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11 Commits

Author SHA1 Message Date
iris-clawd
75ef1ecf91 test: add test script for LinearTool 2026-04-20 15:08:13 -03:00
iris-clawd
7961678879 feat: add LinearTool for Linear project management API 2026-04-20 15:08:04 -03:00
Greyson LaLonde
01b8437940 fix: handle BaseModel result in guardrail retry loop
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The guardrail retry path passed a Pydantic object directly to
TaskOutput.raw (which expects a string), causing a ValidationError
when output_pydantic is set and a guardrail fails. Mirror the
BaseModel check from the initial execution path into both sync
and async retry loops.

Closes #5544 (part 1)
2026-04-21 01:59:42 +08:00
Lorenze Jay
2c08f54341 feat: add Daytona sandbox tools for enhanced functionality (#5530)
* feat: add Daytona sandbox tools for enhanced functionality

- Introduced DaytonaBaseTool as a shared base for tools interacting with Daytona sandboxes.
- Added DaytonaExecTool for executing shell commands within a sandbox.
- Implemented DaytonaFileTool for managing files (read, write, delete, etc.) in a sandbox.
- Created DaytonaPythonTool for running Python code in a sandbox environment.
- Updated pyproject.toml to include Daytona as a dependency.

* chore: update tool specifications

* refactor: enhance error handling and logging in Daytona tools

- Added logging for best-effort cleanup failures in DaytonaBaseTool and DaytonaFileTool to aid in debugging.
- Improved error message for ImportError in DaytonaPythonTool to provide clearer guidance on SDK compatibility issues.

* linted

* addressing comment

* pinning version

* supporting append

* chore: update tool specifications

---------

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
2026-04-20 10:17:11 -07:00
Greyson LaLonde
bc1f1b85a4 docs: update changelog and version for v1.14.3a1 2026-04-21 00:59:07 +08:00
Greyson LaLonde
0b408534ab feat: bump versions to 1.14.3a1 2026-04-21 00:53:50 +08:00
Greyson LaLonde
48f391092c fix: preserve thought_signature in Gemini streaming tool calls
Gemini thinking models (2.5+, 3.x) require thought_signature on
functionCall parts when sent back in conversation history. The streaming
path was extracting only name/args into plain dicts, losing the
signature. Return raw Part objects (matching the non-streaming path)
so the executor preserves them via raw_tool_call_parts.
2026-04-21 00:01:55 +08:00
Greyson LaLonde
ae242c507d feat: add checkpoint and fork support to standalone agents
Add fork classmethod, _restore_runtime, and _restore_event_scope
to BaseAgent. Fix from_checkpoint to set runtime state on the
event bus and restore event scopes. Store kickoff event ID across
checkpoints to skip re-emission on resume. Handle agent entity
type in checkpoint CLI and TUI.
2026-04-20 22:47:37 +08:00
alex-clawd
0b120fac90 fix: use future dates in checkpoint prune tests to prevent time-dependent failures (#5543)
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The test_older_than tests in both JSON and SQLite prune suites used
hardcoded 2026-04-17 timestamps for the 'new' checkpoint. Once that
date passes, the checkpoint is older than 1 day and gets pruned along
with the 'old' one, causing assert count >= 1 to fail (count=0).

Use 2099-01-01 for the 'new' checkpoint so tests remain stable.

Co-authored-by: Joao Moura <joaomdmoura@gmail.com>
2026-04-20 01:27:12 -03:00
Greyson LaLonde
f879909526 fix: emit task_started on fork resume, redesign checkpoint TUI
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Redesign checkpoint TUI with tabbed detail panel, collapsible
agent rosters, keybinding actions, and human-readable timestamps.
2026-04-18 04:19:31 +08:00
Greyson LaLonde
c9b0004d0e fix: correct dry-run order and handle checked-out stale branch in devtools release
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- Move _update_all_versions inside each dry-run branch so output order matches actual execution
- Switch to main before deleting the stale local branch in create_or_reset_branch
2026-04-17 23:26:52 +08:00
35 changed files with 2535 additions and 289 deletions

124
crewai/tools/linear_tool.py Normal file
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@@ -0,0 +1,124 @@
import os
from enum import Enum
from typing import Any, Type
import httpx
from crewai.tools import BaseTool
from pydantic import BaseModel, Field
LINEAR_API_URL = "https://api.linear.app/graphql"
class LinearAction(str, Enum):
MY_ISSUES = "my_issues"
LIST_TEAMS = "list_teams"
LIST_PROJECTS = "list_projects"
class LinearToolInput(BaseModel):
action: LinearAction = Field(
description=(
"Action to perform: "
"'my_issues' — fetch issues assigned to the authenticated user; "
"'list_teams' — list all teams in the workspace; "
"'list_projects' — list all projects in the workspace."
)
)
first: int = Field(
default=25,
ge=1,
le=250,
description="Maximum number of records to return (1250).",
)
_QUERIES: dict[LinearAction, str] = {
LinearAction.MY_ISSUES: """
query MyIssues($first: Int!) {
viewer {
assignedIssues(first: $first, orderBy: updatedAt) {
nodes {
id
identifier
title
state { name }
priority
url
updatedAt
}
}
}
}
""",
LinearAction.LIST_TEAMS: """
query ListTeams($first: Int!) {
teams(first: $first) {
nodes {
id
name
key
description
}
}
}
""",
LinearAction.LIST_PROJECTS: """
query ListProjects($first: Int!) {
projects(first: $first, orderBy: updatedAt) {
nodes {
id
name
description
state
url
updatedAt
}
}
}
""",
}
def _extract(action: LinearAction, data: dict) -> list[dict]:
if action == LinearAction.MY_ISSUES:
return data["viewer"]["assignedIssues"]["nodes"]
if action == LinearAction.LIST_TEAMS:
return data["teams"]["nodes"]
if action == LinearAction.LIST_PROJECTS:
return data["projects"]["nodes"]
return []
class LinearTool(BaseTool):
name: str = "Linear API Tool"
description: str = (
"Interact with the Linear project management API. "
"Supports fetching your assigned issues, listing teams, and listing projects."
)
args_schema: Type[BaseModel] = LinearToolInput
def _run(self, action: LinearAction, first: int = 25) -> Any:
api_key = os.environ.get("LINEAR_API_KEY", "")
if not api_key:
raise EnvironmentError("LINEAR_API_KEY environment variable is not set.")
query = _QUERIES[action]
payload = {"query": query, "variables": {"first": first}}
headers = {
"Authorization": api_key,
"Content-Type": "application/json",
}
response = httpx.post(
LINEAR_API_URL,
json=payload,
headers=headers,
timeout=15,
)
response.raise_for_status()
body = response.json()
if "errors" in body:
raise RuntimeError(f"Linear API errors: {body['errors']}")
return _extract(action, body["data"])

View File

@@ -4,6 +4,31 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
icon: "clock"
mode: "wide"
---
<Update label="21 أبريل 2026">
## v1.14.3a1
[عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a1)
## ما الذي تغير
### الميزات
- إضافة دعم نقاط التحقق والفروع لوكلاء مستقلين
### إصلاحات الأخطاء
- الحفاظ على thought_signature في استدعاءات أداة البث Gemini
- إصدار task_started عند استئناف الفرع وإعادة تصميم واجهة المستخدم النصية لنقاط التحقق
- تصحيح ترتيب التشغيل الجاف ومعالجة الفرع القديم الذي تم التحقق منه في إصدار أدوات التطوير
- استخدام تواريخ مستقبلية في اختبارات تقليم نقاط التحقق لمنع الفشل المعتمد على الوقت (#5543)
### الوثائق
- تحديث سجل التغييرات والإصدار لـ v1.14.2
## المساهمون
@alex-clawd, @greysonlalonde
</Update>
<Update label="17 أبريل 2026">
## v1.14.2

View File

@@ -4,6 +4,31 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
icon: "clock"
mode: "wide"
---
<Update label="Apr 21, 2026">
## v1.14.3a1
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a1)
## What's Changed
### Features
- Add checkpoint and fork support to standalone agents
### Bug Fixes
- Preserve thought_signature in Gemini streaming tool calls
- Emit task_started on fork resume and redesign checkpoint TUI
- Correct dry-run order and handle checked-out stale branch in devtools release
- Use future dates in checkpoint prune tests to prevent time-dependent failures (#5543)
### Documentation
- Update changelog and version for v1.14.2
## Contributors
@alex-clawd, @greysonlalonde
</Update>
<Update label="Apr 17, 2026">
## v1.14.2

View File

@@ -4,6 +4,31 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
icon: "clock"
mode: "wide"
---
<Update label="2026년 4월 21일">
## v1.14.3a1
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a1)
## 변경 사항
### 기능
- 독립형 에이전트에 체크포인트 및 포크 지원 추가
### 버그 수정
- Gemini 스트리밍 도구 호출에서 thought_signature 보존
- 포크 재개 시 task_started 방출 및 체크포인트 TUI 재설계
- dry-run 순서 수정 및 devtools 릴리스에서 체크아웃된 오래된 브랜치 처리
- 체크포인트 가지치기 테스트에서 미래 날짜 사용하여 시간 의존성 실패 방지 (#5543)
### 문서
- v1.14.2에 대한 변경 로그 및 버전 업데이트
## 기여자
@alex-clawd, @greysonlalonde
</Update>
<Update label="2026년 4월 17일">
## v1.14.2

View File

@@ -4,6 +4,31 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
icon: "clock"
mode: "wide"
---
<Update label="21 abr 2026">
## v1.14.3a1
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a1)
## O que Mudou
### Funcionalidades
- Adicionar suporte a checkpoint e fork para agentes autônomos
### Correções de Bugs
- Preservar thought_signature nas chamadas da ferramenta de streaming Gemini
- Emitir task_started na retomada do fork e redesenhar a TUI de checkpoint
- Corrigir a ordem do dry-run e lidar com branch desatualizada em release do devtools
- Usar datas futuras nos testes de poda de checkpoint para evitar falhas dependentes do tempo (#5543)
### Documentação
- Atualizar changelog e versão para v1.14.2
## Contribuidores
@alex-clawd, @greysonlalonde
</Update>
<Update label="17 abr 2026">
## v1.14.2

View File

@@ -152,4 +152,4 @@ __all__ = [
"wrap_file_source",
]
__version__ = "1.14.2"
__version__ = "1.14.3a1"

View File

@@ -10,7 +10,7 @@ requires-python = ">=3.10, <3.14"
dependencies = [
"pytube~=15.0.0",
"requests>=2.33.0,<3",
"crewai==1.14.2",
"crewai==1.14.3a1",
"tiktoken~=0.8.0",
"beautifulsoup4~=4.13.4",
"python-docx~=1.2.0",
@@ -139,6 +139,9 @@ contextual = [
"contextual-client>=0.1.0",
"nest-asyncio>=1.6.0",
]
daytona = [
"daytona~=0.140.0",
]
[tool.uv]

