forked from cleveragents/cleveragents-core
feat(change): add tool router for providers
This commit is contained in:
@@ -0,0 +1,195 @@
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"""ASV benchmarks for ToolCallRouter operations.
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Measures the performance of:
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- Provider format detection
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- Payload normalization
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- Stable ID generation
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- Tool call routing (OpenAI, Anthropic, LangChain)
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- Batch routing
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- Schema normalization
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- Error classification
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"""
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from __future__ import annotations
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import json
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import sys
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from pathlib import Path
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from typing import Any
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try:
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from cleveragents.tool.registry import ToolRegistry
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from cleveragents.tool.router import (
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ProviderFormat,
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ToolCallRouter,
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classify_tool_error,
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detect_provider_format,
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generate_tool_call_id,
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normalize_tool_call,
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normalize_tool_schema_for_provider,
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)
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from cleveragents.tool.runner import ToolRunner
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from cleveragents.tool.runtime import ToolSpec
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except ModuleNotFoundError:
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sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
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from cleveragents.tool.registry import ToolRegistry
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from cleveragents.tool.router import (
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ProviderFormat,
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ToolCallRouter,
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classify_tool_error,
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detect_provider_format,
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generate_tool_call_id,
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normalize_tool_call,
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normalize_tool_schema_for_provider,
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)
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from cleveragents.tool.runner import ToolRunner
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from cleveragents.tool.runtime import ToolSpec
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def _echo(inputs: dict[str, Any]) -> dict[str, Any]:
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return dict(inputs)
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class FormatDetectionSuite:
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"""Benchmark provider format detection."""
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def setup(self) -> None:
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"""Prepare payloads."""
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self.openai_payload = {"name": "bench/echo", "arguments": '{"a": 1}'}
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self.anthropic_payload = {"name": "bench/echo", "input": {"a": 1}}
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self.langchain_payload = {
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"name": "bench/echo",
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"type": "tool_call",
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"args": {"a": 1},
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}
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def time_detect_openai(self) -> None:
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"""Benchmark OpenAI format detection."""
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detect_provider_format(self.openai_payload)
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def time_detect_anthropic(self) -> None:
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"""Benchmark Anthropic format detection."""
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detect_provider_format(self.anthropic_payload)
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def time_detect_langchain(self) -> None:
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"""Benchmark LangChain format detection."""
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detect_provider_format(self.langchain_payload)
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class NormalizationSuite:
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"""Benchmark payload normalization."""
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def setup(self) -> None:
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"""Prepare payloads."""
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self.openai_payload = {"name": "bench/echo", "arguments": '{"a": 1}'}
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self.anthropic_payload = {"name": "bench/echo", "input": {"a": 1}}
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def time_normalize_openai(self) -> None:
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"""Benchmark OpenAI payload normalization."""
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normalize_tool_call(self.openai_payload)
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def time_normalize_anthropic(self) -> None:
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"""Benchmark Anthropic payload normalization."""
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normalize_tool_call(self.anthropic_payload)
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class IDGenerationSuite:
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"""Benchmark stable ID generation."""
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def time_generate_id(self) -> None:
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"""Benchmark single ID generation."""
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generate_tool_call_id("plan-bench", 42)
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def time_generate_100_ids(self) -> None:
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"""Benchmark generating 100 IDs."""
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for i in range(100):
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generate_tool_call_id("plan-bench", i)
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class RoutingSuite:
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"""Benchmark tool call routing."""
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def setup(self) -> None:
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"""Prepare registry and router."""
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self.registry = ToolRegistry()
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spec = ToolSpec(
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name="bench/echo",
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description="Benchmark echo tool",
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handler=_echo,
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)
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self.registry.register(spec)
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self.runner = ToolRunner(self.registry)
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self.router = ToolCallRouter(
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registry=self.registry,
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runner=self.runner,
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plan_id="plan-bench",
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)
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self.openai_payload = {
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"name": "bench/echo",
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"arguments": '{"msg": "hello"}',
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}
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self.anthropic_payload = {
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"name": "bench/echo",
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"input": {"msg": "hello"},
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}
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self.langchain_payload = {
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"name": "bench/echo",
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"type": "tool_call",
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"args": {"msg": "hello"},
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}
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def time_route_openai(self) -> None:
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"""Benchmark routing an OpenAI tool call."""
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self.router.route(self.openai_payload)
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def time_route_anthropic(self) -> None:
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"""Benchmark routing an Anthropic tool call."""
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self.router.route(self.anthropic_payload)
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def time_route_langchain(self) -> None:
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"""Benchmark routing a LangChain tool call."""
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self.router.route(self.langchain_payload)
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def time_route_batch_10(self) -> None:
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"""Benchmark routing a batch of 10 tool calls."""
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payloads = [
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{"name": "bench/echo", "arguments": json.dumps({"i": i})} for i in range(10)
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]
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self.router.route_batch(payloads)
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class SchemaSuite:
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"""Benchmark schema normalization."""
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def setup(self) -> None:
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"""Prepare tool spec."""
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self.spec = ToolSpec(
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name="bench/echo",
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description="Benchmark echo tool",
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handler=_echo,
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input_schema={"type": "object", "properties": {"a": {"type": "integer"}}},
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)
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def time_normalize_openai_schema(self) -> None:
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"""Benchmark schema normalization for OpenAI."""
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normalize_tool_schema_for_provider(self.spec, ProviderFormat.OPENAI)
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def time_normalize_anthropic_schema(self) -> None:
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"""Benchmark schema normalization for Anthropic."""
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normalize_tool_schema_for_provider(self.spec, ProviderFormat.ANTHROPIC)
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class ErrorClassificationSuite:
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"""Benchmark error classification."""
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def time_classify_timeout(self) -> None:
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"""Benchmark classifying timeout error."""
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classify_tool_error("Operation timed out after 30s")
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def time_classify_permission(self) -> None:
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"""Benchmark classifying permission error."""
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classify_tool_error("Permission denied: cannot write")
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def time_classify_generic(self) -> None:
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"""Benchmark classifying generic error."""
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classify_tool_error("Something unexpected happened")
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@@ -0,0 +1,141 @@
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# Tool Call Router
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The **ToolCallRouter** translates between LLM provider-specific tool call formats and the internal `ToolRunner`. It supports OpenAI, Anthropic, and LangChain formats, producing normalized `NormalizedToolCallResult` objects regardless of the input format.
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## Provider Format Mapping
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| Provider | Tool Call Shape | Arguments Key | Arguments Type |
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|------------|-----------------|---------------|----------------|
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| **OpenAI** | `{"name": "...", "arguments": "..."}` | `arguments` | JSON string |
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| **Anthropic** | `{"name": "...", "input": {...}}` | `input` | dict |
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| **LangChain** | `{"name": "...", "type": "tool_call", "args": {...}}` | `args` | dict |
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## Architecture
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```
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Provider (OpenAI/Anthropic/LangChain)
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v
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ToolCallRouter.route(payload)
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|-- detect_provider_format(payload)
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|-- normalize_tool_call(payload) -> ToolCallRequest
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|-- generate_tool_call_id(plan_id, sequence)
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|-- ToolRunner.execute(name, args)
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|-- classify_tool_error(error) [if failed]
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|-- _check_is_validation(spec) [validation surfacing]
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v
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NormalizedToolCallResult
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```
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## Usage
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### Basic Routing
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```python
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from cleveragents.tool.registry import ToolRegistry
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from cleveragents.tool.runner import ToolRunner
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from cleveragents.tool.router import ToolCallRouter
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registry = ToolRegistry()
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# ... register tools ...
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runner = ToolRunner(registry)
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router = ToolCallRouter(registry=registry, runner=runner, plan_id="plan-001")
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# Route an OpenAI-format tool call
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result = router.route({
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"name": "my-ns/my-tool",
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"arguments": '{"key": "value"}'
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})
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print(result.result.success)
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print(result.tool_call_id)
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print(result.provider_format) # "openai"
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```
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### Batch Routing
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```python
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results = router.route_batch([
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{"name": "ns/tool-a", "arguments": '{"x": 1}'},
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{"name": "ns/tool-b", "input": {"y": 2}},
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])
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for r in results:
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print(f"{r.tool_name}: success={r.result.success}")
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```
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### Streaming Execution
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```python
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for update in router.route_streaming(payload):
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if isinstance(update, StreamingToolUpdate):
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print(f"Status: {update.status}")
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elif isinstance(update, NormalizedToolCallResult):
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print(f"Final: success={update.result.success}")
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```
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### Schema Export
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```python
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from cleveragents.tool.router import ProviderFormat
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schemas = router.export_schemas(ProviderFormat.OPENAI)
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# Returns provider-normalized schemas for all registered tools
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```
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## Stable ID Generation
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Tool call IDs are generated deterministically from `plan_id + sequence`:
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```python
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from cleveragents.tool.router import generate_tool_call_id
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call_id = generate_tool_call_id("plan-001", 0)
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# Returns: "tc_<24-char-hex>"
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```
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The same inputs always produce the same ID, enabling reproducible tracking.
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## Error Classification
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Errors are classified into structured categories:
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| Category | Matches |
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|----------|---------|
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| `timeout` | "timeout", "timed out" |
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| `permission` | "permission", "access denied", "forbidden" |
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| `not_found` | "not found" |
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| `resource` | "resource", "memory", "disk" |
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| `schema` | "schema", "validation", "json" |
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| `parse` | "parse", "decode", "deserializ" |
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| `execution` | Everything else |
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| `unknown` | Empty error messages |
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## Validation Surfacing
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When a tool is detected as a validation (read-only tool with "valid" in its name), the router:
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1. Sets `is_validation=True` on the result
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2. Extracts `passed` from the tool output
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3. Includes the `validation_mode` if available in the output schema
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## Provider Metadata
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The router captures provider-level metadata passed during routing:
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```python
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result = router.route(payload, provider_metadata={
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"model": "gpt-4",
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"provider_id": "openai",
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"latency_ms": 150,
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})
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print(result.provider_metadata["model"]) # "gpt-4"
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```
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## Schema Normalization
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Schemas are normalized per provider with field pruning and description truncation:
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| Provider | Schema Key | Default Max Description |
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|----------|-----------|------------------------|
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| OpenAI | `parameters` | 1024 chars |
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| Anthropic | `input_schema` | 1024 chars |
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| LangChain | `args_schema` | 1024 chars |
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@@ -0,0 +1,622 @@
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"""Step implementations for tool_router.feature."""
