AgentToolkit · zdmwi · Oct 23, 2025 · Oct 22, 2025 · Oct 22, 2025
diff --git a/docs/components/json-processor.md b/docs/components/json-processor.md
@@ -100,7 +100,7 @@ Additional results are reported in [1].
 
 
 ## Getting Started
-Refer to this [README](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/post_tool_reflection_toolkit/code_generation/README.md) for instructions on how to get started with the code.
+Refer to this [README](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/post_tool/code_generation/README.md) for instructions on how to get started with the code.
 See an example in action [here](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/examples/json_processor_getting_started.ipynb).
 
 ## References

diff --git a/docs/components/policy-guard.md b/docs/components/policy-guard.md
@@ -43,7 +43,7 @@ The above figure shows the achieved instruction following (IF) rates for the fou
 Additional results are reported in [1].
 
 ## Getting Started
-Refer to this [README](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/policy_guard_toolkit/README.md) for instructions on how to get started with the code.
+Refer to this [README](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/pre_response/policy_guard/README.md) for instructions on how to get started with the code.
 
 ## References
 [1] Elder, B., et al., "Boosting Instruction Following at Scale," arXiv preprint arXiv: (2025).  https://arxiv.org/abs/2510.14842
diff --git a/docs/components/rag-repair.md b/docs/components/rag-repair.md
@@ -17,7 +17,7 @@ For now, the default retrieval method is using sentence embeddings along with Ch
 #### Input format
 This component expects documents to be provided in a local path. Optionally, documents can be divided into manual pages/documentation in a `man` folder and other documents such as troubleshooting documents in a `doc` folder. If these folders are not provided, then all documents are considered non-documentation. Nested folders are supported for ingesting. Supported files are: `pdf`, `html`, `json`, `jsonl`.
 
-The class `post_tool_reflection_toolkit.core.toolkit.RAGRepairRunInput` expects three main inputs as follows:
+The class `post_tool.core.toolkit.RAGRepairRunInput` expects three main inputs as follows:
 
 1. `messages`: List[BaseMessages], a list of messages from the agent, this is optional but will be used to infer the task at hand if `nl_query` is not provided.
 
@@ -47,7 +47,7 @@ Additional results are reported in [1].
 ![rag_results](../assets/img_ragrepair_eval.png)
 
 ## Getting Started
-Refer to this [README](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/post_tool_reflection_toolkit/rag_repair/README.md) for instructions on how to get started with the code.
+Refer to this [README](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/post_tool/rag_repair/README.md) for instructions on how to get started with the code.
 
 See an example in action [here](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/examples/rag_repair.ipynb).
 

diff --git a/docs/components/refraction.md b/docs/components/refraction.md
@@ -2,7 +2,7 @@
 
 ![refraction_fig_1](refraction/assets/01115a12-5433-4a39-a970-9f33ffc13a67.png)
 
-[//]: # (<img src="https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/pre_tool_reflection_toolkit/refraction/docs/assets/01115a12-5433-4a39-a970-9f33ffc13a67.png"/>)
+[//]: # (<img src="https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/docs/components/refraction/assets/01115a12-5433-4a39-a970-9f33ffc13a67.png"/>)
 
 Refraction is a low-cost (no LLMs!), low-latency, domain-agnostic, data-agnostic, model-agnostic
 approach towards validation and repair for a sequence of tool calls, based on classical AI planning techniques.
@@ -99,7 +99,7 @@ For scaling characteristics, see [here](refraction/05.-Scaling.md).
 5. Scaling characteristics [[link](refraction/05.-Scaling.md)]
 6. Offline API [[link](refraction/07.-Offline-Analysis.md)]
 
-Refer to this [README](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/pre_tool_reflection_toolkit/refraction/README.md) for instructions on how to get started with the code.
+Refer to this [README](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/pre_tool/refraction/README.md) for instructions on how to get started with the code.
 
 ## References
 

diff --git a/docs/components/refraction/02.-The-Refraction-API-|-Inputs-and-Outputs.md b/docs/components/refraction/02.-The-Refraction-API-|-Inputs-and-Outputs.md
@@ -120,7 +120,7 @@ result = refract(
 You can also use the format of tool calling used in
 the [OpenAI](https://platform.openai.com/docs/guides/function-calling)
 and [LangChain](https://langchain-ai.github.io/langgraph/how-tos/tool-calling/#call-tools) tool calling APIs.
-These are very commonly used in LLM prompts. Check [here](https://github.ibm.com/research-dba/refraction/blob/main/tests/from_deployment/test_untyped_inputs.py#L14) for an example.
+These are very commonly used in LLM prompts. Check [here](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/tests/pre_tool/refraction/from_deployment/test_untyped_inputs.py#L14) for an example.
 
