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Model-agnostic agent harness for Elixir

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# Alloy

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**Minimal, OTP-native agent loop for Elixir.**

Alloy is the completion-tool-call loop and nothing else. Send messages to any LLM, execute tool calls, loop until done. Swap providers with one line. No opinions on sessions, persistence, memory, scheduling, or UI — those belong in your application, where OTP already gives you the runtime.

Alloy is a harness, not a framework. Three runtime dependencies, ~9,000 lines — small enough to read in an afternoon, and everything beyond the loop is a [recipe](https://hexdocs.pm/alloy/sub-agents.html) built on the primitives, not a subsystem.

```elixir
{:ok, result} = Alloy.run("Read mix.exs and tell me the version",
  provider: {Alloy.Provider.OpenAI, api_key: System.get_env("OPENAI_API_KEY"), model: "gpt-5.4"},
  tools: [Alloy.Tool.Core.Read]
)

result.text #=> "The version is 0.12.0"
```

## Why Alloy?

Most agent frameworks try to be everything — sessions, memory, RAG, multi-agent orchestration, scheduling, UI. Alloy does one thing well: the agent loop. The BEAM makes the rest cheaper than a framework can: sub-agents are supervised function calls ([recipe](https://hexdocs.pm/alloy/sub-agents.html)), MCP servers mount as tools ([recipe](https://hexdocs.pm/alloy/mcp-tools.html)), parallel tool execution is `Task.Supervisor`, and crash isolation is just processes.

## Design Boundary

Alloy stays minimal by owning protocol and loop concerns, not application
workflows.

What belongs in Alloy:
- Provider wire-format translation
- Tool-call / completion loop mechanics
- Normalized message blocks
- Opaque provider-owned state such as stored response IDs
- Provider response metadata such as citations or server-side tool telemetry

What does not belong in Alloy:
- Sessions and persistence policy
- File storage, indexing, or retrieval workflows
- UI rendering for citations, search, or artifacts
- Scheduling, background job orchestration, or dashboards
- Tenant plans, quotas, billing, or hosted infrastructure policy

Rule of thumb: if the feature is required to speak a provider API correctly,
and could help any Alloy consumer, it likely belongs here. If it needs a
database table, product defaults, UI decisions, or tenancy logic, it belongs in
your application layer.

## Non-goals

Alloy does not own multi-agent orchestration; use the
[sub-agents recipe](https://hexdocs.pm/alloy/sub-agents.html), where an agent
spawns itself via a function call. Alloy does not sandbox your machine; use
`:bash_executor` with containers or another isolated runner. Sessions,
persistence, scheduling, hosted queues, dashboards, and UI stay in your
application layer. The library stays small so those choices remain yours.

## What's in the box

- **6 providers** — Anthropic, Gemini, OpenAI, Codex, xAI, and OpenAICompat (works with any OpenAI-compatible API: Ollama, OpenRouter, DeepSeek, Mistral, Groq, Together, etc.)
- **4 built-in tools** — read, write, edit, bash — plus inline tools defined as data with `Alloy.Tool.inline/1`
- **GenServer agents** — supervised, stateful, message-passing (moving to the optional `alloy_agent` runtime package in 0.13)
- **Streaming** — token-by-token from any provider, unified interface
- **Async dispatch** — `send_message/2` fires non-blocking, result arrives via PubSub
- **Middleware** — custom hooks, tool blocking, argument editing
- **Context compaction** — tool-result clearing plus summary-based compaction when approaching token limits, with configurable reserve and fallback to truncation
- **Memory primitive** — `Alloy.Memory` behaviour for Anthropic's `memory_20250818` tool. Alloy owns the wire format and path validation; you own the store (in-memory, disk, Postgres — whatever fits)
- **Prompt caching** — Anthropic `cache: true` adds cache breakpoints for 60-90% input token savings
- **Reasoning blocks** — DeepSeek/xAI `reasoning_content` parsed as first-class thinking blocks
- **Tool guardrails** — `concurrent?/0` controls parallel execution, `max_result_chars/0` caps output, prompt-too-long auto-recovery. Note: the bash tool's restricted mode is a guardrail, not a sandbox — see [Built-in tools](#built-in-tools)
- **Structured output** — `until_tool` forces the loop to continue until a specific tool is called
- **Provider passthrough** — `extra_body` injects arbitrary provider-specific params (response_format, temperature, reasoning_effort)
- **Telemetry** — run, turn, provider, and compaction lifecycle events for OTEL/logging/metrics
- **Cost guard** — `max_budget_cents` halts the loop before overspending
- **Pluggable model catalog** — `Alloy.ModelCatalog` behaviour; bring your own context-window source (e.g., an `llm_db` adapter)
- **~9,000 lines** — small enough to read, understand, and extend

