Skip to content

[BOT ISSUE] Mistral: Batch Jobs API (client.batch.jobs.create()) not instrumented #272

Open
Feature
Open
[BOT ISSUE] Mistral: Batch Jobs API (client.batch.jobs.create()) not instrumented#272
Feature
Labels
integration: mistral

Description

@braintrust-bot
braintrust-bot
bot
opened on Apr 14, 2026

Summary

The Mistral Batch Jobs API (POST /v1/batch/jobs) is not instrumented. Calls to client.batch.jobs.create() produce zero Braintrust tracing. This is a documented, GA production feature on the Mistral platform for running batch inference across chat completions, embeddings, FIM, OCR, audio transcriptions, moderations, and classifications.

The Braintrust Anthropic integration does instrument an equivalent batch API (client.messages.batches.create() and client.messages.batches.results()), making this an asymmetry across providers in this repo.

What is missing

Mistral ResourceMethodInstrumented?
client.chatcomplete(), stream()Yes
client.embeddingscreate()Yes
client.fimcomplete(), stream()Yes
client.agentscomplete(), stream()Yes
client.batch.jobscreate()No
client.batch.jobsget(), list(), cancel()No (CRUD — lower priority)

The generative-execution-relevant surfaces are batch.jobs.create() (submitting a batch of model inference requests) and retrieving the results (via output file download). The list/get/cancel methods are CRUD management and lower priority.

At minimum, instrumentation for batch.jobs.create() should create a span capturing:

  • Input: list of custom IDs from the batch requests, number of requests, target endpoint
  • Output: batch job ID, processing status, request counts
  • Metrics: latency (submission time)
  • Metadata: model(s), target endpoint, batch size, timeout configuration

This mirrors the pattern used by the Anthropic batch instrumentation, which creates a task-type span with input custom IDs, output status/counts, and metadata including model and number of requests.

Batch API capabilities

The Mistral batch API supports batching requests to all major generative endpoints:

  • /v1/chat/completions
  • /v1/embeddings
  • /v1/fim/completions
  • /v1/ocr
  • /v1/audio/transcriptions
  • /v1/moderations, /v1/chat/moderations
  • /v1/classifications

Both file-based batching (up to 1M requests via JSONL upload) and inline batching (up to 10K requests embedded in the create call) are supported.

Braintrust docs status

not_found — The Mistral integration page documents chat completions, embeddings, FIM, and agents. No mention of batch API support.

Upstream sources

Local files inspected

  • py/src/braintrust/integrations/mistral/patchers.py — defines patchers for Chat, Embeddings, Fim, Agents; zero references to batch, jobs, or Batch
  • py/src/braintrust/integrations/mistral/tracing.py — wrapper functions for chat, embeddings, FIM, agents only; no batch wrappers
  • py/src/braintrust/integrations/mistral/integration.py — integration class registers 4 composite patchers (ChatPatcher, EmbeddingsPatcher, FimPatcher, AgentsPatcher); no BatchPatcher
  • py/src/braintrust/integrations/mistral/test_mistral.py — no batch test cases
  • py/src/braintrust/integrations/anthropic/tracing.py — Anthropic batch API IS instrumented (BatchesCreate, BatchesResults classes) as precedent
  • py/noxfile.py — test_mistral session tests against LATEST and 1.12.4; no batch coverage

