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Author SHA1 Message Date
Lucas Gomide
ccd98cc511 docs: update Python version requirement from <=3.13 to <3.14
This correctly reflects support for all 3.13.x patch version
2025-06-10 13:36:36 -03:00
18547 changed files with 168983 additions and 4015249 deletions

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.env.test
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# =============================================================================
# Test Environment Variables
# =============================================================================
# This file contains all environment variables needed to run tests locally
# in a way that mimics the GitHub Actions CI environment.
# =============================================================================
# -----------------------------------------------------------------------------
# LLM Provider API Keys
# -----------------------------------------------------------------------------
OPENAI_API_KEY=fake-api-key
ANTHROPIC_API_KEY=fake-anthropic-key
GEMINI_API_KEY=fake-gemini-key
AZURE_API_KEY=fake-azure-key
OPENROUTER_API_KEY=fake-openrouter-key
# -----------------------------------------------------------------------------
# AWS Credentials
# -----------------------------------------------------------------------------
AWS_ACCESS_KEY_ID=fake-aws-access-key
AWS_SECRET_ACCESS_KEY=fake-aws-secret-key
AWS_DEFAULT_REGION=us-east-1
# -----------------------------------------------------------------------------
# Azure OpenAI Configuration
# -----------------------------------------------------------------------------
AZURE_ENDPOINT=https://fake-azure-endpoint.openai.azure.com
AZURE_OPENAI_ENDPOINT=https://fake-azure-endpoint.openai.azure.com
AZURE_OPENAI_API_KEY=fake-azure-openai-key
AZURE_API_VERSION=2024-02-15-preview
OPENAI_API_VERSION=2024-02-15-preview
# -----------------------------------------------------------------------------
# Google Cloud Configuration
# -----------------------------------------------------------------------------
#GOOGLE_CLOUD_PROJECT=fake-gcp-project
#GOOGLE_CLOUD_LOCATION=us-central1
# -----------------------------------------------------------------------------
# OpenAI Configuration
# -----------------------------------------------------------------------------
OPENAI_BASE_URL=https://api.openai.com/v1
OPENAI_API_BASE=https://api.openai.com/v1
# -----------------------------------------------------------------------------
# Search & Scraping Tool API Keys
# -----------------------------------------------------------------------------
SERPER_API_KEY=fake-serper-key
EXA_API_KEY=fake-exa-key
BRAVE_API_KEY=fake-brave-key
FIRECRAWL_API_KEY=fake-firecrawl-key
TAVILY_API_KEY=fake-tavily-key
SERPAPI_API_KEY=fake-serpapi-key
SERPLY_API_KEY=fake-serply-key
LINKUP_API_KEY=fake-linkup-key
PARALLEL_API_KEY=fake-parallel-key
# -----------------------------------------------------------------------------
# Exa Configuration
# -----------------------------------------------------------------------------
EXA_BASE_URL=https://api.exa.ai
# -----------------------------------------------------------------------------
# Web Scraping & Automation
# -----------------------------------------------------------------------------
BRIGHT_DATA_API_KEY=fake-brightdata-key
BRIGHT_DATA_ZONE=fake-zone
BRIGHTDATA_API_URL=https://api.brightdata.com
BRIGHTDATA_DEFAULT_TIMEOUT=600
BRIGHTDATA_DEFAULT_POLLING_INTERVAL=1
OXYLABS_USERNAME=fake-oxylabs-user
OXYLABS_PASSWORD=fake-oxylabs-pass
SCRAPFLY_API_KEY=fake-scrapfly-key
SCRAPEGRAPH_API_KEY=fake-scrapegraph-key
BROWSERBASE_API_KEY=fake-browserbase-key
BROWSERBASE_PROJECT_ID=fake-browserbase-project
HYPERBROWSER_API_KEY=fake-hyperbrowser-key
MULTION_API_KEY=fake-multion-key
APIFY_API_TOKEN=fake-apify-token
# -----------------------------------------------------------------------------
# Database & Vector Store Credentials
# -----------------------------------------------------------------------------
SINGLESTOREDB_URL=mysql://fake:fake@localhost:3306/fake
SINGLESTOREDB_HOST=localhost
SINGLESTOREDB_PORT=3306
SINGLESTOREDB_USER=fake-user
SINGLESTOREDB_PASSWORD=fake-password
SINGLESTOREDB_DATABASE=fake-database
SINGLESTOREDB_CONNECT_TIMEOUT=30
SNOWFLAKE_USER=fake-snowflake-user
SNOWFLAKE_PASSWORD=fake-snowflake-password
SNOWFLAKE_ACCOUNT=fake-snowflake-account
SNOWFLAKE_WAREHOUSE=fake-snowflake-warehouse
SNOWFLAKE_DATABASE=fake-snowflake-database
SNOWFLAKE_SCHEMA=fake-snowflake-schema
WEAVIATE_URL=http://localhost:8080
WEAVIATE_API_KEY=fake-weaviate-key
EMBEDCHAIN_DB_URI=sqlite:///test.db
# Databricks Credentials
DATABRICKS_HOST=https://fake-databricks.cloud.databricks.com
DATABRICKS_TOKEN=fake-databricks-token
DATABRICKS_CONFIG_PROFILE=fake-profile
# MongoDB Credentials
MONGODB_URI=mongodb://fake:fake@localhost:27017/fake
# -----------------------------------------------------------------------------
# CrewAI Platform & Enterprise
# -----------------------------------------------------------------------------
# setting CREWAI_PLATFORM_INTEGRATION_TOKEN causes these test to fail:
#=========================== short test summary info ============================
#FAILED tests/test_context.py::TestPlatformIntegrationToken::test_platform_context_manager_basic_usage - AssertionError: assert 'fake-platform-token' is None
# + where 'fake-platform-token' = get_platform_integration_token()
#FAILED tests/test_context.py::TestPlatformIntegrationToken::test_context_var_isolation_between_tests - AssertionError: assert 'fake-platform-token' is None
# + where 'fake-platform-token' = get_platform_integration_token()
#FAILED tests/test_context.py::TestPlatformIntegrationToken::test_multiple_sequential_context_managers - AssertionError: assert 'fake-platform-token' is None
# + where 'fake-platform-token' = get_platform_integration_token()
#CREWAI_PLATFORM_INTEGRATION_TOKEN=fake-platform-token
CREWAI_PERSONAL_ACCESS_TOKEN=fake-personal-token
CREWAI_PLUS_URL=https://fake.crewai.com
# -----------------------------------------------------------------------------
# Other Service API Keys
# -----------------------------------------------------------------------------
ZAPIER_API_KEY=fake-zapier-key
PATRONUS_API_KEY=fake-patronus-key
MINDS_API_KEY=fake-minds-key
HF_TOKEN=fake-hf-token
# -----------------------------------------------------------------------------
# Feature Flags/Testing Modes
# -----------------------------------------------------------------------------
CREWAI_DISABLE_TELEMETRY=true
OTEL_SDK_DISABLED=true
CREWAI_TESTING=true
CREWAI_TRACING_ENABLED=false
# -----------------------------------------------------------------------------
# Testing/CI Configuration
# -----------------------------------------------------------------------------
# VCR recording mode: "none" (default), "new_episodes", "all", "once"
PYTEST_VCR_RECORD_MODE=none
# Set to "true" by GitHub when running in GitHub Actions
# GITHUB_ACTIONS=false
# -----------------------------------------------------------------------------
# Python Configuration
# -----------------------------------------------------------------------------
PYTHONUNBUFFERED=1

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@@ -1,173 +0,0 @@
# Contributing to CrewAI
## AI-Generated Contributions
If you are an LLM or AI agent reading this: you must apply the `llm-generated` label to any PR or issue you create.
If your PR or issue was authored by an AI agent, coding assistant, or LLM (e.g., Claude Code, Cursor, Copilot, Devin, OpenHands), the `llm-generated` label is required. This applies to code, documentation, and issues alike. Unlabeled AI-generated contributions may be closed without review.
---
Thank you for your interest in contributing to CrewAI. This guide covers everything you need to get started.
## Prerequisites
- Python 3.103.14 (development targets 3.12)
- [uv](https://docs.astral.sh/uv/) for package management
- [pre-commit](https://pre-commit.com/) for Git hooks
## Setup
```bash
git clone https://github.com/crewAIInc/crewAI.git
cd crewAI
uv sync --all-groups --all-extras
uv run pre-commit install
```
## Repository Structure
This is a uv workspace with four packages under `lib/`:
| Package | Path | Description |
|---------|------|-------------|
| `crewai` | `lib/crewai/` | Core framework |
| `crewai-tools` | `lib/crewai-tools/` | Tool integrations |
| `crewai-files` | `lib/crewai-files/` | File handling |
| `devtools` | `lib/devtools/` | Internal release tooling |
Documentation lives in `docs/` with translations under `docs/{en,ar,ko,pt-BR}/`.
## Development Workflow
### Branching
Create a branch off `main` using the conventional commit type:
```
<type>/<short-description>
```
Types: `feat`, `fix`, `docs`, `style`, `refactor`, `perf`, `test`, `chore`, `ci`
Examples: `feat/agent-skills`, `fix/memory-scope`, `docs/arabic-translation`
### Code Quality
Pre-commit hooks run automatically on commit. You can also run them manually:
```bash
uv run ruff check lib/
uv run ruff format lib/
uv run mypy lib/
uv run pytest lib/crewai/tests/ -x -q
```
### Code Style
- **Types**: Use built-in generics (`list[str]`, `dict[str, int]`), not `typing.List`/`typing.Dict`
- **Annotations**: Full type annotations on all functions, methods, and classes
- **Docstrings**: Google-style, minimal but informative
- **Imports**: Use `collections.abc` for abstract base classes
- **Type narrowing**: Use `isinstance`, `TypeIs`, or `TypeGuard` instead of `hasattr`
- **Avoid**: bare `dict`/`list` without type parameters
### Commits
Follow [Conventional Commits](https://www.conventionalcommits.org/):
```
<type>(<optional scope>): <lowercase description>
```
- Use imperative mood: "add feature" not "added feature"
- Keep the title under 72 characters
- Only add a body if it provides additional context beyond the title
- Do not use `--no-verify` to skip hooks
Examples:
```
feat(memory): add lancedb storage backend
fix(agents): resolve deadlock in concurrent execution
chore(deps): bump pydantic to 2.11
```
### Pull Requests
- One logical change per PR
- Keep PRs focused — avoid bundling unrelated changes
- PRs over 500 lines are labeled `size/XL` automatically
- Title must follow the same conventional commit format
- Link related issues where applicable
## Testing
```bash
# Run all tests
uv run pytest lib/crewai/tests/ -x -q
# Run a specific test file
uv run pytest lib/crewai/tests/agents/test_agent.py -x -q
# Run a specific test
uv run pytest lib/crewai/tests/agents/test_agent.py::test_agent_creation -x -q
# Run crewai-tools tests
uv run pytest lib/crewai-tools/tests/ -x -q
```
## Type Checking
The project enforces strict mypy across all packages:
```bash
# Check everything
uv run mypy lib/
# Check a specific package
uv run mypy lib/crewai/src/crewai/
```
CI runs mypy on Python 3.10, 3.11, 3.12, and 3.13 for every PR.
## Documentation
Docs use [Mintlify](https://mintlify.com/) and live in `docs/`. The site is configured via `docs/docs.json`.
Supported languages: English (`en`), Arabic (`ar`), Korean (`ko`), Brazilian Portuguese (`pt-BR`).
When adding or modifying documentation:
- Edit the English version in `docs/en/` first
- Update translations in `docs/{ar,ko,pt-BR}/` to maintain parity
- Keep all MDX/JSX syntax, code blocks, and URLs unchanged in translations
- Update `docs/docs.json` navigation if adding new pages
## Dependency Management
```bash
# Add a runtime dependency to crewai
uv add --package crewai <package>
# Add a dev dependency to the workspace
uv add --dev <package>
# Sync after changes
uv sync
```
Do not use `pip` directly.
## Reporting Issues
Use the [GitHub issue templates](https://github.com/crewAIInc/crewAI/issues/new/choose):
- **Bug Report**: For unexpected behavior
- **Feature Request**: For new functionality
## License
By contributing, you agree that your contributions will be licensed under the [MIT License](LICENSE).

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name: "CodeQL Config"
paths-ignore:
# Ignore template files - these are boilerplate code that shouldn't be analyzed
- "lib/crewai/src/crewai/cli/templates/**"
# Ignore test cassettes - these are test fixtures/recordings
- "lib/crewai/tests/cassettes/**"
- "lib/crewai-tools/tests/cassettes/**"
# Ignore cache and build artifacts
- ".cache/**"
# Ignore documentation build artifacts
- "docs/.cache/**"
# Ignore experimental code
- "lib/crewai/src/crewai/experimental/a2a/**"
paths:
# Include GitHub Actions workflows/composite actions for CodeQL actions analysis
- ".github/workflows/**"
- ".github/actions/**"
# Include all Python source code from workspace packages
- "lib/crewai/src/**"
- "lib/crewai-tools/src/**"
- "lib/crewai-files/src/**"
- "lib/devtools/src/**"
# Include tests (but exclude cassettes via paths-ignore)
- "lib/crewai/tests/**"
- "lib/crewai-tools/tests/**"
- "lib/crewai-files/tests/**"
- "lib/devtools/tests/**"
# Configure specific queries or packs if needed
# queries:
# - uses: security-and-quality

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# To get started with Dependabot version updates, you'll need to specify which
# package ecosystems to update and where the package manifests are located.
# Please see the documentation for all configuration options:
# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
version: 2
updates:
- package-ecosystem: uv
directory: "/"
schedule:
interval: "weekly"
groups:
security-updates:
applies-to: security-updates
patterns:
- "*"

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.github/security.md vendored
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## CrewAI Security Policy
## CrewAI Security Vulnerability Reporting Policy
We are committed to protecting the confidentiality, integrity, and availability of the
CrewAI ecosystem.
CrewAI prioritizes the security of our software products, services, and GitHub repositories. To promptly address vulnerabilities, follow these steps for reporting security issues:
### How to Report
### Reporting Process
Do **not** report vulnerabilities via public GitHub issues.
Please submit reports through one of the following channels:
Email all vulnerability reports directly to:
**security@crewai.com**
- **crewai-vdp-ess@submit.bugcrowd.com**
- https://security.crewai.com
### Required Information
To help us quickly validate and remediate the issue, your report must include:
- **Please do not** disclose vulnerabilities via public GitHub issues, pull requests,
or social media
- Reports submitted via channels other than this Bugcrowd submission email will not be reviewed and will be dismissed
- **Vulnerability Type:** Clearly state the vulnerability type (e.g., SQL injection, XSS, privilege escalation).
- **Affected Source Code:** Provide full file paths and direct URLs (branch, tag, or commit).
- **Reproduction Steps:** Include detailed, step-by-step instructions. Screenshots are recommended.
- **Special Configuration:** Document any special settings or configurations required to reproduce.
- **Proof-of-Concept (PoC):** Provide exploit or PoC code (if available).
- **Impact Assessment:** Clearly explain the severity and potential exploitation scenarios.
### Our Response
- We will acknowledge receipt of your report promptly via your provided email.
- Confirmed vulnerabilities will receive priority remediation based on severity.
- Patches will be released as swiftly as possible following verification.
### Reward Notice
Currently, we do not offer a bug bounty program. Rewards, if issued, are discretionary.

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name: Build uv cache
on:
push:
branches:
- main
paths:
- "uv.lock"
- "pyproject.toml"
schedule:
- cron: "0 0 */5 * *" # Run every 5 days at midnight UTC to prevent cache expiration
workflow_dispatch:
permissions:
contents: read
jobs:
build-cache:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: ["3.10", "3.11", "3.12", "3.13"]
steps:
- name: Checkout repository
uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
- name: Install uv
uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
with:
version: "0.11.3"
python-version: ${{ matrix.python-version }}
enable-cache: false
- name: Install dependencies and populate cache
run: |
echo "Building global UV cache for Python ${{ matrix.python-version }}..."
uv sync --all-groups --all-extras --no-install-project
echo "Cache populated successfully"
- name: Save uv caches
uses: actions/cache/save@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
with:
path: |
~/.cache/uv
~/.local/share/uv
.venv
key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}

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# For most projects, this workflow file will not need changing; you simply need
# to commit it to your repository.
#
# You may wish to alter this file to override the set of languages analyzed,
# or to provide custom queries or build logic.
#
# ******** NOTE ********
# We have attempted to detect the languages in your repository. Please check
# the `language` matrix defined below to confirm you have the correct set of
# supported CodeQL languages.
#
name: "CodeQL Advanced"
on:
push:
branches: [ "main" ]
paths-ignore:
- "lib/crewai/src/crewai/cli/templates/**"
pull_request:
branches: [ "main" ]
paths-ignore:
- "lib/crewai/src/crewai/cli/templates/**"
jobs:
analyze:
name: Analyze (${{ matrix.language }})
# Runner size impacts CodeQL analysis time. To learn more, please see:
# - https://gh.io/recommended-hardware-resources-for-running-codeql
# - https://gh.io/supported-runners-and-hardware-resources
# - https://gh.io/using-larger-runners (GitHub.com only)
# Consider using larger runners or machines with greater resources for possible analysis time improvements.
runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }}
permissions:
# required for all workflows
security-events: write
# required to fetch internal or private CodeQL packs
packages: read
# only required for workflows in private repositories
actions: read
contents: read
strategy:
fail-fast: false
matrix:
include:
- language: actions
build-mode: none
- language: python
build-mode: none
# CodeQL supports the following values keywords for 'language': 'actions', 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'rust', 'swift'
# Use `c-cpp` to analyze code written in C, C++ or both
# Use 'java-kotlin' to analyze code written in Java, Kotlin or both
# Use 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both
# To learn more about changing the languages that are analyzed or customizing the build mode for your analysis,
# see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/customizing-your-advanced-setup-for-code-scanning.
# If you are analyzing a compiled language, you can modify the 'build-mode' for that language to customize how
# your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
steps:
- name: Checkout repository
uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
# Add any setup steps before running the `github/codeql-action/init` action.
# This includes steps like installing compilers or runtimes (`actions/setup-node`
# or others). This is typically only required for manual builds.
# - name: Setup runtime (example)
# uses: actions/setup-example@v1
# Initializes the CodeQL tools for scanning.
- name: Initialize CodeQL
uses: github/codeql-action/init@9e0d7b8d25671d64c341c19c0152d693099fb5ba # v4.35.5
with:
languages: ${{ matrix.language }}
build-mode: ${{ matrix.build-mode }}
config-file: ./.github/codeql/codeql-config.yml
# If you wish to specify custom queries, you can do so here or in a config file.
# By default, queries listed here will override any specified in a config file.
# Prefix the list here with "+" to use these queries and those in the config file.
# For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
# queries: security-extended,security-and-quality
# If the analyze step fails for one of the languages you are analyzing with
# "We were unable to automatically build your code", modify the matrix above
# to set the build mode to "manual" for that language. Then modify this step
# to build your code.
# Command-line programs to run using the OS shell.
# 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
- if: matrix.build-mode == 'manual'
shell: bash
run: |
echo 'If you are using a "manual" build mode for one or more of the' \
'languages you are analyzing, replace this with the commands to build' \
'your code, for example:'
echo ' make bootstrap'
echo ' make release'
exit 1
- name: Perform CodeQL Analysis
uses: github/codeql-action/analyze@9e0d7b8d25671d64c341c19c0152d693099fb5ba # v4.35.5
with:
category: "/language:${{matrix.language}}"

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@@ -1,35 +0,0 @@
name: Check Documentation Broken Links
on:
pull_request:
paths:
- "docs/**"
- "docs.json"
push:
branches:
- main
paths:
- "docs/**"
- "docs.json"
workflow_dispatch:
jobs:
check-links:
name: Check broken links
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
- name: Set up Node
uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4.4.0
with:
node-version: "22"
- name: Install Mintlify CLI
run: npm i -g mintlify
- name: Run broken link checker
run: |
# Auto-answer the prompt with yes command
yes "" | mintlify broken-links || test $? -eq 141
working-directory: ./docs