 > Check [here](03.-The-Refraction-API-|-Tool-Calling.md)
 for instructions on refraction while calling tools directly.

diff --git a/docs/components/refraction/03.-The-Refraction-API-|-Tool-Calling.md b/docs/components/refraction/03.-The-Refraction-API-|-Tool-Calling.md
@@ -174,9 +174,3 @@ Executing: var1 = TripadvisorSearchLocation(query="London")
 
 Executing: TripadvisorSearchHotels(param="foo", geoId="123", checkIn="2024-09-05", checkOut="2024-09-15")
 ```
-
-# 3.2 Integration with LangGraph
-
-We can use this idea to augment tool decorators in popular
-agentic frameworks. Head over to [here](08.-Integration-with-LangGraph.md)
-for an example with the LangGraph tool decorator.
diff --git a/docs/components/refraction/09.-Integration-with-Mellea.md b/docs/components/refraction/09.-Integration-with-Mellea.md
@@ -148,9 +148,9 @@ if the following were possible:
 - I could not see any immediate method to access the prompt / instruction
 (and not just the response) inside the requirement.
 This should be possible, and then we would not require any extra inputs (i.e. tools, memory, etc.) at all
-to instantiate the requirement -- we can read off everything from the instruction. [[link](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/pre_tool_reflection_toolkit/refraction/src/integration/mellea_requirement.py#12)]
+to instantiate the requirement -- we can read off everything from the instruction. [[link](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/pre_tool/refraction/src/integration/mellea_requirement.py#L12)]
 
 - Currently, the validation result only allows for a `str` reason. This means that the refractor, in the
 event it could fix a call in situ, as in the example above, it must use the prompting mechanism for resampling.
 An alternative pathway could be to allow for return the fix in the validator, if it is able to fix in place, and this
-saves us an extra sampling cost + avoid the possibility of ending up with a new error in the new sample. [[link](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/pre_tool_reflection_toolkit/refraction/src/integration/mellea_requirement.py#L19)]
+saves us an extra sampling cost + avoid the possibility of ending up with a new error in the new sample. [[link](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/pre_tool/refraction/src/integration/mellea_requirement.py#L19)]
diff --git a/docs/components/refraction/10.-Integration-with-LangFlow.md b/docs/components/refraction/10.-Integration-with-LangFlow.md
@@ -9,7 +9,7 @@
 ## Refraction as a component
 
 You can add refraction as a component. Until we are open-source, you have to do it yourself. Please follow the instructions
-[here](https://docs.langflow.org/components-custom-components), and use the file [here](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/pre_tool_reflection_toolkit/refraction/src/integration/refractor_component.py) as an example. Once you have done so,
+[here](https://docs.langflow.org/components-custom-components), and use the file [here](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/pre_tool/refraction/src/integration/refractor_component.py) as an example. Once you have done so,
 it will appear in the list of components.
 
 ![2ca9337f-48f0-4271-bd5e-981ee258b04c.png](assets%2F2ca9337f-48f0-4271-bd5e-981ee258b04c.png)

diff --git a/docs/components/silent-review.md b/docs/components/silent-review.md
@@ -56,5 +56,5 @@ For more details, refer to the paper: [Invocable APIs derived from NL2SQL datase
 - Average loop counts decrease slightly, showing that fewer iterations are needed to reach successful query completion.
 
 ## Getting Started
-Refer to this [README](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/post_tool_reflection_toolkit/silent_review/README.md) for instructions on how to get started with the code.
+Refer to this [README](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/post_tool/silent_review/README.md) for instructions on how to get started with the code.
 See an example in action [here]( https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/examples/silent_review.ipynb).
diff --git a/docs/components/sparc.md b/docs/components/sparc.md
@@ -142,4 +142,4 @@ These results demonstrate the reflector's strong ability to accurately identify
 
 
 ## Getting Started
-Refer to this [README](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/pre_tool_reflection_toolkit/sparc/README.md) for instructions on how to get started with the code. See an example in action [here]( https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/examples/langgraph_agent_sparc_example.py)
+Refer to this [README](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/pre_tool/sparc/README.md) for instructions on how to get started with the code. See an example in action [here]( https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/examples/langgraph_agent_sparc_example.py)
diff --git a/docs/components/spotlight.md b/docs/components/spotlight.md
@@ -14,7 +14,7 @@ The bar graphs below show how SpotLight leads to improved end to end performance
 ![spotlight_results](../assets/img_spotlight_results.png)
 
 ## Getting Started
-Refer to this [README](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/spotlight_toolkit/README.md) for instructions on how to get started with the code. See an example in action [here](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/examples/spotlight.ipynb)
+Refer to this [README](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/pre_llm/spotlight/README.md) for instructions on how to get started with the code. See an example in action [here](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/examples/spotlight.ipynb)
 
 ## References
 Venkateswaran, Praveen, and Danish Contractor. "Spotlight Your Instructions: Instruction-following with Dynamic Attention Steering." arXiv preprint arXiv:2505.12025 (2025). https://arxiv.org/pdf/2505.12025
Original file line number	Diff line number	Diff line change
Expand Up		@@ -142,4 +142,4 @@ These results demonstrate the reflector's strong ability to accurately identify


		## Getting Started
		Refer to this [README](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/pre_tool_reflection_toolkit/sparc/README.md) for instructions on how to get started with the code. See an example in action [here]( https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/examples/langgraph_agent_sparc_example.py)
		Refer to this [README](https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/altk/pre_tool/sparc/README.md) for instructions on how to get started with the code. See an example in action [here]( https://github.com/AgentToolkit/agent-lifecycle-toolkit/blob/main/examples/langgraph_agent_sparc_example.py)