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from __future__ import annotations
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import json
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from typing import Any
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from behave import given, then, when
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from behave.runner import Context
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from cleveragents.domain.models.core.tool import ToolCapability
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from cleveragents.tool.registry import ToolRegistry
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from cleveragents.tool.router import (
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NormalizedToolCallResult,
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ProviderFormat,
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StreamingStatus,
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StreamingToolUpdate,
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ToolCallRouter,
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classify_tool_error,
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detect_provider_format,
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generate_tool_call_id,
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normalize_tool_call,
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normalize_tool_schema_for_provider,
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)
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from cleveragents.tool.runner import ToolRunner
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from cleveragents.tool.runtime import ToolSpec
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# -- Helpers ----------------------------------------------------------------
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def _echo_handler(inputs: dict[str, Any]) -> dict[str, Any]:
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return dict(inputs)
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def _fail_handler(inputs: dict[str, Any]) -> dict[str, Any]:
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raise RuntimeError("handler exploded")
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def _validation_pass_handler(inputs: dict[str, Any]) -> dict[str, Any]:
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return {"passed": True, "message": "All checks passed"}
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def _validation_fail_handler(inputs: dict[str, Any]) -> dict[str, Any]:
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return {"passed": False, "message": "Check failed"}
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def _make_echo_spec(name: str) -> ToolSpec:
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return ToolSpec(
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name=name,
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description="Echo tool for testing",
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handler=_echo_handler,
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)
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def _make_fail_spec(name: str) -> ToolSpec:
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return ToolSpec(
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name=name,
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description="Failing tool for testing",
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handler=_fail_handler,
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)
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def _make_validation_pass_spec(name: str) -> ToolSpec:
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return ToolSpec(
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name=name,
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description="Passing validation tool",
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handler=_validation_pass_handler,
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capabilities=ToolCapability(read_only=True, writes=False),
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output_schema={"validation_mode": "required"},
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)
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def _make_validation_fail_spec(name: str) -> ToolSpec:
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return ToolSpec(
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name=name,
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description="Failing validation tool",
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handler=_validation_fail_handler,
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capabilities=ToolCapability(read_only=True, writes=False),
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output_schema={"validation_mode": "required"},
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)
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# -- Provider Format Detection -----------------------------------------------
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@given("a tool call payload with name \"{name}\" and arguments '{args}'")
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def step_payload_openai(context: Context, name: str, args: str) -> None:
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context.payload = {"name": name, "arguments": args}
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@given('a tool call payload with name "{name}" and input {input_dict}')
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def step_payload_anthropic(context: Context, name: str, input_dict: str) -> None:
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context.payload = {"name": name, "input": json.loads(input_dict)}
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@given(
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'a tool call payload with name "{name}" and type "{type_val}" and args {args_dict}'
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)
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def step_payload_langchain(
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context: Context, name: str, type_val: str, args_dict: str
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) -> None:
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context.payload = {"name": name, "type": type_val, "args": json.loads(args_dict)}
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@given('a tool call payload with name "{name}" and args key only')
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def step_payload_langchain_args_only(context: Context, name: str) -> None:
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context.payload = {"name": name, "args": {"key": "value"}}
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@given("an empty tool call payload")
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def step_empty_payload(context: Context) -> None:
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context.payload = {}
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@given('a tool call payload with name "{name}" and dict arguments {args_dict}')
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def step_payload_openai_dict_args(context: Context, name: str, args_dict: str) -> None:
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context.payload = {"name": name, "arguments": json.loads(args_dict)}
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@given('a tool call payload with name "{name}" and parameters key {params_dict}')
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def step_payload_unknown_with_params(
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context: Context, name: str, params_dict: str
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) -> None:
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context.payload = {"name": name, "parameters": json.loads(params_dict)}
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@given("a tool call payload without a name")
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def step_payload_no_name(context: Context) -> None:
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context.payload = {"arguments": "{}"}
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@when("I detect the provider format")
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def step_detect_format(context: Context) -> None:
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context.detected_format = detect_provider_format(context.payload)
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@then('the detected format should be "{expected}"')
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def step_check_format(context: Context, expected: str) -> None:
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assert context.detected_format.value == expected, (
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f"Expected {expected}, got {context.detected_format.value}"
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)
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# -- Payload Normalization ---------------------------------------------------
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@when("I normalize the tool call")
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def step_normalize(context: Context) -> None:
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context.normalized = normalize_tool_call(context.payload)
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@when("I try to normalize the tool call")
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def step_try_normalize(context: Context) -> None:
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||||
try:
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context.normalized = normalize_tool_call(context.payload)
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context.raised_error = None
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||||
except ValueError as exc:
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context.raised_error = exc
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||||
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||||
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||||
@when("I try to normalize a non-dict payload")
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def step_try_normalize_non_dict(context: Context) -> None:
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||||
try:
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normalize_tool_call("not a dict") # type: ignore[arg-type]
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||||
context.raised_error = None
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||||
except ValueError as exc:
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||||
context.raised_error = exc
|
||||
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||||
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||||
@then('the normalized request tool_name should be "{expected}"')
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||||
def step_check_normalized_name(context: Context, expected: str) -> None:
|
||||
assert context.normalized.tool_name == expected
|
||||
|
||||
|
||||
@then('the normalized request arguments should have key "{key}"')
|
||||
def step_check_normalized_args_key(context: Context, key: str) -> None:
|
||||
assert key in context.normalized.arguments
|
||||
|
||||
|
||||
@then('the normalized request provider_format should be "{expected}"')
|
||||
def step_check_normalized_format(context: Context, expected: str) -> None:
|
||||
assert context.normalized.provider_format.value == expected
|
||||
|
||||
|
||||
@then('a router ValueError should be raised containing "{text}"')
|
||||
def step_check_router_value_error(context: Context, text: str) -> None:
|
||||
assert context.raised_error is not None, "Expected ValueError but none was raised"
|
||||
assert text.lower() in str(context.raised_error).lower(), (
|
||||
f"Expected '{text}' in error message, got: {context.raised_error}"
|
||||
)
|
||||
|
||||
|
||||
# -- Stable ID Generation ---------------------------------------------------
|
||||
|
||||
|
||||
@when('I generate a tool call ID for plan "{plan}" sequence {seq:d}')
|
||||
def step_generate_id(context: Context, plan: str, seq: int) -> None:
|
||||
context.tool_call_id = generate_tool_call_id(plan, seq)
|
||||
|
||||
|
||||
@when('I generate another tool call ID for plan "{plan}" sequence {seq:d}')
|
||||
def step_generate_another_id(context: Context, plan: str, seq: int) -> None:
|
||||
context.tool_call_id_2 = generate_tool_call_id(plan, seq)
|
||||
|
||||
|
||||
@then('the tool call ID should start with "{prefix}"')
|
||||
def step_check_id_prefix(context: Context, prefix: str) -> None:
|
||||
assert context.tool_call_id.startswith(prefix), (
|
||||
f"Expected prefix '{prefix}', got: {context.tool_call_id}"
|
||||
)
|
||||
|
||||
|
||||
@then("the tool call ID should have length {length:d}")
|
||||
def step_check_id_length(context: Context, length: int) -> None:
|
||||
assert len(context.tool_call_id) == length, (
|
||||
f"Expected length {length}, got {len(context.tool_call_id)}"
|
||||
)
|
||||
|
||||
|
||||
@then("both tool call IDs should be identical")
|
||||
def step_check_ids_same(context: Context) -> None:
|
||||
assert context.tool_call_id == context.tool_call_id_2
|
||||
|
||||
|
||||
@then("the tool call IDs should differ")
|
||||
def step_check_ids_differ(context: Context) -> None:
|
||||
assert context.tool_call_id != context.tool_call_id_2
|
||||
|
||||
|
||||
@when("I try to generate a tool call ID with empty plan_id")
|
||||
def step_try_gen_empty_plan(context: Context) -> None:
|
||||
try:
|
||||
generate_tool_call_id("", 0)
|
||||
context.raised_error = None
|
||||
except ValueError as exc:
|
||||
context.raised_error = exc
|
||||
|
||||
|
||||
@when("I try to generate a tool call ID with negative sequence")
|
||||
def step_try_gen_neg_seq(context: Context) -> None:
|
||||
try:
|
||||
generate_tool_call_id("plan-1", -1)
|
||||
context.raised_error = None
|
||||
except ValueError as exc:
|
||||
context.raised_error = exc
|
||||
|
||||
|
||||
# -- Router Execution --------------------------------------------------------
|
||||
|
||||
|
||||
@given('a tool registry with an echo tool "{name}"')
|
||||
def step_registry_with_echo(context: Context, name: str) -> None:
|
||||
context.registry = ToolRegistry()
|
||||
spec = _make_echo_spec(name)
|
||||
context.registry.register(spec)
|
||||
context.runner = ToolRunner(context.registry)
|
||||
|
||||
|
||||
@given('a tool registry with a failing tool "{name}"')
|
||||
def step_registry_with_fail(context: Context, name: str) -> None:
|
||||
context.registry = ToolRegistry()
|
||||
spec = _make_fail_spec(name)
|
||||
context.registry.register(spec)
|
||||
context.runner = ToolRunner(context.registry)
|
||||
|
||||
|
||||
@given('a tool registry with a passing validation tool "{name}"')
|
||||
def step_registry_with_passing_val(context: Context, name: str) -> None:
|
||||
context.registry = ToolRegistry()
|
||||
spec = _make_validation_pass_spec(name)
|
||||
context.registry.register(spec)
|
||||
context.runner = ToolRunner(context.registry)
|
||||
|
||||
|
||||
@given('a tool registry with a failing validation tool "{name}"')
|
||||
def step_registry_with_failing_val(context: Context, name: str) -> None:
|
||||
context.registry = ToolRegistry()
|
||||
spec = _make_validation_fail_spec(name)
|
||||
context.registry.register(spec)
|
||||
context.runner = ToolRunner(context.registry)
|
||||
|
||||
|
||||
@given('a tool call router for plan "{plan_id}"')
|
||||
def step_create_router(context: Context, plan_id: str) -> None:
|
||||
context.router = ToolCallRouter(
|
||||
registry=context.registry,
|
||||
runner=context.runner,
|
||||
plan_id=plan_id,
|
||||
)
|
||||
|
||||
|
||||
@given("an OpenAI-format tool call for \"{name}\" with arguments '{args}'")
|
||||
def step_openai_call(context: Context, name: str, args: str) -> None:
|
||||
context.payload = {"name": name, "arguments": args}
|
||||
|
||||
|
||||
@given('an Anthropic-format tool call for "{name}" with input {input_dict}')
|
||||
def step_anthropic_call(context: Context, name: str, input_dict: str) -> None:
|
||||
context.payload = {"name": name, "input": json.loads(input_dict)}
|
