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This PR implements the AI Condition Block as requested in issue
AUTOMAT-60. The new block enables users to define conditional logic
using natural language descriptions instead of traditional comparison
operators, while maintaining the same yes/no data pass-through
functionality as the existing ConditionBlock.
## Overview
The AI Condition Block uses Large Language Models to evaluate conditions
written in plain English, such as:
- "the input is the body of an email"
- "the input is a City in the USA"
- "the input is an error or a refusal"
## Key Features
**Natural Language Processing**: Users can express complex conditions in
everyday English rather than programming logic, making agent workflows
more intuitive and accessible.
**Consistent Interface**: Maintains the same input/output schema as the
standard ConditionBlock:
- Boolean `result` output indicating condition evaluation
- `yes_output` and `no_output` for conditional data flow
- Optional custom values for yes/no cases
**Robust Error Handling**: Defaults to `false` on AI evaluation failures
to ensure safe operation and prevent workflow interruption.
**Performance Optimized**: Uses minimal token limits (10 tokens) for
true/false responses to reduce latency and API costs.
## Implementation Details
The block is implemented as `AIConditionBlock` in
`backend/blocks/ai_condition.py` and inherits from `AIBlockBase`
following established platform patterns. It includes:
- Proper LLM integration with credential management
- Token usage tracking and statistics
- Comprehensive test mocking for reliable CI/CD
- Full documentation with examples and use cases
## Use Cases
This block enables more sophisticated conditional logic for:
- **Content Classification**: Automatically categorize text, emails, or
documents
- **Data Validation**: Validate inputs using natural language rules
- **Smart Routing**: Route data based on AI-evaluated conditions
- **Error Detection**: Identify and handle error messages or problematic
inputs
- **Quality Control**: Check content against flexible quality standards
## Testing
The implementation includes comprehensive testing that integrates with
the existing platform test suite. All tests pass, including:
- Unit tests with proper LLM response mocking
- Code quality checks (linting, formatting, type checking)
- Security analysis via CodeQL
- Integration testing to ensure proper block discovery and loading
The block is automatically discovered by the platform's block loading
system and is immediately available for use in agent workflows.
## PR Checklist
- [x] **Have you listed your changes in the description?**
- Added new `AIConditionBlock` in `backend/blocks/ai_condition.py`
- Added comprehensive documentation in
`docs/content/platform/blocks/ai_condition.md`
- Implemented natural language condition evaluation using LLMs
- [x] **Have you included a test plan?**
- Unit tests with mocked LLM responses
- Integration tests for block discovery and loading
- Error handling validation
- Token usage tracking verification
- [x] **Have you tested your changes according to the test plan?**
- All existing tests pass
- Linting and formatting checks pass
- Type checking passes
- Security analysis via CodeQL passes
- Fixed `json_format` parameter to `force_json_output` per recent API
changes
> [!WARNING]
>
> <details>
> <summary>Firewall rules blocked me from connecting to one or more
addresses (expand for details)</summary>
>
> #### I tried to connect to the following addresses, but was blocked by
firewall rules:
>
> - `api.openai.com`
> - Triggering command:
`/home/REDACTED/.cache/pypoetry/virtualenvs/autogpt-platform-backend-Ajv4iu2i-py3.11/bin/python
/home/REDACTED/.cache/pypoetry/virtualenvs/autogpt-platform-backend-Ajv4iu2i-py3.11/bin/pytest
backend/blocks/test/test_block.py::test_available_blocks -k
AIConditionBlock -v` (dns block)
> -
`https://api.github.com/repos/Significant-Gravitas/Significant-Gravitas%2FAutoGPT/languages`
> - Triggering command:
`/home/REDACTED/work/_temp/ghcca-node/node/bin/node --enable-source-maps
/home/REDACTED/work/_temp/copilot-developer-action-main/dist/index.js`
(http block)
>
> If you need me to access, download, or install something from one of
these locations, you can either:
>
> - Configure [Actions setup
steps](https://gh.io/copilot/actions-setup-steps) to set up my
environment, which run before the firewall is enabled
> - Add the appropriate URLs or hosts to the custom allowlist in this
repository's [Copilot coding agent
settings](https://github.com/Significant-Gravitas/AutoGPT/settings/copilot/coding_agent)
(admins only)
>
> </details>
<!-- START COPILOT CODING AGENT SUFFIX -->
<details>
<summary>Original prompt</summary>
> Issue Title: AI Condition Block
> Issue Description: A version of the condition/if block that uses an AI
powered condition.
>
> It should have the same yes/no data pass throughs, as well as
outputting a result Boolean.
>
> The condition is plaintext English, provided by the user, and could be
anything.
>
> e.g
> If `[the input] is the body of an email`
> If `[the input] is a City in the USA`
> If `[the input] is an error or a refusal`
> Fixes https://linear.app/autogpt/issue/AUTOMAT-60/ai-condition-block
>
>
> Comment by User 4bcbb358-1758-43e4-abef-a0a42b63442f:
> 📋 I need a **repo** label on this issue to determine which GitHub
repository to work in.
>
> Please add a repo label to this issue with the format
`owner/repository-name` (e.g., `github/copilot`), then I'll
automatically start working on it!
>
> Comment by User :
> This thread is for an agent session with githubcopilotcodingagent.
>
>
</details>
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<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> Introduces `AIConditionBlock` that uses an LLM to evaluate
natural-language conditions and outputs boolean result with yes/no
pass-through, plus accompanying documentation.
>
> - **Backend**:
> - **New block**: `backend/blocks/ai_condition.py`
> - Evaluates natural-language conditions via `llm_call` using
selectable `LlmModel` and credentials.
> - Parses strict true/false responses (with fallback token matching),
yields `result`, `yes_output`/`no_output`, and `error` on
ambiguity/failure.
> - Tracks token usage via `NodeExecutionStats`; includes test
inputs/mocks and `force_json_output=False`.
> - **Docs**:
> - Adds `docs/content/platform/blocks/ai_condition.md` with usage,
inputs/outputs, examples, and considerations.
>
> <sup>Written by [Cursor
Bugbot](https://cursor.com/dashboard?tab=bugbot) for commit
06e9586bd3. This will update automatically
on new commits. Configure
[here](https://cursor.com/dashboard?tab=bugbot).</sup>
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