## Installation

Add `alloy` to your dependencies in `mix.exs`:

```elixir
def deps do
  [
    {:alloy, "~> 0.12"}
  ]
end
```

> **Note:** the optional `alloy_agent` runtime wrapper (sessions, async
> dispatch, memory stores) referenced by the 0.12.x deprecation notices is
> **not yet published** to Hex. Until it ships, keep using
> `Alloy.Agent.Server` and `Alloy.Session` from this package — the 0.13
> removals will not happen before `alloy_agent` is available.

## Quick Start

Prefer a runnable version? [![Run in Livebook](https://livebook.dev/badge/v1/blue.svg)](https://livebook.dev/run?url=https%3A%2F%2Fraw.githubusercontent.com%2Falloy-ex%2Falloy%2Fmain%2Flivebooks%2Fquickstart.livemd)

### Simple completion

```elixir
{:ok, result} = Alloy.run("What is 2+2?",
  provider: {Alloy.Provider.Anthropic, api_key: "sk-ant-...", model: "claude-sonnet-4-6"}
)

result.text #=> "4"
```

### Agent with tools

```elixir
{:ok, result} = Alloy.run("Read mix.exs and summarize the dependencies",
  provider: {Alloy.Provider.Gemini,
    api_key: "...", model: "gemini-2.5-flash-lite"},
  tools: [Alloy.Tool.Core.Read, Alloy.Tool.Core.Bash],
  max_turns: 10
)
```

Gemini model IDs Alloy now budgets for include `gemini-2.5-pro`,
`gemini-2.5-flash`, `gemini-2.5-flash-lite`, `gemini-3-pro-preview`, and
`gemini-3-flash-preview`.

### Swap providers in one line

```elixir
# The same tools and conversation work with any provider
opts = [tools: [Alloy.Tool.Core.Read], max_turns: 10]

# Anthropic
Alloy.run("Read mix.exs", [{:provider, {Alloy.Provider.Anthropic, api_key: "...", model: "claude-sonnet-4-6"}} | opts])

# OpenAI
Alloy.run("Read mix.exs", [{:provider, {Alloy.Provider.OpenAI, api_key: "...", model: "gpt-5.4"}} | opts])

# Gemini
Alloy.run("Read mix.exs", [{:provider, {Alloy.Provider.Gemini, api_key: "...", model: "gemini-2.5-flash"}} | opts])

# xAI via Responses-compatible API
Alloy.run("Read mix.exs", [{:provider, {Alloy.Provider.OpenAI, api_key: "...", api_url: "https://api.x.ai", model: "grok-4.20-0309-reasoning"}} | opts])

# xAI via chat completions (reasoning models, extra_body)
Alloy.run("Read mix.exs", [{:provider, {Alloy.Provider.OpenAICompat, api_key: "...", api_url: "https://api.x.ai", model: "grok-4.1-fast-reasoning"}} | opts])

# Any OpenAI-compatible API (Ollama, OpenRouter, DeepSeek, Mistral, Groq, etc.)
Alloy.run("Read mix.exs", [{:provider, {Alloy.Provider.OpenAICompat, api_url: "http://localhost:11434", model: "llama4"}} | opts])
```

### Streaming

For a one-shot run, use `Alloy.stream/3`:

```elixir
{:ok, result} =
  Alloy.stream("Explain OTP", fn chunk ->
    IO.write(chunk)
  end,
    provider: {Alloy.Provider.OpenAI, api_key: "...", model: "gpt-5.4"}
  )
```

