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Author SHA1 Message Date
Joao Moura
aee571b775 feat: add support for agents to invoke Flows as tools
Agents can now declare flows=[MyFlow] and invoke them as regular tools.
Each Flow class is wrapped as a FlowTool(BaseTool) — the agent decides
WHEN to use it (via tool selection), the Flow handles HOW (deterministic
execution).

- Add flows field to Agent
- Add FlowTool and create_flow_tools in tools/flow_tool.py
- Export from crewai.__init__
- 14 tests

No dependency changes. No memory changes. Just the flow-tool feature.
2026-04-27 05:10:38 -07:00
Greyson LaLonde
cb46a1c4ba docs: update changelog and version for v1.14.3
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2026-04-25 00:13:43 +08:00
Greyson LaLonde
d9046b98dd feat: bump versions to 1.14.3 2026-04-25 00:04:46 +08:00
Tiago Freire
b0e2fda105 fix(flow): add execution_id separate from state.id
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* fix(flow): add execution_id separate from state.id (COR-48)

  When a consumer passes `id` in `kickoff(inputs=...)`, that value
  overwrites the flow's state.id — which was also being used as the
  execution tracking identity for telemetry, tracing, and external
  correlation. Two kickoffs sharing the same consumer id ended up
  with the same tracking id, breaking any downstream system that
  joins on it.

  Introduces `Flow.execution_id`: a stable per-run identifier stored
  as a `PrivateAttr` on the `Flow` model, exposed via property +
  setter. It defaults to a fresh `uuid4` per instance, is never
  touched by `inputs["id"]`, and can be assigned by outer systems
  that already have an execution identity (e.g. a task id).

  Switches the `current_flow_id` / `current_flow_request_id`
  ContextVars to seed from `execution_id` so OTel spans emitted by
  `FlowTrackable` children correlate on the stable tracking key.

  `state.id` keeps its existing override semantics for
  persistence/restore — consumers resuming a persisted flow via
  `inputs["id"]` work exactly as before.

  Adds tests covering default uniqueness per instance, immunity to
  consumer `inputs["id"]`, context-var propagation, absence from
  serialized state, and parity for dict-state flows.

Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>
2026-04-24 04:48:14 +08:00
Greyson LaLonde
69d777ca50 fix(flow): replay recorded method events on checkpoint resume 2026-04-24 03:41:55 +08:00
Greyson LaLonde
77b2835a1d fix(flow): serialize initial_state class refs as JSON schema
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2026-04-23 21:55:50 +08:00
Lorenze Jay
c77f1632dd fix: preserve metadata-only agent skills
Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>
2026-04-23 19:58:12 +08:00
Greyson LaLonde
69461076df refactor: dedupe checkpoint helpers and tighten state type hints 2026-04-23 19:29:04 +08:00
Greyson LaLonde
55937d7523 feat: emit lifecycle events for checkpoint operations
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2026-04-23 18:47:50 +08:00
Greyson LaLonde
bc2fb71560 docs: update changelog and version for v1.14.3a3
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2026-04-23 05:11:06 +08:00
Greyson LaLonde
3e9deaf9c0 feat: bump versions to 1.14.3a3 2026-04-23 04:55:08 +08:00
Lorenze Jay
3f7637455c feat: supporting e2b 2026-04-23 04:36:33 +08:00
Matt Aitchison
fdf3101b39 feat(azure): fall back to DefaultAzureCredential when no API key
Enables keyless Azure auth (OIDC Workload Identity Federation, Managed
Identity, Azure CLI, env-configured Service Principal) without any
crewAI-specific configuration. Customers whose deployment environment
already sets the standard azure-identity env vars get keyless auth for
free; the existing API-key path is unchanged.

Linear: FAC-40
2026-04-23 04:21:35 +08:00
Greyson LaLonde
c94f2e8f28 fix: upgrade lxml to >=6.1.0 for GHSA-vfmq-68hx-4jfw
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2026-04-23 00:52:36 +08:00
alex-clawd
944fe6d435 docs: remove pricing FAQ from build-with-ai page across all locales (#5586)
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Removes the 'How does pricing work?' accordion from EN, AR, KO, and PT-BR.

Co-authored-by: Joao Moura <joaomdmoura@gmail.com>
2026-04-22 03:56:41 -03:00
iris-clawd
3be2fb65dc perf: lazy-load MCP SDK and event types to reduce cold start by ~29% (#5584)
* perf: defer MCP SDK import by fixing import path in agent/core.py

- Change 'from crewai.mcp import MCPServerConfig' to direct path
  'from crewai.mcp.config import MCPServerConfig' to avoid triggering
  mcp/__init__.py which eagerly loads the full mcp SDK (~300-400ms)
- Move MCPToolResolver import into get_mcp_tools() method body since
  it's only used at runtime, not in type annotations

Saves ~200ms on 'import crewai' cold start.

* perf: lazy-load heavy MCP imports in mcp/__init__.py

MCPClient, MCPToolResolver, BaseTransport, and TransportType now use
__getattr__ lazy loading. These pull in the full mcp SDK (~400ms) but
are only needed at runtime when agents actually connect to MCP servers.

Lightweight config and filter types remain eagerly imported.

* perf: lazy-load all event type modules in events/__init__.py

Previously only agent_events were lazy-loaded; all other event type
modules (crew, flow, knowledge, llm, guardrail, logging, mcp, memory,
reasoning, skill, task, tool_usage) were eagerly imported at package
init time. Since events/__init__.py runs whenever ANY crewai.events.*
submodule is accessed, this loaded ~12 Pydantic model modules
unnecessarily.

Now all event types use the same __getattr__ lazy-loading pattern,
with TYPE_CHECKING imports preserved for IDE/type-checker support.

Saves ~550ms on 'import crewai' cold start.

* chore: remove UNKNOWN.egg-info from version control

* fix: add MCPToolResolver to TYPE_CHECKING imports

Fixes F821 (ruff) and name-defined (mypy) from lazy-loading the
MCP import. The type annotation on _mcp_resolver needs the name
available at type-check time.

* fix: bump lxml to >=5.4.0 for GHSA-vfmq-68hx-4jfw

lxml 5.3.2 has a known vulnerability. Bump to 5.4.0+ which
includes the fix (libxml2 2.13.8). The previous <5.4.0 pin
was for etree import issues that have since been resolved.

* fix: bump exclude-newer to 2026-04-22 for lxml 6.1.0 resolution

lxml 6.1.0 (GHSA fix) was released April 17 but the exclude-newer
date was set to April 17, missing it by timestamp. Bump to April 22.

* perf: add import time benchmark script

scripts/benchmark_import_time.py measures import crewai cold start
in fresh subprocesses. Supports --runs, --json (for CI), and
--threshold (fail if median exceeds N seconds).

The companion GitHub Action workflow needs to be pushed separately
(requires workflow scope).

* new action

* Potential fix for pull request finding 'CodeQL / Workflow does not contain permissions'

Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>

---------

Co-authored-by: Joao Moura <joaomdmoura@gmail.com>
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
2026-04-22 02:17:33 -03:00
55 changed files with 5581 additions and 1158 deletions

1
.gitignore vendored
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@@ -30,3 +30,4 @@ chromadb-*.lock
.crewai/memory
blogs/*
secrets/*
UNKNOWN.egg-info/

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@@ -4,6 +4,80 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
icon: "clock"
mode: "wide"
---
<Update label="25 أبريل 2026">
## v1.14.3
[عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3)
## ما الذي تغير
### الميزات
- إضافة أحداث دورة الحياة لعمليات نقطة التحقق
- إضافة دعم لـ e2b
- الرجوع إلى DefaultAzureCredential عند عدم توفير مفتاح API في تكامل Azure
- إضافة دعم Bedrock V4
- إضافة أدوات Daytona sandbox لوظائف محسّنة
- إضافة دعم نقطة التحقق والتفرع للوكلاء المستقلين
### إصلاحات الأخطاء
- إصلاح execution_id ليكون منفصلًا عن state.id
- حل مشكلة إعادة تشغيل أحداث الطريقة المسجلة عند استئناف نقطة التحقق
- إصلاح تسلسل مراجع class initial_state كـ JSON schema
- الحفاظ على مهارات الوكلاء التي تحتوي على بيانات وصفية فقط
- تمرير أسماء @CrewBase الضمنية إلى أحداث الطاقم
- دمج بيانات التنفيذ عند تهيئة دفعة مكررة
- إصلاح تسلسل حقول مراجع class Task لنقاط التحقق
- التعامل مع نتيجة BaseModel في حلقة إعادة المحاولة guardrail
- الحفاظ على thought_signature في استدعاءات أدوات Gemini للبث
- إصدار task_started عند استئناف التفرع وإعادة تصميم واجهة المستخدم النصية لنقطة التحقق
- استخدام تواريخ مستقبلية في اختبارات تقليم نقطة التحقق لمنع الفشل المعتمد على الوقت
- إصلاح ترتيب التشغيل الجاف والتعامل مع الفرع القديم الذي تم التحقق منه في إصدار أدوات التطوير
- ترقية lxml إلى >=6.1.0 لرقعة الأمان
- رفع python-dotenv إلى >=1.2.2 لرقعة الأمان
### الوثائق
- تحديث سجل التغييرات والإصدار لـ v1.14.3
- إضافة صفحة "بناء باستخدام الذكاء الاصطناعي" وتحديث التنقل لجميع اللغات
- إزالة الأسئلة الشائعة حول التسعير من صفحة البناء باستخدام الذكاء الاصطناعي عبر جميع المواقع
### الأداء
- تحسين MCP SDK وأنواع الأحداث لتقليل بدء التشغيل البارد بنسبة ~29%
### إعادة الهيكلة
- إعادة هيكلة مساعدي نقطة التحقق للقضاء على التكرار وتشديد تلميحات نوع الحالة
## المساهمون
@MatthiasHowellYopp, @akaKuruma, @alex-clawd, @github-actions[bot], @github-advanced-security[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @mattatcha, @renatonitta
</Update>
<Update label="23 أبريل 2026">
## v1.14.3a3
[عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a3)
## ما الذي تغير
### الميزات
- إضافة دعم لـ e2b
- تنفيذ التراجع إلى DefaultAzureCredential عند عدم توفير مفتاح API
### إصلاحات الأخطاء
- ترقية lxml إلى >=6.1.0 لمعالجة مشكلة الأمان GHSA-vfmq-68hx-4jfw
### الوثائق
- إزالة الأسئلة الشائعة حول التسعير من صفحة البناء باستخدام الذكاء الاصطناعي عبر جميع اللغات
### الأداء
- تحسين وقت بدء التشغيل البارد بنسبة ~29% من خلال التحميل الكسول لمجموعة أدوات MCP وأنواع الأحداث
## المساهمون
@alex-clawd, @github-advanced-security[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @mattatcha
</Update>
<Update label="22 أبريل 2026">
## v1.14.3a2

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@@ -207,9 +207,6 @@ CrewAI AMP مُصمَّم لفرق الإنتاج. إليك ما تحصل علي
- **Factory (استضافة ذاتية)** — على بنيتك التحتية لسيطرة كاملة على البيانات
- **هجين** — دمج السحابة والاستضافة الذاتية حسب حساسية البيانات
</Accordion>
<Accordion title="كيف يعمل التسعير؟">
سجّل في [app.crewai.com](https://app.crewai.com) لمعرفة الخطط الحالية. تسعير المؤسسات وFactory متاح عند الطلب.
</Accordion>
</AccordionGroup>
<Card title="استكشف CrewAI AMP →" icon="arrow-right" href="https://app.crewai.com">

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@@ -1,180 +0,0 @@
---
title: Daytona Sandbox Tools
description: Run shell commands, execute Python, and manage files inside isolated [Daytona](https://www.daytona.io/) sandboxes.
icon: box
mode: "wide"
---
# Daytona Sandbox Tools
## Description
The Daytona sandbox tools give CrewAI agents access to isolated, ephemeral compute environments powered by [Daytona](https://www.daytona.io/). Three tools are available so you can give an agent exactly the capabilities it needs:
- **`DaytonaExecTool`** — run any shell command inside a sandbox.
- **`DaytonaPythonTool`** — execute a block of Python source code inside a sandbox.
- **`DaytonaFileTool`** — read, write, append, list, delete, and inspect files inside a sandbox.
All three tools share the same sandbox lifecycle controls, so you can mix and match them while keeping state in a single persistent sandbox.
## Installation
```shell
uv add "crewai-tools[daytona]"
# or
pip install "crewai-tools[daytona]"
```
Set your API key:
```shell
export DAYTONA_API_KEY="your-api-key"
```
`DAYTONA_API_URL` and `DAYTONA_TARGET` are also respected if set.
## Sandbox Lifecycle
All three tools inherit lifecycle controls from `DaytonaBaseTool`:
| Mode | How to enable | Sandbox created | Sandbox deleted |
|------|--------------|-----------------|-----------------|
| **Ephemeral** (default) | `persistent=False` (default) | On every `_run` call | At the end of that same call |
| **Persistent** | `persistent=True` | Lazily on first use | At process exit (via `atexit`), or manually via `tool.close()` |
| **Attach** | `sandbox_id="<id>"` | Never — attaches to an existing sandbox | Never — the tool will not delete a sandbox it did not create |
Ephemeral mode is the safe default: nothing leaks if the agent forgets to clean up. Use persistent mode when you want filesystem state or installed packages to carry across multiple tool calls — this is typical when pairing `DaytonaFileTool` with `DaytonaExecTool`.
## Examples
### One-shot Python execution (ephemeral)
```python Code
from crewai_tools import DaytonaPythonTool
tool = DaytonaPythonTool()
result = tool.run(code="print(sum(range(10)))")
print(result)
# {"exit_code": 0, "result": "45\n", "artifacts": None}
```
### Multi-step shell session (persistent)
```python Code
from crewai_tools import DaytonaExecTool, DaytonaFileTool
exec_tool = DaytonaExecTool(persistent=True)
file_tool = DaytonaFileTool(persistent=True)
# Install a package, then write and run a script — all in the same sandbox
exec_tool.run(command="pip install httpx -q")
file_tool.run(action="write", path="/workspace/fetch.py", content="import httpx; print(httpx.get('https://httpbin.org/get').status_code)")
exec_tool.run(command="python /workspace/fetch.py")
```
<Note>
Each tool instance maintains its own persistent sandbox. To share **one** sandbox across two tools, create the first tool, grab its sandbox id via `tool._persistent_sandbox.id`, and pass it to the second tool via `sandbox_id=...`.
</Note>
### Attach to an existing sandbox
```python Code
from crewai_tools import DaytonaExecTool
tool = DaytonaExecTool(sandbox_id="my-long-lived-sandbox")
result = tool.run(command="ls /workspace")
```
### Custom sandbox parameters
Pass Daytona's `CreateSandboxFromSnapshotParams` kwargs via `create_params`:
```python Code
from crewai_tools import DaytonaExecTool
tool = DaytonaExecTool(
persistent=True,
create_params={
"language": "python",
"env_vars": {"MY_FLAG": "1"},
"labels": {"owner": "crewai-agent"},
},
)
```
### Agent integration
```python Code
from crewai import Agent, Task, Crew
from crewai_tools import DaytonaExecTool, DaytonaPythonTool, DaytonaFileTool
exec_tool = DaytonaExecTool(persistent=True)
python_tool = DaytonaPythonTool(persistent=True)
file_tool = DaytonaFileTool(persistent=True)
coder = Agent(
role="Sandbox Engineer",
goal="Write and run code in an isolated environment",
backstory="An engineer who uses Daytona sandboxes to safely execute code and manage files.",
tools=[exec_tool, python_tool, file_tool],
verbose=True,
)
task = Task(
description="Write a Python script that prints the first 10 Fibonacci numbers, save it to /workspace/fib.py, and run it.",
expected_output="The first 10 Fibonacci numbers printed to stdout.",
agent=coder,
)
crew = Crew(agents=[coder], tasks=[task])
result = crew.kickoff()
```
## Parameters
### Shared (`DaytonaBaseTool`)
All three tools accept these parameters at initialization:
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `api_key` | `str \| None` | `$DAYTONA_API_KEY` | Daytona API key. Falls back to the `DAYTONA_API_KEY` env var. |
| `api_url` | `str \| None` | `$DAYTONA_API_URL` | Daytona API URL override. |
| `target` | `str \| None` | `$DAYTONA_TARGET` | Daytona target region. |
| `persistent` | `bool` | `False` | Reuse one sandbox across all calls and delete it at process exit. |
| `sandbox_id` | `str \| None` | `None` | Attach to an existing sandbox by id or name. |
| `create_params` | `dict \| None` | `None` | Extra kwargs forwarded to `CreateSandboxFromSnapshotParams` (e.g. `language`, `env_vars`, `labels`). |
| `sandbox_timeout` | `float` | `60.0` | Timeout in seconds for sandbox create/delete operations. |
### `DaytonaExecTool`
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `command` | `str` | ✓ | Shell command to execute. |
| `cwd` | `str \| None` | | Working directory inside the sandbox. |
| `env` | `dict[str, str] \| None` | | Extra environment variables for this command. |
| `timeout` | `int \| None` | | Maximum seconds to wait for the command. |
### `DaytonaPythonTool`
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `code` | `str` | ✓ | Python source code to execute. |
| `argv` | `list[str] \| None` | | Argument vector forwarded via `CodeRunParams`. |
| `env` | `dict[str, str] \| None` | | Environment variables forwarded via `CodeRunParams`. |
| `timeout` | `int \| None` | | Maximum seconds to wait for execution. |
### `DaytonaFileTool`
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `action` | `str` | ✓ | One of: `read`, `write`, `append`, `list`, `delete`, `mkdir`, `info`. |
| `path` | `str` | ✓ | Absolute path inside the sandbox. |
| `content` | `str \| None` | | Content to write or append. Required for `append`. |
| `binary` | `bool` | | If `True`, `content` is base64 on write; returns base64 on read. |
| `recursive` | `bool` | | For `delete`: remove directories recursively. |
| `mode` | `str` | | For `mkdir`: octal permission string (default `"0755"`). |
<Tip>
For files larger than a few KB, create the file first with `action="write"` and empty content, then send the body via multiple `action="append"` calls of ~4 KB each to stay within tool-call payload limits.
</Tip>

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@@ -4,6 +4,80 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
icon: "clock"
mode: "wide"
---
<Update label="Apr 25, 2026">
## v1.14.3
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3)
## What's Changed
### Features
- Add lifecycle events for checkpoint operations
- Add support for e2b
- Fall back to DefaultAzureCredential when no API key is provided in Azure integration
- Add Bedrock V4 support
- Add Daytona sandbox tools for enhanced functionality
- Add checkpoint and fork support to standalone agents
### Bug Fixes
- Fix execution_id to be separate from state.id
- Resolve replay of recorded method events on checkpoint resume
- Fix serialization of initial_state class references as JSON schema
- Preserve metadata-only agent skills
- Propagate implicit @CrewBase names to crew events
- Merge execution metadata on duplicate batch initialization
- Fix serialization of Task class-reference fields for checkpointing
- Handle BaseModel result in guardrail retry loop
- Preserve thought_signature in Gemini streaming tool calls
- Emit task_started on fork resume and redesign checkpoint TUI
- Use future dates in checkpoint prune tests to prevent time-dependent failures
- Fix dry-run order and handle checked-out stale branch in devtools release
- Upgrade lxml to >=6.1.0 for security patch
- Bump python-dotenv to >=1.2.2 for security patch
### Documentation
- Update changelog and version for v1.14.3
- Add 'Build with AI' page and update navigation for all languages
- Remove pricing FAQ from build-with-ai page across all locales
### Performance
- Optimize MCP SDK and event types to reduce cold start by ~29%
### Refactoring
- Refactor checkpoint helpers to eliminate duplication and tighten state type hints
## Contributors
@MatthiasHowellYopp, @akaKuruma, @alex-clawd, @github-actions[bot], @github-advanced-security[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @mattatcha, @renatonitta
</Update>
<Update label="Apr 23, 2026">
## v1.14.3a3
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a3)
## What's Changed
### Features
- Add support for e2b
- Implement fallback to DefaultAzureCredential when no API key is provided
### Bug Fixes
- Upgrade lxml to >=6.1.0 to address security issue GHSA-vfmq-68hx-4jfw
### Documentation
- Remove pricing FAQ from build-with-ai page across all locales
### Performance
- Improve cold start time by ~29% through lazy-loading of MCP SDK and event types
## Contributors
@alex-clawd, @github-advanced-security[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @mattatcha
</Update>
<Update label="Apr 22, 2026">
## v1.14.3a2

View File

@@ -207,9 +207,6 @@ CrewAI AMP is built for production teams. Here's what you get beyond deployment.
- **Factory (self-hosted)** — run on your own infrastructure for full data control
- **Hybrid** — mix cloud and self-hosted based on sensitivity requirements
</Accordion>
<Accordion title="How does pricing work?">
Sign up at [app.crewai.com](https://app.crewai.com) to see current plans. Enterprise and Factory pricing is available on request.
</Accordion>
</AccordionGroup>
<Card title="Explore CrewAI AMP →" icon="arrow-right" href="https://app.crewai.com">

