refactor: enhance memory handling with read-only support

- Updated memory-related classes and methods to support read-only functionality, allowing for silent no-ops when attempting to remember data in read-only mode. - Modified the LiteAgent and CrewAgentExecutorMixin classes to check for read-only status before saving memories. - Adjusted MemorySlice and Memory classes to reflect changes in behavior when read-only is enabled. - Updated tests to verify that memory operations behave correctly under read-only conditions.
refactor: update memory recall limit and formatting in Agent class
2026-02-26 15:58:14 +00:00 · 2026-02-26 00:10:25 -08:00 · 2026-02-26 00:01:09 -08:00 · 2026-02-25 21:34:45 -08:00 · 2026-02-25 21:30:02 -08:00
22 changed files with 262 additions and 421 deletions
--- a/docs/en/concepts/llms.mdx
+++ b/docs/en/concepts/llms.mdx
@@ -106,15 +106,6 @@ There are different places in CrewAI code where you can specify the model to use
  </Tab>
 </Tabs>

-<Info>
-  CrewAI provides native SDK integrations for OpenAI, Anthropic, Google (Gemini API), Azure, and AWS Bedrock — no extra install needed beyond the provider-specific extras (e.g. `uv add "crewai[openai]"`).
-
-  All other providers are powered by **LiteLLM**. If you plan to use any of them, add it as a dependency to your project:
-  ```bash
-  uv add 'crewai[litellm]'
-  ```
-</Info>
-
 ## Provider Configuration Examples

 CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities.
@@ -284,11 +275,6 @@ In this section, you'll find detailed examples that help you select, configure,
    | `meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8` | 128k | 4028 | Text, Image | Text |
    | `meta_llama/Llama-3.3-70B-Instruct` | 128k | 4028 | Text | Text |
    | `meta_llama/Llama-3.3-8B-Instruct` | 128k | 4028 | Text | Text |
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Anthropic">
@@ -585,11 +571,6 @@ In this section, you'll find detailed examples that help you select, configure,
    | gemini-1.5-flash               | 1M tokens      | Balanced multimodal model, good for most tasks                    |
    | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
    | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Azure">
@@ -785,11 +766,6 @@ In this section, you'll find detailed examples that help you select, configure,
        model="sagemaker/<my-endpoint>"
    )
    ```
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Mistral">
@@ -805,11 +781,6 @@ In this section, you'll find detailed examples that help you select, configure,
        temperature=0.7
    )
    ```
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Nvidia NIM">
@@ -896,11 +867,6 @@ In this section, you'll find detailed examples that help you select, configure,
    | rakuten/rakutenai-7b-instruct                                     | 1,024 tokens   | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
    | rakuten/rakutenai-7b-chat                                         | 1,024 tokens   | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
    | baichuan-inc/baichuan2-13b-chat                                  | 4,096 tokens   | Support Chinese and English chat, coding, math, instruction following, solving quizzes |
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Local NVIDIA NIM Deployed using WSL2">
@@ -941,11 +907,6 @@ In this section, you'll find detailed examples that help you select, configure,

        # ...
    ```
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Groq">
@@ -967,11 +928,6 @@ In this section, you'll find detailed examples that help you select, configure,
    | Llama 3.1 70B/8B  | 131,072 tokens   | High-performance, large context tasks      |
    | Llama 3.2 Series  | 8,192 tokens     | General-purpose tasks                      |
    | Mixtral 8x7B      | 32,768 tokens    | Balanced performance and context           |
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="IBM watsonx.ai">
@@ -994,11 +950,6 @@ In this section, you'll find detailed examples that help you select, configure,
        base_url="https://api.watsonx.ai/v1"
    )
    ```
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Ollama (Local LLMs)">
@@ -1012,11 +963,6 @@ In this section, you'll find detailed examples that help you select, configure,
        base_url="http://localhost:11434"
    )
    ```
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Fireworks AI">
@@ -1032,11 +978,6 @@ In this section, you'll find detailed examples that help you select, configure,
        temperature=0.7
    )
    ```
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Perplexity AI">
@@ -1052,11 +993,6 @@ In this section, you'll find detailed examples that help you select, configure,
        base_url="https://api.perplexity.ai/"
    )
    ```
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Hugging Face">
@@ -1071,11 +1007,6 @@ In this section, you'll find detailed examples that help you select, configure,
        model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct"
    )
    ```
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="SambaNova">
@@ -1099,11 +1030,6 @@ In this section, you'll find detailed examples that help you select, configure,
    | Llama 3.2 Series   | 8,192 tokens           | General-purpose, multimodal tasks            |
    | Llama 3.3 70B      | Up to 131,072 tokens   | High-performance and output quality          |
    | Qwen2 familly      | 8,192 tokens           | High-performance and output quality          |
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Cerebras">
@@ -1129,11 +1055,6 @@ In this section, you'll find detailed examples that help you select, configure,
      - Good balance of speed and quality
      - Support for long context windows
    </Info>
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Open Router">
@@ -1156,11 +1077,6 @@ In this section, you'll find detailed examples that help you select, configure,
      - openrouter/deepseek/deepseek-r1
      - openrouter/deepseek/deepseek-chat
    </Info>
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Nebius AI Studio">
@@ -1183,11 +1099,6 @@ In this section, you'll find detailed examples that help you select, configure,
      - Competitive pricing
      - Good balance of speed and quality
    </Info>
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>
 </AccordionGroup>

--- a/docs/en/learn/llm-connections.mdx
+++ b/docs/en/learn/llm-connections.mdx
@@ -7,7 +7,7 @@ mode: "wide"

 ## Connect CrewAI to LLMs

-CrewAI connects to LLMs through native SDK integrations for the most popular providers (OpenAI, Anthropic, Google Gemini, Azure, and AWS Bedrock), and uses LiteLLM as a flexible fallback for all other providers.
+CrewAI uses LiteLLM to connect to a wide variety of Language Models (LLMs). This integration provides extensive versatility, allowing you to use models from numerous providers with a simple, unified interface.