For a persistent agent process with conversation state, use `Alloy.Agent.Server.stream_chat/4`:

```elixir
{:ok, agent} = Alloy.Agent.Server.start_link(
  provider: {Alloy.Provider.OpenAI, api_key: "...", model: "gpt-5.4"},
  tools: [Alloy.Tool.Core.Read]
)

{:ok, result} = Alloy.Agent.Server.stream_chat(agent, "Explain OTP", fn chunk ->
  IO.write(chunk)  # Print each token as it arrives
end)
```

All providers support streaming. If a custom provider doesn't implement
`stream/4`, the turn loop falls back to `complete/3` automatically.

`Alloy.run/2` remains the buffered convenience API. Use `Alloy.stream/3`
when you want the same one-shot flow with token streaming.

### Provider-owned state

Some provider APIs expose server-side state such as stored response IDs.
That transport concern lives in Alloy; your app decides whether and how to
persist it.

Results expose provider-owned state in `result.metadata.provider_state`:

```elixir
{:ok, result} =
  Alloy.run("Read the repo",
    provider: {Alloy.Provider.OpenAI,
      api_key: System.get_env("XAI_API_KEY"),
      api_url: "https://api.x.ai",
      model: "grok-4.20-0309-reasoning",
      store: true
    }
  )

provider_state = result.metadata.provider_state
```

Pass that state back to the same provider on the next turn to continue a
provider-native conversation:

```elixir
{:ok, next_result} =
  Alloy.run("Keep going",
    messages: result.messages,
    provider: {Alloy.Provider.OpenAI,
      api_key: System.get_env("XAI_API_KEY"),
      api_url: "https://api.x.ai",
      model: "grok-4.20-0309-reasoning",
      provider_state: provider_state
    }
  )
```

For native OpenAI Responses, Alloy automatically preserves opaque reasoning
items across stateless tool-call turns. When `store` is not `true` and no
`previous_response_id` / `provider_state.response_id` is present, the request
adds `include: ["reasoning.encrypted_content"]` and echoes returned reasoning
items back verbatim before their related function calls; `result.text` ignores
those opaque blocks.

### Provider-native tools and citations

Responses-compatible providers can expose built-in server-side tools without
leaking those wire details into your app layer.

For xAI search tools:

```elixir
{:ok, result} =
  Alloy.run("Summarise the latest xAI docs updates",
    provider: {Alloy.Provider.OpenAI,
      api_key: System.get_env("XAI_API_KEY"),
      api_url: "https://api.x.ai",
      model: "grok-4.20-0309-reasoning",
      web_search: %{allowed_domains: ["docs.x.ai"]},
      include: ["inline_citations"]
    }
  )
```

Citation metadata is exposed in two places:
- `result.metadata.provider_response.citations` for provider-level citation data
- assistant text blocks may include `:annotations` for inline citation spans

### Overriding model metadata

Alloy derives the compaction budget from the configured provider model when it
knows that model's context window. If you need to support a just-released model
before Alloy ships a catalog update, override it in config:

```elixir
{:ok, result} = Alloy.run("Summarise this repository",
  provider: {Alloy.Provider.OpenAI, api_key: "...", model: "gpt-5.4-2026-03-05"},
  model_metadata_overrides: %{
    "gpt-5.4" => 900_000,
    "acme-reasoner" => %{limit: 640_000, suffix_patterns: ["", ~r/^-\d{4}\.\d{2}$/]}
  }
)
```

Set `max_tokens` explicitly when you want a fixed compaction budget. Otherwise
Alloy derives it from the current model, including after
`Alloy.Agent.Server.set_model/2` switches to a different provider model.