View File

@@ -1,180 +0,0 @@
---
title: Daytona Sandbox Tools
description: Run shell commands, execute Python, and manage files inside isolated [Daytona](https://www.daytona.io/) sandboxes.
icon: box
mode: "wide"
---
# Daytona Sandbox Tools
## Description
The Daytona sandbox tools give CrewAI agents access to isolated, ephemeral compute environments powered by [Daytona](https://www.daytona.io/). Three tools are available so you can give an agent exactly the capabilities it needs:
- **`DaytonaExecTool`** — run any shell command inside a sandbox.
- **`DaytonaPythonTool`** — execute a block of Python source code inside a sandbox.
- **`DaytonaFileTool`** — read, write, append, list, delete, and inspect files inside a sandbox.
All three tools share the same sandbox lifecycle controls, so you can mix and match them while keeping state in a single persistent sandbox.
## Installation
```shell
uv add "crewai-tools[daytona]"
# or
pip install "crewai-tools[daytona]"
```
Set your API key:
```shell
export DAYTONA_API_KEY="your-api-key"
```
`DAYTONA_API_URL` and `DAYTONA_TARGET` are also respected if set.
## Sandbox Lifecycle
All three tools inherit lifecycle controls from `DaytonaBaseTool`:
| Mode | How to enable | Sandbox created | Sandbox deleted |
|------|--------------|-----------------|-----------------|
| **Ephemeral** (default) | `persistent=False` (default) | On every `_run` call | At the end of that same call |
| **Persistent** | `persistent=True` | Lazily on first use | At process exit (via `atexit`), or manually via `tool.close()` |
| **Attach** | `sandbox_id="<id>"` | Never — attaches to an existing sandbox | Never — the tool will not delete a sandbox it did not create |
Ephemeral mode is the safe default: nothing leaks if the agent forgets to clean up. Use persistent mode when you want filesystem state or installed packages to carry across multiple tool calls — this is typical when pairing `DaytonaFileTool` with `DaytonaExecTool`.
## Examples
### One-shot Python execution (ephemeral)
```python Code
from crewai_tools import DaytonaPythonTool
tool = DaytonaPythonTool()
result = tool.run(code="print(sum(range(10)))")
print(result)
# {"exit_code": 0, "result": "45\n", "artifacts": None}
```
### Multi-step shell session (persistent)
```python Code
from crewai_tools import DaytonaExecTool, DaytonaFileTool
exec_tool = DaytonaExecTool(persistent=True)
file_tool = DaytonaFileTool(persistent=True)
# Install a package, then write and run a script — all in the same sandbox
exec_tool.run(command="pip install httpx -q")
file_tool.run(action="write", path="/workspace/fetch.py", content="import httpx; print(httpx.get('https://httpbin.org/get').status_code)")
exec_tool.run(command="python /workspace/fetch.py")
```
<Note>
Each tool instance maintains its own persistent sandbox. To share **one** sandbox across two tools, create the first tool, grab its sandbox id via `tool._persistent_sandbox.id`, and pass it to the second tool via `sandbox_id=...`.
</Note>
### Attach to an existing sandbox
```python Code
from crewai_tools import DaytonaExecTool
tool = DaytonaExecTool(sandbox_id="my-long-lived-sandbox")
result = tool.run(command="ls /workspace")
```
### Custom sandbox parameters
Pass Daytona's `CreateSandboxFromSnapshotParams` kwargs via `create_params`:
```python Code
from crewai_tools import DaytonaExecTool
tool = DaytonaExecTool(
persistent=True,
create_params={
"language": "python",
"env_vars": {"MY_FLAG": "1"},
"labels": {"owner": "crewai-agent"},
},
)
```
### Agent integration
```python Code
from crewai import Agent, Task, Crew
from crewai_tools import DaytonaExecTool, DaytonaPythonTool, DaytonaFileTool
exec_tool = DaytonaExecTool(persistent=True)
python_tool = DaytonaPythonTool(persistent=True)
file_tool = DaytonaFileTool(persistent=True)
coder = Agent(
role="Sandbox Engineer",
goal="Write and run code in an isolated environment",
backstory="An engineer who uses Daytona sandboxes to safely execute code and manage files.",
tools=[exec_tool, python_tool, file_tool],
verbose=True,
)
task = Task(
description="Write a Python script that prints the first 10 Fibonacci numbers, save it to /workspace/fib.py, and run it.",
expected_output="The first 10 Fibonacci numbers printed to stdout.",
agent=coder,
)
crew = Crew(agents=[coder], tasks=[task])
result = crew.kickoff()
```
## Parameters
### Shared (`DaytonaBaseTool`)
All three tools accept these parameters at initialization:
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `api_key` | `str \| None` | `$DAYTONA_API_KEY` | Daytona API key. Falls back to the `DAYTONA_API_KEY` env var. |
| `api_url` | `str \| None` | `$DAYTONA_API_URL` | Daytona API URL override. |
| `target` | `str \| None` | `$DAYTONA_TARGET` | Daytona target region. |
| `persistent` | `bool` | `False` | Reuse one sandbox across all calls and delete it at process exit. |
| `sandbox_id` | `str \| None` | `None` | Attach to an existing sandbox by id or name. |
| `create_params` | `dict \| None` | `None` | Extra kwargs forwarded to `CreateSandboxFromSnapshotParams` (e.g. `language`, `env_vars`, `labels`). |
| `sandbox_timeout` | `float` | `60.0` | Timeout in seconds for sandbox create/delete operations. |
### `DaytonaExecTool`
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `command` | `str` | ✓ | Shell command to execute. |
| `cwd` | `str \| None` | | Working directory inside the sandbox. |
| `env` | `dict[str, str] \| None` | | Extra environment variables for this command. |
| `timeout` | `int \| None` | | Maximum seconds to wait for the command. |
### `DaytonaPythonTool`
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `code` | `str` | ✓ | Python source code to execute. |
| `argv` | `list[str] \| None` | | Argument vector forwarded via `CodeRunParams`. |
| `env` | `dict[str, str] \| None` | | Environment variables forwarded via `CodeRunParams`. |
| `timeout` | `int \| None` | | Maximum seconds to wait for execution. |
### `DaytonaFileTool`
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `action` | `str` | ✓ | One of: `read`, `write`, `append`, `list`, `delete`, `mkdir`, `info`. |
| `path` | `str` | ✓ | Absolute path inside the sandbox. |
| `content` | `str \| None` | | Content to write or append. Required for `append`. |
| `binary` | `bool` | | If `True`, `content` is base64 on write; returns base64 on read. |
| `recursive` | `bool` | | For `delete`: remove directories recursively. |
| `mode` | `str` | | For `mkdir`: octal permission string (default `"0755"`). |
<Tip>
For files larger than a few KB, create the file first with `action="write"` and empty content, then send the body via multiple `action="append"` calls of ~4 KB each to stay within tool-call payload limits.
</Tip>

View File

@@ -4,6 +4,80 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
icon: "clock"
mode: "wide"
---
<Update label="2026년 4월 25일">
## v1.14.3
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3)
## 변경 사항
### 기능
- 체크포인트 작업을 위한 생명주기 이벤트 추가
- e2b 지원 추가
- Azure 통합에서 API 키가 제공되지 않을 경우 DefaultAzureCredential로 대체
- Bedrock V4 지원 추가
- 향상된 기능을 위한 Daytona 샌드박스 도구 추가
- 독립형 에이전트에 체크포인트 및 포크 지원 추가
### 버그 수정
- execution_id를 state.id와 분리되도록 수정
- 체크포인트 재개 시 기록된 메서드 이벤트 재생 문제 해결
- initial_state 클래스 참조의 JSON 스키마 직렬화 수정
- 메타데이터 전용 에이전트 기술 보존
- 암묵적인 @CrewBase 이름을 크루 이벤트로 전파
- 중복 배치 초기화 시 실행 메타데이터 병합
- 체크포인트를 위한 Task 클래스 참조 필드의 직렬화 수정
- 가드레일 재시도 루프에서 BaseModel 결과 처리
- Gemini 스트리밍 도구 호출에서 thought_signature 보존
- 포크 재개 시 task_started 방출 및 체크포인트 TUI 재설계
- 체크포인트 가지치기 테스트에서 미래 날짜 사용하여 시간 의존적 실패 방지
- 드라이 런 주문 수정 및 devtools 릴리스에서 체크아웃된 오래된 브랜치 처리
- 보안 패치를 위해 lxml을 >=6.1.0으로 업그레이드
- 보안 패치를 위해 python-dotenv를 >=1.2.2로 업그레이드
### 문서
- v1.14.3에 대한 변경 로그 및 버전 업데이트
- 'AI로 빌드하기' 페이지 추가 및 모든 언어에 대한 내비게이션 업데이트
- 모든 로케일에서 build-with-ai 페이지의 가격 FAQ 제거
### 성능
- MCP SDK 및 이벤트 유형 최적화하여 콜드 스타트를 약 29% 감소
### 리팩토링
- 중복 제거 및 상태 유형 힌트를 강화하기 위해 체크포인트 헬퍼 리팩토링
## 기여자
@MatthiasHowellYopp, @akaKuruma, @alex-clawd, @github-actions[bot], @github-advanced-security[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @mattatcha, @renatonitta
</Update>
<Update label="2026년 4월 23일">
## v1.14.3a3
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a3)
## 변경 사항
### 기능
- e2b 지원 추가
- API 키가 제공되지 않을 경우 DefaultAzureCredential로 대체 구현
### 버그 수정
- 보안 문제 GHSA-vfmq-68hx-4jfw를 해결하기 위해 lxml을 >=6.1.0으로 업그레이드
### 문서
- 모든 지역에서 build-with-ai 페이지의 가격 FAQ 제거
### 성능
- MCP SDK 및 이벤트 유형의 지연 로딩을 통해 콜드 스타트 시간을 약 29% 개선
## 기여자
@alex-clawd, @github-advanced-security[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @mattatcha
</Update>
<Update label="2026년 4월 22일">
## v1.14.3a2

View File

@@ -207,9 +207,6 @@ CrewAI AMP는 프로덕션 팀을 위해 만들어졌습니다. 배포 외에
- **Factory(셀프 호스팅)** — 데이터 통제를 위해 자체 인프라에서 실행
- **하이브리드** — 민감도에 따라 클라우드와 셀프 호스팅을 혼합
</Accordion>
<Accordion title="가격은 어떻게 되나요?">
[app.crewai.com](https://app.crewai.com)에 가입하면 현재 요금제를 확인할 수 있습니다. 엔터프라이즈 및 Factory 가격은 문의 시 안내합니다.
</Accordion>
</AccordionGroup>
<Card title="CrewAI AMP 살펴보기 →" icon="arrow-right" href="https://app.crewai.com">

View File

@@ -1,180 +0,0 @@
---
title: Daytona Sandbox Tools
description: Run shell commands, execute Python, and manage files inside isolated [Daytona](https://www.daytona.io/) sandboxes.
icon: box
mode: "wide"
---
# Daytona Sandbox Tools
## Description
The Daytona sandbox tools give CrewAI agents access to isolated, ephemeral compute environments powered by [Daytona](https://www.daytona.io/). Three tools are available so you can give an agent exactly the capabilities it needs:
- **`DaytonaExecTool`** — run any shell command inside a sandbox.
- **`DaytonaPythonTool`** — execute a block of Python source code inside a sandbox.
- **`DaytonaFileTool`** — read, write, append, list, delete, and inspect files inside a sandbox.
All three tools share the same sandbox lifecycle controls, so you can mix and match them while keeping state in a single persistent sandbox.
## Installation
```shell
uv add "crewai-tools[daytona]"
# or
pip install "crewai-tools[daytona]"
```
Set your API key:
```shell
export DAYTONA_API_KEY="your-api-key"
```
`DAYTONA_API_URL` and `DAYTONA_TARGET` are also respected if set.
## Sandbox Lifecycle
All three tools inherit lifecycle controls from `DaytonaBaseTool`:
| Mode | How to enable | Sandbox created | Sandbox deleted |
|------|--------------|-----------------|-----------------|
| **Ephemeral** (default) | `persistent=False` (default) | On every `_run` call | At the end of that same call |
| **Persistent** | `persistent=True` | Lazily on first use | At process exit (via `atexit`), or manually via `tool.close()` |
| **Attach** | `sandbox_id="<id>"` | Never — attaches to an existing sandbox | Never — the tool will not delete a sandbox it did not create |
Ephemeral mode is the safe default: nothing leaks if the agent forgets to clean up. Use persistent mode when you want filesystem state or installed packages to carry across multiple tool calls — this is typical when pairing `DaytonaFileTool` with `DaytonaExecTool`.
## Examples
### One-shot Python execution (ephemeral)
```python Code
from crewai_tools import DaytonaPythonTool
tool = DaytonaPythonTool()
result = tool.run(code="print(sum(range(10)))")
print(result)
# {"exit_code": 0, "result": "45\n", "artifacts": None}
```
### Multi-step shell session (persistent)
```python Code
from crewai_tools import DaytonaExecTool, DaytonaFileTool
exec_tool = DaytonaExecTool(persistent=True)
file_tool = DaytonaFileTool(persistent=True)
# Install a package, then write and run a script — all in the same sandbox
exec_tool.run(command="pip install httpx -q")
file_tool.run(action="write", path="/workspace/fetch.py", content="import httpx; print(httpx.get('https://httpbin.org/get').status_code)")
exec_tool.run(command="python /workspace/fetch.py")
```
<Note>
Each tool instance maintains its own persistent sandbox. To share **one** sandbox across two tools, create the first tool, grab its sandbox id via `tool._persistent_sandbox.id`, and pass it to the second tool via `sandbox_id=...`.
</Note>
### Attach to an existing sandbox
```python Code
from crewai_tools import DaytonaExecTool
tool = DaytonaExecTool(sandbox_id="my-long-lived-sandbox")
result = tool.run(command="ls /workspace")
```
### Custom sandbox parameters
Pass Daytona's `CreateSandboxFromSnapshotParams` kwargs via `create_params`:
```python Code
from crewai_tools import DaytonaExecTool
tool = DaytonaExecTool(
persistent=True,
create_params={
"language": "python",
"env_vars": {"MY_FLAG": "1"},
"labels": {"owner": "crewai-agent"},
},
)
```
### Agent integration
```python Code
from crewai import Agent, Task, Crew
from crewai_tools import DaytonaExecTool, DaytonaPythonTool, DaytonaFileTool
exec_tool = DaytonaExecTool(persistent=True)
python_tool = DaytonaPythonTool(persistent=True)
file_tool = DaytonaFileTool(persistent=True)
coder = Agent(
role="Sandbox Engineer",
goal="Write and run code in an isolated environment",
backstory="An engineer who uses Daytona sandboxes to safely execute code and manage files.",
tools=[exec_tool, python_tool, file_tool],
verbose=True,
)
task = Task(
description="Write a Python script that prints the first 10 Fibonacci numbers, save it to /workspace/fib.py, and run it.",
expected_output="The first 10 Fibonacci numbers printed to stdout.",
agent=coder,
)
crew = Crew(agents=[coder], tasks=[task])
result = crew.kickoff()
```
## Parameters
### Shared (`DaytonaBaseTool`)
All three tools accept these parameters at initialization:
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `api_key` | `str \| None` | `$DAYTONA_API_KEY` | Daytona API key. Falls back to the `DAYTONA_API_KEY` env var. |
| `api_url` | `str \| None` | `$DAYTONA_API_URL` | Daytona API URL override. |
| `target` | `str \| None` | `$DAYTONA_TARGET` | Daytona target region. |
| `persistent` | `bool` | `False` | Reuse one sandbox across all calls and delete it at process exit. |
| `sandbox_id` | `str \| None` | `None` | Attach to an existing sandbox by id or name. |
| `create_params` | `dict \| None` | `None` | Extra kwargs forwarded to `CreateSandboxFromSnapshotParams` (e.g. `language`, `env_vars`, `labels`). |
| `sandbox_timeout` | `float` | `60.0` | Timeout in seconds for sandbox create/delete operations. |
### `DaytonaExecTool`
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `command` | `str` | ✓ | Shell command to execute. |
| `cwd` | `str \| None` | | Working directory inside the sandbox. |
| `env` | `dict[str, str] \| None` | | Extra environment variables for this command. |
| `timeout` | `int \| None` | | Maximum seconds to wait for the command. |
### `DaytonaPythonTool`
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `code` | `str` | ✓ | Python source code to execute. |
| `argv` | `list[str] \| None` | | Argument vector forwarded via `CodeRunParams`. |
| `env` | `dict[str, str] \| None` | | Environment variables forwarded via `CodeRunParams`. |
| `timeout` | `int \| None` | | Maximum seconds to wait for execution. |
### `DaytonaFileTool`
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `action` | `str` | ✓ | One of: `read`, `write`, `append`, `list`, `delete`, `mkdir`, `info`. |
| `path` | `str` | ✓ | Absolute path inside the sandbox. |
| `content` | `str \| None` | | Content to write or append. Required for `append`. |
| `binary` | `bool` | | If `True`, `content` is base64 on write; returns base64 on read. |
| `recursive` | `bool` | | For `delete`: remove directories recursively. |
| `mode` | `str` | | For `mkdir`: octal permission string (default `"0755"`). |
<Tip>
For files larger than a few KB, create the file first with `action="write"` and empty content, then send the body via multiple `action="append"` calls of ~4 KB each to stay within tool-call payload limits.
</Tip>

View File

@@ -4,6 +4,80 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
icon: "clock"
mode: "wide"
---
<Update label="25 abr 2026">
## v1.14.3
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3)
## O que Mudou
### Recursos
- Adicionar eventos de ciclo de vida para operações de checkpoint
- Adicionar suporte para e2b
- Reverter para DefaultAzureCredential quando nenhuma chave de API for fornecida na integração com o Azure
- Adicionar suporte ao Bedrock V4
- Adicionar ferramentas de sandbox Daytona para funcionalidade aprimorada
- Adicionar suporte a checkpoint e fork para agentes autônomos
### Correções de Bugs
- Corrigir execution_id para ser separado de state.id
- Resolver a reprodução de eventos de método gravados na retomada do checkpoint
- Corrigir a serialização de referências de classe initial_state como esquema JSON
- Preservar habilidades de agente somente de metadados
- Propagar nomes implícitos @CrewBase para eventos da equipe
- Mesclar metadados de execução na inicialização de lote duplicado
- Corrigir a serialização de campos de referência de classe Task para checkpointing
- Lidar com o resultado BaseModel no loop de retry do guardrail
- Preservar thought_signature em chamadas de ferramentas de streaming Gemini
- Emitir task_started na retomada do fork e redesenhar TUI de checkpoint
- Usar datas futuras em testes de poda de checkpoint para evitar falhas dependentes do tempo
- Corrigir a ordem de dry-run e lidar com branch obsoleta verificada na liberação do devtools
- Atualizar lxml para >=6.1.0 para patch de segurança
- Aumentar python-dotenv para >=1.2.2 para patch de segurança
### Documentação
- Atualizar changelog e versão para v1.14.3
- Adicionar página 'Construir com IA' e atualizar navegação para todos os idiomas
- Remover FAQ de preços da página construir-com-ia em todos os locais
### Desempenho
- Otimizar MCP SDK e tipos de eventos para reduzir o tempo de inicialização a frio em ~29%
### Refatoração
- Refatorar auxiliares de checkpoint para eliminar duplicação e apertar dicas de tipo de estado
## Contribuidores
@MatthiasHowellYopp, @akaKuruma, @alex-clawd, @github-actions[bot], @github-advanced-security[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @mattatcha, @renatonitta
</Update>
<Update label="23 abr 2026">
## v1.14.3a3
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a3)
## O que Mudou
### Recursos
- Adicionar suporte para e2b
- Implementar fallback para DefaultAzureCredential quando nenhuma chave de API for fornecida
### Correções de Bugs
- Atualizar lxml para >=6.1.0 para resolver problema de segurança GHSA-vfmq-68hx-4jfw
### Documentação
- Remover FAQ de preços da página build-with-ai em todos os locais
### Desempenho
- Melhorar o tempo de inicialização a frio em ~29% através do carregamento preguiçoso do SDK MCP e tipos de eventos
## Contributors
@alex-clawd, @github-advanced-security[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @mattatcha
</Update>
<Update label="22 abr 2026">
## v1.14.3a2

View File

@@ -207,9 +207,6 @@ O CrewAI AMP foi feito para equipes em produção. Além da implantação, você
- **Factory (self-hosted)** — na sua infraestrutura para controle total dos dados
- **Híbrido** — combine nuvem e self-hosted conforme a sensibilidade dos dados
</Accordion>
<Accordion title="Como funciona o preço?">
Cadastre-se em [app.crewai.com](https://app.crewai.com) para ver os planos atuais. Preços enterprise e Factory sob consulta.
</Accordion>
</AccordionGroup>
<Card title="Conheça o CrewAI AMP →" icon="arrow-right" href="https://app.crewai.com">