View File

@@ -59,6 +59,11 @@ from crewai_tools.tools.dalle_tool.dalle_tool import DallETool
from crewai_tools.tools.databricks_query_tool.databricks_query_tool import (
DatabricksQueryTool,
)
from crewai_tools.tools.daytona_sandbox_tool import (
DaytonaExecTool,
DaytonaFileTool,
DaytonaPythonTool,
)
from crewai_tools.tools.directory_read_tool.directory_read_tool import (
DirectoryReadTool,
)
@@ -232,6 +237,9 @@ __all__ = [
"DOCXSearchTool",
"DallETool",
"DatabricksQueryTool",
"DaytonaExecTool",
"DaytonaFileTool",
"DaytonaPythonTool",
"DirectoryReadTool",
"DirectorySearchTool",
"EXASearchTool",
@@ -305,4 +313,4 @@ __all__ = [
"ZapierActionTools",
]
__version__ = "1.14.2"
__version__ = "1.14.3a1"

View File

@@ -48,6 +48,11 @@ from crewai_tools.tools.dalle_tool.dalle_tool import DallETool
from crewai_tools.tools.databricks_query_tool.databricks_query_tool import (
DatabricksQueryTool,
)
from crewai_tools.tools.daytona_sandbox_tool import (
DaytonaExecTool,
DaytonaFileTool,
DaytonaPythonTool,
)
from crewai_tools.tools.directory_read_tool.directory_read_tool import (
DirectoryReadTool,
)
@@ -217,6 +222,9 @@ __all__ = [
"DOCXSearchTool",
"DallETool",
"DatabricksQueryTool",
"DaytonaExecTool",
"DaytonaFileTool",
"DaytonaPythonTool",
"DirectoryReadTool",
"DirectorySearchTool",
"EXASearchTool",

View File

@@ -0,0 +1,107 @@
# Daytona Sandbox Tools
Run shell commands, execute Python, and manage files inside a [Daytona](https://www.daytona.io/) sandbox. Daytona provides isolated, ephemeral compute environments suitable for agent-driven code execution.
Three tools are provided so you can pick what the agent actually needs:
- **`DaytonaExecTool`** — run a shell command (`sandbox.process.exec`).
- **`DaytonaPythonTool`** — run a Python script (`sandbox.process.code_run`).
- **`DaytonaFileTool`** — read / write / list / delete files (`sandbox.fs.*`).
## Installation
```shell
uv add "crewai-tools[daytona]"
# or
pip install "crewai-tools[daytona]"
```
Set the API key:
```shell
export DAYTONA_API_KEY="..."
```
`DAYTONA_API_URL` and `DAYTONA_TARGET` are also respected if set.
## Sandbox lifecycle
All three tools share the same lifecycle controls from `DaytonaBaseTool`:
| Mode | When the sandbox is created | When it is deleted |
| --- | --- | --- |
| **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 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 steps — this is typical when pairing `DaytonaFileTool` with `DaytonaExecTool`.
## Examples
### One-shot Python execution (ephemeral)
```python
from crewai_tools import DaytonaPythonTool
tool = DaytonaPythonTool()
result = tool.run(code="print(sum(range(10)))")
```
### Multi-step shell session (persistent)
```python
from crewai_tools import DaytonaExecTool, DaytonaFileTool
exec_tool = DaytonaExecTool(persistent=True)
file_tool = DaytonaFileTool(persistent=True)
# Agent writes a script, then runs it — both share the same sandbox instance
# because they each keep their own persistent sandbox. If you need the *same*
# sandbox across two tools, create one tool, grab the sandbox id via
# `tool._persistent_sandbox.id`, and pass it to the other via `sandbox_id=...`.
```
### Attach to an existing sandbox
```python
from crewai_tools import DaytonaExecTool
tool = DaytonaExecTool(sandbox_id="my-long-lived-sandbox")
```
### Custom create params
Pass Daytona's `CreateSandboxFromSnapshotParams` kwargs via `create_params`:
```python
tool = DaytonaExecTool(
persistent=True,
create_params={
"language": "python",
"env_vars": {"MY_FLAG": "1"},
"labels": {"owner": "crewai-agent"},
},
)
```
## Tool arguments
### `DaytonaExecTool`
- `command: str` — shell command to run.
- `cwd: str | None` — working directory.
- `env: dict[str, str] | None` — extra env vars for this command.
- `timeout: int | None` — seconds.
### `DaytonaPythonTool`
- `code: str` — Python source to execute.
- `argv: list[str] | None` — argv forwarded via `CodeRunParams`.
- `env: dict[str, str] | None` — env vars forwarded via `CodeRunParams`.
- `timeout: int | None` — seconds.
### `DaytonaFileTool`
- `action: "read" | "write" | "list" | "delete" | "mkdir" | "info"`
- `path: str` — absolute path inside the sandbox.
- `content: str | None` — required for `write`.
- `binary: bool` — if `True`, `content` is base64 on write / returned as base64 on read.
- `recursive: bool` — for `delete`, removes directories recursively.
- `mode: str` — for `mkdir`, octal permission string (default `"0755"`).

View File

@@ -0,0 +1,13 @@
from crewai_tools.tools.daytona_sandbox_tool.daytona_base_tool import DaytonaBaseTool
from crewai_tools.tools.daytona_sandbox_tool.daytona_exec_tool import DaytonaExecTool
from crewai_tools.tools.daytona_sandbox_tool.daytona_file_tool import DaytonaFileTool
from crewai_tools.tools.daytona_sandbox_tool.daytona_python_tool import (
DaytonaPythonTool,
)
__all__ = [
"DaytonaBaseTool",
"DaytonaExecTool",
"DaytonaFileTool",
"DaytonaPythonTool",
]

View File

@@ -0,0 +1,198 @@
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
logger = logging.getLogger(__name__)
class DaytonaBaseTool(BaseTool):
"""Shared base for tools that act on a Daytona sandbox.
Lifecycle modes:
- persistent=False (default): create a fresh sandbox per `_run` call and
delete 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 delete 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 deleted by the tool.
"""
model_config = ConfigDict(arbitrary_types_allowed=True)
package_dependencies: list[str] = Field(default_factory=lambda: ["daytona"])
api_key: str | None = Field(
default_factory=lambda: os.getenv("DAYTONA_API_KEY"),
description="Daytona API key. Falls back to DAYTONA_API_KEY env var.",
json_schema_extra={"required": False},
)
api_url: str | None = Field(
default_factory=lambda: os.getenv("DAYTONA_API_URL"),
description="Daytona API URL override. Falls back to DAYTONA_API_URL env var.",
json_schema_extra={"required": False},
)
target: str | None = Field(
default_factory=lambda: os.getenv("DAYTONA_TARGET"),
description="Daytona target region. Falls back to DAYTONA_TARGET env var.",
json_schema_extra={"required": False},
)
persistent: bool = Field(
default=False,
description=(
"If True, reuse one sandbox across all calls to this tool instance "
"and delete it at process exit. Default False creates and deletes a "
"fresh sandbox per call."
),
)
sandbox_id: str | None = Field(
default=None,
description=(
"Attach to an existing sandbox by id or name instead of creating a "
"new one. The tool will never delete a sandbox it did not create."
),
)
create_params: dict[str, Any] | None = Field(
default=None,
description=(
"Optional kwargs forwarded to CreateSandboxFromSnapshotParams when "
"creating a sandbox (e.g. language, snapshot, env_vars, labels)."
),
)
sandbox_timeout: float = Field(
default=60.0,
description="Timeout in seconds for sandbox create/delete operations.",
)
env_vars: list[EnvVar] = Field(
default_factory=lambda: [
EnvVar(
name="DAYTONA_API_KEY",
description="API key for Daytona sandbox service",
required=False,
),
EnvVar(
name="DAYTONA_API_URL",
description="Daytona API base URL (optional)",
required=False,
),
EnvVar(
name="DAYTONA_TARGET",
description="Daytona target region (optional)",
required=False,
),
]
)
_client: Any | None = PrivateAttr(default=None)
_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_sdk(cls) -> dict[str, Any]:
if cls._sdk_cache:
return cls._sdk_cache
try:
from daytona import (
CreateSandboxFromSnapshotParams,
Daytona,
DaytonaConfig,
)
except ImportError as exc:
raise ImportError(
"The 'daytona' package is required for Daytona sandbox tools. "
"Install it with: uv add daytona (or) pip install daytona"
) from exc
cls._sdk_cache = {
"Daytona": Daytona,
"DaytonaConfig": DaytonaConfig,
"CreateSandboxFromSnapshotParams": CreateSandboxFromSnapshotParams,
}
return cls._sdk_cache
def _get_client(self) -> Any:
if self._client is not None:
return self._client
sdk = self._import_sdk()
config_kwargs: dict[str, Any] = {}
if self.api_key:
config_kwargs["api_key"] = self.api_key
if self.api_url:
config_kwargs["api_url"] = self.api_url
if self.target:
config_kwargs["target"] = self.target
config = sdk["DaytonaConfig"](**config_kwargs) if config_kwargs else None
self._client = sdk["Daytona"](config) if config else sdk["Daytona"]()
return self._client
def _build_create_params(self) -> Any | None:
if not self.create_params:
return None
sdk = self._import_sdk()
return sdk["CreateSandboxFromSnapshotParams"](**self.create_params)
def _acquire_sandbox(self) -> tuple[Any, bool]:
"""Return (sandbox, should_delete_after_use)."""
client = self._get_client()
if self.sandbox_id:
return client.get(self.sandbox_id), False
if self.persistent:
with self._lock:
if self._persistent_sandbox is None:
self._persistent_sandbox = client.create(
self._build_create_params(),
timeout=self.sandbox_timeout,
)
if not self._cleanup_registered:
atexit.register(self.close)
self._cleanup_registered = True
return self._persistent_sandbox, False
sandbox = client.create(
self._build_create_params(),
timeout=self.sandbox_timeout,
)
return sandbox, True
def _release_sandbox(self, sandbox: Any, should_delete: bool) -> None:
if not should_delete:
return
try:
sandbox.delete(timeout=self.sandbox_timeout)
except Exception:
logger.debug(
"Best-effort sandbox cleanup failed after ephemeral use; "
"the sandbox may need manual deletion.",
exc_info=True,
)
def close(self) -> None:
"""Delete 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.delete(timeout=self.sandbox_timeout)
except Exception:
logger.debug(
"Best-effort persistent sandbox cleanup failed at close(); "
"the sandbox may need manual deletion.",
exc_info=True,
)