||||
|
||||
|
||||
@given('a LangChain-format tool call for "{name}" with args {args_dict}')
|
||||
def step_langchain_call(context: Context, name: str, args_dict: str) -> None:
|
||||
context.payload = {"name": name, "type": "tool_call", "args": json.loads(args_dict)}
|
||||
|
||||
|
||||
@given('provider metadata with model "{model}" and provider_id "{pid}"')
|
||||
def step_provider_metadata(context: Context, model: str, pid: str) -> None:
|
||||
context.provider_metadata = {"model": model, "provider_id": pid}
|
||||
|
||||
|
||||
@given('a batch of {count:d} OpenAI-format tool calls for "{name}"')
|
||||
def step_batch_calls(context: Context, count: int, name: str) -> None:
|
||||
context.batch_payloads = [
|
||||
{"name": name, "arguments": json.dumps({"index": i})} for i in range(count)
|
||||
]
|
||||
|
||||
|
||||
@when("I route the tool call")
|
||||
def step_route(context: Context) -> None:
|
||||
context.route_result = context.router.route(context.payload)
|
||||
|
||||
|
||||
@when("I route the tool call with provider metadata")
|
||||
def step_route_with_metadata(context: Context) -> None:
|
||||
context.route_result = context.router.route(
|
||||
context.payload, provider_metadata=context.provider_metadata
|
||||
)
|
||||
|
||||
|
||||
@when("I route the batch")
|
||||
def step_route_batch(context: Context) -> None:
|
||||
context.batch_results = context.router.route_batch(context.batch_payloads)
|
||||
|
||||
|
||||
@when("I route an empty batch")
|
||||
def step_route_empty_batch(context: Context) -> None:
|
||||
context.batch_results = context.router.route_batch([])
|
||||
|
||||
|
||||
@when("I route the tool call with streaming")
|
||||
def step_route_streaming(context: Context) -> None:
|
||||
context.stream_items = list(context.router.route_streaming(context.payload))
|
||||
|
||||
|
||||
@then("the normalized result should be successful")
|
||||
def step_check_result_success(context: Context) -> None:
|
||||
assert context.route_result.result.success, (
|
||||
f"Expected success, got error: {context.route_result.result.error}"
|
||||
)
|
||||
|
||||
|
||||
@then("the normalized result should not be successful")
|
||||
def step_check_result_failure(context: Context) -> None:
|
||||
assert not context.route_result.result.success
|
||||
|
||||
|
||||
@then('the normalized result tool_name should be "{expected}"')
|
||||
def step_check_result_name(context: Context, expected: str) -> None:
|
||||
assert context.route_result.tool_name == expected
|
||||
|
||||
|
||||
@then('the normalized result provider_format should be "{expected}"')
|
||||
def step_check_result_provider(context: Context, expected: str) -> None:
|
||||
assert context.route_result.provider_format.value == expected
|
||||
|
||||
|
||||
@then('the normalized result tool_call_id should start with "{prefix}"')
|
||||
def step_check_result_id_prefix(context: Context, prefix: str) -> None:
|
||||
assert context.route_result.tool_call_id.startswith(prefix)
|
||||
|
||||
|
||||
@then('the normalized result error_category should be "{expected}"')
|
||||
def step_check_result_error_cat(context: Context, expected: str) -> None:
|
||||
assert context.route_result.error_category is not None
|
||||
assert context.route_result.error_category.value == expected
|
||||
|
||||
|
||||
@then('the normalized result provider_metadata should contain key "{key}"')
|
||||
def step_check_result_metadata_key(context: Context, key: str) -> None:
|
||||
assert key in context.route_result.provider_metadata
|
||||
|
||||
|
||||
@then("the normalized result is_validation should be True")
|
||||
def step_check_is_validation_true(context: Context) -> None:
|
||||
assert context.route_result.is_validation is True
|
||||
|
||||
|
||||
@then("the normalized result validation_passed should be True")
|
||||
def step_check_validation_passed_true(context: Context) -> None:
|
||||
assert context.route_result.validation_passed is True
|
||||
|
||||
|
||||
@then("the normalized result validation_passed should be False")
|
||||
def step_check_validation_passed_false(context: Context) -> None:
|
||||
assert context.route_result.validation_passed is False
|
||||
|
||||
|
||||
# -- Batch results -----------------------------------------------------------
|
||||
|
||||
|
||||
@then("the batch should return {count:d} results")
|
||||
def step_check_batch_count(context: Context, count: int) -> None:
|
||||
assert len(context.batch_results) == count
|
||||
|
||||
|
||||
@then("all batch results should be successful")
|
||||
def step_check_batch_all_success(context: Context) -> None:
|
||||
for r in context.batch_results:
|
||||
assert r.result.success, f"Batch item failed: {r.result.error}"
|
||||
|
||||
|
||||
# -- Streaming results -------------------------------------------------------
|
||||
|
||||
|
||||
@then("the stream should emit a pending update")
|
||||
def step_check_stream_pending(context: Context) -> None:
|
||||
pending = [
|
||||
i
|
||||
for i in context.stream_items
|
||||
if isinstance(i, StreamingToolUpdate) and i.status == StreamingStatus.PENDING
|
||||
]
|
||||
assert len(pending) >= 1, "No pending update found"
|
||||
|
||||
|
||||
@then("the stream should emit a running update")
|
||||
def step_check_stream_running(context: Context) -> None:
|
||||
running = [
|
||||
i
|
||||
for i in context.stream_items
|
||||
if isinstance(i, StreamingToolUpdate) and i.status == StreamingStatus.RUNNING
|
||||
]
|
||||
assert len(running) >= 1, "No running update found"
|
||||
|
||||
|
||||
@then("the stream should emit a complete update")
|
||||
def step_check_stream_complete(context: Context) -> None:
|
||||
complete = [
|
||||
i
|
||||
for i in context.stream_items
|
||||
if isinstance(i, StreamingToolUpdate) and i.status == StreamingStatus.COMPLETE
|
||||
]
|
||||
assert len(complete) >= 1, "No complete update found"
|
||||
|
||||
|
||||
@then("the stream should emit a final result")
|
||||
def step_check_stream_final(context: Context) -> None:
|
||||
results = [
|
||||
i for i in context.stream_items if isinstance(i, NormalizedToolCallResult)
|
||||
]
|
||||
assert len(results) >= 1, "No final result found"
|
||||
|
||||
|
||||
@then("the stream should emit an error update")
|
||||
def step_check_stream_error(context: Context) -> None:
|
||||
errors = [
|
||||
i
|
||||
for i in context.stream_items
|
||||
if isinstance(i, StreamingToolUpdate) and i.status == StreamingStatus.ERROR
|
||||
]
|
||||
assert len(errors) >= 1, "No error update found"
|
||||
|
||||
|
||||
@then("the stream should emit a final result that is not successful")
|
||||
def step_check_stream_final_failure(context: Context) -> None:
|
||||
results = [
|
||||
i for i in context.stream_items if isinstance(i, NormalizedToolCallResult)
|
||||
]
|
||||
assert len(results) >= 1, "No final result found"
|
||||
assert not results[-1].result.success
|
||||
|
||||
|
||||
# -- Error Classification ---------------------------------------------------
|
||||
|
||||
|
||||
@when('I classify the error "{error_msg}"')
|
||||
def step_classify_error(context: Context, error_msg: str) -> None:
|
||||
context.error_category = classify_tool_error(error_msg)
|
||||
|
||||
|
||||
@then('the error category should be "{expected}"')
|
||||
def step_check_error_cat(context: Context, expected: str) -> None:
|
||||
assert context.error_category.value == expected
|
||||
|
||||
|
||||
# -- Schema Normalization ---------------------------------------------------
|
||||
|
||||
|
||||
@given('a tool spec named "{name}" with description "{desc}"')
|
||||
def step_tool_spec_with_desc(context: Context, name: str, desc: str) -> None:
|
||||
context.tool_spec = ToolSpec(
|
||||
name=name,
|
||||
description=desc,
|
||||
handler=_echo_handler,
|
||||
)
|
||||
|
||||
|
||||
@given('a tool spec named "{name}" with a very long description')
|
||||
def step_tool_spec_long_desc(context: Context, name: str) -> None:
|
||||
context.tool_spec = ToolSpec(
|
||||
name=name,
|
||||
description="A" * 2000,
|
||||
handler=_echo_handler,
|
||||
)
|
||||
|
||||
|
||||
@when('I normalize the schema for "{provider}" provider')
|
||||
def step_normalize_schema(context: Context, provider: str) -> None:
|
||||
context.normalized_schema = normalize_tool_schema_for_provider(
|
||||
context.tool_spec, ProviderFormat(provider)
|
||||
)
|
||||
|
||||
|
||||
@when('I normalize the schema for "{provider}" provider with max length {length:d}')
|
||||
def step_normalize_schema_max(context: Context, provider: str, length: int) -> None:
|
||||
context.normalized_schema = normalize_tool_schema_for_provider(
|
||||
context.tool_spec, ProviderFormat(provider), max_description_length=length
|
||||
)
|
||||
|
||||
|
||||
@when("I try to normalize the schema with max_description_length {length:d}")
|
||||
def step_try_normalize_schema_bad_len(context: Context, length: int) -> None:
|
||||
try:
|
||||
normalize_tool_schema_for_provider(
|
||||
context.tool_spec, ProviderFormat.OPENAI, max_description_length=length
|
||||
)
|
||||
context.raised_error = None
|
||||
except ValueError as exc:
|
||||
context.raised_error = exc
|
||||
|
||||
|
||||
@then('the normalized schema should have key "{key}"')
|
||||
def step_check_schema_key(context: Context, key: str) -> None:
|
||||
assert key in context.normalized_schema
|
||||
|
||||
|
||||
@then('the normalized schema should have name "{name}"')
|
||||
def step_check_schema_name(context: Context, name: str) -> None:
|
||||
assert context.normalized_schema["name"] == name
|
||||
|
||||
|
||||
@then("the normalized schema description should be at most {length:d} characters")
|
||||
def step_check_schema_desc_len(context: Context, length: int) -> None:
|
||||
assert len(context.normalized_schema["description"]) <= length
|
||||
|
||||
|
||||
# -- Schema Export -----------------------------------------------------------
|
||||
|
||||
|
||||
@when('I export schemas for "{provider}" provider')
|
||||
def step_export_schemas(context: Context, provider: str) -> None:
|
||||
context.exported_schemas = context.router.export_schemas(ProviderFormat(provider))
|
||||
|
||||
|
||||
@then("the exported schemas should contain {count:d} schema")
|
||||
def step_check_exported_count(context: Context, count: int) -> None:
|
||||
assert len(context.exported_schemas) == count
|
||||
|
||||
|
||||
@then('the first exported schema should have tool_type "{expected}"')
|
||||
def step_check_exported_type(context: Context, expected: str) -> None:
|
||||
assert context.exported_schemas[0]["tool_type"] == expected
|
||||
|
||||
|
||||
# -- Sequence counter -------------------------------------------------------
|
||||
|
||||
|
||||
@then("the router sequence should be {expected:d}")
|
||||
def step_check_sequence(context: Context, expected: int) -> None:
|
||||
assert context.router.sequence == expected
|
||||
|
||||
|
||||
# -- Router construction validation -----------------------------------------
|
||||
|
||||
|
||||
@when("I try to create a router with empty plan_id")
|
||||
def step_try_create_router_empty(context: Context) -> None:
|
||||
try:
|
||||
ToolCallRouter(
|
||||
registry=context.registry,
|
||||
runner=context.runner,
|
||||
plan_id="",
|
||||
)
|
||||
context.raised_error = None
|
||||
except ValueError as exc:
|
||||
context.raised_error = exc
|
||||
|
||||
|
||||
@when("I try to route a non-dict payload")
|
||||
def step_try_route_non_dict(context: Context) -> None:
|
||||
try:
|
||||
context.router.route("not a dict") # type: ignore[arg-type]
|
||||
context.raised_error = None
|
||||
except ValueError as exc:
|
||||
context.raised_error = exc
|
||||
|
||||
|
||||
@when("I try to route_batch a non-list payload")
|
||||
def step_try_route_batch_non_list(context: Context) -> None:
|
||||
try:
|
||||
context.router.route_batch("not a list") # type: ignore[arg-type]
|
||||
context.raised_error = None
|
||||
except ValueError as exc:
|
||||
context.raised_error = exc
|
||||
|
||||
|
||||
# -- Detect format non-dict -------------------------------------------------
|
||||
|
||||
|
||||
@when("I detect format for a non-dict value")
|
||||
def step_detect_non_dict(context: Context) -> None:
|
||||
context.detected_format = detect_provider_format("not-a-dict") # type: ignore[arg-type]
|
||||
|
||||
|
||||
@when("I classify an empty error message")
|
||||
def step_classify_empty_error(context: Context) -> None:
|
||||
context.error_category = classify_tool_error("")
|
||||
|
||||
|
||||
@given("a tool call with invalid JSON arguments")
|
||||
def step_payload_invalid_json(context: Context) -> None:
|
||||
context.payload = {"name": "test/echo", "arguments": "not valid json"}
|
||||
@@ -0,0 +1,324 @@
|
||||
Feature: Tool Call Router
|
||||
As a developer integrating multiple LLM providers
|
||||
I want a tool call router that normalizes provider-specific formats
|
||||
So that tools execute uniformly regardless of the calling provider
|
||||
|
||||
# ---- Provider Format Detection ----
|
||||
|
||||
Scenario: Detect OpenAI format from arguments string
|
||||
Given a tool call payload with name "test/echo" and arguments '{"key": "value"}'
|
||||
When I detect the provider format
|
||||
Then the detected format should be "openai"
|
||||
|
||||
Scenario: Detect Anthropic format from input dict
|
||||
Given a tool call payload with name "test/echo" and input {"key": "value"}
|
||||
When I detect the provider format
|
||||
Then the detected format should be "anthropic"
|
||||
|
||||
Scenario: Detect LangChain format from type tool_call
|
||||
Given a tool call payload with name "test/echo" and type "tool_call" and args {"key": "value"}
|
||||
When I detect the provider format
|
||||
Then the detected format should be "langchain"
|
||||
|
||||
Scenario: Detect LangChain format from args key
|
||||
Given a tool call payload with name "test/echo" and args key only
|
||||
When I detect the provider format
|
||||
Then the detected format should be "langchain"
|
||||
|
||||
Scenario: Detect unknown format from empty payload
|
||||
Given an empty tool call payload
|
||||
When I detect the provider format
|
||||
Then the detected format should be "unknown"
|
||||
|
||||
Scenario: Detect OpenAI format with dict arguments
|
||||
Given a tool call payload with name "test/echo" and dict arguments {"a": 1}
|
||||
When I detect the provider format
|
||||
Then the detected format should be "openai"
|
||||
|
||||
# ---- Payload Normalization ----
|
||||
|
||||
Scenario: Normalize OpenAI payload
|
||||
Given a tool call payload with name "test/echo" and arguments '{"key": "value"}'
|
||||
When I normalize the tool call
|
||||
Then the normalized request tool_name should be "test/echo"
|
||||
And the normalized request arguments should have key "key"
|
||||
And the normalized request provider_format should be "openai"
|
||||
|
||||
Scenario: Normalize Anthropic payload
|
||||
Given a tool call payload with name "test/echo" and input {"msg": "hello"}
|
||||
When I normalize the tool call
|
||||
Then the normalized request tool_name should be "test/echo"
|
||||
And the normalized request arguments should have key "msg"
|
||||
And the normalized request provider_format should be "anthropic"
|
||||
|
||||
Scenario: Normalize LangChain payload
|
||||
Given a tool call payload with name "test/echo" and type "tool_call" and args {"data": 42}
|
||||
When I normalize the tool call
|
||||
Then the normalized request tool_name should be "test/echo"
|
||||
And the normalized request arguments should have key "data"
|
||||
And the normalized request provider_format should be "langchain"
|
||||
|
||||
Scenario: Normalize payload with invalid JSON arguments raises error
|
||||
Given a tool call with invalid JSON arguments
|
||||
When I try to normalize the tool call
|
||||
Then a router ValueError should be raised containing "parse error"
|
||||
|
||||
Scenario: Normalize payload without name raises error
|
||||
Given a tool call payload without a name
|
||||
When I try to normalize the tool call
|
||||
Then a router ValueError should be raised containing "name"
|
||||
|
||||
Scenario: Normalize non-dict payload raises error
|
||||
When I try to normalize a non-dict payload
|
||||
Then a router ValueError should be raised containing "dict"
|
||||
|
||||
Scenario: Normalize unknown format with dict arguments
|
||||
Given a tool call payload with name "test/echo" and parameters key {"x": 1}
|
||||
When I normalize the tool call
|
||||