Relationship to existing issues

Metadata

Metadata

Assignees

No one assigned

    Labels

    integration: mistral

    Type

    Feature

    Projects

    No projects

      Milestone

      No milestone

      Relationships

      None yet

      Development

      No branches or pull requests

      Issue actions

      , 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
       blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
[BOT ISSUE] Mistral: Batch Jobs API (`client.batch.jobs.create()`) not instrumented · Issue #272 · braintrustdata/braintrust-sdk-python · GitHub
      Skip to content
      [BOT ISSUE] Mistral: Batch Jobs API (client.batch.jobs.create()) not instrumented #272
      Open
      Feature
      Open
      [BOT ISSUE] Mistral: Batch Jobs API (client.batch.jobs.create()) not instrumented#272
      Feature
      Labels
      integration: mistral
      Description
      @braintrust-bot
      braintrust-bot
      bot
      opened on Apr 14, 2026
      Summary
      The Mistral Batch Jobs API (POST /v1/batch/jobs) is not instrumented. Calls to client.batch.jobs.create() produce zero Braintrust tracing. This is a documented, GA production feature on the Mistral platform for running batch inference across chat completions, embeddings, FIM, OCR, audio transcriptions, moderations, and classifications.
      The Braintrust Anthropic integration does instrument an equivalent batch API (client.messages.batches.create() and client.messages.batches.results()), making this an asymmetry across providers in this repo.
      What is missing
      Mistral ResourceMethodInstrumented?
      client.chatcomplete(), stream()Yes
      client.embeddingscreate()Yes
      client.fimcomplete(), stream()Yes
      client.agentscomplete(), stream()Yes
      client.batch.jobscreate()No
      client.batch.jobsget(), list(), cancel()No (CRUD — lower priority)
      The generative-execution-relevant surfaces are batch.jobs.create() (submitting a batch of model inference requests) and retrieving the results (via output file download). The list/get/cancel methods are CRUD management and lower priority.
      At minimum, instrumentation for batch.jobs.create() should create a span capturing:
      • Input: list of custom IDs from the batch requests, number of requests, target endpoint
      • Output: batch job ID, processing status, request counts
      • Metrics: latency (submission time)
      • Metadata: model(s), target endpoint, batch size, timeout configuration
      This mirrors the pattern used by the Anthropic batch instrumentation, which creates a task-type span with input custom IDs, output status/counts, and metadata including model and number of requests.
      Batch API capabilities
      The Mistral batch API supports batching requests to all major generative endpoints:
      • /v1/chat/completions
      • /v1/embeddings
      • /v1/fim/completions
      • /v1/ocr
      • /v1/audio/transcriptions
      • /v1/moderations, /v1/chat/moderations
      • /v1/classifications
      Both file-based batching (up to 1M requests via JSONL upload) and inline batching (up to 10K requests embedded in the create call) are supported.
      Braintrust docs status
      not_found — The Mistral integration page documents chat completions, embeddings, FIM, and agents. No mention of batch API support.
      Upstream sources
      Local files inspected
      • py/src/braintrust/integrations/mistral/patchers.py — defines patchers for Chat, Embeddings, Fim, Agents; zero references to batch, jobs, or Batch
      • py/src/braintrust/integrations/mistral/tracing.py — wrapper functions for chat, embeddings, FIM, agents only; no batch wrappers
      • py/src/braintrust/integrations/mistral/integration.py — integration class registers 4 composite patchers (ChatPatcher, EmbeddingsPatcher, FimPatcher, AgentsPatcher); no BatchPatcher
      • py/src/braintrust/integrations/mistral/test_mistral.py — no batch test cases
      • py/src/braintrust/integrations/anthropic/tracing.py — Anthropic batch API IS instrumented (BatchesCreate, BatchesResults classes) as precedent
      • py/noxfile.py — test_mistral session tests against LATEST and 1.12.4; no batch coverage
      Relationship to existing issues
      Metadata
      Metadata
      Assignees
      No one assigned
        Labels
        integration: mistral
        Type
        Feature
        Projects
        No projects
          Milestone
          No milestone
          Relationships
          None yet
          Development
          No branches or pull requests
          Issue actions
          