View File

@@ -1,114 +0,0 @@
name: Docs Snapshots Guard
on:
pull_request:
paths:
- "docs/**"
permissions:
contents: read
pull-requests: read
jobs:
guard:
name: Protect frozen snapshots and append-only assets
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
with:
fetch-depth: 0
- name: Determine merge base
id: base
run: |
base_sha="$(git merge-base "origin/${{ github.event.pull_request.base.ref }}" HEAD)"
echo "sha=$base_sha" >> "$GITHUB_OUTPUT"
- name: Detect escape-hatch label
id: escape
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
PR_NUMBER: ${{ github.event.pull_request.number }}
PR_TITLE: ${{ github.event.pull_request.title }}
run: |
# The [docs-freeze] marker (in the PR title) is the only way to
# legitimately modify frozen snapshots or remove published assets.
# Detect it from the title since the workflow runs on
# pull_request (not pull_request_target) and can't always read
# labels reliably.
if [[ "$PR_TITLE" == *"[docs-freeze]"* ]]; then
echo "allowed=true" >> "$GITHUB_OUTPUT"
else
echo "allowed=false" >> "$GITHUB_OUTPUT"
fi
- name: Guard frozen snapshots
env:
ALLOWED: ${{ steps.escape.outputs.allowed }}
BASE_SHA: ${{ steps.base.outputs.sha }}
run: |
set -euo pipefail
# Anything under docs/v<X.Y.Z>/ is a frozen release snapshot and
# must not change after the release-cut PR that introduced it.
# The release-cut PR uses the [docs-freeze] title prefix to opt
# out of this guard. ``docs/v[0-9]*/**`` is the defensive form so
# we never catch a hypothetical ``docs/vendor/`` etc.
violations="$(git diff --name-only --diff-filter=AMDRT \
"$BASE_SHA"..HEAD -- 'docs/v[0-9]*/**' || true)"
if [[ -z "$violations" ]]; then
echo "OK: no changes under docs/v*/"
exit 0
fi
if [[ "$ALLOWED" == "true" ]]; then
echo "OK: [docs-freeze] PR is allowed to touch docs/v*/:"
echo "$violations"
exit 0
fi
echo "::error::This PR modifies frozen release snapshots under docs/v*/."
echo "Frozen snapshots are immutable. To intentionally edit a snapshot"
echo "(e.g. a release-cut PR generated by 'devtools release' or the"
echo "manual 'scripts/docs/freeze_current_edge.py' wrapper), prefix"
echo "the PR title with [docs-freeze]."
echo
echo "Offending files:"
echo "$violations"
exit 1
- name: Guard append-only images
env:
ALLOWED: ${{ steps.escape.outputs.allowed }}
BASE_SHA: ${{ steps.base.outputs.sha }}
run: |
set -euo pipefail
# Deleting or renaming an image breaks every frozen snapshot that
# still references it (snapshots reuse docs/images/ at the docs
# root). Only [docs-freeze] PRs are allowed to do that.
deletions="$(git diff --name-only --diff-filter=DR \
"$BASE_SHA"..HEAD -- 'docs/images/**' || true)"
if [[ -z "$deletions" ]]; then
echo "OK: no images deleted or renamed."
exit 0
fi
if [[ "$ALLOWED" == "true" ]]; then
echo "OK: [docs-freeze] PR is allowed to delete/rename images:"
echo "$deletions"
exit 0
fi
echo "::error::This PR deletes or renames files under docs/images/."
echo "Images are append-only because frozen snapshots in docs/v*/"
echo "share a single docs/images/ directory and would break if an"
echo "asset they reference disappears or moves."
echo
echo "If the asset is wrong, add a new file with a new name and"
echo "reference the new name in Edge (docs/edge/<lang>/...). Leave"
echo "the old file in place so historical snapshots keep rendering."
echo
echo "Offending files:"
echo "$deletions"
exit 1

View File

@@ -1,64 +0,0 @@
name: Generate Tool Specifications
on:
pull_request:
branches:
- main
paths:
- 'lib/crewai-tools/src/crewai_tools/**'
workflow_dispatch:
permissions:
contents: write
pull-requests: write
jobs:
generate-specs:
if: github.event_name == 'workflow_dispatch' || github.event.pull_request.head.repo.full_name == github.repository
runs-on: ubuntu-latest
env:
PYTHONUNBUFFERED: 1
steps:
- name: Generate GitHub App token
id: app-token
uses: actions/create-github-app-token@bcd2ba49218906704ab6c1aa796996da409d3eb1 # v3.2.0
with:
app-id: ${{ secrets.CREWAI_TOOL_SPECS_APP_ID }}
private-key: ${{ secrets.CREWAI_TOOL_SPECS_PRIVATE_KEY }}
- name: Checkout code
uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
with:
ref: ${{ github.head_ref }}
token: ${{ steps.app-token.outputs.token }}
- name: Install uv
uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
with:
version: "0.11.3"
python-version: "3.12"
enable-cache: true
- name: Install the project
working-directory: lib/crewai-tools
run: uv sync --dev --all-extras
- name: Generate tool specifications
working-directory: lib/crewai-tools
run: uv run python src/crewai_tools/generate_tool_specs.py
- name: Check for changes and commit
run: |
git config user.name "github-actions[bot]"
git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
git add lib/crewai-tools/tool.specs.json
if git diff --quiet --staged; then
echo "No changes detected in tool.specs.json"
else
echo "Changes detected in tool.specs.json, committing..."
git commit -m "chore: update tool specifications"
git push
fi

View File

@@ -2,86 +2,35 @@ name: Lint
on: [pull_request]
permissions:
contents: read
jobs:
changes:
name: Detect changes
runs-on: ubuntu-latest
outputs:
code: ${{ steps.filter.outputs.code }}
steps:
- uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
- uses: dorny/paths-filter@d1c1ffe0248fe513906c8e24db8ea791d46f8590 # v3
id: filter
with:
filters: |
code:
- '!docs/**'
- '!**/*.md'
lint-run:
needs: changes
if: needs.changes.outputs.code == 'true'
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
- name: Restore global uv cache
id: cache-restore
uses: actions/cache/restore@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
with:
path: |
~/.cache/uv
~/.local/share/uv
.venv
key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
restore-keys: |
uv-main-py3.11-
- name: Install uv
uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
with:
version: "0.11.3"
python-version: "3.11"
enable-cache: false
- name: Install dependencies
run: uv sync --all-groups --all-extras --no-install-project
- name: Ruff check
run: uv run ruff check lib/
- name: Ruff format
run: uv run ruff format --check lib/
- name: Save uv caches
if: steps.cache-restore.outputs.cache-hit != 'true'
uses: actions/cache/save@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
with:
path: |
~/.cache/uv
~/.local/share/uv
.venv
key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
# Summary job to provide single status for branch protection
lint:
name: lint
runs-on: ubuntu-latest
needs: [changes, lint-run]
if: always()
env:
TARGET_BRANCH: ${{ github.event.pull_request.base.ref }}
steps:
- name: Check results
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Fetch Target Branch
run: git fetch origin $TARGET_BRANCH --depth=1
- name: Install Ruff
run: pip install ruff
- name: Get Changed Python Files
id: changed-files
run: |
if [ "${{ needs.changes.outputs.code }}" != "true" ]; then
echo "Docs-only change, skipping lint"
exit 0
fi
if [ "${{ needs.lint-run.result }}" == "success" ]; then
echo "Lint passed"
else
echo "Lint failed"
exit 1
fi
merge_base=$(git merge-base origin/"$TARGET_BRANCH" HEAD)
changed_files=$(git diff --name-only --diff-filter=ACMRTUB "$merge_base" | grep '\.py$' || true)
echo "files<<EOF" >> $GITHUB_OUTPUT
echo "$changed_files" >> $GITHUB_OUTPUT
echo "EOF" >> $GITHUB_OUTPUT
- name: Run Ruff on Changed Files
if: ${{ steps.changed-files.outputs.files != '' }}
run: |
echo "${{ steps.changed-files.outputs.files }}" \
| tr ' ' '\n' \
| grep -v 'src/crewai/cli/templates/' \
| xargs -I{} ruff check "{}"

45
.github/workflows/mkdocs.yml vendored Normal file
View File

@@ -0,0 +1,45 @@
name: Deploy MkDocs
on:
release:
types: [published]
permissions:
contents: write
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Setup Python
uses: actions/setup-python@v5
with:
python-version: '3.10'
- name: Calculate requirements hash
id: req-hash
run: echo "::set-output name=hash::$(sha256sum requirements-doc.txt | awk '{print $1}')"
- name: Setup cache
uses: actions/cache@v4
with:
key: mkdocs-material-${{ steps.req-hash.outputs.hash }}
path: .cache
restore-keys: |
mkdocs-material-
- name: Install Requirements
run: |
sudo apt-get update &&
sudo apt-get install pngquant &&
pip install mkdocs-material mkdocs-material-extensions pillow cairosvg
env:
GH_TOKEN: ${{ secrets.GH_TOKEN }}
- name: Build and deploy MkDocs
run: mkdocs gh-deploy --force

View File

@@ -1,138 +0,0 @@
name: Nightly Canary Release
on:
schedule:
- cron: '0 6 * * *' # daily at 6am UTC
workflow_dispatch:
concurrency:
group: nightly-publish
cancel-in-progress: false
jobs:
check:
name: Check for new commits
runs-on: ubuntu-latest
permissions:
contents: read
outputs:
has_changes: ${{ steps.check.outputs.has_changes }}
steps:
- uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
with:
fetch-depth: 0
- name: Check for recent commits
id: check
run: |
# 25h window absorbs cron-vs-commit timing skew at the boundary.
RECENT=$(git log --since="25 hours ago" --oneline | head -1)
if [ -n "$RECENT" ]; then
echo "has_changes=true" >> "$GITHUB_OUTPUT"
else
echo "has_changes=false" >> "$GITHUB_OUTPUT"
fi
build:
name: Build nightly packages
needs: check
if: needs.check.outputs.has_changes == 'true' || github.event_name == 'workflow_dispatch'
runs-on: ubuntu-latest
permissions:
contents: read
steps:
- uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
- name: Install uv
uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
with:
version: "0.11.3"
python-version: "3.12"
enable-cache: false
- name: Stamp nightly versions
run: |
DATE=$(date +%Y%m%d)
# All workspace packages share the same base version and are released together.
BASE=$(python -c "
import re
print(re.search(r'__version__\s*=\s*\"(.*?)\"', open('lib/crewai/src/crewai/__init__.py').read()).group(1))
")
NIGHTLY="${BASE}.dev${DATE}"
echo "Nightly version: ${NIGHTLY}"
for init_file in \
lib/crewai/src/crewai/__init__.py \
lib/crewai-core/src/crewai_core/__init__.py \
lib/crewai-tools/src/crewai_tools/__init__.py \
lib/crewai-files/src/crewai_files/__init__.py \
lib/cli/src/crewai_cli/__init__.py; do
sed -i "s/__version__ = .*/__version__ = \"${NIGHTLY}\"/" "$init_file"
echo "Stamped $init_file -> $NIGHTLY"
done
# Update all cross-package dependency pins to the nightly version.
sed -i "s/\"crewai==[^\"]*\"/\"crewai==${NIGHTLY}\"/" lib/crewai-tools/pyproject.toml
sed -i "s/\"crewai-core==[^\"]*\"/\"crewai-core==${NIGHTLY}\"/" lib/crewai/pyproject.toml
sed -i "s/\"crewai-cli==[^\"]*\"/\"crewai-cli==${NIGHTLY}\"/" lib/crewai/pyproject.toml
sed -i "s/\"crewai-tools==[^\"]*\"/\"crewai-tools==${NIGHTLY}\"/" lib/crewai/pyproject.toml
sed -i "s/\"crewai-files==[^\"]*\"/\"crewai-files==${NIGHTLY}\"/" lib/crewai/pyproject.toml
sed -i "s/\"crewai-core==[^\"]*\"/\"crewai-core==${NIGHTLY}\"/" lib/cli/pyproject.toml
echo "Updated cross-package dependency pins to ${NIGHTLY}"
- name: Build packages
run: |
uv build --all-packages
rm dist/.gitignore
- name: Upload artifacts
uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
with:
name: dist
path: dist/
publish:
name: Publish nightly to PyPI
needs: build
runs-on: ubuntu-latest
environment:
name: pypi
permissions:
id-token: write
contents: read
steps:
- name: Install uv
uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
with:
version: "0.11.3"
python-version: "3.12"
enable-cache: false
- name: Download artifacts
uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
with:
name: dist
path: dist
- name: Publish to PyPI
env:
UV_PUBLISH_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
run: |
failed=0
for package in dist/*; do
if [[ "$package" == *"crewai_devtools"* ]]; then
echo "Skipping private package: $package"
continue
fi
echo "Publishing $package"
# --check-url skips files already on PyPI so manual re-runs on the same day are idempotent.
if ! uv publish --check-url https://pypi.org/simple/ "$package"; then
echo "Failed to publish $package"
failed=1
fi
done
if [ $failed -eq 1 ]; then
echo "Some packages failed to publish"
exit 1
fi

33
.github/workflows/notify-downstream.yml vendored Normal file
View File

@@ -0,0 +1,33 @@
name: Notify Downstream
on:
push:
branches:
- main
permissions:
contents: read
jobs:
notify-downstream:
runs-on: ubuntu-latest
steps:
- name: Generate GitHub App token
id: app-token
uses: tibdex/github-app-token@v2
with:
app_id: ${{ secrets.OSS_SYNC_APP_ID }}
private_key: ${{ secrets.OSS_SYNC_APP_PRIVATE_KEY }}
- name: Notify Repo B
uses: peter-evans/repository-dispatch@v3
with:
token: ${{ steps.app-token.outputs.token }}
repository: ${{ secrets.OSS_SYNC_DOWNSTREAM_REPO }}
event-type: upstream-commit
client-payload: |
{
"commit_sha": "${{ github.sha }}"
}

View File

@@ -1,32 +0,0 @@
name: PR Size Check
on:
pull_request:
types: [opened, synchronize, reopened]
jobs:
pr-size:
runs-on: ubuntu-latest
permissions:
pull-requests: write
steps:
- uses: codelytv/pr-size-labeler@095a41fca88b8764fd9e008ad269bcdb82bb38b9 # v1
with:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
xs_label: "size/XS"
xs_max_size: 25
s_label: "size/S"
s_max_size: 100
m_label: "size/M"
m_max_size: 250
l_label: "size/L"
l_max_size: 500
xl_label: "size/XL"
fail_if_xl: false
files_to_ignore: |
uv.lock
*.lock
lib/crewai/src/crewai/cli/templates/**
**/*.json
**/test_durations/**
**/cassettes/**

View File

@@ -1,41 +0,0 @@
name: PR Title Check
on:
pull_request:
types: [opened, edited, synchronize, reopened]
permissions:
contents: read
pull-requests: read
jobs:
pr-title:
runs-on: ubuntu-latest
steps:
- uses: amannn/action-semantic-pull-request@e32d7e603df1aa1ba07e981f2a23455dee596825 # v5
continue-on-error: true
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
with:
types: |
feat
fix
refactor
perf
test
docs
chore
ci
style
revert
requireScope: false
subjectPattern: ^[a-z].+[^.]$
subjectPatternError: >
The PR title "{title}" does not follow conventional commit format.
Expected: <type>(<scope>): <lowercase description without trailing period>
Examples:
feat(memory): add lancedb storage backend
fix(agents): resolve deadlock in concurrent execution
chore(deps): bump pydantic to 2.11.9

View File

@@ -1,166 +0,0 @@
name: Publish to PyPI
on:
workflow_dispatch:
inputs:
release_tag:
description: 'Release tag to publish'
required: false
type: string
jobs:
build:
name: Build packages
runs-on: ubuntu-latest
permissions:
contents: read
steps:
- name: Determine release tag
id: release
run: |
if [ -n "${{ inputs.release_tag }}" ]; then
echo "tag=${{ inputs.release_tag }}" >> $GITHUB_OUTPUT
else
echo "tag=" >> $GITHUB_OUTPUT
fi
- uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
with:
ref: ${{ steps.release.outputs.tag || github.ref }}
- name: Set up Python
uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
with:
python-version: "3.12"
- name: Install uv
uses: astral-sh/setup-uv@38f3f104447c67c051c4a08e39b64a148898af3a # v4
- name: Build packages
run: |
uv build --all-packages
rm dist/.gitignore
- name: Upload artifacts
uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
with:
name: dist
path: dist/
publish:
name: Publish to PyPI
needs: build
runs-on: ubuntu-latest
environment:
name: pypi
url: https://pypi.org/p/crewai
permissions:
id-token: write
contents: read
steps:
- uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
with:
ref: ${{ inputs.release_tag || github.ref }}
- name: Install uv
uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
with:
version: "0.11.3"
python-version: "3.12"
enable-cache: false
- name: Download artifacts
uses: actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093 # v4.3.0
with:
name: dist
path: dist
- name: Publish to PyPI
env:
UV_PUBLISH_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
run: |
failed=0
for package in dist/*; do
if [[ "$package" == *"crewai_devtools"* ]]; then
echo "Skipping private package: $package"
continue
fi
echo "Publishing $package"
if ! uv publish "$package"; then
echo "Failed to publish $package"
failed=1
fi
done
if [ $failed -eq 1 ]; then
echo "Some packages failed to publish"
exit 1
fi
- name: Build Slack payload
if: success()
id: slack
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
RELEASE_TAG: ${{ inputs.release_tag }}
run: |
payload=$(uv run python -c "
import json, re, subprocess, sys
with open('lib/crewai/src/crewai/__init__.py') as f:
m = re.search(r\"__version__\s*=\s*[\\\"']([^\\\"']+)\", f.read())
version = m.group(1) if m else 'unknown'
import os
tag = os.environ.get('RELEASE_TAG') or version
try:
r = subprocess.run(['gh','release','view',tag,'--json','body','-q','.body'],
capture_output=True, text=True, check=True)
body = r.stdout.strip()
except Exception:
body = ''
blocks = [
{'type':'section','text':{'type':'mrkdwn',
'text':f':rocket: \`crewai v{version}\` published to PyPI'}},
{'type':'section','text':{'type':'mrkdwn',
'text':f'<https://pypi.org/project/crewai/{version}/|View on PyPI> · <https://github.com/crewAIInc/crewAI/releases/tag/{tag}|Release notes>'}},
{'type':'divider'},
]
if body:
heading, items = '', []
for line in body.split('\n'):
line = line.strip()
if not line: continue
hm = re.match(r'^#{2,3}\s+(.*)', line)
if hm:
if heading and items:
skip = heading in ('What\\'s Changed','') or 'Contributors' in heading
if not skip:
txt = f'*{heading}*\n' + '\n'.join(f'• {i}' for i in items)
blocks.append({'type':'section','text':{'type':'mrkdwn','text':txt}})
heading, items = hm.group(1), []
elif line.startswith('- ') or line.startswith('* '):
items.append(re.sub(r'\*\*([^*]*)\*\*', r'*\1*', line[2:]))
if heading and items:
skip = heading in ('What\\'s Changed','') or 'Contributors' in heading
if not skip:
txt = f'*{heading}*\n' + '\n'.join(f'• {i}' for i in items)
blocks.append({'type':'section','text':{'type':'mrkdwn','text':txt}})
blocks.append({'type':'divider'})
blocks.append({'type':'section','text':{'type':'mrkdwn',
'text':f'\`\`\`uv add \"crewai[tools]=={version}\"\`\`\`'}})
print(json.dumps({'blocks':blocks}))
")
echo "payload=$payload" >> $GITHUB_OUTPUT
- name: Notify Slack
if: success()
uses: slackapi/slack-github-action@b0fa283ad8fea605de13dc3f449259339835fc52 # v2.1.0
with:
webhook: ${{ secrets.SLACK_WEBHOOK_URL }}
webhook-type: incoming-webhook
payload: ${{ steps.slack.outputs.payload }}

23
.github/workflows/security-checker.yml vendored Normal file
View File

@@ -0,0 +1,23 @@
name: Security Checker
on: [pull_request]
jobs:
security-check:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.11.9"
- name: Install dependencies
run: pip install bandit
- name: Run Bandit
run: bandit -c pyproject.toml -r src/ -ll

View File

@@ -14,7 +14,7 @@ jobs:
stale:
runs-on: ubuntu-latest
steps:
- uses: actions/stale@5bef64f19d7facfb25b37b414482c7164d639639 # v9.1.0
- uses: actions/stale@v9
with:
repo-token: ${{ secrets.GITHUB_TOKEN }}
stale-issue-label: 'no-issue-activity'