View File

@@ -0,0 +1,59 @@
from __future__ import annotations
from builtins import type as type_
from typing import Any
from pydantic import BaseModel, Field
from crewai_tools.tools.daytona_sandbox_tool.daytona_base_tool import DaytonaBaseTool
class DaytonaExecToolSchema(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 work dir.",
)
env: dict[str, str] | None = Field(
default=None,
description="Optional environment variables to set for this command.",
)
timeout: int | None = Field(
default=None,
description="Maximum seconds to wait for the command to finish.",
)
class DaytonaExecTool(DaytonaBaseTool):
"""Run a shell command inside a Daytona sandbox."""
name: str = "Daytona Sandbox Exec"
description: str = (
"Execute a shell command inside a Daytona sandbox and return the exit "
"code and combined output. Use this to run builds, package installs, "
"git operations, or any one-off shell command."
)
args_schema: type_[BaseModel] = DaytonaExecToolSchema
def _run(
self,
command: str,
cwd: str | None = None,
env: dict[str, str] | None = None,
timeout: int | None = None,
) -> Any:
sandbox, should_delete = self._acquire_sandbox()
try:
response = sandbox.process.exec(
command,
cwd=cwd,
env=env,
timeout=timeout,
)
return {
"exit_code": getattr(response, "exit_code", None),
"result": getattr(response, "result", None),
"artifacts": getattr(response, "artifacts", None),
}
finally:
self._release_sandbox(sandbox, should_delete)

View File

@@ -0,0 +1,205 @@
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.daytona_sandbox_tool.daytona_base_tool import DaytonaBaseTool
logger = logging.getLogger(__name__)
FileAction = Literal["read", "write", "append", "list", "delete", "mkdir", "info"]
class DaytonaFileToolSchema(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)."
),
)
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': treat content as base64 and upload raw bytes. "
"For 'read': return contents as base64 instead of decoded utf-8."
),
)
recursive: bool = Field(
default=False,
description="For action='delete': remove directories recursively.",
)
mode: str = Field(
default="0755",
description="For action='mkdir': octal permission string (default 0755).",
)
@model_validator(mode="after")
def _validate_action_args(self) -> DaytonaFileToolSchema:
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 DaytonaFileTool(DaytonaBaseTool):
"""Read, write, and manage files inside a Daytona 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 = "Daytona Sandbox Files"
description: str = (
"Perform filesystem operations inside a Daytona sandbox: read a file, "
"write content to a path, append content to an existing file, list a "
"directory, delete a path, make a directory, or fetch file metadata. "
"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] = DaytonaFileToolSchema
def _run(
self,
action: FileAction,
path: str,
content: str | None = None,
binary: bool = False,
recursive: bool = False,
mode: str = "0755",
) -> Any:
sandbox, should_delete = 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)
if action == "delete":
sandbox.fs.delete_file(path, recursive=recursive)
return {"status": "deleted", "path": path}
if action == "mkdir":
sandbox.fs.create_folder(path, mode)
return {"status": "created", "path": path, "mode": mode}
if action == "info":
return self._info(sandbox, path)
raise ValueError(f"Unknown action: {action}")
finally:
self._release_sandbox(sandbox, should_delete)
def _read(self, sandbox: Any, path: str, *, binary: bool) -> dict[str, Any]:
data: bytes = sandbox.fs.download_file(path)
if binary:
return {
"path": path,
"encoding": "base64",
"content": base64.b64encode(data).decode("ascii"),
}
try:
return {"path": path, "encoding": "utf-8", "content": data.decode("utf-8")}
except UnicodeDecodeError:
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 = base64.b64decode(content) if binary else content.encode("utf-8")
self._ensure_parent_dir(sandbox, path)
sandbox.fs.upload_file(payload, path)
return {"status": "written", "path": path, "bytes": len(payload)}
def _append(
self, sandbox: Any, path: str, content: str, *, binary: bool
) -> dict[str, Any]:
chunk = base64.b64decode(content) if binary else content.encode("utf-8")
self._ensure_parent_dir(sandbox, path)
try:
existing: bytes = sandbox.fs.download_file(path)
except Exception:
existing = b""
payload = existing + chunk
sandbox.fs.upload_file(payload, path)
return {
"status": "appended",
"path": path,
"appended_bytes": len(chunk),
"total_bytes": len(payload),
}
@staticmethod
def _ensure_parent_dir(sandbox: Any, path: str) -> None:
"""Make sure the parent directory of `path` exists.
Daytona's upload returns 400 if the parent directory is missing. We
best-effort mkdir the parent; any error (e.g. already exists) is
swallowed because `create_folder` is not idempotent on the server.
"""
parent = posixpath.dirname(path)
if not parent or parent in ("/", "."):
return
try:
sandbox.fs.create_folder(parent, "0755")
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) -> dict[str, Any]:
entries = sandbox.fs.list_files(path)
return {
"path": path,
"entries": [self._file_info_to_dict(entry) for entry in entries],
}
def _info(self, sandbox: Any, path: str) -> dict[str, Any]:
return self._file_info_to_dict(sandbox.fs.get_file_info(path))
@staticmethod
def _file_info_to_dict(info: Any) -> dict[str, Any]:
fields = (
"name",
"size",
"mode",
"permissions",
"is_dir",
"mod_time",
"owner",
"group",
)
return {field: getattr(info, field, None) for field in fields}

View File

@@ -0,0 +1,82 @@
from __future__ import annotations
from builtins import type as type_
from typing import Any
from pydantic import BaseModel, Field
from crewai_tools.tools.daytona_sandbox_tool.daytona_base_tool import DaytonaBaseTool
class DaytonaPythonToolSchema(BaseModel):
code: str = Field(
...,
description="Python source to execute inside the sandbox.",
)
argv: list[str] | None = Field(
default=None,
description="Optional argv passed to the script (forwarded as params.argv).",
)
env: dict[str, str] | None = Field(
default=None,
description="Optional environment variables for the run (forwarded as params.env).",
)
timeout: int | None = Field(
default=None,
description="Maximum seconds to wait for the code to finish.",
)
class DaytonaPythonTool(DaytonaBaseTool):
"""Run Python source inside a Daytona sandbox."""
name: str = "Daytona Sandbox Python"
description: str = (
"Execute a block of Python code inside a Daytona sandbox and return the "
"exit code, captured stdout, and any produced artifacts. Use this for "
"data processing, quick scripts, or analysis that should run in an "
"isolated environment."
)
args_schema: type_[BaseModel] = DaytonaPythonToolSchema
def _run(
self,
code: str,
argv: list[str] | None = None,
env: dict[str, str] | None = None,
timeout: int | None = None,
) -> Any:
sandbox, should_delete = self._acquire_sandbox()
try:
params = self._build_code_run_params(argv=argv, env=env)
response = sandbox.process.code_run(code, params=params, timeout=timeout)
return {
"exit_code": getattr(response, "exit_code", None),
"result": getattr(response, "result", None),
"artifacts": getattr(response, "artifacts", None),
}
finally:
self._release_sandbox(sandbox, should_delete)
def _build_code_run_params(
self,
argv: list[str] | None,
env: dict[str, str] | None,
) -> Any | None:
if argv is None and env is None:
return None
try:
from daytona import CodeRunParams
except ImportError as exc:
raise ImportError(
"Could not import daytona.CodeRunParams while building "
"argv/env for sandbox.process.code_run. This usually means the "
"installed 'daytona' SDK is too old or incompatible. Upgrade "
"with: pip install -U 'crewai-tools[daytona]'"
) from exc
kwargs: dict[str, Any] = {}
if argv is not None:
kwargs["argv"] = argv
if env is not None:
kwargs["env"] = env
return CodeRunParams(**kwargs)

View File

@@ -6976,6 +6976,634 @@
"type": "object"
}
},
{
"description": "Execute a shell command inside a Daytona sandbox and return the exit code and combined output. Use this to run builds, package installs, git operations, or any one-off shell command.",
"env_vars": [
{
"default": null,
"description": "API key for Daytona sandbox service",
"name": "DAYTONA_API_KEY",
"required": false
},
{
"default": null,
"description": "Daytona API base URL (optional)",
"name": "DAYTONA_API_URL",
"required": false
},
{
"default": null,
"description": "Daytona target region (optional)",
"name": "DAYTONA_TARGET",
"required": false
}
],
"humanized_name": "Daytona 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 a Daytona sandbox.",
"properties": {
"api_key": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Daytona API key. Falls back to DAYTONA_API_KEY env var.",
"required": false,
"title": "Api Key"
},
"api_url": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Daytona API URL override. Falls back to DAYTONA_API_URL env var.",
"required": false,
"title": "Api Url"
},
"create_params": {
"anyOf": [
{
"additionalProperties": true,
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional kwargs forwarded to CreateSandboxFromSnapshotParams when creating a sandbox (e.g. language, snapshot, env_vars, labels).",
"title": "Create Params"
},
"persistent": {
"default": false,
"description": "If True, reuse one sandbox across all calls to this tool instance and delete it at process exit. Default False creates and deletes 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 or name instead of creating a new one. The tool will never delete a sandbox it did not create.",
"title": "Sandbox Id"
},
"sandbox_timeout": {
"default": 60.0,
"description": "Timeout in seconds for sandbox create/delete operations.",
"title": "Sandbox Timeout",
"type": "number"
},
"target": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Daytona target region. Falls back to DAYTONA_TARGET env var.",
"required": false,
"title": "Target"
}
},
"required": [],
"title": "DaytonaExecTool",
"type": "object"
},
"name": "DaytonaExecTool",
"package_dependencies": [
"daytona"
],
"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 work dir.",
"title": "Cwd"
},
"env": {
"anyOf": [
{
"additionalProperties": {
"type": "string"
},
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional environment variables to set for this command.",
"title": "Env"
},
"timeout": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"default": null,
"description": "Maximum seconds to wait for the command to finish.",
"title": "Timeout"
}
},
"required": [
"command"
],
"title": "DaytonaExecToolSchema",
"type": "object"
}
},
{
"description": "Perform filesystem operations inside a Daytona sandbox: read a file, write content to a path, append content to an existing file, list a directory, delete a path, make a directory, or fetch file metadata. 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 Daytona sandbox service",
"name": "DAYTONA_API_KEY",
"required": false
},
{
"default": null,
"description": "Daytona API base URL (optional)",
"name": "DAYTONA_API_URL",
"required": false
},
{
"default": null,
"description": "Daytona target region (optional)",
"name": "DAYTONA_TARGET",
"required": false
}
],
"humanized_name": "Daytona 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 a Daytona sandbox.\n\nNotes:\n - Most useful with `persistent=True` or an explicit `sandbox_id`. With the\n default ephemeral mode, files disappear when this tool call finishes.",
"properties": {
"api_key": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Daytona API key. Falls back to DAYTONA_API_KEY env var.",
"required": false,
"title": "Api Key"
},
"api_url": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Daytona API URL override. Falls back to DAYTONA_API_URL env var.",
"required": false,
"title": "Api Url"
},
"create_params": {
"anyOf": [
{
"additionalProperties": true,
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional kwargs forwarded to CreateSandboxFromSnapshotParams when creating a sandbox (e.g. language, snapshot, env_vars, labels).",
"title": "Create Params"
},
"persistent": {
"default": false,
"description": "If True, reuse one sandbox across all calls to this tool instance and delete it at process exit. Default False creates and deletes 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 or name instead of creating a new one. The tool will never delete a sandbox it did not create.",
"title": "Sandbox Id"
},
"sandbox_timeout": {
"default": 60.0,
"description": "Timeout in seconds for sandbox create/delete operations.",
"title": "Sandbox Timeout",
"type": "number"
},
"target": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Daytona target region. Falls back to DAYTONA_TARGET env var.",
"required": false,
"title": "Target"
}
},
"required": [],
"title": "DaytonaFileTool",
"type": "object"
},
"name": "DaytonaFileTool",
"package_dependencies": [
"daytona"
],
"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).",
"enum": [
"read",
"write",
"append",
"list",
"delete",
"mkdir",
"info"
],
"title": "Action",
"type": "string"
},
"binary": {
"default": false,
"description": "For 'write': 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"
},
"mode": {
"default": "0755",
"description": "For action='mkdir': octal permission string (default 0755).",
"title": "Mode",
"type": "string"
},
"path": {
"description": "Absolute path inside the sandbox.",
"title": "Path",
"type": "string"
},
"recursive": {
"default": false,
"description": "For action='delete': remove directories recursively.",
"title": "Recursive",
"type": "boolean"
}
},
"required": [
"action",
"path"
],
"title": "DaytonaFileToolSchema",
"type": "object"
}
},
{
"description": "Execute a block of Python code inside a Daytona sandbox and return the exit code, captured stdout, and any produced artifacts. Use this for data processing, quick scripts, or analysis that should run in an isolated environment.",
"env_vars": [
{
"default": null,
"description": "API key for Daytona sandbox service",
"name": "DAYTONA_API_KEY",
"required": false
},
{
"default": null,
"description": "Daytona API base URL (optional)",
"name": "DAYTONA_API_URL",
"required": false
},
{
"default": null,
"description": "Daytona target region (optional)",
"name": "DAYTONA_TARGET",
"required": false
}
],
"humanized_name": "Daytona 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 source inside a Daytona sandbox.",
"properties": {
"api_key": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Daytona API key. Falls back to DAYTONA_API_KEY env var.",
"required": false,
"title": "Api Key"
},
"api_url": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Daytona API URL override. Falls back to DAYTONA_API_URL env var.",
"required": false,
"title": "Api Url"
},
"create_params": {
"anyOf": [
{
"additionalProperties": true,
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional kwargs forwarded to CreateSandboxFromSnapshotParams when creating a sandbox (e.g. language, snapshot, env_vars, labels).",
"title": "Create Params"
},
"persistent": {
"default": false,
"description": "If True, reuse one sandbox across all calls to this tool instance and delete it at process exit. Default False creates and deletes 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 or name instead of creating a new one. The tool will never delete a sandbox it did not create.",
"title": "Sandbox Id"
},
"sandbox_timeout": {
"default": 60.0,
"description": "Timeout in seconds for sandbox create/delete operations.",
"title": "Sandbox Timeout",
"type": "number"
},
"target": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Daytona target region. Falls back to DAYTONA_TARGET env var.",
"required": false,
"title": "Target"
}
},
"required": [],
"title": "DaytonaPythonTool",
"type": "object"
},
"name": "DaytonaPythonTool",
"package_dependencies": [
"daytona"
],
"run_params_schema": {
"properties": {
"argv": {
"anyOf": [
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional argv passed to the script (forwarded as params.argv).",
"title": "Argv"
},
"code": {
"description": "Python source to execute inside the sandbox.",
"title": "Code",
"type": "string"
},
"env": {
"anyOf": [
{
"additionalProperties": {
"type": "string"
},
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional environment variables for the run (forwarded as params.env).",
"title": "Env"
},
"timeout": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"default": null,
"description": "Maximum seconds to wait for the code to finish.",
"title": "Timeout"
}
},
"required": [
"code"
],
"title": "DaytonaPythonToolSchema",
"type": "object"
}
},
{
"description": "A tool that can be used to recursively list a directory's content.",
"env_vars": [],