### Pluggable model catalog

The built-in catalog is deliberately small — Alloy is a harness, not a model
database, and a hand-curated list will always lag the release treadmill. For a
richer source of truth, implement the `Alloy.ModelCatalog` behaviour and pass
it via `model_catalog:`. For example, backed by
[`llm_db`](https://hex.pm/packages/llm_db) (45+ providers, maintained against
models.dev):

```elixir
defmodule MyApp.LlmDbCatalog do
  @behaviour Alloy.ModelCatalog

@impl true
  def context_window(model) do
    case LLMDb.model(model) do
      %{context_window: limit} when is_integer(limit) -> limit
      _ -> nil
    end
  end
end

{:ok, result} = Alloy.run("Summarise this repository",
  provider: {Alloy.Provider.OpenAI, api_key: "...", model: "gpt-5.4"},
  model_catalog: MyApp.LlmDbCatalog
)
```

Resolution order: explicit `max_tokens` → `model_metadata_overrides` →
`model_catalog` → default window. Returning `nil` for an unknown model is
fine — Alloy falls back to the default.

Use `compaction:` when you want to tune how much room Alloy reserves before it
compacts older context:

```elixir
{:ok, result} = Alloy.run("Summarise this repository",
  provider: {Alloy.Provider.OpenAI, api_key: "...", model: "gpt-5.4"},
  compaction: [
    reserve_tokens: 12_000,
    keep_recent_tokens: 8_000,
    clear_tool_results: true,
    keep_recent_tool_results: 3,
    fallback: :truncate
  ]
)
```

Alloy first clears old `tool_result` and `server_tool_result` content in one
batch, preserving the newest `keep_recent_tool_results` results, then only
calls the summarizer if the run is still over budget. Batched clearing matters
with prompt caching: clearing invalidates cached prefixes, so Alloy amortizes
that cost instead of dripping changes across turns.

Set `summary_system_prompt:` and `summary_prompt:` inside `compaction:` when
your application needs to own the handoff format. Both values must be strings;
omitting them uses Alloy's default summary prompts.

### Cost guard

Cap how much an agent run can spend:

```elixir
{:ok, result} = Alloy.run("Research this codebase thoroughly",
  provider: {Alloy.Provider.Anthropic, api_key: "...", model: "claude-sonnet-4-6"},
  tools: [Alloy.Tool.Core.Read, Alloy.Tool.Core.Bash],
  max_budget_cents: 50
)

case result.status do
  :completed -> IO.puts(result.text)
  :budget_exceeded -> IO.puts("Stopped: spent #{result.usage.estimated_cost_cents}¢")
end
```

Set `max_budget_cents: nil` (default) for no limit.

### Anthropic prompt caching

Enable prompt caching to save 60-90% on input tokens. Alloy automatically adds
`cache_control` breakpoints to the system prompt and last tool definition:

```elixir
{:ok, result} = Alloy.run("Explain this codebase",
  provider: {Alloy.Provider.Anthropic,
    api_key: "...", model: "claude-sonnet-4-6",
    cache: true
  },
  tools: [Alloy.Tool.Core.Read, Alloy.Tool.Core.Bash],
  system_prompt: "You are a senior Elixir developer."
)

# Cache usage is reported in result.usage
result.usage.cache_creation_input_tokens  #=> 1500
result.usage.cache_read_input_tokens      #=> 1500  (on subsequent calls)
```

### Memory (Anthropic `memory_20250818`)

Alloy exposes memory as a behaviour — `Alloy.Memory` — matching the split
Anthropic uses in their own Python SDK: Alloy owns the protocol (six
commands on a `/memories/` tree, return-string formats, path validation);
your code owns the backing store. No bytes touch Anthropic's servers.

```elixir
defmodule MyApp.Memory.Disk do
  @behaviour Alloy.Memory

@impl true
  def view(store, path), do: # read from disk
  @impl true
  def create(store, path, text), do: # write
  @impl true
  def str_replace(store, path, old, new), do: # ...
  @impl true
  def insert(store, path, line, text), do: # ...
  @impl true
  def delete(store, path), do: # ...
  @impl true
  def rename(store, old_path, new_path), do: # ...
end

{:ok, result} = Alloy.run("Remember the user prefers SI units",
  provider: {Alloy.Provider.Anthropic, api_key: "sk-ant-...", model: "claude-sonnet-4-6"},
  memory: {MyApp.Memory.Disk, root: "/var/agent/memories"}
)
```

When `:memory` is set, Alloy injects the `memory_20250818` tool into the
Anthropic request and adds the `context-management-2025-06-27` beta
header. Memory tool calls are routed through `Alloy.Memory.Router`
(not the general tool executor) so the typed-tool contract stays clean.

