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* feat: adopt directory-based docs versioning with Edge channel Switch docs.crewai.com from navigation-only versioning (every version selector entry rendered the same docs/<lang>/* source files) to Mintlify's directory-based versioning so each version selector entry renders its own snapshot. Add an "Edge" channel under docs/edge/<lang>/* that always reflects main HEAD for unreleased work, eliminating pre-release leakage onto frozen release labels. External links to canonical /<lang>/* URLs are preserved via wildcard redirects that always land on the current default version. Layout: - docs/edge/<lang>/* rolling source (you edit here) - docs/edge/enterprise-api.*.yaml - docs/v<X.Y.Z>/<lang>/* frozen, immutable snapshots - docs/v<X.Y.Z>/enterprise-api.*.yaml - docs/images/ shared, append-only - docs/docs.json nav + redirects URLs follow the Mintlify-idiomatic shape: /edge/<lang>/<page> for Edge, /v<X.Y.Z>/<lang>/<page> for every frozen snapshot. The wildcard redirects /<lang>/:slug* -> /<default>/<lang>/:slug* keep stale links working, and every freeze rewrites them (plus all per-section/per-page redirects) so destinations always resolve to the current default without depending on a second redirect hop. Release flow integration (devtools release): - New module crewai_devtools.docs_versioning.freeze() materialises docs/v<X.Y.Z>/ from docs/edge/, rewrites openapi: refs inside the snapshot, inserts the version into every language block in docs.json, and refreshes all redirect destinations. - _update_docs_and_create_pr() in cli.py now calls that freeze during Phase 2 of devtools release. Edge changelogs are updated first (so the snapshot freeze picks them up), then the snapshot is staged alongside docs.json, branched as docs/freeze-v<X.Y.Z>, and the PR is titled [docs-freeze] docs: snapshot and changelog for v<X.Y.Z> — the title prefix the new CI guard reads. - The PR still gates tag, GitHub release, PyPI publish, and the enterprise release as before; no new PRs are added. - Pre-releases (1.X.YaN, 1.X.YbN, ...) skip the snapshot — they ride Edge — and the docs PR title omits the [docs-freeze] prefix. - docs_check (AI-generated docs scaffolding) writes to docs/edge/<lang>/* so newly-generated unreleased docs land in Edge and never accidentally touch a frozen snapshot. Migration scripts (one-shot): - scripts/docs/freeze_historical_versions.py reconstructs all 16 historical snapshots (v1.10.0 .. v1.14.7) from git tags via git archive | tar, rewriting openapi: MDX refs so each snapshot reads its own enterprise-api YAML rather than the live one. - scripts/docs/prefix_version_paths.py one-shot-migrates docs.json: rewrites every page path in 16 versioned blocks to point under docs/v<X.Y.Z>/, inserts a new Edge entry per language, tags v1.14.7 as Latest (default), prunes pages whose target file doesn't exist in the snapshot (e.g. docs/ar/ didn't exist before v1.12.0), and writes the wildcard + per-section redirects. - scripts/docs/freeze_current_edge.py is now a thin CLI wrapper around docs_versioning.freeze for manual one-off freezes (e.g. retroactively snapshotting a forgotten release). CI guards (.github/workflows/docs-snapshots.yml): - Frozen snapshots under docs/v[0-9]*/ are immutable; only PRs whose title contains [docs-freeze] (i.e. release-cut PRs generated by devtools release or the manual wrapper) may modify them. - Images under docs/images/ are append-only since snapshots share a single image directory. Deleting or renaming an image breaks every historical snapshot that still references it. Restored docs/images/crewai-otel-export.png from PR #3673; it was deleted in PR #4908 but v1.10.0 / v1.10.1 snapshots still reference it. Restoring instead of editing the snapshots preserves historical rendering fidelity and validates the new append-only rule retroactively. Tests: - lib/devtools/tests/test_docs_versioning.py covers the freeze: file copy, openapi rewrite, version insertion, default demotion, redirect upserts, per-section redirect rewriting, idempotency, and invalid inputs. Verified locally with mintlify broken-links: 0 broken links across the full site (Edge + 16 frozen versions, 4 locales). AGENTS.md (repo root) is the contributor guide for the new model; RELEASING.md is the release-cut runbook; README's Contribution section links to both. Co-authored-by: Cursor <cursoragent@cursor.com> * style: resolve linter issues --------- Co-authored-by: Cursor <cursoragent@cursor.com>
238 lines
8.3 KiB
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238 lines
8.3 KiB
Plaintext
---
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title: Braintrust
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description: Braintrust integration for CrewAI with OpenTelemetry tracing and evaluation
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icon: magnifying-glass-chart
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mode: "wide"
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---
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# Braintrust Integration
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This guide demonstrates how to integrate **Braintrust** with **CrewAI** using OpenTelemetry for comprehensive tracing and evaluation. By the end of this guide, you will be able to trace your CrewAI agents, monitor their performance, and evaluate their outputs using Braintrust's powerful observability platform.