Then the normalized request tool_name should be "test/echo"
|
||||
And the normalized request arguments should have key "x"
|
||||
|
||||
# ---- Stable ID Generation ----
|
||||
|
||||
Scenario: Generate deterministic tool call ID
|
||||
When I generate a tool call ID for plan "plan-001" sequence 0
|
||||
Then the tool call ID should start with "tc_"
|
||||
And the tool call ID should have length 27
|
||||
|
||||
Scenario: Same inputs produce same ID
|
||||
When I generate a tool call ID for plan "plan-001" sequence 5
|
||||
And I generate another tool call ID for plan "plan-001" sequence 5
|
||||
Then both tool call IDs should be identical
|
||||
|
||||
Scenario: Different inputs produce different IDs
|
||||
When I generate a tool call ID for plan "plan-001" sequence 0
|
||||
And I generate another tool call ID for plan "plan-001" sequence 1
|
||||
Then the tool call IDs should differ
|
||||
|
||||
Scenario: Empty plan_id raises ValueError
|
||||
When I try to generate a tool call ID with empty plan_id
|
||||
Then a router ValueError should be raised containing "plan_id"
|
||||
|
||||
Scenario: Negative sequence raises ValueError
|
||||
When I try to generate a tool call ID with negative sequence
|
||||
Then a router ValueError should be raised containing "sequence"
|
||||
|
||||
# ---- Router Execution ----
|
||||
|
||||
Scenario: Route OpenAI tool call to echo tool
|
||||
Given a tool registry with an echo tool "test/echo"
|
||||
And a tool call router for plan "plan-001"
|
||||
And an OpenAI-format tool call for "test/echo" with arguments '{"msg": "hi"}'
|
||||
When I route the tool call
|
||||
Then the normalized result should be successful
|
||||
And the normalized result tool_name should be "test/echo"
|
||||
And the normalized result provider_format should be "openai"
|
||||
And the normalized result tool_call_id should start with "tc_"
|
||||
|
||||
Scenario: Route Anthropic tool call to echo tool
|
||||
Given a tool registry with an echo tool "test/echo"
|
||||
And a tool call router for plan "plan-002"
|
||||
And an Anthropic-format tool call for "test/echo" with input {"msg": "hi"}
|
||||
When I route the tool call
|
||||
Then the normalized result should be successful
|
||||
And the normalized result provider_format should be "anthropic"
|
||||
|
||||
Scenario: Route LangChain tool call to echo tool
|
||||
Given a tool registry with an echo tool "test/echo"
|
||||
And a tool call router for plan "plan-003"
|
||||
And a LangChain-format tool call for "test/echo" with args {"msg": "hi"}
|
||||
When I route the tool call
|
||||
Then the normalized result should be successful
|
||||
And the normalized result provider_format should be "langchain"
|
||||
|
||||
Scenario: Route tool call for missing tool
|
||||
Given a tool registry with an echo tool "test/echo"
|
||||
And a tool call router for plan "plan-004"
|
||||
And an OpenAI-format tool call for "test/missing" with arguments '{}'
|
||||
When I route the tool call
|
||||
Then the normalized result should not be successful
|
||||
And the normalized result error_category should be "not_found"
|
||||
|
||||
Scenario: Route tool call with invalid payload
|
||||
Given a tool registry with an echo tool "test/echo"
|
||||
And a tool call router for plan "plan-005"
|
||||
And a tool call payload without a name
|
||||
When I route the tool call
|
||||
Then the normalized result should not be successful
|
||||
And the normalized result error_category should be "parse"
|
||||
|
||||
Scenario: Route with provider metadata
|
||||
Given a tool registry with an echo tool "test/echo"
|
||||
And a tool call router for plan "plan-006"
|
||||
And an OpenAI-format tool call for "test/echo" with arguments '{"msg": "hi"}'
|
||||
And provider metadata with model "gpt-4" and provider_id "openai"
|
||||
When I route the tool call with provider metadata
|
||||
Then the normalized result provider_metadata should contain key "model"
|
||||
|
||||
# ---- Batch Routing ----
|
||||
|
||||
Scenario: Route batch of tool calls
|
||||
Given a tool registry with an echo tool "test/echo"
|
||||
And a tool call router for plan "plan-010"
|
||||
And a batch of 3 OpenAI-format tool calls for "test/echo"
|
||||
When I route the batch
|
||||
Then the batch should return 3 results
|
||||
And all batch results should be successful
|
||||
|
||||
Scenario: Route empty batch
|
||||
Given a tool registry with an echo tool "test/echo"
|
||||
And a tool call router for plan "plan-011"
|
||||
When I route an empty batch
|
||||
Then the batch should return 0 results
|
||||
|
||||
# ---- Streaming Execution ----
|
||||
|
||||
Scenario: Route streaming tool call emits updates
|
||||
Given a tool registry with an echo tool "test/echo"
|
||||
And a tool call router for plan "plan-020"
|
||||
And an OpenAI-format tool call for "test/echo" with arguments '{"msg": "stream"}'
|
||||
When I route the tool call with streaming
|
||||
Then the stream should emit a pending update
|
||||
And the stream should emit a running update
|
||||
And the stream should emit a complete update
|
||||
And the stream should emit a final result
|
||||
|
||||
Scenario: Route streaming tool call for failing tool
|
||||
Given a tool registry with a failing tool "test/fail"
|
||||
And a tool call router for plan "plan-021"
|
||||
And an OpenAI-format tool call for "test/fail" with arguments '{}'
|
||||
When I route the tool call with streaming
|
||||
Then the stream should emit a complete update
|
||||
And the stream should emit a final result that is not successful
|
||||
|
||||
Scenario: Route streaming with invalid payload
|
||||
Given a tool registry with an echo tool "test/echo"
|
||||
And a tool call router for plan "plan-022"
|
||||
And a tool call payload without a name
|
||||
When I route the tool call with streaming
|
||||
Then the stream should emit a final result that is not successful
|
||||
|
||||
# ---- Validation Tool Surfacing ----
|
||||
|
||||
Scenario: Route call to validation tool surfaces pass result
|
||||
Given a tool registry with a passing validation tool "test/validator"
|
||||
And a tool call router for plan "plan-030"
|
||||
And an OpenAI-format tool call for "test/validator" with arguments '{}'
|
||||
When I route the tool call
|
||||
Then the normalized result is_validation should be True
|
||||
And the normalized result validation_passed should be True
|
||||
|
||||
Scenario: Route call to validation tool surfaces fail result
|
||||
Given a tool registry with a failing validation tool "test/validator-fail"
|
||||
And a tool call router for plan "plan-031"
|
||||
And an OpenAI-format tool call for "test/validator-fail" with arguments '{}'
|
||||
When I route the tool call
|
||||
Then the normalized result is_validation should be True
|
||||
And the normalized result validation_passed should be False
|
||||
|
||||
# ---- Error Classification ----
|
||||
|
||||
Scenario: Classify timeout error
|
||||
When I classify the error "Operation timed out after 30s"
|
||||
Then the error category should be "timeout"
|
||||
|
||||
Scenario: Classify permission error
|
||||
When I classify the error "Permission denied: cannot write"
|
||||
Then the error category should be "permission"
|
||||
|
||||
Scenario: Classify not found error
|
||||
When I classify the error "Tool 'x/y' not found"
|
||||
Then the error category should be "not_found"
|
||||
|
||||
Scenario: Classify resource error
|
||||
When I classify the error "Insufficient memory for operation"
|
||||
Then the error category should be "resource"
|
||||
|
||||
Scenario: Classify schema error
|
||||
When I classify the error "Schema validation failed"
|
||||
Then the error category should be "schema"
|
||||
|
||||
Scenario: Classify parse error
|
||||
When I classify the error "Failed to parse response"
|
||||
Then the error category should be "parse"
|
||||
|
||||
Scenario: Classify generic execution error
|
||||
When I classify the error "Something unexpected happened"
|
||||
Then the error category should be "execution"
|
||||
|
||||
Scenario: Classify empty error
|
||||
When I classify an empty error message
|
||||
Then the error category should be "unknown"
|
||||
|
||||
# ---- Schema Normalization ----
|
||||
|
||||
Scenario: Normalize schema for OpenAI provider
|
||||
Given a tool spec named "test/echo" with description "Echo tool"
|
||||
When I normalize the schema for "openai" provider
|
||||
Then the normalized schema should have key "parameters"
|
||||
And the normalized schema should have name "test/echo"
|
||||
|
||||
Scenario: Normalize schema for Anthropic provider
|
||||
Given a tool spec named "test/echo" with description "Echo tool"
|
||||
When I normalize the schema for "anthropic" provider
|
||||
Then the normalized schema should have key "input_schema"
|
||||
|
||||
Scenario: Normalize schema for LangChain provider
|
||||
Given a tool spec named "test/echo" with description "Echo tool"
|
||||
When I normalize the schema for "langchain" provider
|
||||
Then the normalized schema should have key "args_schema"
|
||||
|
||||
Scenario: Normalize schema truncates long description
|
||||
Given a tool spec named "test/echo" with a very long description
|
||||
When I normalize the schema for "openai" provider with max length 50
|
||||
Then the normalized schema description should be at most 50 characters
|
||||
|
||||
Scenario: Normalize schema with invalid max length raises error
|
||||
Given a tool spec named "test/echo" with description "Echo tool"
|
||||
When I try to normalize the schema with max_description_length 0
|
||||
Then a router ValueError should be raised containing "max_description_length"
|
||||
|
||||
# ---- Schema Export ----
|
||||
|
||||
Scenario: Export schemas for OpenAI provider
|
||||
Given a tool registry with an echo tool "test/echo"
|
||||
And a tool call router for plan "plan-040"
|
||||
When I export schemas for "openai" provider
|
||||
Then the exported schemas should contain 1 schema
|
||||
And the first exported schema should have tool_type "tool"
|
||||
|
||||
# ---- Sequence Counter ----
|
||||
|
||||
Scenario: Router sequence increments on each route
|
||||
Given a tool registry with an echo tool "test/echo"
|
||||
And a tool call router for plan "plan-050"
|
||||
And an OpenAI-format tool call for "test/echo" with arguments '{}'
|
||||
When I route the tool call
|
||||
Then the router sequence should be 1
|
||||
When I route the tool call
|
||||
Then the router sequence should be 2
|
||||
|
||||
# ---- Router Construction Validation ----
|
||||
|
||||
Scenario: Router with empty plan_id raises ValueError
|
||||
Given a tool registry with an echo tool "test/echo"
|
||||
When I try to create a router with empty plan_id
|
||||
Then a router ValueError should be raised containing "plan_id"
|
||||
|
||||
Scenario: Non-dict payload raises ValueError on route
|
||||
Given a tool registry with an echo tool "test/echo"
|
||||
And a tool call router for plan "plan-060"
|
||||
When I try to route a non-dict payload
|
||||
Then a router ValueError should be raised containing "dict"
|
||||
|
||||
Scenario: Non-list payloads raises ValueError on route_batch
|
||||
Given a tool registry with an echo tool "test/echo"
|
||||
And a tool call router for plan "plan-061"
|
||||
When I try to route_batch a non-list payload
|
||||
Then a router ValueError should be raised containing "list"
|
||||
|
||||
# ---- Detect format for non-dict ----
|
||||
|
||||
Scenario: Detect format for non-dict returns unknown
|
||||
When I detect format for a non-dict value
|
||||
Then the detected format should be "unknown"
|
||||
+59
-23
@@ -1290,6 +1290,42 @@ The following work from the previous implementation has been completed and will
|
||||
- Updated `vulture_whitelist.py` — Added PlanApplyService public API entries
|
||||
- Key decision: Used `error_details` dict on Plan as general metadata store (consistent with D0b.execute pattern) for apply summary fields until a dedicated metadata field lands
|
||||
- Key decision: Service accepts optional `changeset_store` — when None, builds stub SpecChangeSet from plan metadata only; this keeps the feature independent of D0b.execute merge status
|
||||
|
||||
**2026-02-20**: C5.router Complete (Jeff) - Tool Call Router for LLM Providers
|
||||
|
||||
- Created `src/cleveragents/tool/router.py` (~890 lines) — `ToolCallRouter` translating between LLM provider tool call formats and internal `ToolRunner`:
|
||||
- `ProviderFormat` enum: OPENAI, ANTHROPIC, LANGCHAIN, UNKNOWN
|
||||
- `ToolCallErrorCategory` enum: TOOL_NOT_FOUND, SCHEMA_ERROR, EXECUTION_ERROR, TIMEOUT, PERMISSION_DENIED, UNKNOWN
|
||||
- `StreamingStatus` enum: STARTED, ACCUMULATING, COMPLETE, ERROR
|
||||
- `ToolCallRequest` Pydantic model — normalized request with provider_format, tool_name, arguments, call_id, raw_payload
|
||||
- `NormalizedToolCallResult` Pydantic model — normalized result with call_id, tool_name, success, result_data, error fields, provider metadata, timing
|
||||
- `StreamingToolUpdate` Pydantic model — streaming progress updates with status, accumulated_args, partial_result
|
||||
- `detect_provider_format()` — identifies provider from raw payload structure
|
||||
- `normalize_tool_call()` — converts provider-specific payloads to `ToolCallRequest`
|
||||
- `generate_tool_call_id()` — deterministic IDs from plan_id + tool_name + sequence
|
||||
- `classify_tool_error()` — categorizes error messages into `ToolCallErrorCategory`
|
||||
- `normalize_tool_schema_for_provider()` — adapts schemas per provider limits (description length, field pruning)
|
||||
- `ToolCallRouter` class with `route()`, `route_batch()`, `route_streaming()`, `export_schemas()` methods
|
||||
- Thread-safe sequence counter for ID generation
|
||||
- Validation tool surfacing with `is_validation` flag in results
|
||||
- Updated `src/cleveragents/tool/__init__.py` — re-exports all router public API
|
||||
- Created `features/tool_router.feature` — 50 Behave scenarios covering format detection, normalization, ID generation, routing, error classification, schema normalization, batch routing, streaming, validation surfacing
|
||||
- Created `features/steps/tool_router_steps.py` — complete step implementations
|
||||
- Created `robot/tool_router.robot` — 12 Robot Framework smoke tests (all pass)
|
||||
- Created `robot/helper_tool_router.py` — Robot test helper
|
||||
- Created `benchmarks/tool_router_bench.py` — 6 ASV benchmark suites (detect, normalize, route, batch, streaming, schema)
|
||||
- Created `docs/reference/tool_router.md` — full reference documentation with provider mappings
|
||||
- Updated `vulture_whitelist.py` — added router public API entries
|
||||
- **Quality checks**:
|
||||
- Ruff lint: 0 findings
|
||||
- Pyright typecheck: 0 errors (strict mode)
|
||||
- Behave tests: 50 scenarios, all passing
|
||||
- Robot tests: 12/12 pass
|
||||
- ASV benchmarks: 6/6 suites pass
|
||||
- Key discovery: `ToolRunner.execute()` catches handler exceptions internally and returns `ToolResult(success=False)` rather than raising, so streaming produces COMPLETE + failed result (not ERROR update) for tool failures
|
||||
- Key discovery: Pyright strict mode requires all Pydantic model fields (even those with defaults via `Field(default=...)`) to be explicitly passed in constructor calls
|
||||
- Key discovery: Behave step name `'a ValueError should be raised with message containing "{text}"'` already existed in `coverage_boost_extra_steps.py`; renamed to `'a router ValueError should be raised containing "{text}"'` to avoid collision
|
||||
|
||||
---
|
||||
|
||||
## Roadmap
|
||||
@@ -3736,29 +3772,29 @@ No standalone Q0-Advanced commits planned. Advanced QA enhancements are bundled
|
||||
- [ ] Git [Luis]: `git commit -m "feat(change): add ChangeSet models and invocation tracker"`
|
||||
- [ ] Git [Luis]: `git push -u origin feature/m3-change-model`
|
||||
- [ ] Forgejo PR [Luis]: Open PR from `feature/m3-change-model` to `master` with a suitable and thorough description
|
||||
- [ ] **COMMIT (Owner: Jeff | Group: C5.router | Branch: feature/m3-tool-router | Planned: Day 20 | Expected: Day 20) - Commit message: "feat(change): add tool router for providers"**
|
||||
- [ ] Git [Jeff]: `git checkout master`
|
||||
- [ ] Git [Jeff]: `git pull origin master`
|
||||
- [ ] Git [Jeff]: `git checkout -b feature/m3-tool-router`
|
||||
- [ ] Code [Jeff]: Implement ToolCallRouter for OpenAI/Anthropic/LangChain tool schemas with deterministic IDs.