View File

@@ -1,180 +0,0 @@
---
title: Daytona Sandbox Tools
description: Run shell commands, execute Python, and manage files inside isolated [Daytona](https://www.daytona.io/) sandboxes.
icon: box
mode: "wide"
---
# Daytona Sandbox Tools
## Description
The Daytona sandbox tools give CrewAI agents access to isolated, ephemeral compute environments powered by [Daytona](https://www.daytona.io/). Three tools are available so you can give an agent exactly the capabilities it needs:
- **`DaytonaExecTool`** — run any shell command inside a sandbox.
- **`DaytonaPythonTool`** — execute a block of Python source code inside a sandbox.
- **`DaytonaFileTool`** — read, write, append, list, delete, and inspect files inside a sandbox.
All three tools share the same sandbox lifecycle controls, so you can mix and match them while keeping state in a single persistent sandbox.
## Installation
```shell
uv add "crewai-tools[daytona]"
# or
pip install "crewai-tools[daytona]"
```
Set your API key:
```shell
export DAYTONA_API_KEY="your-api-key"
```
`DAYTONA_API_URL` and `DAYTONA_TARGET` are also respected if set.
## Sandbox Lifecycle
All three tools inherit lifecycle controls from `DaytonaBaseTool`:
| Mode | How to enable | Sandbox created | Sandbox deleted |
|------|--------------|-----------------|-----------------|
| **Ephemeral** (default) | `persistent=False` (default) | On every `_run` call | At the end of that same call |
| **Persistent** | `persistent=True` | Lazily on first use | At process exit (via `atexit`), or manually via `tool.close()` |
| **Attach** | `sandbox_id="<id>"` | Never — attaches to an existing sandbox | Never — the tool will not delete a sandbox it did not create |
Ephemeral mode is the safe default: nothing leaks if the agent forgets to clean up. Use persistent mode when you want filesystem state or installed packages to carry across multiple tool calls — this is typical when pairing `DaytonaFileTool` with `DaytonaExecTool`.
## Examples
### One-shot Python execution (ephemeral)
```python Code
from crewai_tools import DaytonaPythonTool
tool = DaytonaPythonTool()
result = tool.run(code="print(sum(range(10)))")
print(result)
# {"exit_code": 0, "result": "45\n", "artifacts": None}
```
### Multi-step shell session (persistent)
```python Code
from crewai_tools import DaytonaExecTool, DaytonaFileTool
exec_tool = DaytonaExecTool(persistent=True)
file_tool = DaytonaFileTool(persistent=True)
# Install a package, then write and run a script — all in the same sandbox
exec_tool.run(command="pip install httpx -q")
file_tool.run(action="write", path="/workspace/fetch.py", content="import httpx; print(httpx.get('https://httpbin.org/get').status_code)")
exec_tool.run(command="python /workspace/fetch.py")
```
<Note>
Each tool instance maintains its own persistent sandbox. To share **one** sandbox across two tools, create the first tool, grab its sandbox id via `tool._persistent_sandbox.id`, and pass it to the second tool via `sandbox_id=...`.
</Note>
### Attach to an existing sandbox
```python Code
from crewai_tools import DaytonaExecTool
tool = DaytonaExecTool(sandbox_id="my-long-lived-sandbox")
result = tool.run(command="ls /workspace")
```
### Custom sandbox parameters
Pass Daytona's `CreateSandboxFromSnapshotParams` kwargs via `create_params`:
```python Code
from crewai_tools import DaytonaExecTool
tool = DaytonaExecTool(
persistent=True,
create_params={
"language": "python",
"env_vars": {"MY_FLAG": "1"},
"labels": {"owner": "crewai-agent"},
},
)
```
### Agent integration
```python Code
from crewai import Agent, Task, Crew
from crewai_tools import DaytonaExecTool, DaytonaPythonTool, DaytonaFileTool
exec_tool = DaytonaExecTool(persistent=True)
python_tool = DaytonaPythonTool(persistent=True)
file_tool = DaytonaFileTool(persistent=True)
coder = Agent(
role="Sandbox Engineer",
goal="Write and run code in an isolated environment",
backstory="An engineer who uses Daytona sandboxes to safely execute code and manage files.",
tools=[exec_tool, python_tool, file_tool],
verbose=True,
)
task = Task(
description="Write a Python script that prints the first 10 Fibonacci numbers, save it to /workspace/fib.py, and run it.",
expected_output="The first 10 Fibonacci numbers printed to stdout.",
agent=coder,
)
crew = Crew(agents=[coder], tasks=[task])
result = crew.kickoff()
```
## Parameters
### Shared (`DaytonaBaseTool`)
All three tools accept these parameters at initialization:
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `api_key` | `str \| None` | `$DAYTONA_API_KEY` | Daytona API key. Falls back to the `DAYTONA_API_KEY` env var. |
| `api_url` | `str \| None` | `$DAYTONA_API_URL` | Daytona API URL override. |
| `target` | `str \| None` | `$DAYTONA_TARGET` | Daytona target region. |
| `persistent` | `bool` | `False` | Reuse one sandbox across all calls and delete it at process exit. |
| `sandbox_id` | `str \| None` | `None` | Attach to an existing sandbox by id or name. |
| `create_params` | `dict \| None` | `None` | Extra kwargs forwarded to `CreateSandboxFromSnapshotParams` (e.g. `language`, `env_vars`, `labels`). |
| `sandbox_timeout` | `float` | `60.0` | Timeout in seconds for sandbox create/delete operations. |
### `DaytonaExecTool`
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `command` | `str` | ✓ | Shell command to execute. |
| `cwd` | `str \| None` | | Working directory inside the sandbox. |
| `env` | `dict[str, str] \| None` | | Extra environment variables for this command. |
| `timeout` | `int \| None` | | Maximum seconds to wait for the command. |
### `DaytonaPythonTool`
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `code` | `str` | ✓ | Python source code to execute. |
| `argv` | `list[str] \| None` | | Argument vector forwarded via `CodeRunParams`. |
| `env` | `dict[str, str] \| None` | | Environment variables forwarded via `CodeRunParams`. |
| `timeout` | `int \| None` | | Maximum seconds to wait for execution. |
### `DaytonaFileTool`
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `action` | `str` | ✓ | One of: `read`, `write`, `append`, `list`, `delete`, `mkdir`, `info`. |
| `path` | `str` | ✓ | Absolute path inside the sandbox. |
| `content` | `str \| None` | | Content to write or append. Required for `append`. |
| `binary` | `bool` | | If `True`, `content` is base64 on write; returns base64 on read. |
| `recursive` | `bool` | | For `delete`: remove directories recursively. |
| `mode` | `str` | | For `mkdir`: octal permission string (default `"0755"`). |
<Tip>
For files larger than a few KB, create the file first with `action="write"` and empty content, then send the body via multiple `action="append"` calls of ~4 KB each to stay within tool-call payload limits.
</Tip>

View File

@@ -152,4 +152,4 @@ __all__ = [
"wrap_file_source",
]
__version__ = "1.14.3a2"
__version__ = "1.14.3"

View File

@@ -10,7 +10,7 @@ requires-python = ">=3.10, <3.14"
dependencies = [
"pytube~=15.0.0",
"requests>=2.33.0,<3",
"crewai==1.14.3a2",
"crewai==1.14.3",
"tiktoken~=0.8.0",
"beautifulsoup4~=4.13.4",
"python-docx~=1.2.0",
@@ -112,7 +112,7 @@ github = [
]
rag = [
"python-docx>=1.1.0",
"lxml>=5.3.0,<5.4.0", # Pin to avoid etree import issues in 5.4.0
"lxml>=6.1.0,<7", # 6.1.0+ required for GHSA-vfmq-68hx-4jfw (XXE in iterparse)
]
xml = [
"unstructured[local-inference, all-docs]>=0.17.2"
@@ -143,6 +143,11 @@ daytona = [
"daytona~=0.140.0",
]
e2b = [
"e2b~=2.20.0",
"e2b-code-interpreter~=2.6.0",
]
[tool.uv]
exclude-newer = "3 days"

View File

@@ -71,6 +71,11 @@ from crewai_tools.tools.directory_search_tool.directory_search_tool import (
DirectorySearchTool,
)
from crewai_tools.tools.docx_search_tool.docx_search_tool import DOCXSearchTool
from crewai_tools.tools.e2b_sandbox_tool import (
E2BExecTool,
E2BFileTool,
E2BPythonTool,
)
from crewai_tools.tools.exa_tools.exa_search_tool import EXASearchTool
from crewai_tools.tools.file_read_tool.file_read_tool import FileReadTool
from crewai_tools.tools.file_writer_tool.file_writer_tool import FileWriterTool
@@ -242,6 +247,9 @@ __all__ = [
"DaytonaPythonTool",
"DirectoryReadTool",
"DirectorySearchTool",
"E2BExecTool",
"E2BFileTool",
"E2BPythonTool",
"EXASearchTool",
"EnterpriseActionTool",
"FileCompressorTool",
@@ -313,4 +321,4 @@ __all__ = [
"ZapierActionTools",
]
__version__ = "1.14.3a2"
__version__ = "1.14.3"

View File

@@ -60,6 +60,11 @@ from crewai_tools.tools.directory_search_tool.directory_search_tool import (
DirectorySearchTool,
)
from crewai_tools.tools.docx_search_tool.docx_search_tool import DOCXSearchTool
from crewai_tools.tools.e2b_sandbox_tool import (
E2BExecTool,
E2BFileTool,
E2BPythonTool,
)
from crewai_tools.tools.exa_tools.exa_search_tool import EXASearchTool
from crewai_tools.tools.file_read_tool.file_read_tool import FileReadTool
from crewai_tools.tools.file_writer_tool.file_writer_tool import FileWriterTool
@@ -227,6 +232,9 @@ __all__ = [
"DaytonaPythonTool",
"DirectoryReadTool",
"DirectorySearchTool",
"E2BExecTool",
"E2BFileTool",
"E2BPythonTool",
"EXASearchTool",
"FileCompressorTool",
"FileReadTool",

View File

@@ -0,0 +1,120 @@
# E2B Sandbox Tools
Run shell commands, execute Python, and manage files inside an [E2B](https://e2b.dev/) sandbox. E2B provides isolated, ephemeral VMs suitable for agent-driven code execution, with a Jupyter-style code interpreter for rich Python results.
Three tools are provided so you can pick what the agent actually needs:
- **`E2BExecTool`** — run a shell command (`sandbox.commands.run`).
- **`E2BPythonTool`** — run a Python cell in the E2B code interpreter (`sandbox.run_code`), returning stdout/stderr and rich results (charts, dataframes).
- **`E2BFileTool`** — read / write / list / delete files (`sandbox.files.*`).
## Installation
```shell
uv add "crewai-tools[e2b]"
# or
pip install "crewai-tools[e2b]"
```
Set the API key:
```shell
export E2B_API_KEY="..."
```
`E2B_DOMAIN` is also respected if set (for self-hosted or non-default deployments).
## Sandbox lifecycle
All three tools share the same lifecycle controls from `E2BBaseTool`:
| Mode | When the sandbox is created | When it is killed |
| --- | --- | --- |
| **Ephemeral** (default, `persistent=False`) | On every `_run` call | At the end of that same call |
| **Persistent** (`persistent=True`) | Lazily on first use | At process exit (via `atexit`), or manually via `tool.close()` |
| **Attach** (`sandbox_id="…"`) | Never — the tool attaches to an existing sandbox | Never — the tool will not kill a sandbox it did not create |
Ephemeral mode is the safe default: nothing leaks if the agent forgets to clean up. Use persistent mode when you want filesystem state or installed packages to carry across steps — this is typical when pairing `E2BFileTool` with `E2BExecTool`.
E2B sandboxes also auto-expire after an idle timeout. Tune it via `sandbox_timeout` (seconds, default `300`).
## Examples
### One-shot Python execution (ephemeral)
```python
from crewai_tools import E2BPythonTool
tool = E2BPythonTool()
result = tool.run(code="print(sum(range(10)))")
```
### Multi-step shell session (persistent)
```python
from crewai_tools import E2BExecTool, E2BFileTool
exec_tool = E2BExecTool(persistent=True)
file_tool = E2BFileTool(persistent=True)
# Each tool keeps its own persistent sandbox. If you need the *same* sandbox
# across two tools, create one tool, grab the sandbox id via
# `tool._persistent_sandbox.sandbox_id`, and pass it to the other via
# `sandbox_id=...`.
```
### Attach to an existing sandbox
```python
from crewai_tools import E2BExecTool
tool = E2BExecTool(sandbox_id="sbx_...")
```
### Custom create params
```python
tool = E2BExecTool(
persistent=True,
template="my-custom-template",
sandbox_timeout=600,
envs={"MY_FLAG": "1"},
metadata={"owner": "crewai-agent"},
)
```
## Tool arguments
### `E2BExecTool`
- `command: str` — shell command to run.
- `cwd: str | None` — working directory.
- `envs: dict[str, str] | None` — extra env vars for this command.
- `timeout: float | None` — seconds.
### `E2BPythonTool`
- `code: str` — source to execute.
- `language: str | None` — override kernel language (default: Python).
- `envs: dict[str, str] | None` — env vars for the run.
- `timeout: float | None` — seconds.
### `E2BFileTool`
- `action: "read" | "write" | "append" | "list" | "delete" | "mkdir" | "info" | "exists"`
- `path: str` — absolute path inside the sandbox.
- `content: str | None` — required for `append`; optional for `write`.
- `binary: bool` — if `True`, `content` is base64 on write / returned as base64 on read.
- `depth: int` — for `list`, how many levels to recurse (default 1).
## Security considerations
These tools hand the LLM arbitrary shell, Python, and filesystem access inside a remote VM. The threat model to keep in mind:
- **Prompt-injection is a code-execution vector.** If the agent ingests untrusted content (web pages, scraped documents, user-supplied files, emails, search results), a malicious instruction hidden in that content can coerce the agent into issuing commands to `E2BExecTool` / `E2BPythonTool`. Treat any pipeline that feeds untrusted text into an agent that also has these tools as equivalent to remote code execution — the LLM is the attacker's shell.
- **Ephemeral mode (the default) is the main blast-radius control.** A fresh sandbox is created per call and killed at the end, so injected commands cannot persist state, exfiltrate long-lived secrets, or build up tooling across turns. Leave `persistent=False` unless you have a concrete reason to change it.
- **Avoid this specific combination:**
- untrusted content in the agent's context, **plus**
- `persistent=True` or an explicit long-lived `sandbox_id`, **plus**
- a large `sandbox_timeout` or credentials/secrets seeded into the sandbox via `envs`.
That stack lets a single injection pivot into a long-running, credentialed shell that survives across turns. If you must run persistently, also keep `sandbox_timeout` short, scope `envs` to the minimum the task needs, and don't feed the same agent untrusted input.
- **Don't mount production credentials.** Anything you put into `envs`, `metadata`, or files written to the sandbox is reachable from the LLM. Use per-task scoped keys, not your personal API tokens.
- **E2B's VM isolation is the final backstop**, not a license to relax the above — isolation prevents escape to the host, but everything the sandbox can reach (the public internet, any service whose token you dropped in) is still fair game for an injected command.

View File

@@ -0,0 +1,12 @@
from crewai_tools.tools.e2b_sandbox_tool.e2b_base_tool import E2BBaseTool
from crewai_tools.tools.e2b_sandbox_tool.e2b_exec_tool import E2BExecTool
from crewai_tools.tools.e2b_sandbox_tool.e2b_file_tool import E2BFileTool
from crewai_tools.tools.e2b_sandbox_tool.e2b_python_tool import E2BPythonTool
__all__ = [
"E2BBaseTool",
"E2BExecTool",
"E2BFileTool",
"E2BPythonTool",
]

View File

@@ -0,0 +1,197 @@
from __future__ import annotations
import atexit
import logging
import os
import threading
from typing import Any, ClassVar
from crewai.tools import BaseTool, EnvVar
from pydantic import ConfigDict, Field, PrivateAttr, SecretStr
logger = logging.getLogger(__name__)
class E2BBaseTool(BaseTool):
"""Shared base for tools that act on an E2B sandbox.
Lifecycle modes:
- persistent=False (default): create a fresh sandbox per `_run` call and
kill it when the call returns. Safer and stateless — nothing leaks if
the agent forgets cleanup.
- persistent=True: lazily create a single sandbox on first use, cache it
on the instance, and register an atexit hook to kill it at process
exit. Cheaper across many calls and lets files/state carry over.
- sandbox_id=<existing>: attach to a sandbox the caller already owns.
Never killed by the tool.
"""
model_config = ConfigDict(arbitrary_types_allowed=True)
package_dependencies: list[str] = Field(default_factory=lambda: ["e2b"])
api_key: SecretStr | None = Field(
default_factory=lambda: (
SecretStr(val) if (val := os.getenv("E2B_API_KEY")) else None
),
description="E2B API key. Falls back to E2B_API_KEY env var.",
json_schema_extra={"required": False},
repr=False,
)
domain: str | None = Field(
default_factory=lambda: os.getenv("E2B_DOMAIN"),
description="E2B API domain override. Falls back to E2B_DOMAIN env var.",
json_schema_extra={"required": False},
)
template: str | None = Field(
default=None,
description=(
"Optional template/snapshot name or id to create the sandbox from. "
"Defaults to E2B's base template when omitted."
),
)
persistent: bool = Field(
default=False,
description=(
"If True, reuse one sandbox across all calls to this tool instance "
"and kill it at process exit. Default False creates and kills a "
"fresh sandbox per call."
),
)
sandbox_id: str | None = Field(
default=None,
description=(
"Attach to an existing sandbox by id instead of creating a new "
"one. The tool will never kill a sandbox it did not create."
),
)
sandbox_timeout: int = Field(
default=300,
description=(
"Idle timeout in seconds after which E2B auto-kills the sandbox. "
"Applied at create time and when attaching via sandbox_id."
),
)
envs: dict[str, str] | None = Field(
default=None,
description="Environment variables to set inside the sandbox at create time.",
)
metadata: dict[str, str] | None = Field(
default=None,
description="Metadata key-value pairs to attach to the sandbox at create time.",
)
env_vars: list[EnvVar] = Field(
default_factory=lambda: [
EnvVar(
name="E2B_API_KEY",
description="API key for E2B sandbox service",
required=False,
),
EnvVar(
name="E2B_DOMAIN",
description="E2B API domain (optional)",
required=False,
),
]
)
_persistent_sandbox: Any | None = PrivateAttr(default=None)
_lock: threading.Lock = PrivateAttr(default_factory=threading.Lock)
_cleanup_registered: bool = PrivateAttr(default=False)
_sdk_cache: ClassVar[dict[str, Any]] = {}
@classmethod
def _import_sandbox_class(cls) -> Any:
"""Return the Sandbox class used by this tool.
Subclasses override this to swap in a different SDK (e.g. the code
interpreter sandbox). The default uses plain `e2b.Sandbox`.
"""
cached = cls._sdk_cache.get("e2b.Sandbox")
if cached is not None:
return cached
try:
from e2b import Sandbox # type: ignore[import-untyped]
except ImportError as exc:
raise ImportError(
"The 'e2b' package is required for E2B sandbox tools. "
"Install it with: uv add e2b (or) pip install e2b"
) from exc
cls._sdk_cache["e2b.Sandbox"] = Sandbox
return Sandbox
def _connect_kwargs(self) -> dict[str, Any]:
kwargs: dict[str, Any] = {}
if self.api_key is not None:
kwargs["api_key"] = self.api_key.get_secret_value()
if self.domain:
kwargs["domain"] = self.domain
if self.sandbox_timeout is not None:
kwargs["timeout"] = self.sandbox_timeout
return kwargs
def _create_kwargs(self) -> dict[str, Any]:
kwargs: dict[str, Any] = self._connect_kwargs()
if self.template is not None:
kwargs["template"] = self.template
if self.envs is not None:
kwargs["envs"] = self.envs
if self.metadata is not None:
kwargs["metadata"] = self.metadata
return kwargs
def _acquire_sandbox(self) -> tuple[Any, bool]:
"""Return (sandbox, should_kill_after_use)."""
sandbox_cls = self._import_sandbox_class()
if self.sandbox_id:
return (
sandbox_cls.connect(self.sandbox_id, **self._connect_kwargs()),
False,
)
if self.persistent:
with self._lock:
if self._persistent_sandbox is None:
self._persistent_sandbox = sandbox_cls.create(
**self._create_kwargs()
)
if not self._cleanup_registered:
atexit.register(self.close)
self._cleanup_registered = True
return self._persistent_sandbox, False
sandbox = sandbox_cls.create(**self._create_kwargs())
return sandbox, True
def _release_sandbox(self, sandbox: Any, should_kill: bool) -> None:
if not should_kill:
return
try:
sandbox.kill()
except Exception:
logger.debug(
"Best-effort sandbox cleanup failed after ephemeral use; "
"the sandbox may need manual termination.",
exc_info=True,
)
def close(self) -> None:
"""Kill the cached persistent sandbox if one exists."""
with self._lock:
sandbox = self._persistent_sandbox
self._persistent_sandbox = None
if sandbox is None:
return
try:
sandbox.kill()
except Exception:
logger.debug(
"Best-effort persistent sandbox cleanup failed at close(); "
"the sandbox may need manual termination.",
exc_info=True,
)

View File

@@ -0,0 +1,62 @@
from __future__ import annotations
from builtins import type as type_
from typing import Any
from pydantic import BaseModel, Field
from crewai_tools.tools.e2b_sandbox_tool.e2b_base_tool import E2BBaseTool
class E2BExecToolSchema(BaseModel):
command: str = Field(..., description="Shell command to execute in the sandbox.")
cwd: str | None = Field(
default=None,
description="Working directory to run the command in. Defaults to the sandbox home dir.",
)
envs: dict[str, str] | None = Field(
default=None,
description="Optional environment variables to set for this command.",
)
timeout: float | None = Field(
default=None,
description="Maximum seconds to wait for the command to finish.",
)
class E2BExecTool(E2BBaseTool):
"""Run a shell command inside an E2B sandbox."""
name: str = "E2B Sandbox Exec"
description: str = (
"Execute a shell command inside an E2B sandbox and return the exit "
"code, stdout, and stderr. Use this to run builds, package installs, "
"git operations, or any one-off shell command."
)
args_schema: type_[BaseModel] = E2BExecToolSchema
def _run(
self,
command: str,
cwd: str | None = None,
envs: dict[str, str] | None = None,
timeout: float | None = None,
) -> Any:
sandbox, should_kill = self._acquire_sandbox()
try:
run_kwargs: dict[str, Any] = {}
if cwd is not None:
run_kwargs["cwd"] = cwd
if envs is not None:
run_kwargs["envs"] = envs
if timeout is not None:
run_kwargs["timeout"] = timeout
result = sandbox.commands.run(command, **run_kwargs)
return {
"exit_code": getattr(result, "exit_code", None),
"stdout": getattr(result, "stdout", None),
"stderr": getattr(result, "stderr", None),
"error": getattr(result, "error", None),
}
finally:
self._release_sandbox(sandbox, should_kill)

