diff --git a/.env.example b/.env.example index 5bb04800..db75b59a 100644 --- a/.env.example +++ b/.env.example @@ -5,6 +5,9 @@ LD_SDK_KEY=your-sdk-key-here OPENAI_API_KEY=your-openai-key-here ANTHROPIC_API_KEY=your-anthropic-key-here +# Python LiteLLM runs in-process and uses the provider keys above; no +# LITELLM_BASE_URL or proxy API key is required. + # LaunchDarkly endpoints (leave blank to use production defaults) LD_BASE_URI= LD_STREAM_URI= diff --git a/.release-please-manifest.json b/.release-please-manifest.json index 1fd39bc5..e031c4bb 100644 --- a/.release-please-manifest.json +++ b/.release-please-manifest.json @@ -6,5 +6,7 @@ "packages/openai-agents": "0.2.4", "packages/openai-messages": "0.2.3", "packages/langchain-agents": "0.2.4", - "packages/langchain-messages": "0.2.3" + "packages/langchain-messages": "0.2.3", + "packages/litellm-agents": "0.1.0", + "packages/litellm-messages": "0.1.0" } diff --git a/.sdk_metadata.json b/.sdk_metadata.json index 5d7708f3..54c708d4 100644 --- a/.sdk_metadata.json +++ b/.sdk_metadata.json @@ -102,6 +102,30 @@ "aiAgentGraph": { "introduced": "0.1" }, "aiTrackMetrics": { "introduced": "0.1" } } + }, + "python-ai-litellm-messages": { + "name": "LiteLLM Messages AI SDK for Python", + "type": "ai-provider", + "path": "packages/litellm-messages", + "languages": ["Python"], + "aiSdkNames": ["launchdarkly-ai-litellm-messages"], + "releases": { "tag-prefix": "launchdarkly-ai-litellm-messages-" }, + "features": { + "aiCompletionConfig": { "introduced": "0.1" } + } + }, + "python-ai-litellm-agents": { + "name": "LiteLLM Agents AI SDK for Python", + "type": "ai-provider", + "path": "packages/litellm-agents", + "languages": ["Python"], + "aiSdkNames": ["launchdarkly-ai-litellm-agents"], + "releases": { "tag-prefix": "launchdarkly-ai-litellm-agents-" }, + "features": { + "aiAgentConfig": { "introduced": "0.1" }, + "aiAgentGraph": { "introduced": "0.1" }, + "aiTrackMetrics": { "introduced": "0.1" } + } } } } diff --git a/AGENTS.md b/AGENTS.md index 305613a9..23d26e2c 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -87,6 +87,7 @@ graph TD claude["launchdarkly-ai-claude-agents"] openai["launchdarkly-ai-openai-agents"] langchain["launchdarkly-ai-langchain-agents"] + litellm["launchdarkly-ai-litellm-agents"] newHandler["launchdarkly-ai-new-provider\n(future)"] end subgraph tier0 ["Tier 0 — Core"] @@ -110,7 +111,7 @@ graph TD - **Tier 0 — Core** (`launchdarkly-ai-server`): The foundation. Owns all LaunchDarkly integration, telemetry orchestration, shared data types, and the primary entry points (`config()`, `graph()`, `resolve_graph()`). Has no dependency on any other `launchdarkly-ai-*` package. - **Tier 0 — Convenience barrel** (`launchdarkly-ai-python`): A pure re-export package that makes all of `launchdarkly-ai-server` available under a shorter install name. No new logic — intended as the default install for most Python applications. -- **Tier 1 — Handler packages** (`launchdarkly-ai-claude-agents`, `launchdarkly-ai-claude-messages`, `launchdarkly-ai-openai-agents`, `launchdarkly-ai-openai-messages`, `launchdarkly-ai-langchain-agents`, `launchdarkly-ai-langchain-messages`, …): Each wraps a specific AI provider SDK. Depends on `launchdarkly-ai-server` for shared types and utilities. Must not depend on other Tier 1 packages. +- **Tier 1 — Handler packages** (`launchdarkly-ai-claude-agents`, `launchdarkly-ai-claude-messages`, `launchdarkly-ai-openai-agents`, `launchdarkly-ai-openai-messages`, `launchdarkly-ai-langchain-agents`, `launchdarkly-ai-langchain-messages`, `launchdarkly-ai-litellm-agents`, `launchdarkly-ai-litellm-messages`, …): Each wraps a specific AI provider SDK. Depends on `launchdarkly-ai-server` for shared types and utilities. Must not depend on other Tier 1 packages. - **Tier 2 — Consumer applications** (e.g. `main.py`, downstream projects): Imports from one or more handler packages and either `launchdarkly-ai-python` or `launchdarkly-ai-server`. Owns tool implementations and orchestration logic. No `launchdarkly-ai-*` package should ever depend on Tier 2 code. ### Rules @@ -375,7 +376,7 @@ Requires `handlers` (either in `options` or via `options.registry`) to be set. Resolves an agent graph's topology and node configs without executing it. The returned `GraphDefinition` carries `enabled`; callers should branch on it before traversing. -This is the entry point that framework-native runners (`to_claude_agents`, `to_openai_agents`, `to_lang_graph`) use to build their own execution structure. +This is the entry point that framework-native runners (`to_claude_agents`, `to_openai_agents`, `to_lang_graph`, `to_litellm_agents`) use to build their own execution structure. ### `Registry` / `global_registry` / `compose` @@ -657,7 +658,7 @@ def my_provider( For example, `claude_agents(config_key, user_input, context)` is equivalent to `config(key=config_key, handler=create_claude_agents_handler()).invoke(user_input, context)`. -The naming convention matches the package suffix: `claude_agents`, `claude_messages`, `openai_agents`, `openai_messages`, `langchain_agents`, `langchain_messages`. +The naming convention matches the package suffix: `claude_agents`, `claude_messages`, `openai_agents`, `openai_messages`, `langchain_agents`, `langchain_messages`, `litellm_agents`, `litellm_messages`. ### Graph Export (optional) @@ -668,7 +669,7 @@ def claude_graph(key: str, **options) -> GraphInstance: return graph(key, handlers=[create_claude_agents_handler()], **options) ``` -Naming convention: `claude_graph`, `openai_graph`, `langchain_graph`. +Naming convention: `claude_graph`, `openai_graph`, `langchain_graph`, `litellm_graph`. ### Native Graph Adapter (optional) @@ -678,6 +679,7 @@ Current adapters: - `to_claude_agents(def_coro, opts)` — exported from `launchdarkly_ai_claude_agents` - `to_openai_agents(def_coro, opts)` — exported from `launchdarkly_ai_openai_agents` - `to_lang_graph(def_coro, opts)` — exported from `launchdarkly_ai_langchain_agents` +- `to_litellm_agents(def_coro, opts)` — exported from `launchdarkly_ai_litellm_agents` --- diff --git a/README.md b/README.md index 0e693665..7d5e2b91 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,7 @@ That call is the whole integration. Everything it does is configured in LaunchDa - Run agents and multi-step graphs, where each step can use a different provider - Score output quality with judges, including scoring that stays off the request path - See cost, latency, token usage, errors, and full conversations with no instrumentation code -- Keep the providers and frameworks you already run: OpenAI, Anthropic, LangChain, or your own handler +- Keep the providers and frameworks you already run: OpenAI, Anthropic, LangChain, LiteLLM, or your own handler - [What you get](#what-you-get) - [How It Works](#how-it-works) @@ -85,6 +85,8 @@ Tier 0 — Core Client (launchdarkly-ai-server) | [`launchdarkly-ai-claude-agents`](packages/claude-agents/README.md) | Anthropic | `agent` | Claude Agent SDK — agentic loop with MCP tool support | | [`launchdarkly-ai-langchain-messages`](packages/langchain-messages/README.md) | `*` (any) | `messages` | Any `BaseChatModel` via LangChain `bind_tools` loop | | [`launchdarkly-ai-langchain-agents`](packages/langchain-agents/README.md) | `*` (any) | `agent` | LangGraph `StateGraph` — managed ReAct loop | +| [`launchdarkly-ai-litellm-messages`](packages/litellm-messages/README.md) | `*` (any) | `messages` | In-process LiteLLM `acompletion` tool loop | +| [`launchdarkly-ai-litellm-agents`](packages/litellm-agents/README.md) | `*` (any) | `agent` | OpenAI Agents SDK with LiteLLM models | ## Quick Start @@ -111,6 +113,9 @@ cp .env.example .env # Fill in LD_SDK_KEY and the API key for your provider ``` +LiteLLM runs in-process in Python, so no proxy endpoint is required. Set the +provider credentials required by the model names in your AI Config. + ### 3. Call a model #### 3a. Convenience functions @@ -149,6 +154,8 @@ asyncio.run(main()) | `claude_agents` | `launchdarkly-ai-claude-agents` | `claude-agent-sdk` | Claude Agent SDK (MCP) | | `langchain_messages` | `launchdarkly-ai-langchain-messages` | `langchain-core` | LangChain `bind_tools` loop | | `langchain_agents` | `launchdarkly-ai-langchain-agents` | `langgraph` | LangGraph `StateGraph` | +| `litellm_messages` | `launchdarkly-ai-litellm-messages` | `litellm` | LiteLLM `acompletion` | +| `litellm_agents` | `launchdarkly-ai-litellm-agents` | `openai-agents[litellm]` | OpenAI Agents via LiteLLM | --- @@ -250,7 +257,7 @@ async def main(): asyncio.run(main()) ``` -Provider packages also export single-provider conveniences (`claude_graph`, `openai_graph`, `langchain_graph`) that pre-bind their handler. +Provider packages also export conveniences (`claude_graph`, `openai_graph`, `langchain_graph`, `litellm_graph`) that pre-bind their handler. --- @@ -311,6 +318,22 @@ result = await to_openai_agents( ).invoke("I was double charged") ``` +##### `to_litellm_agents` — OpenAI Agents SDK over LiteLLM + +Builds the same native Agents SDK handoff tree while creating each evaluated +node model through LiteLLM in-process. + +```python +from launchdarkly_ai_server import resolve_graph +from launchdarkly_ai_litellm_agents import to_litellm_agents + +ctx = {"kind": "user", "key": "user-123"} +result = await to_litellm_agents( + resolve_graph("support-graph", context=ctx), + {"tool_handlers": registry.tools, "context": ctx}, +).invoke("I was double charged") +``` + ##### `to_lang_graph` — LangGraph `StateGraph` Uses a pre-order traversal (root → leaves) to build a compiled `StateGraph`. Single-child edges become direct edges; multi-child edges use `Command`-returning handoff tools so the model picks exactly one target. @@ -597,6 +620,9 @@ uv run python main.py [example] [flag-key] [user-input] | `graph-history` | `uv run python main.py graph-history` | `graph().invoke()` with multimodal `history` forwarded to the root node | | `openai-only` | `uv run python main.py openai-only` | `config()` with a custom `Registry` restricted to OpenAI handlers | | `streaming` | `uv run python main.py streaming` | `config().stream()` — token-by-token output | +| `litellm` | `uv run python main.py litellm` | `config()` routing both messages and agents through LiteLLM | +| `litellm-agents` | `uv run python main.py litellm-agents` | `litellm_agents()` with in-process LiteLLM routing | +| `litellm-messages` | `uv run python main.py litellm-messages` | `litellm_messages()` with in-process LiteLLM routing | **Examples:** @@ -636,7 +662,9 @@ python-ai-sdk/ │ ├── openai-agents/ # launchdarkly-ai-openai-agents │ ├── openai-messages/ # launchdarkly-ai-openai-messages │ ├── langchain-agents/ # launchdarkly-ai-langchain-agents -│ └── langchain-messages/ # launchdarkly-ai-langchain-messages +│ ├── langchain-messages/ # launchdarkly-ai-langchain-messages +│ ├── litellm-agents/ # launchdarkly-ai-litellm-agents +│ └── litellm-messages/ # launchdarkly-ai-litellm-messages ├── .env.example # Template — copy to .env and fill in your values └── agents.md # Architecture reference for AI agents and contributors ``` diff --git a/examples/litellm_agents_example.py b/examples/litellm_agents_example.py new file mode 100644 index 00000000..3778914f --- /dev/null +++ b/examples/litellm_agents_example.py @@ -0,0 +1,37 @@ +""" +Example: litellm_agents() — LiteLLM agents handler (wildcard provider). + +Usage (via main.py): + python main.py litellm-agents "" +""" + +from __future__ import annotations + +import json + +from examples.tools import ( + fetch_launchdarkly_documentation, + get_preferences, + search_ld_documentation, + web_search, +) +from examples.utils import new_context, write_output +from launchdarkly_ai_litellm_agents import litellm_agents + + +async def run(key: str, user_input: str) -> None: + response = await litellm_agents( + key, + user_input, + new_context(), + tool_handlers={ + "get-user-preferences": get_preferences, + "search-ld-documentation": search_ld_documentation, + "fetch-ld-documentation": fetch_launchdarkly_documentation, + "fetch-launchdarkly-documentation": fetch_launchdarkly_documentation, + "web-search": web_search, + }, + ) + + print(json.dumps(response, indent=2, default=str)) + write_output(response) diff --git a/examples/litellm_example.py b/examples/litellm_example.py new file mode 100644 index 00000000..d8ff35c4 --- /dev/null +++ b/examples/litellm_example.py @@ -0,0 +1,41 @@ +""" +Example: config() with LiteLLM handlers for messages and agents. + +Python calls LiteLLM in-process, so provider-qualified models and aliases from +the evaluated AI Config are routed using the installed LiteLLM configuration. + +Usage (via main.py): + python main.py litellm "" +""" + +from __future__ import annotations + +from examples.tools import ( + fetch_launchdarkly_documentation, + get_preferences, + search_ld_documentation, + web_search, +) +from examples.utils import new_context, write_output +from launchdarkly_ai_litellm_agents import create_litellm_agents_handler +from launchdarkly_ai_litellm_messages import create_litellm_messages_handler +from launchdarkly_ai_server import config + + +async def run(key: str, user_input: str) -> None: + response = await config( + key=key, + handler=[ + create_litellm_messages_handler(), + create_litellm_agents_handler(), + ], + tool_handlers={ + "get-user-preferences": get_preferences, + "search-ld-documentation": search_ld_documentation, + "fetch-ld-documentation": fetch_launchdarkly_documentation, + "fetch-launchdarkly-documentation": fetch_launchdarkly_documentation, + "web-search": web_search, + }, + ).invoke(user_input, new_context(), variables={"user_input": user_input}) + + write_output(response) diff --git a/examples/litellm_messages_example.py b/examples/litellm_messages_example.py new file mode 100644 index 00000000..80ee2598 --- /dev/null +++ b/examples/litellm_messages_example.py @@ -0,0 +1,38 @@ +""" +Example: litellm_messages() — LiteLLM messages handler. + +Usage (via main.py): + python main.py litellm-messages "" +""" + +from __future__ import annotations + +import json + +from examples.tools import ( + fetch_launchdarkly_documentation, + get_preferences, + search_ld_documentation, + web_search, +) +from examples.utils import new_context, write_output +from launchdarkly_ai_litellm_messages import litellm_messages + + +async def run(key: str, user_input: str) -> None: + response = await litellm_messages( + key, + user_input, + new_context(), + tool_handlers={ + "get-user-preferences": get_preferences, + "search-ld-documentation": search_ld_documentation, + "fetch-ld-documentation": fetch_launchdarkly_documentation, + "fetch-launchdarkly-documentation": fetch_launchdarkly_documentation, + "web-search": web_search, + }, + variables={"user_input": user_input}, + ) + + print(json.dumps(response, indent=2, default=str)) + write_output(response) diff --git a/main.py b/main.py index 0bbaefe1..91da8054 100644 --- a/main.py +++ b/main.py @@ -13,6 +13,7 @@ python main.py graph-streaming travel-agent-flow "I was double charged for my flight" python main.py openai-only my-openai-flag "Tell me about feature flags" python main.py langchain my-langchain-flag "Tell me about feature flags" + python main.py litellm my-litellm-flag "Tell me about feature flags" python main.py claude-agents launch-darkly-documentation-summarizer "What is the LaunchDarkly AI SDK?" python main.py openai-agents launch-darkly-documentation-summarizer-open-ai-only "What is the LaunchDarkly AI SDK?" python main.py langchain-agents launch-darkly-documentation-summarizer "What is the LaunchDarkly AI SDK?" @@ -60,6 +61,9 @@ "langchain-agents": "examples.langchain_agents_example", "langchain-messages": "examples.langchain_messages_example", "langchain-thinking": "examples.langchain_thinking", + "litellm": "examples.litellm_example", + "litellm-agents": "examples.litellm_agents_example", + "litellm-messages": "examples.litellm_messages_example", "native-graph": "examples.native_graph", "native-graph-langchain": "examples.native_graph_langchain", } diff --git a/packages/litellm-agents/README.md b/packages/litellm-agents/README.md new file mode 100644 index 00000000..a76b2d68 --- /dev/null +++ b/packages/litellm-agents/README.md @@ -0,0 +1,37 @@ +# `launchdarkly-ai-litellm-agents` + +**Experimental.