Tools

A "tool" is a function the model can call — a weather lookup, a database query, an action in your app. ALLM ships a synchronous tool loop that handles the round-trip: the model emits a tool call, your code runs the tool, the result feeds back to the model, the model produces a final reply. This guide covers the auto-loop, manual mode, per-tool manual control, and the {:ask_user, _} suspension protocol.

Declaring a tool

A tool has a name, a description, a JSON Schema for its arguments, and an executor function:

weather = ALLM.tool(
  name: "get_weather",
  description: "Returns the current weather for a city.",
  schema: %{
    "type" => "object",
    "properties" => %{
      "city" => %{"type" => "string"}
    },
    "required" => ["city"]
  }
)

ALLM.tool/1 returns a %ALLM.Tool{} struct. Pass it to ALLM.request/2 (or ALLM.chat/3 directly) via the :tools opt:

req = ALLM.request([ALLM.user("Weather in Boston?")], tools: [weather])

The model now knows the tool exists. To actually run it when the model asks, configure a tool executor on the engine.

The default tool executor

ALLM.ToolExecutor.Default ships with the library. It takes a map of tool-name → 1-arity function:

engine = ALLM.Engine.new(
  adapter: ALLM.Providers.OpenAI,
  model: "gpt-4.1-mini",
  tool_executor: {ALLM.ToolExecutor.Default, tools: %{
    "get_weather" => fn %{"city" => city} ->
      {:ok, %{temperature: 62, conditions: "sunny", city: city}}
    end
  }}
)

The function receives the parsed argument map and must return one of:

• {:ok, term} — JSON-encodable result. Default encoder is ALLM.ToolResultEncoder.JSON.
• {:error, reason} — tool raised a domain error. The chat loop continues by feeding the error back to the model (it can recover or abandon).
• {:ask_user, prompt, metadata} — suspend the loop and ask the user.

The auto-loop

Pass the request to chat/3. The loop handles the round-trip:

iex> engine = ALLM.Engine.new(
...>   adapter: ALLM.Providers.Fake,
...>   adapter_opts: [scripts: [
...>     [
...>       {:tool_call, %{id: "call_1", name: "get_weather", args: %{"city" => "Boston"}}},
...>       {:finish, :tool_calls}
...>     ],
...>     [
...>       {:text, "It's 62F and sunny in Boston."},
...>       {:finish, :stop}
...>     ]
...>   ]],
...>   tool_executor: {ALLM.ToolExecutor.Default, tools: %{
...>     "get_weather" => fn _args -> {:ok, %{temperature: 62}} end
...>   }}
...> )
iex> weather = ALLM.tool(name: "get_weather", description: "weather", schema: %{"type" => "object"})
iex> req = ALLM.request([ALLM.user("Weather?")], tools: [weather])
iex> {:ok, %ALLM.ChatResult{final_response: %ALLM.Response{output_text: text}}} =
...>   ALLM.chat(engine, req)
iex> text
"It's 62F and sunny in Boston."

The loop ran two round-trips: the first produced a tool call, the executor ran the tool, the result fed back in, and the second round-trip produced the final assistant text.

step/3 is the same minus the loop — one round-trip, one %StepResult{} returned. Use it when you want explicit control over each iteration.

Manual mode (engine-wide)

Sometimes you don't want the loop to run tools at all — you want the model's tool calls returned to your code so you can audit them, queue them, or run them in a different process. Pass mode: :manual on the engine:

engine = ALLM.Engine.new(
  adapter: ALLM.Providers.OpenAI,
  model: "gpt-4.1-mini",
  mode: :manual
)

Now chat/3 halts after one round-trip whenever the model emits tool calls. The %ChatResult{} carries halted_reason: :tool_calls and the calls live on the final response's tool_calls field. You're responsible for executing them and constructing a :tool message containing each result, then re-issuing chat/3 with the augmented thread.

Per-tool manual control

Mix-and-match: most tools auto, one tool manual. Set manual: true on the tool definition:

auto_tool = ALLM.tool(name: "get_weather", description: "...", schema: %{...})

manual_tool = ALLM.tool(
  name: "confirm_action",
  description: "Asks the user to confirm an irreversible action.",
  schema: %{...},
  manual: true
)

req = ALLM.request([ALLM.user("...")], tools: [auto_tool, manual_tool])

Under mode: :auto (the default), the chat orchestrator runs the auto bucket eagerly. If the model ALSO calls a manual tool in the same round, the loop halts with halted_reason: :manual_tool_calls and the manual subset surfaces in metadata.manual_tool_calls (for chat/3/stream/3) or Session.pending_tool_calls (for Session.start/3).