 <Note>
    By default, CrewAI uses the `gpt-4o-mini` model. This is determined by the `OPENAI_MODEL_NAME` environment variable, which defaults to "gpt-4o-mini" if not set.
@@ -41,14 +41,6 @@ LiteLLM supports a wide range of providers, including but not limited to:

 For a complete and up-to-date list of supported providers, please refer to the [LiteLLM Providers documentation](https://docs.litellm.ai/docs/providers).

-<Info>
-  To use any provider not covered by a native integration, add LiteLLM as a dependency to your project:
-  ```bash
-  uv add 'crewai[litellm]'
-  ```
-  Native providers (OpenAI, Anthropic, Google Gemini, Azure, AWS Bedrock) use their own SDK extras — see the [Provider Configuration Examples](/en/concepts/llms#provider-configuration-examples).
-</Info>
-
 ## Changing the LLM

 To use a different LLM with your CrewAI agents, you have several options:
--- a/docs/en/observability/tracing.mdx
+++ b/docs/en/observability/tracing.mdx
@@ -35,7 +35,7 @@ Visit [app.crewai.com](https://app.crewai.com) and create your free account. Thi
 If you haven't already, install CrewAI with the CLI tools:

 ```bash
-uv add 'crewai[tools]'
+uv add crewai[tools]
 ```

 Then authenticate your CLI with your CrewAI AMP account:
--- a/docs/ko/concepts/llms.mdx
+++ b/docs/ko/concepts/llms.mdx
@@ -105,15 +105,6 @@ CrewAI 코드 내에는 사용할 모델을 지정할 수 있는 여러 위치
  </Tab>
 </Tabs>

-<Info>
-  CrewAI는 OpenAI, Anthropic, Google (Gemini API), Azure, AWS Bedrock에 대해 네이티브 SDK 통합을 제공합니다 — 제공자별 extras(예: `uv add "crewai[openai]"`) 외에 추가 설치가 필요하지 않습니다.
-
-  그 외 모든 제공자는 **LiteLLM**을 통해 지원됩니다. 이를 사용하려면 프로젝트에 의존성으로 추가하세요:
-  ```bash
-  uv add 'crewai[litellm]'
-  ```
-</Info>
-
 ## 공급자 구성 예시

 CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양한 LLM 공급자를 지원합니다.
@@ -223,11 +214,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
    | `meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8` | 128k | 4028 | 텍스트, 이미지     | 텍스트           |
    | `meta_llama/Llama-3.3-70B-Instruct`               | 128k | 4028       | 텍스트            | 텍스트           |
    | `meta_llama/Llama-3.3-8B-Instruct`                | 128k | 4028       | 텍스트            | 텍스트           |
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Anthropic">
@@ -368,11 +354,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
    | gemini-1.5-flash                 | 1M 토큰         | 밸런스 잡힌 멀티모달 모델, 대부분의 작업에 적합                         |
    | gemini-1.5-flash-8B              | 1M 토큰         | 가장 빠르고, 비용 효율적, 고빈도 작업에 적합                            |
    | gemini-1.5-pro                   | 2M 토큰         | 최고의 성능, 논리적 추론, 코딩, 창의적 협업 등 다양한 추론 작업에 적합   |
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Azure">
@@ -458,11 +439,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
        model="sagemaker/<my-endpoint>"
    )
    ```
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Mistral">
@@ -478,11 +454,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
        temperature=0.7
    )
    ```
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Nvidia NIM">
@@ -569,11 +540,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
    | rakuten/rakutenai-7b-instruct                                          | 1,024 토큰     | 언어 이해, 추론, 텍스트 생성이 탁월한 최첨단 LLM                         |
    | rakuten/rakutenai-7b-chat                                              | 1,024 토큰     | 언어 이해, 추론, 텍스트 생성이 탁월한 최첨단 LLM                         |
    | baichuan-inc/baichuan2-13b-chat                                        | 4,096 토큰     | 중국어 및 영어 대화, 코딩, 수학, 지시 따르기, 퀴즈 풀이 지원             |
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Local NVIDIA NIM Deployed using WSL2">
@@ -614,11 +580,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양

        # ...
    ```
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Groq">
@@ -640,11 +601,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
    | Llama 3.1 70B/8B| 131,072 토큰      | 고성능, 대용량 문맥 작업         |
    | Llama 3.2 Series| 8,192 토큰        | 범용 작업                        |
    | Mixtral 8x7B    | 32,768 토큰       | 성능과 문맥의 균형               |
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="IBM watsonx.ai">
@@ -667,11 +623,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
        base_url="https://api.watsonx.ai/v1"
    )
    ```
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Ollama (Local LLMs)">
@@ -685,11 +636,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
        base_url="http://localhost:11434"
    )
    ```
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Fireworks AI">
@@ -705,11 +651,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
        temperature=0.7
    )
    ```
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Perplexity AI">
@@ -725,11 +666,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
        base_url="https://api.perplexity.ai/"
    )
    ```
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Hugging Face">
@@ -744,11 +680,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
        model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct"
    )
    ```
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="SambaNova">
@@ -772,11 +703,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
    | Llama 3.2 Series| 8,192 토큰         | 범용, 멀티모달 작업                  |
    | Llama 3.3 70B   | 최대 131,072 토큰   | 고성능, 높은 출력 품질               |
    | Qwen2 familly   | 8,192 토큰         | 고성능, 높은 출력 품질               |
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Cerebras">
@@ -802,11 +728,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
      - 속도와 품질의 우수한 밸런스
      - 긴 컨텍스트 윈도우 지원
    </Info>
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Open Router">
@@ -829,11 +750,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
      - openrouter/deepseek/deepseek-r1
      - openrouter/deepseek/deepseek-chat
    </Info>
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Nebius AI Studio">
@@ -856,11 +772,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
      - 경쟁력 있는 가격
      - 속도와 품질의 우수한 밸런스
    </Info>
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>
 </AccordionGroup>

--- a/docs/ko/learn/llm-connections.mdx
+++ b/docs/ko/learn/llm-connections.mdx
@@ -7,7 +7,7 @@ mode: "wide"

 ## CrewAI를 LLM에 연결하기

-CrewAI는 가장 인기 있는 제공자(OpenAI, Anthropic, Google Gemini, Azure, AWS Bedrock)에 대해 네이티브 SDK 통합을 통해 LLM에 연결하며, 그 외 모든 제공자에 대해서는 LiteLLM을 유연한 폴백으로 사용합니다.
+CrewAI는 LiteLLM을 사용하여 다양한 언어 모델(LLM)에 연결합니다. 이 통합은 높은 다양성을 제공하여, 여러 공급자의 모델을 간단하고 통합된 인터페이스로 사용할 수 있게 해줍니다.