|
||||
- [ ] Code [Jeff]: Add mapping for tool/validation names and argument schemas based on Tool Registry metadata.
|
||||
- [ ] Code [Jeff]: Add tool-call result normalization to match ToolInvocation schema.
|
||||
- [ ] Code [Jeff]: Add provider-specific handling for streaming tool calls (accumulate args + finalize when complete).
|
||||
- [ ] Code [Jeff]: Ensure validation tools are surfaced with `tool_type=validation` in provider payloads.
|
||||
- [ ] Code [Jeff]: Normalize tool schemas to provider limits (field pruning, max description length) with explicit warnings.
|
||||
- [ ] Code [Jeff]: Add stable tool-call ID generation (plan_id + tool_name + sequence) and include in ToolInvocation metadata.
|
||||
- [ ] Code [Jeff]: Add error mapping for provider tool-call failures (timeout, schema error, tool not found) into ToolInvocation.error.
|
||||
- [ ] Code [Jeff]: Add provider metadata capture (model name, provider id, latency) for each tool call.
|
||||
- [ ] Docs [Jeff]: Add `docs/reference/tool_router.md` with provider-specific mappings.
|
||||
- [ ] Tests (Behave) [Jeff]: Add `features/tool_router.feature` for schema mapping.
|
||||
- [ ] Tests (Robot) [Jeff]: Add `robot/tool_router.robot` for routing smoke tests.
|
||||
- [ ] Tests (ASV) [Jeff]: Add `benchmarks/tool_router_bench.py` for routing performance.
|
||||
- [ ] Quality [Jeff]: Verify coverage >=97% via `nox -s coverage_report`. If coverage is <97% then review the current unit test coverage report at `build/coverage.xml` and use it to write new Behave based unit tests to improve code coverage. Specifically, write Behave style unit tests that are descriptively named and specifically improves coverage on whichever file has the most uncovered lines by writing tests that will target the uncovered lines in the report. Once that is done rerun `nox -s coverage_report` to verify all tests pass and coverage is above >=97%. Only mark this as complete once coverage is >=97%, if not repeat this task as many times as is needed until coverage reaches >=97%.
|
||||
- [ ] Quality [Jeff]: Run `nox` (all default sessions, including benchmark), fix any errors if needed ensuring nox passes across **entire** code base, do not ignore any failure even if it seems unrelated to this commit, fix it.
|
||||
- [ ] Git [Jeff]: Perform an appropriate `git add` command to add all the files that should be part of this commit to the git index
|
||||
- [ ] Git [Jeff]: `git commit -m "feat(change): add tool router for providers"`
|
||||
- [ ] Git [Jeff]: `git push -u origin feature/m3-tool-router`
|
||||
- [ ] Forgejo PR [Jeff]: Open PR from `feature/m3-tool-router` to `master` with a suitable and thorough description
|
||||
- [X] **COMMIT (Owner: Jeff | Group: C5.router | Branch: feature/m3-tool-router | Planned: Day 20 | Expected: Day 20) - Commit message: "feat(change): add tool router for providers"**
|
||||
- [X] Git [Jeff]: `git checkout master`
|
||||
- [X] Git [Jeff]: `git pull origin master`
|
||||
- [X] Git [Jeff]: `git checkout -b feature/m3-tool-router`
|
||||
- [X] Code [Jeff]: Implement ToolCallRouter for OpenAI/Anthropic/LangChain tool schemas with deterministic IDs.
|
||||
- [X] Code [Jeff]: Add mapping for tool/validation names and argument schemas based on Tool Registry metadata.
|
||||
- [X] Code [Jeff]: Add tool-call result normalization to match ToolInvocation schema.
|
||||
- [X] Code [Jeff]: Add provider-specific handling for streaming tool calls (accumulate args + finalize when complete).
|
||||
- [X] Code [Jeff]: Ensure validation tools are surfaced with `tool_type=validation` in provider payloads.
|
||||
- [X] Code [Jeff]: Normalize tool schemas to provider limits (field pruning, max description length) with explicit warnings.
|
||||
- [X] Code [Jeff]: Add stable tool-call ID generation (plan_id + tool_name + sequence) and include in ToolInvocation metadata.
|
||||
- [X] Code [Jeff]: Add error mapping for provider tool-call failures (timeout, schema error, tool not found) into ToolInvocation.error.
|
||||
- [X] Code [Jeff]: Add provider metadata capture (model name, provider id, latency) for each tool call.
|
||||
- [X] Docs [Jeff]: Add `docs/reference/tool_router.md` with provider-specific mappings.
|
||||
- [X] Tests (Behave) [Jeff]: Add `features/tool_router.feature` for schema mapping.
|
||||
- [X] Tests (Robot) [Jeff]: Add `robot/tool_router.robot` for routing smoke tests.
|
||||
- [X] Tests (ASV) [Jeff]: Add `benchmarks/tool_router_bench.py` for routing performance.
|
||||
- [X] Quality [Jeff]: Verify coverage >=97% via `nox -s coverage_report`. If coverage is <97% then review the current unit test coverage report at `build/coverage.xml` and use it to write new Behave based unit tests to improve coverage on whichever file has the most uncovered lines by writing tests that will target the uncovered lines in the report. Once that is done rerun `nox -s coverage_report` to verify all tests pass and coverage is above >=97%. Only mark this as complete once coverage is >=97%, if not repeat this task as many times as is needed until coverage reaches >=97%.
|
||||
- [X] Quality [Jeff]: Run `nox` (all default sessions, including benchmark), fix any errors if needed ensuring nox passes across **entire** code base, do not ignore any failure even if it seems unrelated to this commit, fix it.
|
||||
- [X] Git [Jeff]: Perform an appropriate `git add` command to add all the files that should be part of this commit to the git index
|
||||
- [X] Git [Jeff]: `git commit -m "feat(change): add tool router for providers"`
|
||||
- [X] Git [Jeff]: `git push -u origin feature/m3-tool-router`
|
||||
- [ ] Forgejo PR [Jeff]: Open PR from `feature/m3-tool-router` to `master` with a suitable and thorough description
|
||||
- [ ] **COMMIT (Owner: Luis | Group: C5.diff | Branch: feature/m3-diff-review | Planned: Day 20 | Expected: Day 20) - Commit message: "feat(change): add diff review artifacts"**
|
||||
- [ ] Git [Luis]: `git checkout master`
|
||||
- [ ] Git [Luis]: `git pull origin master`
|
||||
|
||||
@@ -0,0 +1,175 @@
|
||||
"""Helper script for Robot Framework tool router smoke tests."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
# Ensure src is importable when run from workspace root
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
|
||||
|
||||
from cleveragents.tool.registry import ToolRegistry
|
||||
from cleveragents.tool.router import (
|
||||
NormalizedToolCallResult,
|
||||
ProviderFormat,
|
||||
StreamingToolUpdate,
|
||||
ToolCallRouter,
|
||||
classify_tool_error,
|
||||
detect_provider_format,
|
||||
generate_tool_call_id,
|
||||
normalize_tool_call,
|
||||
normalize_tool_schema_for_provider,
|
||||
)
|
||||
from cleveragents.tool.runner import ToolRunner
|
||||
from cleveragents.tool.runtime import ToolSpec
|
||||
|
||||
|
||||
def _echo(inputs: dict[str, Any]) -> dict[str, Any]:
|
||||
return dict(inputs)
|
||||
|
||||
|
||||
def _fail(inputs: dict[str, Any]) -> dict[str, Any]:
|
||||
raise RuntimeError("boom")
|
||||
|
||||
|
||||
def test_detect_openai() -> None:
|
||||
fmt = detect_provider_format({"name": "x/y", "arguments": '{"a": 1}'})
|
||||
assert fmt == ProviderFormat.OPENAI
|
||||
print("detect-openai-ok")
|
||||
|
||||
|
||||
def test_detect_anthropic() -> None:
|
||||
fmt = detect_provider_format({"name": "x/y", "input": {"a": 1}})
|
||||
assert fmt == ProviderFormat.ANTHROPIC
|
||||
print("detect-anthropic-ok")
|
||||
|
||||
|
||||
def test_detect_langchain() -> None:
|
||||
fmt = detect_provider_format({"name": "x/y", "type": "tool_call", "args": {}})
|
||||
assert fmt == ProviderFormat.LANGCHAIN
|
||||
print("detect-langchain-ok")
|
||||
|
||||
|
||||
def test_normalize_openai() -> None:
|
||||
req = normalize_tool_call({"name": "x/y", "arguments": '{"a": 1}'})
|
||||
assert req.tool_name == "x/y"
|
||||
assert req.arguments["a"] == 1
|
||||
assert req.provider_format == ProviderFormat.OPENAI
|
||||
print("normalize-openai-ok")
|
||||
|
||||
|
||||
def test_stable_id() -> None:
|
||||
id1 = generate_tool_call_id("plan-1", 0)
|
||||
id2 = generate_tool_call_id("plan-1", 0)
|
||||
assert id1 == id2
|
||||
assert id1.startswith("tc_")
|
||||
print("stable-id-ok")
|
||||
|
||||
|
||||
def test_route_openai() -> None:
|
||||
registry = ToolRegistry()
|
||||
spec = ToolSpec(name="smoke/echo", description="Echo", handler=_echo)
|
||||
registry.register(spec)
|
||||
runner = ToolRunner(registry)
|
||||
router = ToolCallRouter(registry=registry, runner=runner, plan_id="plan-1")
|
||||
result = router.route({"name": "smoke/echo", "arguments": '{"msg": "hi"}'})
|
||||
assert result.result.success
|
||||
assert result.provider_format == ProviderFormat.OPENAI
|
||||
print("route-openai-ok")
|
||||
|
||||
|
||||
def test_route_anthropic() -> None:
|
||||
registry = ToolRegistry()
|
||||
spec = ToolSpec(name="smoke/echo", description="Echo", handler=_echo)
|
||||
registry.register(spec)
|
||||
runner = ToolRunner(registry)
|
||||
router = ToolCallRouter(registry=registry, runner=runner, plan_id="plan-2")
|
||||
result = router.route({"name": "smoke/echo", "input": {"msg": "hi"}})
|
||||
assert result.result.success
|
||||
assert result.provider_format == ProviderFormat.ANTHROPIC
|
||||
print("route-anthropic-ok")
|
||||
|
||||
|
||||
def test_route_error() -> None:
|
||||
registry = ToolRegistry()
|
||||
spec = ToolSpec(name="smoke/fail", description="Fail", handler=_fail)
|
||||
registry.register(spec)
|
||||
runner = ToolRunner(registry)
|
||||
router = ToolCallRouter(registry=registry, runner=runner, plan_id="plan-3")
|
||||
result = router.route({"name": "smoke/fail", "arguments": "{}"})
|
||||
assert not result.result.success
|
||||
print("route-error-ok")
|
||||
|
||||
|
||||
def test_error_classify() -> None:
|
||||
cat = classify_tool_error("Operation timed out")
|
||||
assert cat.value == "timeout"
|
||||
cat2 = classify_tool_error("Permission denied")
|
||||
assert cat2.value == "permission"
|
||||
print("error-classify-ok")
|
||||
|
||||
|
||||
def test_schema_normalize() -> None:
|
||||
spec = ToolSpec(name="smoke/echo", description="Echo tool", handler=_echo)
|
||||
schema = normalize_tool_schema_for_provider(spec, ProviderFormat.OPENAI)
|
||||
assert "parameters" in schema
|
||||
schema2 = normalize_tool_schema_for_provider(spec, ProviderFormat.ANTHROPIC)
|
||||
assert "input_schema" in schema2
|
||||
print("schema-normalize-ok")
|
||||
|
||||
|
||||
def test_batch_route() -> None:
|
||||
registry = ToolRegistry()
|
||||
spec = ToolSpec(name="smoke/echo", description="Echo", handler=_echo)
|
||||
registry.register(spec)
|
||||
runner = ToolRunner(registry)
|
||||
router = ToolCallRouter(registry=registry, runner=runner, plan_id="plan-batch")
|
||||
payloads = [
|
||||
{"name": "smoke/echo", "arguments": json.dumps({"i": i})} for i in range(3)
|
||||
]
|
||||
results = router.route_batch(payloads)
|
||||
assert len(results) == 3
|
||||
assert all(r.result.success for r in results)
|
||||
print("batch-route-ok")
|
||||
|
||||
|
||||
def test_streaming() -> None:
|
||||
registry = ToolRegistry()
|
||||
spec = ToolSpec(name="smoke/echo", description="Echo", handler=_echo)
|
||||
registry.register(spec)
|
||||
runner = ToolRunner(registry)
|
||||
router = ToolCallRouter(registry=registry, runner=runner, plan_id="plan-stream")
|
||||
items = list(
|
||||
router.route_streaming({"name": "smoke/echo", "arguments": '{"msg": "s"}'})
|
||||
)
|
||||
updates = [i for i in items if isinstance(i, StreamingToolUpdate)]
|
||||
results = [i for i in items if isinstance(i, NormalizedToolCallResult)]
|
||||
assert len(updates) >= 2
|
||||
assert len(results) == 1
|
||||
assert results[0].result.success
|
||||
print("streaming-ok")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
cmd = sys.argv[1] if len(sys.argv) > 1 else "detect_openai"
|
||||
commands = {
|
||||
"detect_openai": test_detect_openai,
|
||||
"detect_anthropic": test_detect_anthropic,
|
||||
"detect_langchain": test_detect_langchain,
|
||||
"normalize_openai": test_normalize_openai,
|
||||
"stable_id": test_stable_id,
|
||||
"route_openai": test_route_openai,
|
||||
"route_anthropic": test_route_anthropic,
|
||||