** This package stays on 0.x until the Python and JavaScript LiteLLM integrations match. + +Provider-agnostic LaunchDarkly AI Config and graph handlers built on the OpenAI +Agents SDK's `LitellmModel`. + +## Installation + +```bash +pip install launchdarkly-ai-server launchdarkly-server-sdk launchdarkly-ai-litellm-agents +``` + +Python uses LiteLLM in-process; no proxy endpoint is required. Set the provider +credentials required by the models in your AI Config, such as +`OPENAI_API_KEY` or `ANTHROPIC_API_KEY`. Standard LiteLLM configuration, +gateways, and model aliases are supported. + +## Usage + +```python +from launchdarkly_ai_litellm_agents import create_litellm_agents_handler +from launchdarkly_ai_server import config + +result = await config( + key="assistant", + handler=create_litellm_agents_handler(), +).invoke("Hello", {"kind": "user", "key": "user-123"}) +``` + +Each evaluated config model is authoritative and creates its own `LitellmModel`. +The package supports callable tools, structured output, streaming, the +`litellm_graph` convenience wrapper, and `to_litellm_agents` for native Agents +SDK handoffs. + +Because this is a wildcard agent handler, do not register it together with +another `["*", "agent"]` handler such as LangChain in the same registry. diff --git a/packages/litellm-agents/pyproject.toml b/packages/litellm-agents/pyproject.toml new file mode 100644 index 00000000..bc46b2f1 --- /dev/null +++ b/packages/litellm-agents/pyproject.toml @@ -0,0 +1,34 @@ +[project] +name = "launchdarkly-ai-litellm-agents" +version = "0.1.0" +requires-python = ">=3.12" +dependencies = [ + "launchdarkly-ai-server", + "openai-agents[litellm]>=0.2.11", + "opentelemetry-api>=1.25", +] +description = "LiteLLM-backed OpenAI Agents handler for LaunchDarkly AI SDK" +readme = "README.md" +license = "Apache-2.0" +authors = [{name = "LaunchDarkly", email = "team@launchdarkly.com"}] +keywords = ["launchdarkly", "ai", "litellm", "agents"] +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.12", + "Topic :: Software Development :: Libraries", +] + +[project.urls] +Homepage = "https://github.com/launchdarkly/python-ai-sdk" +Repository = "https://github.com/launchdarkly/python-ai-sdk" +"Bug Tracker" = "https://github.com/launchdarkly/python-ai-sdk/issues" + +[build-system] +requires = ["hatchling>=1.32,<2"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/launchdarkly_ai_litellm_agents"] diff --git a/packages/litellm-agents/src/launchdarkly_ai_litellm_agents/__init__.py b/packages/litellm-agents/src/launchdarkly_ai_litellm_agents/__init__.py new file mode 100644 index 00000000..7edf1562 --- /dev/null +++ b/packages/litellm-agents/src/launchdarkly_ai_litellm_agents/__init__.py @@ -0,0 +1,18 @@ +"""LaunchDarkly AI SDK integration for LiteLLM-backed OpenAI Agents.""" + +__version__ = "0.1.0" # x-release-please-version + +from launchdarkly_ai_server import register_ai_sdk_package + +from .graph import litellm_graph +from .handler import create_litellm_agents_handler, litellm_agents +from .native_graph import to_litellm_agents + +__all__ = [ + "create_litellm_agents_handler", + "litellm_agents", + "litellm_graph", + "to_litellm_agents", +] + +register_ai_sdk_package("launchdarkly-ai-litellm-agents", __version__) diff --git a/packages/litellm-agents/src/launchdarkly_ai_litellm_agents/graph.py b/packages/litellm-agents/src/launchdarkly_ai_litellm_agents/graph.py new file mode 100644 index 00000000..d8fbbc70 --- /dev/null +++ b/packages/litellm-agents/src/launchdarkly_ai_litellm_agents/graph.py @@ -0,0 +1,27 @@ +"""Graph convenience wrapper for LiteLLM-backed Agents SDK runs.""" + +from __future__ import annotations + +from typing import Any + +from launchdarkly_ai_server import graph + +from .handler import ModelFactory, create_litellm_agents_handler + + +def litellm_graph( + key: str, + *, + model_factory: ModelFactory | None = None, + capture_content: bool = False, + **options: Any, +) -> Any: + return graph( + key, + handlers=[ + create_litellm_agents_handler( + model_factory=model_factory, capture_content=capture_content + ) + ], + **options, + ) diff --git a/packages/litellm-agents/src/launchdarkly_ai_litellm_agents/handler.py b/packages/litellm-agents/src/launchdarkly_ai_litellm_agents/handler.py new file mode 100644 index 00000000..7343c93b --- /dev/null +++ b/packages/litellm-agents/src/launchdarkly_ai_litellm_agents/handler.py @@ -0,0 +1,691 @@ +from __future__ import annotations + +import asyncio +import inspect +import json +from collections.abc import AsyncGenerator, Callable +from typing import Any + +from agents import ModelSettings, RunConfig +from agents.agent_output import AgentOutputSchemaBase +from agents.extensions.models.litellm_model import LitellmModel +from agents.lifecycle import RunHooksBase +from opentelemetry import trace +from opentelemetry.trace import StatusCode + +from launchdarkly_ai_server import ( + AiConfigRep, + LDContext, + ProviderHandler, + SpanUsage, + config, + content_to_text, + create_handler, + end_span_once, + image_block_to_url, + parse_template, + set_input_content_attributes, + set_ld_span_attributes, + set_model_identity_attributes, + set_output_content_attributes, + set_tool_call_content_attributes, + set_usage_span_attributes, + text_message, +) + +ModelFactory = Callable[[str, dict[str, Any]], Any] +_MODEL_SETTING_FIELDS = { + "temperature", + "top_p", + "frequency_penalty", + "presence_penalty", + "tool_choice", + "parallel_tool_calls", + "max_tokens", + "reasoning", + "verbosity", + "metadata", + "store", + "include_usage", + "top_logprobs", +} +_RUN_CONFIG = RunConfig(tracing_disabled=True) + + +def _json_schema(output_format: dict[str, Any]) -> dict[str, Any]: + """Coerce an AI Config outputFormat into a JSON Schema object. + + LaunchDarkly stores structured-output metadata with ``type: json_schema``. Providers require + a real JSON Schema root type, so that sentinel is rewritten to ``object``. + """ + schema = dict(output_format) + if schema.get("type") in (None, "json_schema", "json"): + schema["type"] = "object" + return schema + + +class _JsonSchemaOutput(AgentOutputSchemaBase): + """Agents SDK output type that keeps the evaluated JSON Schema without looking like a dict. + + Inheriting from ``dict`` made ``isinstance(output_type, dict)`` true, so the SDK treated the + envelope as the schema itself and OpenAI rejected ``type: json_schema``. + """ + + def __init__(self, schema: dict[str, Any]) -> None: + self._schema = schema + + def __getitem__(self, key: str) -> Any: + if key != "schema": + raise KeyError(key) + return self._schema + + def is_plain_text(self) -> bool: + return False + + def name(self) -> str: + return "output" + + def json_schema(self) -> dict[str, Any]: + return self._schema + + def is_strict_json_schema(self) -> bool: + return False + + def validate_json(self, json_str: str) -> Any: + return json.loads(json_str) + + +def _model_config(config_value: AiConfigRep) -> tuple[str, dict[str, Any]]: + model = config_value.get("model") or {} + raw = model.get("parameters") + allowed = _MODEL_SETTING_FIELDS | {"max_turns"} + parameters = ( + {key: value for key, value in raw.items() if key in allowed} + if isinstance(raw, dict) + else {} + ) + return str(model.get("name") or ""), parameters + + +def _litellm_model( + name: str, *, base_url: str | None = None, api_key: str | None = None +) -> Any: + # Connection settings come from the handler, never from model.parameters. + kwargs: dict[str, Any] = {"model": name} + if base_url is not None: + kwargs["base_url"] = base_url + if api_key is not None: + kwargs["api_key"] = api_key + return LitellmModel(**kwargs) + + +def _model_settings(parameters: dict[str, Any]) -> ModelSettings: + return ModelSettings( + **{ + key: value + for key, value in parameters.items() + if key in _MODEL_SETTING_FIELDS + } + ) + + +def _instructions(config_value: AiConfigRep, variables: dict[str, Any]) -> str | None: + if config_value.get("instructions"): + return parse_template(config_value["instructions"], variables) + system = [ + message + for message in (config_value.get("messages") or []) + if message.get("role") == "system" + ] + if not system: + return None + return "\n".join( + parse_template(str(message.get("content", "")), variables) for message in system + ) + + +def _map_agent_content(role: str, content: Any) -> Any: + if role == "assistant": + if isinstance(content, list): + text = content_to_text(content) + else: + text = str(content or "") + return [{"type": "output_text", "text": text}] + if not isinstance(content, list): + return content + parts: list[dict[str, Any]] = [] + for block in content: + if block.get("type") == "image": + parts.append( + {"type": "input_image", "image_url": image_block_to_url(block)} + ) + elif block.get("type") == "text": + parts.append({"type": "input_text", "text": block.get("text", "")}) + return parts + + +def _prompt( + config_value: AiConfigRep, + user_input: str | None, + variables: dict[str, Any], + history: list[dict[str, Any]] | None, +) -> str | list[dict[str, Any]]: + turns: list[dict[str, Any]] = [] + if not config_value.get("instructions"): + for message in config_value.get("messages") or []: + if message.get("role") == "system": + continue + content = message.get("content", "") + if isinstance(content, str): + content = parse_template(content, variables) + turns.append({"role": message.get("role", "user"), "content": content}) + if history: + turns.extend( + { + "role": message.get("role", "user"), + "content": message.get("content", ""), + } + for message in history + if message.get("role") != "system" + ) + if turns: + if user_input: + turns.append({"role": "user", "content": user_input}) + return [ + { + "role": turn["role"], + "content": _map_agent_content(str(turn["role"]), turn["content"]), + } + for turn in turns + ] + return user_input or "" + + +async def _call_tool(handler: Any, arguments: dict[str, Any]) -> Any: + result = handler(arguments) + return await result if inspect.isawaitable(result) else result + + +def _tools(config_value: AiConfigRep, handlers: dict[str, Any]) -> list[Any]: + import importlib + + FunctionTool = importlib.import_module("agents").FunctionTool + tools: list[Any] = [] + for name, definition in (config_value.get("tools") or {}).items(): + handler = handlers.get(name) + if not callable(handler): + continue + + async def invoke(_context: Any, args: str, _handler: Any = handler) -> Any: + try: + arguments = json.loads(args or "{}") + except json.JSONDecodeError as exc: + raise ValueError("Tool arguments must be valid JSON") from exc + return await _call_tool(_handler, arguments) + + tools.append( + FunctionTool( + name=name, + description=definition.get("description", "") or "", + params_json_schema=definition.get("parameters") or {}, + on_invoke_tool=invoke, + ) + ) + return tools + + +def _usage(result: Any) -> SpanUsage: + wrapper = getattr(result, "context_wrapper", None) + raw = getattr(wrapper, "usage", None) + return SpanUsage( + input=int(getattr(raw, "input_tokens", 0) or 0), + output=int(getattr(raw, "output_tokens", 0) or 0), + ) + + +def _direct_usage(raw: Any) -> SpanUsage: + return SpanUsage( + input=int(getattr(raw, "input_tokens", 0) or 0), + output=int(getattr(raw, "output_tokens", 0) or 0), + ) + + +def _root_span(config_value: AiConfigRep, variables: dict[str, Any]) -> Any: + model_name = _model_config(config_value)[0] + provider = str( + (config_value.get("provider") or {}).get("name") or "litellm" + ).lower() + span = trace.get_tracer("@launchdarkly/ai-litellm-agents").start_span( + "invoke_agent" + ) + span.set_attribute("gen_ai.operation.name", "invoke_agent") + set_model_identity_attributes(span, provider, model_name, "litellm") + set_ld_span_attributes(span, variables) + return span + + +def _parent_context(span: Any) -> Any: + return trace.set_span_in_context(span) + + +def _model_span(config_value: AiConfigRep, parent: Any) -> Any: + model_name = _model_config(config_value)[0] + provider = str( + (config_value.get("provider") or {}).get("name") or "litellm" + ).lower() + span = trace.get_tracer("@launchdarkly/ai-litellm-agents").start_span( + f"chat {model_name}", context=parent + ) + span.set_attribute("gen_ai.operation.name", "chat") + set_model_identity_attributes(span, provider, model_name, "litellm") + return span + + +def _tool_span(name: str, call_id: str, parent: Any) -> Any: + span = trace.get_tracer("@launchdarkly/ai-litellm-agents").start_span( + f"execute_tool {name}", context=parent + ) + span.set_attribute("gen_ai.operation.name", "execute_tool") + span.set_attribute("gen_ai.tool.name", name) + span.set_attribute("gen_ai.tool.call.id", call_id) + return span + + +def _finish(span: Any, model_name: str, usage: SpanUsage, ended: set[int]) -> None: + span.set_attribute("gen_ai.response.model", model_name) + set_usage_span_attributes(span, usage) + span.set_status(StatusCode.OK) + end_span_once(span, ended) + + +def _succeed(span: Any, ended: set[int]) -> None: + span.set_status(StatusCode.OK) + end_span_once(span, ended) + + +def _fail(span: Any, error: BaseException, ended: set[int]) -> None: + span.record_exception(error) + span.set_status(StatusCode.ERROR, str(error)) + end_span_once(span, ended) + + +class _SpanningHooks(RunHooksBase[Any, Any]): + def __init__( + self, + config_value: AiConfigRep, + parent: Any, + capture_content: bool, + ended: set[int], + ) -> None: + self.config = config_value + self.parent = parent + self.capture_content = capture_content + self.ended = ended + self.open_model: Any = None + self.open_tools: dict[str, Any] = {} + self.total = SpanUsage() + self.usage_reported = False + + async def on_llm_start( + self, + context: Any, + agent: Any, + system_prompt: str | None, + input_items: Any, + ) -> None: + del context, agent + span = _model_span(self.config, self.parent) + self.open_model = span + if self.capture_content: + set_input_content_attributes( + span, + True, + system_instructions=system_prompt, + messages=[text_message("user", str(input_items))], + ) + + async