 <Note>
    기본적으로 CrewAI는 `gpt-4o-mini` 모델을 사용합니다. 이는 `OPENAI_MODEL_NAME` 환경 변수에 의해 결정되며, 설정되지 않은 경우 기본값은 "gpt-4o-mini"입니다.
@@ -41,14 +41,6 @@ LiteLLM은 다음을 포함하되 이에 국한되지 않는 다양한 프로바

 지원되는 프로바이더의 전체 및 최신 목록은 [LiteLLM 프로바이더 문서](https://docs.litellm.ai/docs/providers)를 참조하세요.

-<Info>
-  네이티브 통합에서 지원하지 않는 제공자를 사용하려면 LiteLLM을 프로젝트에 의존성으로 추가하세요:
-  ```bash
-  uv add 'crewai[litellm]'
-  ```
-  네이티브 제공자(OpenAI, Anthropic, Google Gemini, Azure, AWS Bedrock)는 자체 SDK extras를 사용합니다 — [공급자 구성 예시](/ko/concepts/llms#공급자-구성-예시)를 참조하세요.
-</Info>
-
 ## LLM 변경하기

 CrewAI agent에서 다른 LLM을 사용하려면 여러 가지 방법이 있습니다:
--- a/docs/ko/observability/tracing.mdx
+++ b/docs/ko/observability/tracing.mdx
@@ -35,7 +35,7 @@ crewai login
 아직 설치하지 않았다면 CLI 도구와 함께 CrewAI를 설치하세요:

 ```bash
-uv add 'crewai[tools]'
+uv add crewai[tools]
 ```

 그런 다음 CrewAI AMP 계정으로 CLI를 인증하세요:
--- a/docs/pt-BR/concepts/llms.mdx
+++ b/docs/pt-BR/concepts/llms.mdx
@@ -105,15 +105,6 @@ Existem diferentes locais no código do CrewAI onde você pode especificar o mod
  </Tab>
 </Tabs>

-<Info>
-  O CrewAI oferece integrações nativas via SDK para OpenAI, Anthropic, Google (Gemini API), Azure e AWS Bedrock — sem necessidade de instalação extra além dos extras específicos do provedor (ex.: `uv add "crewai[openai]"`).
-
-  Todos os outros provedores são alimentados pelo **LiteLLM**. Se você planeja usar algum deles, adicione-o como dependência ao seu projeto:
-  ```bash
-  uv add 'crewai[litellm]'
-  ```
-</Info>
-
 ## Exemplos de Configuração de Provedores

 O CrewAI suporta uma grande variedade de provedores de LLM, cada um com recursos, métodos de autenticação e capacidades de modelo únicos.
@@ -223,11 +214,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
    | `meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8` | 128k | 4028 | Texto, Imagem | Texto |
    | `meta_llama/Llama-3.3-70B-Instruct` | 128k | 4028 | Texto | Texto |
    | `meta_llama/Llama-3.3-8B-Instruct` | 128k | 4028 | Texto | Texto |
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Anthropic">
@@ -368,11 +354,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
    | gemini-1.5-flash                 | 1M tokens          | Modelo multimodal equilibrado, bom para maioria das tarefas         |
    | gemini-1.5-flash-8B              | 1M tokens          | Mais rápido, mais eficiente em custo, adequado para tarefas de alta frequência |
    | gemini-1.5-pro                   | 2M tokens          | Melhor desempenho para uma ampla variedade de tarefas de raciocínio, incluindo lógica, codificação e colaboração criativa |
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Azure">
@@ -457,11 +438,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
        model="sagemaker/<my-endpoint>"
    )
    ```
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Mistral">
@@ -477,11 +453,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
        temperature=0.7
    )
    ```
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Nvidia NIM">
@@ -568,11 +539,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
    | rakuten/rakutenai-7b-instruct                                            | 1.024 tokens       | LLM topo de linha, compreensão, raciocínio e geração textual.|
    | rakuten/rakutenai-7b-chat                                                | 1.024 tokens       | LLM topo de linha, compreensão, raciocínio e geração textual.|
    | baichuan-inc/baichuan2-13b-chat                                          | 4.096 tokens       | Suporte a chat em chinês/inglês, programação, matemática, seguir instruções, resolver quizzes.|
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Local NVIDIA NIM Deployed using WSL2">
@@ -613,11 +579,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co

        # ...
    ```
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Groq">
@@ -639,11 +600,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
    | Llama 3.1 70B/8B  | 131.072 tokens      | Alta performance e tarefas de contexto grande|
    | Llama 3.2 Série   | 8.192 tokens        | Tarefas gerais                          |
    | Mixtral 8x7B      | 32.768 tokens       | Equilíbrio entre performance e contexto  |
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="IBM watsonx.ai">
@@ -666,11 +622,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
        base_url="https://api.watsonx.ai/v1"
    )
    ```
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Ollama (LLMs Locais)">
@@ -684,11 +635,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
        base_url="http://localhost:11434"
    )
    ```
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Fireworks AI">
@@ -704,11 +650,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
        temperature=0.7
    )
    ```
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Perplexity AI">
@@ -724,11 +665,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
        base_url="https://api.perplexity.ai/"
    )
    ```
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Hugging Face">
@@ -743,11 +679,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
        model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct"
    )
    ```
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="SambaNova">
@@ -771,11 +702,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
    | Llama 3.2 Série   | 8.192 tokens              | Tarefas gerais e multimodais                 |
    | Llama 3.3 70B     | Até 131.072 tokens        | Desempenho e qualidade de saída elevada      |
    | Família Qwen2     | 8.192 tokens              | Desempenho e qualidade de saída elevada      |
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Cerebras">
@@ -801,11 +727,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
      - Equilíbrio entre velocidade e qualidade
      - Suporte a longas janelas de contexto
    </Info>
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>

