Refresh docs and consolidate AFK public API

docs/index.mdx

@@ -55,7 +55,7 @@ from afk.core import Runner
 
 agent = Agent(
     name="assistant",
-    model="gpt-4.1-mini",
+    model="gpt-5.5",
     instructions="Answer directly with concrete detail.",
 )
 

docs/library/a2a.mdx

@@ -116,7 +116,7 @@ from afk.agents import Agent, A2AServiceHost, APIKeyA2AAuthProvider
 from afk.core import Runner
 
 # Define the agent
-analyzer = Agent(name="analyzer", model="gpt-4.1-mini", instructions="Analyze data.")
+analyzer = Agent(name="analyzer", model="gpt-5.5", instructions="Analyze data.")
 
 # Create auth provider
 auth = APIKeyA2AAuthProvider(

docs/library/agent-skills.mdx

@@ -103,7 +103,7 @@ from afk.agents import Agent
 
 agent = Agent(
     name="devops-agent",
-    model="gpt-4.1-mini",
+    model="gpt-5.5",
     instructions="""
     You are a DevOps assistant. Use your skills to help with
     deployment, monitoring, and infrastructure tasks.
@@ -181,7 +181,7 @@ The agent autonomously decides which skills to read based on the user's question
     ```python
     agent = Agent(
         name="reader",
-        model="gpt-4.1-mini",
+        model="gpt-5.5",
         skills_dir="skills/",
         skill_tools=["list_skills", "read_skill_md", "read_skill_file"],
         # ← No run_skill_command

docs/library/agentic-behavior.mdx

@@ -13,14 +13,14 @@ from afk.agents import Agent
 from afk.core import Runner
 
 # Specialist agents
-researcher = Agent(name="researcher", model="gpt-4.1-mini", instructions="Find facts.")
-writer = Agent(name="writer", model="gpt-4.1-mini", instructions="Write summaries.")
-reviewer = Agent(name="reviewer", model="gpt-4.1-mini", instructions="Review for accuracy.")
+researcher = Agent(name="researcher", model="gpt-5.5", instructions="Find facts.")
+writer = Agent(name="writer", model="gpt-5.5", instructions="Write summaries.")
+reviewer = Agent(name="reviewer", model="gpt-5.5", instructions="Review for accuracy.")
 
 # Coordinator
 coordinator = Agent(
     name="coordinator",
-    model="gpt-4.1-mini",
+    model="gpt-5.5",
     instructions="""
     1. Ask 'researcher' for facts
     2. Ask 'writer' to summarize

docs/library/agentic-levels.mdx

@@ -12,7 +12,7 @@ AFK applications progress through five levels of capability. Each level adds fea
     One agent, one model, no tools. The simplest possible setup.
 
     ```python
-    agent = Agent(name="classifier", model="gpt-4.1-mini", instructions="Classify as positive/negative/neutral.")
+    agent = Agent(name="classifier", model="gpt-5.5", instructions="Classify as positive/negative/neutral.")
     result = runner.run_sync(agent, user_message="Great product!")
     ```
 
@@ -31,7 +31,7 @@ AFK applications progress through five levels of capability. Each level adds fea
     @tool(args_model=SearchArgs, name="search", description="Search knowledge base.")
     def search(args: SearchArgs) -> dict: ...
 
-    agent = Agent(name="helper", model="gpt-4.1-mini", tools=[search])
+    agent = Agent(name="helper", model="gpt-5.5", tools=[search])
     ```
 
     **AFK features added:** `@tool`, Pydantic models, `FailSafeConfig`
@@ -48,7 +48,7 @@ AFK applications progress through five levels of capability. Each level adds fea
     ```python
     coordinator = Agent(
         name="coordinator",
-        model="gpt-4.1-mini",
+        model="gpt-5.5",
         subagents=[researcher, writer, reviewer],
     )
     ```