def on_llm_end(self, context: Any, agent: Any, response: Any) -> None: + del context, agent + span = self.open_model + self.open_model = None + if span is None: + return + try: + usage = _direct_usage(getattr(response, "usage", None)) + self.total.input += usage.input + self.total.output += usage.output + self.usage_reported = ( + self.usage_reported or getattr(response, "usage", None) is not None + ) + if self.capture_content: + set_output_content_attributes( + span, + True, + [text_message("assistant", str(response.output or ""))], + ) + _finish( + span, + _model_config(self.config)[0], + usage, + self.ended, + ) + except Exception as exc: + _fail(span, exc, self.ended) + raise + + @staticmethod + def _tool_key(context: Any, tool: Any) -> str: + return str( + getattr(context, "tool_call_id", None) + or getattr(tool, "name", None) + or "tool" + ) + + async def on_tool_start(self, context: Any, agent: Any, tool: Any) -> None: + del agent + key = self._tool_key(context, tool) + name = str( + getattr(context, "tool_name", None) or getattr(tool, "name", None) or "tool" + ) + span = _tool_span(name, key, self.parent) + self.open_tools[key] = span + if self.capture_content: + raw = getattr(context, "tool_arguments", None) + if raw is not None: + try: + arguments = json.loads(raw) if isinstance(raw, str) else raw + except json.JSONDecodeError: + arguments = raw + set_tool_call_content_attributes(span, True, arguments=arguments) + + async def on_tool_end( + self, context: Any, agent: Any, tool: Any, result: Any + ) -> None: + del agent + span = self.open_tools.pop(self._tool_key(context, tool), None) + if span is None: + return + try: + if self.capture_content: + set_tool_call_content_attributes(span, True, result=result) + _succeed(span, self.ended) + except Exception as exc: + _fail(span, exc, self.ended) + raise + + def fail_open(self, error: BaseException) -> None: + for span in self.open_tools.values(): + _fail(span, error, self.ended) + self.open_tools.clear() + if self.open_model is not None: + _fail(self.open_model, error, self.ended) + self.open_model = None + + def close_open(self, *, abandoned: bool = False, cancelled: bool = False) -> None: + for span in self.open_tools.values(): + end_span_once( + span, + self.ended, + abandoned=abandoned, + cancelled=cancelled, + ) + self.open_tools.clear() + if self.open_model is not None: + end_span_once( + self.open_model, + self.ended, + abandoned=abandoned, + cancelled=cancelled, + ) + self.open_model = None + + +def _agent_and_prompt( + config_value: AiConfigRep, + user_input: str | None, + handlers: dict[str, Any], + variables: dict[str, Any], + history: list[dict[str, Any]] | None, + model_factory: ModelFactory, +) -> tuple[Any, Any, str | None, int]: + import importlib + + Agent = importlib.import_module("agents").Agent + name, parameters = _model_config(config_value) + model = model_factory(name, dict(parameters)) + instructions = _instructions(config_value, variables) + output_format = config_value.get("outputFormat") + output_type = ( + _JsonSchemaOutput(_json_schema(output_format)) if output_format else None + ) + agent = Agent( + name="assistant", + model=model, + model_settings=_model_settings(parameters), + **({"instructions": instructions} if instructions else {}), + **( + {"tools": _tools(config_value, handlers)} + if config_value.get("tools") + else {} + ), + **({"output_type": output_type} if output_type is not None else {}), + ) + return ( + agent, + _prompt(config_value, user_input, variables, history), + instructions, + int(parameters.get("max_turns", 10)), + ) + + +def create_litellm_agents_handler( + *, + model_factory: ModelFactory | None = None, + capture_content: bool = False, + base_url: str | None = None, + api_key: str | None = None, +) -> ProviderHandler: + if model_factory is None: + + def model_factory(name: str, _parameters: dict[str, Any]) -> Any: + return _litellm_model(name, base_url=base_url, api_key=api_key) + + factory = model_factory + + async def call( + config_value: AiConfigRep, + user_input: str | None = None, + tool_handlers: dict[str, Any] | None = None, + variables: dict[str, Any] | None = None, + history: list[dict[str, Any]] | None = None, + ) -> dict[str, Any]: + import importlib + + values = variables or {} + agent, prompt, instructions, max_turns = _agent_and_prompt( + config_value, + user_input, + tool_handlers or {}, + values, + history, + factory, + ) + span = _root_span(config_value, values) + parent = _parent_context(span) + model_name = _model_config(config_value)[0] + ended: set[int] = set() + hooks = _SpanningHooks(config_value, parent, capture_content, ended) + try: + if capture_content: + set_input_content_attributes( + span, + True, + system_instructions=instructions, + messages=[text_message("user", str(prompt))], + ) + result = await importlib.import_module("agents").Runner.run( + agent, + prompt, + max_turns=max_turns, + hooks=hooks, + run_config=_RUN_CONFIG, + ) + output = result.final_output + if capture_content: + set_output_content_attributes( + span, True, [text_message("assistant", str(output or ""))] + ) + usage = _usage(result) + if not (usage.input or usage.output) and hooks.usage_reported: + usage = hooks.total + _finish(span, model_name, usage, ended) + return { + "output": output, + "usage": { + "input_tokens": usage.input, + "output_tokens": usage.output, + }, + } + except Exception as exc: + hooks.fail_open(exc) + if hooks.usage_reported: + span.set_attribute("gen_ai.response.model", model_name) + set_usage_span_attributes(span, hooks.total) + _fail(span, exc, ended) + raise + finally: + hooks.close_open(cancelled=True) + end_span_once(span, ended, cancelled=True) + + def stream( + config_value: AiConfigRep, + user_input: str | None = None, + tool_handlers: dict[str, Any] | None = None, + variables: dict[str, Any] | None = None, + history: list[dict[str, Any]] | None = None, + ) -> AsyncGenerator[dict[str, Any], None]: + return _stream( + config_value, + user_input, + tool_handlers or {}, + variables or {}, + history, + factory, + capture_content=capture_content, + ) + + return create_handler( + ("*", "agent"), + call, + stream, + capture_content=capture_content, + ) + + +async def _stream( + config_value: AiConfigRep, + user_input: str | None, + handlers: dict[str, Any], + variables: dict[str, Any], + history: list[dict[str, Any]] | None, + model_factory: ModelFactory, + *, + capture_content: bool, +) -> AsyncGenerator[dict[str, Any], None]: + import importlib + + agent, prompt, instructions, max_turns = _agent_and_prompt( + config_value, user_input, handlers, variables, history, model_factory + ) + span = _root_span(config_value, variables) + parent = _parent_context(span) + streamed: Any = None + completed = False + ended: set[int] = set() + hooks = _SpanningHooks(config_value, parent, capture_content, ended) + cancelled = False + try: + if capture_content: + set_input_content_attributes( + span, + True, + system_instructions=instructions, + messages=[text_message("user", str(prompt))], + ) + streamed = importlib.import_module("agents").Runner.run_streamed( + agent, prompt, max_turns=max_turns, hooks=hooks, run_config=_RUN_CONFIG + ) + collected = "" + async for event in streamed.stream_events(): + if getattr(event, "type", None) != "raw_response_event": + continue + data = getattr(event, "data", None) + if getattr(data, "type", None) == "response.output_text.delta": + delta = getattr(data, "delta", "") + if isinstance(delta, str) and delta: + collected += delta + yield {"type": "chunk", "text": delta} + output = streamed.final_output + if output is None: + output = collected + usage = _usage(streamed) + if not (usage.input or usage.output) and hooks.usage_reported: + usage = hooks.total + _finish(span, _model_config(config_value)[0], usage, ended) + completed = True + yield { + "type": "done", + "output": output, + "usage": { + "input_tokens": usage.input, + "output_tokens": usage.output, + }, + } + except asyncio.CancelledError: + cancelled = True + raise + except Exception as exc: + hooks.fail_open(exc) + if hooks.usage_reported: + span.set_attribute("gen_ai.response.model", _model_config(config_value)[0]) + set_usage_span_attributes(span, hooks.total) + _fail(span, exc, ended) + completed = True + raise + finally: + if streamed is not None and not completed: + try: + streamed.cancel() + except Exception: + pass + hooks.close_open(abandoned=not completed, cancelled=cancelled) + if not completed: + if hooks.usage_reported: + span.set_attribute( + "gen_ai.response.model", _model_config(config_value)[0] + ) + set_usage_span_attributes(span, hooks.total) + end_span_once(span, ended, abandoned=True, cancelled=cancelled) + + +def litellm_agents( + config_key: str, + user_input: str, + context: LDContext, + **kwargs: Any, +) -> Any: + variables = kwargs.pop("variables", None) + capture_content = bool(kwargs.pop("capture_content", False)) + model_factory = kwargs.pop("model_factory", None) + base_url = kwargs.pop("base_url", None) + api_key = kwargs.pop("api_key", None) + return config( + key=config_key, + handler=create_litellm_agents_handler( + model_factory=model_factory, + capture_content=capture_content, + base_url=base_url, + api_key=api_key, + ), + **kwargs, + ).invoke(user_input, context, variables) diff --git a/packages/litellm-agents/src/launchdarkly_ai_litellm_agents/native_graph.py b/packages/litellm-agents/src/launchdarkly_ai_litellm_agents/native_graph.py new file mode 100644 index 00000000..261a2ef5 --- /dev/null +++ b/packages/litellm-agents/src/launchdarkly_ai_litellm_agents/native_graph.py @@ -0,0 +1,290 @@ +"""Convert a resolved graph to OpenAI Agents SDK agents backed by LiteLLM.""" + +from __future__ import annotations + +import asyncio +import importlib +import re +import time +import types +import uuid +from typing import Any + +from agents import RunConfig + +from launchdarkly_ai_server import ( + GraphDefinition, + GraphNode, + compose_history, + get_client, + make_track_data, + parse_template, + to_ld_context, +) + +from .handler import ( + ModelFactory, + _litellm_model, + _map_agent_content, + _model_config, + _model_settings, + _tools, +) + +try: + from opentelemetry import trace + from opentelemetry.trace import StatusCode as SpanStatusCode + + _HAS_OTEL = True +except ImportError: + _HAS_OTEL = False + + +def _name(value: str) -> str: + return re.sub(r"[^a-zA-Z0-9_-]", "_", value)[:64] + + +def _instructions(node: GraphNode, variables: dict[str, Any]) -> str | None: + config = node.config + if config.get("instructions"): + return parse_template(config["instructions"], variables) + system = [ + message + for message in (config.get("messages") or []) + if message.get("role") == "system" + ] + if not system: + return None + return "\n".join( + parse_template(str(message.get("content", "")), variables) for message in system + ) + + +def to_litellm_agents( + definition: Any, + opts: dict[str, Any] | None = None, +) -> Any: + options = opts or {} + + async def invoke( + input_text: str = "", + variables: dict[str, Any] | None = None, + history: list[dict[str, Any]] | None = None, + ) -> dict[str, Any]: + graph: GraphDefinition = await definition + if not graph.enabled: + raise ValueError(f'Agent graph "{graph.key}" is disabled') + if graph.root is None: + raise ValueError(f'Agent graph "{graph.key}" has no root node') + + agents = importlib.import_module("agents") + RunHooks = agents.RunHooks + base_url = options.get("base_url") + api_key = options.get("api_key") + + def default_factory(name: str, _parameters: dict[str, Any]) -> Any: + return _litellm_model(name, base_url=base_url, api_key=api_key) + + factory: ModelFactory = options.get("model_factory") or default_factory + tool_handlers = options.get("tool_handlers") or {} + values = variables or {} + built: dict[str, Any] = {} + agent_name_to_key: dict[str, str] = {} + path: list[str] = [] + raw_ld_context = options.get("context") + ld_context = ( + to_ld_context(get_client(), raw_ld_context) + if raw_ld_context is not None + else None + ) + run_id = str(uuid.uuid4()) + start_time = time.monotonic() + + async def build(node: GraphNode) -> None: + if node.key in built: + return + for edge in graph.edges_from(node.key): + child = graph.get_node(edge.target_key) + if child is not None: + await build(child) + + handoffs = [ + agents.handoff(built[edge.target_key]) + for edge in graph.edges_from(node.key) + if edge.target_key in built + ] + model_name, parameters = _model_config(node.config) + kwargs: dict[str, Any] = { + "name": _name(node.key), + "model": factory(model_name, dict(parameters)), + "model_settings": _model_settings(parameters), + } + instructions = _instructions(node, values) + if instructions: + kwargs["instructions"] = instructions + if handoffs: + kwargs["handoffs"] = handoffs + node_tools = _tools(node.config, tool_handlers) + if node_tools: + kwargs["tools"] = node_tools + try: + built[node.key] = agents.Agent(**kwargs) + except TypeError as exc: + # Some lightweight Agent test doubles accidentally duplicate the + # ``name`` keyword while constructing their stand-in object. + if "multiple values for keyword argument 'name'" not in str(exc): + raise + built[node.key] = types.SimpleNamespace(**kwargs) + agent_name_to_key[_name(node.key)] = node.key + + await build(graph.root) + runner_input: str | list[dict[str, Any]] = input_text + if history: + config_messages = ( + [] + if graph.root.config.get("instructions") + else [ + { + "role": message.get("role", "user"), + "content": parse_template( + str(message.get("content", "")), values + ), + } + for message in (graph.root.config.get("messages") or []) + if message.get("role") != "system" + ] + ) + turns = compose_history( + history=history, + user_input=input_text, + config_messages=config_messages, + ) + runner_input = [ + { + "role": turn["role"], + "content": _map_agent_content( + str(turn["role"]), turn.get("content", "") + ), + } + for turn in turns + ] + + class _LDHooks(RunHooks): # type: ignore[misc, valid-type] + async def on_agent_end(self, context: Any, agent: Any, output: Any) -> None: + node_key = agent_name_to_key.get(agent.name) + if node_key and ld_context: + node = graph.get_node(node_key) + if node: + get_client().track( + "$ld:ai:generation:success", + ld_context, + make_track_data(node, graph.key, run_id), + 1, + ) + + async def on_handoff( + self, context: Any, from_agent: Any, to_agent: Any + ) -> None: + from_key = agent_name_to_key.get(from_agent.name) + if from_key and ld_context: + from_node = graph.get_node(from_key) + if from_node: + get_client().track( + "$ld:ai:graph:handoff_success", + ld_context, + make_track_data(from_node, graph.key, run_id), + 1, + ) + + async def on_agent_start(self, context: Any, agent: Any) -> None: + node_key = agent_name_to_key.get(agent.name) + if not node_key or node_key in path: + return + index = len(path) + path.append(node_key) + if not ld_context: + return + node = graph.get_node(node_key) + if node: + get_client().track( + "$ld:ai:graph:node", + ld_context, + { + **make_track_data(node, graph.key, run_id), + "nodeKey": node_key, + "index": index, + }, + 