https://github.com/relevanceai/manage-aiworkforce
Designed to allow Relevance AI users to manage their environments locally and deploy from files or enable Git deployment.
https://github.com/relevanceai/manage-aiworkforce
Last synced: 3 months ago
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Designed to allow Relevance AI users to manage their environments locally and deploy from files or enable Git deployment.
- Host: GitHub
- URL: https://github.com/relevanceai/manage-aiworkforce
- Owner: RelevanceAI
- License: apache-2.0
- Created: 2025-02-13T07:27:47.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-27T02:14:44.000Z (11 months ago)
- Last Synced: 2025-05-27T03:26:33.898Z (11 months ago)
- Language: Python
- Homepage: https://relevanceai.com/
- Size: 43.9 KB
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Manage Your AI Workforce
Designed to allow Relevance AI users to manage their Relevance AI environments locally and deploy from files or Git to any Relevance AI project.
*Note: This codebase is not designed to be used as a package or imported into your project. Use these functions as your building blocks to create your CI-CD pipelines.*
## Usage
The package can be used in two major ways:
### 1. Exporting your assets from a Relevance AI project to Local Files
The example script `examples/from_relevanceai_to_local.py` fetches agents and tools (with optional support for knowledge sets) from your Relevance AI project and writes them to local JSON files. This helps you:
### 2. Pushing Local file assets to a Relevance AI project
The example script `examples/from_local_to_relevanceai.py` reads local JSON files (for agents and tools) and pushes them to your production Relevance AI environment. This is useful for:
### 3. Retrigger Failed Conversations
The example script `examples/trigger_conversations_from_failure.py` shows how you can identify and regenerate conversations that have errored, starting them from just before the last error.
### 4. Agent costs of past conversations between a given timeframe.
The example script `examples/get_agent_conversation_costs.py` calculates the costs of past conversations for a given agent within a specified timeframe. This is useful for understanding the cost distribution and usage patterns of your agents over time. Note: May not properly count subagents' costs.
## API Functions
The package provides core functions to interact directly with the Relevance AI API:
- **Agents**
- `get_all_agents`
- `create_agent`
- `get_agent_tools`
- `delete_agent`
- `update_agent`
- `schedule_message_to_agent`
- `get_agent_analytics`
- `save_agents_to_file`
- **Knowledge**
- `get_all_knowledge`
- `get_knowledge`
- `delete_knowledge`
- `add_knowledge_data`
- `get_knowledge_metadata`
- **Tools**
- `get_tool`
- `get_all_tools`
- `create_tools`
- `delete_tools`
- `get_tool_run_history`
- `trigger_tool`
- `poll_tool_run`
- `update_tool`
- `save_tools_to_file`
- **Conversations**
- `get_conversations`
- `get_list_conversation_studio_history`
- `get_conversation_actions`
- `retrigger_conversation_after_message`
- `trigger_agent_debug_conversation`
- `get_trigger_message`
- `get_conversations_where_specific_tool_failed`
- `get_conversations_between_dates`
- **Snippets**
- `upsert_snippet`
## Contributing
Contributions to enhance features or extend functionality are welcome! If you have suggestions or improvements, please open an issue or submit a pull request.
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Manage your environment seamlessly with manage-aiworkforce.