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> **What is Braintrust?** [Braintrust](https://www.braintrust.dev) is an AI evaluation and observability platform that provides comprehensive tracing, evaluation, and monitoring for AI applications with built-in experiment tracking and performance analytics.
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## Get Started
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We'll walk through a simple example of using CrewAI and integrating it with Braintrust via OpenTelemetry for comprehensive observability and evaluation.
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### Step 1: Install Dependencies
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```bash
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uv add braintrust[otel] crewai crewai-tools opentelemetry-instrumentation-openai opentelemetry-instrumentation-crewai python-dotenv
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```
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### Step 2: Set Up Environment Variables
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Setup Braintrust API keys and configure OpenTelemetry to send traces to Braintrust. You'll need a Braintrust API key and your OpenAI API key.
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```python
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import os
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from getpass import getpass
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# Get your Braintrust credentials
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BRAINTRUST_API_KEY = getpass("🔑 Enter your Braintrust API Key: ")
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# Get API keys for services
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OPENAI_API_KEY = getpass("🔑 Enter your OpenAI API key: ")
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# Set environment variables
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os.environ["BRAINTRUST_API_KEY"] = BRAINTRUST_API_KEY
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os.environ["BRAINTRUST_PARENT"] = "project_name:crewai-demo"
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os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
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```
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### Step 3: Initialize OpenTelemetry with Braintrust
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Initialize the Braintrust OpenTelemetry instrumentation to start capturing traces and send them to Braintrust.
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```python
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import os
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from typing import Any, Dict
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from braintrust.otel import BraintrustSpanProcessor
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from crewai import Agent, Crew, Task
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from crewai.llm import LLM
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from opentelemetry import trace
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from opentelemetry.instrumentation.crewai import CrewAIInstrumentor
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from opentelemetry.instrumentation.openai import OpenAIInstrumentor
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from opentelemetry.sdk.trace import TracerProvider
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def setup_tracing() -> None:
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"""Setup OpenTelemetry tracing with Braintrust."""
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current_provider = trace.get_tracer_provider()
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if isinstance(current_provider, TracerProvider):
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provider = current_provider
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else:
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provider = TracerProvider()
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trace.set_tracer_provider(provider)
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provider.add_span_processor(BraintrustSpanProcessor())
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CrewAIInstrumentor().instrument(tracer_provider=provider)
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OpenAIInstrumentor().instrument(tracer_provider=provider)
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setup_tracing()
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```
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### Step 4: Create a CrewAI Application
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We'll create a CrewAI application where two agents collaborate to research and write a blog post about AI advancements, with comprehensive tracing enabled.
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```python
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from crewai import Agent, Crew, Process, Task
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from crewai_tools import SerperDevTool
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def create_crew() -> Crew:
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"""Create a crew with multiple agents for comprehensive tracing."""
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llm = LLM(model="gpt-4o-mini")
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search_tool = SerperDevTool()
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# Define agents with specific roles
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researcher = Agent(
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role="Senior Research Analyst",
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goal="Uncover cutting-edge developments in AI and data science",
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backstory="""You work at a leading tech think tank.
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Your expertise lies in identifying emerging trends.
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You have a knack for dissecting complex data and presenting actionable insights.""",
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verbose=True,
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allow_delegation=False,
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llm=llm,
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tools=[search_tool],
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)
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writer = Agent(
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role="Tech Content Strategist",
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goal="Craft compelling content on tech advancements",
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backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
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You transform complex concepts into compelling narratives.""",
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verbose=True,
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allow_delegation=True,
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llm=llm,
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)
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# Create tasks for your agents
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research_task = Task(
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description="""Conduct a comprehensive analysis of the latest advancements in {topic}.