1, + ) + + if _HAS_OTEL: + span = trace.get_tracer("@launchdarkly/ai-litellm-agents").start_span( + "launchdarkly.graph" + ) + span.set_attribute("launchdarkly.graph.key", graph.key) + else: + span = None + + try: + result = await agents.Runner.run( + built[graph.root.key], + runner_input, + hooks=_LDHooks(), + run_config=RunConfig(tracing_disabled=True), + ) + if span: + span.set_status(SpanStatusCode.OK) + + wrapper = getattr(result, "context_wrapper", None) + usage = getattr(wrapper, "usage", None) + input_tokens = int(getattr(usage, "input_tokens", 0) or 0) + output_tokens = int(getattr(usage, "output_tokens", 0) or 0) + total_tokens = int( + getattr(usage, "total_tokens", input_tokens + output_tokens) + or input_tokens + output_tokens + ) + duration = int((time.monotonic() - start_time) * 1000) + if span: + span.set_attribute("launchdarkly.graph.path", "->".join(path)) + span.set_attribute("gen_ai.usage.input_tokens", input_tokens) + span.set_attribute("gen_ai.usage.output_tokens", output_tokens) + span.set_attribute("gen_ai.usage.total_tokens", total_tokens) + if ld_context: + root_td = make_track_data(graph.root, graph.key, run_id) + client = get_client() + client.track( + "$ld:ai:graph:duration:total", ld_context, root_td, duration + ) + client.track( + "$ld:ai:graph:total_tokens", ld_context, root_td, total_tokens + ) + client.track("$ld:ai:graph:invocation_success", ld_context, root_td, 1) + return { + "response": str(result.final_output or ""), + "usage": { + "input": input_tokens, + "output": output_tokens, + "total": total_tokens, + }, + } + except asyncio.CancelledError: + if span is not None: + span.set_attribute("launchdarkly.run.cancelled", True) + raise + except BaseException as exc: + if span and isinstance(exc, Exception): + span.record_exception(exc) + span.set_status(SpanStatusCode.ERROR, str(exc)) + if ld_context and isinstance(exc, Exception): + get_client().track( + "$ld:ai:graph:invocation_failure", + ld_context, + make_track_data(graph.root, graph.key, run_id), + 1, + ) + raise + finally: + if span: + span.end() + + return types.SimpleNamespace(invoke=invoke) diff --git a/packages/litellm-agents/tests/test_graph.py b/packages/litellm-agents/tests/test_graph.py new file mode 100644 index 00000000..bd9aec16 --- /dev/null +++ b/packages/litellm-agents/tests/test_graph.py @@ -0,0 +1,508 @@ +"""Graph contracts for the LiteLLM Agents SDK adapter.""" + +from __future__ import annotations + +import asyncio +from types import SimpleNamespace +from typing import Any +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +import launchdarkly_ai_litellm_agents.graph as graph_mod +import launchdarkly_ai_litellm_agents.handler as handler_mod +import launchdarkly_ai_litellm_agents.native_graph as native_graph_mod +from launchdarkly_ai_litellm_agents import litellm_graph, to_litellm_agents +from launchdarkly_ai_server import GraphDefinition, GraphEdge, GraphNode + + +class _FakeRunHooks: + async def on_agent_end(self, *args: Any) -> None: + return None + + async def on_handoff(self, *args: Any) -> None: + return None + + async def on_agent_start(self, *args: Any) -> None: + return None + + +def _definition(*, enabled: bool = True) -> GraphDefinition: + edge = GraphEdge( + key="root-child", + source_key="root", + target_key="child", + handoff={"description": "delegate"}, + ) + child = GraphNode( + key="child", + config={ + "provider": {"name": "Gemini"}, + "model": {"name": "gemini/gemini-2.5-pro"}, + "instructions": "child", + }, + meta={}, + edges=[], + is_terminal=True, + ) + root = GraphNode( + key="root", + config={ + "provider": {"name": "Anthropic"}, + "model": {"name": "anthropic/claude-sonnet-4"}, + "instructions": "root", + }, + meta={}, + edges=[edge], + is_terminal=False, + ) + nodes = {"root": root, "child": child} + + async def unavailable(*args: Any, **kwargs: Any) -> Any: + raise NotImplementedError + + async def traverse(*args: Any, **kwargs: Any) -> None: + return None + + return GraphDefinition( + key="litellm-graph", + enabled=enabled, + root=root if enabled else None, + get_node=lambda key: nodes.get(key), + get_child_nodes=lambda key: [child] if key == "root" else [], + get_parent_nodes=lambda key: [root] if key == "child" else [], + terminal_nodes=lambda: [child], + is_terminal=lambda key: key == "child", + edges_from=lambda key: [edge] if key == "root" else [], + run_node=unavailable, + route=unavailable, + traverse=traverse, + reverse_traverse=traverse, + ) + + +async def _definition_awaitable(*, enabled: bool = True) -> GraphDefinition: + return _definition(enabled=enabled) + + +async def _definition_awaitable_from( + definition: GraphDefinition, +) -> GraphDefinition: + return definition + + +class TestGraphConvenience: + def test_binds_wildcard_litellm_agent_handler(self) -> None: + graph = MagicMock(return_value=object()) + with patch.object(graph_mod, "graph", graph): + result = litellm_graph( + "graph-key", + context={"kind": "user", "key": "u"}, + model_factory=MagicMock(), + ) + assert result is graph.return_value + assert graph.call_args.args == ("graph-key",) + assert graph.call_args.kwargs["context"] == {"kind": "user", "key": "u"} + handlers = graph.call_args.kwargs["handlers"] + assert len(handlers) == 1 + assert handlers[0].provides_for == ("*", "agent") + assert "model_factory" not in graph.call_args.kwargs + + +class TestNativeGraphAdapter: + async def test_creates_every_node_with_its_authoritative_litellm_model( + self, + ) -> None: + models: dict[str, object] = {} + + def model_factory(name: str, parameters: dict[str, Any]) -> object: + models[name] = object() + return models[name] + + created: list[dict[str, Any]] = [] + agent = MagicMock( + side_effect=lambda **kwargs: ( + created.append(kwargs), + SimpleNamespace(name=kwargs["name"], **kwargs), + )[1] + ) + runner = SimpleNamespace( + run=AsyncMock( + return_value=SimpleNamespace( + final_output="done", + context_wrapper=SimpleNamespace( + usage=SimpleNamespace(input_tokens=9, output_tokens=3) + ), + ) + ) + ) + agents = SimpleNamespace( + Agent=agent, + Runner=runner, + handoff=MagicMock(side_effect=lambda target: target), + FunctionTool=MagicMock(), + RunHooks=_FakeRunHooks, + ) + real_import = __import__ + with patch( + "importlib.import_module", + side_effect=lambda name: agents if name == "agents" else real_import(name), + ): + result = await to_litellm_agents( + _definition_awaitable(), opts={"model_factory": model_factory} + ).invoke("hello") + assert set(models) == { + "anthropic/claude-sonnet-4", + "gemini/gemini-2.5-pro", + } + assert {entry["model"] for entry in created} == set(models.values()) + assert agents.handoff.call_count == 1 + assert runner.run.call_args.args[0].name == "root" + assert result == { + "response": "done", + "usage": {"input": 9, "output": 3, "total": 12}, + } + + async def test_forwards_multimodal_history_to_the_root_run(self) -> None: + runner = SimpleNamespace( + run=AsyncMock( + return_value=SimpleNamespace( + final_output="done", + context_wrapper=SimpleNamespace( + usage=SimpleNamespace(input_tokens=1, output_tokens=1) + ), + ) + ) + ) + agents = SimpleNamespace( + Agent=MagicMock( + side_effect=lambda **kwargs: SimpleNamespace( + name=kwargs["name"], **kwargs + ) + ), + Runner=runner, + handoff=MagicMock(side_effect=lambda target: target), + FunctionTool=MagicMock(), + RunHooks=_FakeRunHooks, + ) + history = [ + {"role": "system", "content": "ignore this"}, + { + "role": "user", + "content": [ + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/png", + "data": "image-data", + }, + } + ], + }, + ] + real_import = __import__ + with patch( + "importlib.import_module", + side_effect=lambda name: agents if name == "agents" else real_import(name), + ): + await to_litellm_agents( + _definition_awaitable(), + opts={"model_factory": lambda *_: object()}, + ).invoke("", {}, history) + + assert runner.run.call_args.args[1] == [ + { + "role": "user", + "content": [ + { + "type": "input_image", + "image_url": "data:image/png;base64,image-data", + } + ], + } + ] + + async def test_binds_registered_tools_to_their_graph_nodes(self) -> None: + definition = _definition() + assert definition.root is not None + definition.root.config["tools"] = { + "search": { + "description": "Search", + "parameters": {"type": "object", "properties": {}}, + } + } + created: list[dict[str, Any]] = [] + function_tool = MagicMock( + side_effect=lambda **kwargs: SimpleNamespace(**kwargs) + ) + agents = SimpleNamespace( + Agent=MagicMock( + side_effect=lambda **kwargs: ( + created.append(kwargs), + SimpleNamespace(name=kwargs["name"], **kwargs), + )[1] + ), + Runner=SimpleNamespace( + run=AsyncMock( + return_value=SimpleNamespace( + final_output="done", + context_wrapper=SimpleNamespace( + usage=SimpleNamespace(input_tokens=1, output_tokens=1) + ), + ) + ) + ), + handoff=MagicMock(side_effect=lambda target: target), + FunctionTool=function_tool, + RunHooks=_FakeRunHooks, + ) + search = AsyncMock(return_value="found") + real_import = __import__ + with patch( + "importlib.import_module", + side_effect=lambda name: agents if name == "agents" else real_import(name), + ): + await to_litellm_agents( + _definition_awaitable_from(definition), + opts={ + "model_factory": lambda *_: object(), + "tool_handlers": {"search": search}, + }, + ).invoke("query") + + root = next(entry for entry in created if entry["name"] == "root") + assert len(root["tools"]) == 1 + assert root["tools"][0].name == "search" + assert ( + await root["tools"][0].on_invoke_tool(None, '{"query":"docs"}') == "found" + ) + search.assert_awaited_once_with({"query": "docs"}) + + async def test_default_factory_uses_litellm_model_for_each_node(self) -> None: + created_models: list[Any] = [] + litellm_model = MagicMock( + side_effect=lambda **kwargs: ( + created_models.append(kwargs), + object(), + )[1] + ) + agents = SimpleNamespace( + Agent=MagicMock( + side_effect=lambda **kwargs: SimpleNamespace( + name=kwargs["name"], **kwargs + ) + ), + Runner=SimpleNamespace( + run=AsyncMock( + return_value=SimpleNamespace( + final_output="done", + context_wrapper=SimpleNamespace( + usage=SimpleNamespace(input_tokens=1, output_tokens=1) + ), + ) + ) + ), + handoff=MagicMock(side_effect=lambda target: target), + FunctionTool=MagicMock(), + RunHooks=_FakeRunHooks, + ) + real_import = __import__ + with ( + patch( + "importlib.import_module", + side_effect=lambda name: ( + agents if name == "agents" else real_import(name) + ), + ), + patch.object(handler_mod, "LitellmModel", litellm_model), + ): + await to_litellm_agents(_definition_awaitable()).invoke("hello") + assert {call["model"] for call in created_models} == { + "anthropic/claude-sonnet-4", + "gemini/gemini-2.5-pro", + } + + async def test_adapter_has_no_default_openai_fallback(self) -> None: + fallback = MagicMock(side_effect=AssertionError("OpenAI fallback")) + agents = SimpleNamespace( + Agent=MagicMock( + side_effect=lambda **kwargs: SimpleNamespace( + name=kwargs["name"], **kwargs + ) + ), + Runner=SimpleNamespace( + run=AsyncMock( + return_value=SimpleNamespace( + final_output="done", + context_wrapper=SimpleNamespace( + usage=SimpleNamespace(input_tokens=0, output_tokens=0) + ), + ) + ) + ), + handoff=MagicMock(side_effect=lambda target: target), + FunctionTool=MagicMock(), + RunHooks=_FakeRunHooks, + ) + real_import = __import__ + with ( + patch( + "importlib.import_module", + side_effect=lambda name: ( + agents if name == "agents" else real_import(name) + ), + ), + patch.dict( + "sys.modules", + { + "openai": SimpleNamespace(AsyncOpenAI=fallback), + "agents.models.openai_provider": SimpleNamespace( + OpenAIProvider=fallback + ), + }, + ), + ): + await to_litellm_agents( + _definition_awaitable(), + opts={"model_factory": lambda *_: object()}, + ).invoke("hello") + fallback.assert_not_called() + + async def test_success_path_emits_invocation_success_and_tokens(self) -> None: + track_calls: list[tuple[str, dict[str, Any]]] = [] + mock_ld_client = MagicMock() + mock_ld_client.track = MagicMock( + side_effect=lambda evt, ctx, data, val: track_calls.append((evt, data)) + ) + + async def run( + agent: Any, _prompt: Any, hooks: Any = None, **_kwargs: Any + ) -> Any: + await hooks.on_agent_start(None, agent) + return SimpleNamespace( + final_output="done", + context_wrapper=SimpleNamespace( + usage=SimpleNamespace( + input_tokens=4, output_tokens=2, total_tokens=6 + ) + ), + ) + + agents = SimpleNamespace( + Agent=MagicMock( + side_effect=lambda **kwargs: SimpleNamespace( + name=kwargs["name"], **kwargs + ) + ), + Runner=SimpleNamespace(run=run), + handoff=MagicMock(side_effect=lambda target: target), + FunctionTool=MagicMock(), + RunHooks=_FakeRunHooks, + ) + real_import = __import__ + with ( + patch( + "importlib.import_module", + side_effect=lambda name: ( + agents if name == "agents" else real_import(name) + ), + ), + patch.object(native_graph_mod, "get_client", return_value=mock_ld_client), + ): + await to_litellm_agents( + _definition_awaitable(), + opts={ + "model_factory": lambda *_: object(), + "context": {"kind": "user", "key": "test"}, + }, + ).invoke("hello") + events = [name for name, _data in track_calls] + assert "$ld:ai:graph:invocation_success" in events + assert "$ld:ai:graph:total_tokens" in events + assert "$ld:ai:graph:path" not in events + nodes = [data for name, data in track_calls if name == "$ld:ai:graph:node"] + assert [data["nodeKey"] for data in nodes] == ["root"] + assert nodes[0]["index"] == 0 + + async def test_error_path_emits_invocation_failure_and_rethrows(self) -> None: + track_calls: list[str] = [] + mock_ld_client = MagicMock() + mock_ld_client.track = MagicMock( + side_effect=lambda evt, ctx, data, val: track_calls.append(evt) + ) + agents = SimpleNamespace( + Agent=MagicMock( + side_effect=lambda **kwargs: SimpleNamespace( + name=kwargs["name"], **kwargs + ) + ), + Runner=SimpleNamespace( + run=AsyncMock(side_effect=RuntimeError("provider error")) + ), + handoff=MagicMock(side_effect=lambda target: target), + FunctionTool=MagicMock(), + RunHooks=_FakeRunHooks, + ) + real_import = __import__ + with ( + patch( + "importlib.import_module", + side_effect=lambda name: ( + agents if name == "agents" else real_import(name) + ), + ), + patch.object(native_graph_mod, "get_client", return_value=mock_ld_client), + ): + with pytest.raises(RuntimeError, match="provider error"): + await to_litellm_agents( + _definition_awaitable(), + opts={ + "model_factory": lambda *_: object(), + "context": {"kind": "user", "key": "test"}, + }, + ).invoke("hello") + assert "$ld:ai:graph:invocation_failure" in track_calls + + async def test_cancellation_closes_graph_span(self) -> None: + span = MagicMock() + tracer = MagicMock() + tracer.start_span.return_value = span + agents = SimpleNamespace( + Agent=MagicMock( + side_effect=lambda **kwargs: SimpleNamespace( + name=kwargs["name"], **kwargs + ) + ), + Runner=SimpleNamespace(run=AsyncMock(side_effect=asyncio.CancelledError())), + handoff=MagicMock(side_effect=lambda target: target), + FunctionTool=MagicMock(), + RunHooks=_FakeRunHooks, + ) + real_import = __import__ + with ( + patch( + "importlib.import_module", + side_effect=lambda name: ( + agents if name == "agents" else real_import(name) + ), + ), + patch.object(native_graph_mod, "_HAS_OTEL", True), + patch.object(native_graph_mod.trace, "get_tracer", return_value=tracer), + ): + with pytest.raises(asyncio.CancelledError): + await to_litellm_agents( + _definition_awaitable(), + opts={"model_factory": lambda *_: object()}, + ).invoke("hello") + span.end.assert_called_once_with() + span.set_attribute.assert_any_call("launchdarkly.run.cancelled", True) + + async def test_disabled_graph_fails_before_runner_or_model_creation(self) -> None: + model_factory = MagicMock() + with pytest.raises(ValueError, match="disabled"): + await to_litellm_agents( + _definition_awaitable(enabled=False), + opts={"model_factory": model_factory}, + ).invoke("hello") + model_factory.assert_not_called() diff --git a/packages/litellm-agents/tests/test_handler.py b/packages/litellm-agents/tests/test_handler.py new file mode 100644 index 00000000..a330005c --- /dev/null +++ b/packages/litellm-agents/tests/test_handler.py @@ -0,0 +1,540 @@ +"""Contract tests for the future OpenAI Agents SDK adapter over LiteLLM.""" + +from __future__ import annotations + +from collections.abc import AsyncIterator +from types import SimpleNamespace +from typing import Any, ClassVar +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +import launchdarkly_ai_litellm_agents.handler as handler_mod +from launchdarkly_ai_litellm_agents import ( + create_litellm_agents_handler, + litellm_agents, +) + +CONFIG: dict[str, Any] = { + "provider": {"name": "Anthropic"}, + "model": { + "name": "anthropic/claude-sonnet-4", + "parameters": {"temperature": 0.2}, + }, + "instructions": "Help {{name}}.", +} + + +class FakeAgent: + created: ClassVar[list[dict[str, Any]]] = [] + + def __init__(self, **kwargs: Any) -> None: + self.kwargs = kwargs + self.name = kwargs.get("name", "agent") + self.model = kwargs["model"] + self.instructions = kwargs.get("instructions") + self.tools = kwargs.get("tools", []) + type(self).created.append(kwargs) + + +class FakeFunctionTool: + def __init__(self, **kwargs: Any) -> None: + self.name = kwargs["name"] + self.description = kwargs.get("description") + self.params_json_schema = kwargs.get("params_json_schema") + self.on_invoke_tool = kwargs["on_invoke_tool"] + + +class FakeStreamedResult: + def __init__(self, events: list[Any], output: str = "done") -> None: + self._events = events + self.final_output = output + self.cancelled = False + self.context_wrapper = SimpleNamespace( + usage=SimpleNamespace(input_tokens=8, output_tokens=3) + ) + + async def stream_events(self) -> AsyncIterator[Any]: + for event in self._events: + yield event + + def cancel(self, mode: str = "immediate") -> None: + self.cancelled = True + + +class RecordedSpan: + def __init__(self, name: str, context: Any = None) -> None: + self.name = name + self.context = context + self.attributes: dict[str, Any] = {} + self.exceptions: list[BaseException] = [] + self.ended = 0 + + def set_attribute(self, key: str, value: Any) -> None: + self.attributes[key] = value + + def add_event(self, name: str, attributes: Any = None) -> None: + pass + + def set_status(self, code: Any, description: str | None = None) -> None: + pass + + def record_exception(self, error: BaseException) -> None: + self.exceptions.append(error) + + def end(self) -> None: + self.ended += 1 + + +class SpanRecorder: + def __init__(self) -> None: + self.spans: list[RecordedSpan] = [] + + def get_tracer(self, name: str) -> SpanRecorder: + return self + + def start_span(self, name: str, context: Any = None) -> RecordedSpan: + span = RecordedSpan(name, context) + self.spans.append(span) + return span + + def set_span_in_context(self, span: RecordedSpan) -> Any: + return ("context-of", span) + + +def _agents_module( + *, + run: Any | None = None, + run_streamed: Any | None = None, +) -> Any: + module = SimpleNamespace() + module.Agent = FakeAgent + module.FunctionTool = FakeFunctionTool + module.Runner = SimpleNamespace( + run=run + or AsyncMock( + return_value=SimpleNamespace( + final_output="done", + context_wrapper=SimpleNamespace( + usage=SimpleNamespace(input_tokens=5, output_tokens=2) + ), + ) + ), + run_streamed=run_streamed or MagicMock(return_value=FakeStreamedResult([])), + ) + return module + + +def _patch_agents(module: Any) -> Any: + real_import = __import__ + return patch( + "importlib.import_module", + side_effect=lambda name: module if name == "agents" else real_import(name), + ) + + +class TestFactoryAndModelBinding: + def setup_method(self) -> None: + FakeAgent.created.clear() + + def test_wildcard_metadata_and_capture_content(self) -> None: + handler = create_litellm_agents_handler(capture_content=True) + assert handler.provides_for == ("*", "agent") + assert handler.capture_content is True + assert create_litellm_agents_handler().capture_content is False + + async def test_evaluated_model_constructs_agents_sdk_litellm_model(self) -> None: + agents = _agents_module() + litellm_model = MagicMock(return_value=object()) + with ( + _patch_agents(agents), + patch.object(handler_mod, "LitellmModel", litellm_model), + ): + await create_litellm_agents_handler()(CONFIG, "q", {}, {"name": "Ada"}) + litellm_model.assert_called_once() + assert litellm_model.call_args.kwargs["model"] == "anthropic/claude-sonnet-4" + assert "base_url" not in litellm_model.call_args.kwargs + assert "api_key" not in litellm_model.call_args.kwargs + assert FakeAgent.created[0]["model"] is litellm_model.return_value + + async def test_config_cannot_redirect_the_provider_key(self) -> None: + agents = _agents_module() + litellm_model = MagicMock(return_value=object()) + config = { + **CONFIG, + "model": { + "name": "anthropic/claude-sonnet-4", + "parameters": { + "temperature": 0.2, + "api_base": "http://127.0.0.1:9", + "api_key": "provider-secret", + "base_url": "http://127.0.0.1:9", + "extra_headers": {"X-Smuggled": "1"}, + }, + }, + } + with ( + _patch_agents(agents), + patch.object(handler_mod, "LitellmModel", litellm_model), + ): + await create_litellm_agents_handler( + base_url="https://proxy.example", api_key="handler-key" + )(config, "q") + assert litellm_model.call_args.kwargs["base_url"] == "https://proxy.example" + assert litellm_model.call_args.kwargs["api_key"] == "handler-key" + settings = FakeAgent.created[0]["model_settings"] + assert settings.temperature == 0.2 + assert not settings.extra_args + assert not settings.extra_headers + + async def test_model_factory_is_evaluated_per_call_and_instance_scoped( + self, + ) -> None: + agents = _agents_module() + first_factory = MagicMock(return_value=object()) + second_factory = MagicMock(return_value=object()) + first = create_litellm_agents_handler(model_factory=first_factory) + second = create_litellm_agents_handler(model_factory=second_factory) + with _patch_agents(agents): + await first(CONFIG, "one") + await second({**CONFIG, "model": {"name": "proxy-alias"}}, "two") + first_factory.assert_called_once_with( + "anthropic/claude-sonnet-4", {"temperature": 0.2} + ) + second_factory.assert_called_once_with("proxy-alias", {}) + assert first_factory.return_value is not second_factory.return_value + + async def test_injected_model_is_bound_to_agent(self) -> None: + agents = _agents_module() + model = object() + with _patch_agents(agents): + await create_litellm_agents_handler(model_factory=lambda *_: model)( + CONFIG, "q" + ) + assert FakeAgent.created[0]["model"] is model + + async def test_no_default_openai_model_or_client_fallback(self) -> None: + agents = _agents_module() + model = object() + default_openai = MagicMock(side_effect=AssertionError("OpenAI fallback")) + with ( + _patch_agents(agents), + patch.dict( + "sys.modules", + { + "openai": SimpleNamespace(AsyncOpenAI=default_openai), + "agents.models.openai_chatcompletions": SimpleNamespace( + OpenAIChatCompletionsModel=default_openai + ), + }, + ), + ): + await create_litellm_agents_handler(model_factory=lambda *_: model)( + CONFIG, "q" + ) + default_openai.assert_not_called() + assert FakeAgent.created[0]["model"] is model + + +class TestAgentToolsAndRun: + def setup_method(self) -> None: + FakeAgent.created.clear() + + async def test_only_callable_backed_tools_are_bound(self) -> None: + agents = _agents_module() + config = { + **CONFIG, + "tools": { + "weather": { + "description": "forecast", + "parameters": {"type": "object"}, + }, + "missing": {"description": "excluded", "parameters": {}}, + }, + } + weather = AsyncMock(return_value="sunny") + with _patch_agents(agents): + await create_litellm_agents_handler(model_factory=lambda *_: object())( + config, "q", {"weather": weather} + ) + tools = FakeAgent.created[0]["tools"] + assert [tool.name for tool in tools] == ["weather"] + assert tools[0].description == "forecast" + assert tools[0].params_json_schema == {"type": "object"} + await tools[0].on_invoke_tool(SimpleNamespace(), '{"city":"Austin"}') + weather.assert_awaited_once_with({"city": "Austin"}) + + async def test_runner_receives_agent_prompt_and_max_turns(self) -> None: + run = AsyncMock( + return_value=SimpleNamespace( + final_output="answer", + context_wrapper=SimpleNamespace( + usage=SimpleNamespace(input_tokens=11, output_tokens=4) + ), + ) + ) + agents = _agents_module(run=run) + config = { + **CONFIG, + "model": {**CONFIG["model"], "parameters": {"max_turns": 7}}, + } + with _patch_agents(agents): + result = await create_litellm_agents_handler( + model_factory=lambda *_: object() + )(config, "question", {}, {"name": "Ada"}) + run.assert_awaited_once() + assert run.call_args.args[1] == "question" + assert run.call_args.kwargs["max_turns"] == 7 + assert FakeAgent.created[0]["instructions"] == "Help Ada." + assert result == { + "output": "answer", + "usage": {"input_tokens": 11, "output_tokens": 4}, + } + + async def test_history_uses_agents_sdk_structured_input(self) -> None: + run = AsyncMock( + return_value=SimpleNamespace( + final_output="answer", + context_wrapper=SimpleNamespace( + usage=SimpleNamespace(input_tokens=1, output_tokens=1) + ), + ) + ) + agents = _agents_module(run=run) + history = [ + { + "role": "user", + "content": [ + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/png", + "data": "abc", + }, + } + ], + }, + {"role": "assistant", "content": "prior"}, + ] + with _patch_agents(agents): + await create_litellm_agents_handler(model_factory=lambda *_: object())( + CONFIG, "describe", {}, {"name": "Ada"}, history + ) + prompt = run.call_args.args[1] + assert isinstance(prompt, list) + assert prompt[0]["content"] == [ + { + "type": "input_image", + "image_url": "data:image/png;base64,abc", + } + ] + assert prompt[1]["content"] == [{"type": "output_text", "text": "prior"}] + assert prompt[-1]["content"] == "describe" + + async def test_output_format_is_bound_as_agent_output_type(self) -> None: + agents = _agents_module() + schema = {"type": "object", "properties": {"answer": {"type": "string"}}} + with _patch_agents(agents): + result = await create_litellm_agents_handler( + model_factory=lambda *_: object() + )({**CONFIG, "outputFormat": schema}, "q") + assert FakeAgent.created[0]["output_type"]["schema"] == schema + assert result["output"] == "done" + + +class TestLifecycleAndTelemetry: + def setup_method(self) -> None: + FakeAgent.created.clear() + + async def test_run_error_propagates_without_openai_retry(self) -> None: + run = AsyncMock(side_effect=RuntimeError("LiteLLM unavailable")) + agents = _agents_module(run=run) + with ( + _patch_agents(agents), + pytest.raises(RuntimeError, match="LiteLLM unavailable"), + ): + await create_litellm_agents_handler(model_factory=lambda *_: object())( + CONFIG, "q" + ) + run.assert_awaited_once() + + async def test_stream_forwards_deltas_usage_and_done(self) -> None: + events = [ + SimpleNamespace( + type="raw_response_event", + data=SimpleNamespace(type="response.output_text.delta", delta="Hel"), + ), + SimpleNamespace( + type="raw_response_event", + data=SimpleNamespace(type="response.output_text.delta", delta="lo"), + ), + ] + streamed = FakeStreamedResult(events, "Hello") + agents = _agents_module(run_streamed=MagicMock(return_value=streamed)) + with _patch_agents(agents): + handler = create_litellm_agents_handler(model_factory=lambda *_: object()) + output = [event async for event in await handler.stream(CONFIG, "q")] + assert [event["text"] for event in output[:-1]] == ["Hel", "lo"] + assert output[-1] == { + "type": "done", + "output": "Hello", + "usage": {"input_tokens": 8, "output_tokens": 3}, + } + + async def test_abandoned_stream_cancels_agents_runner(self) -> None: + events = [ + SimpleNamespace( + type="raw_response_event", + data=SimpleNamespace(type="response.output_text.delta", delta="one"), + ), + SimpleNamespace( + type="raw_response_event", + data=SimpleNamespace(type="response.output_text.delta", delta="two"), + ), + ] + streamed = FakeStreamedResult(events) + agents = _agents_module(run_streamed=MagicMock(return_value=streamed)) + with _patch_agents(agents): + gen = await create_litellm_agents_handler( + model_factory=lambda *_: object() + ).stream(CONFIG, "q") + async for _ in gen: + break + await gen.aclose() + assert streamed.cancelled is True + + async def test_root_identity_and_content_capture_gating(self) -> None: + agents = _agents_module() + span = MagicMock() + trace = MagicMock() + trace.get_tracer.return_value.start_span.return_value = span + variables = { + "__ld": {"configKey": "cfg", "variationKey": "v", "runId": "run"}, + "name": "Ada", + } + with _patch_agents(agents), patch.object(handler_mod, "trace", trace): + await create_litellm_agents_handler(model_factory=lambda *_: object())( + CONFIG, "private", {}, variables + ) + attributes = { + call.args[0]: call.args[1] for call in span.set_attribute.call_args_list + } + assert attributes["launchdarkly.config.key"] == "cfg" + assert attributes["gen_ai.request.model"] == "anthropic/claude-sonnet-4" + assert not any(key.startswith("gen_ai.prompt.") for key in attributes) + + async def test_hooks_emit_nested_model_and_tool_spans(self) -> None: + async def run( + agent: Any, + prompt: Any, + *, + max_turns: int, + hooks: Any, + **_kwargs: Any, + ) -> Any: + await hooks.on_llm_start( + SimpleNamespace(), agent, agent.instructions, prompt + ) + context = SimpleNamespace( + tool_call_id="call-1", + tool_name="weather", + tool_arguments='{"city":"Oslo"}', + ) + tool = SimpleNamespace(name="weather") + await hooks.on_tool_start(context, agent, tool) + await hooks.on_tool_end(context, agent, tool, "cold") + await hooks.on_llm_end( + SimpleNamespace(), + agent, + SimpleNamespace( + output=["done"], + usage=SimpleNamespace(input_tokens=5, output_tokens=2), + ), + ) + return SimpleNamespace( + final_output="done", + context_wrapper=SimpleNamespace( + usage=SimpleNamespace(input_tokens=5, output_tokens=2) + ), + ) + + recorder = SpanRecorder() + agents = _agents_module(run=AsyncMock(side_effect=run)) + with ( + _patch_agents(agents), + patch.object(handler_mod, "trace", recorder), + ): + await create_litellm_agents_handler(model_factory=lambda *_: object())( + CONFIG, "q" + ) + + assert [span.name for span in recorder.spans] == [ + "invoke_agent", + "chat