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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>
149 lines
4.7 KiB
Plaintext
149 lines
4.7 KiB
Plaintext
---
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title: Traces
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description: "Using Traces to monitor your Crews"
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icon: "timeline"
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mode: "wide"
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---
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## Overview
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Traces provide comprehensive visibility into your crew executions, helping you monitor performance, debug issues, and optimize your AI agent workflows.
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## What are Traces?
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Traces in CrewAI AMP are detailed execution records that capture every aspect of your crew's operation, from initial inputs to final outputs. They record:
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- Agent thoughts and reasoning
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- Task execution details
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- Tool usage and outputs
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- Token consumption metrics
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- Execution times
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- Cost estimates
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<Frame></Frame>
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## Accessing Traces
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<Steps>
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<Step title="Navigate to the Traces Tab">
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Once in your CrewAI AMP dashboard, click on the **Traces** to view all execution records.
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</Step>
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<Step title="Select an Execution">
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You'll see a list of all crew executions, sorted by date. Click on any execution to view its detailed trace.
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</Step>
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</Steps>
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## Understanding the Trace Interface
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The trace interface is divided into several sections, each providing different insights into your crew's execution:
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### 1. Execution Summary
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The top section displays high-level metrics about the execution:
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- **Total Tokens**: Number of tokens consumed across all tasks
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- **Prompt Tokens**: Tokens used in prompts to the LLM
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- **Completion Tokens**: Tokens generated in LLM responses
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- **Requests**: Number of API calls made
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- **Execution Time**: Total duration of the crew run
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- **Estimated Cost**: Approximate cost based on token usage
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<Frame></Frame>
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### 2. Tasks & Agents
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This section shows all tasks and agents that were part of the crew execution:
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- Task name and agent assignment
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- Agents and LLMs used for each task
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- Status (completed/failed)
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- Individual execution time of the task
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<Frame></Frame>
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### 3. Final Output
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Displays the final result produced by the crew after all tasks are completed.
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<Frame></Frame>
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### 4. Execution Timeline
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A visual representation of when each task started and ended, helping you identify bottlenecks or parallel execution patterns.
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<Frame></Frame>
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### 5. Detailed Task View
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When you click on a specific task in the timeline or task list, you'll see:
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<Frame></Frame>
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- **Task Key**: Unique identifier for the task
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- **Task ID**: Technical identifier in the system
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- **Status**: Current state (completed/running/failed)
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- **Agent**: Which agent performed the task
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- **LLM**: Language model used for this task
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- **Start/End Time**: When the task began and completed
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- **Execution Time**: Duration of this specific task
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- **Task Description**: What the agent was instructed to do
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- **Expected Output**: What output format was requested
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- **Input**: Any input provided to this task from previous tasks
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- **Output**: The actual result produced by the agent
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## Using Traces for Debugging
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Traces are invaluable for troubleshooting issues with your crews:
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<Steps>
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<Step title="Identify Failure Points">
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When a crew execution doesn't produce the expected results, examine the trace to find where things went wrong. Look for:
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- Failed tasks
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- Unexpected agent decisions
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- Tool usage errors
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- Misinterpreted instructions
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<Frame>
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</Frame>
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</Step>
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<Step title="Optimize Performance">
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Use execution metrics to identify performance bottlenecks:
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- Tasks that took longer than expected
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- Excessive token usage
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- Redundant tool operations
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- Unnecessary API calls
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</Step>
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<Step title="Improve Cost Efficiency">
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Analyze token usage and cost estimates to optimize your crew's efficiency:
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- Consider using smaller models for simpler tasks
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- Refine prompts to be more concise
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- Cache frequently accessed information
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- Structure tasks to minimize redundant operations
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</Step>
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</Steps>
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## Performance and batching
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CrewAI batches trace uploads to reduce overhead on high-volume runs:
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- A TraceBatchManager buffers events and sends them in batches via the Plus API client
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- Reduces network chatter and improves reliability on flaky connections
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- Automatically enabled in the default trace listener; no configuration needed
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This yields more stable tracing under load while preserving detailed task/agent telemetry.
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<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
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Contact our support team for assistance with trace analysis or any other
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CrewAI AMP features.
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</Card>
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