View File

@@ -0,0 +1,220 @@
from __future__ import annotations
import base64
from builtins import type as type_
import logging
import posixpath
from typing import Any, Literal
from pydantic import BaseModel, Field, model_validator
from crewai_tools.tools.e2b_sandbox_tool.e2b_base_tool import E2BBaseTool
logger = logging.getLogger(__name__)
FileAction = Literal[
"read", "write", "append", "list", "delete", "mkdir", "info", "exists"
]
class E2BFileToolSchema(BaseModel):
action: FileAction = Field(
...,
description=(
"The filesystem action to perform: 'read' (returns file contents), "
"'write' (create or replace a file with content), 'append' (append "
"content to an existing file — use this for writing large files in "
"chunks to avoid hitting tool-call size limits), 'list' (lists a "
"directory), 'delete' (removes a file/dir), 'mkdir' (creates a "
"directory), 'info' (returns file metadata), 'exists' (returns a "
"boolean for whether the path exists)."
),
)
path: str = Field(..., description="Absolute path inside the sandbox.")
content: str | None = Field(
default=None,
description=(
"Content to write or append. If omitted for 'write', an empty file "
"is created. For files larger than a few KB, prefer one 'write' "
"with empty content followed by multiple 'append' calls of ~4KB "
"each to stay within tool-call payload limits."
),
)
binary: bool = Field(
default=False,
description=(
"For 'write'/'append': treat content as base64 and upload raw "
"bytes. For 'read': return contents as base64 instead of decoded "
"utf-8."
),
)
depth: int = Field(
default=1,
description="For action='list': how many levels deep to recurse (default 1).",
)
@model_validator(mode="after")
def _validate_action_args(self) -> E2BFileToolSchema:
if self.action == "append" and self.content is None:
raise ValueError(
"action='append' requires 'content'. Pass the chunk to append "
"in the 'content' field."
)
return self
class E2BFileTool(E2BBaseTool):
"""Read, write, and manage files inside an E2B sandbox.
Notes:
- Most useful with `persistent=True` or an explicit `sandbox_id`. With
the default ephemeral mode, files disappear when this tool call
finishes.
"""
name: str = "E2B Sandbox Files"
description: str = (
"Perform filesystem operations inside an E2B sandbox: read a file, "
"write content to a path, append content to an existing file, list a "
"directory, delete a path, make a directory, fetch file metadata, or "
"check whether a path exists. For files larger than a few KB, create "
"the file with action='write' and empty content, then send the body "
"via multiple 'append' calls of ~4KB each to stay within tool-call "
"payload limits."
)
args_schema: type_[BaseModel] = E2BFileToolSchema
def _run(
self,
action: FileAction,
path: str,
content: str | None = None,
binary: bool = False,
depth: int = 1,
) -> Any:
sandbox, should_kill = self._acquire_sandbox()
try:
if action == "read":
return self._read(sandbox, path, binary=binary)
if action == "write":
return self._write(sandbox, path, content or "", binary=binary)
if action == "append":
return self._append(sandbox, path, content or "", binary=binary)
if action == "list":
return self._list(sandbox, path, depth=depth)
if action == "delete":
sandbox.files.remove(path)
return {"status": "deleted", "path": path}
if action == "mkdir":
created = sandbox.files.make_dir(path)
return {"status": "created", "path": path, "created": bool(created)}
if action == "info":
return self._info(sandbox, path)
if action == "exists":
return {"path": path, "exists": bool(sandbox.files.exists(path))}
raise ValueError(f"Unknown action: {action}")
finally:
self._release_sandbox(sandbox, should_kill)
def _read(self, sandbox: Any, path: str, *, binary: bool) -> dict[str, Any]:
if binary:
data: bytes = sandbox.files.read(path, format="bytes")
return {
"path": path,
"encoding": "base64",
"content": base64.b64encode(data).decode("ascii"),
}
try:
content: str = sandbox.files.read(path)
return {"path": path, "encoding": "utf-8", "content": content}
except UnicodeDecodeError:
data = sandbox.files.read(path, format="bytes")
return {
"path": path,
"encoding": "base64",
"content": base64.b64encode(data).decode("ascii"),
"note": "File was not valid utf-8; returned as base64.",
}
def _write(
self, sandbox: Any, path: str, content: str, *, binary: bool
) -> dict[str, Any]:
payload: str | bytes = base64.b64decode(content) if binary else content
self._ensure_parent_dir(sandbox, path)
sandbox.files.write(path, payload)
size = (
len(payload)
if isinstance(payload, (bytes, bytearray))
else len(payload.encode("utf-8"))
)
return {"status": "written", "path": path, "bytes": size}
def _append(
self, sandbox: Any, path: str, content: str, *, binary: bool
) -> dict[str, Any]:
chunk: bytes = base64.b64decode(content) if binary else content.encode("utf-8")
self._ensure_parent_dir(sandbox, path)
try:
existing: bytes = sandbox.files.read(path, format="bytes")
except Exception:
existing = b""
payload = existing + chunk
sandbox.files.write(path, payload)
return {
"status": "appended",
"path": path,
"appended_bytes": len(chunk),
"total_bytes": len(payload),
}
@staticmethod
def _ensure_parent_dir(sandbox: Any, path: str) -> None:
parent = posixpath.dirname(path)
if not parent or parent in ("/", "."):
return
try:
sandbox.files.make_dir(parent)
except Exception:
logger.debug(
"Best-effort parent-directory create failed for %s; "
"assuming it already exists and proceeding with the write.",
parent,
exc_info=True,
)
def _list(self, sandbox: Any, path: str, *, depth: int) -> dict[str, Any]:
entries = sandbox.files.list(path, depth=depth)
return {
"path": path,
"entries": [self._entry_to_dict(e) for e in entries],
}
def _info(self, sandbox: Any, path: str) -> dict[str, Any]:
return self._entry_to_dict(sandbox.files.get_info(path))
@staticmethod
def _entry_to_dict(entry: Any) -> dict[str, Any]:
fields = (
"name",
"path",
"type",
"size",
"mode",
"permissions",
"owner",
"group",
"modified_time",
"symlink_target",
)
result: dict[str, Any] = {}
for field in fields:
value = getattr(entry, field, None)
if value is not None and field == "modified_time":
result[field] = (
value.isoformat() if hasattr(value, "isoformat") else str(value)
)
else:
result[field] = value
return result

View File

@@ -0,0 +1,133 @@
from __future__ import annotations
from builtins import type as type_
from typing import Any, ClassVar
from pydantic import BaseModel, Field
from crewai_tools.tools.e2b_sandbox_tool.e2b_base_tool import E2BBaseTool
class E2BPythonToolSchema(BaseModel):
code: str = Field(
...,
description="Python source to execute inside the sandbox.",
)
language: str | None = Field(
default=None,
description=(
"Override the execution language (e.g. 'python', 'r', 'javascript'). "
"Defaults to Python when omitted."
),
)
envs: dict[str, str] | None = Field(
default=None,
description="Optional environment variables for the run.",
)
timeout: float | None = Field(
default=None,
description="Maximum seconds to wait for the code to finish.",
)
class E2BPythonTool(E2BBaseTool):
"""Run Python code inside an E2B code interpreter sandbox.
Uses `e2b_code_interpreter`, which runs cells in a persistent Jupyter-style
kernel so state (imports, variables) carries across calls when
`persistent=True`.
"""
name: str = "E2B Sandbox Python"
description: str = (
"Execute a block of Python code inside an E2B code interpreter sandbox "
"and return captured stdout, stderr, the final expression value, and "
"any rich results (charts, dataframes). Use this for data processing, "
"quick scripts, or analysis that should run in an isolated environment."
)
args_schema: type_[BaseModel] = E2BPythonToolSchema
package_dependencies: list[str] = Field(
default_factory=lambda: ["e2b_code_interpreter"],
)
_ci_cache: ClassVar[dict[str, Any]] = {}
@classmethod
def _import_sandbox_class(cls) -> Any:
cached = cls._ci_cache.get("Sandbox")
if cached is not None:
return cached
try:
from e2b_code_interpreter import Sandbox # type: ignore[import-untyped]
except ImportError as exc:
raise ImportError(
"The 'e2b_code_interpreter' package is required for the E2B "
"Python tool. Install it with: "
"uv add e2b-code-interpreter (or) "
"pip install e2b-code-interpreter"
) from exc
cls._ci_cache["Sandbox"] = Sandbox
return Sandbox
def _run(
self,
code: str,
language: str | None = None,
envs: dict[str, str] | None = None,
timeout: float | None = None,
) -> Any:
sandbox, should_kill = self._acquire_sandbox()
try:
run_kwargs: dict[str, Any] = {}
if language is not None:
run_kwargs["language"] = language
if envs is not None:
run_kwargs["envs"] = envs
if timeout is not None:
run_kwargs["timeout"] = timeout
execution = sandbox.run_code(code, **run_kwargs)
return self._serialize_execution(execution)
finally:
self._release_sandbox(sandbox, should_kill)
@staticmethod
def _serialize_execution(execution: Any) -> dict[str, Any]:
logs = getattr(execution, "logs", None)
error = getattr(execution, "error", None)
results = getattr(execution, "results", None) or []
return {
"text": getattr(execution, "text", None),
"stdout": list(getattr(logs, "stdout", []) or []) if logs else [],
"stderr": list(getattr(logs, "stderr", []) or []) if logs else [],
"error": (
{
"name": getattr(error, "name", None),
"value": getattr(error, "value", None),
"traceback": getattr(error, "traceback", None),
}
if error
else None
),
"results": [E2BPythonTool._serialize_result(r) for r in results],
"execution_count": getattr(execution, "execution_count", None),
}
@staticmethod
def _serialize_result(result: Any) -> dict[str, Any]:
fields = (
"text",
"html",
"markdown",
"svg",
"png",
"jpeg",
"pdf",
"latex",
"json",
"javascript",
"data",
"is_main_result",
"extra",
)
return {field: getattr(result, field, None) for field in fields}

View File

@@ -8734,6 +8734,668 @@
"type": "object"
}
},
{
"description": "Execute a shell command inside an E2B sandbox and return the exit code, stdout, and stderr. Use this to run builds, package installs, git operations, or any one-off shell command.",
"env_vars": [
{
"default": null,
"description": "API key for E2B sandbox service",
"name": "E2B_API_KEY",
"required": false
},
{
"default": null,
"description": "E2B API domain (optional)",
"name": "E2B_DOMAIN",
"required": false
}
],
"humanized_name": "E2B Sandbox Exec",
"init_params_schema": {
"$defs": {
"EnvVar": {
"properties": {
"default": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Default"
},
"description": {
"title": "Description",
"type": "string"
},
"name": {
"title": "Name",
"type": "string"
},
"required": {
"default": true,
"title": "Required",
"type": "boolean"
}
},
"required": [
"name",
"description"
],
"title": "EnvVar",
"type": "object"
}
},
"description": "Run a shell command inside an E2B sandbox.",
"properties": {
"api_key": {
"anyOf": [
{
"format": "password",
"type": "string",
"writeOnly": true
},
{
"type": "null"
}
],
"description": "E2B API key. Falls back to E2B_API_KEY env var.",
"required": false,
"title": "Api Key"
},
"domain": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "E2B API domain override. Falls back to E2B_DOMAIN env var.",
"required": false,
"title": "Domain"
},
"envs": {
"anyOf": [
{
"additionalProperties": {
"type": "string"
},
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "Environment variables to set inside the sandbox at create time.",
"title": "Envs"
},
"metadata": {
"anyOf": [
{
"additionalProperties": {
"type": "string"
},
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "Metadata key-value pairs to attach to the sandbox at create time.",
"title": "Metadata"
},
"persistent": {
"default": false,
"description": "If True, reuse one sandbox across all calls to this tool instance and kill it at process exit. Default False creates and kills a fresh sandbox per call.",
"title": "Persistent",
"type": "boolean"
},
"sandbox_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Attach to an existing sandbox by id instead of creating a new one. The tool will never kill a sandbox it did not create.",
"title": "Sandbox Id"
},
"sandbox_timeout": {
"default": 300,
"description": "Idle timeout in seconds after which E2B auto-kills the sandbox. Applied at create time and when attaching via sandbox_id.",
"title": "Sandbox Timeout",
"type": "integer"
},
"template": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional template/snapshot name or id to create the sandbox from. Defaults to E2B's base template when omitted.",
"title": "Template"
}
},
"required": [],
"title": "E2BExecTool",
"type": "object"
},
"name": "E2BExecTool",
"package_dependencies": [
"e2b"
],
"run_params_schema": {
"properties": {
"command": {
"description": "Shell command to execute in the sandbox.",
"title": "Command",
"type": "string"
},
"cwd": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Working directory to run the command in. Defaults to the sandbox home dir.",
"title": "Cwd"
},
"envs": {
"anyOf": [
{
"additionalProperties": {
"type": "string"
},
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional environment variables to set for this command.",
"title": "Envs"
},
"timeout": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"description": "Maximum seconds to wait for the command to finish.",
"title": "Timeout"
}
},
"required": [
"command"
],
"title": "E2BExecToolSchema",
"type": "object"
}
},
{
"description": "Perform filesystem operations inside an E2B sandbox: read a file, write content to a path, append content to an existing file, list a directory, delete a path, make a directory, fetch file metadata, or check whether a path exists. For files larger than a few KB, create the file with action='write' and empty content, then send the body via multiple 'append' calls of ~4KB each to stay within tool-call payload limits.",
"env_vars": [
{
"default": null,
"description": "API key for E2B sandbox service",
"name": "E2B_API_KEY",
"required": false
},
{
"default": null,
"description": "E2B API domain (optional)",
"name": "E2B_DOMAIN",
"required": false
}
],
"humanized_name": "E2B Sandbox Files",
"init_params_schema": {
"$defs": {
"EnvVar": {
"properties": {
"default": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Default"
},
"description": {
"title": "Description",
"type": "string"
},
"name": {
"title": "Name",
"type": "string"
},
"required": {
"default": true,
"title": "Required",
"type": "boolean"
}
},
"required": [
"name",
"description"
],
"title": "EnvVar",
"type": "object"
}
},
"description": "Read, write, and manage files inside an E2B sandbox.\n\nNotes:\n - Most useful with `persistent=True` or an explicit `sandbox_id`. With\n the default ephemeral mode, files disappear when this tool call\n finishes.",
"properties": {
"api_key": {
"anyOf": [
{
"format": "password",
"type": "string",
"writeOnly": true
},
{
"type": "null"
}
],
"description": "E2B API key. Falls back to E2B_API_KEY env var.",
"required": false,
"title": "Api Key"
},
"domain": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "E2B API domain override. Falls back to E2B_DOMAIN env var.",
"required": false,
"title": "Domain"
},
"envs": {
"anyOf": [
{
"additionalProperties": {
"type": "string"
},
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "Environment variables to set inside the sandbox at create time.",
"title": "Envs"
},
"metadata": {
"anyOf": [
{
"additionalProperties": {
"type": "string"
},
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "Metadata key-value pairs to attach to the sandbox at create time.",
"title": "Metadata"
},
"persistent": {
"default": false,
"description": "If True, reuse one sandbox across all calls to this tool instance and kill it at process exit. Default False creates and kills a fresh sandbox per call.",
"title": "Persistent",
"type": "boolean"
},
"sandbox_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Attach to an existing sandbox by id instead of creating a new one. The tool will never kill a sandbox it did not create.",
"title": "Sandbox Id"
},
"sandbox_timeout": {
"default": 300,
"description": "Idle timeout in seconds after which E2B auto-kills the sandbox. Applied at create time and when attaching via sandbox_id.",
"title": "Sandbox Timeout",
"type": "integer"
},
"template": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional template/snapshot name or id to create the sandbox from. Defaults to E2B's base template when omitted.",
"title": "Template"
}
},
"required": [],
"title": "E2BFileTool",
"type": "object"
},
"name": "E2BFileTool",
"package_dependencies": [
"e2b"
],
"run_params_schema": {
"properties": {
"action": {
"description": "The filesystem action to perform: 'read' (returns file contents), 'write' (create or replace a file with content), 'append' (append content to an existing file \u2014 use this for writing large files in chunks to avoid hitting tool-call size limits), 'list' (lists a directory), 'delete' (removes a file/dir), 'mkdir' (creates a directory), 'info' (returns file metadata), 'exists' (returns a boolean for whether the path exists).",
"enum": [
"read",
"write",
"append",
"list",
"delete",
"mkdir",
"info",
"exists"
],
"title": "Action",
"type": "string"
},
"binary": {
"default": false,
"description": "For 'write'/'append': treat content as base64 and upload raw bytes. For 'read': return contents as base64 instead of decoded utf-8.",
"title": "Binary",
"type": "boolean"
},
"content": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Content to write or append. If omitted for 'write', an empty file is created. For files larger than a few KB, prefer one 'write' with empty content followed by multiple 'append' calls of ~4KB each to stay within tool-call payload limits.",
"title": "Content"
},
"depth": {
"default": 1,
"description": "For action='list': how many levels deep to recurse (default 1).",
"title": "Depth",
"type": "integer"
},
"path": {
"description": "Absolute path inside the sandbox.",
"title": "Path",
"type": "string"
}
},
"required": [
"action",
"path"
],
"title": "E2BFileToolSchema",
"type": "object"
}
},
{
"description": "Execute a block of Python code inside an E2B code interpreter sandbox and return captured stdout, stderr, the final expression value, and any rich results (charts, dataframes). Use this for data processing, quick scripts, or analysis that should run in an isolated environment.",
"env_vars": [
{
"default": null,
"description": "API key for E2B sandbox service",
"name": "E2B_API_KEY",
"required": false
},
{
"default": null,
"description": "E2B API domain (optional)",
"name": "E2B_DOMAIN",
"required": false
}
],
"humanized_name": "E2B Sandbox Python",
"init_params_schema": {
"$defs": {
"EnvVar": {
"properties": {
"default": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Default"
},
"description": {
"title": "Description",
"type": "string"
},
"name": {
"title": "Name",
"type": "string"
},
"required": {
"default": true,
"title": "Required",
"type": "boolean"
}
},
"required": [
"name",
"description"
],
"title": "EnvVar",
"type": "object"
}
},
"description": "Run Python code inside an E2B code interpreter sandbox.\n\nUses `e2b_code_interpreter`, which runs cells in a persistent Jupyter-style\nkernel so state (imports, variables) carries across calls when\n`persistent=True`.",
"properties": {
"api_key": {
"anyOf": [
{
"format": "password",
"type": "string",
"writeOnly": true
},
{
"type": "null"
}
],
"description": "E2B API key. Falls back to E2B_API_KEY env var.",
"required": false,
"title": "Api Key"
},
"domain": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "E2B API domain override. Falls back to E2B_DOMAIN env var.",
"required": false,
"title": "Domain"
},
"envs": {
"anyOf": [
{
"additionalProperties": {
"type": "string"
},
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "Environment variables to set inside the sandbox at create time.",
"title": "Envs"
},
"metadata": {
"anyOf": [
{
"additionalProperties": {
"type": "string"
},
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "Metadata key-value pairs to attach to the sandbox at create time.",
"title": "Metadata"
},
"persistent": {
"default": false,
"description": "If True, reuse one sandbox across all calls to this tool instance and kill it at process exit. Default False creates and kills a fresh sandbox per call.",
"title": "Persistent",
"type": "boolean"
},
"sandbox_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Attach to an existing sandbox by id instead of creating a new one. The tool will never kill a sandbox it did not create.",
"title": "Sandbox Id"
},
"sandbox_timeout": {
"default": 300,
"description": "Idle timeout in seconds after which E2B auto-kills the sandbox. Applied at create time and when attaching via sandbox_id.",
"title": "Sandbox Timeout",
"type": "integer"
},
"template": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional template/snapshot name or id to create the sandbox from. Defaults to E2B's base template when omitted.",
"title": "Template"
}
},
"required": [],
"title": "E2BPythonTool",
"type": "object"
},
"name": "E2BPythonTool",
"package_dependencies": [
"e2b_code_interpreter"
],
"run_params_schema": {
"properties": {
"code": {
"description": "Python source to execute inside the sandbox.",
"title": "Code",
"type": "string"
},
"envs": {
"anyOf": [
{
"additionalProperties": {
"type": "string"
},
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional environment variables for the run.",
"title": "Envs"
},
"language": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Override the execution language (e.g. 'python', 'r', 'javascript'). Defaults to Python when omitted.",
"title": "Language"
},
"timeout": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"description": "Maximum seconds to wait for the code to finish.",
"title": "Timeout"
}
},
"required": [
"code"
],
"title": "E2BPythonToolSchema",
"type": "object"
}
},
{
"description": "Search the internet using Exa",
"env_vars": [

View File

@@ -55,7 +55,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
[project.optional-dependencies]
tools = [
"crewai-tools==1.14.3a2",
"crewai-tools==1.14.3",
]
embeddings = [
"tiktoken~=0.8.0"
@@ -94,6 +94,7 @@ google-genai = [
]
azure-ai-inference = [
"azure-ai-inference~=1.0.0b9",
"azure-identity>=1.17.0,<2",
]
anthropic = [
"anthropic~=0.73.0",

View File

@@ -48,7 +48,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
_suppress_pydantic_deprecation_warnings()
__version__ = "1.14.3a2"
__version__ = "1.14.3"
_LAZY_IMPORTS: dict[str, tuple[str, str]] = {
"Memory": ("crewai.memory.unified_memory", "Memory"),
@@ -94,10 +94,16 @@ try:
}
from crewai.tools.base_tool import BaseTool as _BaseTool
from crewai.tools.flow_tool import (
FlowTool as _FlowTool,
create_flow_tools as _create_flow_tools,
)
from crewai.tools.structured_tool import CrewStructuredTool as _CrewStructuredTool
_base_namespace["BaseTool"] = _BaseTool
_base_namespace["CrewStructuredTool"] = _CrewStructuredTool
_base_namespace["FlowTool"] = _FlowTool
_base_namespace["create_flow_tools"] = _create_flow_tools # type: ignore[assignment]
try:
from crewai.a2a.config import (