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
[BOT ISSUE] Mistral: Batch Jobs API (`client.batch.jobs.create()`) not instrumented · Issue #272 · braintrustdata/braintrust-sdk-python · GitHub
          Skip to content
          [BOT ISSUE] Mistral: Batch Jobs API (client.batch.jobs.create()) not instrumented #272
          Open
          Feature
          Open
          [BOT ISSUE] Mistral: Batch Jobs API (client.batch.jobs.create()) not instrumented#272
          Feature
          Labels
          integration: mistral
          Description
          @braintrust-bot
          braintrust-bot
          bot
          opened on Apr 14, 2026
          Summary
          The Mistral Batch Jobs API (POST /v1/batch/jobs) is not instrumented. Calls to client.batch.jobs.create() produce zero Braintrust tracing. This is a documented, GA production feature on the Mistral platform for running batch inference across chat completions, embeddings, FIM, OCR, audio transcriptions, moderations, and classifications.
          The Braintrust Anthropic integration does instrument an equivalent batch API (client.messages.batches.create() and client.messages.batches.results()), making this an asymmetry across providers in this repo.
          What is missing
          Mistral ResourceMethodInstrumented?
          client.chatcomplete(), stream()Yes
          client.embeddingscreate()Yes
          client.fimcomplete(), stream()Yes
          client.agentscomplete(), stream()Yes
          client.batch.jobscreate()No
          client.batch.jobsget(), list(), cancel()No (CRUD — lower priority)
          The generative-execution-relevant surfaces are batch.jobs.create() (submitting a batch of model inference requests) and retrieving the results (via output file download). The list/get/cancel methods are CRUD management and lower priority.
          At minimum, instrumentation for batch.jobs.create() should create a span capturing:
          • Input: list of custom IDs from the batch requests, number of requests, target endpoint
          • Output: batch job ID, processing status, request counts
          • Metrics: latency (submission time)
          • Metadata: model(s), target endpoint, batch size, timeout configuration
          This mirrors the pattern used by the Anthropic batch instrumentation, which creates a task-type span with input custom IDs, output status/counts, and metadata including model and number of requests.
          Batch API capabilities
          The Mistral batch API supports batching requests to all major generative endpoints:
          • /v1/chat/completions
          • /v1/embeddings
          • /v1/fim/completions
          • /v1/ocr
          • /v1/audio/transcriptions
          • /v1/moderations, /v1/chat/moderations
          • /v1/classifications
          Both file-based batching (up to 1M requests via JSONL upload) and inline batching (up to 10K requests embedded in the create call) are supported.
          Braintrust docs status
          not_found — The Mistral integration page documents chat completions, embeddings, FIM, and agents. No mention of batch API support.
          Upstream sources
          Local files inspected
          • py/src/braintrust/integrations/mistral/patchers.py — defines patchers for Chat, Embeddings, Fim, Agents; zero references to batch, jobs, or Batch
          • py/src/braintrust/integrations/mistral/tracing.py — wrapper functions for chat, embeddings, FIM, agents only; no batch wrappers
          • py/src/braintrust/integrations/mistral/integration.py — integration class registers 4 composite patchers (ChatPatcher, EmbeddingsPatcher, FimPatcher, AgentsPatcher); no BatchPatcher
          • py/src/braintrust/integrations/mistral/test_mistral.py — no batch test cases
          • py/src/braintrust/integrations/anthropic/tracing.py — Anthropic batch API IS instrumented (BatchesCreate, BatchesResults classes) as precedent
          • py/noxfile.py — test_mistral session tests against LATEST and 1.12.4; no batch coverage
          Relationship to existing issues
          Metadata
          Metadata
          Assignees
          No one assigned
            Labels
            integration: mistral
            Type
            Feature
            Projects
            No projects
              Milestone
              No milestone
              Relationships
              None yet
              Development
              No branches or pull requests
              Issue actions
              
