracking * Better reasoning block display and interaction ## v5.6.2 ### Added * Fade-up and shimmer animations for ChatRow, ChatView, and ReasoningBlock components * Enhanced visual feedback with smooth animation effects ### Changed * UI cleanups across chat components * Improved chat message rendering and layout ## v5.6.0 ### Added * Split high think models for better model organization * Enhanced OpenRouter model providers hook ### Changed * Tool cleanups and optimizations * Improved model selection handling ## v5.5.7 ### Added * Web fetch and web search tools for enhanced information retrieval * Model state per task - each task now maintains its own model selection state independently ### Changed * Cleaned up chat theme for improved visual consistency * Enhanced webview message handling for better task management ### Fixed * Fixed @ mentions when editing messages ## v5.5.4 ### Added * Better follow up question UI for improved user interaction ### Changed * Enhanced queue and sending prevention for better chat UX * Improved file list with scrollable container and max height ### Fixed * Fixed activity bar icon display * Fixed authentication navigation issues ## v5.5.3 ### Added * Plan file tools (readPlanFile, listPlanFiles) for better plan management * Image input modality support for enhanced interactions * Plan memory manager for improved plan tracking ### Changed * Enhanced UI improvements across multiple components * Updated PostHog telemetry integration * Improved folder mention chip UI ### Fixed * Limited background task height for better layout management ## v5.5.2 ### Added * Background tasks support for multi-chats - now you can run multiple chat tasks simultaneously without blocking each other * Single file accept metrics - track detailed metrics when accepting individual file edits * Model state per task - each task now maintains its own model selection state independently * Bottom anchor for changes and navigation - improved UI with anchored navigation controls * Todo IDE plan view - enhanced plan viewing experience in the IDE ### Changed * Chat performance improvements - optimized rendering and message handling for smoother chat experience * UI updates across multiple components for better consistency and user experience * Enhanced file edit review controller with improved functionality * Updated task metadata handling for better state management ### Fixed * Fixed plan implementation issues for better plan execution * Fixed chat new lines and paste cursor positioning for improved text input experience * Fixed mention chip alignment for better visual consistency * Auto reject tools and send new message - improved tool rejection workflow ## v5.4.1 ### Added * Sticky user messages for better chat navigation * Pro model info display in settings * Custom icons utility for improved UI components ### Changed * Updated profile page with usage data * Streamlined model names and auto model selection * Improved codebase indexing tool UI * Enhanced task history list * Updated notifications integration * UI updates for code reviews ### Fixed * Fixed file path bug in code reviews ## v5.4.0 ### Added * Open plan in editor functionality for better plan management * Better fuzzy search for improved file context matching ### Changed * Cleaner chat interface with improved UI * Better diff view for edit tool * Edit tool improvements with context search enhancements * Minor UI update to task header ## v5.3.4 ### Fixed * Fixed multi-window authentication synchronization * Fixed beta model access for axon-code-2-pro ## v5.3.2 ### Added * Beta models gating for axon-code-2-pro with backend API integration * Custom icons utility for improved UI components ### Changed * Set temperature to 0.2 for OpenRouter provider * Enhanced file edit tool with improved functionality * Updated kilocode models configuration * Cleaned up chat text area component * Updated and optimized test files * UI improvements across chat components ## v5.3.1 ### Added * Retry button for 5xx streaming failures ### Fixed * Fixed use\_skill tool being included in system prompt when no skills are available ## v5.3.0 ### Added * New skill tool functionality for enhanced code assistance * Skill parser and type definitions for skill management * Comprehensive test coverage for skill tool components * Enhanced code index orchestrator with skill integration * State manager for skill tool operations ### Changed * Updated tool type definitions to support skill tools * Enhanced assistant message presentation with skill support * Improved code index manager with skill-related functionality * Updated dependencies in package.json ## v5.0.0 🚀 **Orbital v5 is Here!