"route_error": test_route_error,
|
||||
"error_classify": test_error_classify,
|
||||
"schema_normalize": test_schema_normalize,
|
||||
"batch_route": test_batch_route,
|
||||
"streaming": test_streaming,
|
||||
}
|
||||
handler = commands.get(cmd)
|
||||
if handler is None:
|
||||
print(f"Unknown command: {cmd}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
handler()
|
||||
@@ -0,0 +1,81 @@
|
||||
*** Settings ***
|
||||
Documentation Smoke tests for tool call router
|
||||
Resource ${CURDIR}/common.resource
|
||||
Suite Setup Setup Test Environment
|
||||
Suite Teardown Cleanup Test Environment
|
||||
|
||||
*** Variables ***
|
||||
${HELPER_SCRIPT} robot/helper_tool_router.py
|
||||
|
||||
*** Test Cases ***
|
||||
Detect OpenAI Format
|
||||
[Documentation] Detect OpenAI tool call format from payload
|
||||
${result}= Run Process ${PYTHON} ${HELPER_SCRIPT} detect_openai cwd=${WORKSPACE}
|
||||
Should Be Equal As Integers ${result.rc} 0
|
||||
Should Contain ${result.stdout} detect-openai-ok
|
||||
|
||||
Detect Anthropic Format
|
||||
[Documentation] Detect Anthropic tool call format from payload
|
||||
${result}= Run Process ${PYTHON} ${HELPER_SCRIPT} detect_anthropic cwd=${WORKSPACE}
|
||||
Should Be Equal As Integers ${result.rc} 0
|
||||
Should Contain ${result.stdout} detect-anthropic-ok
|
||||
|
||||
Detect LangChain Format
|
||||
[Documentation] Detect LangChain tool call format from payload
|
||||
${result}= Run Process ${PYTHON} ${HELPER_SCRIPT} detect_langchain cwd=${WORKSPACE}
|
||||
Should Be Equal As Integers ${result.rc} 0
|
||||
Should Contain ${result.stdout} detect-langchain-ok
|
||||
|
||||
Normalize OpenAI Payload
|
||||
[Documentation] Normalize OpenAI format tool call to internal request
|
||||
${result}= Run Process ${PYTHON} ${HELPER_SCRIPT} normalize_openai cwd=${WORKSPACE}
|
||||
Should Be Equal As Integers ${result.rc} 0
|
||||
Should Contain ${result.stdout} normalize-openai-ok
|
||||
|
||||
Stable ID Generation
|
||||
[Documentation] Generate deterministic tool call IDs
|
||||
${result}= Run Process ${PYTHON} ${HELPER_SCRIPT} stable_id cwd=${WORKSPACE}
|
||||
Should Be Equal As Integers ${result.rc} 0
|
||||
Should Contain ${result.stdout} stable-id-ok
|
||||
|
||||
Route OpenAI Tool Call
|
||||
[Documentation] Route an OpenAI format tool call through the router
|
||||
${result}= Run Process ${PYTHON} ${HELPER_SCRIPT} route_openai cwd=${WORKSPACE}
|
||||
Should Be Equal As Integers ${result.rc} 0
|
||||
Should Contain ${result.stdout} route-openai-ok
|
||||
|
||||
Route Anthropic Tool Call
|
||||
[Documentation] Route an Anthropic format tool call through the router
|
||||
${result}= Run Process ${PYTHON} ${HELPER_SCRIPT} route_anthropic cwd=${WORKSPACE}
|
||||
Should Be Equal As Integers ${result.rc} 0
|
||||
Should Contain ${result.stdout} route-anthropic-ok
|
||||
|
||||
Route Error Normalisation
|
||||
[Documentation] Tool errors are normalized into NormalizedToolCallResult
|
||||
${result}= Run Process ${PYTHON} ${HELPER_SCRIPT} route_error cwd=${WORKSPACE}
|
||||
Should Be Equal As Integers ${result.rc} 0
|
||||
Should Contain ${result.stdout} route-error-ok
|
||||
|
||||
Error Classification
|
||||
[Documentation] Error messages are classified into categories
|
||||
${result}= Run Process ${PYTHON} ${HELPER_SCRIPT} error_classify cwd=${WORKSPACE}
|
||||
Should Be Equal As Integers ${result.rc} 0
|
||||
Should Contain ${result.stdout} error-classify-ok
|
||||
|
||||
Schema Normalization
|
||||
[Documentation] Tool schemas are normalized for different providers
|
||||
${result}= Run Process ${PYTHON} ${HELPER_SCRIPT} schema_normalize cwd=${WORKSPACE}
|
||||
Should Be Equal As Integers ${result.rc} 0
|
||||
Should Contain ${result.stdout} schema-normalize-ok
|
||||
|
||||
Batch Route Tool Calls
|
||||
[Documentation] Route multiple tool calls as a batch
|
||||
${result}= Run Process ${PYTHON} ${HELPER_SCRIPT} batch_route cwd=${WORKSPACE}
|
||||
Should Be Equal As Integers ${result.rc} 0
|
||||
Should Contain ${result.stdout} batch-route-ok
|
||||
|
||||
Streaming Tool Call
|
||||
[Documentation] Route a tool call with streaming updates
|
||||
${result}= Run Process ${PYTHON} ${HELPER_SCRIPT} streaming cwd=${WORKSPACE}
|
||||
Should Be Equal As Integers ${result.rc} 0
|
||||
Should Contain ${result.stdout} streaming-ok
|
||||
@@ -3,7 +3,8 @@
|
||||
Re-exports public types from the runtime, runner, and registry modules
|
||||
(C0.runtime) and the four-stage tool lifecycle with capability enforcement,
|
||||
JSON Schema validation, per-plan activation caching, execution tracing,
|
||||
and cancellation propagation (C5.lifecycle).
|
||||
and cancellation propagation (C5.lifecycle). Also re-exports the
|
||||
tool call router for provider format translation (C5.router).
|
||||
"""
|
||||
|
||||
from cleveragents.tool.context import (
|
||||
@@ -32,6 +33,20 @@ from cleveragents.tool.lifecycle import (
|
||||
ToolResult as LifecycleToolResult,
|
||||
)
|
||||
from cleveragents.tool.registry import ToolRegistry
|
||||
from cleveragents.tool.router import (
|
||||
NormalizedToolCallResult,
|
||||
ProviderFormat,
|
||||
StreamingStatus,
|
||||
StreamingToolUpdate,
|
||||
ToolCallErrorCategory,
|
||||
ToolCallRequest,
|
||||
ToolCallRouter,
|
||||
classify_tool_error,
|
||||
detect_provider_format,
|
||||
generate_tool_call_id,
|
||||
normalize_tool_call,
|
||||
normalize_tool_schema_for_provider,
|
||||
)
|
||||
from cleveragents.tool.runner import ToolRunner
|
||||
from cleveragents.tool.runtime import ToolError, ToolResult, ToolSpec
|
||||
from cleveragents.tool.schema_validator import (
|
||||
@@ -46,8 +61,15 @@ __all__ = [
|
||||
"Change",
|
||||
"ChangeOperation",
|
||||
"LifecycleToolResult",
|
||||
"NormalizedToolCallResult",
|
||||
"ProviderFormat",
|
||||
"StreamingStatus",
|
||||
"StreamingToolUpdate",
|
||||
"ToolAccessDeniedError",
|
||||
"ToolActivationError",
|
||||
"ToolCallErrorCategory",
|
||||
"ToolCallRequest",
|
||||
"ToolCallRouter",
|
||||
"ToolCancelledError",
|
||||
"ToolCheckpointRequiredError",
|
||||
"ToolDeactivationError",
|
||||
@@ -66,6 +88,11 @@ __all__ = [
|
||||
"ToolRuntimeError",
|
||||
"ToolSchemaValidationError",
|
||||
"ToolSpec",
|
||||
"classify_tool_error",
|
||||
"detect_provider_format",
|
||||
"generate_tool_call_id",
|
||||
"normalize_tool_call",
|
||||
"normalize_tool_schema_for_provider",
|
||||
"validate_tool_input",
|
||||
"validate_tool_output",
|
||||
]
|
||||
|
||||
@@ -0,0 +1,895 @@
|
||||
"""Tool call router for LLM provider format translation.
|
||||
|
||||
Central router that translates between different LLM provider tool call
|
||||
formats and the internal ``ToolRunner``. Supports OpenAI, Anthropic,
|
||||
and LangChain formats with normalized ``ToolResult`` output.
|
||||
|
||||
## Supported Provider Formats
|
||||
|
||||
| Provider | Tool Call Shape |
|
||||
|------------|--------------------------------------------------------|
|
||||
| OpenAI | ``{"name": "...", "arguments": "..."}`` (JSON string) |
|
||||
| Anthropic | ``{"name": "...", "input": {...}}`` (dict) |
|
||||
| LangChain | ``{"name": "...", "args": {...}, "type": "tool_call"}`` |
|
||||
|
||||
## Features
|
||||
|
||||
- **Format detection** -- auto-detects provider format from payload shape
|
||||
- **Stable ID generation** -- deterministic IDs from plan_id + sequence
|
||||
- **Validation surfacing** -- validation tools include pass/fail metadata
|
||||
- **Error mapping** -- provider-specific error details
|
||||
- **Provider metadata capture** -- tracks which provider was used
|
||||
|
||||
Based on ``docs/specification.md`` and ``implementation_plan.md`` task C5.router.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import logging
|
||||
import threading
|
||||
import time
|
||||
from collections.abc import Generator
|
||||
from enum import StrEnum
|
||||
from typing import Any
|
||||
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
from cleveragents.tool.registry import ToolRegistry
|
||||
from cleveragents.tool.runner import ToolRunner
|
||||
from cleveragents.tool.runtime import ToolError, ToolResult, ToolSpec
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Provider format enum
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class ProviderFormat(StrEnum):
|
||||
"""Supported LLM provider tool call formats."""
|
||||
|
||||
OPENAI = "openai"
|
||||
ANTHROPIC = "anthropic"
|
||||
LANGCHAIN = "langchain"
|
||||
UNKNOWN = "unknown"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Error category enum
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class ToolCallErrorCategory(StrEnum):
|
||||
"""Structured error categories for tool call failures."""
|
||||
|
||||
TIMEOUT = "timeout"
|
||||
PERMISSION = "permission"
|
||||
RESOURCE = "resource"
|
||||
SCHEMA = "schema"
|
||||
NOT_FOUND = "not_found"
|
||||
EXECUTION = "execution"
|
||||
PARSE = "parse"
|
||||
UNKNOWN = "unknown"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Streaming status
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class StreamingStatus(StrEnum):
|
||||
"""Status of a streaming tool call."""
|
||||
|
||||
PENDING = "pending"
|
||||
RUNNING = "running"
|
||||
COMPLETE = "complete"
|
||||
ERROR = "error"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Normalized tool call request
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class ToolCallRequest(BaseModel):
|
||||
"""Normalized internal representation of a tool call from any provider.
|
||||
|
||||
Created by the router during format detection and normalization.
|
||||
"""
|
||||
|
||||
tool_name: str = Field(..., min_length=1, description="Tool name to invoke")
|
||||
arguments: dict[str, Any] = Field(
|
||||
default_factory=dict, description="Tool arguments as a dict"
|
||||
)
|
||||
call_id: str = Field(default="", description="Provider-assigned call ID (if any)")
|
||||
provider_format: ProviderFormat = Field(
|
||||
ProviderFormat.UNKNOWN, description="Detected provider format"
|
||||
)
|
||||
|
||||
model_config = ConfigDict(
|
||||
str_strip_whitespace=True,
|
||||
validate_assignment=True,
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Normalized tool call result
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class NormalizedToolCallResult(BaseModel):
|
||||
"""Normalized result from a routed tool call.
|
||||
|
||||
Includes the tool name, arguments, result, duration, provider
|
||||
metadata, and any error information regardless of the original
|
||||
provider format.
|
||||
"""
|
||||
|
||||
tool_call_id: str = Field(..., description="Stable tool call ID")
|
||||
tool_name: str = Field(..., description="Name of the tool invoked")
|
||||
arguments: dict[str, Any] = Field(
|
||||
default_factory=dict, description="Arguments passed to the tool"
|
||||
)
|
||||
result: ToolResult = Field(..., description="Execution result from the runner")
|
||||
duration_ms: float = Field(0.0, ge=0.0, description="Wall-clock duration (ms)")
|
||||
provider_format: ProviderFormat = Field(
|
||||
ProviderFormat.UNKNOWN, description="Provider format used"
|
||||
)
|
||||
provider_metadata: dict[str, Any] = Field(
|
||||
default_factory=dict, description="Provider-specific metadata"
|
||||
)
|
||||
error_category: ToolCallErrorCategory | None = Field(
|
||||
default=None, description="Error category if failed"
|
||||
)
|
||||
error_details: str | None = Field(
|
||||
default=None, description="Detailed error message"
|
||||
)
|
||||
is_validation: bool = Field(False, description="Whether the tool is a validation")
|
||||
validation_passed: bool | None = Field(
|
||||
default=None,
|
||||
description="Validation pass/fail (None if not a validation)",
|
||||
)
|
||||
validation_mode: str | None = Field(
|
||||
default=None,
|
||||
description="Validation mode (required/informational) if applicable",
|
||||
)
|
||||
|
||||
model_config = ConfigDict(
|
||||
str_strip_whitespace=True,
|
||||
validate_assignment=True,
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Streaming update
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class StreamingToolUpdate(BaseModel):
|
||||
"""Intermediate status update for a streaming tool call."""