  <Accordion title="Open Router">
@@ -828,11 +749,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
      - openrouter/deepseek/deepseek-r1
      - openrouter/deepseek/deepseek-chat
    </Info>
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
  </Accordion>
 </AccordionGroup>

--- a/docs/pt-BR/learn/llm-connections.mdx
+++ b/docs/pt-BR/learn/llm-connections.mdx
@@ -7,7 +7,7 @@ mode: "wide"

 ## Conecte o CrewAI a LLMs

-O CrewAI conecta-se a LLMs por meio de integrações nativas via SDK para os provedores mais populares (OpenAI, Anthropic, Google Gemini, Azure e AWS Bedrock), e usa o LiteLLM como alternativa flexível para todos os demais provedores.
+O CrewAI utiliza o LiteLLM para conectar-se a uma grande variedade de Modelos de Linguagem (LLMs). Essa integração proporciona grande versatilidade, permitindo que você utilize modelos de inúmeros provedores por meio de uma interface simples e unificada.

 <Note>
    Por padrão, o CrewAI usa o modelo `gpt-4o-mini`. Isso é determinado pela variável de ambiente `OPENAI_MODEL_NAME`, que tem como padrão "gpt-4o-mini" se não for definida.
@@ -40,14 +40,6 @@ O LiteLLM oferece suporte a uma ampla gama de provedores, incluindo, mas não se

 Para uma lista completa e sempre atualizada dos provedores suportados, consulte a [documentação de Provedores do LiteLLM](https://docs.litellm.ai/docs/providers).

-<Info>
-  Para usar qualquer provedor não coberto por uma integração nativa, adicione o LiteLLM como dependência ao seu projeto:
-  ```bash
-  uv add 'crewai[litellm]'
-  ```
-  Provedores nativos (OpenAI, Anthropic, Google Gemini, Azure, AWS Bedrock) usam seus próprios extras de SDK — consulte os [Exemplos de Configuração de Provedores](/pt-BR/concepts/llms#exemplos-de-configuração-de-provedores).
-</Info>
-
 ## Alterando a LLM

 Para utilizar uma LLM diferente com seus agentes CrewAI, você tem várias opções:
--- a/lib/crewai-tools/pyproject.toml
+++ b/lib/crewai-tools/pyproject.toml
@@ -8,12 +8,10 @@ authors = [
 ]
 requires-python = ">=3.10, <3.14"
 dependencies = [
-    "lancedb~=0.5.4",
    "pytube~=15.0.0",
    "requests~=2.32.5",
    "docker~=7.1.0",
    "crewai==1.9.3",
-    "lancedb~=0.5.4",
    "tiktoken~=0.8.0",
    "beautifulsoup4~=4.13.4",
    "python-docx~=1.2.0",
--- a/lib/crewai/pyproject.toml
+++ b/lib/crewai/pyproject.toml
@@ -42,7 +42,7 @@ dependencies = [
    "mcp~=1.26.0",
    "uv~=0.9.13",
    "aiosqlite~=0.21.0",
-    "lancedb>=0.4.0",
+    "lancedb>=0.29.2",
 ]

 [project.urls]
--- a/lib/crewai/src/crewai/agent/core.py
+++ b/lib/crewai/src/crewai/agent/core.py
@@ -384,10 +384,10 @@ class Agent(BaseAgent):
                )
                if unified_memory is not None:
                    query = task.description
-                    matches = unified_memory.recall(query, limit=10)
+                    matches = unified_memory.recall(query, limit=5)
                    if matches:
                        memory = "Relevant memories:\n" + "\n".join(
-                            f"- {m.record.content}" for m in matches
+                            m.format() for m in matches
                        )
                if memory.strip() != "":
                    task_prompt += self.i18n.slice("memory").format(memory=memory)
@@ -622,10 +622,10 @@ class Agent(BaseAgent):
                )
                if unified_memory is not None:
                    query = task.description
-                    matches = unified_memory.recall(query, limit=10)
+                    matches = unified_memory.recall(query, limit=5)
                    if matches:
                        memory = "Relevant memories:\n" + "\n".join(
-                            f"- {m.record.content}" for m in matches
+                            m.format() for m in matches
                        )
                if memory.strip() != "":
                    task_prompt += self.i18n.slice("memory").format(memory=memory)
@@ -1811,11 +1811,11 @@ class Agent(BaseAgent):
                    ),
                )
                start_time = time.time()
-                matches = agent_memory.recall(formatted_messages, limit=10)
+                matches = agent_memory.recall(formatted_messages, limit=5)
                memory_block = ""
                if matches:
                    memory_block = "Relevant memories:\n" + "\n".join(
-                        f"- {m.record.content}" for m in matches
+                        m.format() for m in matches
                    )
                if memory_block:
                    formatted_messages += "\n\n" + self.i18n.slice("memory").format(
--- a/lib/crewai/src/crewai/agents/agent_builder/base_agent_executor_mixin.py
+++ b/lib/crewai/src/crewai/agents/agent_builder/base_agent_executor_mixin.py
@@ -30,7 +30,7 @@ class CrewAgentExecutorMixin:
        memory = getattr(self.agent, "memory", None) or (
            getattr(self.crew, "_memory", None) if self.crew else None
        )
-        if memory is None or not self.task:
+        if memory is None or not self.task or getattr(memory, "_read_only", False):
            return
        if (
            f"Action: {sanitize_tool_name('Delegate work to coworker')}"
--- a/lib/crewai/src/crewai/cli/memory_tui.py
+++ b/lib/crewai/src/crewai/cli/memory_tui.py
@@ -290,13 +290,20 @@ class MemoryTUI(App[None]):
        if self._memory is None:
            panel.update(self._init_error or "No memory loaded.")
            return
+        display_limit = 1000
        info = self._memory.info(path)
        self._last_scope_info = info
-        self._entries = self._memory.list_records(scope=path, limit=200)
+        self._entries = self._memory.list_records(scope=path, limit=display_limit)
        panel.update(_format_scope_info(info))
        panel.border_title = "Detail"
        entry_list = self.query_one("#entry-list", OptionList)
-        entry_list.border_title = f"Entries ({len(self._entries)})"
+        capped = info.record_count > display_limit
+        count_label = (
+            f"Entries (showing {display_limit} of {info.record_count} — display limit)"
+            if capped
+            else f"Entries ({len(self._entries)})"
+        )
+        entry_list.border_title = count_label
        self._populate_entry_list()