** ### ✨ Major Highlights 🧠 **Auto-Generated Memories** Orbital now generates and references memories from past chats, ensuring previously used context is instantly recalled when building or updating your codebase. 🔧 **Revamped Context Agents & File Edits** Completely redesigned tools (read, edit, search, list) with dramatically reduced error rates for more reliable code modifications. 🏢 **Enterprise Code Review Support** Full support for self-deployed platforms with enhanced AI code review capabilities. 💎 **Major UI Overhaul** Completely refreshed interface with improved auth flow and review-only mode. ### Added * Auto-generated memories for past chats with quick context recall * Revamped context agents and file edit tools (read, edit, search, list) with reduced error rates * Enterprise code review support for self-deployed platforms * Major UI improvements across the board * Enhanced auth flow with review-only mode * Line counter tracking (lines added, updated, deleted) * "View Diff" button to see pending changes in VS Code editor * Enhanced file edit review with accept/reject functionality * File changes list persists on task abortion * Elapsed time tracking for reasoning blocks ("Thinking for Xs") * Animated loading indicators with MatterProgressIndicator component * Context window usage tracking * Credits usage display on hover * Chat title fetching and display * Enhanced webview message handling * Streamlined chat interface with better UX * Settings UI cleanup * Updated translations for multiple languages * Added memories locale files for better i18n support ### Changed * Improved file edit and code review workflows * Enhanced model provider integration * Better error handling and UI consistency * Improved cancel button functionality ### Fixed * Fixed message queuing after rejecting exec\_cmd tool * Fixed stream tool calling issues when tools got stuck * Notify LLM if files have been changed by the user post its own edits ## v4.204.1 ### Added * CLI authentication wizard with browser-based OAuth flow * Browser authentication utilities for secure token management * Welcome message utilities for CLI user onboarding ### Changed * Refactored AcceptRejectButtons component for improved multi-edit handling * Enhanced ChatTextArea with better cancel button functionality * Updated ChatView component for improved message queue management * Improved Logo component with better styling and responsiveness * Enhanced AuthWizard with comprehensive authentication flow * Updated CLI package configuration and validation ### Fixed * Fixed code paths return value issue in AuthWizard * Improved cancel button UI and functionality * Enhanced CLI authentication initialization process ## v4.204.0 ### Added * Support for axon-code-2-preview model in OpenRouter provider * Enhanced file edit review controller with better diff handling * Improved UI refactors across multiple components ### Changed * Refactored kilocode-models configuration for better model management * Updated useOpenRouterModelProviders hook for enhanced provider integration * Enhanced FileEditReviewController for improved file edit reviews * Improved QueuedMessages component for better message handling * Updated CSS styling for consistent UI appearance * Enhanced slash commands utilities for better command processing ### Fixed * Cleaned up AI code reviews card for better user experience * Fixed various UI inconsistencies across components * Improved error handling in webview message handler ## v4.203.0 ### Added * Enhanced chat text area with improved mention chip functionality * New mention chip demo component for better user interaction ### Changed * Refactored ChatTextArea component for better performance and maintainability * Updated ChatRow component with streamlined UI elements * Improved CodeAccordian component for enhanced code display * Enhanced useOpenRouterModelProviders hook for better model management * Updated prettyModelName utility for improved model name formatting * Refined KiloTaskHeader component with better task management * Improved GhostServiceSettings component for enhanced configuration ### Fixed * Better handling of chat text area interactions * Improved mention chip display and functionality * Enhanced UI cleanup and consistency across components ## v4.202.0 ### Added * New utilities for generating unified diffs (`buildFileEditDiff`, `computeDiffStats`) and classifying file icons (`getFileIconClass`). * `context` prop to `ErrorRow` for displaying additional error context. * `headerContent` prop to `CodeAccordian` for custom header rendering. ### Changed * **Revamped Error Display:** Introduced a compact, single-line error display with an interactive info icon that reveals full details in a tooltip. * **Enhanced File Edit Viewer:** Significantly improved the `fileEdit` display in chat, now showing unified diffs, line-by-line