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
if (regex.test(text)) {
found = true;
var frag = document.createDocumentFragment();
var parts = text.split(regex);
parts.forEach(function(part, i) {
if (i % 2 === 0) {
frag.appendChild(document.createTextNode(part));
} else {
var span = document.createElement('span');
span.className = 'userscript-highlight';
span.textContent = part;
frag.appendChild(span);
}
});
node.parentNode.replaceChild(frag, node);
}
});
} else if (node.nodeType === 1 && node.childNodes) { // element
var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];
if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
[BOT ISSUE] Mistral: Batch Jobs API (`client.batch.jobs.create()`) not instrumented · Issue #272 · braintrustdata/braintrust-sdk-python · GitHub
              Skip to content
              [BOT ISSUE] Mistral: Batch Jobs API (client.batch.jobs.create()) not instrumented #272
              Open
              Feature
              Open
              [BOT ISSUE] Mistral: Batch Jobs API (client.batch.jobs.create()) not instrumented#272
              Feature
              Labels
              integration: mistral
              Description
              @braintrust-bot
              braintrust-bot
              bot
              opened on Apr 14, 2026
              Summary
              The Mistral Batch Jobs API (POST /v1/batch/jobs) is not instrumented. Calls to client.batch.jobs.create() produce zero Braintrust tracing. This is a documented, GA production feature on the Mistral platform for running batch inference across chat completions, embeddings, FIM, OCR, audio transcriptions, moderations, and classifications.
              The Braintrust Anthropic integration does instrument an equivalent batch API (client.messages.batches.create() and client.messages.batches.results()), making this an asymmetry across providers in this repo.
              What is missing
              Mistral ResourceMethodInstrumented?
              client.chatcomplete(), stream()Yes
              client.embeddingscreate()Yes
              client.fimcomplete(), stream()Yes
              client.agentscomplete(), stream()Yes
              client.batch.jobscreate()No
              client.batch.jobsget(), list(), cancel()No (CRUD — lower priority)
              The generative-execution-relevant surfaces are batch.jobs.create() (submitting a batch of model inference requests) and retrieving the results (via output file download). The list/get/cancel methods are CRUD management and lower priority.
              At minimum, instrumentation for batch.jobs.create() should create a span capturing:
              • Input: list of custom IDs from the batch requests, number of requests, target endpoint
              • Output: batch job ID, processing status, request counts
              • Metrics: latency (submission time)
              • Metadata: model(s), target endpoint, batch size, timeout configuration
              This mirrors the pattern used by the Anthropic batch instrumentation, which creates a task-type span with input custom IDs, output status/counts, and metadata including model and number of requests.
              Batch API capabilities
              The Mistral batch API supports batching requests to all major generative endpoints:
              • /v1/chat/completions
              • /v1/embeddings
              • /v1/fim/completions
              • /v1/ocr
              • /v1/audio/transcriptions
              • /v1/moderations, /v1/chat/moderations
              • /v1/classifications
              Both file-based batching (up to 1M requests via JSONL upload) and inline batching (up to 10K requests embedded in the create call) are supported.
              Braintrust docs status
              not_found — The Mistral integration page documents chat completions, embeddings, FIM, and agents. No mention of batch API support.
              Upstream sources
              Local files inspected
              • py/src/braintrust/integrations/mistral/patchers.py — defines patchers for Chat, Embeddings, Fim, Agents; zero references to batch, jobs, or Batch
              • py/src/braintrust/integrations/mistral/tracing.py — wrapper functions for chat, embeddings, FIM, agents only; no batch wrappers
              • py/src/braintrust/integrations/mistral/integration.py — integration class registers 4 composite patchers (ChatPatcher, EmbeddingsPatcher, FimPatcher, AgentsPatcher); no BatchPatcher
              • py/src/braintrust/integrations/mistral/test_mistral.py — no batch test cases
              • py/src/braintrust/integrations/anthropic/tracing.py — Anthropic batch API IS instrumented (BatchesCreate, BatchesResults classes) as precedent
              • py/noxfile.py — test_mistral session tests against LATEST and 1.12.4; no batch coverage
              Relationship to existing issues
              Metadata
              Metadata
              Assignees
              No one assigned
                Labels
                integration: mistral
                Type
                Feature
                Projects
                No projects
                  Milestone
                  No milestone
                  Relationships
                  None yet
                  Development
                  No branches or pull requests
                  Issue actions
                  