    def on_option_list_option_highlighted(
@@ -376,6 +383,11 @@ class MemoryTUI(App[None]):
                return

            info_lines: list[str] = []
+            info_lines.append(
+                "[dim italic]Searched the full dataset"
+                + (f" within [bold]{scope}[/]" if scope else "")
+                + " using the recall flow (semantic + recency + importance).[/]\n"
+            )
            if not self._custom_embedder:
                info_lines.append(
                    "[dim italic]Note: Using default OpenAI embedder. "
--- a/lib/crewai/src/crewai/lite_agent.py
+++ b/lib/crewai/src/crewai/lite_agent.py
@@ -599,8 +599,8 @@ class LiteAgent(FlowTrackable, BaseModel):
            )

    def _save_to_memory(self, output_text: str) -> None:
-        """Extract discrete memories from the run and remember each. No-op if _memory is None."""
-        if self._memory is None:
+        """Extract discrete memories from the run and remember each. No-op if _memory is None or read-only."""
+        if self._memory is None or getattr(self._memory, "_read_only", False):
            return
        input_str = self._get_last_user_content() or "User request"
        try:
--- a/lib/crewai/src/crewai/llm.py
+++ b/lib/crewai/src/crewai/llm.py
@@ -427,7 +427,7 @@ class LLM(BaseLLM):
                f"installed.\n\n"
                f"To fix this, either:\n"
                f"  1. Install LiteLLM for broad model support: "
-                f"uv add 'crewai[litellm]'\n"
+                f"uv add litellm\n"
                f"or\n"
                f"pip install litellm\n\n"
                f"For more details, see: "
--- a/lib/crewai/src/crewai/memory/memory_scope.py
+++ b/lib/crewai/src/crewai/memory/memory_scope.py
@@ -145,7 +145,7 @@ class MemoryScope:


 class MemorySlice:
-    """View over multiple scopes: recall searches all, remember requires explicit scope unless read_only."""
+    """View over multiple scopes: recall searches all, remember is a no-op when read_only."""

    def __init__(
        self,
@@ -160,7 +160,7 @@ class MemorySlice:
            memory: The underlying Memory instance.
            scopes: List of scope paths to include.
            categories: Optional category filter for recall.
-            read_only: If True, remember() raises PermissionError.
+            read_only: If True, remember() is a silent no-op.
        """
        self._memory = memory
        self._scopes = [s.rstrip("/") or "/" for s in scopes]
@@ -176,10 +176,10 @@ class MemorySlice:
        importance: float | None = None,
        source: str | None = None,
        private: bool = False,
-    ) -> MemoryRecord:
-        """Remember into an explicit scope. Required when read_only=False."""
+    ) -> MemoryRecord | None:
+        """Remember into an explicit scope. No-op when read_only=True."""
        if self._read_only:
-            raise PermissionError("This MemorySlice is read-only")
+            return None
        return self._memory.remember(
            content,
            scope=scope,
--- a/lib/crewai/src/crewai/memory/storage/lancedb_storage.py
+++ b/lib/crewai/src/crewai/memory/storage/lancedb_storage.py
@@ -53,6 +53,7 @@ class LanceDBStorage:
        path: str | Path | None = None,
        table_name: str = "memories",
        vector_dim: int | None = None,
+        compact_every: int = 100,
    ) -> None:
        """Initialize LanceDB storage.

@@ -64,6 +65,10 @@ class LanceDBStorage:
            vector_dim: Dimensionality of the embedding vector. When ``None``
                  (default), the dimension is auto-detected from the existing
                  table schema or from the first saved embedding.
+            compact_every: Number of ``save()`` calls between automatic
+                  background compactions.  Each ``save()`` creates one new
+                  fragment file; compaction merges them, keeping query
+                  performance consistent.  Set to 0 to disable.
        """
        if path is None:
            storage_dir = os.environ.get("CREWAI_STORAGE_DIR")
@@ -78,6 +83,22 @@ class LanceDBStorage:
        self._table_name = table_name
        self._db = lancedb.connect(str(self._path))

+        # On macOS and Linux the default per-process open-file limit is 256.
+        # A LanceDB table stores one file per fragment (one fragment per save()
+        # call by default).  With hundreds of fragments, a single full-table
+        # scan opens all of them simultaneously, exhausting the limit.
+        # Raise it proactively so scans on large tables never hit OS error 24.
+        try:
+            import resource
+            soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
+            if soft < 4096:
+                resource.setrlimit(resource.RLIMIT_NOFILE, (min(hard, 4096), hard))
+        except Exception:  # noqa: S110
+            pass  # Windows or already at the max hard limit — safe to ignore
+
+        self._compact_every = compact_every
+        self._save_count = 0
+
        # Get or create a shared write lock for this database path.
        resolved = str(self._path.resolve())
        with LanceDBStorage._path_locks_guard:
@@ -91,6 +112,11 @@ class LanceDBStorage:
        try:
            self._table: lancedb.table.Table | None = self._db.open_table(self._table_name)
            self._vector_dim: int = self._infer_dim_from_table(self._table)
+            # Best-effort: create the scope index if it doesn't exist yet.
+            self._ensure_scope_index()
+            # Compact in the background if the table has accumulated many
+            # fragments from previous runs (each save() creates one).
+            self._compact_if_needed()
        except Exception:
            self._table = None
            self._vector_dim = vector_dim or 0  # 0 = not yet known
@@ -178,6 +204,56 @@ class LanceDBStorage:
        table.delete("id = '__schema_placeholder__'")
        return table

