---
title: "Upgrading CrewAI"
description: "How to upgrade CrewAI in your project and adapt to breaking changes between versions."
icon: "arrow-up-circle"
---

## Overview

CrewAI releases ship new capabilities regularly. This guide walks you through the practical steps to keep your installation up to date — both the CLI and your project's virtual environment.

If you're starting fresh, see [Installation](/en/installation). If you're coming from another framework, see [Migrating from LangGraph](/en/guides/migration/migrating-from-langgraph).

---

## The Two Things You Might Want to Upgrade

CrewAI lives in two places on your machine, and they upgrade independently:

| What | How it's installed | How to upgrade |
|---|---|---|
| The **global `crewai` CLI** | `uv tool install crewai` | `uv tool install crewai --upgrade` |
| The **project venv** (what your code runs) | `crewai install` / `uv sync` | `uv add "crewai[...]>=X.Y.Z"` then `crewai install` |

These can — and often do — get out of sync. Running `crewai --version` tells you the CLI version. Running `uv pip show crewai` inside your project tells you the venv version. If they differ, that's normal; what matters for your running code is the venv version.

## Why `crewai install` Alone Doesn't Upgrade

`crewai install` is a thin wrapper around `uv sync`. It installs exactly what the current `uv.lock` file says — it does **not** bump any version constraints.

If your `pyproject.toml` says `crewai>=1.11.1` and the lock file resolved to `1.11.1`, running `crewai install` will keep you on `1.11.1` forever, even if `1.14.4` is available.

To actually upgrade, you need to:

1. Update the version constraint in `pyproject.toml`
2. Re-solve the lock file
3. Sync the venv

`uv add` does all three in one shot.

## How to Upgrade Your Project

```bash
# Bump the constraint and re-lock in one command
uv add "crewai[tools]>=1.14.4"

# Sync the venv (crewai install calls uv sync under the hood)
crewai install

# Verify
uv pip show crewai
# → Version: 1.14.4
```

Replace `[tools]` with whatever extras your project uses (e.g. `[tools,anthropic]`). Check your `pyproject.toml` `dependencies` list if you're unsure.

<Note>
  `uv add` updates both `pyproject.toml` **and** `uv.lock` atomically. If you edit `pyproject.toml` manually, you still need to run `uv lock --upgrade-package crewai` to re-solve the lock file before `crewai install` will pick up the new version.
</Note>

## Upgrading the Global CLI

The global CLI is separate from your project. Upgrade it with:

```bash
uv tool install crewai --upgrade
```

If your shell warns about `PATH` after the upgrade, refresh it:

```bash
uv tool update-shell
```

This does **not** touch your project's venv — you still need `uv add` + `crewai install` inside the project.

## Verify Both Are in Sync

```bash
# Global CLI version
crewai --version

# Project venv version
uv pip show crewai | grep Version
```

They don't need to match — but your project venv version is what matters for runtime behavior.

<Note>
  CrewAI requires `Python >=3.10, <3.14`. If `uv` was installed against an older interpreter, recreate the project venv with a supported Python before running `crewai install`.
</Note>

---

## Breaking Changes & Migration Notes

Most upgrades only require small adjustments. The areas below are the ones that break silently or with confusing tracebacks.

### Import paths: tools and `BaseTool`

The canonical import location for tools is `crewai.tools`. Older paths still surface in tutorials but should be updated.

```python
# Before
from crewai_tools import BaseTool
from crewai.agents.tools import tool

# After
from crewai.tools import BaseTool, tool
```

The `@tool` decorator and `BaseTool` subclass both live in `crewai.tools`. `AgentFinish` and other internal-agent symbols are no longer part of the public surface — if you were importing them, switch to event listeners or `Task` callbacks instead.

### `Agent` parameter changes

```python
from crewai import Agent

agent = Agent(
    role="Researcher",
    goal="Find authoritative sources on {topic}",
    backstory="You are a careful, source-driven researcher.",
    llm="gpt-4o-mini",   # string model name OR an LLM object
    verbose=True,        # bool, not an int level
    max_iter=15,         # default has changed across versions — set explicitly
    allow_delegation=False,
)
```

- `llm` accepts either a string model name (resolved via the configured provider) or an `LLM` object for fine-grained control.
- `verbose` is a plain `bool`. Passing an integer no longer toggles log levels.
- `max_iter` defaults have shifted between releases. If your agent silently stops looping after the first tool call, set `max_iter` explicitly.

### `Crew` parameters

```python
from crewai import Crew, Process

crew = Crew(
    agents=[...],
    tasks=[...],
    process=Process.sequential,   # or Process.hierarchical
    memory=True,
    cache=True,
    embedder={"provider": "openai", "config": {"model": "text-embedding-3-large"}},
)
```

- `process=Process.hierarchical` requires either `manager_llm=` or `manager_agent=`. Without one, kickoff raises at validation time.
- `memory=True` with a non-default embedding provider needs an `embedder` dict — see [Memory & embedder config](#memory-embedder-config) below.

### `Task` structured output

Use `output_pydantic`, `output_json`, or `output_file` to coerce a task's result into a typed shape:

```python
from pydantic import BaseModel
from crewai import Task

class Article(BaseModel):
    title: str
    body: str

write = Task(
    description="Write an article about {topic}",
    expected_output="A short article with a title and body",
    agent=writer,
    output_pydantic=Article,        # the class, NOT an instance
    output_file="output/article.md",
)
```

`output_pydantic` takes the **class** itself. Passing `Article(title="", body="")` is a common mistake and fails with a confusing validation error.

### Memory & embedder config {#memory-embedder-config}

If `memory=True` and you're not using the default OpenAI `text-embedding-3-large` embeddings, you must pass an `embedder`:

```python
crew = Crew(
    agents=[...],
    tasks=[...],
    memory=True,
    embedder={
        "provider": "ollama",
        "config": {"model": "nomic-embed-text"},
    },
)
```

Set the relevant provider credentials (`OPENAI_API_KEY`, `OLLAMA_HOST`, etc.) in your `.env` file. Memory storage paths are project-local by default. Existing local memory stores created with 1536-dimensional embeddings may not be compatible with the default OpenAI `text-embedding-3-large` embedder, which uses 3072 dimensions. If you hit a dimension mismatch, delete the project's memory directory, run `crewai reset-memories -m`, or explicitly configure the older embedder model until you migrate.