docs/library/agents.mdx

@@ -12,7 +12,7 @@ from afk.agents import Agent
 
 agent = Agent(
     name="assistant",                          # ← Identity (used in logs, telemetry)
-    model="gpt-4.1-mini",                      # ← Which LLM to use
+    model="gpt-5.5",                      # ← Which LLM to use
     instructions="Be helpful and concise.",     # ← System prompt
 )
 ```
@@ -61,7 +61,7 @@ Those are the fields most examples should set. `model` is the only required cons
     ```python
     agent = Agent(
         name="classifier",
-        model="gpt-4.1-mini",
+        model="gpt-5.5",
         instructions="""
         Classify the input into one of: positive, negative, neutral.
         Output only the label.
@@ -81,19 +81,19 @@ Those are the fields most examples should set. `model` is the only required cons
     ```python
     researcher = Agent(
         name="researcher",
-        model="gpt-4.1-mini",
+        model="gpt-5.5",
         instructions="Find relevant facts. Be thorough.",
     )
 
     writer = Agent(
         name="writer",
-        model="gpt-4.1-mini",
+        model="gpt-5.5",
         instructions="Write clear, concise summaries.",
     )
 
     coordinator = Agent(
         name="coordinator",
-        model="gpt-4.1-mini",
+        model="gpt-5.5",
         instructions="""
         You manage a team:
         - Delegate research to 'researcher'
@@ -139,7 +139,7 @@ from afk.agents import Agent, FailSafeConfig
 
 agent = Agent(
     name="safe-agent",
-    model="gpt-4.1-mini",
+    model="gpt-5.5",
     instructions="...",
     tools=[...],
     fail_safe=FailSafeConfig(
@@ -155,7 +155,7 @@ agent = Agent(
         subagent_failure_policy="continue",               # "continue" | "retry_then_fail" | "skip_action"
 
         # Fallback model chain for LLM resilience
-        fallback_model_chain=["gpt-4.1-mini", "gpt-4.1-nano"],
+        fallback_model_chain=["gpt-5.5", "gpt-5.5"],
     ),
 )
 ```

docs/library/building-with-ai.mdx

@@ -32,7 +32,7 @@ flowchart LR
     ```python
     agent = Agent(
         name="classifier",
-        model="gpt-4.1-mini",
+        model="gpt-5.5",
         instructions="""
         Classify the support ticket into exactly one category:
         billing, technical, account, feature-request, other.
@@ -58,7 +58,7 @@ flowchart LR
 
     agent = Agent(
         name="rag-agent",
-        model="gpt-4.1-mini",
+        model="gpt-5.5",
         instructions="""
         Always search the knowledge base before answering.
         Cite sources. If no relevant docs found, say so.
@@ -84,7 +84,7 @@ flowchart LR
 
     agent = Agent(
         name="coder",
-        model="gpt-4.1",
+        model="gpt-5.5",
         instructions="""
         Write code, then run the tests to verify.
         Fix any failures before presenting the final version.
@@ -110,7 +110,7 @@ flowchart LR
     ```python
     coordinator = Agent(
         name="coordinator",
-        model="gpt-4.1-mini",
+        model="gpt-5.5",
         instructions="""
         Route each request to the appropriate specialist:
         - Technical questions → 'engineer'

docs/library/checkpoint-schema.mdx

@@ -78,7 +78,7 @@ flowchart TB
 from afk.agents import Agent
 from afk.core import Runner
 
-agent = Agent(name="analyst", model="gpt-4.1-mini", instructions="Analyze data.")
+agent = Agent(name="analyst", model="gpt-5.5", instructions="Analyze data.")
 runner = Runner()
 
 # Start a run that might be interrupted

docs/library/core-runner.mdx

@@ -11,7 +11,7 @@ The **Runner** is the execution engine that runs agents. It manages the step loo
 from afk.agents import Agent
 from afk.core import Runner
 
-agent = Agent(name="demo", model="gpt-4.1-mini", instructions="Be helpful.")
+agent = Agent(name="demo", model="gpt-5.5", instructions="Be helpful.")
 runner = Runner()
 
 # Synchronous (simplest)

docs/library/deployment.mdx

@@ -120,7 +120,7 @@ Create `config/production.yaml`:
 
 ```yaml
 agent:
-  default_model: gpt-4.1-mini
+  default_model: gpt-5.5
   default_fail_safe:
     max_steps: 20
     max_tool_calls: 10

docs/library/environment-variables.mdx

@@ -17,7 +17,7 @@ These variables configure the default LLM provider and behavior. They are read b
 | ------------------------------- | -------------- | ------------------------------------------------------------ |
 | `AFK_LLM_PROVIDER`              | `litellm`      | Default provider id (`openai`, `litellm`, `anthropic_agent`) |
 | `AFK_LLM_PROVIDER_ORDER`        | _(none)_       | Comma-separated provider preference order for routing helpers |
-| `AFK_LLM_MODEL`                 | `gpt-4.1-mini` | Default model name                                           |
+| `AFK_LLM_MODEL`                 | `gpt-5.5` | Default model name                                           |
 | `AFK_EMBED_MODEL`               | _(none)_       | Embedding model for vector operations                        |
 | `AFK_LLM_API_BASE_URL`          | _(none)_       | Provider API base URL                                        |
 | `AFK_LLM_API_KEY`               | _(none)_       | Provider API key                                             |

docs/library/evals.mdx

@@ -14,7 +14,7 @@ from afk.evals import EvalCase, EvalSuiteConfig, StateCompletedAssertion, run_su
 
 agent = Agent(
     name="classifier",
-    model="gpt-4.1-mini",
+    model="gpt-5.5",
     instructions="Classify as: billing, technical, account, other. Output only the label.",
 )
 

docs/library/failure-policy-matrix.mdx

@@ -65,7 +65,7 @@ agent = Agent(
     ...,
     fail_safe=FailSafeConfig(
         llm_failure_policy="degrade",              # "fail" or "degrade"
-        fallback_model_chain=["gpt-4.1-mini"],     # Try cheaper model
+        fallback_model_chain=["gpt-5.5"],     # Try cheaper model
     ),
 )
 ```