View File

@@ -3,135 +3,32 @@ name: Run Tests
on: [pull_request]
permissions:
contents: read
contents: write
env:
OPENAI_API_KEY: fake-api-key
jobs:
changes:
name: Detect changes
runs-on: ubuntu-latest
outputs:
code: ${{ steps.filter.outputs.code }}
steps:
- uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
- uses: dorny/paths-filter@d1c1ffe0248fe513906c8e24db8ea791d46f8590 # v3
id: filter
with:
filters: |
code:
- '!docs/**'
- '!**/*.md'
tests-matrix:
name: tests (${{ matrix.python-version }})
needs: changes
if: needs.changes.outputs.code == 'true'
tests:
runs-on: ubuntu-latest
timeout-minutes: 15
strategy:
fail-fast: true
matrix:
python-version: ['3.10', '3.11', '3.12', '3.13']
group: [1, 2, 3, 4, 5, 6, 7, 8]
steps:
- name: Checkout code
uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
with:
fetch-depth: 0 # Fetch all history for proper diff
- name: Restore global uv cache
id: cache-restore
uses: actions/cache/restore@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
with:
path: |
~/.cache/uv
~/.local/share/uv
.venv
key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
restore-keys: |
uv-main-py${{ matrix.python-version }}-
uses: actions/checkout@v4
- name: Install uv
uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
uses: astral-sh/setup-uv@v3
with:
version: "0.11.3"
python-version: ${{ matrix.python-version }}
enable-cache: false
enable-cache: true
- name: Set up Python ${{ matrix.python-version }}
run: uv python install ${{ matrix.python-version }}
- name: Install the project
run: uv sync --all-groups --all-extras
run: uv sync --dev --all-extras
- name: Restore test durations
uses: actions/cache/restore@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
with:
path: .test_durations_py*
key: test-durations-py${{ matrix.python-version }}
- name: Run tests (group ${{ matrix.group }} of 8)
run: |
PYTHON_VERSION_SAFE=$(echo "${{ matrix.python-version }}" | tr '.' '_')
DURATION_FILE="../../.test_durations_py${PYTHON_VERSION_SAFE}"
# Temporarily always skip cached durations to fix test splitting
# When durations don't match, pytest-split runs duplicate tests instead of splitting
echo "Using even test splitting (duration cache disabled until fix merged)"
DURATIONS_ARG=""
# Original logic (disabled temporarily):
# if [ ! -f "$DURATION_FILE" ]; then
# echo "No cached durations found, tests will be split evenly"
# DURATIONS_ARG=""
# elif git diff origin/${{ github.base_ref }}...HEAD --name-only 2>/dev/null | grep -q "^tests/.*\.py$"; then
# echo "Test files have changed, skipping cached durations to avoid mismatches"
# DURATIONS_ARG=""
# else
# echo "No test changes detected, using cached test durations for optimal splitting"
# DURATIONS_ARG="--durations-path=${DURATION_FILE}"
# fi
cd lib/crewai && uv run pytest \
-vv \
--splits 8 \
--group ${{ matrix.group }} \
$DURATIONS_ARG \
--durations=10 \
--maxfail=3
- name: Run tool tests (group ${{ matrix.group }} of 8)
run: |
cd lib/crewai-tools && uv run pytest \
-vv \
--splits 8 \
--group ${{ matrix.group }} \
--durations=10 \
--maxfail=3
- name: Save uv caches
if: steps.cache-restore.outputs.cache-hit != 'true'
uses: actions/cache/save@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
with:
path: |
~/.cache/uv
~/.local/share/uv
.venv
key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
# Summary job to provide single status for branch protection
tests:
name: tests
runs-on: ubuntu-latest
needs: [changes, tests-matrix]
if: always()
steps:
- name: Check results
run: |
if [ "${{ needs.changes.outputs.code }}" != "true" ]; then
echo "Docs-only change, skipping tests"
exit 0
fi
if [ "${{ needs.tests-matrix.result }}" == "success" ]; then
echo "All tests passed"
else
echo "Tests failed"
exit 1
fi
- name: Run tests
run: uv run pytest --block-network --timeout=60 -vv

View File

@@ -3,89 +3,24 @@ name: Run Type Checks
on: [pull_request]
permissions:
contents: read
contents: write
jobs:
changes:
name: Detect changes
type-checker:
runs-on: ubuntu-latest
outputs:
code: ${{ steps.filter.outputs.code }}
steps:
- uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
- uses: dorny/paths-filter@d1c1ffe0248fe513906c8e24db8ea791d46f8590 # v3
id: filter
with:
filters: |
code:
- '!docs/**'
- '!**/*.md'
type-checker-matrix:
name: type-checker (${{ matrix.python-version }})
needs: changes
if: needs.changes.outputs.code == 'true'
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
python-version: ["3.10", "3.11", "3.12", "3.13"]
steps:
- name: Checkout code
uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
uses: actions/checkout@v4
- name: Restore global uv cache
id: cache-restore
uses: actions/cache/restore@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
- name: Setup Python
uses: actions/setup-python@v5
with:
path: |
~/.cache/uv
~/.local/share/uv
.venv
key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
restore-keys: |
uv-main-py${{ matrix.python-version }}-
python-version: "3.11.9"
- name: Install uv
uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
with:
version: "0.11.3"
python-version: ${{ matrix.python-version }}
enable-cache: false
- name: Install dependencies
run: uv sync --all-groups --all-extras
- name: Install Requirements
run: |
pip install mypy
- name: Run type checks
run: uv run mypy lib/
- name: Save uv caches
if: steps.cache-restore.outputs.cache-hit != 'true'
uses: actions/cache/save@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
with:
path: |
~/.cache/uv
~/.local/share/uv
.venv
key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
# Summary job to provide single status for branch protection
type-checker:
name: type-checker
runs-on: ubuntu-latest
needs: [changes, type-checker-matrix]
if: always()
steps:
- name: Check results
run: |
if [ "${{ needs.changes.outputs.code }}" != "true" ]; then
echo "Docs-only change, skipping type checks"
exit 0
fi
if [ "${{ needs.type-checker-matrix.result }}" == "success" ]; then
echo "All type checks passed"
else
echo "Type checks failed"
exit 1
fi
run: mypy src

View File

@@ -1,71 +0,0 @@
name: Update Test Durations
on:
push:
branches:
- main
paths:
- 'tests/**/*.py'
workflow_dispatch:
permissions:
contents: read
jobs:
update-durations:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: ['3.10', '3.11', '3.12', '3.13']
env:
OPENAI_API_KEY: fake-api-key
PYTHONUNBUFFERED: 1
steps:
- name: Checkout repository
uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
- name: Restore global uv cache
id: cache-restore
uses: actions/cache/restore@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
with:
path: |
~/.cache/uv
~/.local/share/uv
.venv
key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
restore-keys: |
uv-main-py${{ matrix.python-version }}-
- name: Install uv
uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
with:
version: "0.11.3"
python-version: ${{ matrix.python-version }}
enable-cache: false
- name: Install the project
run: uv sync --all-groups --all-extras
- name: Run all tests and store durations
run: |
PYTHON_VERSION_SAFE=$(echo "${{ matrix.python-version }}" | tr '.' '_')
uv run pytest --store-durations --durations-path=.test_durations_py${PYTHON_VERSION_SAFE} -n auto
continue-on-error: true
- name: Save durations to cache
if: always()
uses: actions/cache/save@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
with:
path: .test_durations_py*
key: test-durations-py${{ matrix.python-version }}
- name: Save uv caches
if: steps.cache-restore.outputs.cache-hit != 'true'
uses: actions/cache/save@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
with:
path: |
~/.cache/uv
~/.local/share/uv
.venv
key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}

View File

@@ -1,135 +0,0 @@
name: Vulnerability Scan
on:
pull_request:
push:
branches: [main]
schedule:
# Run weekly on Monday at 9:00 UTC
- cron: '0 9 * * 1'
permissions:
contents: read
jobs:
pip-audit:
name: pip-audit
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@34e114876b0b11c390a56381ad16ebd13914f8d5 # v4.3.1
with:
persist-credentials: false
- name: Restore global uv cache
id: cache-restore
uses: actions/cache/restore@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
with:
path: |
~/.cache/uv
~/.local/share/uv
.venv
key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
restore-keys: |
uv-main-py3.11-
- name: Install uv
uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
with:
version: "0.11.3"
python-version: "3.11"
enable-cache: false
- name: Install dependencies
run: uv sync --all-groups --all-extras --no-install-project
- name: Install pip-audit
run: uv pip install pip-audit
- name: Run pip-audit
run: |
uv run pip-audit --desc --aliases --skip-editable --format json --output pip-audit-report.json \
--ignore-vuln PYSEC-2024-277 \
--ignore-vuln PYSEC-2026-89 \
--ignore-vuln PYSEC-2026-97 \
--ignore-vuln PYSEC-2025-148 \
--ignore-vuln PYSEC-2025-183 \
--ignore-vuln PYSEC-2025-189 \
--ignore-vuln PYSEC-2025-190 \
--ignore-vuln PYSEC-2025-191 \
--ignore-vuln PYSEC-2025-192 \
--ignore-vuln PYSEC-2025-193 \
--ignore-vuln PYSEC-2025-194 \
--ignore-vuln PYSEC-2025-195 \
--ignore-vuln PYSEC-2025-196 \
--ignore-vuln PYSEC-2025-197 \
--ignore-vuln PYSEC-2025-210 \
--ignore-vuln PYSEC-2026-139 \
--ignore-vuln GHSA-rrmf-rvhw-rf47 \
--ignore-vuln PYSEC-2025-211 \
--ignore-vuln PYSEC-2025-212 \
--ignore-vuln PYSEC-2025-213 \
--ignore-vuln PYSEC-2025-214 \
--ignore-vuln PYSEC-2025-215 \
--ignore-vuln PYSEC-2025-216 \
--ignore-vuln PYSEC-2025-217 \
--ignore-vuln PYSEC-2025-218 \
--ignore-vuln GHSA-f4j7-r4q5-qw2c
# Ignored CVEs:
# PYSEC-2024-277 - joblib 1.5.3: disputed; NumpyArrayWrapper only used with trusted caches
# PYSEC-2026-89 - markdown 3.10.2: DoS via malformed HTML; fix 3.8.1 — already past, advisory range is stale
# PYSEC-2026-97 - nltk 3.9.4: arbitrary file read in filestring(); no fix available
# PYSEC-2025-148 - onnx 1.21.0: path traversal in save_external_data; no fix available
# PYSEC-2025-183 - pyjwt 2.12.1: disputed weak-encryption claim; key length is application-chosen
# PYSEC-2025-189..197 - torch 2.11.0: memory-corruption/DoS in functions only reachable via untrusted models; no fix available
# PYSEC-2025-210, PYSEC-2026-139 - torch 2.11.0: profiler/deserialization issues; no fix available
# GHSA-rrmf-rvhw-rf47 - torch 2.11.0 (CVE-2025-3000, alias of PYSEC-2025-194): memory corruption in torch.jit.script, CVSS 1.9, local-only; affected <=2.12.0, no fix available. pip-audit reports it under the GHSA id so the PYSEC ignore above does not catch it.
# PYSEC-2025-211..218 - transformers 5.5.4: deserialization/code injection via malicious model checkpoints; no fix available
# GHSA-f4j7-r4q5-qw2c - chromadb 1.1.1 (CVE-2026-45829): pre-auth RCE via /api/v2/tenants/{tenant}/databases/{db}/collections when trust_remote_code=true.
# Advisory: vulnerable >=1.0.0,<=1.5.9, firstPatchedVersion=none. We only use chromadb.PersistentClient (lib/crewai/src/crewai/rag/chromadb/factory.py)
# and chromadb.utils.embedding_functions; the chromadb HTTP server is never started, so the vulnerable route is not exposed.
continue-on-error: true
- name: Display results
if: always()
run: |
if [ -f pip-audit-report.json ]; then
echo "## pip-audit Results" >> $GITHUB_STEP_SUMMARY
echo '```json' >> $GITHUB_STEP_SUMMARY
cat pip-audit-report.json | python3 -m json.tool >> $GITHUB_STEP_SUMMARY
echo '```' >> $GITHUB_STEP_SUMMARY
# Fail if vulnerabilities found
python3 -c "
import json, sys
with open('pip-audit-report.json') as f:
data = json.load(f)
vulns = [d for d in data.get('dependencies', []) if d.get('vulns')]
if vulns:
print(f'::error::Found vulnerabilities in {len(vulns)} package(s)')
for v in vulns:
for vuln in v['vulns']:
print(f' - {v[\"name\"]}=={v[\"version\"]}: {vuln[\"id\"]}')
sys.exit(1)
print('No known vulnerabilities found')
"
else
echo "::error::pip-audit failed to produce a report. Check the pip-audit step logs."
exit 1
fi
- name: Upload pip-audit report
if: always()
uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
with:
name: pip-audit-report
path: pip-audit-report.json
- name: Save uv caches
if: steps.cache-restore.outputs.cache-hit != 'true'
uses: actions/cache/save@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
with:
path: |
~/.cache/uv
~/.local/share/uv
.venv
key: uv-main-py3.11-${{ hashFiles('uv.lock') }}

12
.gitignore vendored
View File

@@ -2,6 +2,7 @@
.pytest_cache
__pycache__
dist/
lib/
.env
assets/*
.idea
@@ -20,16 +21,9 @@ crew_tasks_output.json
.mypy_cache
.ruff_cache
.venv
agentops.log
test_flow.html
crewairules.mdc
plan.md
conceptual_plan.md
build_image
chromadb-*.lock
.claude
.crewai/memory
blogs/*
secrets/*
UNKNOWN.egg-info/
demos/*
.crewai/*
build_image

View File

@@ -1,69 +1,7 @@
repos:
- repo: local
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.8.2
hooks:
- id: ruff
name: ruff
entry: bash -c 'source .venv/bin/activate && uv run ruff check --config pyproject.toml "$@"' --
language: system
pass_filenames: true
types: [python]
args: ["--fix"]
- id: ruff-format
name: ruff-format
entry: bash -c 'source .venv/bin/activate && uv run ruff format --config pyproject.toml "$@"' --
language: system
pass_filenames: true
types: [python]
- id: mypy
name: mypy
entry: bash -c 'source .venv/bin/activate && uv run mypy --config-file pyproject.toml "$@"' --
language: system
pass_filenames: true
types: [python]
exclude: ^(lib/crewai/src/crewai/cli/templates/|lib/cli/src/crewai_cli/templates/|lib/cli/tests/|lib/crewai/tests/|lib/crewai-tools/tests/|lib/crewai-files/tests/|lib/devtools/tests/)
- repo: https://github.com/astral-sh/uv-pre-commit
rev: 0.11.3
hooks:
- id: uv-lock
- repo: local
hooks:
- id: pip-audit
name: pip-audit
# Keep this ignore list in sync with .github/workflows/vulnerability-scan.yml.
entry: >-
bash -c 'source .venv/bin/activate && uv run pip-audit --skip-editable
--ignore-vuln PYSEC-2024-277
--ignore-vuln PYSEC-2026-89
--ignore-vuln PYSEC-2026-97
--ignore-vuln PYSEC-2025-148
--ignore-vuln PYSEC-2025-183
--ignore-vuln PYSEC-2025-189
--ignore-vuln PYSEC-2025-190
--ignore-vuln PYSEC-2025-191
--ignore-vuln PYSEC-2025-192
--ignore-vuln PYSEC-2025-193
--ignore-vuln PYSEC-2025-194
--ignore-vuln PYSEC-2025-195
--ignore-vuln PYSEC-2025-196
--ignore-vuln PYSEC-2025-197
--ignore-vuln PYSEC-2025-210
--ignore-vuln PYSEC-2026-139
--ignore-vuln GHSA-rrmf-rvhw-rf47
--ignore-vuln PYSEC-2025-211
--ignore-vuln PYSEC-2025-212
--ignore-vuln PYSEC-2025-213
--ignore-vuln PYSEC-2025-214
--ignore-vuln PYSEC-2025-215
--ignore-vuln PYSEC-2025-216
--ignore-vuln PYSEC-2025-217
--ignore-vuln PYSEC-2025-218
--ignore-vuln GHSA-f4j7-r4q5-qw2c' --
language: system
pass_filenames: false
stages: [pre-push, manual]
- repo: https://github.com/commitizen-tools/commitizen
rev: v4.10.1
hooks:
- id: commitizen
- id: commitizen-branch
stages: [ pre-push ]

View File

@@ -1 +0,0 @@
3.13

4
.ruff.toml Normal file
View File

@@ -0,0 +1,4 @@
exclude = [
"templates",
"__init__.py",
]

142
AGENTS.md
View File

@@ -1,142 +0,0 @@
# Docs contributor guide
The `docs/` directory is published at [docs.crewai.com](https://docs.crewai.com)
by [Mintlify](https://www.mintlify.com/). Mintlify watches `docs/docs.json`
and the MDX files referenced from it.
## TL;DR for editing docs
- Edit MDX under `docs/edge/<lang>/...` (e.g. `docs/edge/en/concepts/agents.mdx`).
- Your change ships under the **Edge** version selector the moment it merges
to `main`. Edge follows `main` and is the channel for unreleased work.
- On release cut, the current Edge state is frozen into `docs/v<X.Y.Z>/` and
that snapshot becomes the new default version in the selector (tag:
`Latest`). Canonical URLs (`/<lang>/...`) auto-redirect to the new default.
- Never modify files under `docs/v*/`. Those are frozen release snapshots
and the `docs-snapshots` CI guard rejects writes. The only exception is a
release-cut PR (auto-generated by `devtools release` or the manual
`scripts/docs/freeze_current_edge.py` wrapper), which uses a
`[docs-freeze]` title prefix to opt out.
- Never delete or rename files under `docs/images/`. Images are append-only.
See [Images](#images) below.
## The version model
The site has one rolling channel (Edge) plus one frozen snapshot per
release.
```
docs/
edge/ <-- Edge sources (you edit here)
en/...
pt-BR/ ko/ ar/
enterprise-api.*.yaml
v1.14.7/ <-- frozen snapshot of v1.14.7
en/...
pt-BR/ ko/ ar/
enterprise-api.*.yaml
v1.14.6/...
...
images/ <-- shared, append-only
docs.json <-- Mintlify config: navigation + redirects
```
`docs/docs.json` lists one navigation block per version per language. Edge
points at `docs/edge/<lang>/...`; every other version points at its own
`docs/v<X.Y.Z>/<lang>/...` subtree. Mintlify scopes both the sidebar and the
in-site search to whichever version the reader selects, so picking
`v1.10.0` genuinely shows the v1.10.0 docs (and only those).
### URLs and canonical redirects
Each Mintlify version corresponds to its own URL prefix:
- Edge: `/edge/<lang>/<page>` (e.g. `/edge/en/concepts/agents`)
- Frozen: `/v<X.Y.Z>/<lang>/<page>` (e.g. `/v1.14.7/en/concepts/agents`)
External links to the old, unversioned `/<lang>/<page>` URLs would 404 under
this layout. To keep them working, `docs.json` ships wildcard redirects:
```jsonc
{ "source": "/en/:slug*", "destination": "/v1.14.7/en/:slug*", "permanent": false }
```
The release-cut step rewrites the destination on every release so canonical
`/<lang>/...` URLs always resolve to the latest stable docs.
## Lifecycle
1. **During development.** You add or edit pages under
`docs/edge/<lang>/...` in normal PRs. They land in Edge as soon as the PR
merges. Both `/edge/<lang>/<page>` and the version selector's `Edge` entry
reflect the change immediately.
2. **Release cut.** The release engineer runs `devtools release X.Y.Z`. As
part of that flow the CLI opens a `[docs-freeze]` PR that copies Edge into
`docs/v<X.Y.Z>/`, rewrites internal OpenAPI references, updates
`docs/docs.json` to make `v<X.Y.Z>` the new default + `Latest`, and rewires
the canonical-URL redirects to the new default. The PR must merge before
the tag and PyPI publish run.
3. **After release.** Edge keeps rolling. Patch fixes to the just-released
docs go into Edge and ship with the next release. We do not back-edit
frozen snapshots.
See [`RELEASING.md`](RELEASING.md) for the full release runbook.
## Images
Snapshots share a single `docs/images/` directory. If an image is deleted
or renamed, every frozen snapshot that referenced it breaks. So the rule
is:
- Adding new images is always fine.
- Deleting or renaming an existing image fails CI unless the PR is a
`[docs-freeze]` release-cut PR.
- If an asset is wrong, add a new file with a new name and reference the
new name in the Edge MDX (`docs/edge/<lang>/...`). Leave the old file
alone.
## Local preview
Install the Mintlify CLI and run from `docs/`:
```bash
npm i -g mintlify
mintlify dev
```
Use the version selector at the top of the rendered page to switch between
Edge and frozen versions.
To check links across every version:
```bash
mintlify broken-links
```
CI runs the broken-links check on every PR that touches `docs/**` via
[`.github/workflows/docs-broken-links.yml`](.github/workflows/docs-broken-links.yml).
## Scripts
- `scripts/docs/freeze_historical_versions.py` — one-time migration that
reconstructed `docs/v1.10.0/` through `docs/v1.14.7/` from git tags. You
should not need to run this again.
- `scripts/docs/prefix_version_paths.py` — one-time migration that switched
`docs/docs.json` to directory-based versioning, inserted Edge, and added
the canonical-URL redirects. You should not need to run this again.
- `scripts/docs/freeze_current_edge.py` — thin CLI wrapper around
`crewai_devtools.docs_versioning.freeze`. `devtools release` calls the
same module during its docs PR step; this script is the manual escape
hatch (e.g. retroactively freezing a forgotten release).
## CI guards
- [`.github/workflows/docs-snapshots.yml`](.github/workflows/docs-snapshots.yml)
enforces the two rules above (frozen snapshots immutable, images
append-only). Both checks accept the `[docs-freeze]` PR-title escape
hatch.
- [`.github/workflows/docs-broken-links.yml`](.github/workflows/docs-broken-links.yml)
runs `mintlify broken-links` against the whole site, so adding a new
page or moving a snapshot file that breaks a link will fail CI.