View File

@@ -78,14 +78,14 @@ from crewai.knowledge.knowledge import Knowledge
from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
from crewai.lite_agent_output import LiteAgentOutput
from crewai.llms.base_llm import BaseLLM
from crewai.mcp import MCPServerConfig
from crewai.mcp.tool_resolver import MCPToolResolver
from crewai.mcp.config import MCPServerConfig
from crewai.rag.embeddings.types import EmbedderConfig
from crewai.security.fingerprint import Fingerprint
from crewai.skills.loader import activate_skill, discover_skills
from crewai.skills.models import INSTRUCTIONS, Skill as SkillModel
from crewai.state.checkpoint_config import CheckpointConfig, apply_checkpoint
from crewai.tools.agent_tools.agent_tools import AgentTools
from crewai.tools.flow_tool import create_flow_tools
from crewai.types.callback import SerializableCallable
from crewai.utilities.agent_utils import (
get_tool_names,
@@ -119,6 +119,7 @@ if TYPE_CHECKING:
from crewai.a2a.config import A2AClientConfig, A2AConfig, A2AServerConfig
from crewai.agents.agent_builder.base_agent import PlatformAppOrAction
from crewai.mcp.tool_resolver import MCPToolResolver
from crewai.task import Task
from crewai.tools.base_tool import BaseTool
from crewai.tools.structured_tool import CrewStructuredTool
@@ -305,6 +306,10 @@ class Agent(BaseAgent):
Can be a single A2AConfig/A2AClientConfig/A2AServerConfig, or a list of any number of A2AConfig/A2AClientConfig with a single A2AServerConfig.
""",
)
flows: list[Any] | None = Field(
default=None,
description="Flow classes that the agent can invoke as tools. Each entry is a Flow subclass (not an instance).",
)
agent_executor: CrewAgentExecutor | AgentExecutor | None = Field(
default=None, description="An instance of the CrewAgentExecutor class."
)
@@ -347,6 +352,7 @@ class Agent(BaseAgent):
)
self.set_skills()
self._set_flow_tools()
if self.reasoning and self.planning_config is None:
warnings.warn(
@@ -394,15 +400,17 @@ class Agent(BaseAgent):
self,
resolved_crew_skills: list[SkillModel] | None = None,
) -> None:
"""Resolve skill paths and activate skills to INSTRUCTIONS level.
"""Resolve skill paths while preserving explicit disclosure levels.
Path entries trigger discovery and activation. Pre-loaded Skill objects
below INSTRUCTIONS level are activated. Crew-level skills are merged in
with event emission so observability is consistent regardless of origin.
Path entries trigger discovery and activation because directory-based
skills opt into eager loading. Pre-loaded Skill objects keep their
current disclosure level so callers can attach METADATA-only skills and
progressively activate them later. Crew-level skills are merged in with
event emission so observability is consistent regardless of origin.
Args:
resolved_crew_skills: Pre-resolved crew skills (already discovered
and activated). When provided, avoids redundant discovery per agent.
resolved_crew_skills: Pre-resolved crew skills. When provided,
avoids redundant discovery per agent.
"""
from crewai.crew import Crew
@@ -443,8 +451,7 @@ class Agent(BaseAgent):
elif isinstance(item, SkillModel):
if item.name not in seen:
seen.add(item.name)
activated = activate_skill(item, source=self)
if activated is item and item.disclosure_level >= INSTRUCTIONS:
if item.disclosure_level >= INSTRUCTIONS:
crewai_event_bus.emit(
self,
event=SkillActivatedEvent(
@@ -454,10 +461,20 @@ class Agent(BaseAgent):
disclosure_level=item.disclosure_level,
),
)
resolved.append(activated)
resolved.append(item)
self.skills = resolved if resolved else None
def _set_flow_tools(self) -> None:
"""Convert Flow classes in ``self.flows`` to tools and merge them."""
if not self.flows:
return
flow_tools = create_flow_tools(self.flows)
if flow_tools:
if self.tools is None:
self.tools = []
self.tools.extend(flow_tools)
def _is_any_available_memory(self) -> bool:
"""Check if unified memory is available (agent or crew)."""
if getattr(self, "memory", None):
@@ -1120,6 +1137,8 @@ class Agent(BaseAgent):
Delegates to :class:`~crewai.mcp.tool_resolver.MCPToolResolver`.
"""
self._cleanup_mcp_clients()
from crewai.mcp.tool_resolver import MCPToolResolver
self._mcp_resolver = MCPToolResolver(agent=self, logger=self._logger)
return self._mcp_resolver.resolve(mcps)

View File

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
authors = [{ name = "Your Name", email = "you@example.com" }]
requires-python = ">=3.10,<3.14"
dependencies = [
"crewai[tools]==1.14.3a2"
"crewai[tools]==1.14.3"
]
[project.scripts]

View File

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
authors = [{ name = "Your Name", email = "you@example.com" }]
requires-python = ">=3.10,<3.14"
dependencies = [
"crewai[tools]==1.14.3a2"
"crewai[tools]==1.14.3"
]
[project.scripts]

View File

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
readme = "README.md"
requires-python = ">=3.10,<3.14"
dependencies = [
"crewai[tools]==1.14.3a2"
"crewai[tools]==1.14.3"
]
[tool.crewai]

View File

@@ -6,111 +6,20 @@ This module provides the event infrastructure that allows users to:
- Build custom logging and analytics
- Extend CrewAI with custom event handlers
- Declare handler dependencies for ordered execution
Event type classes are lazy-loaded on first access to avoid importing
~12 Pydantic model modules (and their transitive deps) at package init time.
"""
from __future__ import annotations
import importlib
from typing import TYPE_CHECKING, Any
from crewai.events.base_event_listener import BaseEventListener
from crewai.events.depends import Depends
from crewai.events.event_bus import crewai_event_bus
from crewai.events.handler_graph import CircularDependencyError
from crewai.events.types.crew_events import (
CrewKickoffCompletedEvent,
CrewKickoffFailedEvent,
CrewKickoffStartedEvent,
CrewTestCompletedEvent,
CrewTestFailedEvent,
CrewTestResultEvent,
CrewTestStartedEvent,
CrewTrainCompletedEvent,
CrewTrainFailedEvent,
CrewTrainStartedEvent,
)
from crewai.events.types.flow_events import (
FlowCreatedEvent,
FlowEvent,
FlowFinishedEvent,
FlowPlotEvent,
FlowStartedEvent,
HumanFeedbackReceivedEvent,
HumanFeedbackRequestedEvent,
MethodExecutionFailedEvent,
MethodExecutionFinishedEvent,
MethodExecutionStartedEvent,
)
from crewai.events.types.knowledge_events import (
KnowledgeQueryCompletedEvent,
KnowledgeQueryFailedEvent,
KnowledgeQueryStartedEvent,
KnowledgeRetrievalCompletedEvent,
KnowledgeRetrievalStartedEvent,
KnowledgeSearchQueryFailedEvent,
)
from crewai.events.types.llm_events import (
LLMCallCompletedEvent,
LLMCallFailedEvent,
LLMCallStartedEvent,
LLMStreamChunkEvent,
)
from crewai.events.types.llm_guardrail_events import (
LLMGuardrailCompletedEvent,
LLMGuardrailStartedEvent,
)
from crewai.events.types.logging_events import (
AgentLogsExecutionEvent,
AgentLogsStartedEvent,
)
from crewai.events.types.mcp_events import (
MCPConfigFetchFailedEvent,
MCPConnectionCompletedEvent,
MCPConnectionFailedEvent,
MCPConnectionStartedEvent,
MCPToolExecutionCompletedEvent,
MCPToolExecutionFailedEvent,
MCPToolExecutionStartedEvent,
)
from crewai.events.types.memory_events import (
MemoryQueryCompletedEvent,
MemoryQueryFailedEvent,
MemoryQueryStartedEvent,
MemoryRetrievalCompletedEvent,
MemoryRetrievalFailedEvent,
MemoryRetrievalStartedEvent,
MemorySaveCompletedEvent,
MemorySaveFailedEvent,
MemorySaveStartedEvent,
)
from crewai.events.types.reasoning_events import (
AgentReasoningCompletedEvent,
AgentReasoningFailedEvent,
AgentReasoningStartedEvent,
ReasoningEvent,
)
from crewai.events.types.skill_events import (
SkillActivatedEvent,
SkillDiscoveryCompletedEvent,
SkillDiscoveryStartedEvent,
SkillEvent,
SkillLoadFailedEvent,
SkillLoadedEvent,
)
from crewai.events.types.task_events import (
TaskCompletedEvent,
TaskEvaluationEvent,
TaskFailedEvent,
TaskStartedEvent,
)
from crewai.events.types.tool_usage_events import (
ToolExecutionErrorEvent,
ToolSelectionErrorEvent,
ToolUsageErrorEvent,
ToolUsageEvent,
ToolUsageFinishedEvent,
ToolUsageStartedEvent,
ToolValidateInputErrorEvent,
)
if TYPE_CHECKING:
@@ -125,6 +34,250 @@ if TYPE_CHECKING:
LiteAgentExecutionErrorEvent,
LiteAgentExecutionStartedEvent,
)
from crewai.events.types.checkpoint_events import (
CheckpointBaseEvent,
CheckpointCompletedEvent,
CheckpointFailedEvent,
CheckpointForkBaseEvent,
CheckpointForkCompletedEvent,
CheckpointForkStartedEvent,
CheckpointPrunedEvent,
CheckpointRestoreBaseEvent,
CheckpointRestoreCompletedEvent,
CheckpointRestoreFailedEvent,
CheckpointRestoreStartedEvent,
CheckpointStartedEvent,
)
from crewai.events.types.crew_events import (
CrewKickoffCompletedEvent,
CrewKickoffFailedEvent,
CrewKickoffStartedEvent,
CrewTestCompletedEvent,
CrewTestFailedEvent,
CrewTestResultEvent,
CrewTestStartedEvent,
CrewTrainCompletedEvent,
CrewTrainFailedEvent,
CrewTrainStartedEvent,
)
from crewai.events.types.flow_events import (
FlowCreatedEvent,
FlowEvent,
FlowFinishedEvent,
FlowPlotEvent,
FlowStartedEvent,
HumanFeedbackReceivedEvent,
HumanFeedbackRequestedEvent,
MethodExecutionFailedEvent,
MethodExecutionFinishedEvent,
MethodExecutionStartedEvent,
)
from crewai.events.types.knowledge_events import (
KnowledgeQueryCompletedEvent,
KnowledgeQueryFailedEvent,
KnowledgeQueryStartedEvent,
KnowledgeRetrievalCompletedEvent,
KnowledgeRetrievalStartedEvent,
KnowledgeSearchQueryFailedEvent,
)
from crewai.events.types.llm_events import (
LLMCallCompletedEvent,
LLMCallFailedEvent,
LLMCallStartedEvent,
LLMStreamChunkEvent,
)
from crewai.events.types.llm_guardrail_events import (
LLMGuardrailCompletedEvent,
LLMGuardrailStartedEvent,
)
from crewai.events.types.logging_events import (
AgentLogsExecutionEvent,
AgentLogsStartedEvent,
)
from crewai.events.types.mcp_events import (
MCPConfigFetchFailedEvent,
MCPConnectionCompletedEvent,
MCPConnectionFailedEvent,
MCPConnectionStartedEvent,
MCPToolExecutionCompletedEvent,
MCPToolExecutionFailedEvent,
MCPToolExecutionStartedEvent,
)
from crewai.events.types.memory_events import (
MemoryQueryCompletedEvent,
MemoryQueryFailedEvent,
MemoryQueryStartedEvent,
MemoryRetrievalCompletedEvent,
MemoryRetrievalFailedEvent,
MemoryRetrievalStartedEvent,
MemorySaveCompletedEvent,
MemorySaveFailedEvent,
MemorySaveStartedEvent,
)
from crewai.events.types.reasoning_events import (
AgentReasoningCompletedEvent,
AgentReasoningFailedEvent,
AgentReasoningStartedEvent,
ReasoningEvent,
)
from crewai.events.types.skill_events import (
SkillActivatedEvent,
SkillDiscoveryCompletedEvent,
SkillDiscoveryStartedEvent,
SkillEvent,
SkillLoadFailedEvent,
SkillLoadedEvent,
)
from crewai.events.types.task_events import (
TaskCompletedEvent,
TaskEvaluationEvent,
TaskFailedEvent,
TaskStartedEvent,
)
from crewai.events.types.tool_usage_events import (
ToolExecutionErrorEvent,
ToolSelectionErrorEvent,
ToolUsageErrorEvent,
ToolUsageEvent,
ToolUsageFinishedEvent,
ToolUsageStartedEvent,
ToolValidateInputErrorEvent,
)
# Map every event class name → its module path for lazy loading
_LAZY_EVENT_MAPPING: dict[str, str] = {
# agent_events
"AgentEvaluationCompletedEvent": "crewai.events.types.agent_events",
"AgentEvaluationFailedEvent": "crewai.events.types.agent_events",
"AgentEvaluationStartedEvent": "crewai.events.types.agent_events",
"AgentExecutionCompletedEvent": "crewai.events.types.agent_events",
"AgentExecutionErrorEvent": "crewai.events.types.agent_events",
"AgentExecutionStartedEvent": "crewai.events.types.agent_events",
"LiteAgentExecutionCompletedEvent": "crewai.events.types.agent_events",
"LiteAgentExecutionErrorEvent": "crewai.events.types.agent_events",
"LiteAgentExecutionStartedEvent": "crewai.events.types.agent_events",
# checkpoint_events
"CheckpointBaseEvent": "crewai.events.types.checkpoint_events",
"CheckpointCompletedEvent": "crewai.events.types.checkpoint_events",
"CheckpointFailedEvent": "crewai.events.types.checkpoint_events",
"CheckpointForkBaseEvent": "crewai.events.types.checkpoint_events",
"CheckpointForkCompletedEvent": "crewai.events.types.checkpoint_events",
"CheckpointForkStartedEvent": "crewai.events.types.checkpoint_events",
"CheckpointPrunedEvent": "crewai.events.types.checkpoint_events",
"CheckpointRestoreBaseEvent": "crewai.events.types.checkpoint_events",
"CheckpointRestoreCompletedEvent": "crewai.events.types.checkpoint_events",
"CheckpointRestoreFailedEvent": "crewai.events.types.checkpoint_events",
"CheckpointRestoreStartedEvent": "crewai.events.types.checkpoint_events",
"CheckpointStartedEvent": "crewai.events.types.checkpoint_events",
# crew_events
"CrewKickoffCompletedEvent": "crewai.events.types.crew_events",
"CrewKickoffFailedEvent": "crewai.events.types.crew_events",
"CrewKickoffStartedEvent": "crewai.events.types.crew_events",
"CrewTestCompletedEvent": "crewai.events.types.crew_events",
"CrewTestFailedEvent": "crewai.events.types.crew_events",
"CrewTestResultEvent": "crewai.events.types.crew_events",
"CrewTestStartedEvent": "crewai.events.types.crew_events",
"CrewTrainCompletedEvent": "crewai.events.types.crew_events",
"CrewTrainFailedEvent": "crewai.events.types.crew_events",
"CrewTrainStartedEvent": "crewai.events.types.crew_events",
# flow_events
"FlowCreatedEvent": "crewai.events.types.flow_events",
"FlowEvent": "crewai.events.types.flow_events",
"FlowFinishedEvent": "crewai.events.types.flow_events",
"FlowPlotEvent": "crewai.events.types.flow_events",
"FlowStartedEvent": "crewai.events.types.flow_events",
"HumanFeedbackReceivedEvent": "crewai.events.types.flow_events",
"HumanFeedbackRequestedEvent": "crewai.events.types.flow_events",
"MethodExecutionFailedEvent": "crewai.events.types.flow_events",
"MethodExecutionFinishedEvent": "crewai.events.types.flow_events",
"MethodExecutionStartedEvent": "crewai.events.types.flow_events",
# knowledge_events
"KnowledgeQueryCompletedEvent": "crewai.events.types.knowledge_events",
"KnowledgeQueryFailedEvent": "crewai.events.types.knowledge_events",
"KnowledgeQueryStartedEvent": "crewai.events.types.knowledge_events",
"KnowledgeRetrievalCompletedEvent": "crewai.events.types.knowledge_events",
"KnowledgeRetrievalStartedEvent": "crewai.events.types.knowledge_events",
"KnowledgeSearchQueryFailedEvent": "crewai.events.types.knowledge_events",
# llm_events
"LLMCallCompletedEvent": "crewai.events.types.llm_events",
"LLMCallFailedEvent": "crewai.events.types.llm_events",
"LLMCallStartedEvent": "crewai.events.types.llm_events",
"LLMStreamChunkEvent": "crewai.events.types.llm_events",
# llm_guardrail_events
"LLMGuardrailCompletedEvent": "crewai.events.types.llm_guardrail_events",
"LLMGuardrailStartedEvent": "crewai.events.types.llm_guardrail_events",
# logging_events
"AgentLogsExecutionEvent": "crewai.events.types.logging_events",
"AgentLogsStartedEvent": "crewai.events.types.logging_events",
# mcp_events
"MCPConfigFetchFailedEvent": "crewai.events.types.mcp_events",
"MCPConnectionCompletedEvent": "crewai.events.types.mcp_events",
"MCPConnectionFailedEvent": "crewai.events.types.mcp_events",
"MCPConnectionStartedEvent": "crewai.events.types.mcp_events",
"MCPToolExecutionCompletedEvent": "crewai.events.types.mcp_events",
"MCPToolExecutionFailedEvent": "crewai.events.types.mcp_events",
"MCPToolExecutionStartedEvent": "crewai.events.types.mcp_events",
# memory_events
"MemoryQueryCompletedEvent": "crewai.events.types.memory_events",
"MemoryQueryFailedEvent": "crewai.events.types.memory_events",
"MemoryQueryStartedEvent": "crewai.events.types.memory_events",
"MemoryRetrievalCompletedEvent": "crewai.events.types.memory_events",
"MemoryRetrievalFailedEvent": "crewai.events.types.memory_events",
"MemoryRetrievalStartedEvent": "crewai.events.types.memory_events",
"MemorySaveCompletedEvent": "crewai.events.types.memory_events",
"MemorySaveFailedEvent": "crewai.events.types.memory_events",
"MemorySaveStartedEvent": "crewai.events.types.memory_events",
# reasoning_events
"AgentReasoningCompletedEvent": "crewai.events.types.reasoning_events",
"AgentReasoningFailedEvent": "crewai.events.types.reasoning_events",
"AgentReasoningStartedEvent": "crewai.events.types.reasoning_events",
"ReasoningEvent": "crewai.events.types.reasoning_events",
# skill_events
"SkillActivatedEvent": "crewai.events.types.skill_events",
"SkillDiscoveryCompletedEvent": "crewai.events.types.skill_events",
"SkillDiscoveryStartedEvent": "crewai.events.types.skill_events",
"SkillEvent": "crewai.events.types.skill_events",
"SkillLoadFailedEvent": "crewai.events.types.skill_events",
"SkillLoadedEvent": "crewai.events.types.skill_events",
# task_events
"TaskCompletedEvent": "crewai.events.types.task_events",
"TaskEvaluationEvent": "crewai.events.types.task_events",
"TaskFailedEvent": "crewai.events.types.task_events",
"TaskStartedEvent": "crewai.events.types.task_events",
# tool_usage_events
"ToolExecutionErrorEvent": "crewai.events.types.tool_usage_events",
"ToolSelectionErrorEvent": "crewai.events.types.tool_usage_events",
"ToolUsageErrorEvent": "crewai.events.types.tool_usage_events",
"ToolUsageEvent": "crewai.events.types.tool_usage_events",
"ToolUsageFinishedEvent": "crewai.events.types.tool_usage_events",
"ToolUsageStartedEvent": "crewai.events.types.tool_usage_events",
"ToolValidateInputErrorEvent": "crewai.events.types.tool_usage_events",
}
_extension_exports: dict[str, Any] = {}
def __getattr__(name: str) -> Any:
"""Lazy import for event types and registered extensions."""
if name in _LAZY_EVENT_MAPPING:
module_path = _LAZY_EVENT_MAPPING[name]
module = importlib.import_module(module_path)
val = getattr(module, name)
globals()[name] = val # cache for subsequent access
return val
if name in _extension_exports:
value = _extension_exports[name]
if isinstance(value, str):
module_path, _, attr_name = value.rpartition(".")
if module_path:
module = importlib.import_module(module_path)
return getattr(module, attr_name)
return importlib.import_module(value)
return value
msg = f"module {__name__!r} has no attribute {name!r}"
raise AttributeError(msg)
__all__ = [
@@ -140,6 +293,18 @@ __all__ = [
"AgentReasoningFailedEvent",
"AgentReasoningStartedEvent",
"BaseEventListener",
"CheckpointBaseEvent",
"CheckpointCompletedEvent",
"CheckpointFailedEvent",
"CheckpointForkBaseEvent",
"CheckpointForkCompletedEvent",
"CheckpointForkStartedEvent",
"CheckpointPrunedEvent",
"CheckpointRestoreBaseEvent",
"CheckpointRestoreCompletedEvent",
"CheckpointRestoreFailedEvent",
"CheckpointRestoreStartedEvent",
"CheckpointStartedEvent",
"CircularDependencyError",
"CrewKickoffCompletedEvent",
"CrewKickoffFailedEvent",
@@ -214,42 +379,3 @@ __all__ = [
"_extension_exports",
"crewai_event_bus",
]
_AGENT_EVENT_MAPPING = {
"AgentEvaluationCompletedEvent": "crewai.events.types.agent_events",
"AgentEvaluationFailedEvent": "crewai.events.types.agent_events",
"AgentEvaluationStartedEvent": "crewai.events.types.agent_events",
"AgentExecutionCompletedEvent": "crewai.events.types.agent_events",
"AgentExecutionErrorEvent": "crewai.events.types.agent_events",
"AgentExecutionStartedEvent": "crewai.events.types.agent_events",
"LiteAgentExecutionCompletedEvent": "crewai.events.types.agent_events",
"LiteAgentExecutionErrorEvent": "crewai.events.types.agent_events",
"LiteAgentExecutionStartedEvent": "crewai.events.types.agent_events",
}
_extension_exports: dict[str, Any] = {}
def __getattr__(name: str) -> Any:
"""Lazy import for agent events and registered extensions."""
if name in _AGENT_EVENT_MAPPING:
import importlib
module_path = _AGENT_EVENT_MAPPING[name]
module = importlib.import_module(module_path)
return getattr(module, name)
if name in _extension_exports:
import importlib
value = _extension_exports[name]
if isinstance(value, str):
module_path, _, attr_name = value.rpartition(".")
if module_path:
module = importlib.import_module(module_path)
return getattr(module, attr_name)
return importlib.import_module(value)
return value
msg = f"module {__name__!r} has no attribute {name!r}"
raise AttributeError(msg)

