alfin06/swegent-issues-filtered huggingface dataset:
[Feature Request] Integrate ACI Docs

### Required prerequisites

- [x] I have searched the [Issue Tracker](https://github.com/camel-ai/camel/issues) and [Discussions](https://github.com/camel-ai/camel/discussions) that this hasn't already been reported. (+1 or comment there if it has.)
- [x] Consider asking first in a [Discussion](https://github.com/camel-ai/camel/discussions/new).

### Motivation

[ACI.dev](https://github.com/aipotheosis-labs/aci) is the open source platform that connects your AI agents to 600+ tool integrations. Integrate [ACI.dev](https://github.com/aipotheosis-labs/aci) with CAMEL agents to let them seamlessly interact with these external apps.

ACI enables agents to connect with Various Tools and work with them, which can allow users make tool call with more tools.


### Solution

Let CAMEL agents support ACI usage and give the documentation of the example. 

### Alternatives

_No response_

### Additional context

_No response_

Labels: enhancement
State: closed
[BUG]

### Description

Whenever i wanted to my agent to product a pydantic model output by giving a pydantic model to output_pydantic parameter, it is working somtimes or failing most of the time by giving this message 
Failed to add to long term memory: Failed to convert text into a Pydantic model due to error: No module named 'google.genai' irrespective of success or failure. Because of this i cannot use that task in the next flow.

### Steps to Reproduce

Create a simple agent and pydantic model and in agent , give that pydantic model as a output_pydantic, we can see the error

### Expected behavior

It should work without any error message and the next agent should understand that it is a pydantic model and the second agent should be able convert that into a string automatically and and work from there.  Or we should have the option of what key value pair from the pydantic model of the first agent can be given as a input to second agent. 

### Screenshots/Code snippets

class ResponseFormat(BaseModel):
    string : str = Field(description = 'string needs to manitained')

class FirstFlowCrew:

    def first_crew(self, param):
        first_agent = Agent(role = "role mentioned",
                                goal = dedent( f"""Goals mentioned"""),
                                backstory = dedent("""Back story mentioned"""),
                                tools=[tool mentioned],
                                verbose=True,
                                allow_delegation=True,
                                memory = True
                            )

       task_1 = Task(description = "Task description mentioned",
                                   expected_output = "expected output mentioned.",
                                   agent = first_agent
                                )
        

        second_agent = Agent(role='role mentioned',
                                   goal = dedent("""goal mentioned"""),
                                    backstory= "back story mentioned",
                                    
                                )
        
        task_2 = Task(description = "task mentioned",
                                 expected_output = "expected output metioned",
                                 agent = second_agent,
                                output_pydantic=ResponseFormat
                                )

        first_crew = Crew(agents = [first_agent, second_agent], 
                    tasks = [task_1, task_2], 
                    process = 'sequential',
                    verbose = True,
                    memory=True
                    )
        
        result = first_crew.kickoff()
        return result

### Operating System

Other (specify in additional context)

### Python Version

3.12

### crewAI Version

0.108.0

### crewAI Tools Version

0.38.1

### Virtual Environment

Venv

### Evidence

![Image](https://github.com/user-attachments/assets/77095421-3c7f-4e0a-9904-72868397e8fb)

### Possible Solution

None

### Additional context

None

Labels: bug
State: closed
[Feature Request] ChatAgent support `.to_mcp` to convert into MCP server

### Required prerequisites

- [x] I have searched the [Issue Tracker](https://github.com/camel-ai/camel/issues) and [Discussions](https://github.com/camel-ai/camel/discussions) that this hasn't already been reported. (+1 or comment there if it has.)
- [ ] Consider asking first in a [Discussion](https://github.com/camel-ai/camel/discussions/new).

### Motivation

currently we support convert one ChatAgent as MCP Server but it still require user to write some code to make it, a more user-friendly way would be adding `.to_mcp` method that support direct converting

### Solution

_No response_

### Alternatives

_No response_

### Additional context

_No response_

Labels: enhancement, P1
State: closed
[Bug]: reflection_with_llm issue with local server

### Describe the bug

There seems to be an issue with generating the reflection_with_llm summary when working with locally deployed models. 

Below is a simple snippet that when run with my local model, it generates the conversation correctly, but the ChatResult summary is empty. When I run it using gpt4, it generates the summary correctly. 