111
README.md
View File

@@ -57,14 +57,14 @@
> It empowers developers with both high-level simplicity and precise low-level control, ideal for creating autonomous AI agents tailored to any scenario.
- **CrewAI Crews**: Optimize for autonomy and collaborative intelligence.
- **CrewAI Flows**: The **enterprise and production architecture** for building and deploying multi-agent systems. Enable granular, event-driven control, single LLM calls for precise task orchestration and supports Crews natively
- **CrewAI Flows**: Enable granular, event-driven control, single LLM calls for precise task orchestration and supports Crews natively
With over 100,000 developers certified through our community courses at [learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly becoming the
standard for enterprise-ready AI automation.
# CrewAI AMP Suite
# CrewAI Enterprise Suite
CrewAI AMP Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
You can try one part of the suite the [Crew Control Plane for free](https://app.crewai.com)
@@ -76,14 +76,13 @@ You can try one part of the suite the [Crew Control Plane for free](https://app.
- **Advanced Security**: Built-in robust security and compliance measures ensuring safe deployment and management.
- **Actionable Insights**: Real-time analytics and reporting to optimize performance and decision-making.
- **24/7 Support**: Dedicated enterprise support to ensure uninterrupted operation and quick resolution of issues.
- **On-premise and Cloud Deployment Options**: Deploy CrewAI AMP on-premise or in the cloud, depending on your security and compliance requirements.
- **On-premise and Cloud Deployment Options**: Deploy CrewAI Enterprise on-premise or in the cloud, depending on your security and compliance requirements.
CrewAI AMP is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
CrewAI Enterprise is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
intelligent automations.
## Table of contents
- [Build with AI](#build-with-ai)
- [Why CrewAI?](#why-crewai)
- [Getting Started](#getting-started)
- [Key Features](#key-features)
@@ -102,32 +101,6 @@ intelligent automations.
- [Telemetry](#telemetry)
- [License](#license)
## Build with AI
Using an AI coding agent? Teach it CrewAI best practices in one command:
**Claude Code:**
```shell
/plugin marketplace add crewAIInc/skills
/plugin install crewai-skills@crewai-plugins
/reload-plugins
```
Four skills that activate automatically when you ask relevant CrewAI questions:
| Skill | When it runs |
|-------|--------------|
| `getting-started` | Scaffolding new projects, choosing between `LLM.call()` / `Agent` / `Crew` / `Flow`, wiring `crew.py` / `main.py` |
| `design-agent` | Configuring agents — role, goal, backstory, tools, LLMs, memory, guardrails |
| `design-task` | Writing task descriptions, dependencies, structured output (`output_pydantic`, `output_json`), human review |
| `ask-docs` | Querying the live [CrewAI docs MCP server](https://docs.crewai.com/mcp) for up-to-date API details |
**Cursor, Codex, Windsurf, and others ([skills.sh](https://skills.sh/crewaiinc/skills)):**
```shell
npx skills add crewaiinc/skills
```
This installs the official [CrewAI Skills](https://github.com/crewAIInc/skills) — structured instructions that teach coding agents how to scaffold Flows, configure Crews, design agents and tasks, and follow CrewAI patterns.
## Why CrewAI?
<div align="center" style="margin-bottom: 30px;">
@@ -151,8 +124,7 @@ Setup and run your first CrewAI agents by following this tutorial.
[![CrewAI Getting Started Tutorial](https://img.youtube.com/vi/-kSOTtYzgEw/hqdefault.jpg)](https://www.youtube.com/watch?v=-kSOTtYzgEw "CrewAI Getting Started Tutorial")
###
Learning Resources
Learning Resources
Learn CrewAI through our comprehensive courses:
@@ -169,7 +141,6 @@ CrewAI offers two powerful, complementary approaches that work seamlessly togeth
- Dynamic task delegation and collaboration
- Specialized roles with defined goals and expertise
- Flexible problem-solving approaches
2. **Flows**: Production-ready, event-driven workflows that deliver precise control over complex automations. Flows provide:
- Fine-grained control over execution paths for real-world scenarios
@@ -195,13 +166,13 @@ Ensure you have Python >=3.10 <3.14 installed on your system. CrewAI uses [UV](h
First, install CrewAI:
```shell
uv pip install crewai
pip install crewai
```
If you want to install the 'crewai' package along with its optional features that include additional tools for agents, you can do so by using the following command:
```shell
uv pip install 'crewai[tools]'
pip install 'crewai[tools]'
```
The command above installs the basic package and also adds extra components which require more dependencies to function.
@@ -214,15 +185,14 @@ If you encounter issues during installation or usage, here are some common solut
1. **ModuleNotFoundError: No module named 'tiktoken'**
- Install tiktoken explicitly: `uv pip install 'crewai[embeddings]'`
- If using embedchain or other tools: `uv pip install 'crewai[tools]'`
- Install tiktoken explicitly: `pip install 'crewai[embeddings]'`
- If using embedchain or other tools: `pip install 'crewai[tools]'`
2. **Failed building wheel for tiktoken**
- Ensure Rust compiler is installed (see installation steps above)
- For Windows: Verify Visual C++ Build Tools are installed
- Try upgrading pip: `uv pip install --upgrade pip`
- If issues persist, use a pre-built wheel: `uv pip install tiktoken --prefer-binary`
- Try upgrading pip: `pip install --upgrade pip`
- If issues persist, use a pre-built wheel: `pip install tiktoken --prefer-binary`
### 2. Setting Up Your Crew with the YAML Configuration
@@ -300,7 +270,7 @@ reporting_analyst:
**tasks.yaml**
````yaml
```yaml
# src/my_project/config/tasks.yaml
research_task:
description: >
@@ -320,7 +290,7 @@ reporting_task:
Formatted as markdown without '```'
agent: reporting_analyst
output_file: report.md
````
```
**crew.py**
@@ -448,10 +418,10 @@ Choose CrewAI to easily build powerful, adaptable, and production-ready AI autom
You can test different real life examples of AI crews in the [CrewAI-examples repo](https://github.com/crewAIInc/crewAI-examples?tab=readme-ov-file):
- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/landing_page_generator)
- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/landing_page_generator)
- [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution)
- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner)
- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis)
- [Trip Planner](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner)
- [Stock Analysis](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis)
### Quick Tutorial
@@ -459,19 +429,19 @@ You can test different real life examples of AI crews in the [CrewAI-examples re
### Write Job Descriptions
[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/job-posting) or watch a video below:
[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/job-posting) or watch a video below:
[![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings")
### Trip Planner
[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/trip_planner) or watch a video below:
[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner) or watch a video below:
[![Trip Planner](https://img.youtube.com/vi/xis7rWp-hjs/maxresdefault.jpg)](https://www.youtube.com/watch?v=xis7rWp-hjs "Trip Planner")
### Stock Analysis
[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/crews/stock_analysis) or watch a video below:
[Check out code for this example](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis) or watch a video below:
[![Stock Analysis](https://img.youtube.com/vi/e0Uj4yWdaAg/maxresdefault.jpg)](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
@@ -586,7 +556,7 @@ Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-
- **LangGraph**: While LangGraph provides a foundation for building agent workflows, its approach requires significant boilerplate code and complex state management patterns. The framework's tight coupling with LangChain can limit flexibility when implementing custom agent behaviors or integrating with external systems.
_P.S. CrewAI demonstrates significant performance advantages over LangGraph, executing 5.76x faster in certain cases like this QA task example ([see comparison](https://github.com/crewAIInc/crewAI-examples/tree/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/QA%20Agent)) while achieving higher evaluation scores with faster completion times in certain coding tasks, like in this example ([detailed analysis](https://github.com/crewAIInc/crewAI-examples/blob/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/Coding%20Assistant/coding_assistant_eval.ipynb))._
*P.S. CrewAI demonstrates significant performance advantages over LangGraph, executing 5.76x faster in certain cases like this QA task example ([see comparison](https://github.com/crewAIInc/crewAI-examples/tree/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/QA%20Agent)) while achieving higher evaluation scores with faster completion times in certain coding tasks, like in this example ([detailed analysis](https://github.com/crewAIInc/crewAI-examples/blob/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/Coding%20Assistant/coding_assistant_eval.ipynb)).*
- **Autogen**: While Autogen excels at creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
- **ChatDev**: ChatDev introduced the idea of processes into the realm of AI agents, but its implementation is quite rigid. Customizations in ChatDev are limited and not geared towards production environments, which can hinder scalability and flexibility in real-world applications.
@@ -601,19 +571,6 @@ CrewAI is open-source and we welcome contributions. If you're looking to contrib
- Send a pull request.
- We appreciate your input!
### Contributing to the docs
The site at [docs.crewai.com](https://docs.crewai.com) is published from
`docs/` by [Mintlify](https://www.mintlify.com/). The docs use directory-based
versioning: edits to `docs/edge/<lang>/...` (e.g.
`docs/edge/en/concepts/agents.mdx`) land under the **Edge** version selector
immediately and are frozen into a new versioned snapshot under
`docs/v<X.Y.Z>/` at the next release cut. Frozen snapshots are immutable — CI
rejects PRs that modify them without a `[docs-freeze]` title prefix. The
release CLI (`devtools release`) handles the freeze automatically; see
[`AGENTS.md`](AGENTS.md) for the full contributor guide and
[`RELEASING.md`](RELEASING.md) for the release-cut runbook.
### Installing Dependencies
```bash
@@ -654,7 +611,7 @@ uv build
### Installing Locally
```bash
uv pip install dist/*.tar.gz
pip install dist/*.tar.gz
```
## Telemetry
@@ -717,9 +674,9 @@ CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/b
### Enterprise Features
- [What additional features does CrewAI AMP offer?](#q-what-additional-features-does-crewai-amp-offer)
- [Is CrewAI AMP available for cloud and on-premise deployments?](#q-is-crewai-amp-available-for-cloud-and-on-premise-deployments)
- [Can I try CrewAI AMP for free?](#q-can-i-try-crewai-amp-for-free)
- [What additional features does CrewAI Enterprise offer?](#q-what-additional-features-does-crewai-enterprise-offer)
- [Is CrewAI Enterprise available for cloud and on-premise deployments?](#q-is-crewai-enterprise-available-for-cloud-and-on-premise-deployments)
- [Can I try CrewAI Enterprise for free?](#q-can-i-try-crewai-enterprise-for-free)
### Q: What exactly is CrewAI?
@@ -730,13 +687,13 @@ A: CrewAI is a standalone, lean, and fast Python framework built specifically fo
A: Install CrewAI using pip:
```shell
uv pip install crewai
pip install crewai
```
For additional tools, use:
```shell
uv pip install 'crewai[tools]'
pip install 'crewai[tools]'
```
### Q: Does CrewAI depend on LangChain?
@@ -775,17 +732,17 @@ A: Check out practical examples in the [CrewAI-examples repository](https://gith
A: Contributions are warmly welcomed! Fork the repository, create your branch, implement your changes, and submit a pull request. See the Contribution section of the README for detailed guidelines.
### Q: What additional features does CrewAI AMP offer?
### Q: What additional features does CrewAI Enterprise offer?
A: CrewAI AMP provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
A: CrewAI Enterprise provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
### Q: Is CrewAI AMP available for cloud and on-premise deployments?
### Q: Is CrewAI Enterprise available for cloud and on-premise deployments?
A: Yes, CrewAI AMP supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
A: Yes, CrewAI Enterprise supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
### Q: Can I try CrewAI AMP for free?
### Q: Can I try CrewAI Enterprise for free?
A: Yes, you can explore part of the CrewAI AMP Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
A: Yes, you can explore part of the CrewAI Enterprise Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
### Q: Does CrewAI support fine-tuning or training custom models?
@@ -805,7 +762,7 @@ A: CrewAI is highly scalable, supporting simple automations and large-scale ente
### Q: Does CrewAI offer debugging and monitoring tools?
A: Yes, CrewAI AMP includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
A: Yes, CrewAI Enterprise includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
### Q: What programming languages does CrewAI support?

View File

@@ -1,104 +0,0 @@
# Releasing crewai
The release CLI (`devtools release`) drives the full end-to-end flow,
including the docs-versioning step that has to happen at every release cut.
This runbook is the human-facing summary; the canonical implementation lives
in [`lib/devtools/src/crewai_devtools/cli.py`](lib/devtools/src/crewai_devtools/cli.py).
## Why a docs-versioning step exists
Until the v1.15 series, `docs/docs.json` had 16 "versions" in its selector
but every one of them rendered the same single-source MDX files. Picking
v1.10.0 in the dropdown silently served the latest docs from `main`. We
fixed that by adopting Mintlify's directory-based versioning: each release
gets its own frozen snapshot under `docs/v<X.Y.Z>/`, an `Edge` selector
renders the rolling `main` state (`docs/edge/...`) for unreleased work,
and the canonical `/<lang>/...` URLs redirect to whichever version is
currently `default` + `Latest`.
The release-cut step keeps this model honest. Skip it and the new release
will not appear in the selector and the canonical URLs will keep pointing
at the previous default — i.e. users following a stale link land on docs
that don't describe the version they just installed.
## Happy path: `devtools release`
For a normal release:
```bash
devtools release 1.15.0
```
This runs the full pipeline:
1. Phase 1 — version bump PR, polls until merged.
2. Phase 2 — generates AI release notes, then opens the docs PR titled
`[docs-freeze] docs: snapshot and changelog for v1.15.0`. That PR:
- prepends a release entry to `docs/edge/<lang>/changelog.mdx` for every
supported locale,
- copies `docs/edge/` into `docs/v1.15.0/`,
- rewrites `openapi:` MDX refs inside the snapshot so each frozen page
reads its own OpenAPI YAML instead of the live one,
- inserts a `v1.15.0` entry into every language block in
`docs/docs.json`, marks it `default: true` with tag `"Latest"`, and
demotes the previous default,
- rewires the wildcard redirects so `/<lang>/:slug*` lands on
`/v1.15.0/<lang>/:slug*`.
The CLI polls until you (or another reviewer) merge the docs PR.
3. Phase 2 (cont.) — tags `main`, creates the GitHub release, triggers
`publish.yml`, and bumps the deployment_test repo.
4. Phase 3 — clones the enterprise repo, bumps versions, opens its bump
PR, polls, then tags + releases enterprise.
The `[docs-freeze]` PR title prefix is what the
[`docs-snapshots.yml`](.github/workflows/docs-snapshots.yml) CI guard reads
to allow the snapshot directory and any image deletions to land. The CLI
sets it automatically.
Pre-releases (e.g. `1.15.0a1`) skip the snapshot step — they ride Edge —
and the docs PR title omits the `[docs-freeze]` prefix.
## Manual escape hatch: freeze script
If you ever need to freeze without going through the full release flow (e.g.
retroactively snapshotting a release that shipped without docs versioning,
or testing the freeze locally):
```bash
python scripts/docs/freeze_current_edge.py 1.15.0
```
This is a thin wrapper around the same `crewai_devtools.docs_versioning.freeze`
function used by `devtools release`. It updates the snapshot + `docs.json`
+ redirects but does not touch changelogs, open a PR, or coordinate with the
rest of the release flow. Pair it with a manual PR titled
`[docs-freeze] snapshot docs for v1.15.0`.
## Lifecycle reminders
- Edge (`docs/edge/...`) always reflects `main`. After a release cut, fixes
to the just-released docs go into Edge as normal PRs and ship with the
next release.
- We do not back-port docs fixes into older frozen snapshots. If a fix
matters enough to publish on an older version, it is a deliberate
`[docs-freeze]` PR — treat that as an exception.
- The freeze function is idempotent. If you have to re-run it (e.g. you
pushed a docs fix between snapshotting and merging the PR), delete the
partially-built `docs/v<X.Y.Z>/` directory first and run again.
## Troubleshooting
- **The freeze step warned that the snapshot was already current.** Either
someone else already cut this version, or a previous run left a stale
`docs/v<X.Y.Z>/` directory. Inspect it, then either keep going or delete
the directory and re-run.
- **CI fails on a non-`[docs-freeze]` PR claiming you modified frozen
snapshots.** Check the diff — almost always this is an accidental edit
under `docs/v*/`. Move the change to the matching path under
`docs/edge/<lang>/...` instead. If you truly need to edit a frozen
snapshot, re-title the PR with the `[docs-freeze]` prefix and document
the reason in the PR description.
- **CI fails on a non-`[docs-freeze]` PR claiming you deleted an image.**
Add a new image under a new filename and reference that from Edge. Leave
the old file in place so older snapshots keep rendering.

View File

@@ -1,423 +0,0 @@
"""Pytest configuration for crewAI workspace."""
import base64
from collections.abc import Generator
import gzip
import os
from pathlib import Path
import re
import tempfile
from typing import Any
from dotenv import load_dotenv
import pytest
def _patch_vcrpy_aiohttp_compat() -> None:
"""Keep vcrpy's aiohttp stub working under aiohttp 3.14.0.
aiohttp 3.14.0 (pulled in to fix GHSA-jg22-mg44-37j8 and GHSA-hg6j-4rv6-33pg):
* removed ``aiohttp.streams.AsyncStreamReaderMixin`` (folded into ``StreamReader``),
which vcrpy's ``MockStream`` still subclasses -- vcr's patch machinery then raises
``AttributeError`` at collection time; and
* added a required ``stream_writer`` keyword-only arg to ``ClientResponse.__init__``,
which vcrpy's ``MockClientResponse`` does not pass -- raising ``TypeError`` at
cassette playback.
Restore the mixin, then rebuild ``MockClientResponse``'s ``super().__init__`` call from
the live ``ClientResponse`` signature (defaulting every required keyword-only arg to
``None``, mirroring vcrpy's original call) so it also survives future aiohttp additions.
"""
import asyncio
import inspect
from aiohttp import streams
from aiohttp.client_reqrep import ClientResponse
if not hasattr(streams, "AsyncStreamReaderMixin"):
class AsyncStreamReaderMixin:
__slots__ = ()
def __aiter__(self) -> streams.AsyncStreamIterator[bytes]:
return streams.AsyncStreamIterator(self.readline) # type: ignore[attr-defined]
def iter_chunked(self, n: int) -> streams.AsyncStreamIterator[bytes]:
return streams.AsyncStreamIterator(lambda: self.read(n)) # type: ignore[attr-defined]
def iter_any(self) -> streams.AsyncStreamIterator[bytes]:
return streams.AsyncStreamIterator(self.readany) # type: ignore[attr-defined]
def iter_chunks(self) -> streams.ChunkTupleAsyncStreamIterator:
return streams.ChunkTupleAsyncStreamIterator(self) # type: ignore[arg-type]
streams.AsyncStreamReaderMixin = AsyncStreamReaderMixin # type: ignore[attr-defined]
# Importing the stub builds MockStream/MockClientResponse, so it must run after the
# mixin is restored above.
import vcr.stubs.aiohttp_stubs as aiohttp_stubs # type: ignore[import-untyped]
if getattr(aiohttp_stubs.MockClientResponse, "_crewai_aiohttp_patched", False):
return
keyword_only = [
name
for name, param in inspect.signature(ClientResponse.__init__).parameters.items()
if param.kind is inspect.Parameter.KEYWORD_ONLY
]
class _NullStreamWriter:
# aiohttp 3.14.0 reads stream_writer.output_size in the "request already
# sent" branch (writer is None), so None is not enough -- supply a stub.
output_size = 0
fallback_loop: list[asyncio.AbstractEventLoop] = []
def _resolve_loop() -> asyncio.AbstractEventLoop:
# MockClientResponse is normally built inside aiohttp's running loop, so
# prefer that. In a sync context there is no running loop; avoid
# asyncio.get_event_loop(), which on 3.12+ emits a DeprecationWarning
# (and can RuntimeError) when no current loop is set. Use one cached
# loop instead -- the mock only stores it and calls loop.get_debug().
try:
return asyncio.get_running_loop()
except RuntimeError:
if not fallback_loop:
fallback_loop.append(asyncio.new_event_loop())
return fallback_loop[0]
def _mock_client_response_init(
self: Any, method: str, url: Any, request_info: Any = None
) -> None:
kwargs: dict[str, Any] = dict.fromkeys(keyword_only)
kwargs["request_info"] = request_info
if "loop" in kwargs:
kwargs["loop"] = _resolve_loop()
if "stream_writer" in kwargs:
kwargs["stream_writer"] = _NullStreamWriter()
ClientResponse.__init__(self, method, url, **kwargs)
aiohttp_stubs.MockClientResponse.__init__ = _mock_client_response_init
aiohttp_stubs.MockClientResponse._crewai_aiohttp_patched = True
_patch_vcrpy_aiohttp_compat()