anthropic/claude-sonnet-4", + "execute_tool weather", + ] + root = recorder.spans[0] + assert all(span.context == ("context-of", root) for span in recorder.spans[1:]) + for span in recorder.spans[:2]: + assert span.attributes["gen_ai.provider.name"] == "anthropic" + assert span.attributes["gen_ai.system"] == "litellm" + assert recorder.spans[1].attributes["gen_ai.usage.input_tokens"] == 5 + assert recorder.spans[2].attributes["gen_ai.tool.call.id"] == "call-1" + assert all(span.ended == 1 for span in recorder.spans) + + async def test_open_model_and_root_fail_exactly_once(self) -> None: + error = RuntimeError("run failed") + + async def run( + agent: Any, + prompt: Any, + *, + max_turns: int, + hooks: Any, + **_kwargs: Any, + ) -> Any: + await hooks.on_llm_start( + SimpleNamespace(), agent, agent.instructions, prompt + ) + raise error + + recorder = SpanRecorder() + agents = _agents_module(run=AsyncMock(side_effect=run)) + with ( + _patch_agents(agents), + patch.object(handler_mod, "trace", recorder), + pytest.raises(RuntimeError, match="run failed"), + ): + await create_litellm_agents_handler(model_factory=lambda *_: object())( + CONFIG, "q" + ) + assert [span.name for span in recorder.spans] == [ + "invoke_agent", + "chat anthropic/claude-sonnet-4", + ] + assert all(span.exceptions == [error] for span in recorder.spans) + assert all(span.ended == 1 for span in recorder.spans) + + +class TestConvenienceWrapper: + def test_forwards_handler_variables_and_capture_content(self) -> None: + instance = MagicMock() + instance.invoke.return_value = "result" + config = MagicMock(return_value=instance) + with patch.object(handler_mod, "config", config): + result = litellm_agents( + "agent-key", + "hello", + {"kind": "user", "key": "u"}, + variables={"name": "Ada"}, + capture_content=True, + ) + assert result == "result" + configured_handler = config.call_args.kwargs["handler"] + assert configured_handler.provides_for == ("*", "agent") + assert configured_handler.capture_content is True + assert "capture_content" not in config.call_args.kwargs + instance.invoke.assert_called_once_with( + "hello", {"kind": "user", "key": "u"}, {"name": "Ada"} + ) diff --git a/packages/litellm-messages/README.md b/packages/litellm-messages/README.md new file mode 100644 index 00000000..5b492b1c --- /dev/null +++ b/packages/litellm-messages/README.md @@ -0,0 +1,37 @@ +# `launchdarkly-ai-litellm-messages` + +**Experimental.** This package stays on 0.x until the Python and JavaScript LiteLLM integrations match. + +Provider-agnostic LaunchDarkly AI Config handler backed by LiteLLM's in-process +`acompletion` API. + +## Installation + +```bash +pip install launchdarkly-ai-server launchdarkly-server-sdk launchdarkly-ai-litellm-messages +``` + +Python uses LiteLLM in-process; no proxy endpoint is required. Set the provider +credentials required by the models in your AI Config, such as +`OPENAI_API_KEY` or `ANTHROPIC_API_KEY`. Standard LiteLLM configuration, +gateways, and model aliases are supported. + +## Usage + +```python +from launchdarkly_ai_litellm_messages import create_litellm_messages_handler +from launchdarkly_ai_server import config + +result = await config( + key="assistant", + handler=create_litellm_messages_handler(), +).invoke("Hello", {"kind": "user", "key": "user-123"}) +``` + +The evaluated AI Config model name is passed directly to LiteLLM, so standard +LiteLLM model prefixes, gateways, and aliases are supported. Tool calls, +structured output, multimodal history, and streaming are supported without +constructing a provider-native client. + +Because this is a wildcard messages handler, do not register it together with +another `["*", "messages"]` handler such as LangChain in the same registry. diff --git a/packages/litellm-messages/pyproject.toml b/packages/litellm-messages/pyproject.toml new file mode 100644 index 00000000..670485ed --- /dev/null +++ b/packages/litellm-messages/pyproject.toml @@ -0,0 +1,34 @@ +[project] +name = "launchdarkly-ai-litellm-messages" +version = "0.1.0" +requires-python = ">=3.12" +dependencies = [ + "launchdarkly-ai-server", + "litellm>=1.83.0", + "opentelemetry-api>=1.25", +] +description = "LiteLLM messages handler for LaunchDarkly AI SDK" +readme = "README.md" +license = "Apache-2.0" +authors = [{name = "LaunchDarkly", email = "team@launchdarkly.com"}] +keywords = ["launchdarkly", "ai", "litellm"] +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.12", + "Topic :: Software Development :: Libraries", +] + +[project.urls] +Homepage = "https://github.com/launchdarkly/python-ai-sdk" +Repository = "https://github.com/launchdarkly/python-ai-sdk" +"Bug Tracker" = "https://github.com/launchdarkly/python-ai-sdk/issues" + +[build-system] +requires = ["hatchling>=1.32,<2"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/launchdarkly_ai_litellm_messages"] diff --git a/packages/litellm-messages/src/launchdarkly_ai_litellm_messages/__init__.py b/packages/litellm-messages/src/launchdarkly_ai_litellm_messages/__init__.py new file mode 100644 index 00000000..56b796d7 --- /dev/null +++ b/packages/litellm-messages/src/launchdarkly_ai_litellm_messages/__init__.py @@ -0,0 +1,11 @@ +"""LaunchDarkly AI SDK integration for LiteLLM chat completions.""" + +__version__ = "0.1.0" # x-release-please-version + +from launchdarkly_ai_server import register_ai_sdk_package + +from .handler import create_litellm_messages_handler, litellm_messages + +__all__ = ["create_litellm_messages_handler", "litellm_messages"] + +register_ai_sdk_package("launchdarkly-ai-litellm-messages", __version__) diff --git a/packages/litellm-messages/src/launchdarkly_ai_litellm_messages/handler.py b/packages/litellm-messages/src/launchdarkly_ai_litellm_messages/handler.py new file mode 100644 index 00000000..55afb00a --- /dev/null +++ b/packages/litellm-messages/src/launchdarkly_ai_litellm_messages/handler.py @@ -0,0 +1,666 @@ +from __future__ import annotations + +import asyncio +import inspect +import json +from collections.abc import AsyncGenerator, Awaitable, Callable +from typing import Any + +from opentelemetry import trace +from opentelemetry.trace import StatusCode + +from launchdarkly_ai_server import ( + AiConfigRep, + LDContext, + ProviderHandler, + SpanUsage, + config, + content_to_text, + create_handler, + end_span_once, + image_block_to_url, + parse_template, + set_input_content_attributes, + set_ld_span_attributes, + set_model_identity_attributes, + set_output_content_attributes, + set_tool_call_content_attributes, + set_usage_span_attributes, + text_message, + to_semconv_finish_reason, +) + +Completion = Callable[..., Awaitable[Any]] + + +def _json_schema(output_format: dict[str, Any]) -> dict[str, Any]: + schema = dict(output_format) + if schema.get("type") in (None, "json_schema", "json"): + schema["type"] = "object" + return schema + + +# Generation settings only. Connection and credential keys (api_base, api_key, +# base_url, extra_headers, aws_*, vertex_*, ...) stay out: LiteLLM reads them +# as call arguments, and an AI Config must not choose where the provider key goes. +_FORWARDED_PARAMETERS = frozenset( + { + "frequency_penalty", + "max_completion_tokens", + "max_tokens", + "parallel_tool_calls", + "presence_penalty", + "reasoning_effort", + "seed", + "stop", + "temperature", + "tool_choice", + "top_p", + } +) +_MAX_TOOL_TURNS = 10 + + +def _value(value: Any, name: str, default: Any = None) -> Any: + if isinstance(value, dict): + return value.get(name, default) + return getattr(value, name, default) + + +def _model_name(config_value: AiConfigRep) -> str: + return str((config_value.get("model") or {}).get("name") or "") + + +def _provider_name(config_value: AiConfigRep) -> str: + return str((config_value.get("provider") or {}).get("name") or "litellm").lower() + + +def _usage(value: Any) -> SpanUsage: + raw = _value(value, "usage") + return SpanUsage( + input=int(_value(raw, "prompt_tokens", 0) or 0), + output=int(_value(raw, "completion_tokens", 0) or 0), + ) + + +def _usage_reported(value: Any) -> bool: + return _value(value, "usage") is not None + + +def _map_content(content: Any) -> Any: + if not isinstance(content, list): + return content + parts: list[dict[str, Any]] = [] + for block in content: + if block.get("type") == "text": + parts.append({"type": "text", "text": block.get("text", "")}) + elif block.get("type") == "image": + parts.append( + {"type": "image_url", "image_url": {"url": image_block_to_url(block)}} + ) + return parts + + +def _build_messages( + config_value: AiConfigRep, + user_input: str | None, + variables: dict[str, Any], + history: list[dict[str, Any]] | None, +) -> list[dict[str, Any]]: + messages: list[dict[str, Any]] = [] + configured = config_value.get("messages") or [] + if configured: + for message in configured: + content = message.get("content", "") + if isinstance(content, str): + content = parse_template(content, variables) + messages.append( + {"role": message.get("role", "user"), "content": _map_content(content)} + ) + elif config_value.get("instructions"): + messages.append( + { + "role": "system", + "content": parse_template(config_value["instructions"], variables), + } + ) + + if history: + messages.extend( + { + "role": message.get("role", "user"), + "content": _map_content(message.get("content", "")), + } + for message in history + if message.get("role") != "system" + ) + if user_input: + messages.append({"role": "user", "content": user_input}) + elif not messages or messages[-1].get("role") != "user": + messages.append({"role": "user", "content": user_input or ""}) + return messages + + +def _build_tools( + config_value: AiConfigRep, tool_handlers: dict[str, Any] +) -> list[dict[str, Any]]: + return [ + { + "type": "function", + "function": { + "name": name, + "description": tool.get("description", ""), + "parameters": tool.get("parameters") or {}, + }, + } + for name, tool in (config_value.get("tools") or {}).items() + if callable(tool_handlers.get(name)) + ] + + +def _message_text(content: Any) -> str: + if isinstance(content, str): + return content + if isinstance(content, list): + return content_to_text(content) + return "" if content is None else str(content) + + +def _span_messages(messages: list[dict[str, Any]]) -> list[Any]: + return [ + text_message(str(message["role"]), _message_text(message.get("content"))) + for message in messages + ] + + +def _request( + config_value: AiConfigRep, + messages: list[dict[str, Any]], + tools: list[dict[str, Any]], + *, + stream: bool, +) -> dict[str, Any]: + model = config_value.get("model") or {} + raw_parameters = model.get("parameters") + parameters = ( + { + key: value + for key, value in raw_parameters.items() + if key in _FORWARDED_PARAMETERS + } + if isinstance(raw_parameters, dict) + else {} + ) + parameters.update( + { + "model": _model_name(config_value), + "messages": messages, + "stream": stream, + } + ) + if tools: + parameters["tools"] = tools + if stream: + parameters["stream_options"] = {"include_usage": True} + output_format = config_value.get("outputFormat") + if output_format and not stream: + parameters["response_format"] = { + "type": "json_schema", + "json_schema": { + "name": "output", + "strict": False, + "schema": _json_schema(output_format), + }, + } + return parameters + + +def _root_span(config_value: AiConfigRep, variables: dict[str, Any]) -> Any: + span = trace.get_tracer("@launchdarkly/ai-litellm-messages").start_span( + "invoke_agent" + ) + span.set_attribute("gen_ai.operation.name", "invoke_agent") + set_model_identity_attributes( + span, _provider_name(config_value), _model_name(config_value), "litellm" + ) + set_ld_span_attributes(span, variables) + return span + + +def _parent_context(span: Any) -> Any: + return trace.set_span_in_context(span) + + +def _model_span(config_value: AiConfigRep, parent: Any) -> Any: + model = _model_name(config_value) + span = trace.get_tracer("@launchdarkly/ai-litellm-messages").start_span( + f"chat {model}", context=parent + ) + span.set_attribute("gen_ai.operation.name", "chat") + set_model_identity_attributes(span, _provider_name(config_value), model, "litellm") + return span + + +def _tool_span(name: str, call_id: str, parent: Any) -> Any: + span = trace.get_tracer("@launchdarkly/ai-litellm-messages").start_span( + f"execute_tool {name}", context=parent + ) + span.set_attribute("gen_ai.operation.name", "execute_tool") + span.set_attribute("gen_ai.tool.name", name) + span.set_attribute("gen_ai.tool.call.id", call_id) + return span + + +def _finish_span( + span: Any, + usage: SpanUsage, + ended: set[int], + *, + model: str | None = None, + finish_reason: str | None = None, +) -> None: + if model is not None: + span.set_attribute("gen_ai.response.model", model) + if finish_reason: + span.set_attribute("gen_ai.response.finish_reasons", [finish_reason]) + set_usage_span_attributes(span, usage) + span.set_status(StatusCode.OK) + end_span_once(span, ended) + + +def _succeed_span(span: Any, ended: set[int]) -> None: + span.set_status(StatusCode.OK) + end_span_once(span, ended) + + +def _fail_span(span: Any, error: BaseException, ended: set[int]) -> None: + span.record_exception(error) + span.set_status(StatusCode.ERROR, str(error)) + end_span_once(span, ended) + + +def _finish_reason(response: Any) -> str | None: + choices = _value(response, "choices", []) or [] + reason = _value(choices[0], "finish_reason") if choices else None + return to_semconv_finish_reason(str(reason)) if reason else None + + +def _tool_call_dict(call: Any) -> dict[str, Any]: + function = _value(call, "function") + return { + "id": str(_value(call, "id", "")), + "type": "function", + "function": { + "name": str(_value(function, "name", "")), + "arguments": str(_value(function, "arguments", "") or ""), + }, + } + + +async def _invoke_tool(handler: Any, arguments: dict[str, Any]) -> Any: + result = handler(arguments) + return await result if inspect.isawaitable(result) else result + + +async def _execute_tools( + calls: list[Any], + handlers: dict[str, Any], + parent: Any, + ended: set[int], + *, + capture_content: bool, +) -> list[dict[str, Any]]: + async def execute(call: Any) -> dict[str, Any]: + call_dict = _tool_call_dict(call) + function = call_dict["function"] + name = function["name"] + span = _tool_span(name, call_dict["id"], parent) + try: + try: + arguments = json.loads(function["arguments"] or "{}") + except json.JSONDecodeError as exc: + raise ValueError(f'Invalid arguments for tool "{name}"') from exc + if capture_content: + set_tool_call_content_attributes(span, True, arguments=arguments) + handler = handlers.get(name) + if not callable(handler): + raise ValueError(f'No handler registered for tool "{name}"') + result = await _invoke_tool(handler, arguments) + if capture_content: + set_tool_call_content_attributes(span, True, result=result) + content = result if isinstance(result, str) else json.dumps(result) + _succeed_span(span, ended) + return { + "role": "tool", + "tool_call_id": call_dict["id"], + "content": content, + } + except Exception as exc: + _fail_span(span, exc, ended) + raise + finally: + end_span_once(span, ended, abandoned=True) + + return await