### Steps to reproduce

from autogen import GroupChatManager, GroupChat, config_list_from_json, ConversableAgent, UserProxyAgent

config_list = config_list_from_json(
    env_or_file="OAI_CONFIG_LIST",
    file_location=".",
    filter_dict={
        "model": ["TheBloke/Mistral-7B-Instruct-v0.2-GGUF/mistral-7b-instruct-v0.2.Q6_K.gguf"],
    },
)
llm_config = {
    "cache_seed": False,  # change the cache_seed for different trials
    "temperature": 0,
    "config_list": config_list,
    "timeout": 120,
}

user_proxy_auto = UserProxyAgent(
    name="user_proxy_auto",
    code_execution_config=False,
    llm_config=llm_config
)

drafter_agent = ConversableAgent(
    name="drafter",
    llm_config=llm_config,
    system_message="You are an assistant in charge of drafting the answer for the task.",
)
reviewer_agent = ConversableAgent(
    name="reviewer",
    llm_config=llm_config,
    system_message="You are an assistant in charge of reviewing the drafted answer and assess its quality in terms of tackling the task successfully and effectively. You can make adjustments directly, request a completely new draft while providing a framework to approach the task more effectively, or approve the answer as is. If the task is complete, end the task with `TERMINATE`",
)
group_chat = GroupChat(
    agents=[drafter_agent, reviewer_agent, user_proxy_auto],
    messages=[],
    max_round=4,
    speaker_selection_method="round_robin"
)

chat_manager = GroupChatManager(
    groupchat=group_chat,
    llm_config=llm_config
)

chat_result = user_proxy_auto.initiate_chat(
    chat_manager, 
    message="List 5 roles/positions that benefit strongly from a high EQ, and list your reasons.", 
    clear_history=True, 
    summary_args={"summary_prompt": "List the final answer to the task."},
    summary_method="reflection_with_llm"
)

print(f'chat_result history: {chat_result.chat_history}')
print(f'chat_result summary: {chat_result.summary}')

### Model Used

TheBloke/Mistral-7B-Instruct-v0.2-GGUF/mistral-7b-instruct-v0.2.Q6_K.gguf -> LMStudio
gpt-4-turbo-2024-04-09 -> OpenAI

### Expected Behavior

reflection_with_llm not generating the output when using local model

### Screenshots and logs

_No response_

### Additional Information

Name: pyautogen
Version: 0.2.23

Labels: models
State: closed
[Bug]: Runtime logging not supported with GPTAssistantAgent

### Describe the bug

I followed the example mentioned in https://microsoft.github.io/autogen/docs/notebooks/agentchat_logging/
But when i replaced the AssistantAgent, with GPTAssistantAgent. The logs werent captured in the DB. No new entry added.

### Steps to reproduce

In the example https://microsoft.github.io/autogen/docs/notebooks/agentchat_logging/

just replace assistant with 

assistant = GPTAssistantAgent(
    name="assistant",
    llm_config=llm_config
)

and then try to get logs from sqlite, using steps given in same doc.

### Model Used

_No response_

### Expected Behavior

_No response_

### Screenshots and logs

_No response_

### Additional Information

_No response_

Labels: logging
State: closed
failed to get next task: %!w(*fmt.wrapError=&{failed to get next command: no tool calls found 0x14000113a40})

The LLM model is trying to choose between available instruments (open a web browser, edit code, execute a shell command, etc.), but sometimes it fails to choose one, and this error occurs. I will open a new GitHub issue.

Labels: bug, good first issue
State: closed
[Feature Request] Add Crynux as a LLM provider

### Required prerequisites

- [x] I have searched the [Issue Tracker](https://github.com/camel-ai/camel/issues) and [Discussions](https://github.com/camel-ai/camel/discussions) that this hasn't already been reported. (+1 or comment there if it has.)
- [ ] Consider asking first in a [Discussion](https://github.com/camel-ai/camel/discussions/new).

### Motivation

We'd like to add Crynux as a LLM provider
Crynux offers extremely low cost for LLM inference, and free API key (with low QPS) for developers to play with.


### Solution

_No response_

### Alternatives

_No response_

### Additional context

_No response_

Labels: New Feature
State: closed
[Feature Request]: Authentication of gemini pro models using service account file on google cloud

### Is your feature request related to a problem? Please describe.

All enterprise cloud projects use service account file keys to authenticate. Autogen llm configuration does not support the service account file keys at the moment, rather authenticates with api keys. Pleaase make this feature available.