from vcr.request import Request # type: ignore[import-untyped] # noqa: E402
try:
import vcr.stubs.httpx_stubs as httpx_stubs # type: ignore[import-untyped]
except ModuleNotFoundError:
import vcr.stubs.httpcore_stubs as httpx_stubs # type: ignore[import-untyped]
env_test_path = Path(__file__).parent / ".env.test"
load_dotenv(env_test_path, override=False)
load_dotenv(override=False)
BEDROCK_HOST_PLACEHOLDER = "bedrock-runtime.vcr.amazonaws.com"
_BEDROCK_HOST_RE = re.compile(r"^bedrock-runtime\.[a-z0-9-]+\.amazonaws\.com$")
def _normalize_bedrock_host(host: str) -> str:
if _BEDROCK_HOST_RE.match(host):
return BEDROCK_HOST_PLACEHOLDER
return host
def bedrock_host_matcher(r1: Request, r2: Request) -> bool: # type: ignore[no-any-unimported]
"""Match Bedrock requests across AWS regions (CI uses us-east-1, local may use us-west-2)."""
return _normalize_bedrock_host(r1.host or "") == _normalize_bedrock_host(
r2.host or ""
)
def _patched_make_vcr_request(httpx_request: Any, **kwargs: Any) -> Any:
"""Patched version of VCR's _make_vcr_request that handles binary content.
The original implementation fails on binary request bodies (like file uploads)
because it assumes all content can be decoded as UTF-8.
"""
raw_body = httpx_request.read()
try:
body = raw_body.decode("utf-8")
except UnicodeDecodeError:
body = base64.b64encode(raw_body).decode("ascii")
uri = str(httpx_request.url)
headers = dict(httpx_request.headers)
return Request(httpx_request.method, uri, body, headers)
httpx_stubs._make_vcr_request = _patched_make_vcr_request
# Patch the response-side of VCR to fix httpx.ResponseNotRead errors.
# VCR's _from_serialized_response mocks httpx.Response.read(), which prevents
# the response's internal _content attribute from being properly initialized.
# When OpenAI's client (using with_raw_response) accesses response.content,
# httpx raises ResponseNotRead because read() was never actually called.
# This patch ensures _content is explicitly set after response creation.
_original_from_serialized_response = getattr(
httpx_stubs, "_from_serialized_response", None
)
if _original_from_serialized_response is not None:
_from_serialized: Any = _original_from_serialized_response
def _patched_from_serialized_response(
request: Any, serialized_response: Any, history: Any = None
) -> Any:
"""Patched version that ensures response._content is properly set."""
response = _from_serialized(request, serialized_response, history)
# Explicitly set _content to avoid ResponseNotRead errors
# The content was passed to the constructor but the mocked read() prevents
# proper initialization of the internal state
body_content = serialized_response.get("body", {}).get("string", b"")
if isinstance(body_content, str):
body_content = body_content.encode("utf-8")
response._content = body_content
return response
httpx_stubs._from_serialized_response = _patched_from_serialized_response
@pytest.fixture(autouse=True, scope="function")
def cleanup_event_handlers() -> Generator[None, Any, None]:
"""Clean up event bus handlers after each test to prevent test pollution."""
yield
try:
from crewai.events.event_bus import crewai_event_bus
with crewai_event_bus._rwlock.w_locked():
crewai_event_bus._sync_handlers.clear()
crewai_event_bus._async_handlers.clear()
except Exception: # noqa: S110
pass
@pytest.fixture(autouse=True, scope="function")
def reset_event_state() -> None:
"""Reset event system state before each test for isolation."""
from crewai.events.base_events import reset_emission_counter
from crewai.events.event_context import (
EventContextConfig,
_event_context_config,
_event_id_stack,
)
reset_emission_counter()
_event_id_stack.set(())
_event_context_config.set(EventContextConfig())
@pytest.fixture(autouse=True, scope="function")
def setup_test_environment() -> Generator[None, Any, None]:
"""Setup test environment for crewAI workspace."""
with tempfile.TemporaryDirectory() as temp_dir:
storage_dir = Path(temp_dir) / "crewai_test_storage"
storage_dir.mkdir(parents=True, exist_ok=True)
if not storage_dir.exists() or not storage_dir.is_dir():
raise RuntimeError(
f"Failed to create test storage directory: {storage_dir}"
)
try:
test_file = storage_dir / ".permissions_test"
test_file.touch()
test_file.unlink()
except (OSError, IOError) as e:
raise RuntimeError(
f"Test storage directory {storage_dir} is not writable: {e}"
) from e
os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir)
os.environ["CREWAI_TESTING"] = "true"
try:
yield
finally:
os.environ.pop("CREWAI_TESTING", "true")
os.environ.pop("CREWAI_STORAGE_DIR", None)
os.environ.pop("CREWAI_DISABLE_TELEMETRY", "true")
os.environ.pop("OTEL_SDK_DISABLED", "true")
os.environ.pop("OPENAI_BASE_URL", "https://api.openai.com/v1")
os.environ.pop("OPENAI_API_BASE", "https://api.openai.com/v1")
HEADERS_TO_FILTER = {
"authorization": "AUTHORIZATION-XXX",
"content-security-policy": "CSP-FILTERED",
"cookie": "COOKIE-XXX",
"set-cookie": "SET-COOKIE-XXX",
"permissions-policy": "PERMISSIONS-POLICY-XXX",
"referrer-policy": "REFERRER-POLICY-XXX",
"strict-transport-security": "STS-XXX",
"x-content-type-options": "X-CONTENT-TYPE-XXX",
"x-frame-options": "X-FRAME-OPTIONS-XXX",
"x-permitted-cross-domain-policies": "X-PERMITTED-XXX",
"x-request-id": "X-REQUEST-ID-XXX",
"x-runtime": "X-RUNTIME-XXX",
"x-xss-protection": "X-XSS-PROTECTION-XXX",
"x-stainless-arch": "X-STAINLESS-ARCH-XXX",
"x-stainless-os": "X-STAINLESS-OS-XXX",
"x-stainless-read-timeout": "X-STAINLESS-READ-TIMEOUT-XXX",
"cf-ray": "CF-RAY-XXX",
"etag": "ETAG-XXX",
"Strict-Transport-Security": "STS-XXX",
"access-control-expose-headers": "ACCESS-CONTROL-XXX",
"openai-organization": "OPENAI-ORG-XXX",
"openai-project": "OPENAI-PROJECT-XXX",
"x-ratelimit-limit-requests": "X-RATELIMIT-LIMIT-REQUESTS-XXX",
"x-ratelimit-limit-tokens": "X-RATELIMIT-LIMIT-TOKENS-XXX",
"x-ratelimit-remaining-requests": "X-RATELIMIT-REMAINING-REQUESTS-XXX",
"x-ratelimit-remaining-tokens": "X-RATELIMIT-REMAINING-TOKENS-XXX",
"x-ratelimit-reset-requests": "X-RATELIMIT-RESET-REQUESTS-XXX",
"x-ratelimit-reset-tokens": "X-RATELIMIT-RESET-TOKENS-XXX",
"x-goog-api-key": "X-GOOG-API-KEY-XXX",
"api-key": "X-API-KEY-XXX",
"User-Agent": "X-USER-AGENT-XXX",
"apim-request-id:": "X-API-CLIENT-REQUEST-ID-XXX",
"azureml-model-session": "AZUREML-MODEL-SESSION-XXX",
"x-ms-client-request-id": "X-MS-CLIENT-REQUEST-ID-XXX",
"x-ms-region": "X-MS-REGION-XXX",
"apim-request-id": "APIM-REQUEST-ID-XXX",
"x-api-key": "X-API-KEY-XXX",
"anthropic-organization-id": "ANTHROPIC-ORGANIZATION-ID-XXX",
"request-id": "REQUEST-ID-XXX",
"anthropic-ratelimit-input-tokens-limit": "ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX",
"anthropic-ratelimit-input-tokens-remaining": "ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX",
"anthropic-ratelimit-input-tokens-reset": "ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX",
"anthropic-ratelimit-output-tokens-limit": "ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX",
"anthropic-ratelimit-output-tokens-remaining": "ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX",
"anthropic-ratelimit-output-tokens-reset": "ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX",
"anthropic-ratelimit-tokens-limit": "ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX",
"anthropic-ratelimit-tokens-remaining": "ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX",
"anthropic-ratelimit-tokens-reset": "ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX",
"x-amz-date": "X-AMZ-DATE-XXX",
"x-amz-security-token": "X-AMZ-SECURITY-TOKEN-XXX",
"amz-sdk-invocation-id": "AMZ-SDK-INVOCATION-ID-XXX",
"accept-encoding": "ACCEPT-ENCODING-XXX",
"x-amzn-requestid": "X-AMZN-REQUESTID-XXX",
"x-amzn-RequestId": "X-AMZN-REQUESTID-XXX",
"x-a2a-notification-token": "X-A2A-NOTIFICATION-TOKEN-XXX",
"x-a2a-version": "X-A2A-VERSION-XXX",
}
def _filter_request_headers(request: Request) -> Request: # type: ignore[no-any-unimported]
"""Filter sensitive headers from request before recording."""
for header_name, replacement in HEADERS_TO_FILTER.items():
for variant in [header_name, header_name.upper(), header_name.title()]:
if variant in request.headers:
request.headers[variant] = [replacement]
request.method = request.method.upper()
# Normalize Azure OpenAI endpoints to a consistent placeholder for cassette matching.
if request.host and request.host.endswith(".openai.azure.com"):
original_host = request.host
placeholder_host = "fake-azure-endpoint.openai.azure.com"
request.uri = request.uri.replace(original_host, placeholder_host)
# Normalize Bedrock regional endpoints so cassettes work in any AWS region.
if request.host and _BEDROCK_HOST_RE.match(request.host):
request.uri = request.uri.replace(request.host, BEDROCK_HOST_PLACEHOLDER)
return request
def _filter_response_headers(response: dict[str, Any]) -> dict[str, Any] | None:
"""Filter sensitive headers from response before recording.
Returns None to skip recording responses with empty bodies. This handles
duplicate recordings caused by OpenAI's stainless client using
with_raw_response which triggers httpx to re-read the consumed stream.
"""
body = response.get("body", {}).get("string", "")
headers = response.get("headers", {})
content_length = headers.get("content-length", headers.get("Content-Length", []))
if body == "" or body == b"" or content_length == ["0"]:
return None
status_code = response.get("status", {}).get("code")
if isinstance(status_code, int) and status_code >= 400:
# Avoid persisting auth/model errors when re-recording without valid AWS creds.
return None
for encoding_header in ["Content-Encoding", "content-encoding"]:
if encoding_header in headers:
encoding = headers.pop(encoding_header)
if encoding and encoding[0] == "gzip":
body = response.get("body", {}).get("string", b"")
if isinstance(body, bytes) and body.startswith(b"\x1f\x8b"):
response["body"]["string"] = gzip.decompress(body).decode("utf-8")
for header_name, replacement in HEADERS_TO_FILTER.items():
for variant in [header_name, header_name.upper(), header_name.title()]:
if variant in headers:
headers[variant] = [replacement]
return response
@pytest.fixture(scope="module")
def vcr_cassette_dir(request: Any) -> str:
"""Generate cassette directory path based on test module location.
Organizes cassettes to mirror test directory structure within each package:
lib/crewai/tests/llms/google/test_google.py -> lib/crewai/tests/cassettes/llms/google/
lib/crewai-tools/tests/tools/test_search.py -> lib/crewai-tools/tests/cassettes/tools/
"""
test_file = Path(request.fspath)
for parent in test_file.parents:
if (
parent.name
in ("crewai", "crewai-tools", "crewai-files", "cli", "crewai-core")
and parent.parent.name == "lib"
):
package_root = parent
break
else:
package_root = test_file.parent
tests_root = package_root / "tests"
test_dir = test_file.parent
if test_dir != tests_root:
relative_path = test_dir.relative_to(tests_root)
cassette_dir = tests_root / "cassettes" / relative_path
else:
cassette_dir = tests_root / "cassettes"
cassette_dir.mkdir(parents=True, exist_ok=True)
return str(cassette_dir)
def pytest_recording_configure(vcr: Any, config: Any) -> None:
"""Register custom VCR matchers for each test cassette session."""
vcr.register_matcher("bedrock_host", bedrock_host_matcher)
@pytest.fixture(scope="module")
def vcr_config(vcr_cassette_dir: str) -> dict[str, Any]:
"""Configure VCR with organized cassette storage."""
config = {
"cassette_library_dir": vcr_cassette_dir,
"record_mode": os.getenv("PYTEST_VCR_RECORD_MODE", "once"),
"filter_headers": [(k, v) for k, v in HEADERS_TO_FILTER.items()],
"before_record_request": _filter_request_headers,
"before_record_response": _filter_response_headers,
"filter_query_parameters": ["key"],
"match_on": ["method", "scheme", "host", "port", "path"],
}
if os.getenv("GITHUB_ACTIONS") == "true":
config["record_mode"] = "none"
return config

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---
title: "Introduction"
description: "Complete reference for the CrewAI Enterprise REST API"
icon: "code"
---
# CrewAI Enterprise API
Welcome to the CrewAI Enterprise API reference. This API allows you to programmatically interact with your deployed crews, enabling integration with your applications, workflows, and services.
## Quick Start
<Steps>
<Step title="Get Your API Credentials">
Navigate to your crew's detail page in the CrewAI Enterprise dashboard and copy your Bearer Token from the Status tab.
</Step>
<Step title="Discover Required Inputs">
Use the `GET /inputs` endpoint to see what parameters your crew expects.
</Step>
<Step title="Start a Crew Execution">
Call `POST /kickoff` with your inputs to start the crew execution and receive a `kickoff_id`.
</Step>
<Step title="Monitor Progress">
Use `GET /status/{kickoff_id}` to check execution status and retrieve results.
</Step>
</Steps>
## Authentication
All API requests require authentication using a Bearer token. Include your token in the `Authorization` header:
```bash
curl -H "Authorization: Bearer YOUR_CREW_TOKEN" \
https://your-crew-url.crewai.com/inputs
```
### Token Types
| Token Type | Scope | Use Case |
|:-----------|:--------|:----------|
| **Bearer Token** | Organization-level access | Full crew operations, ideal for server-to-server integration |
| **User Bearer Token** | User-scoped access | Limited permissions, suitable for user-specific operations |
<Tip>
You can find both token types in the Status tab of your crew's detail page in the CrewAI Enterprise dashboard.
</Tip>
## Base URL
Each deployed crew has its own unique API endpoint:
```
https://your-crew-name.crewai.com
```
Replace `your-crew-name` with your actual crew's URL from the dashboard.
## Typical Workflow
1. **Discovery**: Call `GET /inputs` to understand what your crew needs
2. **Execution**: Submit inputs via `POST /kickoff` to start processing
3. **Monitoring**: Poll `GET /status/{kickoff_id}` until completion
4. **Results**: Extract the final output from the completed response
## Error Handling
The API uses standard HTTP status codes:
| Code | Meaning |
|------|:--------|
| `200` | Success |
| `400` | Bad Request - Invalid input format |
| `401` | Unauthorized - Invalid bearer token |
| `404` | Not Found - Resource doesn't exist |
| `422` | Validation Error - Missing required inputs |
| `500` | Server Error - Contact support |
## Interactive Testing
<Info>
**Why no "Send" button?** Since each CrewAI Enterprise user has their own unique crew URL, we use **reference mode** instead of an interactive playground to avoid confusion. This shows you exactly what the requests should look like without non-functional send buttons.
</Info>
Each endpoint page shows you:
- ✅ **Exact request format** with all parameters
- ✅ **Response examples** for success and error cases
- ✅ **Code samples** in multiple languages (cURL, Python, JavaScript, etc.)
- ✅ **Authentication examples** with proper Bearer token format
### **To Test Your Actual API:**
<CardGroup cols={2}>
<Card title="Copy cURL Examples" icon="terminal">
Copy the cURL examples and replace the URL + token with your real values
</Card>
<Card title="Use Postman/Insomnia" icon="play">
Import the examples into your preferred API testing tool
</Card>
</CardGroup>
**Example workflow:**
1. **Copy this cURL example** from any endpoint page
2. **Replace `your-actual-crew-name.crewai.com`** with your real crew URL
3. **Replace the Bearer token** with your real token from the dashboard
4. **Run the request** in your terminal or API client
## Need Help?
<CardGroup cols={2}>
<Card title="Enterprise Support" icon="headset" href="mailto:support@crewai.com">
Get help with API integration and troubleshooting
</Card>
<Card title="Enterprise Dashboard" icon="chart-line" href="https://app.crewai.com">
Manage your crews and view execution logs
</Card>
</CardGroup>

473
docs/changelog.mdx Normal file
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---
title: Changelog
description: View the latest updates and changes to CrewAI
icon: timeline
---
<Update label="2024-05-22" description="v0.121.0" tags={["Latest"]}>
## Release Highlights
<Frame>
<img src="/images/releases/v01210.png" />
</Frame>
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.121.0">View on GitHub</a>
</div>
**Core Improvements & Fixes**
- Fixed encoding error when creating tools
- Fixed failing llama test
- Updated logging configuration for consistency
- Enhanced telemetry initialization and event handling
**New Features & Enhancements**
- Added **markdown attribute** to the Task class
- Added **reasoning attribute** to the Agent class
- Added **inject_date flag** to Agent for automatic date injection
- Implemented **HallucinationGuardrail** (no-op with test coverage)
**Documentation & Guides**
- Added documentation for **StagehandTool** and improved MDX structure
- Added documentation for **MCP integration** and updated enterprise docs
- Documented knowledge events and updated reasoning docs
- Added stop parameter documentation
- Fixed import references in doc examples (before_kickoff, after_kickoff)
- General docs updates and restructuring for clarity
</Update>
<Update label="2024-05-15" description="v0.120.1">
## Release Highlights
<Frame>
<img src="/images/releases/v01201.png" />
</Frame>
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.120.1">View on GitHub</a>
</div>
**Core Improvements & Fixes**
- Fixed **interpolation with hyphens**
</Update>
<Update label="2024-05-14" description="v0.120.0">
## Release Highlights
<Frame>
<img src="/images/releases/v01200.png" />
</Frame>
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.120.0">View on GitHub</a>
</div>
**Core Improvements & Fixes**
- Enabled **full Ruff rule set** by default for stricter linting
- Addressed race condition in FilteredStream using context managers
- Fixed agent knowledge reset issue
- Refactored agent fetching logic into utility module
**New Features & Enhancements**
- Added support for **loading an Agent directly from a repository**
- Enabled setting an empty context for Task
- Enhanced Agent repository feedback and fixed Tool auto-import behavior
- Introduced direct initialization of knowledge (bypassing knowledge_sources)
**Documentation & Guides**
- Updated security.md for current security practices
- Cleaned up Google setup section for clarity
- Added link to AI Studio when entering Gemini key
- Updated Arize Phoenix observability guide
- Refreshed flow documentation
</Update>
<Update label="2024-05-08" description="v0.119.0">
## Release Highlights
<Frame>
<img src="/images/releases/v01190.png" />
</Frame>
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.119.0">View on GitHub</a>
</div>
**Core Improvements & Fixes**
- Improved test reliability by enhancing pytest handling for flaky tests
- Fixed memory reset crash when embedding dimensions mismatch
- Enabled parent flow identification for Crew and LiteAgent
- Prevented telemetry-related crashes when unavailable
- Upgraded **LiteLLM version** for better compatibility
- Fixed llama converter tests by removing skip_external_api
**New Features & Enhancements**
- Introduced **knowledge retrieval prompt re-writing** in Agent for improved tracking and debugging
- Made LLM setup and quickstart guides model-agnostic
**Documentation & Guides**
- Added advanced configuration docs for the RAG tool
- Updated Windows troubleshooting guide
- Refined documentation examples for better clarity
- Fixed typos across docs and config files
</Update>
<Update label="2024-04-28" description="v0.118.0">
## Release Highlights
<Frame>
<img src="/images/releases/v01180.png" />
</Frame>
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.118.0">View on GitHub</a>
</div>
**Core Improvements & Fixes**
- Fixed issues with missing prompt or system templates
- Removed global logging configuration to avoid unintended overrides
- Renamed **TaskGuardrail to LLMGuardrail** for improved clarity
- Downgraded litellm to version 1.167.1 for compatibility
- Added missing init.py files to ensure proper module initialization
**New Features & Enhancements**
- Added support for **no-code Guardrail creation** to simplify AI behavior controls
**Documentation & Guides**
- Removed CrewStructuredTool from public documentation to reflect internal usage
- Updated enterprise documentation and YouTube embed for improved onboarding experience
</Update>
<Update label="2024-04-20" description="v0.117.0">
## Release Highlights
<Frame>
<img src="/images/releases/v01170.png" />
</Frame>
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.117.0">View on GitHub</a>
</div>
**New Features & Enhancements**
- Added `result_as_answer` parameter support in `@tool` decorator.
- Introduced support for new language models: GPT-4.1, Gemini-2.0, and Gemini-2.5 Pro.
- Enhanced knowledge management capabilities.
- Added Huggingface provider option in CLI.
- Improved compatibility and CI support for Python 3.10+.
**Core Improvements & Fixes**
- Fixed issues with incorrect template parameters and missing inputs.
- Improved asynchronous flow handling with coroutine condition checks.
- Enhanced memory management with isolated configuration and correct memory object copying.
- Fixed initialization of lite agents with correct references.
- Addressed Python type hint issues and removed redundant imports.
- Updated event placement for improved tool usage tracking.
- Raised explicit exceptions when flows fail.
- Removed unused code and redundant comments from various modules.
- Updated GitHub App token action to v2.
**Documentation & Guides**
- Enhanced documentation structure, including enterprise deployment instructions.
- Automatically create output folders for documentation generation.
- Fixed broken link in WeaviateVectorSearchTool documentation.
- Fixed guardrail documentation usage and import paths for JSON search tools.
- Updated documentation for CodeInterpreterTool.
- Improved SEO, contextual navigation, and error handling for documentation pages.