docs/library/how-to-use-afk.mdx

@@ -15,7 +15,7 @@ from afk.core import Runner
 
 agent = Agent(
     name="ticket-classifier",
-    model="gpt-4.1-mini",
+    model="gpt-5.5",
     instructions="""
     Classify the support ticket as exactly one of:
     billing, technical, account, or other.
@@ -55,7 +55,7 @@ def lookup_ticket(args: TicketArgs) -> dict:
 
 agent = Agent(
     name="ticket-agent",
-    model="gpt-4.1-mini",
+    model="gpt-5.5",
     instructions="Look up tickets before answering. Never modify data.",
     tools=[lookup_ticket],
     fail_safe=FailSafeConfig(

docs/library/learn-in-15-minutes.mdx

@@ -13,7 +13,7 @@ from afk.core import Runner
 
 agent = Agent(
     name="tutor",
-    model="gpt-4.1-mini",
+    model="gpt-5.5",
     instructions="Explain programming concepts in plain language.",
 )
 
@@ -53,7 +53,7 @@ def search_docs(args: LookupArgs) -> dict:
 
 agent = Agent(
     name="research-tutor",
-    model="gpt-4.1-mini",
+    model="gpt-5.5",
     instructions="Search docs before answering user questions.",
     tools=[search_docs],
 )
@@ -82,7 +82,7 @@ from afk.core import Runner
 
 agent = Agent(
     name="streamer",
-    model="gpt-4.1-mini",
+    model="gpt-5.5",
     instructions="Explain topics clearly.",
 )
 
@@ -118,7 +118,7 @@ from afk.core import Runner
 
 agent = Agent(
     name="memory-tutor",
-    model="gpt-4.1-mini",
+    model="gpt-5.5",
     instructions="Remember the user's earlier questions in this thread.",
 )
 
@@ -157,7 +157,7 @@ from afk.core import Runner, RunnerConfig
 
 agent = Agent(
     name="bounded-agent",
-    model="gpt-4.1-mini",
+    model="gpt-5.5",
     instructions="Help users, but stop if the task becomes too large.",
     fail_safe=FailSafeConfig(
         max_steps=8,

docs/library/llm-interaction.mdx

@@ -31,7 +31,7 @@ Each step in this flow is described in detail below.
 
 At each step of the agent loop, the runner builds an `LLMRequest` and sends it to the configured LLM provider. Here is what goes into the request:
 
-**Model resolution.** The agent's `model` field (e.g., `"gpt-4.1-mini"`) is resolved through the model resolution chain. This maps the requested model name to a provider, adapter, and normalized model identifier. Custom `model_resolver` functions can override this mapping.
+**Model resolution.** The agent's `model` field (e.g., `"gpt-5.5"`) is resolved through the model resolution chain. This maps the requested model name to a provider, adapter, and normalized model identifier. Custom `model_resolver` functions can override this mapping.
 
 **Message history.** The runner maintains a list of `Message` objects representing the conversation history. This includes:
 - A `system` message containing the agent's instructions, skill manifests, and (when enabled) an untrusted-data preamble.
@@ -48,7 +48,7 @@ At each step of the agent loop, the runner builds an `LLMRequest` and sends it t
 ```python
 # Simplified view of what the runner builds
 request = LLMRequest(
-    model="gpt-4.1-mini",
+    model="gpt-5.5",
     request_id=f"{run_id}:step:{step}",
     messages=messages,           # Full conversation history
     tools=tool_definitions,      # OpenAI-format function tools

docs/library/mcp-server.mdx

@@ -111,7 +111,7 @@ Discover and use tools from any MCP server:
 
     agent = Agent(
         name="assistant",
-        model="gpt-4.1-mini",
+        model="gpt-5.5",
         instructions="Use available tools to help the user.",
         tools=tools,  # ← MCP tools work like local tools
     )

docs/library/memory.mdx

@@ -12,7 +12,7 @@ import asyncio
 from afk.agents import Agent
 from afk.core import Runner
 
-agent = Agent(name="tutor", model="gpt-4.1-mini", instructions="You are a Python tutor.")
+agent = Agent(name="tutor", model="gpt-5.5", instructions="You are a Python tutor.")
 
 async def main():
     runner = Runner()

docs/library/mental-model.mdx

@@ -54,7 +54,7 @@ flowchart LR
     # The Decision Loop is shaped by these fields
     agent = Agent(
         instructions="...",    # ← What the model knows
-        model="gpt-4.1-mini", # ← How the model thinks
+        model="gpt-5.5", # ← How the model thinks
         tools=[...],           # ← What the model can do
     )
     ```