|
||||
|
||||
tool_call_id: str = Field(..., description="Stable tool call ID")
|
||||
tool_name: str = Field(..., description="Name of the tool")
|
||||
status: StreamingStatus = Field(..., description="Current status")
|
||||
elapsed_ms: float = Field(0.0, ge=0.0, description="Time elapsed so far")
|
||||
partial_output: dict[str, Any] | None = Field(
|
||||
default=None, description="Partial output (if available)"
|
||||
)
|
||||
|
||||
model_config = ConfigDict(
|
||||
str_strip_whitespace=True,
|
||||
validate_assignment=True,
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# ID generation
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def generate_tool_call_id(plan_id: str, sequence: int) -> str:
|
||||
"""Generate a deterministic tool call ID from plan_id + sequence.
|
||||
|
||||
Uses SHA-256 to produce a stable, unique identifier that is
|
||||
reproducible given the same inputs.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
plan_id:
|
||||
The plan identifier.
|
||||
sequence:
|
||||
The sequence number within the plan.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str:
|
||||
A hex-encoded deterministic ID (first 26 characters).
|
||||
"""
|
||||
if not plan_id:
|
||||
raise ValueError("plan_id must not be empty")
|
||||
if sequence < 0:
|
||||
raise ValueError("sequence must be non-negative")
|
||||
|
||||
raw = f"{plan_id}:{sequence}"
|
||||
digest = hashlib.sha256(raw.encode("utf-8")).hexdigest()
|
||||
return f"tc_{digest[:24]}"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Format detection
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def detect_provider_format(payload: dict[str, Any]) -> ProviderFormat:
|
||||
"""Detect the provider format from a tool call payload.
|
||||
|
||||
Heuristics:
|
||||
- OpenAI: has ``"arguments"`` key with a string value (JSON-encoded)
|
||||
- Anthropic: has ``"input"`` key with a dict value
|
||||
- LangChain: has ``"type"`` key equal to ``"tool_call"`` or ``"args"`` key
|
||||
|
||||
Parameters
|
||||
----------
|
||||
payload:
|
||||
The raw tool call payload from the provider.
|
||||
|
||||
Returns
|
||||
-------
|
||||
ProviderFormat:
|
||||
The detected provider format.
|
||||
"""
|
||||
if not isinstance(payload, dict):
|
||||
return ProviderFormat.UNKNOWN
|
||||
|
||||
# LangChain: has "type": "tool_call" or "args" key
|
||||
if payload.get("type") == "tool_call":
|
||||
return ProviderFormat.LANGCHAIN
|
||||
if "args" in payload and "name" in payload:
|
||||
return ProviderFormat.LANGCHAIN
|
||||
|
||||
# OpenAI: "arguments" is a JSON string
|
||||
if "arguments" in payload and isinstance(payload.get("arguments"), str):
|
||||
return ProviderFormat.OPENAI
|
||||
|
||||
# Anthropic: "input" is a dict
|
||||
if "input" in payload and isinstance(payload.get("input"), dict):
|
||||
return ProviderFormat.ANTHROPIC
|
||||
|
||||
# OpenAI variant: "arguments" is already a dict (some wrappers pre-parse)
|
||||
if "arguments" in payload and isinstance(payload.get("arguments"), dict):
|
||||
return ProviderFormat.OPENAI
|
||||
|
||||
return ProviderFormat.UNKNOWN
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Payload normalization
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def normalize_tool_call(payload: dict[str, Any]) -> ToolCallRequest:
|
||||
"""Normalize a provider-specific tool call payload into a ToolCallRequest.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
payload:
|
||||
The raw tool call payload from the provider.
|
||||
|
||||
Returns
|
||||
-------
|
||||
ToolCallRequest:
|
||||
Normalized request with extracted name and arguments.
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError:
|
||||
If the payload cannot be parsed or has no tool name.
|
||||
"""
|
||||
if not isinstance(payload, dict):
|
||||
raise ValueError("Tool call payload must be a dict")
|
||||
|
||||
provider = detect_provider_format(payload)
|
||||
name = payload.get("name")
|
||||
if not name or not isinstance(name, str):
|
||||
raise ValueError("Tool call payload must include a non-empty 'name' field")
|
||||
|
||||
call_id = str(payload.get("id", ""))
|
||||
arguments: dict[str, Any] = {}
|
||||
|
||||
if provider == ProviderFormat.OPENAI:
|
||||
raw_args = payload.get("arguments", "{}")
|
||||
if isinstance(raw_args, str):
|
||||
try:
|
||||
arguments = json.loads(raw_args)
|
||||
except json.JSONDecodeError as exc:
|
||||
raise ValueError(f"OpenAI arguments JSON parse error: {exc}") from exc
|
||||
elif isinstance(raw_args, dict):
|
||||
arguments = raw_args
|
||||
else:
|
||||
raise ValueError(
|
||||
f"OpenAI arguments must be str or dict, got {type(raw_args).__name__}"
|
||||
)
|
||||
|
||||
elif provider == ProviderFormat.ANTHROPIC:
|
||||
raw_input = payload.get("input", {})
|
||||
if isinstance(raw_input, dict):
|
||||
arguments = raw_input
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Anthropic input must be a dict, got {type(raw_input).__name__}"
|
||||
)
|
||||
|
||||
elif provider == ProviderFormat.LANGCHAIN:
|
||||
raw_args = payload.get("args", {})
|
||||
if isinstance(raw_args, dict):
|
||||
arguments = raw_args
|
||||
else:
|
||||
raise ValueError(
|
||||
f"LangChain args must be a dict, got {type(raw_args).__name__}"
|
||||
)
|
||||
|
||||
else:
|
||||
# Unknown: try to extract arguments from common keys
|
||||
for key in ("arguments", "input", "args", "parameters"):
|
||||
raw = payload.get(key)
|
||||
if isinstance(raw, dict):
|
||||
arguments = raw
|
||||
break
|
||||
if isinstance(raw, str):
|
||||
try:
|
||||
parsed = json.loads(raw)
|
||||
if isinstance(parsed, dict):
|
||||
arguments = parsed
|
||||
break
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
|
||||
return ToolCallRequest(
|
||||
tool_name=name,
|
||||
arguments=arguments,
|
||||
call_id=call_id,
|
||||
provider_format=provider,
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Error mapping
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def classify_tool_error(error_msg: str) -> ToolCallErrorCategory:
|
||||
"""Classify an error message into a structured category.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
error_msg:
|
||||
The error message to classify.
|
||||
|
||||
Returns
|
||||
-------
|
||||
ToolCallErrorCategory:
|
||||
The determined error category.
|
||||
"""
|
||||
if not error_msg:
|
||||
return ToolCallErrorCategory.UNKNOWN
|
||||
|
||||
lower = error_msg.lower()
|
||||
|
||||
if "timeout" in lower or "timed out" in lower:
|
||||
return ToolCallErrorCategory.TIMEOUT
|
||||
if "permission" in lower or "access denied" in lower or "forbidden" in lower:
|
||||
return ToolCallErrorCategory.PERMISSION
|
||||
if "not found" in lower:
|
||||
return ToolCallErrorCategory.NOT_FOUND
|
||||
if "resource" in lower or "memory" in lower or "disk" in lower:
|
||||
return ToolCallErrorCategory.RESOURCE
|
||||
if "schema" in lower or "validation" in lower or "json" in lower:
|
||||
return ToolCallErrorCategory.SCHEMA
|
||||
if "parse" in lower or "decode" in lower or "deserializ" in lower:
|
||||
return ToolCallErrorCategory.PARSE
|
||||
|
||||
return ToolCallErrorCategory.EXECUTION
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Schema normalization
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
_MAX_DESCRIPTION_LENGTH = 1024
|
||||
|
||||
|
||||
def normalize_tool_schema_for_provider(
|
||||
spec: ToolSpec,
|
||||
provider: ProviderFormat,
|
||||
max_description_length: int = _MAX_DESCRIPTION_LENGTH,
|
||||
) -> dict[str, Any]:
|
||||
"""Normalize a ToolSpec's schema for a specific provider.
|
||||
|
||||
Applies provider-specific field pruning and limits:
|
||||
- Truncates descriptions to ``max_description_length``
|
||||
- Produces provider-appropriate schema shape
|
||||
|
||||
Parameters
|
||||
----------
|
||||
spec:
|
||||
The tool specification.
|
||||
provider:
|
||||
Target provider format.
|
||||
max_description_length:
|
||||
Maximum description length (default 1024).
|
||||
|
||||
Returns
|
||||
-------
|
||||
dict:
|
||||
Provider-normalized tool schema dict.
|
||||
"""
|
||||
if max_description_length < 1:
|
||||
raise ValueError("max_description_length must be at least 1")
|
||||
|
||||
description = spec.description
|
||||
truncated = False
|
||||
if len(description) > max_description_length:
|
||||
description = description[:max_description_length]
|
||||
truncated = True
|
||||
|
||||
schema: dict[str, Any] = {
|
||||
"name": spec.name,
|
||||
"description": description,
|
||||
}
|
||||
|
||||
if truncated:
|
||||
logger.warning(
|
||||
"Tool description truncated for provider",
|
||||
extra={
|
||||
"tool": spec.name,
|
||||
"provider": provider.value,
|
||||
"original_length": len(spec.description),
|
||||
"max_length": max_description_length,
|
||||
},
|
||||
)
|
||||
|
||||
default: dict[str, Any] = {"type": "object", "properties": {}}
|
||||
if provider == ProviderFormat.OPENAI:
|
||||
schema["parameters"] = spec.input_schema or default
|
||||
elif provider == ProviderFormat.ANTHROPIC:
|
||||
schema["input_schema"] = spec.input_schema or default
|
||||
elif provider == ProviderFormat.LANGCHAIN:
|
||||
schema["args_schema"] = spec.input_schema or default
|
||||
else:
|
||||
schema["input_schema"] = spec.input_schema or default
|
||||
|
||||
return schema
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# ToolCallRouter
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class ToolCallRouter:
|
||||
"""Central router translating between LLM provider tool call formats.
|
||||
|
||||
Accepts tool calls in OpenAI, Anthropic, or LangChain format, routes
|
||||
them through the ``ToolRunner``, and returns normalized
|
||||
``NormalizedToolCallResult`` objects.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
registry:
|
||||
The ``ToolRegistry`` to look up tools.
|
||||
runner:
|
||||
The ``ToolRunner`` for executing tools.
|
||||
plan_id:
|
||||
Plan ID for stable tool call ID generation.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
registry: ToolRegistry,
|
||||
runner: ToolRunner,
|
||||
plan_id: str,
|
||||
) -> None:
|
||||
if not plan_id:
|
||||
raise ValueError("plan_id must not be empty")
|
||||
|
||||
self._registry = registry
|
||||
self._runner = runner
|
||||
self._plan_id = plan_id
|
||||
self._sequence = 0
|
||||
self._lock = threading.RLock()
|
||||
|
||||
@property
|
||||
def plan_id(self) -> str:
|
||||
"""Return the plan ID for this router."""
|
||||
return self._plan_id
|
||||
|
||||
@property
|
||||
def sequence(self) -> int:
|
||||
"""Return the current sequence number."""
|
||||
with self._lock:
|
||||
return self._sequence
|
||||
|
||||
def _next_sequence(self) -> int:
|
||||
"""Get and increment the sequence number (thread-safe)."""
|
||||
with self._lock:
|
||||
seq = self._sequence
|
||||
self._sequence += 1
|
||||
return seq
|
||||
|
||||
# -- Route tool call -------------------------------------------------------
|
||||
|
||||
def route(
|
||||
self,
|
||||
payload: dict[str, Any],
|
||||
provider_metadata: dict[str, Any] | None = None,
|
||||
) -> NormalizedToolCallResult:
|
||||
"""Route a tool call from any supported provider format.
|
||||
|
||||
Detects the provider format, normalizes the payload, executes
|
||||
the tool, and returns a normalized result.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
payload:
|
||||
The raw tool call payload from the provider.
|
||||
provider_metadata:
|
||||
Optional provider-level metadata (model name, latency, etc.).
|
||||
|
||||
Returns
|
||||
-------
|
||||
NormalizedToolCallResult:
|
||||
The normalized result from execution.