additions/deletions, and a more detailed header with file path and diff statistics. * **Improved Batch File Permission UI:** Updated the UI for batch file permissions, including better display of file paths and line snippets. * **Redesigned Accept/Reject Buttons:** Overhauled the UI for accepting/rejecting file changes, featuring an expandable list of affected files with individual diff statistics and a consolidated action footer. * **General UI/Styling Refinements:** Applied various styling and layout improvements across `ToolUseBlock`, `ToolUseBlockHeader`, `ModelSelector`, and `CodeAccordian` components for a cleaner user experience. ### Fixed * Corrected line snippet formatting for file paths in `BatchFilePermission` and `ChatRow`. * Implemented click-outside-to-close functionality for the new error tooltip in `ErrorRow`. * Improved accessibility with better keyboard navigation support. ## v4.201.0 ### Added * Enhanced file edit review functionality with improved user experience * Better webview message handling for file edit operations ### Changed * Improved file edit review interface with refined accept/reject buttons * Updated ChatTextArea component for better user interaction * Enhanced OpenRouter provider integration * Streamlined UI components for improved consistency * Updated translations for ar, ca, cs, de, es, fr, hi, id, it, ja, ko, nl, pl, pt-BR, ru, th, tr, uk, vi, zh-CN and zh-TW ### Fixed * Better handling of file edit review edge cases * Improved localization support for chat components ## v4.200.0 ### Added * New file edit review system with accept/reject functionality * Enhanced monthly quota management within chat interface * Improved mentions cleanup and handling * New AcceptRejectButtons component for better user interaction ### Changed * Updated UI components across dialogs, dropdowns, popovers, and selects * Enhanced chat interface with improved user experience * Refined chat row component for better readability * Updated tool use block styling for improved visual consistency * Streamlined chat message layout and interactions ### Fixed * Better handling of file edit operations * Improved quota tracking and display * Enhanced error handling in chat interface ## v4.123.1 ### Changed * Refined chat UI components with improved styling and consistency * Updated todo list display with better iconography using CheckCircle components * Enhanced timestamp display with improved font sizing * Streamlined chat message layout by removing unnecessary borders * Improved color scheme consistency across UI components * Updated font weights and sizes for better visual hierarchy ### Fixed * Better handling of todo list status indicators * Improved color variable references for consistent theming ## v4.123.0 ### Added * Enhanced chat interface with improved user experience * Cleaner message display and layout optimization ### Changed * Refined chat row component for better readability * Updated tool use block styling for improved visual consistency * Enhanced internationalization support for chat components ### Fixed * Improved message formatting and spacing * Better handling of long messages in chat interface ## v4.122.0 ### Added * Enhanced credit management system with improved warnings * Better error handling for insufficient credit scenarios * Comprehensive test coverage for error utilities ### Changed * Updated credit warning messages to be more user-friendly * Improved low credit warning UI components * Enhanced error messaging for better user guidance ### Fixed * Better handling of credit limit scenarios * Improved error message display and formatting ## v4.121.0 ### Added * Refined codebase search functionality with improved accuracy * Enhanced search result filtering and scoring * Better integration with codebase indexing system ### Changed * Optimized search result limits (reduced from 200 to 5 max results) * Improved search score thresholds (minimum score: 0.5) * Enhanced model provider integration * Streamlined codebase search UI components ### Fixed * Better handling of search result pagination * Improved search result display formatting * Enhanced model selection interface ## v4.120.0 ### Added * Complete codebase indexing system with backend integration * Vector store migration to backend services * Enhanced embedding endpoint functionality * Improved package cleanup and optimization ### Changed * Migrated vector store operations to backend for better performance * Refactored codebase indexing architecture for scalability * Enhanced UI components for better user experience * Improved model provider configurations ### Fixed * Resolved embedding endpoint connectivity issues * Fixed codebase indexing performance bottlenecks * Enhanced error handling in vector store operations ## v4.119.0 ### Added * Hardcoded model IDs for improved Axon integration * Enhanced tool optimization for better performance * Credit usage display per model for better cost tracking * Improved model selection interface ### Changed * Optimized API calls to reduce latency * Enhanced model provider architecture * Improved credit management and display * Streamlined tool execution pipeline ### Fixed * Better handling of model selection edge cases * Improved credit tracking accuracy * Enhanced error handling in model provider operations ## v4.118.0 ### Added * Initial release with core functionality * Basic chat interface and model integration * Foundational codebase indexing capabilities ### Features * AI-powered code assistance * Multi-model support through OpenRouter * Basic autocomplete and code suggestions * Initial marketplace integration # Install MatterAI On Bitbucket Source: https://docs.matterai.so/connectors/atlassian-bitbucket Install MatterAI on Bitbucket for AI Code Reviews and more All Pricing and Features are same on Bitbucket as on Github # MatterAI Bitbucket Integration MatterAI seamlessly integrates with Atlassian Bitbucket to enhance your development workflow by: * Automatically initiating AI-powered code reviews for new merge requests * Displaying intelligent review comments directly within merge requests * Providing real-time assistance through the MatterAI bot Follow these steps to integrate MatterAI with your Bitbucket repositories: ## Step 1: Choose Your Bitbucket Access Token Type To enable MatterAI to interact with your Bitbucket repositories, you'll need to provide an access token with appropriate permissions: * **Personal API Token**: Create a dedicated MatterAI service account and generate a API Token for it * **Group API Token**: For Bitbucket Premium/Ultimate users, generate a token that automatically creates a bot user ## Step 2: Set Up a API Token We recommend creating a dedicated service account for MatterAI with these best practices: 1. Create a new **Bitbucket user specifically for MatterAI integration** 2. Name the account **matterai-yourdomain** for easy identification. Keep the email you create with handy, it will be used later. 3. Use a \*\*dedicated email address \*\*for this account 4. Upload the **MatterAI logo** as the profile picture. You can find it here: [MatterAI Logo](https://raw.githubusercontent.com/GravityCloudAI/public-assets/refs/heads/main/logos/matter-logo.png) 5. Ensure this user has at least **Product Admin** access to your target repositories Setting up MatterAI service account ## Step 3: Generate Your API Token Create a new API token for the created user with the following scopes * read:account * read:user:bitbucket * write:issue:bitbucket * read:issue:bitbucket * read:workspace:bitbucket * admin:project:bitbucket * write:webhook:bitbucket * read:webhook:bitbucket * read:pipeline:bitbucket * read:runner:bitbucket * read:repository:bitbucket * write:repository:bitbucket * read:pullrequest:bitbucket * write:pullrequest:bitbucket Setting up MatterAI service account Setting up MatterAI service account Setting up MatterAI service account Setting up MatterAI service account Setting up MatterAI service account Copy the API Token, it will be used in MatterAI Connector. ## Step 4: Setup MatterAI Webhook 1. Navigate to **Bitbucket** -> **Repositories** -> **Your Repository** -> **Repository Settings** -> **Webhooks** 2. Click on **Add Webhook** 3. Enter the following URL: **[https://api.matterai.so/api/v1/bitbucket/webhook](https://api.matterai.so/api/v1/bitbucket/webhook)** 4. Generate a **Secret Token** and copy it, this will be used to store in MatterAI Connectors 5. Select the **Triggers** for the Webhook, MatterAI requires the following trigger under **Pull Requests**: `Create`, `Updated`, `Approved`, `Changes Request created`, `Changes Request removed`, `Merged`, `Comment created`, `Comment updated`, `Comment deleted`, `Comment resolved`, `Comment reopened` 6. Save the Webhook Setting up MatterAI service account Setting up MatterAI service account ## Step 5: Setup MatterAI Connector 1. Navigate to **MatterAI** -> **Connectors** -> **Bitbucket** and click on **Connect**. Console URL: [https://app.matterai.so/connectors](https://app.matterai.so/connectors) 2. Enter the **Bitbucket User Email**(`user is the one you created API Token for`), **Bitbucket API Token** and **Webhook Secret Token** in the form. 3. Click on **Save** 4. You are all set! MatterAI will start processing your Pull Requests Setting up MatterAI service account Setting up MatterAI service account ### AI Pull Request Summary Get PR summaries in your Bitbucket pull requests, always upto date on all the commits. Setting up MatterAI service account Unlike Github/GitLab, Bitbucket does not support Mermaid