The store term (second element of `{module, opts}`) is opaque — pass a
keyword list, a map, a `pid()`, or a struct, whichever your store needs.
Alloy does not bake session scoping into the contract; if you want
per-session memory trees, thread `session_id: "..."` through your store
opts and namespace inside your implementation.

As of 0.12.0, memory is Anthropic-only — configuring `:memory` with any
other provider raises at `Alloy.run/2` entry. Other providers will be
wired as they ship their own memory primitives.

### Reasoning model support (DeepSeek, xAI)

OpenAI-compatible reasoning models that return `reasoning_content` (DeepSeek-R1,
xAI Grok reasoning variants) are automatically parsed into thinking blocks:

```elixir
{:ok, result} = Alloy.run("Solve this step by step",
  provider: {Alloy.Provider.OpenAICompat,
    api_url: "https://api.x.ai",
    api_key: "...", model: "grok-4.1-fast-reasoning"
  }
)

# Thinking blocks are preserved in message content
[thinking, text] = hd(result.messages).content
thinking.type     #=> "thinking"
thinking.thinking #=> "Step 1: Let me consider..."
text.type         #=> "text"
text.text         #=> "The answer is 42."
```

### Provider-specific parameters (extra_body)

Pass arbitrary provider-specific parameters via `extra_body`. It merges last,
so it can override any default field:

```elixir
{:ok, result} = Alloy.run("Return JSON",
  provider: {Alloy.Provider.OpenAICompat,
    api_url: "https://api.deepseek.com",
    api_key: "...", model: "deepseek-chat",
    extra_body: %{
      "response_format" => %{"type" => "json_object"},
      "temperature" => 0.3
    }
  }
)
```

Works for any provider param: `reasoning_effort`, `max_completion_tokens`,
`presence_penalty`, etc.

### Telemetry

Alloy emits telemetry events for observability. Attach handlers for OTEL,
logging, or custom metrics:

```elixir
:telemetry.attach_many("my-handler", [
  [:alloy, :run, :start],
  [:alloy, :run, :stop],
  [:alloy, :turn, :start],
  [:alloy, :turn, :stop],
  [:alloy, :provider, :request],
  [:alloy, :compaction, :cleared],
  [:alloy, :compaction, :done],
  [:alloy, :tool, :start],
  [:alloy, :tool, :stop],
  [:alloy, :event]
], &MyApp.Telemetry.handle_event/4, nil)
```

| Event | Measurements | Metadata |
|-------|-------------|----------|
| `[:alloy, :run, :start]` | `system_time` | `model` |
| `[:alloy, :run, :stop]` | `duration_ms` | `status`, `turns`, `model` |
| `[:alloy, :turn, :start]` | `system_time` | `turn` |
| `[:alloy, :turn, :stop]` | — | `turn`, `status` |
| `[:alloy, :provider, :request]` | `duration_ms` | `provider`, `model`, `streaming`, `attempt`, `result` |
| `[:alloy, :compaction, :cleared]` | `results_cleared`, `bytes_cleared` | `turn` |
| `[:alloy, :compaction, :done]` | `messages_before`, `messages_after` | `turn` |
| `[:alloy, :tool, :start]` | — | tool identity, correlation |
| `[:alloy, :tool, :stop]` | `duration_ms` | tool identity, result |

### Structured output with `until_tool`

Force the model to call a specific tool before the loop completes. This is more
reliable than response format instructions because the tool schema is validated
at the API level:

```elixir
defmodule SubmitAnswer do
  @behaviour Alloy.Tool
  def name, do: "submit_answer"
  def description, do: "Submit your final answer as structured data."
  def input_schema do
    %{type: "object", properties: %{
      answer: %{type: "string"},
      confidence: %{type: "number", minimum: 0, maximum: 1}
    }, required: ["answer", "confidence"]}
  end
  def execute(input, _ctx), do: {:ok, "Received: #{input["answer"]}"}
end

{:ok, result} = Alloy.run("What is the capital of France?",
  provider: {Alloy.Provider.Anthropic, api_key: "...", model: "claude-sonnet-4-6"},
  tools: [SubmitAnswer],
  until_tool: "submit_answer"
)
```