View File

@@ -64,6 +64,22 @@ P = ParamSpec("P")
R = TypeVar("R")
_replaying: contextvars.ContextVar[bool] = contextvars.ContextVar(
"crewai_event_replaying", default=False
)
def is_replaying() -> bool:
"""Return True if the current context is dispatching a replayed event.
Listeners with side effects (checkpoint writes, external API calls that
should not be repeated) should early-return when this is true. Listeners
whose purpose is reconstructing timeline state (trace batch, console
formatter) should ignore the flag and process replayed events normally.
"""
return _replaying.get()
class CrewAIEventsBus:
"""Singleton event bus for handling events in CrewAI.
@@ -261,6 +277,11 @@ class CrewAIEventsBus:
self._runtime_state = state
self._registered_entity_ids = {id(e) for e in state.root}
@property
def runtime_state(self) -> RuntimeState | None:
"""The RuntimeState currently attached to the bus, if any."""
return self._runtime_state
def register_entity(self, entity: Any) -> None:
"""Add an entity to the RuntimeState, creating it if needed.
@@ -568,6 +589,87 @@ class CrewAIEventsBus:
return None
async def _acall_handlers_replaying(
self,
source: Any,
event: BaseEvent,
handlers: AsyncHandlerSet,
) -> None:
"""Call async handlers with the replaying flag set on the loop thread."""
token = _replaying.set(True)
try:
await self._acall_handlers(source, event, handlers)
finally:
_replaying.reset(token)
async def _emit_with_dependencies_replaying(
self, source: Any, event: BaseEvent
) -> None:
"""Dependency-aware dispatch with the replaying flag set."""
token = _replaying.set(True)
try:
await self._emit_with_dependencies(source, event)
finally:
_replaying.reset(token)
def replay(self, source: Any, event: BaseEvent) -> Future[None] | None:
"""Dispatch a previously-recorded event without mutating its fields.
Unlike :meth:`emit`, this does not run ``_prepare_event`` (so stored
event ids and ``emission_sequence`` are preserved) and does not
re-record the event. Listeners can call :func:`is_replaying` to
opt out of side-effectful processing.
Args:
source: The emitting object.
event: The previously-recorded event to dispatch.
Returns:
Future that completes when handlers finish, or None if no handlers.
"""
event_type = type(event)
with self._rwlock.r_locked():
if self._shutting_down:
return None
has_dependencies = event_type in self._handler_dependencies
sync_handlers = self._sync_handlers.get(event_type, frozenset())
async_handlers = self._async_handlers.get(event_type, frozenset())
if not sync_handlers and not async_handlers:
return None
self._ensure_executor_initialized()
self._has_pending_events = True
token = _replaying.set(True)
try:
if has_dependencies:
return self._track_future(
asyncio.run_coroutine_threadsafe(
self._emit_with_dependencies_replaying(source, event),
self._loop,
)
)
if sync_handlers:
ctx = contextvars.copy_context()
sync_future = self._sync_executor.submit(
ctx.run, self._call_handlers, source, event, sync_handlers
)
self._track_future(sync_future)
if not async_handlers:
return sync_future
return self._track_future(
asyncio.run_coroutine_threadsafe(
self._acall_handlers_replaying(source, event, async_handlers),
self._loop,
)
)
finally:
_replaying.reset(token)
def flush(self, timeout: float | None = 30.0) -> bool:
"""Block until all pending event handlers complete.

View File

@@ -30,6 +30,17 @@ from crewai.events.types.agent_events import (
AgentExecutionStartedEvent,
LiteAgentExecutionCompletedEvent,
)
from crewai.events.types.checkpoint_events import (
CheckpointCompletedEvent,
CheckpointFailedEvent,
CheckpointForkCompletedEvent,
CheckpointForkStartedEvent,
CheckpointPrunedEvent,
CheckpointRestoreCompletedEvent,
CheckpointRestoreFailedEvent,
CheckpointRestoreStartedEvent,
CheckpointStartedEvent,
)
from crewai.events.types.crew_events import (
CrewKickoffCompletedEvent,
CrewKickoffFailedEvent,
@@ -183,4 +194,13 @@ EventTypes = (
| MCPToolExecutionCompletedEvent
| MCPToolExecutionFailedEvent
| MCPConfigFetchFailedEvent
| CheckpointStartedEvent
| CheckpointCompletedEvent
| CheckpointFailedEvent
| CheckpointForkStartedEvent
| CheckpointForkCompletedEvent
| CheckpointRestoreStartedEvent
| CheckpointRestoreCompletedEvent
| CheckpointRestoreFailedEvent
| CheckpointPrunedEvent
)

View File

@@ -0,0 +1,97 @@
"""Event family for automatic state checkpointing and forking."""
from typing import Literal
from crewai.events.base_events import BaseEvent
class CheckpointBaseEvent(BaseEvent):
"""Base event for checkpoint lifecycle operations."""
type: str
location: str
provider: str
trigger: str | None = None
branch: str | None = None
parent_id: str | None = None
class CheckpointStartedEvent(CheckpointBaseEvent):
"""Event emitted immediately before a checkpoint is written."""
type: Literal["checkpoint_started"] = "checkpoint_started"
class CheckpointCompletedEvent(CheckpointBaseEvent):
"""Event emitted when a checkpoint has been written successfully."""
type: Literal["checkpoint_completed"] = "checkpoint_completed"
checkpoint_id: str
duration_ms: float
class CheckpointFailedEvent(CheckpointBaseEvent):
"""Event emitted when a checkpoint write fails."""
type: Literal["checkpoint_failed"] = "checkpoint_failed"
error: str
class CheckpointPrunedEvent(CheckpointBaseEvent):
"""Event emitted after pruning old checkpoints from a branch."""
type: Literal["checkpoint_pruned"] = "checkpoint_pruned"
removed_count: int
max_checkpoints: int
class CheckpointForkBaseEvent(BaseEvent):
"""Base event for fork lifecycle operations on a RuntimeState."""
type: str
branch: str
parent_branch: str | None = None
parent_checkpoint_id: str | None = None
class CheckpointForkStartedEvent(CheckpointForkBaseEvent):
"""Event emitted immediately before a fork relabels the branch."""
type: Literal["checkpoint_fork_started"] = "checkpoint_fork_started"
class CheckpointForkCompletedEvent(CheckpointForkBaseEvent):
"""Event emitted after a fork has established the new branch."""
type: Literal["checkpoint_fork_completed"] = "checkpoint_fork_completed"
class CheckpointRestoreBaseEvent(BaseEvent):
"""Base event for checkpoint restore lifecycle operations."""
type: str
location: str
provider: str | None = None
class CheckpointRestoreStartedEvent(CheckpointRestoreBaseEvent):
"""Event emitted immediately before a checkpoint restore begins."""
type: Literal["checkpoint_restore_started"] = "checkpoint_restore_started"
class CheckpointRestoreCompletedEvent(CheckpointRestoreBaseEvent):
"""Event emitted when a checkpoint has been restored successfully."""
type: Literal["checkpoint_restore_completed"] = "checkpoint_restore_completed"
checkpoint_id: str
branch: str | None = None
parent_id: str | None = None
duration_ms: float
class CheckpointRestoreFailedEvent(CheckpointRestoreBaseEvent):
"""Event emitted when a checkpoint restore fails."""
type: Literal["checkpoint_restore_failed"] = "checkpoint_restore_failed"
error: str

View File

@@ -45,6 +45,7 @@ from pydantic import (
BeforeValidator,
ConfigDict,
Field,
PlainSerializer,
PrivateAttr,
SerializeAsAny,
ValidationError,
@@ -58,6 +59,7 @@ from crewai.events.event_bus import crewai_event_bus
from crewai.events.event_context import (
get_current_parent_id,
reset_last_event_id,
restore_event_scope,
triggered_by_scope,
)
from crewai.events.listeners.tracing.trace_listener import (
@@ -157,6 +159,37 @@ def _resolve_persistence(value: Any) -> Any:
return value
_INITIAL_STATE_CLASS_MARKER = "__crewai_pydantic_class_schema__"
def _serialize_initial_state(value: Any) -> Any:
"""Make ``initial_state`` safe for JSON checkpoint serialization.
``BaseModel`` class refs are emitted as their JSON schema under a sentinel
marker key so deserialization can round-trip them back to a class.
``BaseModel`` instances are dumped to JSON (round-trip as plain dicts,
which ``_create_initial_state`` accepts). Bare ``type`` values that are
not ``BaseModel`` subclasses (e.g. ``dict``) are dropped since they
can't be represented in JSON.
"""
if isinstance(value, type):
if issubclass(value, BaseModel):
return {_INITIAL_STATE_CLASS_MARKER: value.model_json_schema()}
return None
if isinstance(value, BaseModel):
return value.model_dump(mode="json")
return value
def _deserialize_initial_state(value: Any) -> Any:
"""Rehydrate a class ref serialized by :func:`_serialize_initial_state`."""
if isinstance(value, dict) and _INITIAL_STATE_CLASS_MARKER in value:
from crewai.utilities.pydantic_schema_utils import create_model_from_schema
return create_model_from_schema(value[_INITIAL_STATE_CLASS_MARKER])
return value
class FlowState(BaseModel):
"""Base model for all flow states, ensuring each state has a unique ID."""
@@ -908,7 +941,11 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
entity_type: Literal["flow"] = "flow"
initial_state: Any = Field(default=None)
initial_state: Annotated[ # type: ignore[type-arg]
type[BaseModel] | type[dict] | dict[str, Any] | BaseModel | None,
BeforeValidator(_deserialize_initial_state),
PlainSerializer(_serialize_initial_state, return_type=Any, when_used="json"),
] = Field(default=None)
name: str | None = Field(default=None)
tracing: bool | None = Field(default=None)
stream: bool = Field(default=False)
@@ -980,13 +1017,18 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
A Flow instance on the new branch. Call kickoff() to run.
"""
flow = cls.from_checkpoint(config)
state = crewai_event_bus._runtime_state
state = crewai_event_bus.runtime_state
if state is None:
raise RuntimeError(
"Cannot fork: no runtime state on the event bus. "
"Ensure from_checkpoint() succeeded before calling fork()."
)
state.fork(branch)
new_id = str(uuid4())
if isinstance(flow._state, dict):
flow._state["id"] = new_id
else:
object.__setattr__(flow._state, "id", new_id)
return flow
checkpoint_completed_methods: set[str] | None = Field(default=None)
@@ -1008,6 +1050,8 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
}
if self.checkpoint_state is not None:
self._restore_state(self.checkpoint_state)
restore_event_scope(())
reset_last_event_id()
_methods: dict[FlowMethodName, FlowMethod[Any, Any]] = PrivateAttr(
default_factory=dict
@@ -1030,6 +1074,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
_human_feedback_method_outputs: dict[str, Any] = PrivateAttr(default_factory=dict)
_input_history: list[InputHistoryEntry] = PrivateAttr(default_factory=list)
_state: Any = PrivateAttr(default=None)
_execution_id: str = PrivateAttr(default_factory=lambda: str(uuid4()))
def __class_getitem__(cls: type[Flow[T]], item: type[T]) -> type[Flow[T]]: # type: ignore[override]
class _FlowGeneric(cls): # type: ignore[valid-type,misc]
@@ -1820,6 +1865,27 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
except (AttributeError, TypeError):
return "" # Safely handle any unexpected attribute access issues
@property
def execution_id(self) -> str:
"""Stable identifier for this flow execution.
Separate from ``flow_id`` / ``state.id``, which consumers may
override via ``kickoff(inputs={"id": ...})`` to resume a persisted
flow. ``execution_id`` is never affected by ``inputs`` and stays
stable for the lifetime of a single run, so it is the correct key
for telemetry, tracing, and any external correlation that must
uniquely identify a single execution even when callers pass an
``id`` in ``inputs``.
Defaults to a fresh ``uuid4`` per ``Flow`` instance; assign to
override when an outer system already has an execution identity.
"""
return self._execution_id
@execution_id.setter
def execution_id(self, value: str) -> None:
self._execution_id = value
def _initialize_state(self, inputs: dict[str, Any]) -> None:
"""Initialize or update flow state with new inputs.
@@ -2133,9 +2199,9 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
flow_id_token = None
request_id_token = None
if current_flow_id.get() is None:
flow_id_token = current_flow_id.set(self.flow_id)
flow_id_token = current_flow_id.set(self.execution_id)
if current_flow_request_id.get() is None:
request_id_token = current_flow_request_id.set(self.flow_id)
request_id_token = current_flow_request_id.set(self.execution_id)
try:
# Reset flow state for fresh execution unless restoring from persistence
@@ -2214,6 +2280,9 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
if inputs is not None and "id" not in inputs:
self._initialize_state(inputs)
if self._is_execution_resuming:
await self._replay_recorded_events()
try:
# Determine which start methods to execute at kickoff
# Conditional start methods (with __trigger_methods__) are only triggered by their conditions
@@ -2361,6 +2430,44 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
"""
return await self.kickoff_async(inputs, input_files, from_checkpoint)
async def _replay_recorded_events(self) -> None:
"""Dispatch recorded ``MethodExecution*`` events from the event record."""
state = crewai_event_bus.runtime_state
if state is None:
return
record = state.event_record
if len(record) == 0:
return
replayable = (
MethodExecutionStartedEvent,
MethodExecutionFinishedEvent,
MethodExecutionFailedEvent,
)
flow_name = self.name or self.__class__.__name__
nodes = sorted(
(
n
for n in record.all_nodes()
if isinstance(n.event, replayable)
and n.event.flow_name == flow_name
and n.event.method_name in self._completed_methods
),
key=lambda n: n.event.emission_sequence or 0,
)
for node in nodes:
future = crewai_event_bus.replay(self, node.event)
if future is not None:
try:
await asyncio.wrap_future(future)
except Exception:
logger.warning(
"Replayed event handler failed: %s",
node.event.type,
exc_info=True,
)
async def _execute_start_method(self, start_method_name: FlowMethodName) -> None:
"""Executes a flow's start method and its triggered listeners.

View File

@@ -183,11 +183,6 @@ class AzureCompletion(BaseLLM):
AzureCompletion._is_azure_openai_endpoint(self.endpoint)
)
if not self.api_key:
raise ValueError(
"Azure API key is required. Set AZURE_API_KEY environment "
"variable or pass api_key parameter."
)
if not self.endpoint:
raise ValueError(
"Azure endpoint is required. Set AZURE_ENDPOINT environment "
@@ -195,12 +190,39 @@ class AzureCompletion(BaseLLM):
)
client_kwargs: dict[str, Any] = {
"endpoint": self.endpoint,
"credential": AzureKeyCredential(self.api_key),
"credential": self._resolve_credential(),
}
if self.api_version:
client_kwargs["api_version"] = self.api_version
return client_kwargs
def _resolve_credential(self) -> Any:
"""Return an Azure credential, preferring the API key when set.
Without an API key, fall back to ``DefaultAzureCredential`` from
``azure-identity``. That chain auto-detects the standard keyless
paths the customer's environment may provide — OIDC Workload
Identity Federation (``AZURE_FEDERATED_TOKEN_FILE`` +
``AZURE_TENANT_ID`` + ``AZURE_CLIENT_ID``), Managed Identity on
AKS/Azure VMs, environment-configured service principals, and
developer tools like the Azure CLI. Installing ``azure-identity``
is what enables these paths; without it we raise the existing
API-key error.
"""
if self.api_key:
return AzureKeyCredential(self.api_key)
try:
from azure.identity import DefaultAzureCredential
except ImportError:
raise ValueError(
"Azure API key is required when azure-identity is not "
"installed. Set AZURE_API_KEY, or install azure-identity "
'for keyless auth: uv add "crewai[azure-ai-inference]"'
) from None
return DefaultAzureCredential()
def _get_sync_client(self) -> Any:
if self._client is None:
self._client = self._build_sync_client()

View File

@@ -2,9 +2,17 @@
This module provides native MCP client functionality, allowing CrewAI agents
to connect to any MCP-compliant server using various transport types.
Heavy imports (MCPClient, MCPToolResolver, BaseTransport, TransportType) are
lazy-loaded on first access to avoid pulling in the ``mcp`` SDK (~400ms)
when only lightweight config/filter types are needed.
"""
from crewai.mcp.client import MCPClient
from __future__ import annotations
import importlib
from typing import TYPE_CHECKING, Any
from crewai.mcp.config import (
MCPServerConfig,
MCPServerHTTP,
@@ -18,8 +26,28 @@ from crewai.mcp.filters import (
create_dynamic_tool_filter,
create_static_tool_filter,
)
from crewai.mcp.tool_resolver import MCPToolResolver
from crewai.mcp.transports.base import BaseTransport, TransportType
if TYPE_CHECKING:
from crewai.mcp.client import MCPClient
from crewai.mcp.tool_resolver import MCPToolResolver
from crewai.mcp.transports.base import BaseTransport, TransportType
_LAZY: dict[str, tuple[str, str]] = {
"MCPClient": ("crewai.mcp.client", "MCPClient"),
"MCPToolResolver": ("crewai.mcp.tool_resolver", "MCPToolResolver"),
"BaseTransport": ("crewai.mcp.transports.base", "BaseTransport"),
"TransportType": ("crewai.mcp.transports.base", "TransportType"),
}
def __getattr__(name: str) -> Any:
if name in _LAZY:
mod_path, attr = _LAZY[name]
mod = importlib.import_module(mod_path)
val = getattr(mod, attr)
globals()[name] = val # cache for subsequent access
return val
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
__all__ = [

View File

@@ -10,12 +10,22 @@ from __future__ import annotations
import json
import logging
import threading
import time
from typing import Any
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.crew import Crew
from crewai.events.base_events import BaseEvent
from crewai.events.event_bus import CrewAIEventsBus, crewai_event_bus
from crewai.events.event_bus import CrewAIEventsBus, crewai_event_bus, is_replaying
from crewai.events.types.checkpoint_events import (
CheckpointBaseEvent,
CheckpointCompletedEvent,
CheckpointFailedEvent,
CheckpointForkBaseEvent,
CheckpointPrunedEvent,
CheckpointRestoreBaseEvent,
CheckpointStartedEvent,
)
from crewai.flow.flow import Flow
from crewai.state.checkpoint_config import CheckpointConfig
from crewai.state.runtime import RuntimeState, _prepare_entities
@@ -53,12 +63,26 @@ def _resolve(value: CheckpointConfig | bool | None) -> CheckpointConfig | None |
if isinstance(value, CheckpointConfig):
_ensure_handlers_registered()
return value
if value is True:
if value:
_ensure_handlers_registered()
return CheckpointConfig()
if value is False:
return _SENTINEL
return None # None = inherit
return None
def _resolve_from_agent(agent: BaseAgent) -> CheckpointConfig | None:
"""Resolve a checkpoint config starting from an agent, walking to its crew."""
result = _resolve(agent.checkpoint)
if isinstance(result, CheckpointConfig):
return result
if result is _SENTINEL:
return None
crew = agent.crew
if isinstance(crew, Crew):
crew_result = _resolve(crew.checkpoint)
return crew_result if isinstance(crew_result, CheckpointConfig) else None
return None
def _find_checkpoint(source: Any) -> CheckpointConfig | None:
@@ -77,28 +101,11 @@ def _find_checkpoint(source: Any) -> CheckpointConfig | None:
result = _resolve(source.checkpoint)
return result if isinstance(result, CheckpointConfig) else None
if isinstance(source, BaseAgent):
result = _resolve(source.checkpoint)
if isinstance(result, CheckpointConfig):
return result
if result is _SENTINEL:
return None
crew = source.crew
if isinstance(crew, Crew):
result = _resolve(crew.checkpoint)
return result if isinstance(result, CheckpointConfig) else None
return None
return _resolve_from_agent(source)
if isinstance(source, Task):
agent = source.agent
if isinstance(agent, BaseAgent):
result = _resolve(agent.checkpoint)
if isinstance(result, CheckpointConfig):
return result
if result is _SENTINEL:
return None
crew = agent.crew
if isinstance(crew, Crew):
result = _resolve(crew.checkpoint)
return result if isinstance(result, CheckpointConfig) else None
return _resolve_from_agent(agent)
return None
return None
@@ -107,27 +114,106 @@ def _do_checkpoint(
state: RuntimeState, cfg: CheckpointConfig, event: BaseEvent | None = None
) -> None:
"""Write a checkpoint and prune old ones if configured."""
_prepare_entities(state.root)
payload = state.model_dump(mode="json")
if event is not None:
payload["trigger"] = event.type
data = json.dumps(payload)
location = cfg.provider.checkpoint(
data,
cfg.location,
parent_id=state._parent_id,
branch=state._branch,
)
state._chain_lineage(cfg.provider, location)
provider_name: str = type(cfg.provider).__name__
trigger: str | None = event.type if event is not None else None
context: dict[str, Any] = {
"task_id": event.task_id if event is not None else None,
"task_name": event.task_name if event is not None else None,
"agent_id": event.agent_id if event is not None else None,
"agent_role": event.agent_role if event is not None else None,
}
checkpoint_id: str = cfg.provider.extract_id(location)
parent_id_snapshot: str | None = state._parent_id
branch_snapshot: str = state._branch
crewai_event_bus.emit(
cfg,
CheckpointStartedEvent(
location=cfg.location,
provider=provider_name,
trigger=trigger,
branch=branch_snapshot,
parent_id=parent_id_snapshot,
**context,
),
)
start: float = time.perf_counter()
try:
_prepare_entities(state.root)
payload = state.model_dump(mode="json")
if event is not None:
payload["trigger"] = event.type
data = json.dumps(payload)
location = cfg.provider.checkpoint(
data,
cfg.location,
parent_id=parent_id_snapshot,
branch=branch_snapshot,
)
state._chain_lineage(cfg.provider, location)
checkpoint_id: str = cfg.provider.extract_id(location)
except Exception as exc:
crewai_event_bus.emit(
cfg,
CheckpointFailedEvent(
location=cfg.location,
provider=provider_name,
trigger=trigger,
branch=branch_snapshot,
parent_id=parent_id_snapshot,
error=str(exc),
**context,
),
)
raise
duration_ms: float = (time.perf_counter() - start) * 1000.0
msg: str = (
f"Checkpoint saved. Resume with: crewai checkpoint resume {checkpoint_id}"
)
logger.info(msg)
crewai_event_bus.emit(
cfg,
CheckpointCompletedEvent(
location=location,
provider=provider_name,
trigger=trigger,
branch=branch_snapshot,
parent_id=parent_id_snapshot,
checkpoint_id=checkpoint_id,
duration_ms=duration_ms,
**context,
),
)
if cfg.max_checkpoints is not None:
cfg.provider.prune(cfg.location, cfg.max_checkpoints, branch=state._branch)
try:
removed_count: int = cfg.provider.prune(
cfg.location, cfg.max_checkpoints, branch=branch_snapshot
)
except Exception:
logger.warning(
"Checkpoint prune failed for %s (branch=%s)",
cfg.location,
branch_snapshot,
exc_info=True,
)
return
crewai_event_bus.emit(
cfg,
CheckpointPrunedEvent(
location=cfg.location,
provider=provider_name,
trigger=trigger,
branch=branch_snapshot,
parent_id=parent_id_snapshot,
removed_count=removed_count,
max_checkpoints=cfg.max_checkpoints,
**context,
),
)
def _should_checkpoint(source: Any, event: BaseEvent) -> CheckpointConfig | None:
@@ -142,6 +228,13 @@ def _should_checkpoint(source: Any, event: BaseEvent) -> CheckpointConfig | None
def _on_any_event(source: Any, event: BaseEvent, state: Any) -> None:
"""Sync handler registered on every event class."""
if is_replaying():
return
if isinstance(
event,
(CheckpointBaseEvent, CheckpointForkBaseEvent, CheckpointRestoreBaseEvent),
):
return
cfg = _should_checkpoint(source, event)
if cfg is None:
return
@@ -161,7 +254,8 @@ def _register_all_handlers(event_bus: CrewAIEventsBus) -> None:
seen: set[type] = set()
def _collect(cls: type[BaseEvent]) -> None:
for sub in cls.__subclasses__():
subclasses: list[type[BaseEvent]] = cls.__subclasses__()
for sub in subclasses:
if sub not in seen:
seen.add(sub)
type_field = sub.model_fields.get("type")

