semvec/pypi-packages huggingface dataset:
meshagent-mcp 0.27.0
Computer Building Blocks for MCP
Requires Python: >=3.13

# [Meshagent](https://www.meshagent.com)

## MeshAgent MCP

The ``meshagent.mcp`` package allows you to use any MCP server as a MeshAgent tool. 

### MCPTool
Wrap an MCP tool with ``MCPTool`` so it conforms to the MeshAgent Tool syntax. For more details on how to do this check out the [MeshAgent MCP docs](https://docs.meshagent.com/mcp-overview/overview).

### MCPToolkit
The ``MCPToolkit`` bundles multiple ``MCPTool`` instances into a toolkit that agents can invoke just like built-in MeshAgent tools or custom tools.

```Python Python
import mcp
from meshagent.mcp import MCPToolkit

session = mcp.ClientSession(...)
toolkit = MCPToolkit(
    name="mcp-tools",
    session=session,
    tools=[...]
)
```

---
### Learn more about MeshAgent on our website or check out the docs for additional examples!

**Website**: [www.meshagent.com](https://www.meshagent.com/)

**Documentation**: [docs.meshagent.com](https://docs.meshagent.com/)

---
meshagent-anthropic 0.27.0
Anthropic Building Blocks for Meshagent
Requires Python: >=3.13

# [Meshagent](https://www.meshagent.com)

## MeshAgent Anthropic
The `meshagent.anthropic` package provides adapters to integrate Anthropic models (Messages API) with MeshAgent tools and agents.

### Messages Adapter
- `AnthropicMessagesAdapter`: wraps the Anthropic Messages API. It turns `Toolkit` objects into Anthropic tool definitions, executes tool calls, and returns the final assistant response.

```Python Python
from meshagent.anthropic import AnthropicMessagesAdapter

adapter = AnthropicMessagesAdapter(model="claude-3-5-sonnet-latest")
```

### Tool Response Adapter
`AnthropicMessagesToolResponseAdapter` converts a tool's structured response into Anthropic `tool_result` blocks that can be inserted back into the conversation.

---
### Learn more about MeshAgent on our website or check out the docs for additional examples!

**Website**: [www.meshagent.com](https://www.meshagent.com/)

**Documentation**: [docs.meshagent.com](https://docs.meshagent.com/)

---
meshagent 0.27.0
Meshagent
Requires Python: >=3.13

# [Meshagent](https://www.meshagent.com)

# MeshAgent Python SDK

MeshAgent is your platform to create, deploy, and manage AI agents collaboratively and at scale, securely and in real time. 

MeshAgent removes the infrastructure headaches of building and shipping AI Agents. It spins up secure, real-time "Rooms" that connect humans, agents, and shared context -- letting you launch, share, and refine agents in hours instead of weeks.

---

**Documentation**: [docs.meshagent.com](https://docs.meshagent.com/)

**Website**: [www.meshagent.com](https://www.meshagent.com/)

**MeshAgent Studio**: [studio.meshagent.com](https://studio.meshagent.com/)

---

## Why MeshAgent? 

- **Real-time multi-user "Rooms" for true human-AI teamwork**: Secure workspaces, "Rooms", automatically spin-up when someone joins, tracking presence, chat, documents, and agent activity, and disappear when everyone leaves -- so people and agents stay in sync. 

- **Accelerated feedback loop and shortened development cycle**: Invite a colleague or stakeholder into the MeshAgent Studio Room so they can see, hear, and interact with your agent immediately -- letting you demo, gather feedback, and iterate on the agent on the same day.

- **Shareable links that showcase your agent**: After you deploy an agent, MeshAgent provides you a URL so colleagues or customers can jump straight into the Room, try the agent, and give feedback -- no code checkout, IDE, or GPU required. 

- **Reusable and extensibly agent tools**: MeshAgent ships prebuilt, reusable toolkits for common Room interactions (e.g., file and document handling, media generation, dynamic UI interactions) and high-demand tasks (e.g., web search or document-to-Markdown conversion). Support for custom tools and the Model Context Protocol (MCP) enables you to seamlessly integrate any MCP-compatible service into a Room without changes to your applications. Tools are centrally managed like agents, allowing you to deploy them once and reuse them effortlessly.

- **Built-in Observability**: Streaming logs and traces give you real-time insight into every message, agent action, and error. 

- **Secure and elastic by default**: Project-scoped keys, on-demand provisioning, and auto-scaling infrastructure keep Rooms private, secure, and ready for your usage needs.

- **Support across languages**: The same feature set (Rooms, Agents, Tools, Messaging, Queues, Storage, Documents, etc.) is exposed through official Python, Javascript, Typescript, and Dart SDKs, so every team member can work in their preferred language. 

## Getting Setup

Follow our [Getting Started Documentation](https://docs.meshagent.com/introduction/get_started) to setup your MeshAgent account, create your first project, and start building agents!