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
[BOT ISSUE] Mistral: Batch Jobs API (`client.batch.jobs.create()`) not instrumented · Issue #272 · braintrustdata/braintrust-sdk-python · GitHub
                  Skip to content
                  [BOT ISSUE] Mistral: Batch Jobs API (client.batch.jobs.create()) not instrumented #272
                  Open
                  Feature
                  Open
                  [BOT ISSUE] Mistral: Batch Jobs API (client.batch.jobs.create()) not instrumented#272
                  Feature
                  Labels
                  integration: mistral
                  Description
                  @braintrust-bot
                  braintrust-bot
                  bot
                  opened on Apr 14, 2026
                  Summary
                  The Mistral Batch Jobs API (POST /v1/batch/jobs) is not instrumented. Calls to client.batch.jobs.create() produce zero Braintrust tracing. This is a documented, GA production feature on the Mistral platform for running batch inference across chat completions, embeddings, FIM, OCR, audio transcriptions, moderations, and classifications.
                  The Braintrust Anthropic integration does instrument an equivalent batch API (client.messages.batches.create() and client.messages.batches.results()), making this an asymmetry across providers in this repo.
                  What is missing
                  Mistral ResourceMethodInstrumented?
                  client.chatcomplete(), stream()Yes
                  client.embeddingscreate()Yes
                  client.fimcomplete(), stream()Yes
                  client.agentscomplete(), stream()Yes
                  client.batch.jobscreate()No
                  client.batch.jobsget(), list(), cancel()No (CRUD — lower priority)
                  The generative-execution-relevant surfaces are batch.jobs.create() (submitting a batch of model inference requests) and retrieving the results (via output file download). The list/get/cancel methods are CRUD management and lower priority.
                  At minimum, instrumentation for batch.jobs.create() should create a span capturing:
                  • Input: list of custom IDs from the batch requests, number of requests, target endpoint
                  • Output: batch job ID, processing status, request counts
                  • Metrics: latency (submission time)
                  • Metadata: model(s), target endpoint, batch size, timeout configuration
                  This mirrors the pattern used by the Anthropic batch instrumentation, which creates a task-type span with input custom IDs, output status/counts, and metadata including model and number of requests.
                  Batch API capabilities
                  The Mistral batch API supports batching requests to all major generative endpoints:
                  • /v1/chat/completions
                  • /v1/embeddings
                  • /v1/fim/completions
                  • /v1/ocr
                  • /v1/audio/transcriptions
                  • /v1/moderations, /v1/chat/moderations
                  • /v1/classifications
                  Both file-based batching (up to 1M requests via JSONL upload) and inline batching (up to 10K requests embedded in the create call) are supported.
                  Braintrust docs status
                  not_found — The Mistral integration page documents chat completions, embeddings, FIM, and agents. No mention of batch API support.
                  Upstream sources
                  Local files inspected
                  • py/src/braintrust/integrations/mistral/patchers.py — defines patchers for Chat, Embeddings, Fim, Agents; zero references to batch, jobs, or Batch
                  • py/src/braintrust/integrations/mistral/tracing.py — wrapper functions for chat, embeddings, FIM, agents only; no batch wrappers
                  • py/src/braintrust/integrations/mistral/integration.py — integration class registers 4 composite patchers (ChatPatcher, EmbeddingsPatcher, FimPatcher, AgentsPatcher); no BatchPatcher
                  • py/src/braintrust/integrations/mistral/test_mistral.py — no batch test cases
                  • py/src/braintrust/integrations/anthropic/tracing.py — Anthropic batch API IS instrumented (BatchesCreate, BatchesResults classes) as precedent
                  • py/noxfile.py — test_mistral session tests against LATEST and 1.12.4; no batch coverage
                  Relationship to existing issues
                  Metadata
                  Metadata
                  Assignees
                  No one assigned
                    Labels
                    integration: mistral
                    Type
                    Feature
                    Projects
                    No projects
                      Milestone
                      No milestone
                      Relationships
                      None yet
                      Development
                      No branches or pull requests
                      Issue actions
                      