+    def _ensure_scope_index(self) -> None:
+        """Create a BTREE scalar index on the ``scope`` column if not present.
+
+        A scalar index lets LanceDB skip a full table scan when filtering by
+        scope prefix, which is the hot path for ``list_records``,
+        ``get_scope_info``, and ``list_scopes``.  The call is best-effort:
+        if the table is empty or the index already exists the exception is
+        swallowed silently.
+        """
+        if self._table is None:
+            return
+        try:
+            self._table.create_scalar_index("scope", index_type="BTREE", replace=False)
+        except Exception:  # noqa: S110
+            pass  # index already exists, table empty, or unsupported version
+
+    # ------------------------------------------------------------------
+    # Automatic background compaction
+    # ------------------------------------------------------------------
+
+    def _compact_if_needed(self) -> None:
+        """Spawn a background compaction on startup.
+
+        Called whenever an existing table is opened so that fragments
+        accumulated in previous sessions are silently merged before the
+        first query.  ``optimize()`` returns quickly when the table is
+        already compact, so the cost is negligible in the common case.
+        """
+        if self._table is None or self._compact_every <= 0:
+            return
+        self._compact_async()
+
+    def _compact_async(self) -> None:
+        """Fire-and-forget: compact the table in a daemon background thread."""
+        threading.Thread(
+            target=self._compact_safe,
+            daemon=True,
+            name="lancedb-compact",
+        ).start()
+
+    def _compact_safe(self) -> None:
+        """Run ``table.optimize()`` in a background thread, absorbing errors."""
+        try:
+            if self._table is not None:
+                self._table.optimize()
+                # Refresh the scope index so new fragments are covered.
+                self._ensure_scope_index()
+        except Exception:
+            _logger.debug("LanceDB background compaction failed", exc_info=True)
+
    def _ensure_table(self, vector_dim: int | None = None) -> lancedb.table.Table:
        """Return the table, creating it lazily if needed.

@@ -239,6 +315,7 @@ class LanceDBStorage:
            if r.embedding and len(r.embedding) > 0:
                dim = len(r.embedding)
                break
+        is_new_table = self._table is None
        with self._write_lock:
            self._ensure_table(vector_dim=dim)
            rows = [self._record_to_row(r) for r in records]
@@ -246,6 +323,13 @@ class LanceDBStorage:
                if r["vector"] is None or len(r["vector"]) != self._vector_dim:
                    r["vector"] = [0.0] * self._vector_dim
            self._retry_write("add", rows)
+        # Create the scope index on the first save so it covers the initial dataset.
+        if is_new_table:
+            self._ensure_scope_index()
+        # Auto-compact every N saves so fragment files don't pile up.
+        self._save_count += 1
+        if self._compact_every > 0 and self._save_count % self._compact_every == 0:
+            self._compact_async()

    def update(self, record: MemoryRecord) -> None:
        """Update a record by ID. Preserves created_at, updates last_accessed."""
@@ -261,6 +345,10 @@ class LanceDBStorage:
    def touch_records(self, record_ids: list[str]) -> None:
        """Update last_accessed to now for the given record IDs.

+        Uses a single batch ``table.update()`` call instead of N
+        delete-and-re-add cycles, which is both faster and avoids
+        unnecessary write amplification.
+
        Args:
            record_ids: IDs of records to touch.
        """
@@ -268,25 +356,20 @@ class LanceDBStorage:
            return
        with self._write_lock:
            now = datetime.utcnow().isoformat()
-            for rid in record_ids:
-                safe_id = str(rid).replace("'", "''")
-                rows = (
-                    self._table.search([0.0] * self._vector_dim)
-                    .where(f"id = '{safe_id}'")
-                    .limit(1)
-                    .to_list()
-                )
-                if rows:
-                    rows[0]["last_accessed"] = now
-                    self._retry_write("delete", f"id = '{safe_id}'")
-                    self._retry_write("add", [rows[0]])
+            safe_ids = [str(rid).replace("'", "''") for rid in record_ids]
+            ids_expr = ", ".join(f"'{rid}'" for rid in safe_ids)
+            self._retry_write(
+                "update",
+                where=f"id IN ({ids_expr})",
+                values={"last_accessed": now},
+            )

    def get_record(self, record_id: str) -> MemoryRecord | None:
        """Return a single record by ID, or None if not found."""
        if self._table is None:
            return None
        safe_id = str(record_id).replace("'", "''")
-        rows = self._table.search([0.0] * self._vector_dim).where(f"id = '{safe_id}'").limit(1).to_list()
+        rows = self._table.search().where(f"id = '{safe_id}'").limit(1).to_list()
        if not rows:
            return None
        return self._row_to_record(rows[0])
@@ -374,13 +457,31 @@ class LanceDBStorage:
            self._retry_write("delete", where_expr)
            return before - self._table.count_rows()

-    def _scan_rows(self, scope_prefix: str | None = None, limit: int = _SCAN_ROWS_LIMIT) -> list[dict[str, Any]]:
-        """Scan rows optionally filtered by scope prefix."""
+    def _scan_rows(
+        self,
+        scope_prefix: str | None = None,
+        limit: int = _SCAN_ROWS_LIMIT,
+        columns: list[str] | None = None,
+    ) -> list[dict[str, Any]]:
+        """Scan rows optionally filtered by scope prefix.
+
+        Uses a full table scan (no vector query) so the limit is applied after
+        the scope filter, not to ANN candidates before filtering.
+
+        Args:
+            scope_prefix: Optional scope path prefix to filter by.
+            limit: Maximum number of rows to return (applied after filtering).
+            columns: Optional list of column names to fetch.  Pass only the
+                columns you need for metadata operations to avoid reading the
+                heavy ``vector`` column unnecessarily.
+        """
        if self._table is None:
            return []
-        q = self._table.search([0.0] * self._vector_dim)
+        q = self._table.search()
        if scope_prefix is not None and scope_prefix.strip("/"):
            q = q.where(f"scope LIKE '{scope_prefix.rstrip('/')}%'")
+        if columns is not None:
+            q = q.select(columns)
        return q.limit(limit).to_list()

    def list_records(
@@ -406,7 +507,10 @@ class LanceDBStorage:
        prefix = scope if scope != "/" else ""
        if prefix and not prefix.startswith("/"):
            prefix = "/" + prefix
-        rows = self._scan_rows(prefix or None)
+        rows = self._scan_rows(
+            prefix or None,
+            columns=["scope", "categories_str", "created_at"],
+        )
        if not rows:
            return ScopeInfo(
                path=scope or "/",
@@ -453,7 +557,7 @@ class LanceDBStorage:
    def list_scopes(self, parent: str = "/") -> list[str]:
        parent = parent.rstrip("/") or ""
        prefix = (parent + "/") if parent else "/"
-        rows = self._scan_rows(prefix if prefix != "/" else None)
+        rows = self._scan_rows(prefix if prefix != "/" else None, columns=["scope"])
        children: set[str] = set()
        for row in rows:
            sc = str(row.get("scope", ""))
@@ -465,7 +569,7 @@ class LanceDBStorage:
        return sorted(children)