### Middleware: editing tool arguments

Middleware can return `{:edit, modified_call}` from `:before_tool_call` to rewrite
tool arguments before execution (e.g., policy enforcement, input sanitization):

```elixir
defmodule SanitizeBash do
  @behaviour Alloy.Middleware

def call(:before_tool_call, state) do
    call = state.config.context[:current_tool_call]

if call[:name] == "bash" && String.contains?(call[:input]["command"], "rm ") do
      {:edit, %{call | input: %{"command" => "echo 'rm commands are blocked'"}}}
    else
      state
    end
  end

def call(_hook, state), do: state
end
```

### Supervised GenServer agent

```elixir
{:ok, agent} = Alloy.Agent.Server.start_link(
  provider: {Alloy.Provider.Anthropic, api_key: "...", model: "claude-sonnet-4-6"},
  tools: [Alloy.Tool.Core.Read, Alloy.Tool.Core.Edit, Alloy.Tool.Core.Bash],
  system_prompt: "You are a senior Elixir developer."
)

{:ok, response} = Alloy.Agent.Server.chat(agent, "What does this project do?")
{:ok, response} = Alloy.Agent.Server.chat(agent, "Now refactor the main module")
```

### Async dispatch (Phoenix LiveView)

Fire a message without blocking the caller — ideal for LiveView and background jobs:

```elixir
# Subscribe to receive the result
Phoenix.PubSub.subscribe(MyApp.PubSub, "agent:#{session_id}:responses")

# Returns {:ok, request_id} immediately — agent works in the background
{:ok, req_id} = Alloy.Agent.Server.send_message(agent, "Summarise this report",
  request_id: "req-123"
)

# Handle the result whenever it arrives
def handle_info({:agent_response, %{text: text, request_id: "req-123"}}, socket) do
  {:noreply, assign(socket, :response, text)}
end
```

## Providers

| Vendor | Recommended Module | Example Models |
|--------|---------------------|----------------|
| Anthropic | `Alloy.Provider.Anthropic` | `claude-opus-4-6`, `claude-sonnet-4-6`, `claude-haiku-4-5` |
| Gemini | `Alloy.Provider.Gemini` | `gemini-2.5-pro`, `gemini-2.5-flash`, `gemini-3-pro-preview`, `gemma-4-26b-a4b-it` (open-weight) |
| OpenAI | `Alloy.Provider.OpenAI` | `gpt-5.4` |
| xAI | `Alloy.Provider.OpenAI` with `api_url: "https://api.x.ai"` | `grok-4.20-0309-reasoning`, `grok-4.20-multi-agent-0309`, `grok-4.1-fast-reasoning`, `grok-code-fast-1` |
| Other OpenAI-compatible APIs | `Alloy.Provider.OpenAICompat` | `kimi-k2.6` (Moonshot), `qwen3-coder-plus` (1M ctx), `glm-4.6`, `mistral-large-2512`, plus Ollama, OpenRouter, DeepSeek, Groq, Together |

Use `Alloy.Provider.OpenAI` for native Responses APIs like OpenAI and xAI.
Use `Alloy.Provider.Gemini` for Gemini's native GenerateContent API.
Use `Alloy.Provider.OpenAICompat` for chat-completions compatible APIs and local runtimes.

`OpenAICompat` works with any API that implements the OpenAI chat completions format.
Just set `api_url`, `model`, and optionally `api_key` and `chat_path`.