View File

@@ -55,7 +55,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
[project.optional-dependencies]
tools = [
"crewai-tools==1.14.2",
"crewai-tools==1.14.3a1",
]
embeddings = [
"tiktoken~=0.8.0"

View File

@@ -46,7 +46,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
_suppress_pydantic_deprecation_warnings()
__version__ = "1.14.2"
__version__ = "1.14.3a1"
_telemetry_submitted = False

View File

@@ -29,7 +29,7 @@ from pydantic import (
model_validator,
)
from pydantic.functional_serializers import PlainSerializer
from typing_extensions import Self
from typing_extensions import Self, TypeIs
from crewai.agent.planning_config import PlanningConfig
from crewai.agent.utils import (
@@ -133,6 +133,13 @@ _EXECUTOR_CLASS_MAP: dict[str, type] = {
}
def _is_resuming_agent_executor(
executor: CrewAgentExecutor | AgentExecutor | None,
) -> TypeIs[AgentExecutor]:
"""Type guard: True when the executor is resuming from a checkpoint."""
return isinstance(executor, AgentExecutor) and executor._resuming
def _validate_executor_class(value: Any) -> Any:
if isinstance(value, str):
cls = _EXECUTOR_CLASS_MAP.get(value)
@@ -1366,24 +1373,42 @@ class Agent(BaseAgent):
prompt, stop_words, rpm_limit_fn = self._build_execution_prompt(raw_tools)
executor = AgentExecutor(
llm=cast(BaseLLM, self.llm),
agent=self,
prompt=prompt,
max_iter=self.max_iter,
tools=parsed_tools,
tools_names=get_tool_names(parsed_tools),
stop_words=stop_words,
tools_description=render_text_description_and_args(parsed_tools),
tools_handler=self.tools_handler,
original_tools=raw_tools,
step_callback=self.step_callback,
function_calling_llm=self.function_calling_llm,
respect_context_window=self.respect_context_window,
request_within_rpm_limit=rpm_limit_fn,
callbacks=[TokenCalcHandler(self._token_process)],
response_model=response_format,
)
if _is_resuming_agent_executor(self.agent_executor):
executor = self.agent_executor
executor.tools = parsed_tools
executor.tools_names = get_tool_names(parsed_tools)
executor.tools_description = render_text_description_and_args(parsed_tools)
executor.original_tools = raw_tools
executor.prompt = prompt
executor.response_model = response_format
executor.stop_words = stop_words
executor.tools_handler = self.tools_handler
executor.step_callback = self.step_callback
executor.function_calling_llm = cast(
BaseLLM | None, self.function_calling_llm
)
executor.respect_context_window = self.respect_context_window
executor.request_within_rpm_limit = rpm_limit_fn
executor.callbacks = [TokenCalcHandler(self._token_process)]
else:
executor = AgentExecutor(
llm=cast(BaseLLM, self.llm),
agent=self,
prompt=prompt,
max_iter=self.max_iter,
tools=parsed_tools,
tools_names=get_tool_names(parsed_tools),
stop_words=stop_words,
tools_description=render_text_description_and_args(parsed_tools),
tools_handler=self.tools_handler,
original_tools=raw_tools,
step_callback=self.step_callback,
function_calling_llm=self.function_calling_llm,
respect_context_window=self.respect_context_window,
request_within_rpm_limit=rpm_limit_fn,
callbacks=[TokenCalcHandler(self._token_process)],
response_model=response_format,
)
all_files: dict[str, Any] = {}
if isinstance(messages, str):
@@ -1504,14 +1529,17 @@ class Agent(BaseAgent):
)
try:
crewai_event_bus.emit(
self,
event=LiteAgentExecutionStartedEvent(
if self.checkpoint_kickoff_event_id is not None:
self._kickoff_event_id = self.checkpoint_kickoff_event_id
self.checkpoint_kickoff_event_id = None
else:
started_event = LiteAgentExecutionStartedEvent(
agent_info=agent_info,
tools=parsed_tools,
messages=messages,
),
)
)
crewai_event_bus.emit(self, event=started_event)
self._kickoff_event_id = started_event.event_id
output = self._execute_and_build_output(executor, inputs, response_format)
return self._finalize_kickoff(
@@ -1808,14 +1836,17 @@ class Agent(BaseAgent):
)
try:
crewai_event_bus.emit(
self,
event=LiteAgentExecutionStartedEvent(
if self.checkpoint_kickoff_event_id is not None:
self._kickoff_event_id = self.checkpoint_kickoff_event_id
self.checkpoint_kickoff_event_id = None
else:
started_event = LiteAgentExecutionStartedEvent(
agent_info=agent_info,
tools=parsed_tools,
messages=messages,
),
)
)
crewai_event_bus.emit(self, event=started_event)
self._kickoff_event_id = started_event.event_id
output = await self._execute_and_build_output_async(
executor, inputs, response_format

View File

@@ -28,6 +28,9 @@ from crewai.agents.agent_builder.base_agent_executor import BaseAgentExecutor
from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
from crewai.agents.cache.cache_handler import CacheHandler
from crewai.agents.tools_handler import ToolsHandler
from crewai.events.base_events import set_emission_counter
from crewai.events.event_bus import crewai_event_bus
from crewai.events.event_context import restore_event_scope, set_last_event_id
from crewai.knowledge.knowledge import Knowledge
from crewai.knowledge.knowledge_config import KnowledgeConfig
from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
@@ -51,6 +54,7 @@ from crewai.utilities.string_utils import interpolate_only
if TYPE_CHECKING:
from crewai.context import ExecutionContext
from crewai.crew import Crew
from crewai.state.runtime import RuntimeState
def _validate_crew_ref(value: Any) -> Any:
@@ -219,6 +223,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
_original_goal: str | None = PrivateAttr(default=None)
_original_backstory: str | None = PrivateAttr(default=None)
_token_process: TokenProcess = PrivateAttr(default_factory=TokenProcess)
_kickoff_event_id: str | None = PrivateAttr(default=None)
id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
role: str = Field(description="Role of the agent")
goal: str = Field(description="Objective of the agent")
@@ -335,30 +340,90 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
min_length=1,
)
execution_context: ExecutionContext | None = Field(default=None)
checkpoint_kickoff_event_id: str | None = Field(default=None)
@classmethod
def from_checkpoint(cls, config: CheckpointConfig) -> Self:
"""Restore an Agent from a checkpoint.
"""Restore an Agent from a checkpoint, ready to resume via kickoff().
Args:
config: Checkpoint configuration with ``restore_from`` set.
config: Checkpoint configuration with ``restore_from`` set to
the path of the checkpoint to load.
Returns:
An Agent instance. Call kickoff() to resume execution.
"""
from crewai.context import apply_execution_context
from crewai.state.runtime import RuntimeState
state = RuntimeState.from_checkpoint(config, context={"from_checkpoint": True})
crewai_event_bus.set_runtime_state(state)
for entity in state.root:
if isinstance(entity, cls):
if entity.execution_context is not None:
apply_execution_context(entity.execution_context)
if entity.agent_executor is not None:
entity.agent_executor.agent = entity
entity.agent_executor._resuming = True
entity._restore_runtime(state)
return entity
raise ValueError(
f"No {cls.__name__} found in checkpoint: {config.restore_from}"
)
@classmethod
def fork(cls, config: CheckpointConfig, branch: str | None = None) -> Self:
"""Fork an Agent from a checkpoint, creating a new execution branch.
Args:
config: Checkpoint configuration with ``restore_from`` set.
branch: Branch label for the fork. Auto-generated if not provided.
Returns:
An Agent instance on the new branch. Call kickoff() to run.
"""
agent = cls.from_checkpoint(config)
state = crewai_event_bus._runtime_state
if state is None:
raise RuntimeError("Cannot fork: no runtime state on the event bus.")
state.fork(branch)
return agent
def _restore_runtime(self, state: RuntimeState) -> None:
"""Re-create runtime objects after restoring from a checkpoint.
Args:
state: The RuntimeState containing the event record.
"""
if self.agent_executor is not None:
self.agent_executor.agent = self
self.agent_executor._resuming = True
if self.checkpoint_kickoff_event_id is not None:
self._kickoff_event_id = self.checkpoint_kickoff_event_id
self._restore_event_scope(state)
def _restore_event_scope(self, state: RuntimeState) -> None:
"""Rebuild the event scope stack from the checkpoint's event record.
Args:
state: The RuntimeState containing the event record.
"""
stack: list[tuple[str, str]] = []
kickoff_id = self._kickoff_event_id
if kickoff_id:
stack.append((kickoff_id, "lite_agent_execution_started"))
restore_event_scope(tuple(stack))
last_event_id: str | None = None
max_seq = 0
for node in state.event_record.nodes.values():
seq = node.event.emission_sequence or 0
if seq > max_seq:
max_seq = seq
last_event_id = node.event.event_id
if last_event_id is not None:
set_last_event_id(last_event_id)
if max_seq > 0:
set_emission_counter(max_seq)
@model_validator(mode="before")
@classmethod
def process_model_config(cls, values: Any) -> dict[str, Any]:

View File

@@ -106,17 +106,50 @@ def _parse_checkpoint_json(raw: str, source: str) -> dict[str, Any]:
"name": entity.get("name"),
"id": entity.get("id"),
}
raw_agents = entity.get("agents", [])
agents_by_id: dict[str, dict[str, Any]] = {}
parsed_agents: list[dict[str, Any]] = []
for ag in raw_agents:
agent_info: dict[str, Any] = {
"id": ag.get("id", ""),
"role": ag.get("role", ""),
"goal": ag.get("goal", ""),
}
parsed_agents.append(agent_info)
if ag.get("id"):
agents_by_id[str(ag["id"])] = agent_info
if parsed_agents:
info["agents"] = parsed_agents
if tasks:
info["tasks_completed"] = completed
info["tasks_total"] = len(tasks)
info["tasks"] = [
{
parsed_tasks: list[dict[str, Any]] = []
for t in tasks:
task_info: dict[str, Any] = {
"description": t.get("description", ""),
"completed": t.get("output") is not None,
"output": (t.get("output") or {}).get("raw", ""),
}
for t in tasks
]
task_agent = t.get("agent")
if isinstance(task_agent, dict):
task_info["agent_role"] = task_agent.get("role", "")
task_info["agent_id"] = task_agent.get("id", "")
elif isinstance(task_agent, str) and task_agent in agents_by_id:
task_info["agent_role"] = agents_by_id[task_agent].get("role", "")
task_info["agent_id"] = task_agent
parsed_tasks.append(task_info)
info["tasks"] = parsed_tasks
if entity.get("entity_type") == "flow":
completed_methods = entity.get("checkpoint_completed_methods")
if completed_methods:
info["completed_methods"] = sorted(completed_methods)
state = entity.get("checkpoint_state")
if isinstance(state, dict):
info["flow_state"] = state
parsed_entities.append(info)
inputs: dict[str, Any] = {}
@@ -439,6 +472,8 @@ def _entity_type_from_meta(meta: dict[str, Any]) -> str:
for ent in meta.get("entities", []):
if ent.get("type") == "flow":
return "flow"
if ent.get("type") == "agent":
return "agent"
return "crew"
@@ -472,6 +507,11 @@ def resume_checkpoint(location: str, checkpoint_id: str | None) -> None:
flow = Flow.from_checkpoint(config)
result = asyncio.run(flow.kickoff_async(inputs=inputs))
elif entity_type == "agent":
from crewai.agent import Agent
agent = Agent.from_checkpoint(config)
result = asyncio.run(agent.akickoff(messages="Resume execution."))
else:
from crewai.crew import Crew