Diagrams, so we are not able to show them in the pull requests. ### AI Pull Request Code Reviews Get PR code reviews in your Bitbucket pull requests, fix and code fix suggestion that you can apply with 1-click. Unlike Github/GitLab, Bitbucket does not support mulit-line Review Comments, so we are not able to show them in the pull requests. Setting up MatterAI service account # Install MatterAI On Azure DevOps Source: https://docs.matterai.so/connectors/azure-devops Install MatterAI on Azure DevOps for AI Code Reviews and more All Pricing and Features are same on Azure DevOps as on GitHub # MatterAI Azure DevOps Integration MatterAI seamlessly integrates with Azure DevOps to enhance your development workflow by: * Automatically initiating AI-powered code reviews for new pull requests * Displaying intelligent review comments directly within pull requests * Providing real-time assistance through the MatterAI integration Follow these steps to integrate MatterAI with your Azure DevOps repositories: ## Step 1: Create a Personal Access Token 1. Log in to your Azure DevOps organization 2. Click on your profile picture in the top right corner and select **Personal Access Tokens** 3. Click **+ New Token** 4. Enter a descriptive name (e.g., "MatterAI Integration") 5. Set an expiration date for the token 6. Select the organization where you want to use this token Creating Personal Access Token ## Step 2: Configure Token Scopes Select the following scopes for your Personal Access Token: * **Build**: Read & Execute * **Code**: Read & Write * **Pull Request Threads**: Read & Write * **Project and Team**: Read * **User Profile**: Read * **Work Items**: Read & Manage Configuring Token Scopes Make sure to copy and securely save your Personal Access Token immediately after creation, as it will not be shown again. ## Step 3: Connect to MatterAI 1. Navigate to **MatterAI** -> **Connectors** -> **Azure DevOps** and click on **Connect**. Console URL: [https://app.matterai.so/connectors](https://app.matterai.so/connectors) 2. Enter your **Personal Access Token** in the provided field 3. Enter your **Azure DevOps Organization URL** (e.g., `https://dev.azure.com/your-organization`) 4. Click **Save** Connecting Azure DevOps in MatterAI ## Step 4: Test Connection and Manage Projects 1. Once connected, click on **Test Connection** to verify the integration is working properly 2. After successful connection, click on **Manage Projects** to view available repositories 3. MatterAI will retrieve all Azure DevOps projects and repositories associated with your token Testing Connection and Managing Projects ## Step 5: Configure Service Hooks MatterAI requires Service Hooks to receive events from Azure DevOps: 1. The integration will automatically configure the necessary service hooks 2. These hooks notify MatterAI when pull requests are created or updated 3. Ensure your organization allows service hook creation for the integration Configuring Service Hooks ## Step 6: Select Repositories 1. Browse through your Azure DevOps projects 2. Select the specific repositories you want to enable for AI code reviews 3. You can enable multiple repositories across different projects Selecting Repositories ## Step 7: Enable Projects 1. Toggle the switch to enable MatterAI for each selected repository 2. MatterAI will automatically configure the required service hooks for all enabled repositories 3. The integration is now active and will process new pull requests Enabling Projects This automated flow works on all Azure DevOps service tiers and supports both cloud-hosted and self-hosted Azure DevOps Server instances. ## Done, Your Integration is Complete! Integration Complete ## Azure DevOps Code Reviews ### AI Pull Request Summary Get comprehensive PR summaries in your Azure DevOps pull requests, always up to date with all the commits and changes. MatterAI analyzes the entire pull request diff and provides: * Overview of changes * Key modifications summary * Potential issues and suggestions * Code quality insights Creating Personal Access Token ### AI Review Comments Receive intelligent code review comments directly in your Azure DevOps pull requests with: * **Issue Detection**: Identify bugs, security vulnerabilities, and code smells * **Fix Suggestions**: Get specific recommendations for improvements * **One-Click Apply**: Apply suggested code fixes with a single click * **Best Practices**: Learn about language-specific and framework-specific best practices Creating Personal Access Token ### Security Analysis MatterAI performs comprehensive security scanning on your Azure DevOps pull requests: * Detects common security vulnerabilities (OWASP Top 10) * Identifies hardcoded secrets and credentials * Flags insecure dependencies * Provides remediation