    def list_categories(self, scope_prefix: str | None = None) -> dict[str, int]:
-        rows = self._scan_rows(scope_prefix)
+        rows = self._scan_rows(scope_prefix, columns=["categories_str"])
        counts: dict[str, int] = {}
        for row in rows:
            cat_str = row.get("categories_str") or "[]"
@@ -498,6 +602,21 @@ class LanceDBStorage:
        if prefix:
            self._table.delete(f"scope >= '{prefix}' AND scope < '{prefix}/\uFFFF'")

+    def optimize(self) -> None:
+        """Compact the table synchronously and refresh the scope index.
+
+        Under normal usage this is called automatically in the background
+        (every ``compact_every`` saves and on startup when the table is
+        fragmented).  Call this explicitly only when you need the compaction
+        to be complete before the next operation — for example immediately
+        after a large bulk import, before a latency-sensitive recall.
+        It is a no-op if the table does not exist.
+        """
+        if self._table is None:
+            return
+        self._table.optimize()
+        self._ensure_scope_index()
+
    async def asave(self, records: list[MemoryRecord]) -> None:
        self.save(records)

--- a/lib/crewai/src/crewai/memory/types.py
+++ b/lib/crewai/src/crewai/memory/types.py
@@ -87,6 +87,22 @@ class MemoryMatch(BaseModel):
        description="Information the system looked for but could not find.",
    )

+    def format(self) -> str:
+        """Format this match as a human-readable string including metadata.
+
+        Returns:
+            A multi-line string with score, content, categories, and non-empty
+            metadata fields.
+        """
+        lines = [f"- (score={self.score:.2f}) {self.record.content}"]
+        if self.record.categories:
+            lines.append(f"  categories: {', '.join(self.record.categories)}")
+        if self.record.metadata:
+            for key, value in self.record.metadata.items():
+                if value:
+                    lines.append(f"  {key}: {value}")
+        return "\n".join(lines)
+

 class ScopeInfo(BaseModel):
    """Information about a scope in the memory hierarchy."""
--- a/lib/crewai/src/crewai/memory/unified_memory.py
+++ b/lib/crewai/src/crewai/memory/unified_memory.py
@@ -88,6 +88,10 @@ class Memory:
        # Queries shorter than this skip LLM analysis (saving ~1-3s).
        # Longer queries (full task descriptions) benefit from LLM distillation.
        query_analysis_threshold: int = 200,
+        # When True, all write operations (remember, remember_many) are silently
+        # skipped. Useful for sharing a read-only view of memory across agents
+        # without any of them persisting new memories.
+        read_only: bool = False,
    ) -> None:
        """Initialize Memory.

@@ -107,7 +111,9 @@ class Memory:
            complex_query_threshold: For complex queries, explore deeper below this confidence.
            exploration_budget: Number of LLM-driven exploration rounds during deep recall.
            query_analysis_threshold: Queries shorter than this skip LLM analysis during deep recall.
+            read_only: If True, remember() and remember_many() are silent no-ops.
        """
+        self._read_only = read_only
        self._config = MemoryConfig(
            recency_weight=recency_weight,
            semantic_weight=semantic_weight,
@@ -335,11 +341,13 @@ class Memory:
            agent_role: Optional agent role for event metadata.

        Returns:
-            The created MemoryRecord.
+            The created MemoryRecord, or None if this memory is read-only.

        Raises:
            Exception: On save failure (events emitted).
        """
+        if self._read_only:
+            return None  # type: ignore[return-value]
        _source_type = "unified_memory"
        try:
            crewai_event_bus.emit(
@@ -420,7 +428,7 @@ class Memory:
        Returns:
            Empty list (records are not available until the background save completes).
        """
-        if not contents:
+        if not contents or self._read_only:
            return []

        self._submit_save(
--- a/lib/crewai/src/crewai/tools/memory_tools.py
+++ b/lib/crewai/src/crewai/tools/memory_tools.py
@@ -20,14 +20,6 @@ class RecallMemorySchema(BaseModel):
            "or multiple items to search for several things at once."
        ),
    )
-    scope: str | None = Field(
-        default=None,
-        description="Optional scope to narrow the search (e.g. /project/alpha)",
-    )
-    depth: str = Field(
-        default="shallow",
-        description="'shallow' for fast vector search, 'deep' for LLM-analyzed retrieval",
-    )


 class RecallMemoryTool(BaseTool):
@@ -41,32 +33,27 @@ class RecallMemoryTool(BaseTool):
    def _run(
        self,
        queries: list[str] | str,
-        scope: str | None = None,
-        depth: str = "shallow",
        **kwargs: Any,
    ) -> str:
        """Search memory for relevant information.

        Args:
            queries: One or more search queries (string or list of strings).
-            scope: Optional scope prefix to narrow the search.
-            depth: "shallow" for fast vector search, "deep" for LLM-analyzed retrieval.

        Returns:
            Formatted string of matching memories, or a message if none found.
        """
        if isinstance(queries, str):
            queries = [queries]
-        actual_depth = depth if depth in ("shallow", "deep") else "shallow"

        all_lines: list[str] = []
        seen_ids: set[str] = set()
        for query in queries:
-            matches = self.memory.recall(query, scope=scope, limit=5, depth=actual_depth)
+            matches = self.memory.recall(query)
            for m in matches:
                if m.record.id not in seen_ids:
                    seen_ids.add(m.record.id)
-                    all_lines.append(f"- (score={m.score:.2f}) {m.record.content}")
+                    all_lines.append(m.format())

        if not all_lines:
            return "No relevant memories found."
@@ -117,20 +104,28 @@ class RememberTool(BaseTool):
 def create_memory_tools(memory: Any) -> list[BaseTool]:
    """Create Recall and Remember tools for the given memory instance.