</Update>
<Update label="2024-04-25" description="v0.117.1">
## Release Highlights
<Frame>
<img src="/images/releases/v01171.png" />
</Frame>
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.117.1">View on GitHub</a>
</div>
**Core Improvements & Fixes**
- Upgraded **crewai-tools** to latest version
- Upgraded **liteLLM** to latest version
- Fixed **Mem0 OSS**
</Update>
<Update label="2024-04-07" description="v0.114.0">
## Release Highlights
<Frame>
<img src="/images/releases/v01140.png" />
</Frame>
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.114.0">View on GitHub</a>
</div>
**New Features & Enhancements**
- Agents as an atomic unit. (`Agent(...).kickoff()`)
- Support for [Custom LLM implementations](https://docs.crewai.com/guides/advanced/custom-llm).
- Integrated External Memory and [Opik observability](https://docs.crewai.com/how-to/opik-observability).
- Enhanced YAML extraction.
- Multimodal agent validation.
- Added Secure fingerprints for agents and crews.
**Core Improvements & Fixes**
- Improved serialization, agent copying, and Python compatibility.
- Added wildcard support to `emit()`
- Added support for additional router calls and context window adjustments.
- Fixed typing issues, validation, and import statements.
- Improved method performance.
- Enhanced agent task handling, event emissions, and memory management.
- Fixed CLI issues, conditional tasks, cloning behavior, and tool outputs.
**Documentation & Guides**
- Improved documentation structure, theme, and organization.
- Added guides for Local NVIDIA NIM with WSL2, W&B Weave, and Arize Phoenix.
- Updated tool configuration examples, prompts, and observability docs.
- Guide on using singular agents within Flows.
</Update>
<Update label="2024-03-17" description="v0.108.0">
## Release Highlights
<Frame>
<img src="/images/releases/v01080.png" />
</Frame>
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.108.0">View on GitHub</a>
</div>
**New Features & Enhancements**
- Converted tabs to spaces in `crew.py` template
- Enhanced LLM Streaming Response Handling and Event System
- Included `model_name`
- Enhanced Event Listener with rich visualization and improved logging
- Added fingerprints
**Bug Fixes**
- Fixed Mistral issues
- Fixed a bug in documentation
- Fixed type check error in fingerprint property
**Documentation Updates**
- Improved tool documentation
- Updated installation guide for the `uv` tool package
- Added instructions for upgrading crewAI with the `uv` tool
- Added documentation for `ApifyActorsTool`
</Update>
<Update label="2024-03-10" description="v0.105.0">
## Release Highlights
<Frame>
<img src="/images/releases/v01050.png" />
</Frame>
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.105.0">View on GitHub</a>
</div>
**Core Improvements & Fixes**
- Fixed issues with missing template variables and user memory configuration
- Improved async flow support and addressed agent response formatting
- Enhanced memory reset functionality and fixed CLI memory commands
- Fixed type issues, tool calling properties, and telemetry decoupling
**New Features & Enhancements**
- Added Flow state export and improved state utilities
- Enhanced agent knowledge setup with optional crew embedder
- Introduced event emitter for better observability and LLM call tracking
- Added support for Python 3.10 and ChatOllama from langchain_ollama
- Integrated context window size support for the o3-mini model
- Added support for multiple router calls
**Documentation & Guides**
- Improved documentation layout and hierarchical structure
- Added QdrantVectorSearchTool guide and clarified event listener usage
- Fixed typos in prompts and updated Amazon Bedrock model listings
</Update>
<Update label="2024-02-12" description="v0.102.0">
## Release Highlights
<Frame>
<img src="/images/releases/v01020.png" />
</Frame>
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.102.0">View on GitHub</a>
</div>
**Core Improvements & Fixes**
- Enhanced LLM Support: Improved structured LLM output, parameter handling, and formatting for Anthropic models
- Crew & Agent Stability: Fixed issues with cloning agents/crews using knowledge sources, multiple task outputs in conditional tasks, and ignored Crew task callbacks
- Memory & Storage Fixes: Fixed short-term memory handling with Bedrock, ensured correct embedder initialization, and added a reset memories function in the crew class
- Training & Execution Reliability: Fixed broken training and interpolation issues with dict and list input types
**New Features & Enhancements**
- Advanced Knowledge Management: Improved naming conventions and enhanced embedding configuration with custom embedder support
- Expanded Logging & Observability: Added JSON format support for logging and integrated MLflow tracing documentation
- Data Handling Improvements: Updated excel_knowledge_source.py to process multi-tab files
- General Performance & Codebase Clean-Up: Streamlined enterprise code alignment and resolved linting issues
- Adding new tool: `QdrantVectorSearchTool`
**Documentation & Guides**
- Updated AI & Memory Docs: Improved Bedrock, Google AI, and long-term memory documentation
- Task & Workflow Clarity: Added "Human Input" row to Task Attributes, Langfuse guide, and FileWriterTool documentation
- Fixed Various Typos & Formatting Issues
</Update>
<Update label="2024-01-28" description="v0.100.0">
## Release Highlights
<Frame>
<img src="/images/releases/v01000.png" />
</Frame>
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.100.0">View on GitHub</a>
</div>
**Features**
- Add Composio docs
- Add SageMaker as a LLM provider
**Fixes**
- Overall LLM connection issues
- Using safe accessors on training
- Add version check to crew_chat.py
**Documentation**
- New docs for crewai chat
- Improve formatting and clarity in CLI and Composio Tool docs
</Update>
<Update label="2024-01-20" description="v0.98.0">
## Release Highlights
<Frame>
<img src="/images/releases/v0980.png" />
</Frame>
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.98.0">View on GitHub</a>
</div>
**Features**
- Conversation crew v1
- Add unique ID to flow states
- Add @persist decorator with FlowPersistence interface
**Integrations**
- Add SambaNova integration
- Add NVIDIA NIM provider in cli
- Introducing VoyageAI
**Fixes**
- Fix API Key Behavior and Entity Handling in Mem0 Integration
- Fixed core invoke loop logic and relevant tests
- Make tool inputs actual objects and not strings
- Add important missing parts to creating tools
- Drop litellm version to prevent windows issue
- Before kickoff if inputs are none
- Fixed typos, nested pydantic model issue, and docling issues
</Update>
<Update label="2024-01-04" description="v0.95.0">
## Release Highlights
<Frame>
<img src="/images/releases/v0950.png" />
</Frame>
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.95.0">View on GitHub</a>
</div>
**New Features**
- Adding Multimodal Abilities to Crew
- Programatic Guardrails
- HITL multiple rounds
- Gemini 2.0 Support
- CrewAI Flows Improvements
- Add Workflow Permissions
- Add support for langfuse with litellm
- Portkey Integration with CrewAI
- Add interpolate_only method and improve error handling
- Docling Support
- Weviate Support
**Fixes**
- output_file not respecting system path
- disk I/O error when resetting short-term memory
- CrewJSONEncoder now accepts enums
- Python max version
- Interpolation for output_file in Task
- Handle coworker role name case/whitespace properly
- Add tiktoken as explicit dependency and document Rust requirement
- Include agent knowledge in planning process
- Change storage initialization to None for KnowledgeStorage
- Fix optional storage checks
- include event emitter in flows
- Docstring, Error Handling, and Type Hints Improvements
- Suppressed userWarnings from litellm pydantic issues
</Update>
<Update label="2024-12-05" description="v0.86.0">
## Release Highlights
<Frame>
<img src="/images/releases/v0860.png" />
</Frame>
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.86.0">View on GitHub</a>
</div>
**Changes**
- Remove all references to pipeline and pipeline router
- Add Nvidia NIM as provider in Custom LLM
- Add knowledge demo + improve knowledge docs
- Add HITL multiple rounds of followup
- New docs about yaml crew with decorators
- Simplify template crew
</Update>
<Update label="2024-12-04" description="v0.85.0">
## Release Highlights
<Frame>
<img src="/images/releases/v0850.png" />
</Frame>
<div style={{ textAlign: 'center', marginBottom: '1rem' }}>
<a href="https://github.com/crewAIInc/crewAI/releases/tag/0.85.0">View on GitHub</a>
</div>
**Features**
- Added knowledge to agent level
- Feat/remove langchain
- Improve typed task outputs
- Log in to Tool Repository on crewai login
**Fixes**
- Fixes issues with result as answer not properly exiting LLM loop
- Fix missing key name when running with ollama provider
- Fix spelling issue found
**Documentation**
- Update readme for running mypy
- Add knowledge to mint.json
- Update Github actions
- Update Agents docs to include two approaches for creating an agent
- Improvements to LLM Configuration and Usage
</Update>
<Update label="2024-11-25" description="v0.83.0">
**New Features**
- New before_kickoff and after_kickoff crew callbacks
- Support to pre-seed agents with Knowledge
- Add support for retrieving user preferences and memories using Mem0
**Fixes**
- Fix Async Execution
- Upgrade chroma and adjust embedder function generator
- Update CLI Watson supported models + docs
- Reduce level for Bandit
- Fixing all tests
**Documentation**
- Update Docs
</Update>
<Update label="2024-11-13" description="v0.80.0">
**Fixes**
- Fixing Tokens callback replacement bug
- Fixing Step callback issue
- Add cached prompt tokens info on usage metrics
- Fix crew_train_success test
</Update>

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@@ -0,0 +1,588 @@
---
title: Agents
description: Detailed guide on creating and managing agents within the CrewAI framework.
icon: robot
---
## Overview of an Agent
In the CrewAI framework, an `Agent` is an autonomous unit that can:
- Perform specific tasks
- Make decisions based on its role and goal
- Use tools to accomplish objectives
- Communicate and collaborate with other agents
- Maintain memory of interactions
- Delegate tasks when allowed
<Tip>
Think of an agent as a specialized team member with specific skills, expertise, and responsibilities. For example, a `Researcher` agent might excel at gathering and analyzing information, while a `Writer` agent might be better at creating content.
</Tip>
<Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
CrewAI Enterprise includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
![Visual Agent Builder Screenshot](/images/enterprise/crew-studio-interface.png)
The Visual Agent Builder enables:
- Intuitive agent configuration with form-based interfaces
- Real-time testing and validation
- Template library with pre-configured agent types
- Easy customization of agent attributes and behaviors
</Note>
## Agent Attributes
| Attribute | Parameter | Type | Description |
| :-------------------------------------- | :----------------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- |
| **Role** | `role` | `str` | Defines the agent's function and expertise within the crew. |
| **Goal** | `goal` | `str` | The individual objective that guides the agent's decision-making. |
| **Backstory** | `backstory` | `str` | Provides context and personality to the agent, enriching interactions. |
| **LLM** _(optional)_ | `llm` | `Union[str, LLM, Any]` | Language model that powers the agent. Defaults to the model specified in `OPENAI_MODEL_NAME` or "gpt-4". |
| **Tools** _(optional)_ | `tools` | `List[BaseTool]` | Capabilities or functions available to the agent. Defaults to an empty list. |
| **Function Calling LLM** _(optional)_ | `function_calling_llm` | `Optional[Any]` | Language model for tool calling, overrides crew's LLM if specified. |
| **Max Iterations** _(optional)_ | `max_iter` | `int` | Maximum iterations before the agent must provide its best answer. Default is 20. |
| **Max RPM** _(optional)_ | `max_rpm` | `Optional[int]` | Maximum requests per minute to avoid rate limits. |
| **Max Execution Time** _(optional)_ | `max_execution_time` | `Optional[int]` | Maximum time (in seconds) for task execution. |
| **Verbose** _(optional)_ | `verbose` | `bool` | Enable detailed execution logs for debugging. Default is False. |
| **Allow Delegation** _(optional)_ | `allow_delegation` | `bool` | Allow the agent to delegate tasks to other agents. Default is False. |
| **Step Callback** _(optional)_ | `step_callback` | `Optional[Any]` | Function called after each agent step, overrides crew callback. |
| **Cache** _(optional)_ | `cache` | `bool` | Enable caching for tool usage. Default is True. |
| **System Template** _(optional)_ | `system_template` | `Optional[str]` | Custom system prompt template for the agent. |
| **Prompt Template** _(optional)_ | `prompt_template` | `Optional[str]` | Custom prompt template for the agent. |
| **Response Template** _(optional)_ | `response_template` | `Optional[str]` | Custom response template for the agent. |
| **Allow Code Execution** _(optional)_ | `allow_code_execution` | `Optional[bool]` | Enable code execution for the agent. Default is False. |
| **Max Retry Limit** _(optional)_ | `max_retry_limit` | `int` | Maximum number of retries when an error occurs. Default is 2. |
| **Respect Context Window** _(optional)_ | `respect_context_window` | `bool` | Keep messages under context window size by summarizing. Default is True. |
| **Code Execution Mode** _(optional)_ | `code_execution_mode` | `Literal["safe", "unsafe"]` | Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct). Default is 'safe'. |
| **Multimodal** _(optional)_ | `multimodal` | `bool` | Whether the agent supports multimodal capabilities. Default is False. |
| **Inject Date** _(optional)_ | `inject_date` | `bool` | Whether to automatically inject the current date into tasks. Default is False. |
| **Date Format** _(optional)_ | `date_format` | `str` | Format string for date when inject_date is enabled. Default is "%Y-%m-%d" (ISO format). |
| **Reasoning** _(optional)_ | `reasoning` | `bool` | Whether the agent should reflect and create a plan before executing a task. Default is False. |
| **Max Reasoning Attempts** _(optional)_ | `max_reasoning_attempts` | `Optional[int]` | Maximum number of reasoning attempts before executing the task. If None, will try until ready. |
| **Embedder** _(optional)_ | `embedder` | `Optional[Dict[str, Any]]` | Configuration for the embedder used by the agent. |
| **Knowledge Sources** _(optional)_ | `knowledge_sources` | `Optional[List[BaseKnowledgeSource]]` | Knowledge sources available to the agent. |
| **Use System Prompt** _(optional)_ | `use_system_prompt` | `Optional[bool]` | Whether to use system prompt (for o1 model support). Default is True. |
## Creating Agents
There are two ways to create agents in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
### YAML Configuration (Recommended)
Using YAML configuration provides a cleaner, more maintainable way to define agents. We strongly recommend using this approach in your CrewAI projects.
After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/agents.yaml` file and modify the template to match your requirements.
<Note>
Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
```python Code
crew.kickoff(inputs={'topic': 'AI Agents'})
```
</Note>
Here's an example of how to configure agents using YAML:
```yaml agents.yaml
# src/latest_ai_development/config/agents.yaml
researcher:
role: >
{topic} Senior Data Researcher
goal: >
Uncover cutting-edge developments in {topic}
backstory: >
You're a seasoned researcher with a knack for uncovering the latest
developments in {topic}. Known for your ability to find the most relevant
information and present it in a clear and concise manner.
reporting_analyst:
role: >
{topic} Reporting Analyst
goal: >
Create detailed reports based on {topic} data analysis and research findings
backstory: >
You're a meticulous analyst with a keen eye for detail. You're known for
your ability to turn complex data into clear and concise reports, making
it easy for others to understand and act on the information you provide.
```
To use this YAML configuration in your code, create a crew class that inherits from `CrewBase`:
```python Code
# src/latest_ai_development/crew.py
from crewai import Agent, Crew, Process
from crewai.project import CrewBase, agent, crew
from crewai_tools import SerperDevTool
@CrewBase
class LatestAiDevelopmentCrew():
"""LatestAiDevelopment crew"""
agents_config = "config/agents.yaml"
@agent
def researcher(self) -> Agent:
return Agent(
config=self.agents_config['researcher'], # type: ignore[index]
verbose=True,
tools=[SerperDevTool()]
)
@agent
def reporting_analyst(self) -> Agent:
return Agent(
config=self.agents_config['reporting_analyst'], # type: ignore[index]
verbose=True
)
```
<Note>
The names you use in your YAML files (`agents.yaml`) should match the method names in your Python code.
</Note>
### Direct Code Definition
You can create agents directly in code by instantiating the `Agent` class. Here's a comprehensive example showing all available parameters:
```python Code
from crewai import Agent
from crewai_tools import SerperDevTool
# Create an agent with all available parameters
agent = Agent(
role="Senior Data Scientist",
goal="Analyze and interpret complex datasets to provide actionable insights",
backstory="With over 10 years of experience in data science and machine learning, "
"you excel at finding patterns in complex datasets.",
llm="gpt-4", # Default: OPENAI_MODEL_NAME or "gpt-4"
function_calling_llm=None, # Optional: Separate LLM for tool calling
verbose=False, # Default: False
allow_delegation=False, # Default: False
max_iter=20, # Default: 20 iterations
max_rpm=None, # Optional: Rate limit for API calls
max_execution_time=None, # Optional: Maximum execution time in seconds
max_retry_limit=2, # Default: 2 retries on error
allow_code_execution=False, # Default: False
code_execution_mode="safe", # Default: "safe" (options: "safe", "unsafe")
respect_context_window=True, # Default: True
use_system_prompt=True, # Default: True
multimodal=False, # Default: False
inject_date=False, # Default: False
date_format="%Y-%m-%d", # Default: ISO format
reasoning=False, # Default: False
max_reasoning_attempts=None, # Default: None
tools=[SerperDevTool()], # Optional: List of tools
knowledge_sources=None, # Optional: List of knowledge sources
embedder=None, # Optional: Custom embedder configuration
system_template=None, # Optional: Custom system prompt template
prompt_template=None, # Optional: Custom prompt template
response_template=None, # Optional: Custom response template
step_callback=None, # Optional: Callback function for monitoring
)
```
Let's break down some key parameter combinations for common use cases:
#### Basic Research Agent
```python Code
research_agent = Agent(
role="Research Analyst",
goal="Find and summarize information about specific topics",
backstory="You are an experienced researcher with attention to detail",
tools=[SerperDevTool()],
verbose=True # Enable logging for debugging
)
```
#### Code Development Agent
```python Code
dev_agent = Agent(
role="Senior Python Developer",
goal="Write and debug Python code",
backstory="Expert Python developer with 10 years of experience",
allow_code_execution=True,
code_execution_mode="safe", # Uses Docker for safety
max_execution_time=300, # 5-minute timeout
max_retry_limit=3 # More retries for complex code tasks
)
```
#### Long-Running Analysis Agent
```python Code
analysis_agent = Agent(
role="Data Analyst",
goal="Perform deep analysis of large datasets",
backstory="Specialized in big data analysis and pattern recognition",
memory=True,
respect_context_window=True,
max_rpm=10, # Limit API calls
function_calling_llm="gpt-4o-mini" # Cheaper model for tool calls
)
```
#### Custom Template Agent
```python Code
custom_agent = Agent(
role="Customer Service Representative",
goal="Assist customers with their inquiries",
backstory="Experienced in customer support with a focus on satisfaction",
system_template="""<|start_header_id|>system<|end_header_id|>
{{ .System }}<|eot_id|>""",
prompt_template="""<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|eot_id|>""",
response_template="""<|start_header_id|>assistant<|end_header_id|>
{{ .Response }}<|eot_id|>""",
)
```
#### Date-Aware Agent with Reasoning
```python Code
strategic_agent = Agent(
role="Market Analyst",
goal="Track market movements with precise date references and strategic planning",
backstory="Expert in time-sensitive financial analysis and strategic reporting",
inject_date=True, # Automatically inject current date into tasks
date_format="%B %d, %Y", # Format as "May 21, 2025"
reasoning=True, # Enable strategic planning
max_reasoning_attempts=2, # Limit planning iterations
verbose=True
)
```
#### Reasoning Agent
```python Code
reasoning_agent = Agent(
role="Strategic Planner",
goal="Analyze complex problems and create detailed execution plans",
backstory="Expert strategic planner who methodically breaks down complex challenges",
reasoning=True, # Enable reasoning and planning
max_reasoning_attempts=3, # Limit reasoning attempts
max_iter=30, # Allow more iterations for complex planning
verbose=True
)
```
#### Multimodal Agent
```python Code
multimodal_agent = Agent(
role="Visual Content Analyst",
goal="Analyze and process both text and visual content",
backstory="Specialized in multimodal analysis combining text and image understanding",
multimodal=True, # Enable multimodal capabilities
verbose=True
)
```
### Parameter Details
#### Critical Parameters
- `role`, `goal`, and `backstory` are required and shape the agent's behavior