View File

@@ -3,17 +3,20 @@
from __future__ import annotations
from collections import defaultdict
from datetime import datetime
from typing import Any, ClassVar, Literal
from textual.app import App, ComposeResult
from textual.binding import Binding
from textual.containers import Horizontal, Vertical, VerticalScroll
from textual.widgets import (
Button,
Collapsible,
Footer,
Header,
Input,
Static,
TabPane,
TabbedContent,
TextArea,
Tree,
)
@@ -32,6 +35,22 @@ _TERTIARY = "#ffffff"
_DIM = "#888888"
_BG_DARK = "#0d1117"
_BG_PANEL = "#161b22"
_ACCENT = "#c9a227"
_SUCCESS = "#3fb950"
_PENDING = "#e3b341"
_ENTITY_ICONS: dict[str, str] = {
"flow": "",
"crew": "",
"agent": "",
"unknown": "",
}
_ENTITY_COLORS: dict[str, str] = {
"flow": _ACCENT,
"crew": _SECONDARY,
"agent": _PRIMARY,
"unknown": _DIM,
}
def _load_entries(location: str) -> list[dict[str, Any]]:
@@ -40,8 +59,27 @@ def _load_entries(location: str) -> list[dict[str, Any]]:
return _list_json(location)
def _human_ts(ts: str) -> str:
"""Turn '2026-04-17 17:05:00' into a short relative label."""
try:
dt = datetime.strptime(ts, "%Y-%m-%d %H:%M:%S")
except ValueError:
return ts
now = datetime.now()
delta = now.date() - dt.date()
hour = dt.hour % 12 or 12
ampm = "am" if dt.hour < 12 else "pm"
time_str = f"{hour}:{dt.minute:02d}{ampm}"
if delta.days == 0:
return time_str
if delta.days == 1:
return f"yest {time_str}"
if delta.days < 7:
return f"{dt.strftime('%a').lower()} {time_str}"
return f"{dt.strftime('%b')} {dt.day}"
def _short_id(name: str) -> str:
"""Shorten a checkpoint name for tree display."""
if len(name) > 30:
return name[:27] + "..."
return name
@@ -63,29 +101,29 @@ def _entry_id(entry: dict[str, Any]) -> str:
return name
def _build_entity_header(ent: dict[str, Any]) -> str:
"""Build rich text header for an entity (progress bar only)."""
lines: list[str] = []
tasks = ent.get("tasks")
if isinstance(tasks, list):
completed = ent.get("tasks_completed", 0)
total = ent.get("tasks_total", 0)
pct = int(completed / total * 100) if total else 0
bar_len = 20
filled = int(bar_len * completed / total) if total else 0
bar = f"[{_PRIMARY}]{'' * filled}[/][{_DIM}]{'' * (bar_len - filled)}[/]"
lines.append(f"{bar} {completed}/{total} tasks ({pct}%)")
return "\n".join(lines)
def _build_progress_bar(completed: int, total: int, width: int = 20) -> str:
if total == 0:
return f"[{_DIM}]{'' * width}[/] 0/0"
pct = int(completed / total * 100)
filled = int(width * completed / total)
color = _SUCCESS if completed == total else _PRIMARY
bar = f"[{color}]{'' * filled}[/][{_DIM}]{'' * (width - filled)}[/]"
return f"{bar} {completed}/{total} ({pct}%)"
def _entity_icon(etype: str) -> str:
icon = _ENTITY_ICONS.get(etype, _ENTITY_ICONS["unknown"])
color = _ENTITY_COLORS.get(etype, _DIM)
return f"[{color}]{icon}[/]"
# Return type: (location, action, inputs, task_output_overrides, entity_type)
_TuiResult = (
tuple[
str,
str,
dict[str, Any] | None,
dict[int, str] | None,
Literal["crew", "flow"],
Literal["crew", "flow", "agent"],
]
| None
)
@@ -122,7 +160,7 @@ class CheckpointTUI(App[_TuiResult]):
height: 1fr;
}}
#tree-panel {{
width: 45%;
width: 40%;
background: {_BG_PANEL};
border: round {_SECONDARY};
padding: 0 1;
@@ -132,41 +170,81 @@ class CheckpointTUI(App[_TuiResult]):
border: round {_PRIMARY};
}}
#detail-container {{
width: 55%;
width: 60%;
height: 1fr;
}}
#detail-scroll {{
height: 1fr;
background: {_BG_PANEL};
border: round {_SECONDARY};
padding: 1 2;
scrollbar-color: {_PRIMARY};
}}
#detail-scroll:focus-within {{
border: round {_PRIMARY};
}}
#detail-header {{
margin-bottom: 1;
}}
#status {{
height: 1;
padding: 0 2;
color: {_DIM};
}}
#inputs-section {{
display: none;
height: auto;
max-height: 8;
padding: 0 1;
#detail-tabs {{
height: 1fr;
}}
#inputs-section.visible {{
display: block;
TabbedContent > ContentSwitcher {{
background: {_BG_PANEL};
height: 1fr;
}}
#inputs-label {{
height: 1;
TabPane {{
padding: 0;
}}
Tabs {{
background: {_BG_DARK};
}}
Tab {{
background: {_BG_DARK};
color: {_DIM};
padding: 0 2;
}}
Tab.-active {{
background: {_BG_PANEL};
color: {_PRIMARY};
}}
Tab:hover {{
color: {_TERTIARY};
}}
Underline > .underline--bar {{
color: {_SECONDARY};
background: {_BG_DARK};
}}
.tab-scroll {{
background: {_BG_PANEL};
height: 1fr;
padding: 1 2;
scrollbar-color: {_PRIMARY};
}}
.section-header {{
padding: 0 0 0 1;
margin: 1 0 0 0;
}}
.detail-line {{
padding: 0 0 0 1;
}}
.task-label {{
padding: 0 1;
}}
.task-output-editor {{
height: auto;
max-height: 10;
margin: 0 1 1 3;
border: round {_DIM};
}}
.task-output-editor:focus {{
border: round {_PRIMARY};
}}
Collapsible {{
background: {_BG_PANEL};
padding: 0;
margin: 0 0 1 1;
}}
CollapsibleTitle {{
background: {_BG_DARK};
color: {_TERTIARY};
padding: 0 1;
}}
CollapsibleTitle:hover {{
background: {_SECONDARY};
}}
.input-row {{
height: 3;
padding: 0 1;
@@ -180,55 +258,9 @@ class CheckpointTUI(App[_TuiResult]):
.input-row Input {{
width: 1fr;
}}
#no-inputs-label {{
height: 1;
.empty-state {{
color: {_DIM};
padding: 0 1;
}}
#action-buttons {{
height: 3;
align: right middle;
padding: 0 1;
display: none;
}}
#action-buttons.visible {{
display: block;
}}
#action-buttons Button {{
margin: 0 0 0 1;
min-width: 10;
}}
#btn-resume {{
background: {_SECONDARY};
color: {_TERTIARY};
}}
#btn-resume:hover {{
background: {_PRIMARY};
}}
#btn-fork {{
background: {_PRIMARY};
color: {_TERTIARY};
}}
#btn-fork:hover {{
background: {_SECONDARY};
}}
.entity-title {{
padding: 1 1 0 1;
}}
.entity-detail {{
padding: 0 1;
}}
.task-output-editor {{
height: auto;
max-height: 10;
margin: 0 1 1 1;
border: round {_DIM};
}}
.task-output-editor:focus {{
border: round {_PRIMARY};
}}
.task-label {{
padding: 0 1;
padding: 1;
}}
Tree {{
background: {_BG_PANEL};
@@ -242,6 +274,8 @@ class CheckpointTUI(App[_TuiResult]):
BINDINGS: ClassVar[list[Binding | tuple[str, str] | tuple[str, str, str]]] = [
("q", "quit", "Quit"),
("r", "refresh", "Refresh"),
("e", "resume", "Resume"),
("f", "fork", "Fork"),
]
def __init__(self, location: str = "./.checkpoints") -> None:
@@ -256,27 +290,49 @@ class CheckpointTUI(App[_TuiResult]):
yield Header(show_clock=False)
with Horizontal(id="main-layout"):
tree: Tree[dict[str, Any]] = Tree("Checkpoints", id="tree-panel")
tree.show_root = True
tree.show_root = False
tree.guide_depth = 3
yield tree
with Vertical(id="detail-container"):
yield Static("", id="status")
with VerticalScroll(id="detail-scroll"):
yield Static(
f"[{_DIM}]Select a checkpoint from the tree[/]", # noqa: S608
id="detail-header",
)
with Vertical(id="inputs-section"):
yield Static("Inputs", id="inputs-label")
with Horizontal(id="action-buttons"):
yield Button("Resume", id="btn-resume")
yield Button("Fork", id="btn-fork")
with TabbedContent(id="detail-tabs"):
with TabPane("Overview", id="tab-overview"):
with VerticalScroll(classes="tab-scroll"):
yield Static(
f"[{_DIM}]Select a checkpoint from the tree[/]", # noqa: S608
id="overview-empty",
)
with TabPane("Tasks", id="tab-tasks"):
with VerticalScroll(classes="tab-scroll"):
yield Static(
f"[{_DIM}]Select a checkpoint to view tasks[/]",
id="tasks-empty",
)
with TabPane("Inputs", id="tab-inputs"):
with VerticalScroll(classes="tab-scroll"):
yield Static(
f"[{_DIM}]Select a checkpoint to view inputs[/]",
id="inputs-empty",
)
yield Footer()
async def on_mount(self) -> None:
self._refresh_tree()
self.query_one("#tree-panel", Tree).root.expand()
# ── Tree building ──────────────────────────────────────────────
@staticmethod
def _top_level_entity(entry: dict[str, Any]) -> tuple[str, str]:
etype, ename = "unknown", ""
for ent in entry.get("entities", []):
t = ent.get("type", "unknown")
if t == "flow":
return "flow", ent.get("name") or ""
if t == "crew" and etype != "crew":
etype, ename = "crew", ent.get("name") or ""
return etype, ename
def _refresh_tree(self) -> None:
self._entries = _load_entries(self._location)
self._selected_entry = None
@@ -285,45 +341,57 @@ class CheckpointTUI(App[_TuiResult]):
tree.clear()
if not self._entries:
self.query_one("#detail-header", Static).update(
f"[{_DIM}]No checkpoints in {self._location}[/]"
)
self.query_one("#status", Static).update("")
self.sub_title = self._location
self.query_one("#status", Static).update("")
return
# Group by branch
branches: dict[str, list[dict[str, Any]]] = defaultdict(list)
grouped: dict[tuple[str, str], dict[str, list[dict[str, Any]]]] = defaultdict(
lambda: defaultdict(list)
)
for entry in self._entries:
key = self._top_level_entity(entry)
branch = entry.get("branch", "main")
branches[branch].append(entry)
# Index checkpoint names to tree nodes so forks can attach
node_by_name: dict[str, Any] = {}
grouped[key][branch].append(entry)
def _make_label(e: dict[str, Any]) -> str:
name = e.get("name", "")
ts = e.get("ts") or ""
trigger = e.get("trigger") or ""
parts = [f"[bold]{_short_id(name)}[/]"]
if ts:
time_part = ts.split(" ")[-1] if " " in ts else ts
time_part = ts.split(" ")[-1] if " " in ts else ts
total_c, total_t = 0, 0
for ent in e.get("entities", []):
c = ent.get("tasks_completed")
t = ent.get("tasks_total")
if c is not None and t is not None:
total_c += c
total_t += t
parts: list[str] = []
if time_part:
parts.append(f"[{_DIM}]{time_part}[/]")
if trigger:
parts.append(f"[{_PRIMARY}]{trigger}[/]")
return " ".join(parts)
if total_t:
display_c = total_c
if trigger == "task_started" and total_c < total_t:
display_c = total_c + 1
color = _SUCCESS if total_c == total_t else _DIM
parts.append(f"[{color}]{display_c}/{total_t}[/]")
return " ".join(parts) if parts else _short_id(e.get("name", ""))
fork_parents: set[str] = set()
for branch_name, entries in branches.items():
if branch_name == "main" or not entries:
continue
oldest = min(entries, key=lambda e: str(e.get("name", "")))
first_parent = oldest.get("parent_id")
if first_parent:
fork_parents.add(str(first_parent))
for branches in grouped.values():
for branch_name, entries in branches.items():
if branch_name == "main" or not entries:
continue
oldest = min(entries, key=lambda e: str(e.get("name", "")))
first_parent = oldest.get("parent_id")
if first_parent:
fork_parents.add(str(first_parent))
node_by_name: dict[str, Any] = {}
def _add_checkpoint(parent_node: Any, e: dict[str, Any]) -> None:
"""Add a checkpoint node — expandable only if a fork attaches to it."""
cp_id = _entry_id(e)
if cp_id in fork_parents:
node = parent_node.add(
@@ -333,67 +401,97 @@ class CheckpointTUI(App[_TuiResult]):
node = parent_node.add_leaf(_make_label(e), data=e)
node_by_name[cp_id] = node
if "main" in branches:
for entry in reversed(branches["main"]):
_add_checkpoint(tree.root, entry)
type_order = {"flow": 0, "crew": 1}
sorted_keys = sorted(
grouped.keys(), key=lambda k: (type_order.get(k[0], 9), k[1])
)
for etype, ename in sorted_keys:
branches = grouped[(etype, ename)]
icon = _entity_icon(etype)
color = _ENTITY_COLORS.get(etype, _DIM)
total = sum(len(v) for v in branches.values())
label_parts = [f"{icon} [bold {color}]{etype.upper()}[/]"]
if ename:
label_parts.append(f"[bold]{ename}[/]")
label_parts.append(f"[{_DIM}]({total})[/]")
all_entries = [e for bl in branches.values() for e in bl]
timestamps = [str(e.get("ts", "")) for e in all_entries if e.get("ts")]
if timestamps:
latest = max(timestamps)
label_parts.append(f"[{_DIM}]{_human_ts(latest)}[/]")
entity_label = " ".join(label_parts)
entity_node = tree.root.add(entity_label, expand=True)
if "main" in branches:
for entry in reversed(branches["main"]):
_add_checkpoint(entity_node, entry)
fork_branches = [
(name, sorted(entries, key=lambda e: str(e.get("name", ""))))
for name, entries in branches.items()
if name != "main"
]
remaining = fork_branches
max_passes = len(remaining) + 1
while remaining and max_passes > 0:
max_passes -= 1
deferred = []
made_progress = False