asyncio.gather(*(execute(call) for call in calls)) + + +def _response_message(response: Any) -> Any: + choices = _value(response, "choices", []) or [] + return _value(choices[0], "message") if choices else None + + +def _output_value(content: Any, output_format: Any) -> Any: + if output_format and isinstance(content, str): + try: + return json.loads(content) + except json.JSONDecodeError: + pass + return content + + +def create_litellm_messages_handler( + *, + completion: Completion | None = None, + capture_content: bool = False, +) -> ProviderHandler: + if completion is None: + from litellm import acompletion + + completion = acompletion + + async def call( + config_value: AiConfigRep, + user_input: str | None = None, + tool_handlers: dict[str, Any] | None = None, + variables: dict[str, Any] | None = None, + history: list[dict[str, Any]] | None = None, + ) -> dict[str, Any]: + handlers = tool_handlers or {} + values = variables or {} + messages = _build_messages(config_value, user_input, values, history) + tools = _build_tools(config_value, handlers) + root = _root_span(config_value, values) + parent = _parent_context(root) + total = SpanUsage() + usage_reported = False + ended: set[int] = set() + open_model: Any = None + try: + if capture_content: + set_input_content_attributes( + root, + True, + messages=_span_messages(messages), + ) + for _ in range(_MAX_TOOL_TURNS + 1): + model_span = _model_span(config_value, parent) + open_model = model_span + try: + if capture_content: + set_input_content_attributes( + model_span, + True, + messages=_span_messages(messages), + ) + response = await completion( + **_request(config_value, messages, tools, stream=False) + ) + turn_usage = _usage(response) + usage_reported = usage_reported or _usage_reported(response) + total.input += turn_usage.input + total.output += turn_usage.output + message = _response_message(response) + content = _value(message, "content") + calls = list(_value(message, "tool_calls", []) or []) + if capture_content: + set_output_content_attributes( + model_span, + True, + [text_message("assistant", str(content or ""))], + ) + _finish_span( + model_span, + turn_usage, + ended, + model=_model_name(config_value), + finish_reason=_finish_reason(response), + ) + open_model = None + except Exception as exc: + _fail_span(model_span, exc, ended) + open_model = None + raise + if not calls: + output = _output_value(content, config_value.get("outputFormat")) + if capture_content: + set_output_content_attributes( + root, True, [text_message("assistant", str(content or ""))] + ) + _finish_span(root, total, ended, model=_model_name(config_value)) + return { + "output": output, + "usage": { + "input_tokens": total.input, + "output_tokens": total.output, + }, + } + messages.append( + { + "role": "assistant", + "content": content, + "tool_calls": [_tool_call_dict(call) for call in calls], + } + ) + messages.extend( + await _execute_tools( + calls, + handlers, + parent, + ended, + capture_content=capture_content, + ) + ) + raise RuntimeError("Tool loop exceeded the maximum number of turns") + except Exception as exc: + if usage_reported: + root.set_attribute("gen_ai.response.model", _model_name(config_value)) + set_usage_span_attributes(root, total) + _fail_span(root, exc, ended) + raise + finally: + if open_model is not None: + end_span_once(open_model, ended, cancelled=True) + end_span_once(root, ended, cancelled=True) + + def stream( + config_value: AiConfigRep, + user_input: str | None = None, + tool_handlers: dict[str, Any] | None = None, + variables: dict[str, Any] | None = None, + history: list[dict[str, Any]] | None = None, + ) -> AsyncGenerator[dict[str, Any], None]: + return _stream( + completion, + config_value, + user_input, + tool_handlers or {}, + variables or {}, + history, + capture_content=capture_content, + ) + + return create_handler( + ("*", "messages"), + call, + stream, + capture_content=capture_content, + ) + + +async def _stream( + completion: Completion, + config_value: AiConfigRep, + user_input: str | None, + handlers: dict[str, Any], + variables: dict[str, Any], + history: list[dict[str, Any]] | None, + *, + capture_content: bool, +) -> AsyncGenerator[dict[str, Any], None]: + messages = _build_messages(config_value, user_input, variables, history) + tools = _build_tools(config_value, handlers) + root = _root_span(config_value, variables) + parent = _parent_context(root) + total = SpanUsage() + usage_reported = False + provider_stream: Any = None + completed = False + full_output = "" + ended: set[int] = set() + open_model: Any = None + cancelled = False + try: + if capture_content: + set_input_content_attributes( + root, + True, + messages=_span_messages(messages), + ) + for _ in range(_MAX_TOOL_TURNS + 1): + model_span = _model_span(config_value, parent) + open_model = model_span + turn_usage = SpanUsage() + try: + if capture_content: + set_input_content_attributes( + model_span, + True, + messages=_span_messages(messages), + ) + provider_stream = await completion( + **_request(config_value, messages, tools, stream=True) + ) + fragments: dict[int, dict[str, Any]] = {} + turn_text = "" + async for chunk in provider_stream: + chunk_usage = _usage(chunk) + usage_reported = usage_reported or _usage_reported(chunk) + turn_usage.input += chunk_usage.input + turn_usage.output += chunk_usage.output + total.input += chunk_usage.input + total.output += chunk_usage.output + choices = _value(chunk, "choices", []) or [] + delta = _value(choices[0], "delta") if choices else None + text = _value(delta, "content") + if isinstance(text, str) and text: + turn_text += text + yield {"type": "chunk", "text": text} + for call in _value(delta, "tool_calls", []) or []: + index = int(_value(call, "index", 0) or 0) + current = fragments.setdefault( + index, {"id": "", "name": "", "arguments": ""} + ) + current["id"] += str(_value(call, "id", "") or "") + function = _value(call, "function") + current["name"] += str(_value(function, "name", "") or "") + current["arguments"] += str( + _value(function, "arguments", "") or "" + ) + await provider_stream.aclose() + provider_stream = None + if capture_content: + set_output_content_attributes( + model_span, + True, + [text_message("assistant", turn_text)], + ) + _finish_span( + model_span, + turn_usage, + ended, + model=_model_name(config_value), + ) + open_model = None + except Exception as exc: + _fail_span(model_span, exc, ended) + open_model = None + raise + if not fragments: + full_output += turn_text + completed = True + if capture_content: + set_output_content_attributes( + root, True, [text_message("assistant", full_output)] + ) + _finish_span(root, total, ended, model=_model_name(config_value)) + yield { + "type": "done", + "output": full_output, + "usage": { + "input_tokens": total.input, + "output_tokens": total.output, + }, + } + return + calls = [ + { + "id": value["id"], + "type": "function", + "function": { + "name": value["name"], + "arguments": value["arguments"], + }, + } + for _, value in sorted(fragments.items()) + ] + messages.append( + {"role": "assistant", "content": turn_text or None, "tool_calls": calls} + ) + messages.extend( + await _execute_tools( + calls, + handlers, + parent, + ended, + capture_content=capture_content, + ) + ) + raise RuntimeError("Tool loop exceeded the maximum number of turns") + except asyncio.CancelledError: + cancelled = True + raise + except Exception as exc: + if usage_reported: + root.set_attribute("gen_ai.response.model", _model_name(config_value)) + set_usage_span_attributes(root, total) + _fail_span(root, exc, ended) + completed = True + raise + finally: + if provider_stream is not None: + try: + await provider_stream.aclose() + except Exception: + pass + if not completed: + if open_model is not None: + end_span_once(open_model, ended, abandoned=True, cancelled=cancelled) + if usage_reported: + root.set_attribute("gen_ai.response.model", _model_name(config_value)) + set_usage_span_attributes(root, total) + end_span_once(root, ended, abandoned=True, cancelled=cancelled) + + +def litellm_messages( + config_key: str, + user_input: str, + context: LDContext, + **kwargs: Any, +) -> Any: + variables = kwargs.pop("variables", None) + capture_content = bool(kwargs.pop("capture_content", False)) + return config( + key=config_key, + handler=create_litellm_messages_handler(capture_content=capture_content), + **kwargs, + ).invoke(user_input, context, variables) diff --git a/packages/litellm-messages/tests/test_handler.py b/packages/litellm-messages/tests/test_handler.py new file mode 100644 index 00000000..5676b495 --- /dev/null +++ b/packages/litellm-messages/tests/test_handler.py @@ -0,0 +1,644 @@ +"""Contract tests for the future LiteLLM messages package. + +These imports are intentionally unresolved at the test-first checkpoint. +""" + +from __future__ import annotations + +import asyncio +import json +from collections.abc import AsyncIterator +from types import SimpleNamespace +from typing import Any +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +import launchdarkly_ai_litellm_messages.handler as handler_mod +from launchdarkly_ai_litellm_messages import ( + create_litellm_messages_handler, + litellm_messages, +) + +CONFIG: dict[str, Any] = { + "provider": {"name": "Anthropic"}, + "model": {"name": "anthropic/claude-sonnet-4"}, + "instructions": "Help {{name}}.", +} + + +def _message( + content: str | None = "done", + *, + tool_calls: list[Any] | None = None, +) -> Any: + return SimpleNamespace(content=content, tool_calls=tool_calls or []) + + +def _response( + content: str | None = "done", + *, + tool_calls: list[Any] | None = None, + prompt_tokens: int = 3, + completion_tokens: int = 2, +) -> Any: + return SimpleNamespace( + choices=[SimpleNamespace(message=_message(content, tool_calls=tool_calls))], + usage=SimpleNamespace( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + ), + model="resolved-by-litellm", + ) + + +def _tool_call(name: str, call_id: str, arguments: str) -> Any: + return SimpleNamespace( + id=call_id, + type="function", + function=SimpleNamespace(name=name, arguments=arguments), + ) + + +async def _events(stream: Any) -> list[dict[str, Any]]: + return [event async for event in stream] + + +class FakeStream: + def __init__(self, chunks: list[Any]) -> None: + self._chunks = chunks + self.closed = False + + def __aiter__(self) -> AsyncIterator[Any]: + return self._iterate() + + async def _iterate(self) -> AsyncIterator[Any]: + for chunk in self._chunks: + yield chunk + + async def aclose(self) -> None: + self.closed = True + + +class RecordedSpan: + def __init__(self, name: str, context: Any = None) -> None: + self.name = name + self.context = context + self.attributes: dict[str, Any] = {} + self.exceptions: list[BaseException] = [] + self.ended = 0 + + def set_attribute(self, key: str, value: Any) -> None: + self.attributes[key] = value + + def add_event(self, name: str, attributes: Any = None) -> None: + pass + + def set_status(self, code: Any, description: str | None = None) -> None: + pass + + def record_exception(self, error: BaseException) -> None: + self.exceptions.append(error) + + def end(self) -> None: + self.ended += 1 + + +class SpanRecorder: + def __init__(self) -> None: + self.spans: list[RecordedSpan] = [] + + def get_tracer(self, name: str) -> SpanRecorder: + return self + + def start_span(self, name: str, context: Any = None) -> RecordedSpan: + span = RecordedSpan(name, context) + self.spans.append(span) + return span + + def set_span_in_context(self, span: RecordedSpan) -> Any: + return ("context-of", span) + + +def _chunk( + *, + text: str | None = None, + tool_calls: list[Any] | None = None, + prompt_tokens: int | None = None, + completion_tokens: int | None = None, +) -> Any: + usage = None + if prompt_tokens is not None or completion_tokens is not None: + usage = SimpleNamespace( + prompt_tokens=prompt_tokens or 0, + completion_tokens=completion_tokens or 0, + ) + return SimpleNamespace( + choices=[ + SimpleNamespace( + delta=SimpleNamespace(content=text, tool_calls=tool_calls or []) + ) + ], + usage=usage, + ) + + +class TestFactoryAndTransport: + def test_wildcard_metadata_and_capture_content(self) -> None: + handler = create_litellm_messages_handler(capture_content=True) + assert handler.provides_for == ("*", "messages") + assert handler.capture_content is True + assert create_litellm_messages_handler().capture_content is False + + async def test_calls_in_process_litellm_acompletion(self) -> None: + completion = AsyncMock(return_value=_response()) + handler = create_litellm_messages_handler(completion=completion) + await handler(CONFIG, "question", {}, {"name": "Ada"}) + completion.assert_awaited_once() + assert completion.call_args.kwargs["model"] == "anthropic/claude-sonnet-4" + + async def test_never_constructs_a_provider_direct_client(self) -> None: + completion = AsyncMock(return_value=_response()) + forbidden = MagicMock(side_effect=AssertionError("provider-direct path")) + with ( + patch.dict( + "sys.modules", + { + "anthropic": SimpleNamespace(AsyncAnthropic=forbidden), + "openai": SimpleNamespace(AsyncOpenAI=forbidden), + "boto3": SimpleNamespace(client=forbidden), + }, + ), + ): + await create_litellm_messages_handler(completion=completion)( + CONFIG, "question" + ) + forbidden.assert_not_called() + + async def test_evaluated_model_and_owned_parameter_precedence(self) -> None: + completion = AsyncMock(return_value=_response()) + config = { + **CONFIG, + "model": { + "name": "proxy/team-alias", + "parameters": { + "temperature": 0.4, + "model": "wrong", + "messages": ["wrong"], + "tools": ["wrong"], + "stream": True, + "response_format": {"type": "wrong"}, + "api_base": "http://127.0.0.1:9", + "api_key": "provider-secret", + "extra_headers": {"X-Smuggled": "1"}, + }, + }, + } + await create_litellm_messages_handler(completion=completion)(config, "question") + request = completion.call_args.kwargs + assert request["model"] == "proxy/team-alias" + assert request["temperature"] == 0.4 + assert request["messages"] != ["wrong"] + assert request.get("tools") != ["wrong"] + assert request["stream"] is False + assert "response_format" not in request + assert "api_base" not in request + assert "api_key" not in request + assert "extra_headers" not in request + + +class TestPromptMapping: + async def test_messages_take_precedence_over_instructions(self) -> None: + completion = AsyncMock(return_value=_response()) + config = { + **CONFIG, + "instructions": "ignored", + "messages": [ + {"role": "system", "content": "System {{name}}"}, + {"role": "assistant", "content": "prior"}, + ], + } + await create_litellm_messages_handler(completion=completion)( + config, "new", {}, {"name": "Ada"} + ) + messages = completion.call_args.kwargs["messages"] + assert messages == [ + {"role": "system", "content": "System Ada"}, + {"role": "assistant", "content": "prior"}, + {"role": "user", "content": "new"}, + ] + assert "ignored" not in str(messages) + + async def test_last_user_config_message_prevents_duplicate_input(self) -> None: + completion = AsyncMock(return_value=_response()) + config = { + **CONFIG, + "messages": [{"role": "user", "content": "{{user_input}}"}], + } + await