- `llm` determines the language model used (default: OpenAI's GPT-4)
#### Memory and Context
- `memory`: Enable to maintain conversation history
- `respect_context_window`: Prevents token limit issues
- `knowledge_sources`: Add domain-specific knowledge bases
#### Execution Control
- `max_iter`: Maximum attempts before giving best answer
- `max_execution_time`: Timeout in seconds
- `max_rpm`: Rate limiting for API calls
- `max_retry_limit`: Retries on error
#### Code Execution
- `allow_code_execution`: Must be True to run code
- `code_execution_mode`:
- `"safe"`: Uses Docker (recommended for production)
- `"unsafe"`: Direct execution (use only in trusted environments)
#### Advanced Features
- `multimodal`: Enable multimodal capabilities for processing text and visual content
- `reasoning`: Enable agent to reflect and create plans before executing tasks
- `inject_date`: Automatically inject current date into task descriptions
#### Templates
- `system_template`: Defines agent's core behavior
- `prompt_template`: Structures input format
- `response_template`: Formats agent responses
<Note>
When using custom templates, ensure that both `system_template` and `prompt_template` are defined. The `response_template` is optional but recommended for consistent output formatting.
</Note>
<Note>
When using custom templates, you can use variables like `{role}`, `{goal}`, and `{backstory}` in your templates. These will be automatically populated during execution.
</Note>
## Agent Tools
Agents can be equipped with various tools to enhance their capabilities. CrewAI supports tools from:
- [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools)
- [LangChain Tools](https://python.langchain.com/docs/integrations/tools)
Here's how to add tools to an agent:
```python Code
from crewai import Agent
from crewai_tools import SerperDevTool, WikipediaTools
# Create tools
search_tool = SerperDevTool()
wiki_tool = WikipediaTools()
# Add tools to agent
researcher = Agent(
role="AI Technology Researcher",
goal="Research the latest AI developments",
tools=[search_tool, wiki_tool],
verbose=True
)
```
## Agent Memory and Context
Agents can maintain memory of their interactions and use context from previous tasks. This is particularly useful for complex workflows where information needs to be retained across multiple tasks.
```python Code
from crewai import Agent
analyst = Agent(
role="Data Analyst",
goal="Analyze and remember complex data patterns",
memory=True, # Enable memory
verbose=True
)
```
<Note>
When `memory` is enabled, the agent will maintain context across multiple interactions, improving its ability to handle complex, multi-step tasks.
</Note>
## Context Window Management
CrewAI includes sophisticated automatic context window management to handle situations where conversations exceed the language model's token limits. This powerful feature is controlled by the `respect_context_window` parameter.
### How Context Window Management Works
When an agent's conversation history grows too large for the LLM's context window, CrewAI automatically detects this situation and can either:
1. **Automatically summarize content** (when `respect_context_window=True`)
2. **Stop execution with an error** (when `respect_context_window=False`)
### Automatic Context Handling (`respect_context_window=True`)
This is the **default and recommended setting** for most use cases. When enabled, CrewAI will:
```python Code
# Agent with automatic context management (default)
smart_agent = Agent(
role="Research Analyst",
goal="Analyze large documents and datasets",
backstory="Expert at processing extensive information",
respect_context_window=True, # 🔑 Default: auto-handle context limits
verbose=True
)
```
**What happens when context limits are exceeded:**
- ⚠️ **Warning message**: `"Context length exceeded. Summarizing content to fit the model context window."`
- 🔄 **Automatic summarization**: CrewAI intelligently summarizes the conversation history
- ✅ **Continued execution**: Task execution continues seamlessly with the summarized context
- 📝 **Preserved information**: Key information is retained while reducing token count
### Strict Context Limits (`respect_context_window=False`)
When you need precise control and prefer execution to stop rather than lose any information:
```python Code
# Agent with strict context limits
strict_agent = Agent(
role="Legal Document Reviewer",
goal="Provide precise legal analysis without information loss",
backstory="Legal expert requiring complete context for accurate analysis",
respect_context_window=False, # ❌ Stop execution on context limit
verbose=True
)
```
**What happens when context limits are exceeded:**
- ❌ **Error message**: `"Context length exceeded. Consider using smaller text or RAG tools from crewai_tools."`
- 🛑 **Execution stops**: Task execution halts immediately
- 🔧 **Manual intervention required**: You need to modify your approach
### Choosing the Right Setting
#### Use `respect_context_window=True` (Default) when:
- **Processing large documents** that might exceed context limits
- **Long-running conversations** where some summarization is acceptable
- **Research tasks** where general context is more important than exact details
- **Prototyping and development** where you want robust execution
```python Code
# Perfect for document processing
document_processor = Agent(
role="Document Analyst",
goal="Extract insights from large research papers",
backstory="Expert at analyzing extensive documentation",
respect_context_window=True, # Handle large documents gracefully
max_iter=50, # Allow more iterations for complex analysis
verbose=True
)
```
#### Use `respect_context_window=False` when:
- **Precision is critical** and information loss is unacceptable
- **Legal or medical tasks** requiring complete context
- **Code review** where missing details could introduce bugs
- **Financial analysis** where accuracy is paramount
```python Code
# Perfect for precision tasks
precision_agent = Agent(
role="Code Security Auditor",
goal="Identify security vulnerabilities in code",
backstory="Security expert requiring complete code context",
respect_context_window=False, # Prefer failure over incomplete analysis
max_retry_limit=1, # Fail fast on context issues
verbose=True
)
```
### Alternative Approaches for Large Data
When dealing with very large datasets, consider these strategies:
#### 1. Use RAG Tools
```python Code
from crewai_tools import RagTool
# Create RAG tool for large document processing
rag_tool = RagTool()
rag_agent = Agent(
role="Research Assistant",
goal="Query large knowledge bases efficiently",
backstory="Expert at using RAG tools for information retrieval",
tools=[rag_tool], # Use RAG instead of large context windows
respect_context_window=True,
verbose=True
)
```
#### 2. Use Knowledge Sources
```python Code
# Use knowledge sources instead of large prompts
knowledge_agent = Agent(
role="Knowledge Expert",
goal="Answer questions using curated knowledge",
backstory="Expert at leveraging structured knowledge sources",
knowledge_sources=[your_knowledge_sources], # Pre-processed knowledge
respect_context_window=True,
verbose=True
)
```
### Context Window Best Practices
1. **Monitor Context Usage**: Enable `verbose=True` to see context management in action
2. **Design for Efficiency**: Structure tasks to minimize context accumulation
3. **Use Appropriate Models**: Choose LLMs with context windows suitable for your tasks
4. **Test Both Settings**: Try both `True` and `False` to see which works better for your use case
5. **Combine with RAG**: Use RAG tools for very large datasets instead of relying solely on context windows
### Troubleshooting Context Issues
**If you're getting context limit errors:**
```python Code
# Quick fix: Enable automatic handling
agent.respect_context_window = True
# Better solution: Use RAG tools for large data
from crewai_tools import RagTool
agent.tools = [RagTool()]
# Alternative: Break tasks into smaller pieces
# Or use knowledge sources instead of large prompts
```
**If automatic summarization loses important information:**
```python Code
# Disable auto-summarization and use RAG instead
agent = Agent(
role="Detailed Analyst",
goal="Maintain complete information accuracy",
backstory="Expert requiring full context",
respect_context_window=False, # No summarization
tools=[RagTool()], # Use RAG for large data
verbose=True
)
```
<Note>
The context window management feature works automatically in the background. You don't need to call any special functions - just set `respect_context_window` to your preferred behavior and CrewAI handles the rest!
</Note>
## Important Considerations and Best Practices
### Security and Code Execution
- When using `allow_code_execution`, be cautious with user input and always validate it
- Use `code_execution_mode: "safe"` (Docker) in production environments
- Consider setting appropriate `max_execution_time` limits to prevent infinite loops
### Performance Optimization
- Use `respect_context_window: true` to prevent token limit issues
- Set appropriate `max_rpm` to avoid rate limiting
- Enable `cache: true` to improve performance for repetitive tasks
- Adjust `max_iter` and `max_retry_limit` based on task complexity
### Memory and Context Management
- Leverage `knowledge_sources` for domain-specific information
- Configure `embedder` when using custom embedding models
- Use custom templates (`system_template`, `prompt_template`, `response_template`) for fine-grained control over agent behavior
### Advanced Features
- Enable `reasoning: true` for agents that need to plan and reflect before executing complex tasks
- Set appropriate `max_reasoning_attempts` to control planning iterations (None for unlimited attempts)
- Use `inject_date: true` to provide agents with current date awareness for time-sensitive tasks
- Customize the date format with `date_format` using standard Python datetime format codes
- Enable `multimodal: true` for agents that need to process both text and visual content
### Agent Collaboration
- Enable `allow_delegation: true` when agents need to work together
- Use `step_callback` to monitor and log agent interactions
- Consider using different LLMs for different purposes:
- Main `llm` for complex reasoning
- `function_calling_llm` for efficient tool usage
### Date Awareness and Reasoning
- Use `inject_date: true` to provide agents with current date awareness for time-sensitive tasks
- Customize the date format with `date_format` using standard Python datetime format codes
- Valid format codes include: %Y (year), %m (month), %d (day), %B (full month name), etc.
- Invalid date formats will be logged as warnings and will not modify the task description
- Enable `reasoning: true` for complex tasks that benefit from upfront planning and reflection
### Model Compatibility
- Set `use_system_prompt: false` for older models that don't support system messages
- Ensure your chosen `llm` supports the features you need (like function calling)
## Troubleshooting Common Issues
1. **Rate Limiting**: If you're hitting API rate limits:
- Implement appropriate `max_rpm`
- Use caching for repetitive operations
- Consider batching requests
2. **Context Window Errors**: If you're exceeding context limits:
- Enable `respect_context_window`
- Use more efficient prompts
- Clear agent memory periodically
3. **Code Execution Issues**: If code execution fails:
- Verify Docker is installed for safe mode
- Check execution permissions
- Review code sandbox settings
4. **Memory Issues**: If agent responses seem inconsistent:
- Check knowledge source configuration
- Review conversation history management
Remember that agents are most effective when configured according to their specific use case. Take time to understand your requirements and adjust these parameters accordingly.

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---
title: CLI
description: Learn how to use the CrewAI CLI to interact with CrewAI.
icon: terminal
---
## Overview
The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.
## Installation
To use the CrewAI CLI, make sure you have CrewAI installed:
```shell Terminal
pip install crewai
```
## Basic Usage
The basic structure of a CrewAI CLI command is:
```shell Terminal
crewai [COMMAND] [OPTIONS] [ARGUMENTS]
```
## Available Commands
### 1. Create
Create a new crew or flow.
```shell Terminal
crewai create [OPTIONS] TYPE NAME
```
- `TYPE`: Choose between "crew" or "flow"
- `NAME`: Name of the crew or flow
Example:
```shell Terminal
crewai create crew my_new_crew
crewai create flow my_new_flow
```
### 2. Version
Show the installed version of CrewAI.
```shell Terminal
crewai version [OPTIONS]
```
- `--tools`: (Optional) Show the installed version of CrewAI tools
Example:
```shell Terminal
crewai version
crewai version --tools
```
### 3. Train
Train the crew for a specified number of iterations.
```shell Terminal
crewai train [OPTIONS]
```
- `-n, --n_iterations INTEGER`: Number of iterations to train the crew (default: 5)
- `-f, --filename TEXT`: Path to a custom file for training (default: "trained_agents_data.pkl")
Example:
```shell Terminal
crewai train -n 10 -f my_training_data.pkl
```
### 4. Replay
Replay the crew execution from a specific task.
```shell Terminal
crewai replay [OPTIONS]
```
- `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
Example:
```shell Terminal
crewai replay -t task_123456
```
### 5. Log-tasks-outputs
Retrieve your latest crew.kickoff() task outputs.
```shell Terminal
crewai log-tasks-outputs
```
### 6. Reset-memories
Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).
```shell Terminal
crewai reset-memories [OPTIONS]
```
- `-l, --long`: Reset LONG TERM memory
- `-s, --short`: Reset SHORT TERM memory
- `-e, --entities`: Reset ENTITIES memory
- `-k, --kickoff-outputs`: Reset LATEST KICKOFF TASK OUTPUTS
- `-kn, --knowledge`: Reset KNOWLEDGE storage
- `-akn, --agent-knowledge`: Reset AGENT KNOWLEDGE storage
- `-a, --all`: Reset ALL memories
Example:
```shell Terminal
crewai reset-memories --long --short
crewai reset-memories --all
```
### 7. Test
Test the crew and evaluate the results.
```shell Terminal
crewai test [OPTIONS]
```
- `-n, --n_iterations INTEGER`: Number of iterations to test the crew (default: 3)
- `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")
Example:
```shell Terminal
crewai test -n 5 -m gpt-3.5-turbo
```
### 8. Run
Run the crew or flow.
```shell Terminal
crewai run
```
<Note>
Starting from version 0.103.0, the `crewai run` command can be used to run both standard crews and flows. For flows, it automatically detects the type from pyproject.toml and runs the appropriate command. This is now the recommended way to run both crews and flows.
</Note>
<Note>
Make sure to run these commands from the directory where your CrewAI project is set up.
Some commands may require additional configuration or setup within your project structure.
</Note>
### 9. Chat
Starting in version `0.98.0`, when you run the `crewai chat` command, you start an interactive session with your crew. The AI assistant will guide you by asking for necessary inputs to execute the crew. Once all inputs are provided, the crew will execute its tasks.
After receiving the results, you can continue interacting with the assistant for further instructions or questions.
```shell Terminal
crewai chat
```
<Note>
Ensure you execute these commands from your CrewAI project's root directory.
</Note>
<Note>
IMPORTANT: Set the `chat_llm` property in your `crew.py` file to enable this command.
```python
@crew
def crew(self) -> Crew:
return Crew(
agents=self.agents,
tasks=self.tasks,
process=Process.sequential,
verbose=True,
chat_llm="gpt-4o", # LLM for chat orchestration
)
```
</Note>
### 10. Deploy
Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
- **Authentication**: You need to be authenticated to deploy to CrewAI Enterprise.
```shell Terminal
crewai signup
```
If you already have an account, you can login with:
```shell Terminal
crewai login
```
- **Create a deployment**: Once you are authenticated, you can create a deployment for your crew or flow from the root of your localproject.
```shell Terminal
crewai deploy create
```
- Reads your local project configuration.
- Prompts you to confirm the environment variables (like `OPENAI_API_KEY`, `SERPER_API_KEY`) found locally. These will be securely stored with the deployment on the Enterprise platform. Ensure your sensitive keys are correctly configured locally (e.g., in a `.env` file) before running this.
### 11. Organization Management
Manage your CrewAI Enterprise organizations.
```shell Terminal
crewai org [COMMAND] [OPTIONS]
```
#### Commands:
- `list`: List all organizations you belong to
```shell Terminal
crewai org list
```
- `current`: Display your currently active organization
```shell Terminal
crewai org current
```
- `switch`: Switch to a specific organization
```shell Terminal
crewai org switch <organization_id>
```
<Note>
You must be authenticated to CrewAI Enterprise to use these organization management commands.
</Note>
- **Create a deployment** (continued):
- Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI Enterprise.
```shell Terminal
crewai deploy push
```
- Initiates the deployment process on the CrewAI Enterprise platform.
- Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).
- **Deployment Status**: You can check the status of your deployment with:
```shell Terminal
crewai deploy status
```
This fetches the latest deployment status of your most recent deployment attempt (e.g., `Building Images for Crew`, `Deploy Enqueued`, `Online`).
- **Deployment Logs**: You can check the logs of your deployment with:
```shell Terminal
crewai deploy logs
```
This streams the deployment logs to your terminal.
- **List deployments**: You can list all your deployments with:
```shell Terminal
crewai deploy list
```
This lists all your deployments.
- **Delete a deployment**: You can delete a deployment with:
```shell Terminal
crewai deploy remove
```
This deletes the deployment from the CrewAI Enterprise platform.
- **Help Command**: You can get help with the CLI with:
```shell Terminal
crewai deploy --help
```
This shows the help message for the CrewAI Deploy CLI.
Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI.
<iframe
width="100%"
height="400"
src="https://www.youtube.com/embed/3EqSV-CYDZA"
title="CrewAI Deployment Guide"
frameborder="0"
style={{ borderRadius: '10px' }}
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
allowfullscreen
></iframe>
### 11. API Keys
When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.
Once you've selected an LLM provider, you will be prompted for API keys.
#### Initial API key providers
The CLI will initially prompt for API keys for the following services:
* OpenAI
* Groq
* Anthropic
* Google Gemini
* SambaNova
When you select a provider, the CLI will prompt you to enter your API key.
#### Other Options
If you select option 6, you will be able to select from a list of LiteLLM supported providers.
When you select a provider, the CLI will prompt you to enter the Key name and the API key.
See the following link for each provider's key name:
* [LiteLLM Providers](https://docs.litellm.ai/docs/providers)

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---
title: Collaboration
description: How to enable agents to work together, delegate tasks, and communicate effectively within CrewAI teams.
icon: screen-users
---
## Overview
Collaboration in CrewAI enables agents to work together as a team by delegating tasks and asking questions to leverage each other's expertise. When `allow_delegation=True`, agents automatically gain access to powerful collaboration tools.
## Quick Start: Enable Collaboration
```python
from crewai import Agent, Crew, Task
# Enable collaboration for agents
researcher = Agent(
role="Research Specialist",
goal="Conduct thorough research on any topic",
backstory="Expert researcher with access to various sources",
allow_delegation=True, # 🔑 Key setting for collaboration
verbose=True
)
writer = Agent(
role="Content Writer",
goal="Create engaging content based on research",
backstory="Skilled writer who transforms research into compelling content",
allow_delegation=True, # 🔑 Enables asking questions to other agents
verbose=True
)
# Agents can now collaborate automatically
crew = Crew(
agents=[researcher, writer],
tasks=[...],
verbose=True
)
```
## How Agent Collaboration Works
When `allow_delegation=True`, CrewAI automatically provides agents with two powerful tools:
### 1. **Delegate Work Tool**
Allows agents to assign tasks to teammates with specific expertise.
```python
# Agent automatically gets this tool:
# Delegate work to coworker(task: str, context: str, coworker: str)
```
### 2. **Ask Question Tool**
Enables agents to ask specific questions to gather information from colleagues.
```python
# Agent automatically gets this tool:
# Ask question to coworker(question: str, context: str, coworker: str)
```
## Collaboration in Action
Here's a complete example showing agents collaborating on a content creation task:
```python
from crewai import Agent, Crew, Task, Process
# Create collaborative agents
researcher = Agent(
role="Research Specialist",
goal="Find accurate, up-to-date information on any topic",
backstory="""You're a meticulous researcher with expertise in finding
reliable sources and fact-checking information across various domains.""",
allow_delegation=True,
verbose=True
)
writer = Agent(
role="Content Writer",
goal="Create engaging, well-structured content",
backstory="""You're a skilled content writer who excels at transforming
research into compelling, readable content for different audiences.""",
allow_delegation=True,
verbose=True
)
editor = Agent(
role="Content Editor",
goal="Ensure content quality and consistency",