guidance ### Code Quality Improve your codebase quality with AI-powered insights: * Performance optimization suggestions * Code complexity analysis * Maintainability recommendations * Test coverage insights # Figma Integration Source: https://docs.matterai.so/connectors/figma Connect your Figma account to MatterAI # MatterAI Figma Integration Connect your Figma account to MatterAI to seamlessly search, fetch designs, and interact with all your Figma content directly within MatterAI. Follow these steps to integrate MatterAI with your Figma Workspace: ## Step 1: Create a Personal Access Token (PAT) in Figma 1. Open Figma and click your profile avatar in the top left corner, then select **Settings** from the dropdown menu. 2. In the **Account** tab, scroll down to the **Personal access tokens** section. 3. Click the **Generate new token** button. 4. Give your token a name (such as `matterai-figma`) and an expiration date, then click **Generate token**. 5. In the **Scopes** section, grant the required permissions for MatterAI to access your Figma files and design data. Figma PAT permissions 6. Copy the generated personal access token. **Save it somewhere safe**, as you won't be able to retrieve it again once you close the dialog. ## Step 2: Connect to MatterAI 1. Visit the MatterAI web app [https://app.matterai.so/connectors](https://app.matterai.so/connectors) and navigate to **Connectors** on the left menu. 2. Under the **Figma** connector card, click the **Connect** button. 3. In the Figma Settings modal, paste the **Figma Personal Access Token** you copied during the previous step into the token field. 4. Click **Save** to complete the integration. Configure Figma in MatterAI Connectors You are now successfully connected! MatterAI can now interact with your Figma workspace. # Install Github App Source: https://docs.matterai.so/connectors/github Install Github App for CI/CD ## Overview Installing MatterAI's official Github App enables CI/CD for your services ## Steps 1. Visit [https://app.matterai.so/connectors](https://app.matterai.so/connectors) and then click on Connect under Github. MatterAI Connectors 2. On the Github App page, select `Install` or `Configure` MatterAI Connectors 3. Select the organisation or specific repository to install the app to. You can update this list later via the same steps. MatterAI Connectors Once installed, MatterAI will pull the list of repositories. MatterAI does not store any code, the code is only fetched for CI/CD runtime and then purged from an isolated VM # Install MatterAI On GitLab Source: https://docs.matterai.so/connectors/gitlab Install MatterAI on GitLab for AI Code Reviews and more All Pricing and Features are same on GitLab as on Github # MatterAI GitLab Integration MatterAI seamlessly integrates with GitLab to enhance your development workflow by: * Automatically initiating AI-powered code reviews for new merge requests * Displaying intelligent review comments directly within merge requests * Providing real-time assistance through the MatterAI bot Follow these steps to integrate MatterAI with your GitLab repositories: ## Step 1: Create a Dedicated MatterAI User (Recommended, but optional) We recommend creating a dedicated service account for MatterAI with these best practices: 1. Create a new **GitLab user specifically for MatterAI integration** 2. Name the account **matter-ai** for easy identification 3. Use a **dedicated email address** for this account 4. Upload the **MatterAI logo** as the profile picture. You can find it here: [https://raw.githubusercontent.com/GravityCloudAI/public-assets/refs/heads/main/logos/matter-logo.svg](https://raw.githubusercontent.com/GravityCloudAI/public-assets/refs/heads/main/logos/matter-logo.svg) 5. Ensure this user has at least **Maintainer access** to your target repositories ## Step 2: Generate Personal Access Token 1. Log in using the dedicated MatterAI service account 2. Click your avatar on the left sidebar and select **Edit Profile** 3. Choose **Access Tokens** from the left menu 4. Click **Add New Token** 5. Enter a descriptive name and set an expiration date 6. Select the following scopes: **api, read\_repository** 7. Click **Create Personal Access Token** 8. Save the token securely Generating Personal Access Token Generating Personal Access Token Scopes ## Step 3: Connect to MatterAI 1. Navigate to **MatterAI** -> **Connectors** -> **GitLab** and click on **Connect**. Console URL: [https://app.matterai.so/connectors](https://app.matterai.so/connectors) 2. Enter your **Personal Access Token** in the provided field. 3. If you are using a self-hosted instance, enter your **Self-Hosted GitLab URL**. 4. Click **Save**. Connecting GitLab in MatterAI ## Step 4: Manage Projects 1. Once connected, click on **Manage Projects** (or **Fetch Groups**). 