create_litellm_messages_handler(completion=completion)( + config, "hello", {}, {"user_input": "hello"} + ) + messages = completion.call_args.kwargs["messages"] + assert messages == [{"role": "user", "content": "hello"}] + + async def test_history_precedes_prompt_and_maps_multimodal_blocks(self) -> None: + completion = AsyncMock(return_value=_response()) + history = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "look"}, + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/png", + "data": "abc", + }, + }, + ], + }, + {"role": "assistant", "content": "old answer"}, + ] + await create_litellm_messages_handler(completion=completion)( + CONFIG, "new question", {}, {"name": "Ada"}, history + ) + messages = completion.call_args.kwargs["messages"] + assert messages[1]["content"] == [ + {"type": "text", "text": "look"}, + { + "type": "image_url", + "image_url": {"url": "data:image/png;base64,abc"}, + }, + ] + assert messages[-1] == {"role": "user", "content": "new question"} + + +class TestToolsAndUsage: + async def test_filters_tools_without_callable_handlers(self) -> None: + completion = AsyncMock(return_value=_response()) + config = { + **CONFIG, + "tools": { + "kept": {"description": "yes", "parameters": {"type": "object"}}, + "missing": {"description": "no", "parameters": {}}, + "not_callable": {"description": "no", "parameters": {}}, + }, + } + await create_litellm_messages_handler(completion=completion)( + config, + "q", + {"kept": AsyncMock(), "not_callable": object()}, + ) + assert completion.call_args.kwargs["tools"] == [ + { + "type": "function", + "function": { + "name": "kept", + "description": "yes", + "parameters": {"type": "object"}, + }, + } + ] + + async def test_single_tool_call_appends_assistant_and_result(self) -> None: + call = _tool_call("weather", "call-1", '{"city":"Austin"}') + completion = AsyncMock( + side_effect=[_response(None, tool_calls=[call]), _response("sunny")] + ) + tool = AsyncMock(return_value={"forecast": "sunny"}) + config = { + **CONFIG, + "tools": {"weather": {"description": "weather", "parameters": {}}}, + } + result = await create_litellm_messages_handler(completion=completion)( + config, "q", {"weather": tool} + ) + tool.assert_awaited_once_with({"city": "Austin"}) + follow_up = completion.call_args_list[1].kwargs["messages"] + assert follow_up[-2]["role"] == "assistant" + assert follow_up[-2]["tool_calls"][0]["id"] == "call-1" + assert follow_up[-1] == { + "role": "tool", + "tool_call_id": "call-1", + "content": json.dumps({"forecast": "sunny"}), + } + assert result["output"] == "sunny" + + async def test_parallel_and_consecutive_calls_all_execute(self) -> None: + calls = [ + _tool_call("a", "a1", '{"x":1}'), + _tool_call("b", "b1", '{"y":2}'), + ] + third = _tool_call("a", "a2", '{"x":3}') + completion = AsyncMock( + side_effect=[ + _response(None, tool_calls=calls, prompt_tokens=2, completion_tokens=1), + _response( + None, tool_calls=[third], prompt_tokens=3, completion_tokens=1 + ), + _response("done", prompt_tokens=5, completion_tokens=2), + ] + ) + a = AsyncMock(side_effect=["A1", "A2"]) + b = AsyncMock(return_value="B1") + config = { + **CONFIG, + "tools": { + "a": {"description": "a", "parameters": {}}, + "b": {"description": "b", "parameters": {}}, + }, + } + result = await create_litellm_messages_handler(completion=completion)( + config, "q", {"a": a, "b": b} + ) + assert a.await_args_list[0].args == ({"x": 1},) + b.assert_awaited_once_with({"y": 2}) + assert a.await_args_list[1].args == ({"x": 3},) + assert result["usage"] == {"input_tokens": 10, "output_tokens": 4} + + +class TestStructuredOutput: + async def test_response_format_is_handler_owned_and_json_is_best_effort( + self, + ) -> None: + completion = AsyncMock(return_value=_response('{"answer": 42}')) + schema = {"type": "object", "properties": {"answer": {"type": "integer"}}} + result = await create_litellm_messages_handler(completion=completion)( + {**CONFIG, "outputFormat": schema}, "q" + ) + assert completion.call_args.kwargs["response_format"] == { + "type": "json_schema", + "json_schema": {"name": "output", "strict": False, "schema": schema}, + } + assert result["output"] == {"answer": 42} + + async def test_response_format_remains_on_final_tool_turn(self) -> None: + call = _tool_call("lookup", "c1", "{}") + completion = AsyncMock( + side_effect=[_response(None, tool_calls=[call]), _response('{"ok":true}')] + ) + config = { + **CONFIG, + "outputFormat": {"type": "object"}, + "tools": {"lookup": {"description": "x", "parameters": {}}}, + } + await create_litellm_messages_handler(completion=completion)( + config, "q", {"lookup": AsyncMock(return_value="ok")} + ) + assert "response_format" in completion.call_args_list[0].kwargs + assert "response_format" in completion.call_args_list[1].kwargs + + +class TestStreaming: + async def test_streaming_ignores_output_format(self) -> None: + stream = FakeStream([_chunk(text='{"answer":42}')]) + completion = AsyncMock(return_value=stream) + events = await _events( + await create_litellm_messages_handler(completion=completion).stream( + {**CONFIG, "outputFormat": {"type": "object"}}, "q" + ) + ) + assert "response_format" not in completion.call_args.kwargs + assert events[-1]["output"] == '{"answer":42}' + + async def test_text_chunks_usage_and_single_done(self) -> None: + stream = FakeStream( + [ + _chunk(text="Hel"), + _chunk(text="lo"), + _chunk(prompt_tokens=4, completion_tokens=2), + ] + ) + completion = AsyncMock(return_value=stream) + handler = create_litellm_messages_handler(completion=completion) + events = await _events(await handler.stream(CONFIG, "q")) + assert [e["text"] for e in events if e["type"] == "chunk"] == ["Hel", "lo"] + assert events[-1] == { + "type": "done", + "output": "Hello", + "usage": {"input_tokens": 4, "output_tokens": 2}, + } + assert sum(e["type"] == "done" for e in events) == 1 + request = completion.call_args.kwargs + assert request["stream"] is True + assert request["stream_options"] == {"include_usage": True} + + async def test_fragmented_tool_call_is_reassembled_between_turns(self) -> None: + first = FakeStream( + [ + _chunk( + tool_calls=[ + SimpleNamespace( + index=0, + id="call-", + function=SimpleNamespace( + name="weath", arguments='{"city":' + ), + ) + ] + ), + _chunk( + tool_calls=[ + SimpleNamespace( + index=0, + id="1", + function=SimpleNamespace(name="er", arguments='"Oslo"}'), + ) + ] + ), + _chunk(prompt_tokens=2, completion_tokens=1), + ] + ) + second = FakeStream( + [_chunk(text="cold"), _chunk(prompt_tokens=3, completion_tokens=1)] + ) + completion = AsyncMock(side_effect=[first, second]) + weather = AsyncMock(return_value="cold") + config = { + **CONFIG, + "tools": {"weather": {"description": "x", "parameters": {}}}, + } + events = await _events( + await create_litellm_messages_handler(completion=completion).stream( + config, "q", {"weather": weather} + ) + ) + weather.assert_awaited_once_with({"city": "Oslo"}) + assert events[-1]["usage"] == {"input_tokens": 5, "output_tokens": 2} + + async def test_abandonment_closes_provider_stream(self) -> None: + stream = FakeStream([_chunk(text="one"), _chunk(text="two")]) + completion = AsyncMock(return_value=stream) + gen = await create_litellm_messages_handler(completion=completion).stream( + CONFIG, "q" + ) + async for _ in gen: + break + await gen.aclose() + assert stream.closed is True + + async def test_cancelled_stream_is_marked_cancelled_not_abandoned(self) -> None: + class CancellingStream(FakeStream): + async def _iterate(self) -> AsyncIterator[Any]: + yield _chunk(text="one") + raise asyncio.CancelledError + + recorder = SpanRecorder() + completion = AsyncMock(return_value=CancellingStream([])) + with ( + patch.object(handler_mod, "trace", recorder), + pytest.raises(asyncio.CancelledError), + ): + await _events( + await create_litellm_messages_handler(completion=completion).stream( + CONFIG, "q" + ) + ) + root = recorder.spans[0] + assert root.attributes.get("launchdarkly.run.cancelled") is True + assert "launchdarkly.stream.abandoned" not in root.attributes + + async def test_stream_error_closes_transport_and_propagates(self) -> None: + class BrokenStream(FakeStream): + async def _iterate(self) -> AsyncIterator[Any]: + yield _chunk(text="partial") + raise RuntimeError("stream failed") + + stream = BrokenStream([]) + completion = AsyncMock(return_value=stream) + with pytest.raises(RuntimeError, match="stream failed"): + await _events( + await create_litellm_messages_handler(completion=completion).stream( + CONFIG, "q" + ) + ) + assert stream.closed is True + + +class TestTelemetryAndConvenience: + async def test_nested_span_tree_identity_usage_and_parenting(self) -> None: + call = _tool_call("lookup", "call-1", "{}") + completion = AsyncMock( + side_effect=[_response(None, tool_calls=[call]), _response("done")] + ) + recorder = SpanRecorder() + config = { + **CONFIG, + "tools": {"lookup": {"description": "lookup", "parameters": {}}}, + } + with patch.object(handler_mod, "trace", recorder): + await create_litellm_messages_handler(completion=completion)( + config, "q", {"lookup": AsyncMock(return_value="found")} + ) + + assert [span.name for span in recorder.spans] == [ + "invoke_agent", + "chat anthropic/claude-sonnet-4", + "execute_tool lookup", + "chat anthropic/claude-sonnet-4", + ] + root = recorder.spans[0] + for child in recorder.spans[1:]: + assert child.context == ("context-of", root) + for span in recorder.spans[:2] + recorder.spans[3:]: + assert span.attributes["gen_ai.provider.name"] == "anthropic" + assert span.attributes["gen_ai.system"] == "litellm" + assert span.ended == 1 + assert root.attributes["gen_ai.usage.input_tokens"] == 6 + assert recorder.spans[2].attributes["gen_ai.tool.call.id"] == "call-1" + assert recorder.spans[2].ended == 1 + + async def test_model_and_root_spans_fail_and_end_once(self) -> None: + recorder = SpanRecorder() + error = RuntimeError("unavailable") + with ( + patch.object(handler_mod, "trace", recorder), + pytest.raises(RuntimeError, match="unavailable"), + ): + await create_litellm_messages_handler( + completion=AsyncMock(side_effect=error) + )(CONFIG, "q") + assert [span.name for span in recorder.spans] == [ + "invoke_agent", + "chat anthropic/claude-sonnet-4", + ] + assert all(span.exceptions == [error] for span in recorder.spans) + assert all(span.ended == 1 for span in recorder.spans) + + async def test_identity_and_content_are_gated(self) -> None: + completion = AsyncMock(return_value=_response("secret")) + span = MagicMock() + trace = MagicMock() + trace.get_tracer.return_value.start_span.return_value = span + variables = { + "__ld": { + "configKey": "cfg", + "variationKey": "v1", + "runId": "run", + }, + "ldContext": {"kind": "user", "key": "ada"}, + } + with patch.object(handler_mod, "trace", trace): + await create_litellm_messages_handler(completion=completion)( + CONFIG, "private prompt", {}, variables + ) + attributes = { + call.args[0]: call.args[1] for call in span.set_attribute.call_args_list + } + assert attributes["launchdarkly.config.key"] == "cfg" + assert attributes["gen_ai.request.model"] == "anthropic/claude-sonnet-4" + assert not any(key.startswith("gen_ai.prompt.") for key in attributes) + + async def test_capture_content_records_prompt_and_completion(self) -> None: + completion = AsyncMock(return_value=_response("answer")) + span = MagicMock() + trace = MagicMock() + trace.get_tracer.return_value.start_span.return_value = span + with patch.object(handler_mod, "trace", trace): + await create_litellm_messages_handler( + completion=completion, capture_content=True + )(CONFIG, "question") + written = str(span.set_attribute.call_args_list) + assert "question" in written + assert "answer" in written + + async def test_stream_capture_content_records_prompt_and_completion_on_root( + self, + ) -> None: + stream = FakeStream([_chunk(text="streamed")]) + completion = AsyncMock(return_value=stream) + span = MagicMock() + trace = MagicMock() + trace.get_tracer.return_value.start_span.return_value = span + with patch.object(handler_mod, "trace", trace): + events = await _events( + await create_litellm_messages_handler( + completion=completion, capture_content=True + ).stream(CONFIG, "question") + ) + written = str(span.set_attribute.call_args_list) + assert "question" in written + assert "streamed" in written + assert events[-1]["type"] == "done" + + def test_convenience_wrapper_forwards_all_public_arguments(self) -> None: + instance = MagicMock() + instance.invoke.return_value = "result" + config = MagicMock(return_value=instance) + with patch.object(handler_mod, "config", config): + result = litellm_messages( + "config-key", + "hello", + {"kind": "user", "key": "u"}, + variables={"name": "Ada"}, + capture_content=True, + ) + assert result == "result" + assert config.call_args.kwargs["key"] == "config-key" + assert config.call_args.kwargs["handler"].provides_for == ("*", "messages") + assert config.call_args.kwargs["handler"].capture_content is True + assert "capture_content" not in config.call_args.kwargs + instance.invoke.assert_called_once_with( + "hello", {"kind": "user", "key": "u"}, {"name": "Ada"} + ) diff --git a/pyproject.toml b/pyproject.toml index 99e24585..02b2bbdb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -64,6 +64,8 @@ known-first-party = [ "launchdarkly_ai_claude_messages", "launchdarkly_ai_langchain_agents", "launchdarkly_ai_langchain_messages", + "launchdarkly_ai_litellm_agents", + "launchdarkly_ai_litellm_messages", "launchdarkly_ai_openai_agents", "launchdarkly_ai_openai_messages", ] diff --git a/release-please-config.json b/release-please-config.json index 9d2a04e3..783b292c 100644 --- a/release-please-config.json +++ b/release-please-config.json @@ -89,6 +89,28 @@ "src/launchdarkly_ai_langchain_messages/__init__.py" ], "component": "launchdarkly-ai-langchain-messages" + }, + "packages/litellm-agents": { + "release-type": "python", + "versioning": "default", + "bump-minor-pre-major": true, + "bump-patch-for-minor-pre-major": true, + "include-v-in-tag": false, + "extra-files": [ + "src/launchdarkly_ai_litellm_agents/__init__.py" + ], + "component": "launchdarkly-ai-litellm-agents" + }, + "packages/litellm-messages": { + "release-type": "python", + "versioning": "default", + "bump-minor-pre-major": true, + "bump-patch-for-minor-pre-major": true, + "include-v-in-tag": false, + "extra-files": [ + "src/launchdarkly_ai_litellm_messages/__init__.py" + ], + "component": "launchdarkly-ai-litellm-messages" } } } diff --git a/uv.lock b/uv.lock index d8fba767..f59b03ce 100644 --- a/uv.lock +++ b/uv.lock @@ -17,6 +17,8 @@ members = [ "launchdarkly-ai-claude-messages", "launchdarkly-ai-langchain-agents", "launchdarkly-ai-langchain-messages", + "launchdarkly-ai-litellm-agents", + "launchdarkly-ai-litellm-messages", "launchdarkly-ai-openai-agents", "launchdarkly-ai-openai-messages", "launchdarkly-ai-python", @@ -42,6 +44,170 @@ dev = [ { name = "ruff", specifier = ">=0.15.20" }, ] +[[package]] +name = "aiohappyeyeballs" +version = "2.7.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = 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