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docs/CONTRIBUTING_TO_DOCS.md
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# Contributing to DOCS
To allow moving the docs to mslearn later, we are using language pivots as supported with mslearn markdown files.
This means that to make the docs easier to understand for users, we have a [PowerShell script](./generate-language-specific-docs.ps1) that generates language specific versions of the docs in separate folders.
The script strips out any pivots that are for a different language to the target.
Therefore, write your docs in the [docs-templates](./docs-templates/) folder and then
generate the language-specific versions by just running the PowerShell script.
```powershell
.\generate-language-specific-docs.ps1
```
## Using pivots
To have language-specific content, use the `::: zone pivot` syntax in your markdown file.
Note that when using a pivot you always have to have a section for both languages (csharp and python).
```text
::: zone pivot="programming-language-csharp"
C# specific content.
::: zone-end
::: zone pivot="programming-language-python"
Python specific content.
::: zone-end
```
docs/decisions/0007-agent-filtering-middleware.md
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| Haystack | Python | N (Pipeline-based interception) | N/A (Pipeline Components/Routers) | Relies on modular pipelines for implicit interception but lacks explicit middleware/filters; custom components can read/write data flow via routing/transformations, but this is compositional rather than hook-based interception. [Details](#haystack) |
| OpenAI Swarm | Python | N | N/A | No explicit middleware/filters; interception requires custom wrappers or manual handling (e.g., function decorators, client subclassing), lacking native framework support for built-in components to accept such modifications. [Details](#openai-swarm) |
| Atomic Agents | Python | N | N/A (Composable Components) | No explicit middleware/filters; modularity allows composable units but no dedicated interception hooks or callbacks for custom reading/modification mid-execution. [Details](#atomic-agents) |
| Smolagents (Hugging Face)| Python | N | N/A | No explicit support; focuses on simple agent building without interception mechanisms or hooks for reading/modifying execution. [Details](#smolagents) |
| Phidata (Agno) | Python | N | N/A | No explicit middleware/filters; agents use tools/memory but no interception hooks for custom reading/modification of calls. [Details](#phidata) |
| PromptFlow (Microsoft) | Python | N (Tracing only) | Tracing | Supports tracing for LLM interactions, acting as callbacks for debugging/iteration; tracing is read-only for observability/telemetry without options to modify context or intercept calls beyond logging. [Details](#promptflow) |
| Smolagents (Hugging Face)| Python | N | N/A | No explicit support; focuses on simple agent building without interception mechanisms or hooks for reading/modifying execution. [Details](#smolagents-hugging-face) |
| Phidata (Agno) | Python | N | N/A | No explicit middleware/filters; agents use tools/memory but no interception hooks for custom reading/modification of calls. [Details](#phidata-agno) |
| PromptFlow (Microsoft) | Python | N (Tracing only) | Tracing | Supports tracing for LLM interactions, acting as callbacks for debugging/iteration; tracing is read-only for observability/telemetry without options to modify context or intercept calls beyond logging. [Details](#promptflow-microsoft) |
| n8n | JS/TS | Y (read/write) | Callbacks (inherited from LangChain) | AI Agent node uses LangChain under the hood, inheriting callbacks for observability; supports reading/modifying metadata or interrupting flow as in LangChain. [Details](#n8n) |
## Considered Options