2. MatterAI will retrieve all available GitLab Projects/Groups associated with your token. Fetching GitLab Groups ## Step 5: Enable Projects 1. Locate the project or group you wish to enable. 2. Toggle the switch to enable it. You can enable specific projects or an entire group. 3. MatterAI will automatically set up the required webhooks for all enabled projects. Enabling GitLab Projects This automated flow works on all GitLab tiers (Free, Premium, Ultimate) and fully supports self-hosted GitLab instances. Use the toggle to easily manage MatterAI integration for your groups. ## Done, Your Webhooks are auto-configured! Enabling GitLab Projects ## GitLab Code Reviews ### AI Merge Request Summary Get PR summaries in your GitLab merge requests, always upto date on all the commits. Setting up MatterAI service account ### AI Merge Review Threads Get PR summaries in your GitLab merge requests with issues, fix and code fix suggestion that you can apply with 1-click. Setting up MatterAI service account # Jira Integration Source: https://docs.matterai.so/connectors/jira Connect your Atlassian Jira account to MatterAI # MatterAI Jira Integration Connect your Atlassian Jira account to MatterAI to seamlessly search, fetch tasks, and interact with all your Jira content directly within MatterAI. Follow these steps to integrate MatterAI with your Jira Workspace: ## Step 1: Create a Service Account 1. Go to your **Atlassian Administration** dashboard (e.g., `admin.atlassian.com`). 2. Navigate to **Directory** > **Service accounts** from the left sidebar. Service accounts navigation 3. Click the **Create a service account** button in the top right corner. Create a service account button 4. Provide a name (e.g., `matterai-jira`) and description for your service account, then click **Next**. Name your service account 5. In the **Select app roles** step, find **Jira** from the list of apps and assign it the **User** role. Select app roles for Jira ## Step 2: Generate an API Token 1. After the service account is successfully created, click on the **Create credentials** button on the service account page. Create credentials 2. Choose **API token** as your authentication type and click **Next**. Choose API token authentication 3. Give your token a name (such as `matterai-jira-token`), select an expiration date, and click **Next**. Name API token and set expiration 4. In the **Select token scopes** screen, search for and select the `read:jira-work` scope. This allows MatterAI to fetch your Jira issues and project data. Then, click **Next**. Select token scopes 5. Review your API token details. Make sure the configuration is correct, then click **Create**. Review API token 6. Copy the generated API token. **Save it somewhere safe**, as you won't be able to retrieve it again. Click **Done**. Copy API token ## Step 3: Connect to MatterAI 1. Visit the MatterAI web app [https://app.matterai.so/connectors](https://app.matterai.so/connectors) and navigate to **Connectors** on the left menu. 2. Under the **JIRA** connector card, click the **Connect** button. 3. In the JIRA Settings modal, enter your **JIRA Base URL** (e.g., `https://your-company.atlassian.net`). 4. Paste the **JIRA API Token** you copied during the previous step into the token field. 5. Click **Save** to complete the integration. Configure Jira in MatterAI Connectors You are now successfully connected! MatterAI can now interact with your Jira workspace. # MS Teams Source: https://docs.matterai.so/connectors/ms-teams Install MatterAI Microsoft Teams App GA Coming Soon! Contact for private-beta # Install Slack Webhook Source: https://docs.matterai.so/connectors/slack Get PR Merge notifications on Slack ## Overview Adding Slack webhooks to MatterAI enables Code Quality and Vulnerability notifications on merge to your Slack channels. ## Steps 1. Visit [https://app.matterai.so/settings](https://app.matterai.so/settings) and enter your Slack Webhook URL under `PR Merge Alerts` MatterAI Connectors 2. Configure the thresholds for Code Quality and Vulnerability notifications. * Code Quality Percentage: If a PR is merged below this percentage, it will be notified in Slack. * Package Vulnerability: If a PR is merged with an existing package vulnerability, it will be notified in Slack. # Enterprise Configurations Source: https://docs.matterai.so/enterprise/configure Configure MatterAI for your enterprise deployment features as per your organization These instructions are for self-hosted enterprise MatterAI deployments. ## Github App Configuration Once you have created your own GitHub App, you need to configure it for MatterAI. Below are the configuration values needed: * App Name * App ID * Client ID * Client Secret * Private Key (base64 encoded) * Webhook