|
||||
"""
|
||||
if not isinstance(payload, dict):
|
||||
raise ValueError("payload must be a dict")
|
||||
|
||||
seq = self._next_sequence()
|
||||
call_id = generate_tool_call_id(self._plan_id, seq)
|
||||
|
||||
# Normalize payload
|
||||
try:
|
||||
request = normalize_tool_call(payload)
|
||||
except ValueError as exc:
|
||||
return NormalizedToolCallResult(
|
||||
tool_call_id=call_id,
|
||||
tool_name=payload.get("name", "<unknown>"),
|
||||
arguments={},
|
||||
result=ToolResult(
|
||||
success=False,
|
||||
output={},
|
||||
error=f"Payload parse error: {exc}",
|
||||
),
|
||||
duration_ms=0.0,
|
||||
provider_format=ProviderFormat.UNKNOWN,
|
||||
error_category=ToolCallErrorCategory.PARSE,
|
||||
error_details=str(exc),
|
||||
provider_metadata=provider_metadata or {},
|
||||
is_validation=False,
|
||||
)
|
||||
|
||||
# Execute the tool
|
||||
start = time.monotonic()
|
||||
try:
|
||||
result = self._runner.execute(request.tool_name, request.arguments)
|
||||
except ToolError as exc:
|
||||
elapsed = (time.monotonic() - start) * 1000.0
|
||||
error_cat = classify_tool_error(str(exc))
|
||||
return NormalizedToolCallResult(
|
||||
tool_call_id=call_id,
|
||||
tool_name=request.tool_name,
|
||||
arguments=request.arguments,
|
||||
result=ToolResult(
|
||||
success=False,
|
||||
output={},
|
||||
error=str(exc),
|
||||
duration_ms=elapsed,
|
||||
),
|
||||
duration_ms=elapsed,
|
||||
provider_format=request.provider_format,
|
||||
provider_metadata=provider_metadata or {},
|
||||
error_category=error_cat,
|
||||
error_details=str(exc),
|
||||
is_validation=False,
|
||||
)
|
||||
except Exception as exc:
|
||||
elapsed = (time.monotonic() - start) * 1000.0
|
||||
error_cat = classify_tool_error(str(exc))
|
||||
return NormalizedToolCallResult(
|
||||
tool_call_id=call_id,
|
||||
tool_name=request.tool_name,
|
||||
arguments=request.arguments,
|
||||
result=ToolResult(
|
||||
success=False,
|
||||
output={},
|
||||
error=f"{type(exc).__name__}: {exc}",
|
||||
duration_ms=elapsed,
|
||||
),
|
||||
duration_ms=elapsed,
|
||||
provider_format=request.provider_format,
|
||||
provider_metadata=provider_metadata or {},
|
||||
error_category=error_cat,
|
||||
error_details=str(exc),
|
||||
is_validation=False,
|
||||
)
|
||||
|
||||
elapsed = (time.monotonic() - start) * 1000.0
|
||||
|
||||
# Check if tool is a validation and surface pass/fail
|
||||
is_validation = False
|
||||
validation_passed: bool | None = None
|
||||
validation_mode: str | None = None
|
||||
|
||||
spec = self._registry.get(request.tool_name)
|
||||
if spec is not None:
|
||||
is_validation = self._check_is_validation(spec)
|
||||
if is_validation and result.success:
|
||||
validation_passed = result.output.get("passed", None)
|
||||
validation_mode = self._get_validation_mode(spec)
|
||||
|
||||
# Classify error if failed
|
||||
error_cat: ToolCallErrorCategory | None = None
|
||||
error_details: str | None = None
|
||||
if not result.success and result.error:
|
||||
error_cat = classify_tool_error(result.error)
|
||||
error_details = result.error
|
||||
|
||||
return NormalizedToolCallResult(
|
||||
tool_call_id=call_id,
|
||||
tool_name=request.tool_name,
|
||||
arguments=request.arguments,
|
||||
result=result,
|
||||
duration_ms=elapsed,
|
||||
provider_format=request.provider_format,
|
||||
provider_metadata=provider_metadata or {},
|
||||
error_category=error_cat,
|
||||
error_details=error_details,
|
||||
is_validation=is_validation,
|
||||
validation_passed=validation_passed,
|
||||
validation_mode=validation_mode,
|
||||
)
|
||||
|
||||
# -- Batch routing ---------------------------------------------------------
|
||||
|
||||
def route_batch(
|
||||
self,
|
||||
payloads: list[dict[str, Any]],
|
||||
provider_metadata: dict[str, Any] | None = None,
|
||||
) -> list[NormalizedToolCallResult]:
|
||||
"""Route multiple tool calls sequentially.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
payloads:
|
||||
List of raw tool call payloads.
|
||||
provider_metadata:
|
||||
Optional provider-level metadata.
|
||||
|
||||
Returns
|
||||
-------
|
||||
list[NormalizedToolCallResult]:
|
||||
Results for each tool call in order.
|
||||
"""
|
||||
if not isinstance(payloads, list):
|
||||
raise ValueError("payloads must be a list")
|
||||
|
||||
results: list[NormalizedToolCallResult] = []
|
||||
for payload in payloads:
|
||||
result = self.route(payload, provider_metadata=provider_metadata)
|
||||
results.append(result)
|
||||
return results
|
||||
|
||||
# -- Streaming execution ---------------------------------------------------
|
||||
|
||||
def route_streaming(
|
||||
self,
|
||||
payload: dict[str, Any],
|
||||
provider_metadata: dict[str, Any] | None = None,
|
||||
) -> Generator[StreamingToolUpdate | NormalizedToolCallResult]:
|
||||
"""Route a tool call with streaming status updates.
|
||||
|
||||
Yields ``StreamingToolUpdate`` for intermediate progress and a
|
||||
final ``NormalizedToolCallResult`` when complete.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
payload:
|
||||
The raw tool call payload.
|
||||
provider_metadata:
|
||||
Optional provider metadata.
|
||||
|
||||
Yields
|
||||
------
|
||||
StreamingToolUpdate | NormalizedToolCallResult:
|
||||
Intermediate updates followed by the final result.
|
||||
"""
|
||||
if not isinstance(payload, dict):
|
||||
raise ValueError("payload must be a dict")
|
||||
|
||||
seq = self._next_sequence()
|
||||
call_id = generate_tool_call_id(self._plan_id, seq)
|
||||
|
||||
# Normalize
|
||||
try:
|
||||
request = normalize_tool_call(payload)
|
||||
except ValueError as exc:
|
||||
yield NormalizedToolCallResult(
|
||||
tool_call_id=call_id,
|
||||
tool_name=payload.get("name", "<unknown>"),
|
||||
arguments={},
|
||||
result=ToolResult(
|
||||
success=False,
|
||||
output={},
|
||||
error=f"Payload parse error: {exc}",
|
||||
),
|
||||
duration_ms=0.0,
|
||||
provider_format=ProviderFormat.UNKNOWN,
|
||||
error_category=ToolCallErrorCategory.PARSE,
|
||||
error_details=str(exc),
|
||||
provider_metadata=provider_metadata or {},
|
||||
is_validation=False,
|
||||
)
|
||||
return
|
||||
|
||||
# Emit pending
|
||||
yield StreamingToolUpdate(
|
||||
tool_call_id=call_id,
|
||||
tool_name=request.tool_name,
|
||||
status=StreamingStatus.PENDING,
|
||||
elapsed_ms=0.0,
|
||||
)
|
||||
|
||||
# Emit running
|
||||
start = time.monotonic()
|
||||
yield StreamingToolUpdate(
|
||||
tool_call_id=call_id,
|
||||
tool_name=request.tool_name,
|
||||
status=StreamingStatus.RUNNING,
|
||||
elapsed_ms=0.0,
|
||||
)
|
||||
|
||||
# Execute
|
||||
try:
|
||||
result = self._runner.execute(request.tool_name, request.arguments)
|
||||
except (ToolError, Exception) as exc:
|
||||
elapsed = (time.monotonic() - start) * 1000.0
|
||||
error_cat = classify_tool_error(str(exc))
|
||||
|
||||
yield StreamingToolUpdate(
|
||||
tool_call_id=call_id,
|
||||
tool_name=request.tool_name,
|
||||
status=StreamingStatus.ERROR,
|
||||
elapsed_ms=elapsed,
|
||||
)
|
||||
|
||||
yield NormalizedToolCallResult(
|
||||
tool_call_id=call_id,
|
||||
tool_name=request.tool_name,
|
||||
arguments=request.arguments,
|
||||
result=ToolResult(
|
||||
success=False,
|
||||
output={},
|
||||
error=str(exc),
|
||||
duration_ms=elapsed,
|
||||
),
|
||||
duration_ms=elapsed,
|
||||
provider_format=request.provider_format,
|
||||
provider_metadata=provider_metadata or {},
|
||||
error_category=error_cat,
|
||||
error_details=str(exc),
|
||||
is_validation=False,
|
||||
)
|
||||
return
|
||||
|
||||
elapsed = (time.monotonic() - start) * 1000.0
|
||||
|
||||
# Emit complete
|
||||
yield StreamingToolUpdate(
|
||||
tool_call_id=call_id,
|
||||
tool_name=request.tool_name,
|
||||
status=StreamingStatus.COMPLETE,
|
||||
elapsed_ms=elapsed,
|
||||
)
|
||||
|
||||
# Check validation
|
||||
is_validation = False
|
||||
validation_passed: bool | None = None
|
||||
validation_mode: str | None = None
|
||||
spec = self._registry.get(request.tool_name)
|
||||
if spec is not None:
|
||||
is_validation = self._check_is_validation(spec)
|
||||
if is_validation and result.success:
|
||||
validation_passed = result.output.get("passed", None)
|
||||
validation_mode = self._get_validation_mode(spec)
|
||||
|
||||
error_cat_final: ToolCallErrorCategory | None = None
|
||||
error_details_final: str | None = None
|
||||
if not result.success and result.error:
|
||||
error_cat_final = classify_tool_error(result.error)
|
||||
error_details_final = result.error
|
||||
|
||||
yield NormalizedToolCallResult(
|
||||
tool_call_id=call_id,
|
||||
tool_name=request.tool_name,
|
||||
arguments=request.arguments,
|
||||
result=result,
|
||||
duration_ms=elapsed,
|
||||
provider_format=request.provider_format,
|
||||
provider_metadata=provider_metadata or {},
|
||||
error_category=error_cat_final,
|
||||
error_details=error_details_final,
|
||||
is_validation=is_validation,
|
||||
validation_passed=validation_passed,
|
||||
validation_mode=validation_mode,
|
||||
)
|
||||
|
||||
# -- Schema export ---------------------------------------------------------
|
||||
|
||||
def export_schemas(
|
||||
self,
|
||||
provider: ProviderFormat,
|
||||
namespace: str | None = None,
|
||||
max_description_length: int = _MAX_DESCRIPTION_LENGTH,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Export tool schemas normalized for a specific provider.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
provider:
|
||||
The target provider format.
|
||||
namespace:
|
||||
Optional namespace filter.
|
||||
max_description_length:
|
||||
Maximum description length (default 1024).
|
||||
|
||||
Returns
|
||||
-------
|
||||
list[dict]:
|
||||
Provider-normalized tool schemas.
|
||||
"""
|
||||
specs = self._registry.list_tools(namespace=namespace)
|
||||
schemas: list[dict[str, Any]] = []
|
||||
for spec in specs:
|
||||
schema = normalize_tool_schema_for_provider(
|
||||
spec, provider, max_description_length
|
||||
)
|
||||
# Annotate validation tools
|
||||
if self._check_is_validation(spec):
|
||||
schema["tool_type"] = "validation"
|
||||
mode = self._get_validation_mode(spec)
|
||||
if mode:
|
||||
schema["validation_mode"] = mode
|
||||
else:
|
||||
schema["tool_type"] = "tool"
|
||||
schemas.append(schema)
|
||||
return schemas
|
||||
|
||||
# -- Internal helpers ------------------------------------------------------
|
||||
|
||||
@staticmethod
|
||||
def _check_is_validation(spec: ToolSpec) -> bool:
|
||||
"""Check if a ToolSpec represents a validation tool.
|
||||
|
||||
Uses the metadata dict to determine if tool_type is 'validation'.
|
||||
"""
|
||||
# Check capabilities for validation pattern: read_only=True, writes=False
|
||||
# Also check if the name contains 'validation' pattern
|
||||
cap = spec.capabilities
|
||||
# Heuristic: read-only tools with 'valid' in name are validations
|
||||
return cap.read_only and not cap.writes and "valid" in spec.name.lower()
|
||||
|
||||
@staticmethod
|
||||
def _get_validation_mode(spec: ToolSpec) -> str | None:
|
||||
"""Extract the validation mode from a ToolSpec if applicable.
|
||||
|
||||
Checks output_schema metadata for the validation mode field.
|
||||
"""
|
||||
# Check output_schema for mode hint
|
||||
if spec.output_schema and "validation_mode" in spec.output_schema:
|
||||
raw_mode = spec.output_schema["validation_mode"]
|
||||
if isinstance(raw_mode, str):
|
||||
return raw_mode
|
||||
return None
|
||||
@@ -206,3 +206,20 @@ _OP_COLORS # noqa: B018, F821
|
||||
_get_apply_service # noqa: B018, F821
|
||||
plan_diff # noqa: B018, F821
|
||||
plan_artifacts # noqa: B018, F821
|
||||
|
||||
# ToolCallRouter — public API for provider-agnostic tool routing
|
||||
ToolCallRouter # noqa: B018, F821
|
||||
ToolCallRequest # noqa: B018, F821
|
||||
NormalizedToolCallResult # noqa: B018, F821
|
||||
ProviderFormat # noqa: B018, F821
|
||||
StreamingStatus # noqa: B018, F821
|
||||
StreamingToolUpdate # noqa: B018, F821
|
||||
ToolCallErrorCategory # noqa: B018, F821
|
||||
detect_provider_format # noqa: B018, F821
|
||||
normalize_tool_call # noqa: B018, F821
|
||||
generate_tool_call_id # noqa: B018, F821
|
||||
classify_tool_error # noqa: B018, F821
|
||||
normalize_tool_schema_for_provider # noqa: B018, F821
|
||||
route_batch # noqa: B018, F821
|
||||
route_streaming # noqa: B018, F821
|
||||
export_schemas # noqa: B018, F821
|
||||
|
||||
Reference in New Issue
Block a user