+    When memory is read-only (``_read_only=True``), only the RecallMemoryTool
+    is returned — the RememberTool is omitted so agents are never offered a
+    save capability they cannot use.
+
    Args:
        memory: A Memory, MemoryScope, or MemorySlice instance.

    Returns:
-        List containing a RecallMemoryTool and a RememberTool.
+        List containing a RecallMemoryTool and, if not read-only, a RememberTool.
    """
    i18n = get_i18n()
-    return [
+    tools: list[BaseTool] = [
        RecallMemoryTool(
            memory=memory,
            description=i18n.tools("recall_memory"),
        ),
-        RememberTool(
-            memory=memory,
-            description=i18n.tools("save_to_memory"),
-        ),
    ]
+    if not getattr(memory, "_read_only", False):
+        tools.append(
+            RememberTool(
+                memory=memory,
+                description=i18n.tools("save_to_memory"),
+            )
+        )
+    return tools
--- a/lib/crewai/tests/memory/test_unified_memory.py
+++ b/lib/crewai/tests/memory/test_unified_memory.py
@@ -218,14 +218,15 @@ def test_memory_slice_recall(tmp_path: Path, mock_embedder: MagicMock) -> None:
    assert isinstance(matches, list)


-def test_memory_slice_remember_raises_when_read_only(tmp_path: Path, mock_embedder: MagicMock) -> None:
+def test_memory_slice_remember_is_noop_when_read_only(tmp_path: Path, mock_embedder: MagicMock) -> None:
    from crewai.memory.unified_memory import Memory
    from crewai.memory.memory_scope import MemorySlice

    mem = Memory(storage=str(tmp_path / "db7"), llm=MagicMock(), embedder=mock_embedder)
    sl = MemorySlice(mem, ["/a"], read_only=True)
-    with pytest.raises(PermissionError):
-        sl.remember("x", scope="/a")
+    result = sl.remember("x", scope="/a")
+    assert result is None
+    assert mem.list_records() == []


 # --- Flow memory ---
--- a/uv.lock
+++ b/uv.lock
@@ -1204,7 +1204,7 @@ requires-dist = [
    { name = "json-repair", specifier = "~=0.25.2" },
    { name = "json5", specifier = "~=0.10.0" },
    { name = "jsonref", specifier = "~=1.1.0" },
-    { name = "lancedb", specifier = ">=0.4.0" },
+    { name = "lancedb", specifier = ">=0.29.2" },
    { name = "litellm", marker = "extra == 'litellm'", specifier = ">=1.74.9,<3" },
    { name = "mcp", specifier = "~=1.26.0" },
    { name = "mem0ai", marker = "extra == 'mem0'", specifier = "~=0.1.94" },
@@ -1286,7 +1286,6 @@ dependencies = [
    { name = "beautifulsoup4" },
    { name = "crewai" },
    { name = "docker" },
-    { name = "lancedb" },
    { name = "pymupdf" },
    { name = "python-docx" },
    { name = "pytube" },
@@ -1428,7 +1427,6 @@ requires-dist = [
    { name = "firecrawl-py", marker = "extra == 'firecrawl-py'", specifier = ">=1.8.0" },
    { name = "gitpython", marker = "extra == 'github'", specifier = "==3.1.38" },
    { name = "hyperbrowser", marker = "extra == 'hyperbrowser'", specifier = ">=0.18.0" },
-    { name = "lancedb", specifier = "~=0.5.4" },
    { name = "langchain-apify", marker = "extra == 'apify'", specifier = ">=0.1.2,<1.0.0" },
    { name = "linkup-sdk", marker = "extra == 'linkup-sdk'", specifier = ">=0.2.2" },
    { name = "lxml", marker = "extra == 'rag'", specifier = ">=5.3.0,<5.4.0" },
@@ -3226,27 +3224,54 @@ wheels = [
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-version = "0.5.7"
+name = "lance-namespace"
+version = "0.5.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+]
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+wheels = [
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+]
+
+[[package]]
+name = "lance-namespace-urllib3-client"
+version = "0.5.2"
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-    { name = "pylance" },
-    { name = "pyyaml" },
-    { name = "ratelimiter" },
-    { name = "requests" },
-    { name = "retry" },
-    { name = "semver" },
+    { name = "python-dateutil" },
+    { name = "typing-extensions" },
+    { name = "urllib3", version = "1.26.20", source = { registry = "https://pypi.org/simple" }, marker = "platform_python_implementation == 'PyPy'" },
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+[[package]]
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+dependencies = [
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+    { name = "lance-namespace" },
+    { name = "numpy" },
+    { name = "overrides", marker = "python_full_version < '3.12'" },
+    { name = "packaging" },
+    { name = "pyarrow" },
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 [[package]]
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Author	SHA1	Message	Date
Joao Moura	358fd92e6b	refactor: enhance memory handling with read-only support - Updated memory-related classes and methods to support read-only functionality, allowing for silent no-ops when attempting to remember data in read-only mode. - Modified the LiteAgent and CrewAgentExecutorMixin classes to check for read-only status before saving memories. - Adjusted MemorySlice and Memory classes to reflect changes in behavior when read-only is enabled. - Updated tests to verify that memory operations behave correctly under read-only conditions.	2026-02-26 00:10:25 -08:00
Joao Moura	c4d4ea6c71	refactor: update memory recall limit and formatting in Agent class - Reduced the memory recall limit from 10 to 5 in multiple locations within the Agent class. - Updated the memory formatting to use a new `format` method in the MemoryMatch class for improved readability and metadata inclusion.	2026-02-26 00:01:09 -08:00
Joao Moura	24c68d4053	linter	2026-02-25 21:34:45 -08:00
Joao Moura	320326e3e5	chore(deps): update lancedb version and add lance-namespace packages - Updated lancedb dependency version from 0.4.0 to 0.29.2 in multiple files. - Added new packages: lance-namespace and lance-namespace-urllib3-client with version 0.5.2, including their dependencies and installation details. - Enhanced MemoryTUI to display a limit on entries and improved the LanceDBStorage class with automatic background compaction and index creation for better performance.	2026-02-25 21:30:02 -08:00