for branch_name, entries in remaining:
first_parent = entries[0].get("parent_id") if entries else None
if first_parent and str(first_parent) not in node_by_name:
deferred.append((branch_name, entries))
continue
attach_to: Any = entity_node
if first_parent:
attach_to = node_by_name.get(str(first_parent), entity_node)
branch_label = (
f"[bold {_SECONDARY}]{branch_name}[/] "
f"[{_DIM}]({len(entries)})[/]"
)
branch_node = attach_to.add(branch_label, expand=False)
for entry in entries:
_add_checkpoint(branch_node, entry)
made_progress = True
remaining = deferred
if not made_progress:
break
fork_branches = [
(name, sorted(entries, key=lambda e: str(e.get("name", ""))))
for name, entries in branches.items()
if name != "main"
]
remaining = fork_branches
max_passes = len(remaining) + 1
while remaining and max_passes > 0:
max_passes -= 1
deferred = []
made_progress = False
for branch_name, entries in remaining:
first_parent = entries[0].get("parent_id") if entries else None
if first_parent and str(first_parent) not in node_by_name:
deferred.append((branch_name, entries))
continue
attach_to: Any = tree.root
if first_parent:
attach_to = node_by_name.get(str(first_parent), tree.root)
branch_label = (
f"[bold {_SECONDARY}]{branch_name}[/] [{_DIM}]({len(entries)})[/]"
f"[bold {_SECONDARY}]{branch_name}[/] "
f"[{_DIM}]({len(entries)})[/] [{_DIM}](orphaned)[/]"
)
branch_node = attach_to.add(branch_label, expand=False)
branch_node = entity_node.add(branch_label, expand=False)
for entry in entries:
_add_checkpoint(branch_node, entry)
made_progress = True
remaining = deferred
if not made_progress:
break
for branch_name, entries in remaining:
branch_label = (
f"[bold {_SECONDARY}]{branch_name}[/] "
f"[{_DIM}]({len(entries)})[/] [{_DIM}](orphaned)[/]"
)
branch_node = tree.root.add(branch_label, expand=False)
for entry in entries:
_add_checkpoint(branch_node, entry)
count = len(self._entries)
storage = "SQLite" if _is_sqlite(self._location) else "JSON"
self.sub_title = self._location
self.query_one("#status", Static).update(f" {count} checkpoint(s) | {storage}")
async def _show_detail(self, entry: dict[str, Any]) -> None:
"""Update the detail panel for a checkpoint entry."""
self._selected_entry = entry
self.query_one("#action-buttons").add_class("visible")
# ── Detail panel ───────────────────────────────────────────────
detail_scroll = self.query_one("#detail-scroll", VerticalScroll)
# Remove all dynamic children except the header — await so IDs are freed
to_remove = [c for c in detail_scroll.children if c.id != "detail-header"]
for child in to_remove:
async def _clear_scroll(self, tab_id: str) -> VerticalScroll:
tab = self.query_one(f"#{tab_id}", TabPane)
scroll = tab.query_one(VerticalScroll)
for child in list(scroll.children):
await child.remove()
return scroll
async def _show_detail(self, entry: dict[str, Any]) -> None:
self._selected_entry = entry
await self._render_overview(entry)
await self._render_tasks(entry)
await self._render_inputs(entry.get("inputs", {}))
async def _render_overview(self, entry: dict[str, Any]) -> None:
scroll = await self._clear_scroll("tab-overview")
# Header
name = entry.get("name", "")
ts = entry.get("ts") or "unknown"
trigger = entry.get("trigger") or ""
@@ -414,42 +512,115 @@ class CheckpointTUI(App[_TuiResult]):
header_lines.append(f" [bold]Branch[/] [{_SECONDARY}]{branch}[/]")
if parent_id:
header_lines.append(f" [bold]Parent[/] [{_DIM}]{parent_id}[/]")
if "path" in entry:
header_lines.append(f" [bold]Path[/] [{_DIM}]{entry['path']}[/]")
if "db" in entry:
header_lines.append(f" [bold]Database[/] [{_DIM}]{entry['db']}[/]")
self.query_one("#detail-header", Static).update("\n".join(header_lines))
await scroll.mount(Static("\n".join(header_lines)))
for ent in entry.get("entities", []):
etype = ent.get("type", "unknown")
ename = ent.get("name", "unnamed")
icon = _entity_icon(etype)
color = _ENTITY_COLORS.get(etype, _DIM)
eid = str(ent.get("id", ""))[:8]
entity_title = (
f"\n{icon} [bold {color}]{etype.upper()}[/] [bold]{ename}[/]"
)
if eid:
entity_title += f" [{_DIM}]{eid}…[/]"
await scroll.mount(Static(entity_title, classes="section-header"))
await scroll.mount(Static(f"[{_DIM}]{'' * 46}[/]", classes="detail-line"))
if etype == "flow":
methods = ent.get("completed_methods", [])
if methods:
method_list = ", ".join(f"[{_SUCCESS}]{m}[/]" for m in methods)
await scroll.mount(
Static(
f" [bold]Methods[/] {method_list}",
classes="detail-line",
)
)
flow_state = ent.get("flow_state")
if isinstance(flow_state, dict) and flow_state:
state_parts: list[str] = []
for k, v in list(flow_state.items())[:5]:
sv = str(v)
if len(sv) > 40:
sv = sv[:37] + "..."
state_parts.append(f"[{_DIM}]{k}[/]={sv}")
await scroll.mount(
Static(
f" [bold]State[/] {', '.join(state_parts)}",
classes="detail-line",
)
)
agents = ent.get("agents", [])
if agents:
agent_lines: list[Static] = []
for ag in agents:
role = ag.get("role", "unnamed")
goal = ag.get("goal", "")
if len(goal) > 60:
goal = goal[:57] + "..."
agent_line = f" {_entity_icon('agent')} [bold]{role}[/]"
if goal:
agent_line += f"\n [{_DIM}]{goal}[/]"
agent_lines.append(Static(agent_line))
collapsible = Collapsible(
*agent_lines,
title=f"Agents ({len(agents)})",
collapsed=len(agents) > 3,
)
await scroll.mount(collapsible)
async def _render_tasks(self, entry: dict[str, Any]) -> None:
scroll = await self._clear_scroll("tab-tasks")
# Entity details and editable task outputs — mounted flat for scrolling
self._task_output_ids = []
flat_task_idx = 0
has_tasks = False
for ent_idx, ent in enumerate(entry.get("entities", [])):
etype = ent.get("type", "unknown")
ename = ent.get("name", "unnamed")
completed = ent.get("tasks_completed")
total = ent.get("tasks_total")
entity_title = f"[bold {_SECONDARY}]{etype}: {ename}[/]"
if completed is not None and total is not None:
entity_title += f" [{_DIM}]{completed}/{total} tasks[/]"
await detail_scroll.mount(Static(entity_title, classes="entity-title"))
await detail_scroll.mount(
Static(_build_entity_header(ent), classes="entity-detail")
)
icon = _entity_icon(etype)
color = _ENTITY_COLORS.get(etype, _DIM)
tasks = ent.get("tasks", [])
if not tasks:
continue
has_tasks = True
completed = ent.get("tasks_completed", 0)
total = ent.get("tasks_total", 0)
await scroll.mount(
Static(
f"{icon} [bold {color}]{ename}[/] "
f"{_build_progress_bar(completed, total, width=16)}",
classes="section-header",
)
)
for i, task in enumerate(tasks):
desc = str(task.get("description", ""))
if len(desc) > 55:
desc = desc[:52] + "..."
if len(desc) > 50:
desc = desc[:47] + "..."
agent_role = task.get("agent_role", "")
if task.get("completed"):
icon = "[green]✓[/]"
await detail_scroll.mount(
Static(f" {icon} {i + 1}. {desc}", classes="task-label")
)
status_icon = f"[{_SUCCESS}]✓[/]"
task_line = f" {status_icon} {i + 1}. {desc}"
if agent_role:
task_line += (
f" [{_DIM}]→ {_entity_icon('agent')} {agent_role}[/]"
)
await scroll.mount(Static(task_line, classes="task-label"))
output_text = task.get("output", "")
editor_id = f"task-output-{ent_idx}-{i}"
await detail_scroll.mount(
await scroll.mount(
TextArea(
str(output_text),
classes="task-output-editor",
@@ -460,28 +631,25 @@ class CheckpointTUI(App[_TuiResult]):
(flat_task_idx, editor_id, str(output_text))
)
else:
icon = "[yellow]○[/]"
await detail_scroll.mount(
Static(f" {icon} {i + 1}. {desc}", classes="task-label")
)
status_icon = f"[{_PENDING}]○[/]"
task_line = f" {status_icon} {i + 1}. {desc}"
if agent_role:
task_line += (
f" [{_DIM}]→ {_entity_icon('agent')} {agent_role}[/]"
)
await scroll.mount(Static(task_line, classes="task-label"))
flat_task_idx += 1
# Build input fields
await self._build_input_fields(entry.get("inputs", {}))
if not has_tasks:
await scroll.mount(Static(f"[{_DIM}]No tasks[/]", classes="empty-state"))
async def _build_input_fields(self, inputs: dict[str, Any]) -> None:
"""Rebuild the inputs section with one field per input key."""
section = self.query_one("#inputs-section")
# Remove old dynamic children — await so IDs are freed
for widget in list(section.query(".input-row, .no-inputs")):
await widget.remove()
async def _render_inputs(self, inputs: dict[str, Any]) -> None:
scroll = await self._clear_scroll("tab-inputs")
self._input_keys = []
if not inputs:
await section.mount(Static(f"[{_DIM}]No inputs[/]", classes="no-inputs"))
section.add_class("visible")
await scroll.mount(Static(f"[{_DIM}]No inputs[/]", classes="empty-state"))
return
for key, value in inputs.items():
@@ -491,12 +659,11 @@ class CheckpointTUI(App[_TuiResult]):
row.compose_add_child(
Input(value=str(value), placeholder=key, id=f"input-{key}")
)
await section.mount(row)
await scroll.mount(row)
section.add_class("visible")
# ── Data collection ────────────────────────────────────────────
def _collect_inputs(self) -> dict[str, Any] | None:
"""Collect current values from input fields."""
if not self._input_keys:
return None
result: dict[str, Any] = {}
@@ -506,7 +673,6 @@ class CheckpointTUI(App[_TuiResult]):
return result
def _collect_task_overrides(self) -> dict[int, str] | None:
"""Collect edited task outputs. Returns only changed values."""
if not self._task_output_ids or self._selected_entry is None:
return None
overrides: dict[int, str] = {}
@@ -516,38 +682,48 @@ class CheckpointTUI(App[_TuiResult]):
overrides[task_idx] = editor.text
return overrides or None
def _detect_entity_type(self, entry: dict[str, Any]) -> Literal["crew", "flow"]:
"""Infer the top-level entity type from checkpoint entities."""
def _detect_entity_type(
self, entry: dict[str, Any]
) -> Literal["crew", "flow", "agent"]:
for ent in entry.get("entities", []):
if ent.get("type") == "flow":
return "flow"
if ent.get("type") == "agent":
return "agent"
return "crew"
def _resolve_location(self, entry: dict[str, Any]) -> str:
"""Get the restore location string for a checkpoint entry."""
if "path" in entry:
return str(entry["path"])
if _is_sqlite(self._location):
return f"{self._location}#{entry['name']}"
return str(entry.get("name", ""))
# ── Events ─────────────────────────────────────────────────────
async def on_tree_node_highlighted(
self, event: Tree.NodeHighlighted[dict[str, Any]]
) -> None:
if event.node.data is not None:
await self._show_detail(event.node.data)
def on_button_pressed(self, event: Button.Pressed) -> None:
def _exit_with_action(self, action: str) -> None:
if self._selected_entry is None:
self.notify("No checkpoint selected", severity="warning")
return
inputs = self._collect_inputs()
overrides = self._collect_task_overrides()
loc = self._resolve_location(self._selected_entry)
etype = self._detect_entity_type(self._selected_entry)
if event.button.id == "btn-resume":
self.exit((loc, "resume", inputs, overrides, etype))
elif event.button.id == "btn-fork":
self.exit((loc, "fork", inputs, overrides, etype))
name = self._selected_entry.get("name", "")[:30]
self.notify(f"{action.title()}: {name}")
self.exit((loc, action, inputs, overrides, etype))
def action_resume(self) -> None:
self._exit_with_action("resume")
def action_fork(self) -> None:
self._exit_with_action("fork")
def action_refresh(self) -> None:
self._refresh_tree()
@@ -657,6 +833,21 @@ async def _run_checkpoint_tui_async(location: str) -> None:
click.echo(f"\nResult: {getattr(result, 'raw', result)}")
return
if entity_type == "agent":
from crewai.agent import Agent
if action == "fork":
click.echo(f"\nForking agent from: {selected}\n")
agent = Agent.fork(config)
else:
click.echo(f"\nResuming agent from: {selected}\n")
agent = Agent.from_checkpoint(config)
click.echo()
result = await agent.akickoff(messages="Resume execution.")
click.echo(f"\nResult: {getattr(result, 'raw', result)}")
return
from crewai.crew import Crew
if action == "fork":