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Identify key trends, breakthrough technologies, and potential industry impacts.""",
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expected_output="Full analysis report in bullet points",
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agent=researcher,
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)
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writing_task = Task(
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description="""Using the insights provided, develop an engaging blog
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post that highlights the most significant {topic} advancements.
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Your post should be informative yet accessible, catering to a tech-savvy audience.
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Make it sound cool, avoid complex words so it doesn't sound like AI.""",
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expected_output="Full blog post of at least 4 paragraphs",
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agent=writer,
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context=[research_task],
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)
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# Instantiate your crew with a sequential process
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crew = Crew(
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agents=[researcher, writer],
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tasks=[research_task, writing_task],
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verbose=True,
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process=Process.sequential
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)
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return crew
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def run_crew():
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"""Run the crew and return results."""
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crew = create_crew()
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result = crew.kickoff(inputs={"topic": "AI developments"})
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return result
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# Run your crew
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if __name__ == "__main__":
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# Instrumentation is already initialized above in this module
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result = run_crew()
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print(result)
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```
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### Step 5: View Traces in Braintrust
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After running your crew, you can view comprehensive traces in Braintrust through different perspectives:
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<Tabs>
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<Tab title="Trace">
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<Frame>
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<img src="/images/braintrust-trace-view.png" alt="Braintrust Trace View"/>
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</Frame>
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</Tab>
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<Tab title="Timeline">
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<Frame>
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<img src="/images/braintrust-timeline-view.png" alt="Braintrust Timeline View"/>
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</Frame>
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</Tab>
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<Tab title="Thread">
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<Frame>
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<img src="/images/braintrust-thread-view.png" alt="Braintrust Thread View"/>
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</Frame>
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</Tab>
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</Tabs>
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### Step 6: Evaluate via SDK (Experiments)
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You can also run evaluations using Braintrust's Eval SDK. This is useful for comparing versions or scoring outputs offline. Below is a Python example using the `Eval` class with the crew we created above:
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```python
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# eval_crew.py
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from braintrust import Eval
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from autoevals import Levenshtein
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def evaluate_crew_task(input_data):
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"""Task function that wraps our crew for evaluation."""
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crew = create_crew()
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result = crew.kickoff(inputs={"topic": input_data["topic"]})
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return str(result)
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Eval(
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"AI Research Crew", # Project name
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{
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"data": lambda: [
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{"topic": "artificial intelligence trends 2024"},
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{"topic": "machine learning breakthroughs"},
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{"topic": "AI ethics and governance"},
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],
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"task": evaluate_crew_task,
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"scores": [Levenshtein],
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},
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)
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```
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Setup your API key and run:
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```bash
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export BRAINTRUST_API_KEY="YOUR_API_KEY"
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braintrust eval eval_crew.py
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```
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See the [Braintrust Eval SDK guide](https://www.braintrust.dev/docs/start/eval-sdk) for more details.
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### Key Features of Braintrust Integration
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- **Comprehensive Tracing**: Track all agent interactions, tool usage, and LLM calls
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- **Performance Monitoring**: Monitor execution times, token usage, and success rates
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- **Experiment Tracking**: Compare different crew configurations and models
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- **Automated Evaluation**: Set up custom evaluation metrics for crew outputs
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- **Error Tracking**: Monitor and debug failures across your crew executions
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- **Cost Analysis**: Track token usage and associated costs
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### Version Compatibility Information
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- Python 3.8+
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- CrewAI >= 0.86.0
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- Braintrust >= 0.1.0
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- OpenTelemetry SDK >= 1.31.0
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### References
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- [Braintrust Documentation](https://www.braintrust.dev/docs) - Overview of the Braintrust platform
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- [Braintrust CrewAI Integration](https://www.braintrust.dev/docs/integrations/crew-ai) - Official CrewAI integration guide
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- [Braintrust Eval SDK](https://www.braintrust.dev/docs/start/eval-sdk) - Run experiments via the SDK
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- [CrewAI Documentation](https://docs.crewai.com/) - Overview of the CrewAI framework
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- [OpenTelemetry Docs](https://opentelemetry.io/docs/) - OpenTelemetry guide
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- [Braintrust GitHub](https://github.com/braintrustdata/braintrust) - Source code for Braintrust SDK
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