## Next Steps and Examples

To see examples of agents in action and to start building your own agents check out the MeshAgent docs at [docs.meshagent.com](https://docs.meshagent.com/)
meshagent-cli 0.27.0
CLI for Meshagent
Requires Python: >=3.13

# [Meshagent](https://www.meshagent.com)

## MeshAgent CLI

The ``meshagent.cli`` package installs everything you need to streamline room and agent management from your terminal. The CLI assembles submodules for authentication, projects, API keys, participant tokens, messaging, storage, agents, webhooks, and more. 
Check out the [CLI Quickstart](https://docs.meshagent.com/cli/getting_started) for more details.

---
### Learn more about MeshAgent on our website or check out the docs for additional examples!

**Website**: [www.meshagent.com](https://www.meshagent.com/)

**Documentation**: [docs.meshagent.com](https://docs.meshagent.com/)

---
meshagent-agents 0.27.0
Agent Building Blocks for Meshagent
Requires Python: >=3.13

# [Meshagent](https://www.meshagent.com)

### MeshAgent Agents

The ``meshagent.agents`` package provides higher-level agent classes that orchestrate tools and tasks. The primary agents you will build will use the ``TaskRunner``, ``Worker``, and ``ChatBot`` agent types. These agents extend the base ``Agent`` and ``SingleRoomAgent`` classes which setup the fundamentals for working with Agents in MeshAgent Rooms.

Note that agents use LLMAdapters and ToolResponseAdapters to translate between language model calls and tool executions. They also use the ServiceHost to run. 

### Agent
The ``Agent`` base class handles static info such as the agent name, description, and requirements. This class is not used directly, but is the foundation for specialized agents. 

### SingleRoomAgent
The ``SingleRoomAgent``extends the ``Agent`` class, connects to a ``RoomClient``, and installs any declared schemas or toolkits when the agent starts up. All other MeshAgent Agent types extend the ``SingleRoomAgent`` class with additional functionality.

### TaskRunner
The ``TaskRunner`` agent is useful when you want to invoke an agent with a well-defined JSON schemas for input and output. This is important for running agents-as-tools or running remote tasks. Often you will define a ``TaskRunner`` and pass it to a ``ChatBot`` or ``VoiceBot`` as a tool for that agent to use. 

### Worker
The ``Worker`` is a queue-based ``SingleRoomAgent`` that processes queued messages with an LLM and optional tools. This is particularly helpful for running asynchronous jobs. With the ``Worker`` agent you can create a set of tasks that need to run in a Room and the ``Worker`` will execute all of the tasks in the queue. 

### ChatBot
The ``ChatBot`` is a conversational agent derived from the ``SingleRoomAgent``. It wires an LLMAdapter, optoinal tools, and manages chat threads for each user. This means multiple users can be in the same room interacting with a chat agent, but each user will have private messages with the agent. Check out the [Build a Chat Agent](https://docs.meshagent.com/agents/standard/chatbot) example to learn how to create a simple Chat Agent without tools then add built-in MeshAgent tools and custom tools to the agent.

---
### Learn more about MeshAgent on our website or check out the docs for additional examples!

**Website**: [www.meshagent.com](https://www.meshagent.com/)

**Documentation**: [docs.meshagent.com](https://docs.meshagent.com/)

---
meshagent-codex 0.27.0
Codex app-server adapter and chatbot for Meshagent
Requires Python: >=3.13

# meshagent-codex

`meshagent-codex` adds Codex app-server backed agents:
- `CodexChatBot`
- `CodexTaskRunner`

## Included

- `CodexChatBot`: chat agent backed by `codex app-server`
- `CodexTaskRunner`: task runner backed by `codex app-server`

## Example

```python
from meshagent.api.services import ServiceHost
from meshagent.codex import CodexChatBot

service = ServiceHost()


@service.path("/agent")
class MyCodexAgent(CodexChatBot):
    def __init__(self):
        super().__init__(
            name="meshagent.codex-chatbot",
            title="codex chatbot",
            description="chatbot powered by codex app-server",
            rules=["You are a concise assistant."],
            model="codex-mini-latest",
        )
```

By default, the backend launches Codex via `codex app-server`.

You can override transport with environment variables:

- `MESHAGENT_CODEX_COMMAND` to change the launch command.
- `MESHAGENT_CODEX_WS_URL` to connect to an existing Codex app-server websocket instead of launching a local process.
meshagent-otel 0.27.0
CLI for Meshagent
Requires Python: >=3.13

# [Meshagent](https://www.meshagent.com)

## MeshAgent Otel

The ``meshagent.otel`` package is a streamlined way to enable otel in your meshagent projects.

Check out the [CLI Quickstart](https://docs.meshagent.com/cli/getting_started) for more details.

---
### Learn more about MeshAgent on our website or check out the docs for additional examples!

**Website**: [www.meshagent.com](https://www.meshagent.com/)

**Documentation**: [docs.meshagent.com](https://docs.meshagent.com/)

---