### Describe the solution you'd like

Autogen assistant and Userproxy agents to support LLM configurations using service account files.

### Additional context

_No response_

Labels: models
State: closed
[.Net][Feature Request]: Run Agent as Web API

### Is your feature request related to a problem? Please describe.

Starting an openai-chat-compatible web api from an arbitrary agent

### Describe the solution you'd like

```csharp
// Server side
IAgent alice;
IAgent bob;

// add muliple agents to the server
app.UseAgentAsOpenAIChatCompletionEndpoint(alice); // this will start an OpenAI API at /v1/chat/completions and will be triggered when model field equals to "alice"
app.UseAgentAsOpenAIChatCompletionEndpoint(bob); // this will start an OpenAI API at /v1/chat/completions and will be triggered when model field equals to "bob"


app.StartAsync();
```

```csharp
// client side
// Suppose the service is running at localhost:8080
// We can use `OpenAIChatAgent` to consume that backend
var openaiClient = CreateOpenAIClient(client); // check Example 16 on how to connect to a third-party OpenAI API backend
var alice= new OpenAIChatAgent(openaiClient, "alice")
            .RegisterMessageConnector();

await alice.SendAsync("Hello");

// if agent is an `IStreamingAgent`, the service will also support streaming

await foreach(var chunk in alice.GenerateStreamingReply(xx))
{
    // process chunks
}
```


### Additional context
Also cc @ekzhu and @luisquintanilla
_No response_
```[tasklist]
### Tasks
```


State: closed
[Feature Request][.Net]: Count token usage for `OpenAIClientAgent` 

### Is your feature request related to a problem? Please describe.

The token usage information is included in `ChatCompletions`, but `OpenAIClentAgent` returns `ChatCompletions.Choice[0]`, which prevents developers get token count information.

### Describe the solution you'd like

Return `ChatCompletions` in `OpenAIClientAgent` so the token count information can be retrieved in the similar way of the following example

https://microsoft.github.io/autogen-for-net/articles/MistralChatAgent-count-token-usage.html

### Additional context

_No response_

Labels: dotnet
State: closed
[Issue]: Warning from the def _num_token_from_messages is verbose and hard to silence

### Describe the issue

Here is an example output: 
[I 2024-04-27 00:02:01,357.357 autogen.token_count_utils] gpt-4 may update over time. Returning num tokens assuming gpt-4-0613.

However, this is very verbose and frustrating to see printed.  Can we either dynamically retrieve the number of tokens from the model count or toggle this output?  I can work around it but it seems like a fixable bit of code.

### Steps to reproduce
Initialize a compressible agent or series of compressible agents

### Screenshots and logs

Screenshots in comment

### Additional Information

_No response_

State: closed
Write File Tool cannot write Chinese

![image](https://github.com/TransformerOptimus/SuperAGI/assets/100895608/7bf0644d-71f8-4894-800e-8587c385f062)


State: closed
[Bug]: Retrieving existing collection ignores custom embedding_function when using ChromaVectorDB

### Describe the bug

Retrieving existing collection ignores custom embedding_function when using ChromaVectorDB.

### Steps to reproduce

Setup custom embedding function:
```
embeeding_function = embedding_functions.OpenAIEmbeddingFunction(
                api_key="__localai__",
                api_base="http://localhost:8080/v1",
                model_name="bert-minilm-embedding"
            )

rag_vector_db = ChromaVectorDB(path="./data", embedding_function=embeeding_function)

admin_helper = RetrieveUserProxyAgent(
    name="admin_helper",
    description="Assistant who has extra content retrieval power for solving difficult problems.",
    human_input_mode="NEVER",
    is_termination_msg=termination_msg,
    default_auto_reply="Reply `TERMINATE` if the task is done.",
    retrieve_config={
        "task": "qa",
        "vector_db": rag_vector_db,
        "collection_name": "odoo_table_context",
        "docs_path": "./odoo-table-context.jsonl",
        "get_or_create": True,
    },
    max_consecutive_auto_reply=3,
    code_execution_config=False,
)
```

Run it twice.