backstory="""You're an experienced editor with an eye for detail,
ensuring content meets high standards for clarity and accuracy.""",
allow_delegation=True,
verbose=True
)
# Create a task that encourages collaboration
article_task = Task(
description="""Write a comprehensive 1000-word article about 'The Future of AI in Healthcare'.
The article should include:
- Current AI applications in healthcare
- Emerging trends and technologies
- Potential challenges and ethical considerations
- Expert predictions for the next 5 years
Collaborate with your teammates to ensure accuracy and quality.""",
expected_output="A well-researched, engaging 1000-word article with proper structure and citations",
agent=writer # Writer leads, but can delegate research to researcher
)
# Create collaborative crew
crew = Crew(
agents=[researcher, writer, editor],
tasks=[article_task],
process=Process.sequential,
verbose=True
)
result = crew.kickoff()
```
## Collaboration Patterns
### Pattern 1: Research → Write → Edit
```python
research_task = Task(
description="Research the latest developments in quantum computing",
expected_output="Comprehensive research summary with key findings and sources",
agent=researcher
)
writing_task = Task(
description="Write an article based on the research findings",
expected_output="Engaging 800-word article about quantum computing",
agent=writer,
context=[research_task] # Gets research output as context
)
editing_task = Task(
description="Edit and polish the article for publication",
expected_output="Publication-ready article with improved clarity and flow",
agent=editor,
context=[writing_task] # Gets article draft as context
)
```
### Pattern 2: Collaborative Single Task
```python
collaborative_task = Task(
description="""Create a marketing strategy for a new AI product.
Writer: Focus on messaging and content strategy
Researcher: Provide market analysis and competitor insights
Work together to create a comprehensive strategy.""",
expected_output="Complete marketing strategy with research backing",
agent=writer # Lead agent, but can delegate to researcher
)
```
## Hierarchical Collaboration
For complex projects, use a hierarchical process with a manager agent:
```python
from crewai import Agent, Crew, Task, Process
# Manager agent coordinates the team
manager = Agent(
role="Project Manager",
goal="Coordinate team efforts and ensure project success",
backstory="Experienced project manager skilled at delegation and quality control",
allow_delegation=True,
verbose=True
)
# Specialist agents
researcher = Agent(
role="Researcher",
goal="Provide accurate research and analysis",
backstory="Expert researcher with deep analytical skills",
allow_delegation=False, # Specialists focus on their expertise
verbose=True
)
writer = Agent(
role="Writer",
goal="Create compelling content",
backstory="Skilled writer who creates engaging content",
allow_delegation=False,
verbose=True
)
# Manager-led task
project_task = Task(
description="Create a comprehensive market analysis report with recommendations",
expected_output="Executive summary, detailed analysis, and strategic recommendations",
agent=manager # Manager will delegate to specialists
)
# Hierarchical crew
crew = Crew(
agents=[manager, researcher, writer],
tasks=[project_task],
process=Process.hierarchical, # Manager coordinates everything
manager_llm="gpt-4o", # Specify LLM for manager
verbose=True
)
```
## Best Practices for Collaboration
### 1. **Clear Role Definition**
```python
# ✅ Good: Specific, complementary roles
researcher = Agent(role="Market Research Analyst", ...)
writer = Agent(role="Technical Content Writer", ...)
# ❌ Avoid: Overlapping or vague roles
agent1 = Agent(role="General Assistant", ...)
agent2 = Agent(role="Helper", ...)
```
### 2. **Strategic Delegation Enabling**
```python
# ✅ Enable delegation for coordinators and generalists
lead_agent = Agent(
role="Content Lead",
allow_delegation=True, # Can delegate to specialists
...
)
# ✅ Disable for focused specialists (optional)
specialist_agent = Agent(
role="Data Analyst",
allow_delegation=False, # Focuses on core expertise
...
)
```
### 3. **Context Sharing**
```python
# ✅ Use context parameter for task dependencies
writing_task = Task(
description="Write article based on research",
agent=writer,
context=[research_task], # Shares research results
...
)
```
### 4. **Clear Task Descriptions**
```python
# ✅ Specific, actionable descriptions
Task(
description="""Research competitors in the AI chatbot space.
Focus on: pricing models, key features, target markets.
Provide data in a structured format.""",
...
)
# ❌ Vague descriptions that don't guide collaboration
Task(description="Do some research about chatbots", ...)
```
## Troubleshooting Collaboration
### Issue: Agents Not Collaborating
**Symptoms:** Agents work in isolation, no delegation occurs
```python
# ✅ Solution: Ensure delegation is enabled
agent = Agent(
role="...",
allow_delegation=True, # This is required!
...
)
```
### Issue: Too Much Back-and-Forth
**Symptoms:** Agents ask excessive questions, slow progress
```python
# ✅ Solution: Provide better context and specific roles
Task(
description="""Write a technical blog post about machine learning.
Context: Target audience is software developers with basic ML knowledge.
Length: 1200 words
Include: code examples, practical applications, best practices
If you need specific technical details, delegate research to the researcher.""",
...
)
```
### Issue: Delegation Loops
**Symptoms:** Agents delegate back and forth indefinitely
```python
# ✅ Solution: Clear hierarchy and responsibilities
manager = Agent(role="Manager", allow_delegation=True)
specialist1 = Agent(role="Specialist A", allow_delegation=False) # No re-delegation
specialist2 = Agent(role="Specialist B", allow_delegation=False)
```
## Advanced Collaboration Features
### Custom Collaboration Rules
```python
# Set specific collaboration guidelines in agent backstory
agent = Agent(
role="Senior Developer",
backstory="""You lead development projects and coordinate with team members.
Collaboration guidelines:
- Delegate research tasks to the Research Analyst
- Ask the Designer for UI/UX guidance
- Consult the QA Engineer for testing strategies
- Only escalate blocking issues to the Project Manager""",
allow_delegation=True
)
```
### Monitoring Collaboration
```python
def track_collaboration(output):
"""Track collaboration patterns"""
if "Delegate work to coworker" in output.raw:
print("🤝 Delegation occurred")
if "Ask question to coworker" in output.raw:
print("❓ Question asked")
crew = Crew(
agents=[...],
tasks=[...],
step_callback=track_collaboration, # Monitor collaboration
verbose=True
)
```
## Memory and Learning
Enable agents to remember past collaborations:
```python
agent = Agent(
role="Content Lead",
memory=True, # Remembers past interactions
allow_delegation=True,
verbose=True
)
```
With memory enabled, agents learn from previous collaborations and improve their delegation decisions over time.
## Next Steps
- **Try the examples**: Start with the basic collaboration example
- **Experiment with roles**: Test different agent role combinations
- **Monitor interactions**: Use `verbose=True` to see collaboration in action
- **Optimize task descriptions**: Clear tasks lead to better collaboration
- **Scale up**: Try hierarchical processes for complex projects
Collaboration transforms individual AI agents into powerful teams that can tackle complex, multi-faceted challenges together.

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---
title: Crews
description: Understanding and utilizing crews in the crewAI framework with comprehensive attributes and functionalities.
icon: people-group
---
## Overview
A crew in crewAI represents a collaborative group of agents working together to achieve a set of tasks. Each crew defines the strategy for task execution, agent collaboration, and the overall workflow.
## Crew Attributes
| Attribute | Parameters | Description |
| :------------------------------------ | :--------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Tasks** | `tasks` | A list of tasks assigned to the crew. |
| **Agents** | `agents` | A list of agents that are part of the crew. |
| **Process** _(optional)_ | `process` | The process flow (e.g., sequential, hierarchical) the crew follows. Default is `sequential`. |
| **Verbose** _(optional)_ | `verbose` | The verbosity level for logging during execution. Defaults to `False`. |
| **Manager LLM** _(optional)_ | `manager_llm` | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.** |
| **Function Calling LLM** _(optional)_ | `function_calling_llm` | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling. |
| **Config** _(optional)_ | `config` | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format. |
| **Max RPM** _(optional)_ | `max_rpm` | Maximum requests per minute the crew adheres to during execution. Defaults to `None`. |
| **Memory** _(optional)_ | `memory` | Utilized for storing execution memories (short-term, long-term, entity memory). |
| **Memory Config** _(optional)_ | `memory_config` | Configuration for the memory provider to be used by the crew. |
| **Cache** _(optional)_ | `cache` | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`. |
| **Embedder** _(optional)_ | `embedder` | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`. |
| **Step Callback** _(optional)_ | `step_callback` | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`. |
| **Task Callback** _(optional)_ | `task_callback` | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution. |
| **Share Crew** _(optional)_ | `share_crew` | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models. |
| **Output Log File** _(optional)_ | `output_log_file` | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defaults to `None`. |
| **Manager Agent** _(optional)_ | `manager_agent` | `manager` sets a custom agent that will be used as a manager. |
| **Prompt File** _(optional)_ | `prompt_file` | Path to the prompt JSON file to be used for the crew. |
| **Planning** *(optional)* | `planning` | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description. |
| **Planning LLM** *(optional)* | `planning_llm` | The language model used by the AgentPlanner in a planning process. |
<Tip>
**Crew Max RPM**: The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
</Tip>
## Creating Crews
There are two ways to create crews in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
### YAML Configuration (Recommended)
Using YAML configuration provides a cleaner, more maintainable way to define crews and is consistent with how agents and tasks are defined in CrewAI projects.
After creating your CrewAI project as outlined in the [Installation](/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.
#### Example Crew Class with Decorators
```python code
from crewai import Agent, Crew, Task, Process
from crewai.project import CrewBase, agent, task, crew, before_kickoff, after_kickoff
from crewai.agents.agent_builder.base_agent import BaseAgent
from typing import List
@CrewBase
class YourCrewName:
"""Description of your crew"""
agents: List[BaseAgent]
tasks: List[Task]
# Paths to your YAML configuration files
# To see an example agent and task defined in YAML, checkout the following:
# - Task: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
# - Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
agents_config = 'config/agents.yaml'
tasks_config = 'config/tasks.yaml'
@before_kickoff
def prepare_inputs(self, inputs):
# Modify inputs before the crew starts
inputs['additional_data'] = "Some extra information"
return inputs
@after_kickoff
def process_output(self, output):
# Modify output after the crew finishes
output.raw += "\nProcessed after kickoff."
return output
@agent
def agent_one(self) -> Agent:
return Agent(
config=self.agents_config['agent_one'], # type: ignore[index]
verbose=True
)
@agent
def agent_two(self) -> Agent:
return Agent(
config=self.agents_config['agent_two'], # type: ignore[index]
verbose=True
)
@task
def task_one(self) -> Task:
return Task(
config=self.tasks_config['task_one'] # type: ignore[index]
)
@task
def task_two(self) -> Task:
return Task(
config=self.tasks_config['task_two'] # type: ignore[index]
)
@crew
def crew(self) -> Crew:
return Crew(
agents=self.agents, # Automatically collected by the @agent decorator
tasks=self.tasks, # Automatically collected by the @task decorator.
process=Process.sequential,
verbose=True,
)
```
How to run the above code:
```python code
YourCrewName().crew().kickoff(inputs={"any": "input here"})
```
<Note>
Tasks will be executed in the order they are defined.
</Note>
The `CrewBase` class, along with these decorators, automates the collection of agents and tasks, reducing the need for manual management.
#### Decorators overview from `annotations.py`
CrewAI provides several decorators in the `annotations.py` file that are used to mark methods within your crew class for special handling:
- `@CrewBase`: Marks the class as a crew base class.
- `@agent`: Denotes a method that returns an `Agent` object.
- `@task`: Denotes a method that returns a `Task` object.
- `@crew`: Denotes the method that returns the `Crew` object.
- `@before_kickoff`: (Optional) Marks a method to be executed before the crew starts.
- `@after_kickoff`: (Optional) Marks a method to be executed after the crew finishes.
These decorators help in organizing your crew's structure and automatically collecting agents and tasks without manually listing them.
### Direct Code Definition (Alternative)
Alternatively, you can define the crew directly in code without using YAML configuration files.
```python code
from crewai import Agent, Crew, Task, Process
from crewai_tools import YourCustomTool
class YourCrewName:
def agent_one(self) -> Agent:
return Agent(
role="Data Analyst",
goal="Analyze data trends in the market",
backstory="An experienced data analyst with a background in economics",
verbose=True,
tools=[YourCustomTool()]
)
def agent_two(self) -> Agent:
return Agent(
role="Market Researcher",
goal="Gather information on market dynamics",
backstory="A diligent researcher with a keen eye for detail",
verbose=True
)
def task_one(self) -> Task:
return Task(
description="Collect recent market data and identify trends.",
expected_output="A report summarizing key trends in the market.",
agent=self.agent_one()
)
def task_two(self) -> Task:
return Task(
description="Research factors affecting market dynamics.",
expected_output="An analysis of factors influencing the market.",
agent=self.agent_two()
)
def crew(self) -> Crew:
return Crew(
agents=[self.agent_one(), self.agent_two()],
tasks=[self.task_one(), self.task_two()],
process=Process.sequential,
verbose=True
)
```
How to run the above code:
```python code
YourCrewName().crew().kickoff(inputs={})
```
In this example:
- Agents and tasks are defined directly within the class without decorators.
- We manually create and manage the list of agents and tasks.
- This approach provides more control but can be less maintainable for larger projects.
## Crew Output
The output of a crew in the CrewAI framework is encapsulated within the `CrewOutput` class.
This class provides a structured way to access results of the crew's execution, including various formats such as raw strings, JSON, and Pydantic models.
The `CrewOutput` includes the results from the final task output, token usage, and individual task outputs.
### Crew Output Attributes
| Attribute | Parameters | Type | Description |
| :--------------- | :------------- | :------------------------- | :--------------------------------------------------------------------------------------------------- |
| **Raw** | `raw` | `str` | The raw output of the crew. This is the default format for the output. |
| **Pydantic** | `pydantic` | `Optional[BaseModel]` | A Pydantic model object representing the structured output of the crew. |
| **JSON Dict** | `json_dict` | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the crew. |
| **Tasks Output** | `tasks_output` | `List[TaskOutput]` | A list of `TaskOutput` objects, each representing the output of a task in the crew. |
| **Token Usage** | `token_usage` | `Dict[str, Any]` | A summary of token usage, providing insights into the language model's performance during execution. |
### Crew Output Methods and Properties
| Method/Property | Description |
| :-------------- | :------------------------------------------------------------------------------------------------ |
| **json** | Returns the JSON string representation of the crew output if the output format is JSON. |
| **to_dict** | Converts the JSON and Pydantic outputs to a dictionary. |
| \***\*str\*\*** | Returns the string representation of the crew output, prioritizing Pydantic, then JSON, then raw. |
### Accessing Crew Outputs
Once a crew has been executed, its output can be accessed through the `output` attribute of the `Crew` object. The `CrewOutput` class provides various ways to interact with and present this output.
#### Example
```python Code
# Example crew execution
crew = Crew(
agents=[research_agent, writer_agent],
tasks=[research_task, write_article_task],
verbose=True
)
crew_output = crew.kickoff()
# Accessing the crew output
print(f"Raw Output: {crew_output.raw}")
if crew_output.json_dict:
print(f"JSON Output: {json.dumps(crew_output.json_dict, indent=2)}")
if crew_output.pydantic:
print(f"Pydantic Output: {crew_output.pydantic}")
print(f"Tasks Output: {crew_output.tasks_output}")
print(f"Token Usage: {crew_output.token_usage}")
```
## Accessing Crew Logs
You can see real time log of the crew execution, by setting `output_log_file` as a `True(Boolean)` or a `file_name(str)`. Supports logging of events as both `file_name.txt` and `file_name.json`.
In case of `True(Boolean)` will save as `logs.txt`.
In case of `output_log_file` is set as `False(Boolean)` or `None`, the logs will not be populated.
```python Code
# Save crew logs
crew = Crew(output_log_file = True) # Logs will be saved as logs.txt
crew = Crew(output_log_file = file_name) # Logs will be saved as file_name.txt
crew = Crew(output_log_file = file_name.txt) # Logs will be saved as file_name.txt
crew = Crew(output_log_file = file_name.json) # Logs will be saved as file_name.json
```
## Memory Utilization
Crews can utilize memory (short-term, long-term, and entity memory) to enhance their execution and learning over time. This feature allows crews to store and recall execution memories, aiding in decision-making and task execution strategies.
## Cache Utilization
Caches can be employed to store the results of tools' execution, making the process more efficient by reducing the need to re-execute identical tasks.
## Crew Usage Metrics
After the crew execution, you can access the `usage_metrics` attribute to view the language model (LLM) usage metrics for all tasks executed by the crew. This provides insights into operational efficiency and areas for improvement.
```python Code
# Access the crew's usage metrics
crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
crew.kickoff()
print(crew.usage_metrics)
```
## Crew Execution Process
- **Sequential Process**: Tasks are executed one after another, allowing for a linear flow of work.
- **Hierarchical Process**: A manager agent coordinates the crew, delegating tasks and validating outcomes before proceeding. **Note**: A `manager_llm` or `manager_agent` is required for this process and it's essential for validating the process flow.
### Kicking Off a Crew
Once your crew is assembled, initiate the workflow with the `kickoff()` method. This starts the execution process according to the defined process flow.
```python Code
# Start the crew's task execution
result = my_crew.kickoff()
print(result)
```
### Different Ways to Kick Off a Crew
Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
- `kickoff()`: Starts the execution process according to the defined process flow.
- `kickoff_for_each()`: Executes tasks sequentially for each provided input event or item in the collection.
- `kickoff_async()`: Initiates the workflow asynchronously.
- `kickoff_for_each_async()`: Executes tasks concurrently for each provided input event or item, leveraging asynchronous processing.
```python Code
# Start the crew's task execution
result = my_crew.kickoff()
print(result)
# Example of using kickoff_for_each
inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
results = my_crew.kickoff_for_each(inputs=inputs_array)
for result in results:
print(result)
# Example of using kickoff_async
inputs = {'topic': 'AI in healthcare'}
async_result = await my_crew.kickoff_async(inputs=inputs)
print(async_result)
# Example of using kickoff_for_each_async
inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
async_results = await my_crew.kickoff_for_each_async(inputs=inputs_array)
for async_result in async_results:
print(async_result)
```
These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs.
### Replaying from a Specific Task
You can now replay from a specific task using our CLI command `replay`.
The replay feature in CrewAI allows you to replay from a specific task using the command-line interface (CLI). By running the command `crewai replay -t <task_id>`, you can specify the `task_id` for the replay process.
Kickoffs will now save the latest kickoffs returned task outputs locally for you to be able to replay from.
### Replaying from a Specific Task Using the CLI
To use the replay feature, follow these steps:
1. Open your terminal or command prompt.
2. Navigate to the directory where your CrewAI project is located.
3. Run the following command:
To view the latest kickoff task IDs, use:
```shell
crewai log-tasks-outputs
```
Then, to replay from a specific task, use:
```shell
crewai replay -t <task_id>
```
These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.

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