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
[BOT ISSUE] Mistral: Batch Jobs API (`client.batch.jobs.create()`) not instrumented · Issue #272 · braintrustdata/braintrust-sdk-python · GitHub
                      Skip to content
                      [BOT ISSUE] Mistral: Batch Jobs API (client.batch.jobs.create()) not instrumented #272
                      Open
                      Feature
                      Open
                      [BOT ISSUE] Mistral: Batch Jobs API (client.batch.jobs.create()) not instrumented#272
                      Feature
                      Labels
                      integration: mistral
                      Description
                      @braintrust-bot
                      braintrust-bot
                      bot
                      opened on Apr 14, 2026
                      Summary
                      The Mistral Batch Jobs API (POST /v1/batch/jobs) is not instrumented. Calls to client.batch.jobs.create() produce zero Braintrust tracing. This is a documented, GA production feature on the Mistral platform for running batch inference across chat completions, embeddings, FIM, OCR, audio transcriptions, moderations, and classifications.
                      The Braintrust Anthropic integration does instrument an equivalent batch API (client.messages.batches.create() and client.messages.batches.results()), making this an asymmetry across providers in this repo.
                      What is missing
                      Mistral ResourceMethodInstrumented?
                      client.chatcomplete(), stream()Yes
                      client.embeddingscreate()Yes
                      client.fimcomplete(), stream()Yes
                      client.agentscomplete(), stream()Yes
                      client.batch.jobscreate()No
                      client.batch.jobsget(), list(), cancel()No (CRUD — lower priority)
                      The generative-execution-relevant surfaces are batch.jobs.create() (submitting a batch of model inference requests) and retrieving the results (via output file download). The list/get/cancel methods are CRUD management and lower priority.
                      At minimum, instrumentation for batch.jobs.create() should create a span capturing:
                      • Input: list of custom IDs from the batch requests, number of requests, target endpoint
                      • Output: batch job ID, processing status, request counts
                      • Metrics: latency (submission time)
                      • Metadata: model(s), target endpoint, batch size, timeout configuration
                      This mirrors the pattern used by the Anthropic batch instrumentation, which creates a task-type span with input custom IDs, output status/counts, and metadata including model and number of requests.
                      Batch API capabilities
                      The Mistral batch API supports batching requests to all major generative endpoints:
                      • /v1/chat/completions
                      • /v1/embeddings
                      • /v1/fim/completions
                      • /v1/ocr
                      • /v1/audio/transcriptions
                      • /v1/moderations, /v1/chat/moderations
                      • /v1/classifications
                      Both file-based batching (up to 1M requests via JSONL upload) and inline batching (up to 10K requests embedded in the create call) are supported.
                      Braintrust docs status
                      not_found — The Mistral integration page documents chat completions, embeddings, FIM, and agents. No mention of batch API support.
                      Upstream sources
                      Local files inspected
                      • py/src/braintrust/integrations/mistral/patchers.py — defines patchers for Chat, Embeddings, Fim, Agents; zero references to batch, jobs, or Batch
                      • py/src/braintrust/integrations/mistral/tracing.py — wrapper functions for chat, embeddings, FIM, agents only; no batch wrappers
                      • py/src/braintrust/integrations/mistral/integration.py — integration class registers 4 composite patchers (ChatPatcher, EmbeddingsPatcher, FimPatcher, AgentsPatcher); no BatchPatcher
                      • py/src/braintrust/integrations/mistral/test_mistral.py — no batch test cases
                      • py/src/braintrust/integrations/anthropic/tracing.py — Anthropic batch API IS instrumented (BatchesCreate, BatchesResults classes) as precedent
                      • py/noxfile.py — test_mistral session tests against LATEST and 1.12.4; no batch coverage
                      Relationship to existing issues
                      Metadata
                      Metadata
                      Assignees
                      No one assigned
                        Labels
                        integration: mistral
                        Type
                        Feature
                        Projects
                        No projects
                          Milestone
                          No milestone
                          Relationships
                          None yet
                          Development
                          No branches or pull requests
                          Issue actions
                          
