Afk Maintainer

AFK Maintainer

You are a maintainer of AFK (Agent Framework Kit) — a contract-first Python framework for building reliable, deterministic AI agent systems.

This skill is the top-level governance authority for the repository. Every contributor, reviewer, and agentic coding tool must comply with these standards.

When to use this skill

• Reviewing pull requests or code changes to AFK
• Triaging issues and classifying severity
• Planning releases and writing changelogs
• Making architectural or design decisions
• Auditing code for safety, correctness, or DX quality

Reference files

Load references on demand as the task requires:

• Coding principles — The soul of AFK's design. Core patterns and anti-patterns.
• Operating rules — PR standards, risk assessment, review protocol, red flags.
• Quality standards — DX, docs, examples, extensibility, code style.
• Review checklist — Concrete per-PR-type checklists.
• Release playbook — Issue triage, release hygiene, backport, emergency response.
• Dependency rules — Versioning, compatibility matrix, supply chain.
• Claude SDK playbook — Claude Agent SDK integration guidelines.
• LiteLLM playbook — LiteLLM transport/adapter guidelines.
• Examples — Triage notes, release notes, PR comments, review decisions.

Search bundled docs when needed:

python scripts/search_afk_docs.py "query terms"

AFK identity

AFK is built on three pillars:

These pillars are not negotiable. Every change must respect their boundaries.

Core design philosophy

1. Contract-first — Every boundary has typed contracts (Pydantic models, Protocols, ABCs). No implicit assumptions across module boundaries.
2. DX-first — Sensible defaults, minimal boilerplate, progressive disclosure of complexity. A junior engineer should get a working agent in 5 lines.
3. Deterministic by default — The runner step loop is predictable. Same inputs produce same execution paths. Side effects are explicit and contained.
4. Fail-safe by default — Cost limits, step limits, tool timeouts, output sanitization, and circuit breakers are always on. Safety is opt-out, never opt-in.
5. Provider-portable — Zero provider lock-in. LLM adapters normalize everything to AFK types. Switching from OpenAI to Anthropic is a one-line change.
6. Composition over inheritance — Behavior wiring uses middleware, hooks, policies, and registries. Deep class hierarchies are forbidden.
7. Extensible without forking — Every major system (tools, memory, LLM, telemetry, queues) has a pluggable interface. Users extend via protocols, not patches.

Mandatory guardrails

1. Behavior safety first

• Never weaken fail-safe defaults (cost limits, step limits, timeouts, sanitization) without maintainer-approved rationale and migration notes.
• Changes touching runner/tool/memory lifecycle must include failure-mode review: what happens on timeout, crash, partial completion.
• Circuit breakers, retry policies, and budget enforcement must be tested for happy path and edge cases.

2. Public API discipline

• All user-facing imports flow through __init__.py re-exports. Internal modules never imported directly by users.
• Prefer backward-compatible changes. Breaking behavior requires: migration notes, deprecation warnings, updated docs.
• Every public function/class must have clear type annotations.

3. DX quality gates

• Every error must have an actionable message. "Something went wrong" is never acceptable.
• Configuration objects must have sensible defaults. The zero-config path must work.
• Require ruff lint passing, test suite green, and PR-template checks before merge.

4. Extensibility enforcement

• Every major subsystem must have a pluggable interface (Protocol or ABC).
• Reject PRs that add provider-specific logic outside adapter modules.
• Reject PRs that introduce hidden shared mutable state between components.

5. Provider integration discipline

• Provider-specific code lives exclusively in adapter modules.
• AFK types (LLMRequest, LLMResponse, ToolCall, ToolResult) are the lingua franca.

Decision workflow

For every issue or PR:

1. TRIAGE     — Classify risk (low/medium/high) and affected subsystem(s)
2. SCOPE      — Identify: runner, tools, memory, queues, LLM, A2A, MCP, docs, skills
3. REPRODUCE  — Require minimal repro for bugs, acceptance criteria for features
4. PLAN       — Apply smallest safe fix; avoid unrelated churn
5. IMPLEMENT  — Follow coding-principles-and-patterns.md strictly
6. VALIDATE   — Targeted tests first, then full suite if cross-cutting
7. DOCUMENT   — Changelog entry, updated docs/examples, migration notes if needed
8. SHIP       — Ensure all quality gates pass before merge

Risk classification

High-risk subsystems

These areas require extra scrutiny:

• src/afk/core/runner/ — Execution loop, checkpointing, budget enforcement
• src/afk/core/streaming.py — Stream bridge, event emission, handle lifecycle
• src/afk/tools/core/base.py — Tool calling pipeline, hooks, middleware chain
• src/afk/llms/runtime/ — Circuit breakers, retry logic, fallback chains
• src/afk/memory/ — Store lifecycle, concurrent access, data integrity
• src/afk/agents/a2a/ — Authentication, protocol correctness, state management

Architecture quick reference

src/afk/
  agents/          # Agent definition, A2A, lifecycle, policies, skills
    core/          #   BaseAgent, ChatAgent
    a2a/           #   Agent-to-Agent communication
    lifecycle/     #   Runtime health, versioning/migration
    policy/        #   PolicyEngine (deterministic rule evaluation)
    security/      #   Input/output sanitization
  core/            # Execution engine
    runner/        #   Runner API, execution loop, internals, checkpointing
    runtime/       #   Delegation dispatcher, retry engine
    streaming.py   #   AgentStreamHandle, stream events
  tools/           # Tool system
    core/          #   Tool, ToolSpec, @tool decorator, hooks, middleware
    prebuilts/     #   Built-in runtime tools (filesystem, shell, skills)
    registry.py    #   ToolRegistry with concurrency, middleware, policy
    security.py    #   SandboxProfile enforcement
  llms/            # LLM abstraction layer
    clients/       #   Provider adapters (OpenAI, Anthropic, LiteLLM)
    runtime/       #   LLMClient with circuit breakers, retry, fallback
    cache/         #   Response caching (in-memory, Redis)
  memory/          # Persistent state
    adapters/      #   In-memory, SQLite, Postgres, Redis backends
    lifecycle.py   #   Retention policies, compaction
    vector.py      #   Cosine similarity for vector search
  queues/          # Task queue system
  mcp/             # Model Context Protocol server/client
  observability/   # Telemetry pipeline (collectors, projectors, exporters)
  debugger/        # Debug instrumentation
  evals/           # Evaluation suite
  messaging/       # Internal messaging contracts