First run it's alright.
![chromadb py - autogen - Visual Studio Code 29_04_2024 08_01_42](https://github.com/microsoft/autogen/assets/22361729/334fa02a-a2a3-4515-9601-8f48835ff8c7)

Second run gives default embedding function.
![chromadb py - autogen - Visual Studio Code 29_04_2024 08_04_25](https://github.com/microsoft/autogen/assets/22361729/038e9f72-3b95-4445-afd1-0fa57d6db3c6)


### Model Used

_No response_

### Expected Behavior

It should always use the provided embedding function.

### Screenshots and logs

_No response_

### Additional Information

This line should be using the custom embedding function. https://github.com/microsoft/autogen/blob/a9171211c7533fc11e078899720a3847f45807cc/autogen/agentchat/contrib/vectordb/chromadb.py#L129

```
self.active_collection = self.client.get_collection(collection_name, embedding_function=self.embedding_function)
```

Labels: rag
State: closed
[Feature]Huggingface is not mentioned in the provider list but in documentation page also mentioned it as a provider

### Description

In the documentation page you have clearly mentioned about the llm [LLMs
](https://docs.crewai.com/concepts/llms#hugging-face) support for crew projects.But when we try to create a crew in the providers list i couldnt see HUggingface anywhere.

![Image](https://github.com/user-attachments/assets/ad5bee3c-c48d-4be9-a415-356e7739f0f8)

![Image](https://github.com/user-attachments/assets/db018e1a-9cc0-4e6f-ab38-40f60dee5f81)
![Image](https://github.com/user-attachments/assets/c416a914-c4e3-4c8d-a635-7c2c8f687bcb)

### Steps to Reproduce

In console,
1.crewai create crew x_crew
2.You can see top 12  providers list,you cant see huggingface there,so 12th option Others is selected
3.Another window will pop up,In that also i couldnt see any such options 


### Expected behavior

It will be much helpful if huggingface is also there as a provider.

### Screenshots/Code snippets

Sure

![Image](https://github.com/user-attachments/assets/a656bfba-41fa-47a9-a3ce-5b617ad3f210)
![Image](https://github.com/user-attachments/assets/881fd193-92fa-4e7e-bf0f-c0a797be6e18)

### Operating System

Ubuntu 22.04

### Python Version

3.10

### crewAI Version

0.108.0

### crewAI Tools Version

0.40.1

### Virtual Environment

Conda

### Evidence

Already shared

![Image](https://github.com/user-attachments/assets/acaa01c3-75dd-4eb6-aaf3-15edc7ec08ff)
![Image](https://github.com/user-attachments/assets/d419862e-38fb-4848-938c-4b1e739b9aa3)
![Image](https://github.com/user-attachments/assets/ae06b5a4-ce04-423a-9d2f-3b551903c7c3)

### Possible Solution

Include Huggingface also in the providers list if possible,Atleast llama ,phi,like models can be utilised in diverse projects.

### Additional context

None

Labels: bug
State: closed
AstraDB component Ingest all items twice

As I run the flow to ingest data, it does it twice. So each item is captured twice in the AstraDB data collection. 

**Browser and Version**
- Langflow version 1.0.6 running on local a virtual environment

**To Reproduce**
- use the pre-built flow 'Vector Store RAG'
- use the existing flow to ingest any content

Does anyone that experience the same? 




Labels: bug
State: closed
[Feature Request] Add Chroma vector database

### Required prerequisites

- [x] I have searched the [Issue Tracker](https://github.com/camel-ai/camel/issues) and [Discussions](https://github.com/camel-ai/camel/discussions) that this hasn't already been reported. (+1 or comment there if it has.)
- [ ] Consider asking first in a [Discussion](https://github.com/camel-ai/camel/discussions/new).

### Motivation

Add Chroma vector database since some community members asked for.

### Solution

_No response_

### Alternatives

_No response_

### Additional context

_No response_

Labels: P1, RAG
State: closed
[BUG]Mem0 local config not being applied

### Description

The mem0 local config is not being applied when agent memory is being instantiated.

I belive the issue is in storage/mem0_storage on line 50
if mem0_local_config and len(mem0_local_config):
                self.memory = Memory.from_config(config)

Should be

if mem0_local_config and len(mem0_local_config):
                self.memory = Memory.from_config(mem0_local_config)

### Steps to Reproduce

Just try using mem0 with a local config. I am attempting to use Gemini as an embedder but the exceptions I get state the OpenAI API key is not set.