View File

@@ -39,7 +39,8 @@ def _build_event_type_map() -> None:
"""Populate _event_type_map from all BaseEvent subclasses."""
def _collect(cls: type[BaseEvent]) -> None:
for sub in cls.__subclasses__():
subclasses: list[type[BaseEvent]] = cls.__subclasses__()
for sub in subclasses:
type_field = sub.model_fields.get("type")
if type_field and type_field.default:
_event_type_map[type_field.default] = sub
@@ -196,6 +197,21 @@ class EventRecord(BaseModel):
node for node in self.nodes.values() if not node.neighbors("parent")
]
def all_nodes(self) -> list[EventNode]:
"""Return a snapshot of every node under the read lock.
Returns:
A list copy of the current nodes, safe to iterate without holding
the lock.
"""
with self._lock.r_locked():
return list(self.nodes.values())
def clear(self) -> None:
"""Remove all nodes from the record under the write lock."""
with self._lock.w_locked():
self.nodes.clear()
def __len__(self) -> int:
with self._lock.r_locked():
return len(self.nodes)

View File

@@ -61,13 +61,16 @@ class BaseProvider(BaseModel, ABC):
...
@abstractmethod
def prune(self, location: str, max_keep: int, *, branch: str = "main") -> None:
def prune(self, location: str, max_keep: int, *, branch: str = "main") -> int:
"""Remove old checkpoints, keeping at most *max_keep* per branch.
Args:
location: The storage destination passed to ``checkpoint``.
max_keep: Maximum number of checkpoints to retain.
branch: Only prune checkpoints on this branch.
Returns:
The number of checkpoints removed.
"""
...

View File

@@ -95,17 +95,20 @@ class JsonProvider(BaseProvider):
await f.write(data)
return str(file_path)
def prune(self, location: str, max_keep: int, *, branch: str = "main") -> None:
def prune(self, location: str, max_keep: int, *, branch: str = "main") -> int:
"""Remove oldest checkpoint files beyond *max_keep* on a branch."""
_safe_branch(location, branch)
branch_dir = os.path.join(location, branch)
pattern = os.path.join(branch_dir, "*.json")
files = sorted(glob.glob(pattern), key=os.path.getmtime)
removed = 0
for path in files if max_keep == 0 else files[:-max_keep]:
try:
os.remove(path)
removed += 1
except OSError: # noqa: PERF203
logger.debug("Failed to remove %s", path, exc_info=True)
return removed
def extract_id(self, location: str) -> str:
"""Extract the checkpoint ID from a file path.

View File

@@ -111,11 +111,13 @@ class SqliteProvider(BaseProvider):
await db.commit()
return f"{location}#{checkpoint_id}"
def prune(self, location: str, max_keep: int, *, branch: str = "main") -> None:
def prune(self, location: str, max_keep: int, *, branch: str = "main") -> int:
"""Remove oldest checkpoint rows beyond *max_keep* on a branch."""
with sqlite3.connect(location) as conn:
conn.execute(_PRUNE, (branch, branch, max_keep))
cursor = conn.execute(_PRUNE, (branch, branch, max_keep))
removed: int = cursor.rowcount
conn.commit()
return max(removed, 0)
def extract_id(self, location: str) -> str:
"""Extract the checkpoint ID from a ``db_path#id`` string."""

View File

@@ -10,6 +10,7 @@ via ``RuntimeState.model_rebuild()``.
from __future__ import annotations
import logging
import time
from typing import TYPE_CHECKING, Any
import uuid
@@ -23,6 +24,17 @@ from pydantic import (
)
from crewai.context import capture_execution_context
from crewai.events.event_bus import crewai_event_bus
from crewai.events.types.checkpoint_events import (
CheckpointCompletedEvent,
CheckpointFailedEvent,
CheckpointForkCompletedEvent,
CheckpointForkStartedEvent,
CheckpointRestoreCompletedEvent,
CheckpointRestoreFailedEvent,
CheckpointRestoreStartedEvent,
CheckpointStartedEvent,
)
from crewai.state.checkpoint_config import CheckpointConfig
from crewai.state.event_record import EventRecord
from crewai.state.provider.core import BaseProvider
@@ -89,7 +101,7 @@ def _migrate(data: dict[str, Any]) -> dict[str, Any]:
"""
raw = data.get("crewai_version")
current = Version(get_crewai_version())
stored = Version(raw) if raw else Version("0.0.0")
stored = Version(raw) if isinstance(raw, str) and raw else Version("0.0.0")
if raw is None:
logger.warning("Checkpoint has no crewai_version — treating as 0.0.0")
@@ -159,6 +171,63 @@ class RuntimeState(RootModel): # type: ignore[type-arg]
self._checkpoint_id = provider.extract_id(location)
self._parent_id = self._checkpoint_id
def _begin_checkpoint(self, location: str) -> tuple[str, str | None, str, float]:
"""Emit the start event and return the invariant context for a checkpoint."""
provider_name: str = type(self._provider).__name__
parent_id_snapshot: str | None = self._parent_id
branch_snapshot: str = self._branch
crewai_event_bus.emit(
self,
CheckpointStartedEvent(
location=location,
provider=provider_name,
branch=branch_snapshot,
parent_id=parent_id_snapshot,
),
)
return provider_name, parent_id_snapshot, branch_snapshot, time.perf_counter()
def _emit_checkpoint_failed(
self,
location: str,
provider_name: str,
branch_snapshot: str,
parent_id_snapshot: str | None,
exc: Exception,
) -> None:
"""Emit the failure event for a checkpoint write."""
crewai_event_bus.emit(
self,
CheckpointFailedEvent(
location=location,
provider=provider_name,
branch=branch_snapshot,
parent_id=parent_id_snapshot,
error=str(exc),
),
)
def _emit_checkpoint_completed(
self,
result: str,
provider_name: str,
branch_snapshot: str,
parent_id_snapshot: str | None,
start: float,
) -> None:
"""Emit the completion event for a successful checkpoint write."""
crewai_event_bus.emit(
self,
CheckpointCompletedEvent(
location=result,
provider=provider_name,
branch=branch_snapshot,
parent_id=parent_id_snapshot,
checkpoint_id=self._provider.extract_id(result),
duration_ms=(time.perf_counter() - start) * 1000.0,
),
)
def checkpoint(self, location: str) -> str:
"""Write a checkpoint.
@@ -169,14 +238,27 @@ class RuntimeState(RootModel): # type: ignore[type-arg]
Returns:
A location identifier for the saved checkpoint.
"""
_prepare_entities(self.root)
result = self._provider.checkpoint(
self.model_dump_json(),
location,
parent_id=self._parent_id,
branch=self._branch,
provider_name, parent_id_snapshot, branch_snapshot, start = (
self._begin_checkpoint(location)
)
try:
_prepare_entities(self.root)
result = self._provider.checkpoint(
self.model_dump_json(),
location,
parent_id=parent_id_snapshot,
branch=branch_snapshot,
)
self._chain_lineage(self._provider, result)
except Exception as exc:
self._emit_checkpoint_failed(
location, provider_name, branch_snapshot, parent_id_snapshot, exc
)
raise
self._emit_checkpoint_completed(
result, provider_name, branch_snapshot, parent_id_snapshot, start
)
self._chain_lineage(self._provider, result)
return result
async def acheckpoint(self, location: str) -> str:
@@ -189,14 +271,27 @@ class RuntimeState(RootModel): # type: ignore[type-arg]
Returns:
A location identifier for the saved checkpoint.
"""
_prepare_entities(self.root)
result = await self._provider.acheckpoint(
self.model_dump_json(),
location,
parent_id=self._parent_id,
branch=self._branch,
provider_name, parent_id_snapshot, branch_snapshot, start = (
self._begin_checkpoint(location)
)
try:
_prepare_entities(self.root)
result = await self._provider.acheckpoint(
self.model_dump_json(),
location,
parent_id=parent_id_snapshot,
branch=branch_snapshot,
)
self._chain_lineage(self._provider, result)
except Exception as exc:
self._emit_checkpoint_failed(
location, provider_name, branch_snapshot, parent_id_snapshot, exc
)
raise
self._emit_checkpoint_completed(
result, provider_name, branch_snapshot, parent_id_snapshot, start
)
self._chain_lineage(self._provider, result)
return result
def fork(self, branch: str | None = None) -> None:
@@ -211,11 +306,32 @@ class RuntimeState(RootModel): # type: ignore[type-arg]
times without collisions.
"""
if branch:
self._branch = branch
new_branch = branch
elif self._checkpoint_id:
self._branch = f"fork/{self._checkpoint_id}_{uuid.uuid4().hex[:6]}"
new_branch = f"fork/{self._checkpoint_id}_{uuid.uuid4().hex[:6]}"
else:
self._branch = f"fork/{uuid.uuid4().hex[:8]}"
new_branch = f"fork/{uuid.uuid4().hex[:8]}"
parent_branch: str | None = self._branch
parent_checkpoint_id: str | None = self._checkpoint_id
crewai_event_bus.emit(
self,
CheckpointForkStartedEvent(
branch=new_branch,
parent_branch=parent_branch,
parent_checkpoint_id=parent_checkpoint_id,
),
)
self._branch = new_branch
crewai_event_bus.emit(
self,
CheckpointForkCompletedEvent(
branch=new_branch,
parent_branch=parent_branch,
parent_checkpoint_id=parent_checkpoint_id,
),
)
@classmethod
def from_checkpoint(cls, config: CheckpointConfig, **kwargs: Any) -> RuntimeState:
@@ -233,13 +349,41 @@ class RuntimeState(RootModel): # type: ignore[type-arg]
if config.restore_from is None:
raise ValueError("CheckpointConfig.restore_from must be set")
location = str(config.restore_from)
provider = detect_provider(location)
raw = provider.from_checkpoint(location)
state = cls.model_validate_json(raw, **kwargs)
state._provider = provider
checkpoint_id = provider.extract_id(location)
state._checkpoint_id = checkpoint_id
state._parent_id = checkpoint_id
crewai_event_bus.emit(config, CheckpointRestoreStartedEvent(location=location))
start: float = time.perf_counter()
provider_name: str | None = None
try:
provider = detect_provider(location)
provider_name = type(provider).__name__
raw = provider.from_checkpoint(location)
state = cls.model_validate_json(raw, **kwargs)
state._provider = provider
checkpoint_id = provider.extract_id(location)
state._checkpoint_id = checkpoint_id
state._parent_id = checkpoint_id
except Exception as exc:
crewai_event_bus.emit(
config,
CheckpointRestoreFailedEvent(
location=location,
provider=provider_name,
error=str(exc),
),
)
raise
crewai_event_bus.emit(
config,
CheckpointRestoreCompletedEvent(
location=location,
provider=provider_name,
checkpoint_id=checkpoint_id,
branch=state._branch,
parent_id=state._parent_id,
duration_ms=(time.perf_counter() - start) * 1000.0,
),
)
return state
@classmethod
@@ -260,13 +404,41 @@ class RuntimeState(RootModel): # type: ignore[type-arg]
if config.restore_from is None:
raise ValueError("CheckpointConfig.restore_from must be set")
location = str(config.restore_from)
provider = detect_provider(location)
raw = await provider.afrom_checkpoint(location)
state = cls.model_validate_json(raw, **kwargs)
state._provider = provider
checkpoint_id = provider.extract_id(location)
state._checkpoint_id = checkpoint_id
state._parent_id = checkpoint_id
crewai_event_bus.emit(config, CheckpointRestoreStartedEvent(location=location))
start: float = time.perf_counter()
provider_name: str | None = None
try:
provider = detect_provider(location)
provider_name = type(provider).__name__
raw = await provider.afrom_checkpoint(location)
state = cls.model_validate_json(raw, **kwargs)
state._provider = provider
checkpoint_id = provider.extract_id(location)
state._checkpoint_id = checkpoint_id
state._parent_id = checkpoint_id
except Exception as exc:
crewai_event_bus.emit(
config,
CheckpointRestoreFailedEvent(
location=location,
provider=provider_name,
error=str(exc),
),
)
raise
crewai_event_bus.emit(
config,
CheckpointRestoreCompletedEvent(
location=location,
provider=provider_name,
checkpoint_id=checkpoint_id,
branch=state._branch,
parent_id=state._parent_id,
duration_ms=(time.perf_counter() - start) * 1000.0,
),
)
return state

View File

@@ -0,0 +1,82 @@
"""Wrap Flow classes as callable tools so agents can invoke them."""
from __future__ import annotations
import json
from typing import Any
from pydantic import BaseModel, Field
from crewai.tools.base_tool import BaseTool
from crewai.utilities.string_utils import sanitize_tool_name
class FlowToolInputSchema(BaseModel):
"""Default input schema for a FlowTool."""
inputs: str = Field(
default="{}",
description=(
"JSON string of key-value pairs to pass as inputs to the flow. "
"Use '{}' if the flow requires no inputs."
),
)
class FlowTool(BaseTool):
"""Wraps a Flow class as a BaseTool so an agent can invoke it.
The tool instantiates the Flow, calls ``kickoff(inputs=...)`` and returns
the result as a string.
"""
name: str = ""
description: str = ""
flow_class: Any = Field(
default=None,
description="The Flow class (not instance) to wrap.",
exclude=True,
)
args_schema: Any = FlowToolInputSchema
def _run(self, inputs: str = "{}") -> str:
"""Instantiate the Flow, run kickoff, and return the result."""
try:
parsed_inputs = json.loads(inputs) if isinstance(inputs, str) else inputs
except (json.JSONDecodeError, TypeError):
parsed_inputs = {}
if not isinstance(parsed_inputs, dict):
parsed_inputs = {}
flow_instance = self.flow_class()
result = flow_instance.kickoff(inputs=parsed_inputs if parsed_inputs else None)
return str(result)
def create_flow_tools(flows: list[type] | None) -> list[BaseTool]:
"""Convert a list of Flow classes into BaseTool wrappers.
Args:
flows: Flow classes (not instances) to wrap as tools.
Returns:
A list of FlowTool instances ready for agent use.
"""
if not flows:
return []
tools: list[BaseTool] = []
for flow_cls in flows:
name = sanitize_tool_name(flow_cls.__name__)
docstring = (flow_cls.__doc__ or "").strip()
description = docstring if docstring else f"Run the {flow_cls.__name__} flow."
tools.append(
FlowTool(
name=name,
description=description,
flow_class=flow_cls,
)
)
return tools

View File

@@ -0,0 +1,165 @@
"""Tests for event bus replay dispatch and is_replaying flag."""
from __future__ import annotations
from typing import Any
from unittest.mock import patch
from crewai.events.event_bus import _replaying, crewai_event_bus, is_replaying
from crewai.events.types.flow_events import (
MethodExecutionFinishedEvent,
MethodExecutionStartedEvent,
)
def _make_started(method: str, event_id: str, sequence: int) -> MethodExecutionStartedEvent:
"""Build a MethodExecutionStartedEvent with explicit ids/sequence."""
ev = MethodExecutionStartedEvent(
method_name=method,
flow_name="F",
params={},
state={},
)
ev.event_id = event_id
ev.emission_sequence = sequence
return ev
class TestReplayPreservesFields:
"""replay() must not overwrite event_id, parent_event_id, or emission_sequence."""
def test_preserves_ids_and_sequence(self) -> None:
captured: list[MethodExecutionStartedEvent] = []
with crewai_event_bus.scoped_handlers():
@crewai_event_bus.on(MethodExecutionStartedEvent)
def _capture(_: Any, event: MethodExecutionStartedEvent) -> None:
captured.append(event)
ev = _make_started("outline", "orig-id-1", 42)
ev.parent_event_id = "parent-abc"
future = crewai_event_bus.replay(object(), ev)
if future is not None:
future.result(timeout=5.0)
assert len(captured) == 1
assert captured[0].event_id == "orig-id-1"
assert captured[0].parent_event_id == "parent-abc"
assert captured[0].emission_sequence == 42
class TestIsReplayingFlag:
"""is_replaying() must be True inside handlers dispatched via replay()."""
def test_flag_true_during_replay(self) -> None:
seen: list[bool] = []
with crewai_event_bus.scoped_handlers():
@crewai_event_bus.on(MethodExecutionStartedEvent)
def _capture(_: Any, __: MethodExecutionStartedEvent) -> None:
seen.append(is_replaying())
ev = _make_started("m", "id-1", 1)
future = crewai_event_bus.replay(object(), ev)
if future is not None:
future.result(timeout=5.0)
assert seen == [True]
assert is_replaying() is False
def test_flag_false_during_emit(self) -> None:
seen: list[bool] = []
with crewai_event_bus.scoped_handlers():
@crewai_event_bus.on(MethodExecutionStartedEvent)
def _capture(_: Any, __: MethodExecutionStartedEvent) -> None:
seen.append(is_replaying())
ev = _make_started("m", "id-1", 1)
future = crewai_event_bus.emit(object(), ev)
if future is not None:
future.result(timeout=5.0)
assert seen == [False]
class TestCheckpointListenerOptsOut:
"""CheckpointListener must early-return during replay."""
def test_checkpoint_not_written_on_replay(self) -> None:
from crewai.state.checkpoint_config import CheckpointConfig
from crewai.state.checkpoint_listener import _on_any_event
class FlowLike:
entity_type = "flow"
checkpoint = CheckpointConfig(trigger_all=True)
ev = _make_started("m", "id-1", 1)
with patch("crewai.state.checkpoint_listener._do_checkpoint") as do_cp:
token = _replaying.set(True)
try:
_on_any_event(FlowLike(), ev, state=None)
finally:
_replaying.reset(token)
assert do_cp.call_count == 0
class TestFlowResumeReplaysEvents:
"""End-to-end: a resumed flow emits MethodExecution* events for completed methods."""
def test_resume_dispatches_completed_method_events(self, tmp_path) -> None:
from crewai.flow.flow import Flow, listen, start
from crewai.flow.persistence.sqlite import SQLiteFlowPersistence
db_path = tmp_path / "flows.db"
persistence = SQLiteFlowPersistence(str(db_path))
class ThreeStepFlow(Flow[dict]):
@start()
def step_a(self) -> str:
return "a"
@listen(step_a)
def step_b(self) -> str:
return "b"
@listen(step_b)
def step_c(self) -> str:
return "c"
if crewai_event_bus.runtime_state is not None:
crewai_event_bus.runtime_state.event_record.clear()
flow1 = ThreeStepFlow(persistence=persistence)
flow1.kickoff()
flow_id = flow1.state["id"]
captured_started: list[str] = []
captured_finished: list[str] = []
flow2 = ThreeStepFlow(persistence=persistence)
flow2._completed_methods = {"step_a", "step_b"}
with crewai_event_bus.scoped_handlers():
@crewai_event_bus.on(MethodExecutionStartedEvent)
def _cs(_: Any, event: MethodExecutionStartedEvent) -> None:
captured_started.append(event.method_name)
@crewai_event_bus.on(MethodExecutionFinishedEvent)
def _cf(_: Any, event: MethodExecutionFinishedEvent) -> None:
captured_finished.append(event.method_name)
flow2.kickoff(inputs={"id": flow_id})
assert captured_started.count("step_a") == 1
assert captured_started.count("step_b") == 1
assert captured_started.count("step_c") == 1
assert captured_finished.count("step_a") == 1
assert captured_finished.count("step_b") == 1
assert captured_finished.count("step_c") == 1