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]==1.14.2"
"crewai[tools]==1.14.3a1"
]
[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]==1.14.2"
"crewai[tools]==1.14.3a1"
]
[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]==1.14.2"
"crewai[tools]==1.14.3a1"
]
[tool.crewai]

View File

@@ -419,10 +419,32 @@ class Crew(FlowTrackable, BaseModel):
def _restore_runtime(self) -> None:
"""Re-create runtime objects after restoring from a checkpoint."""
from crewai.events.event_bus import crewai_event_bus
started_task_ids: set[str] = set()
state = crewai_event_bus._runtime_state
if state is not None:
for node in state.event_record.nodes.values():
if node.event.type == "task_started" and node.event.task_id:
started_task_ids.add(node.event.task_id)
resuming_task_agent_roles: set[str] = set()
for task in self.tasks:
if (
task.output is None
and task.agent is not None
and str(task.id) in started_task_ids
):
resuming_task_agent_roles.add(task.agent.role)
for agent in self.agents:
agent.crew = self
executor = agent.agent_executor
if executor and executor.messages:
if (
executor
and executor.messages
and agent.role in resuming_task_agent_roles
):
executor.crew = self
executor.agent = agent
executor._resuming = True

View File

@@ -976,6 +976,7 @@ class GeminiCompletion(BaseLLM):
"id": call_id,
"name": part.function_call.name,
"args": args_dict,
"raw_part": part,
}
self._emit_stream_chunk_event(
@@ -1060,29 +1061,20 @@ class GeminiCompletion(BaseLLM):
if call_data.get("name") != STRUCTURED_OUTPUT_TOOL_NAME
}
# If there are function calls but no available_functions,
# return them for the executor to handle
if non_structured_output_calls and not available_functions:
formatted_function_calls = [
{
"id": call_data["id"],
"function": {
"name": call_data["name"],
"arguments": json.dumps(call_data["args"]),
},
"type": "function",
}
raw_parts = [
call_data["raw_part"]
for call_data in non_structured_output_calls.values()
]
self._emit_call_completed_event(
response=formatted_function_calls,
response=raw_parts,
call_type=LLMCallType.TOOL_CALL,
from_task=from_task,
from_agent=from_agent,
messages=self._convert_contents_to_dict(contents),
usage=usage_data,
)
return formatted_function_calls
return raw_parts
# Handle completed function calls (excluding structured_output)
if non_structured_output_calls and available_functions:

View File

@@ -44,9 +44,12 @@ def _sync_checkpoint_fields(entity: object) -> None:
entity: The entity whose private runtime attributes will be
copied into its public checkpoint fields.
"""
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.crew import Crew
from crewai.flow.flow import Flow
if isinstance(entity, BaseAgent):
entity.checkpoint_kickoff_event_id = entity._kickoff_event_id
if isinstance(entity, Flow):
entity.checkpoint_completed_methods = (
set(entity._completed_methods) if entity._completed_methods else None

View File

@@ -1241,12 +1241,26 @@ Follow these guidelines:
tools=tools,
)
pydantic_output, json_output = self._export_output(result)
if isinstance(result, BaseModel):
raw = result.model_dump_json()
if self.output_pydantic:
pydantic_output = result
json_output = None
elif self.output_json:
pydantic_output = None
json_output = result.model_dump()
else:
pydantic_output = None
json_output = None
else:
raw = result
pydantic_output, json_output = self._export_output(result)
task_output = TaskOutput(
name=self.name or self.description,
description=self.description,
expected_output=self.expected_output,
raw=result,
raw=raw,
pydantic=pydantic_output,
json_dict=json_output,
agent=agent.role,
@@ -1337,12 +1351,26 @@ Follow these guidelines:
tools=tools,
)
pydantic_output, json_output = self._export_output(result)
if isinstance(result, BaseModel):
raw = result.model_dump_json()
if self.output_pydantic:
pydantic_output = result
json_output = None
elif self.output_json:
pydantic_output = None
json_output = result.model_dump()
else:
pydantic_output = None
json_output = None
else:
raw = result
pydantic_output, json_output = self._export_output(result)
task_output = TaskOutput(
name=self.name or self.description,
description=self.description,
expected_output=self.expected_output,
raw=result,
raw=raw,
pydantic=pydantic_output,
json_dict=json_output,
agent=agent.role,

View File

@@ -562,3 +562,75 @@ class TestKickoffFromCheckpoint:
)
assert mock_restored.checkpoint.restore_from is None
assert result == "flow_result"
# ---------- Agent checkpoint/fork ----------
class TestAgentCheckpoint:
def _make_agent_state(self) -> RuntimeState:
agent = Agent(role="r", goal="g", backstory="b", llm="gpt-4o-mini")
return RuntimeState(root=[agent])
def test_agent_from_checkpoint_sets_runtime_state(self) -> None:
state = self._make_agent_state()
state._provider = JsonProvider()
with tempfile.TemporaryDirectory() as d:
loc = state.checkpoint(d)
cfg = CheckpointConfig(restore_from=loc)
from crewai.events.event_bus import crewai_event_bus
crewai_event_bus._runtime_state = None
Agent.from_checkpoint(cfg)
assert crewai_event_bus._runtime_state is not None
def test_agent_fork_sets_branch(self) -> None:
state = self._make_agent_state()
state._provider = JsonProvider()
with tempfile.TemporaryDirectory() as d:
loc = state.checkpoint(d)
cfg = CheckpointConfig(restore_from=loc)
from crewai.events.event_bus import crewai_event_bus
Agent.fork(cfg, branch="agent-experiment")
rt = crewai_event_bus._runtime_state
assert rt is not None
assert rt._branch == "agent-experiment"
def test_agent_fork_auto_branch(self) -> None:
state = self._make_agent_state()
state._provider = JsonProvider()
with tempfile.TemporaryDirectory() as d:
loc = state.checkpoint(d)
cfg = CheckpointConfig(restore_from=loc)
from crewai.events.event_bus import crewai_event_bus
Agent.fork(cfg)
rt = crewai_event_bus._runtime_state
assert rt is not None
assert rt._branch.startswith("fork/")
def test_sync_checkpoint_fields_agent(self) -> None:
from crewai.state.runtime import _sync_checkpoint_fields
agent = Agent(role="r", goal="g", backstory="b", llm="gpt-4o-mini")
agent._kickoff_event_id = "evt-123"
_sync_checkpoint_fields(agent)
assert agent.checkpoint_kickoff_event_id == "evt-123"
def test_agent_restore_kickoff_event_id(self) -> None:
agent = Agent(role="r", goal="g", backstory="b", llm="gpt-4o-mini")
agent._kickoff_event_id = "evt-456"
state = RuntimeState(root=[agent])
state._provider = JsonProvider()
with tempfile.TemporaryDirectory() as d:
from crewai.state.runtime import _prepare_entities
_prepare_entities(state.root)
loc = state.checkpoint(d)
cfg = CheckpointConfig(restore_from=loc)
restored = Agent.from_checkpoint(cfg)
assert restored._kickoff_event_id == "evt-456"

View File

@@ -292,7 +292,7 @@ class TestPruneJson:
d, name="20250101T000000_old01111_p-none.json"
)
os.utime(old_path, (0, 0))
_write_json_checkpoint(d, name="20260417T000000_new01111_p-none.json")
_write_json_checkpoint(d, name="20990101T000000_new01111_p-none.json")
deleted = _prune_json(d, keep=None, older_than=timedelta(days=1))
assert deleted == 1
@@ -330,7 +330,7 @@ class TestPruneSqlite:
with tempfile.TemporaryDirectory() as d:
db_path = os.path.join(d, "test.db")
_create_sqlite_checkpoint(db_path, "20200101T000000_old01111")
_create_sqlite_checkpoint(db_path, "20260417T000000_new01111")
_create_sqlite_checkpoint(db_path, "20990101T000000_new01111")
deleted = _prune_sqlite(db_path, keep=None, older_than=timedelta(days=1))
assert deleted >= 1
with sqlite3.connect(db_path) as conn:

View File

@@ -1,3 +1,3 @@
"""CrewAI development tools."""
__version__ = "1.14.2"
__version__ = "1.14.3a1"

View File

@@ -247,6 +247,14 @@ def create_or_reset_branch(branch: str, cwd: Path | None = None) -> None:
remote_exists = False
if local_exists:
current = run_command(
["git", "rev-parse", "--abbrev-ref", "HEAD"], cwd=cwd
).strip()
if current == branch:
console.print(
f"[yellow]![/yellow] Currently on {branch}, switching to main before delete"
)
run_command(["git", "checkout", "main"], cwd=cwd)
console.print(f"[yellow]![/yellow] Deleting local branch {branch}")
run_command(["git", "branch", "-D", branch], cwd=cwd)
@@ -2019,9 +2027,8 @@ def release(
create_or_reset_branch(branch_name)
console.print("[green]✓[/green] Branch created")
_update_all_versions(cwd, lib_dir, version, packages, dry_run)
_update_all_versions(cwd, lib_dir, version, packages, dry_run)
if not dry_run:
console.print("\nCommitting changes...")
run_command(["git", "add", "."])
run_command(["git", "commit", "-m", f"feat: bump versions to {version}"])
@@ -2051,6 +2058,7 @@ def release(
_poll_pr_until_merged(branch_name, "bump PR")
else:
console.print(f"[dim][DRY RUN][/dim] Would create branch: {branch_name}")
_update_all_versions(cwd, lib_dir, version, packages, dry_run)
console.print(
f"[dim][DRY RUN][/dim] Would commit: feat: bump versions to {version}"
)

View File

@@ -0,0 +1,57 @@
"""
Test script for LinearTool — runs against the real Linear API.
Usage:
LINEAR_API_KEY=lin_api_xxxxxxxxxxxx python tests/tools/test_linear_tool.py
Set LINEAR_API_KEY to your actual Personal API key from:
https://linear.app/settings/api (Profile → API → Personal API keys)
"""
import json
import os
import sys
from crewai.tools.linear_tool import LinearAction, LinearTool
def pretty(data: object) -> str:
return json.dumps(data, indent=2, default=str)
def run_test(tool: LinearTool, label: str, action: LinearAction, first: int = 5) -> None:
print(f"\n{'' * 60}")
print(f" {label}")
print(f"{'' * 60}")
try:
result = tool._run(action=action, first=first)
if not result:
print(" (no records returned)")
else:
print(pretty(result))
except Exception as exc:
print(f" ERROR: {exc}", file=sys.stderr)
def main() -> None:
if not os.environ.get("LINEAR_API_KEY"):
print(
"ERROR: Set LINEAR_API_KEY before running.\n"
" export LINEAR_API_KEY=lin_api_xxxxxxxxxxxx",
file=sys.stderr,
)
sys.exit(1)
tool = LinearTool()
run_test(tool, "My assigned issues (up to 5)", LinearAction.MY_ISSUES, first=5)
run_test(tool, "Teams (up to 10)", LinearAction.LIST_TEAMS, first=10)
run_test(tool, "Projects (up to 10)", LinearAction.LIST_PROJECTS, first=10)
print(f"\n{'' * 60}")
print(" All tests complete.")
print(f"{'' * 60}\n")
if __name__ == "__main__":
main()

206
uv.lock generated
View File

@@ -13,7 +13,7 @@ resolution-markers = [
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[options]
exclude-newer = "2026-04-17T16:00:00Z"
exclude-newer = "2026-04-18T07:00:00Z"
[manifest]
members = [
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]
[[package]]
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[[package]]
name = "aioitertools"
version = "0.13.0"
@@ -1474,6 +1486,9 @@ couchbase = [
databricks-sdk = [
{ name = "databricks-sdk" },
]
daytona = [
{ name = "daytona" },
]
exa-py = [
{ name = "exa-py" },
]
@@ -1574,6 +1589,7 @@ requires-dist = [
{ name = "crewai", editable = "lib/crewai" },
{ 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 = "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" },
@@ -1616,7 +1632,7 @@ requires-dist = [
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{ name = "youtube-transcript-api", specifier = "~=1.2.2" },
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