After you've handled the manual tool, append a :tool message containing the result and re-issue chat/3 (or call Session.submit_tool_result/3 then Session.continue/3).

examples/14_per_tool_manual.exs and examples/15_per_tool_manual_session.exs are runnable smoke tests of this flow.

:on_tool_error policy

When a tool returns {:error, reason}, the loop's default behaviour is to feed the error back to the model and continue. Override with :on_tool_error:

ALLM.chat(engine, req, on_tool_error: :halt)

Legal values:

• :continue (default) — feed the error back to the model.
• :halt — halt the loop with halted_reason: :tool_error.

• A 2-arity function fn tool_call, error -> :continue | :halt end — decide per-call.

Ask-user suspension

A tool can return {:ask_user, prompt, metadata} to halt the loop and wait for human input. The chat loop returns with halted_reason: :ask_user; the prompt and metadata live on the result.

ask_tool = fn _args ->
  {:ask_user, "Confirm deleting the production database?", %{action: :delete_db}}
end

Resume by appending the user's reply as a :user message and re-issuing chat/3, or by calling Session.reply/4 if you're using sessions.

examples/09_ask_user.exs is a runnable smoke test.

Streaming tool calls

stream/3 is the streaming version of chat/3. Tool calls arrive as :tool_call_delta events (the argument blob accumulates) followed by a :tool_call event when the call is complete. The auto-loop dispatches the tool, emits a :tool_result event, and continues the loop.

See streaming.md for the full event-shape table.

Handler context (arity-2)

A tool handler may be 1-arity (fn args -> ... end) or 2-arity (fn args, context -> ... end). ALLM detects the arity at dispatch time and routes accordingly.

The arity-2 keyword list carries call context. Standard keys provided by ALLM.ToolExecutor.Default:

handler = fn args, ctx ->
  case Keyword.get(ctx, :context) do
    %{user_id: id} -> {:ok, lookup_for_user(id, args)}
    _ -> {:ok, args}
  end
end

Reach for the 1-arity form when handlers don't need context — it keeps the call site simple. Custom keys in :context are passed through unchanged so tests can inject arbitrary correlation data.

Adapter-call cadence

Each turn of the tool loop consumes two adapter calls: one for the assistant's tool-call request, and one for the post-tool-result assistant turn. Token bills scale with turn_count × 2. Multi-tool turns (parallel tool calls) still count as one assistant call each direction — only the turn count drives the call multiplier.

A loop running three tool-call turns issues six adapter requests. With max_turns: 8 (the library default), the upper bound is sixteen calls per ALLM.chat/3 invocation.

Structured response after tool loop

When you need the post-tool-loop assistant turn to return JSON matching a schema (rather than free-form text), pass both :response_format and structured_finalize: true:

schema = ALLM.json_schema("answer", %{
  "type" => "object",
  "properties" => %{"answer" => %{"type" => "string"}},
  "required" => ["answer"]
})

{:ok, result} =
  ALLM.chat(engine, [ALLM.user("what is 6×7?")],
    response_format: schema,
    structured_finalize: true
  )

{:ok, %{"answer" => "42"}} = Jason.decode(result.final_response.output_text)

structured_finalize: true runs a two-pass orchestration: pass 1 runs the tool loop freely (the model may emit any text or tool calls); pass 2 re-prompts the model with response_format constrained to the schema so the final turn is guaranteed to match.

The result's metadata carries observability for the two passes:

• result.metadata.structured_finalize.pass_1_halted — the halt reason pass 1 reached (typically :completed).
• result.metadata.structured_finalize.pass_1_response — pass 1's raw %Response{} for inspection.

result.steps contains the merged step list from both passes so step indexes remain stable across the two-pass boundary.

Where to next

• sessions.md — multi-turn tool flows with persistence.
• streaming.md — tool calls in the event stream.
• examples/03_single_tool_call.exs — runnable single-tool smoke test.
• examples/04_parallel_tool_calls.exs — two tools in one round.
• examples/07_manual_tool_round_trip.exs — engine-wide manual mode.