## Built-in Tools

| Tool | Module | Description |
|------|--------|-------------|
| **read** | `Alloy.Tool.Core.Read` | Read files from disk |
| **write** | `Alloy.Tool.Core.Write` | Write files to disk |
| **edit** | `Alloy.Tool.Core.Edit` | Search-and-replace editing |
| **bash** | `Alloy.Tool.Core.Bash` | Execute shell commands (restricted shell by default) |

> **Bash is a guardrail, not a sandbox.** Restricted mode (`bash -r`) blocks
> `cd`, `PATH` changes, and redirection — but any interpreter on `PATH`
> (`python3 -c`, `perl -e`) gives full capability, so treat it as a speed
> bump against accidents, not an isolation boundary. For real isolation,
> supply your own `:bash_executor` in context (container, jail, firejail, or
> a remote runner) and restrict file tools with `:allowed_paths`. Treat the
> agent's shell access as you would a contractor's laptop on your network.

### Custom tools

```elixir
defmodule MyApp.Tools.WebSearch do
  @behaviour Alloy.Tool

@impl true
  def name, do: "web_search"

@impl true
  def description, do: "Search the web for information"

@impl true
  def input_schema do
    %{
      type: "object",
      properties: %{query: %{type: "string", description: "Search query"}},
      required: ["query"],
      additionalProperties: false
    }
  end

@impl true
  def strict?, do: true

@impl true
  def execute(%{"query" => query}, _context) do
    # Your implementation here
    {:ok, "Results for: #{query}"}
  end
end
```

Prefer strict mode for tools that have stable schemas. Set `strict?/0` on a
module tool, or `strict: true` on an inline tool, and include
`additionalProperties: false` in the top-level JSON Schema. Alloy validates
that requirement instead of silently rewriting your schema. OpenAI strict mode
also requires every property to be listed in `required`.

For Anthropic advanced tool use, add `input_examples/0` or `input_examples:`
with representative argument maps to improve parameter selection. Set
`defer_loading?/0` or `defer_loading: true` for tools that should be exposed
through Anthropic's deferred-loading path; other providers ignore these fields.

### Inline tools

For one-off tools, or tools discovered at runtime (an MCP server's tool
list, user-defined actions from a database), define a tool as data with
`Alloy.Tool.inline/1` — no module needed. Inline tools and tool modules
mix freely in `tools:`:

```elixir
weather =
  Alloy.Tool.inline(
    name: "get_weather",
    description: "Get current weather for a location",
    input_schema: %{
      type: "object",
      properties: %{location: %{type: "string"}},
      required: ["location"]
    },
    execute: fn %{"location" => loc}, _context ->
      {:ok, MyApp.Weather.current(loc)}
    end
  )

{:ok, result} = Alloy.run("What's the weather in Sydney?",
  provider: provider,
  tools: [weather, Alloy.Tool.Core.Read]
)
```

Optional fields mirror the behaviour's optional callbacks: `concurrent?:`,
`max_result_chars:`, `allowed_callers:`, `result_type:`. See
`Alloy.Tool.Inline` for details.

### Code execution (Anthropic)

Enable Anthropic's server-side code execution sandbox:

```elixir
{:ok, result} = Alloy.run("Calculate the first 20 Fibonacci numbers",
  provider: {Alloy.Provider.Anthropic, api_key: "...", model: "claude-sonnet-4-6"},
  code_execution: true
)
```

## Architecture

```
Alloy.run/2                    One-shot agent loop (pure function)
Alloy.Agent.Server             GenServer wrapper (stateful, supervisable)
Alloy.Agent.Turn               Single turn: call provider → execute tools → return
Alloy.Provider                 Behaviour: translate wire format ↔ Alloy.Message
Alloy.Tool                     Behaviour: name, description, input_schema, execute
Alloy.Middleware               Pipeline: custom hooks, tool blocking
Alloy.Context.Compactor        Automatic conversation summarization
```

Sessions, persistence, multi-agent coordination, scheduling, skills, and UI
belong in your application layer. See [Anvil](https://github.com/alloy-ex/anvil)
for a reference Phoenix application built on Alloy.

## License

MIT — see [LICENSE](LICENSE).

## Releases

Hex.pm publishing is handled by GitHub Actions on `v*` tags.
Successful publishes also dispatch the landing-site version sync workflow.

alloy

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