View File

@@ -389,17 +389,41 @@ def test_azure_raises_error_when_endpoint_missing():
llm._get_sync_client()
def test_azure_raises_error_when_api_key_missing():
"""Credentials are validated lazily: construction succeeds, first
def test_azure_raises_error_when_api_key_missing_without_azure_identity():
"""Without an API key AND without ``azure-identity`` installed,
client build raises the descriptive error."""
from crewai.llms.providers.azure.completion import AzureCompletion
with patch.dict(os.environ, {}, clear=True):
llm = AzureCompletion(
model="gpt-4", endpoint="https://test.openai.azure.com"
)
with pytest.raises(ValueError, match="Azure API key is required"):
llm._get_sync_client()
with patch.dict("sys.modules", {"azure.identity": None}):
llm = AzureCompletion(
model="gpt-4", endpoint="https://test.openai.azure.com"
)
with pytest.raises(ValueError, match="Azure API key is required"):
llm._get_sync_client()
def test_azure_uses_default_credential_when_api_key_missing():
"""With ``azure-identity`` installed, a missing API key falls back to
``DefaultAzureCredential`` instead of raising. This is the path that
enables keyless auth (OIDC WIF on EKS/AKS, Managed Identity, Azure
CLI) without any crewAI-specific config."""
from unittest.mock import MagicMock
from crewai.llms.providers.azure.completion import AzureCompletion
sentinel = MagicMock(name="DefaultAzureCredential()")
with patch.dict(os.environ, {}, clear=True):
with patch(
"azure.identity.DefaultAzureCredential", return_value=sentinel
) as mock_cls:
llm = AzureCompletion(
model="gpt-4",
endpoint="https://test-ai.services.example.com",
)
kwargs = llm._make_client_kwargs()
assert kwargs["credential"] is sentinel
mock_cls.assert_called()
@pytest.mark.asyncio

View File

@@ -4,6 +4,8 @@ from pathlib import Path
import pytest
from crewai import Agent
from crewai.agent.utils import append_skill_context
from crewai.skills.loader import activate_skill, discover_skills, format_skill_context
from crewai.skills.models import INSTRUCTIONS, METADATA
@@ -76,3 +78,23 @@ class TestSkillDiscoveryAndActivation:
all_skills.extend(discover_skills(search_path))
names = {s.name for s in all_skills}
assert names == {"skill-a", "skill-b"}
def test_agent_preserves_metadata_for_discovered_skills(self, tmp_path: Path) -> None:
_create_skill_dir(tmp_path, "travel", body="Use this skill for travel planning.")
discovered = discover_skills(tmp_path)
agent = Agent(
role="Travel Advisor",
goal="Provide personalized travel suggestions.",
backstory="An experienced travel consultant.",
skills=discovered,
)
assert agent.skills is not None
assert agent.skills[0].disclosure_level == METADATA
assert agent.skills[0].instructions is None
prompt = append_skill_context(agent, "Plan a 10-day Japan itinerary.")
assert "## Skill: travel" in prompt
assert "Skill travel" in prompt
assert "Use this skill for travel planning." not in prompt

View File

@@ -11,11 +11,12 @@ from typing import Any
from unittest.mock import MagicMock, patch
import pytest
from pydantic import BaseModel
from crewai.agent.core import Agent
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.crew import Crew
from crewai.flow.flow import Flow, start
from crewai.flow.flow import _INITIAL_STATE_CLASS_MARKER, Flow, start
from crewai.state.checkpoint_config import CheckpointConfig
from crewai.state.checkpoint_listener import (
_find_checkpoint,
@@ -310,6 +311,65 @@ class TestRuntimeStateLineage:
assert state._branch != first
class TestFlowInitialStateSerialization:
"""Regression tests for checkpoint serialization of ``Flow.initial_state``."""
def test_class_ref_serializes_as_schema(self) -> None:
class MyState(BaseModel):
id: str = "x"
foo: str = "bar"
flow = Flow(initial_state=MyState)
state = RuntimeState(root=[flow])
dumped = json.loads(state.model_dump_json())
entity = dumped["entities"][0]
wrapped = entity["initial_state"]
assert isinstance(wrapped, dict)
assert _INITIAL_STATE_CLASS_MARKER in wrapped
assert wrapped[_INITIAL_STATE_CLASS_MARKER].get("title") == "MyState"
def test_class_ref_round_trips_to_basemodel_subclass(self) -> None:
class MyState(BaseModel):
id: str = "x"
foo: str = "bar"
flow = Flow(initial_state=MyState)
raw = RuntimeState(root=[flow]).model_dump_json()
restored = RuntimeState.model_validate_json(
raw, context={"from_checkpoint": True}
)
rehydrated = restored.root[0].initial_state
assert isinstance(rehydrated, type)
assert issubclass(rehydrated, BaseModel)
assert set(rehydrated.model_fields.keys()) == {"id", "foo"}
def test_instance_serializes_as_values(self) -> None:
class MyState(BaseModel):
id: str = "x"
foo: str = "bar"
flow = Flow(initial_state=MyState(foo="baz"))
state = RuntimeState(root=[flow])
dumped = json.loads(state.model_dump_json())
entity = dumped["entities"][0]
assert entity["initial_state"] == {"id": "x", "foo": "baz"}
def test_dict_passthrough(self) -> None:
flow = Flow(initial_state={"id": "x", "foo": "bar"})
state = RuntimeState(root=[flow])
dumped = json.loads(state.model_dump_json())
entity = dumped["entities"][0]
assert entity["initial_state"] == {"id": "x", "foo": "bar"}
def test_dict_round_trips_as_dict(self) -> None:
flow = Flow(initial_state={"id": "x", "foo": "bar"})
raw = RuntimeState(root=[flow]).model_dump_json()
restored = RuntimeState.model_validate_json(
raw, context={"from_checkpoint": True}
)
assert restored.root[0].initial_state == {"id": "x", "foo": "bar"}
# ---------- JsonProvider forking ----------

View File

@@ -4519,8 +4519,8 @@ def test_sets_flow_context_when_using_crewbase_pattern_inside_flow():
flow.kickoff()
assert captured_crew is not None
assert captured_crew._flow_id == flow.flow_id # type: ignore[attr-defined]
assert captured_crew._request_id == flow.flow_id # type: ignore[attr-defined]
assert captured_crew._flow_id == flow.execution_id # type: ignore[attr-defined]
assert captured_crew._request_id == flow.execution_id # type: ignore[attr-defined]
def test_sets_flow_context_when_outside_flow(researcher, writer):
@@ -4554,8 +4554,8 @@ def test_sets_flow_context_when_inside_flow(researcher, writer):
flow = MyFlow()
result = flow.kickoff()
assert result._flow_id == flow.flow_id # type: ignore[attr-defined]
assert result._request_id == flow.flow_id # type: ignore[attr-defined]
assert result._flow_id == flow.execution_id # type: ignore[attr-defined]
assert result._request_id == flow.execution_id # type: ignore[attr-defined]
def test_reset_knowledge_with_no_crew_knowledge(researcher, writer):

View File

@@ -0,0 +1,185 @@
"""Tests for Flow-as-tool functionality."""
from __future__ import annotations
from unittest.mock import MagicMock
from crewai.flow.flow import Flow, start
from crewai.tools.flow_tool import FlowTool, create_flow_tools
# ---------------------------------------------------------------------------
# Test Flow classes
# ---------------------------------------------------------------------------
class SimpleFlow(Flow):
"""A simple flow that greets the user."""
@start()
def greet(self) -> str:
return "Hello from SimpleFlow!"
class MathFlow(Flow):
"""Performs basic math operations."""
@start()
def compute(self) -> str:
return "42"
class NoDocFlow(Flow):
@start()
def run_it(self) -> str:
return "no doc"
# ---------------------------------------------------------------------------
# FlowTool unit tests
# ---------------------------------------------------------------------------
class TestFlowTool:
def test_wrap_simple_flow(self) -> None:
tool = FlowTool(
name="simple_flow",
description="A simple flow that greets the user.",
flow_class=SimpleFlow,
)
assert tool.name == "simple_flow"
assert "greets the user" in tool.description
def test_run_invokes_kickoff(self) -> None:
mock_flow = MagicMock()
mock_flow.return_value = mock_flow # __init__ returns self
mock_flow.kickoff.return_value = "mocked result"
tool = FlowTool(
name="test_flow",
description="test",
flow_class=mock_flow,
)
result = tool._run(inputs="{}")
assert result == "mocked result"
mock_flow.kickoff.assert_called_once()
def test_run_with_json_inputs(self) -> None:
mock_flow = MagicMock()
mock_flow.return_value = mock_flow
mock_flow.kickoff.return_value = "result with inputs"
tool = FlowTool(
name="test_flow",
description="test",
flow_class=mock_flow,
)
result = tool._run(inputs='{"key": "value"}')
assert result == "result with inputs"
mock_flow.kickoff.assert_called_once_with(inputs={"key": "value"})
def test_run_with_invalid_json_defaults_to_empty(self) -> None:
mock_flow = MagicMock()
mock_flow.return_value = mock_flow
mock_flow.kickoff.return_value = "ok"
tool = FlowTool(
name="test_flow",
description="test",
flow_class=mock_flow,
)
result = tool._run(inputs="not valid json")
assert result == "ok"
mock_flow.kickoff.assert_called_once_with(inputs=None)
def test_run_returns_string(self) -> None:
mock_flow = MagicMock()
mock_flow.return_value = mock_flow
mock_flow.kickoff.return_value = 42
tool = FlowTool(
name="test_flow",
description="test",
flow_class=mock_flow,
)
result = tool._run()
assert result == "42"
assert isinstance(result, str)
# ---------------------------------------------------------------------------
# create_flow_tools tests
# ---------------------------------------------------------------------------
class TestCreateFlowTools:
def test_creates_tools_from_flow_classes(self) -> None:
tools = create_flow_tools([SimpleFlow, MathFlow])
assert len(tools) == 2
names = {t.name for t in tools}
assert "simple_flow" in names
assert "math_flow" in names
def test_description_from_docstring(self) -> None:
tools = create_flow_tools([SimpleFlow])
assert len(tools) == 1
assert "greets the user" in tools[0].description
def test_description_fallback_when_no_docstring(self) -> None:
tools = create_flow_tools([NoDocFlow])
assert len(tools) == 1
assert "NoDocFlow" in tools[0].description
def test_empty_list_returns_empty(self) -> None:
assert create_flow_tools([]) == []
def test_none_returns_empty(self) -> None:
assert create_flow_tools(None) == []
def test_tools_are_base_tool_instances(self) -> None:
from crewai.tools.base_tool import BaseTool
tools = create_flow_tools([SimpleFlow])
for tool in tools:
assert isinstance(tool, BaseTool)
# ---------------------------------------------------------------------------
# Agent integration tests
# ---------------------------------------------------------------------------
class TestAgentFlowIntegration:
def test_agent_with_flows_has_flow_tools(self) -> None:
from crewai.agent.core import Agent
agent = Agent(
role="Test Agent",
goal="Test flows",
backstory="I test things",
flows=[SimpleFlow, MathFlow],
)
tool_names = {t.name for t in (agent.tools or [])}
assert "simple_flow" in tool_names
assert "math_flow" in tool_names
def test_agent_without_flows_no_extra_tools(self) -> None:
from crewai.agent.core import Agent
agent = Agent(
role="Test Agent",
goal="Test",
backstory="I test things",
)
# Should not have any flow tools
flow_tool_names = {
t.name for t in (agent.tools or []) if isinstance(t, FlowTool)
}
assert len(flow_tool_names) == 0
def test_flow_tool_executes_real_flow(self) -> None:
"""Test that a FlowTool actually runs the Flow's kickoff."""
tools = create_flow_tools([SimpleFlow])
tool = tools[0]
result = tool.run(inputs="{}")
assert "Hello from SimpleFlow" in result

View File

@@ -0,0 +1,127 @@
"""Regression tests for ``Flow.execution_id``.
``execution_id`` is the stable tracking identifier for a single flow run.
It must stay independent of ``state.id`` so that consumers passing an
``id`` in ``inputs`` (used for persistence restore) cannot destabilize
the identity used by telemetry, tracing, and external correlation.
"""
from __future__ import annotations
from typing import Any
import pytest
from crewai.flow.flow import Flow, FlowState, start
from crewai.flow.flow_context import current_flow_id, current_flow_request_id
class _CaptureState(FlowState):
captured_flow_id: str = ""
captured_state_id: str = ""
captured_current_flow_id: str = ""
captured_execution_id: str = ""
class _IdentityCaptureFlow(Flow[_CaptureState]):
initial_state = _CaptureState
@start()
def capture(self) -> None:
self.state.captured_flow_id = self.flow_id
self.state.captured_state_id = self.state.id
self.state.captured_current_flow_id = current_flow_id.get() or ""
self.state.captured_execution_id = self.execution_id
def test_execution_id_defaults_to_fresh_uuid_per_instance() -> None:
a = _IdentityCaptureFlow()
b = _IdentityCaptureFlow()
assert a.execution_id
assert b.execution_id
assert a.execution_id != b.execution_id
def test_execution_id_survives_consumer_id_in_inputs() -> None:
flow = _IdentityCaptureFlow()
original_execution_id = flow.execution_id
flow.kickoff(inputs={"id": "consumer-supplied-id"})
assert flow.state.id == "consumer-supplied-id"
assert flow.flow_id == "consumer-supplied-id"
assert flow.execution_id == original_execution_id
assert flow.execution_id != "consumer-supplied-id"
def test_two_runs_with_same_consumer_id_have_distinct_execution_ids() -> None:
flow_a = _IdentityCaptureFlow()
flow_b = _IdentityCaptureFlow()
colliding_id = "shared-consumer-id"
flow_a.kickoff(inputs={"id": colliding_id})
flow_b.kickoff(inputs={"id": colliding_id})
assert flow_a.state.id == colliding_id
assert flow_b.state.id == colliding_id
assert flow_a.execution_id != flow_b.execution_id
def test_execution_id_is_writable() -> None:
flow = _IdentityCaptureFlow()
flow.execution_id = "external-task-id"
assert flow.execution_id == "external-task-id"
flow.kickoff(inputs={"id": "consumer-supplied-id"})
assert flow.execution_id == "external-task-id"
assert flow.state.id == "consumer-supplied-id"
def test_current_flow_id_context_var_matches_execution_id() -> None:
flow = _IdentityCaptureFlow()
flow.execution_id = "external-task-id"
flow.kickoff(inputs={"id": "consumer-supplied-id"})
assert flow.state.captured_current_flow_id == "external-task-id"
assert flow.state.captured_flow_id == "consumer-supplied-id"
assert flow.state.captured_execution_id == "external-task-id"
def test_execution_id_not_included_in_serialized_state() -> None:
flow = _IdentityCaptureFlow()
flow.execution_id = "external-task-id"
flow.kickoff()
dumped = flow.state.model_dump()
assert "execution_id" not in dumped
assert "_execution_id" not in dumped
assert dumped["id"] == flow.state.id
def test_dict_state_flow_also_exposes_stable_execution_id() -> None:
class DictFlow(Flow[dict[str, Any]]):
initial_state = dict # type: ignore[assignment]
@start()
def noop(self) -> None:
pass
flow = DictFlow()
original = flow.execution_id
flow.kickoff(inputs={"id": "consumer-supplied-id"})
assert flow.state["id"] == "consumer-supplied-id"
assert flow.execution_id == original
@pytest.fixture(autouse=True)
def _reset_flow_context_vars():
yield
for var in (current_flow_id, current_flow_request_id):
try:
var.set(None)
except LookupError:
# ContextVar was never set in this context; nothing to reset.
pass

View File

@@ -1,3 +1,3 @@
"""CrewAI development tools."""
__version__ = "1.14.3a2"
__version__ = "1.14.3"

View File

@@ -164,7 +164,7 @@ info = "Commits must follow Conventional Commits 1.0.0."
[tool.uv]
# Pinned to include the security patch releases (authlib 1.6.11,
# langchain-text-splitters 1.1.2) uploaded on 2026-04-16.
exclude-newer = "2026-04-17"
exclude-newer = "2026-04-22"
# composio-core pins rich<14 but textual requires rich>=14.
# onnxruntime 1.24+ dropped Python 3.10 wheels; cap it so qdrant[fastembed] resolves on 3.10.

273
uv.lock generated
View File

@@ -13,7 +13,7 @@ resolution-markers = [
]
[options]
exclude-newer = "2026-04-17T16:00:00Z"
exclude-newer = "2026-04-23T07:00:00Z"
[manifest]
members = [
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[[package]]
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version = "1.25.3"
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{ name = "msal" },
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version = "2.6"
source = { registry = "https://pypi.org/simple" }
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[[package]]
name = "browserbase"
version = "1.8.0"
@@ -1296,6 +1321,7 @@ aws = [
]
azure-ai-inference = [
{ name = "azure-ai-inference" },
{ name = "azure-identity" },
]
bedrock = [
{ name = "boto3" },
@@ -1350,6 +1376,7 @@ requires-dist = [
{ name = "anthropic", marker = "extra == 'anthropic'", specifier = "~=0.73.0" },
{ name = "appdirs", specifier = "~=1.4.4" },
{ name = "azure-ai-inference", marker = "extra == 'azure-ai-inference'", specifier = "~=1.0.0b9" },
{ name = "azure-identity", marker = "extra == 'azure-ai-inference'", specifier = ">=1.17.0,<2" },
{ name = "boto3", marker = "extra == 'aws'", specifier = "~=1.42.79" },
{ name = "boto3", marker = "extra == 'bedrock'", specifier = "~=1.42.79" },
{ name = "chromadb", specifier = "~=1.1.0" },
@@ -1489,6 +1516,10 @@ databricks-sdk = [
daytona = [
{ name = "daytona" },
]
e2b = [
{ name = "e2b" },
{ name = "e2b-code-interpreter" },
]
exa-py = [
{ name = "exa-py" },
]
@@ -1590,13 +1621,15 @@ requires-dist = [
{ name = "cryptography", marker = "extra == 'snowflake'", specifier = ">=43.0.3" },
{ name = "databricks-sdk", marker = "extra == 'databricks-sdk'", specifier = ">=0.46.0" },
{ name = "daytona", marker = "extra == 'daytona'", specifier = "~=0.140.0" },
{ name = "e2b", marker = "extra == 'e2b'", specifier = "~=2.20.0" },
{ name = "e2b-code-interpreter", marker = "extra == 'e2b'", specifier = "~=2.6.0" },
{ name = "exa-py", marker = "extra == 'exa-py'", specifier = ">=1.8.7" },
{ name = "firecrawl-py", marker = "extra == 'firecrawl-py'", specifier = ">=1.8.0" },
{ name = "gitpython", marker = "extra == 'github'", specifier = ">=3.1.41,<4" },
{ name = "hyperbrowser", marker = "extra == 'hyperbrowser'", specifier = ">=0.18.0" },
{ name = "langchain-apify", marker = "extra == 'apify'", specifier = ">=0.1.2,<1.0.0" },
{ name = "linkup-sdk", marker = "extra == 'linkup-sdk'", specifier = ">=0.2.2" },
{ name = "lxml", marker = "extra == 'rag'", specifier = ">=5.3.0,<5.4.0" },
{ name = "lxml", marker = "extra == 'rag'", specifier = ">=6.1.0,<7" },
{ name = "mcp", marker = "extra == 'mcp'", specifier = ">=1.6.0" },
{ name = "mcpadapt", marker = "extra == 'mcp'", specifier = ">=0.1.9" },
{ name = "multion", marker = "extra == 'multion'", specifier = ">=1.1.0" },
@@ -1632,7 +1665,7 @@ requires-dist = [
{ name = "weaviate-client", marker = "extra == 'weaviate-client'", specifier = ">=4.10.2" },
{ name = "youtube-transcript-api", specifier = "~=1.2.2" },
]
provides-extras = ["apify", "beautifulsoup4", "bedrock", "browserbase", "composio-core", "contextual", "couchbase", "databricks-sdk", "daytona", "exa-py", "firecrawl-py", "github", "hyperbrowser", "linkup-sdk", "mcp", "mongodb", "multion", "mysql", "oxylabs", "patronus", "postgresql", "qdrant-client", "rag", "scrapegraph-py", "scrapfly-sdk", "selenium", "serpapi", "singlestore", "snowflake", "spider-client", "sqlalchemy", "stagehand", "tavily-python", "weaviate-client", "xml"]
provides-extras = ["apify", "beautifulsoup4", "bedrock", "browserbase", "composio-core", "contextual", "couchbase", "databricks-sdk", "daytona", "e2b", "exa-py", "firecrawl-py", "github", "hyperbrowser", "linkup-sdk", "mcp", "mongodb", "multion", "mysql", "oxylabs", "patronus", "postgresql", "qdrant-client", "rag", "scrapegraph-py", "scrapfly-sdk", "selenium", "serpapi", "singlestore", "snowflake", "spider-client", "sqlalchemy", "stagehand", "tavily-python", "weaviate-client", "xml"]
[[package]]
name = "cryptography"
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