### Expected behavior

The local config should be applied

### Screenshots/Code snippets

embedder_config = {
    "provider": "google",
    "config": {
        "model": "models/text-embedding-004",
        "api_key": GEMINI_API_KEY,
    }
}
----
@crew
    def crew(self) -> Crew:
        """Creates the TedCrew crew"""

        return Crew(
            agents=self.agents,
            tasks=self.tasks,
            process=Process.sequential,
            verbose=True,
            memory=True,
            memory_config={
                "provider": "mem0",
                "config": {"user_id": "tedcrew", 'local_mem0_config': mem0_config},
                "user_memory" : {} #Set user_memory explicitly to a dictionary, we are working on this issue.
            },
            # Long-term memory for persistent storage across sessions
            long_term_memory = LongTermMemory(
                storage=LTMSQLiteStorage(
                    db_path=f"{DATA_DIR}/long_term_memory_storage.db"
                )
            ),
            # Short-term memory for current context using RAG
            short_term_memory = ShortTermMemory(
                storage = RAGStorage(
                        embedder_config=embedder_config,
                        type="short_term",
                        path=f"{DATA_DIR}/"
                    )
            ),
            # Entity memory for tracking key information about entities
            entity_memory = EntityMemory(
                storage=RAGStorage(
                    embedder_config=embedder_config,
                    type="short_term",
                    path=f"{DATA_DIR}/"
                )
            ),
            embedder=embedder_config
        )

### Operating System

macOS Sonoma

### Python Version

3.12

### crewAI Version

0.114.0

### crewAI Tools Version

0.40.1

### Virtual Environment

Venv

### Evidence

<img width="1059" alt="Image" src="https://github.com/user-attachments/assets/bf0f8d50-708d-4cc8-9a2a-e10950a173fa" />

### Possible Solution

I belive the issue is in storage/mem0_storage on line 50
if mem0_local_config and len(mem0_local_config):
                self.memory = Memory.from_config(config)

Should be

if mem0_local_config and len(mem0_local_config):
                self.memory = Memory.from_config(mem0_local_config)

### Additional context

None

Labels: bug
State: closed
[BUG] Workforce accepts empty/meaningless task content as successful completion

### Required prerequisites

- [x] I have read the documentation <https://camel-ai.github.io/camel/camel.html>.
- [x] I have searched the [Issue Tracker](https://github.com/camel-ai/camel/issues) and [Discussions](https://github.com/camel-ai/camel/discussions) that this hasn't already been reported. (+1 or comment there if it has.)
- [x] Consider asking first in a [Discussion](https://github.com/camel-ai/camel/discussions/new).

### What version of camel are you using?

0.2.63

### System information

3.12.4 | packaged by Anaconda, Inc. | (main, Jun 18 2024, 15:03:56) [MSC v.1929 64 bit (AMD64)] win32
0.2.63

### Problem description

The Workforce module has no content validation mechanisms, allowing tasks with empty or meaningless content to be marked as successfully completed. This creates a critical vulnerability for production where users could receive empty results without any error indication.

- Workforce only checks the failed flag in agent responses
- Tasks with {"content": "", "failed": false} are marked as successful
- Empty content triggers extensive task decomposition and processing
- System generates elaborate plans and documentation for completely empty tasks
- No quality checks or minimum content requirements exist

this can cause silent failures that might go unnoticed and are difficult to debug.

### Reproducible example code

The Python snippets:

```python
    # External service returns HTTP 200 but empty response body
    api_response = requests.get("/external-data-service") 
    # Status: 200 OK, but response.json()["content"] = ""
    # Agent marks as successful because HTTP status indicates success
```

```python
   # Query executes successfully but returns no rows
   result = db.query("SELECT report_data WHERE date = ?", target_date)
   # Query succeeds but result set is empty (no data for that date)
   # Agent reports success with empty content
```

```python
   # File exists but is corrupted, empty, or in wrong format
   with open("quarterly_data.csv", "r") as f:
       content = f.read()  # Returns empty string from corrupted file
   # File operation "succeeds" but produces no usable content
```

### Traceback

```pytb

```

### Expected behavior

_No response_

### Additional context

_No response_

Labels: bug, Society
State: closed