Secret Remember to Base64 encode the `private-key.pem`, you can run `base64 -i private-key.pem` and enter the output in the Private Key input box under github app config GitHub App Configuration ## Review Configurations Customize how MatterAI handles code reviews with the following settings: Review Configurations ### **Enable PR Review Comments** When enabled, MatterAI will post detailed review comments on pull requests, highlighting potential issues and suggestions directly in the code diff. #### Enable PR Review on Commits When enabled, MatterAI will automatically review new commits pushed to pull requests, providing feedback on the latest changes. #### Enable Language Rulesets in Review When enabled, MatterAI will apply language-specific rules and best practices during code reviews, ensuring code quality and consistency. #### Enable Similarity Search When enabled, MatterAI will search for similar code patterns across your codebase to identify potential duplications or reusable components. #### Enable Lite Review When enabled, performs a faster, less resource-intensive review that focuses on critical issues only. ### **Enable PR Description Generation** #### Enable PR Description as Comment When enabled, the generated PR description will be posted as a comment instead of updating the PR description directly. #### Enable Quality Recommendations in Summary When enabled, includes code quality improvement recommendations in the review summary. #### Enable Sequence Diagram in Summary When enabled, generates and includes sequence diagrams in the review summary to help visualize complex interactions in the code. ## Disabled Repositories Mark repositories that MatterAI should not review automatically. Manual reviews can be triggered for these repositories using `/matter` [commands](/features/command-list). Disabled Repositories ## Advanced Code Review Repositories Mark repositories that MatterAI should automatically review with advanced settings. Manual reviews can be triggered for these repositories using `/matter review-max`. Advanced Code Review Repositories ## AI Provider Keys ### Setup AI Provider keys are your AI API keys stored securely in MatterAI. You can create and manage them in the MatterAI dashboard. AI Provider Keys ### Anthropic To setup Anthropic, simply add your API key in the MatterAI dashboard. You can get your API key from [Anthropic Console](https://console.anthropic.com/settings/keys) ### OpenAI To setup OpenAI, simply add your API key in the MatterAI dashboard. You can get your API key from [OpenAI Console](https://platform.openai.com/api-keys) ### Gemini To setup Gemini, simply add your API key in the MatterAI dashboard. You can get your API key from [Gemini Console](https://aistudio.google.com/app/apikey) ### Vertex AI To setup Vertex AI, you need to create a service account in Google Cloud Console and required details as below. * Project ID * Project Location (eg; us-central1) * Service Account JSON #### Creating Service Account for Vertex AI Visit GCP Console for Service Accounts and create a new Service Account: [https://console.cloud.google.com/iam-admin/serviceaccounts](https://console.cloud.google.com/iam-admin/serviceaccounts) Create a new Service Account Configure the Service Account with required name and role. * Name: `matterai` * Role: `Vertex AI Colab Service Agent` Configure Service Account Configure Service Account Create a key for the service account and download the JSON file. Create a Key Add the details in MatterAI dashboard. * Project ID * Project Location (eg; us-central1) * Service Account JSON Add the details in MatterAI dashboard ## RuleSets Rulesets are your custom organization-specific AI rules for languages, security and repositories stored securely in MatterAI. You can create and manage them in the MatterAI dashboard. ### Language Rules Create or edit existing language rules in the MatterAI dashboard as per how AI should should review your codebase with specific instructions. Language Rules ### Security Rules Create or edit existing security rules in the MatterAI dashboard as per how AI should should review your codebase with specific instructions. Security Rules ## IDE Code Reviews Transform your development workflow with Matter AI's new asynchronous review engine designed for IDE integration. **Benefits:** * Instant feedback submission without waiting for analysis * IDE stays responsive during complex code reviews * Perfect for large diffs and complex codebases * Built-in retry logic and error handling ### Quick Setup 1. Install the Axon Code extension from [https://www.matterai.so/axon-ai-coding-agent](https://www.matterai.so/axon-ai-coding-agent) for your IDE 2. Keep your MatterAI Enterprise Backend URL and MCP Server Key ready 3. Setup Enterprise Configs