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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>
121 lines
4.6 KiB
Plaintext
121 lines
4.6 KiB
Plaintext
---
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title: "Overview"
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description: "Monitor, evaluate, and optimize your CrewAI agents with comprehensive observability tools"
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icon: "face-smile"
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mode: "wide"
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---
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## Observability for CrewAI
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Observability is crucial for understanding how your CrewAI agents perform, identifying bottlenecks, and ensuring reliable operation in production environments. This section covers various tools and platforms that provide monitoring, evaluation, and optimization capabilities for your agent workflows.
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## Why Observability Matters
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- **Performance Monitoring**: Track agent execution times, token usage, and resource consumption
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- **Quality Assurance**: Evaluate output quality and consistency across different scenarios
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- **Debugging**: Identify and resolve issues in agent behavior and task execution
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- **Cost Management**: Monitor LLM API usage and associated costs
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- **Continuous Improvement**: Gather insights to optimize agent performance over time
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## Available Observability Tools
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### Monitoring & Tracing Platforms
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<CardGroup cols={2}>
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<Card title="LangDB" icon="database" href="/en/observability/langdb">
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End-to-end tracing for CrewAI workflows with automatic agent interaction capture.
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</Card>
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<Card title="OpenLIT" icon="magnifying-glass-chart" href="/en/observability/openlit">
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OpenTelemetry-native monitoring with cost tracking and performance analytics.
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</Card>
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<Card title="MLflow" icon="bars-staggered" href="/en/observability/mlflow">
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Machine learning lifecycle management with tracing and evaluation capabilities.
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</Card>
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<Card title="Langfuse" icon="link" href="/en/observability/langfuse">
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LLM engineering platform with detailed tracing and analytics.
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</Card>
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<Card title="Langtrace" icon="chart-line" href="/en/observability/langtrace">
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Open-source observability for LLMs and agent frameworks.
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</Card>
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<Card title="Arize Phoenix" icon="meteor" href="/en/observability/arize-phoenix">
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AI observability platform for monitoring and troubleshooting.
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</Card>
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<Card title="Portkey" icon="key" href="/en/observability/portkey">
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AI gateway with comprehensive monitoring and reliability features.
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</Card>
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<Card title="Opik" icon="meteor" href="/en/observability/opik">
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Debug, evaluate, and monitor LLM applications with comprehensive tracing.
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</Card>
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<Card title="Weave" icon="network-wired" href="/en/observability/weave">
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Weights & Biases platform for tracking and evaluating AI applications.
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</Card>
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</CardGroup>
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### Evaluation & Quality Assurance
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<CardGroup cols={2}>
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<Card title="Patronus AI" icon="shield-check" href="/en/observability/patronus-evaluation">
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Comprehensive evaluation platform for LLM outputs and agent behaviors.
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</Card>
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</CardGroup>
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## Key Observability Metrics
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### Performance Metrics
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- **Execution Time**: How long agents take to complete tasks
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- **Token Usage**: Input/output tokens consumed by LLM calls
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- **API Latency**: Response times from external services
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- **Success Rate**: Percentage of successfully completed tasks
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### Quality Metrics
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- **Output Accuracy**: Correctness of agent responses
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- **Consistency**: Reliability across similar inputs
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- **Relevance**: How well outputs match expected results
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- **Safety**: Compliance with content policies and guidelines
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### Cost Metrics
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- **API Costs**: Expenses from LLM provider usage
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- **Resource Utilization**: Compute and memory consumption
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- **Cost per Task**: Economic efficiency of agent operations
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- **Budget Tracking**: Monitoring against spending limits
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## Getting Started
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1. **Choose Your Tools**: Select observability platforms that match your needs
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2. **Instrument Your Code**: Add monitoring to your CrewAI applications
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3. **Set Up Dashboards**: Configure visualizations for key metrics
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4. **Define Alerts**: Create notifications for important events
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5. **Establish Baselines**: Measure initial performance for comparison
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6. **Iterate and Improve**: Use insights to optimize your agents
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## Best Practices
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### Development Phase
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- Use detailed tracing to understand agent behavior
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- Implement evaluation metrics early in development
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- Monitor resource usage during testing
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- Set up automated quality checks
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### Production Phase
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- Implement comprehensive monitoring and alerting
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- Track performance trends over time
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- Monitor for anomalies and degradation
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- Maintain cost visibility and control
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### Continuous Improvement
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- Regular performance reviews and optimization
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- A/B testing of different agent configurations
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- Feedback loops for quality improvement
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- Documentation of lessons learned
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Choose the observability tools that best fit your use case, infrastructure, and monitoring requirements to ensure your CrewAI agents perform reliably and efficiently.
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