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
[BOT ISSUE] Mistral: Batch Jobs API (`client.batch.jobs.create()`) not instrumented · Issue #272 · braintrustdata/braintrust-sdk-python · GitHub
                          Skip to content
                          [BOT ISSUE] Mistral: Batch Jobs API (client.batch.jobs.create()) not instrumented #272
                          Open
                          Feature
                          Open
                          [BOT ISSUE] Mistral: Batch Jobs API (client.batch.jobs.create()) not instrumented#272
                          Feature
                          Labels
                          integration: mistral
                          Description
                          @braintrust-bot
                          braintrust-bot
                          bot
                          opened on Apr 14, 2026
                          Summary
                          The Mistral Batch Jobs API (POST /v1/batch/jobs) is not instrumented. Calls to client.batch.jobs.create() produce zero Braintrust tracing. This is a documented, GA production feature on the Mistral platform for running batch inference across chat completions, embeddings, FIM, OCR, audio transcriptions, moderations, and classifications.
                          The Braintrust Anthropic integration does instrument an equivalent batch API (client.messages.batches.create() and client.messages.batches.results()), making this an asymmetry across providers in this repo.
                          What is missing
                          Mistral ResourceMethodInstrumented?
                          client.chatcomplete(), stream()Yes
                          client.embeddingscreate()Yes
                          client.fimcomplete(), stream()Yes
                          client.agentscomplete(), stream()Yes
                          client.batch.jobscreate()No
                          client.batch.jobsget(), list(), cancel()No (CRUD — lower priority)
                          The generative-execution-relevant surfaces are batch.jobs.create() (submitting a batch of model inference requests) and retrieving the results (via output file download). The list/get/cancel methods are CRUD management and lower priority.
                          At minimum, instrumentation for batch.jobs.create() should create a span capturing:
                          • Input: list of custom IDs from the batch requests, number of requests, target endpoint
                          • Output: batch job ID, processing status, request counts
                          • Metrics: latency (submission time)
                          • Metadata: model(s), target endpoint, batch size, timeout configuration
                          This mirrors the pattern used by the Anthropic batch instrumentation, which creates a task-type span with input custom IDs, output status/counts, and metadata including model and number of requests.
                          Batch API capabilities
                          The Mistral batch API supports batching requests to all major generative endpoints:
                          • /v1/chat/completions
                          • /v1/embeddings
                          • /v1/fim/completions
                          • /v1/ocr
                          • /v1/audio/transcriptions
                          • /v1/moderations, /v1/chat/moderations
                          • /v1/classifications
                          Both file-based batching (up to 1M requests via JSONL upload) and inline batching (up to 10K requests embedded in the create call) are supported.
                          Braintrust docs status
                          not_found — The Mistral integration page documents chat completions, embeddings, FIM, and agents. No mention of batch API support.
                          Upstream sources
                          Local files inspected
                          • py/src/braintrust/integrations/mistral/patchers.py — defines patchers for Chat, Embeddings, Fim, Agents; zero references to batch, jobs, or Batch
                          • py/src/braintrust/integrations/mistral/tracing.py — wrapper functions for chat, embeddings, FIM, agents only; no batch wrappers
                          • py/src/braintrust/integrations/mistral/integration.py — integration class registers 4 composite patchers (ChatPatcher, EmbeddingsPatcher, FimPatcher, AgentsPatcher); no BatchPatcher
                          • py/src/braintrust/integrations/mistral/test_mistral.py — no batch test cases
                          • py/src/braintrust/integrations/anthropic/tracing.py — Anthropic batch API IS instrumented (BatchesCreate, BatchesResults classes) as precedent
                          • py/noxfile.py — test_mistral session tests against LATEST and 1.12.4; no batch coverage
                          Relationship to existing issues
                          Metadata
                          Metadata
                          Assignees
                          No one assigned
                            Labels
                            integration: mistral
                            Type
                            Feature
                            Projects
                            No projects
                              Milestone
                              No milestone
                              Relationships
                              None yet
                              Development
                              No branches or pull requests
                              Issue actions
                              
, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
[BOT ISSUE] Mistral: Batch Jobs API (`client.batch.jobs.create()`) not instrumented · Issue #272 · braintrustdata/braintrust-sdk-python · GitHub
                              Skip to content
                              [BOT ISSUE] Mistral: Batch Jobs API (client.batch.jobs.create()) not instrumented #272
                              Open
                              Feature
                              Open
                              [BOT ISSUE] Mistral: Batch Jobs API (client.batch.jobs.create()) not instrumented#272
                              Feature
                              Labels
                              integration: mistral
                              Description
                              @braintrust-bot
                              braintrust-bot
                              bot
                              opened on Apr 14, 2026
                              Summary
                              The Mistral Batch Jobs API (POST /v1/batch/jobs) is not instrumented. Calls to client.batch.jobs.create() produce zero Braintrust tracing. This is a documented, GA production feature on the Mistral platform for running batch inference across chat completions, embeddings, FIM, OCR, audio transcriptions, moderations, and classifications.
                              The Braintrust Anthropic integration does instrument an equivalent batch API (client.messages.batches.create() and client.messages.batches.results()), making this an asymmetry across providers in this repo.
                              What is missing
                              Mistral ResourceMethodInstrumented?
                              client.chatcomplete(), stream()Yes
                              client.embeddingscreate()Yes
                              client.fimcomplete(), stream()Yes
                              client.agentscomplete(), stream()Yes
                              client.batch.jobscreate()No
                              client.batch.jobsget(), list(), cancel()No (CRUD — lower priority)
                              The generative-execution-relevant surfaces are batch.jobs.create() (submitting a batch of model inference requests) and retrieving the results (via output file download). The list/get/cancel methods are CRUD management and lower priority.
                              At minimum, instrumentation for batch.jobs.create() should create a span capturing:
                              • Input: list of custom IDs from the batch requests, number of requests, target endpoint
                              • Output: batch job ID, processing status, request counts
                              • Metrics: latency (submission time)
                              • Metadata: model(s), target endpoint, batch size, timeout configuration
                              This mirrors the pattern used by the Anthropic batch instrumentation, which creates a task-type span with input custom IDs, output status/counts, and metadata including model and number of requests.
                              Batch API capabilities
                              The Mistral batch API supports batching requests to all major generative endpoints:
                              • /v1/chat/completions
                              • /v1/embeddings
                              • /v1/fim/completions
                              • /v1/ocr
                              • /v1/audio/transcriptions
                              • /v1/moderations, /v1/chat/moderations
                              • /v1/classifications
                              Both file-based batching (up to 1M requests via JSONL upload) and inline batching (up to 10K requests embedded in the create call) are supported.
                              Braintrust docs status
                              not_found — The Mistral integration page documents chat completions, embeddings, FIM, and agents. No mention of batch API support.
                              Upstream sources
                              Local files inspected
                              • py/src/braintrust/integrations/mistral/patchers.py — defines patchers for Chat, Embeddings, Fim, Agents; zero references to batch, jobs, or Batch
                              • py/src/braintrust/integrations/mistral/tracing.py — wrapper functions for chat, embeddings, FIM, agents only; no batch wrappers
                              • py/src/braintrust/integrations/mistral/integration.py — integration class registers 4 composite patchers (ChatPatcher, EmbeddingsPatcher, FimPatcher, AgentsPatcher); no BatchPatcher
                              • py/src/braintrust/integrations/mistral/test_mistral.py — no batch test cases
                              • py/src/braintrust/integrations/anthropic/tracing.py — Anthropic batch API IS instrumented (BatchesCreate, BatchesResults classes) as precedent
                              • py/noxfile.py — test_mistral session tests against LATEST and 1.12.4; no batch coverage
                              Relationship to existing issues
                              Metadata
                              Metadata
                              Assignees
                              No one assigned
                                Labels
                                integration: mistral
                                Type
                                Feature
                                Projects
                                No projects
                                  Milestone
                                  No milestone
                                  Relationships
                                  None yet
                                  Development
                                  No branches or pull requests
                                  Issue actions