docs: fix spelling typos in decision records

docs/decisions/0001-agent-run-response.md

@@ -324,7 +324,7 @@ public string? ModelId { get; set; }
 ```
 
 - **PROS**: Agent response types can evolve separately. Types can still resemble MEAI response types to ensure a fimilar experience for developers.
-- **CONS**: No automatic inheritence of new properties from MEAI. (this might also be a pro)
+- **CONS**: No automatic inheritance of new properties from MEAI. (this might also be a pro)
 
 ## Long Running Processes Options
 
@@ -355,7 +355,7 @@ public class ChatFinishReason
 ```
 
 - **PROS**: Fits well into existing `ChatResponse` design.
-- **CONS**: More complex for users to extract the required long running result (can be mitigated with extenion methods)
+- **CONS**: More complex for users to extract the required long running result (can be mitigated with extension methods)
 
 ### Option 2: Add another property on responses for AgentRun
 

docs/decisions/0008-python-subpackages.md

@@ -66,7 +66,7 @@ The goal is to design a subpackage structure for the Python agent framework that
         - makes developer package management more complex, because that meta-extra will include both GA and non-GA packages, so during dev they could use that, but then during prod they have to figure out which one they actually need and make a change in their dependencies, leading to mismatches between dev and prod.
 6. Make all imports happen from `agent_framework.connectors` (or from two or three groups `agent_framework.chat_clients`, `agent_framework.context_providers`, or something similar) while the underlying code comes from different packages.
     * Pros:
-        - best developer experience, since all imports are from the same place and it is easy to find what you need, and we can raise a meaningfull error with which extra to install.
+        - best developer experience, since all imports are from the same place and it is easy to find what you need, and we can raise a meaningful error with which extra to install.
         - easier for users to find and install the right package.
     * Cons:
         - larger overhead in maintaining the `__init__.py` files that do the lazy loading and error handling.

docs/decisions/0019-python-context-compaction-strategy.md

@@ -646,7 +646,7 @@ agent = client.create_agent(
 
 There are two ways we can do this:
 1. Before writing to storage in `after_run`, compaction is called on the new messages,
-    combined with: a new `compact` method, that reads the full history, calls the compaction strategy with the full history, then writes the compacted result back to storage (also requires a `overwrite` flag on the `save_messages` method). This makes removing old messages from storage a explicit action that the user initiaties instead of being implicitly triggered by `after_run` writes, but it also means compaction strategies only see new messages instead of the full history (unless they read it themselves), the `compact` method could then also have a override for the strategy to use (and/or the tokenizer in case of Variant E1/E2/F1/F2).
+    combined with: a new `compact` method, that reads the full history, calls the compaction strategy with the full history, then writes the compacted result back to storage (also requires a `overwrite` flag on the `save_messages` method). This makes removing old messages from storage a explicit action that the user initiates instead of being implicitly triggered by `after_run` writes, but it also means compaction strategies only see new messages instead of the full history (unless they read it themselves), the `compact` method could then also have a override for the strategy to use (and/or the tokenizer in case of Variant E1/E2/F1/F2).
 
     ```python
     class HistoryProvider(ContextProvider):