Metadata-Version: 2.4
Name: lm15
Version: 1.0.0rc3
Summary: Provider-neutral, low-level foundation for LLM APIs: one canonical representation, exact serde, adapters for OpenAI/Anthropic/Gemini and every Chat Completions-compatible server — stdlib-only.
Author-email: Maxime Rivest <mrive052@gmail.com>
License: MIT License
        
        Copyright (c) 2026 lm15 contributors
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
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Project-URL: Homepage, https://lm15.dev
Project-URL: Documentation, https://lm15.dev/docs/
Project-URL: Playground, https://lm15.dev/playground/
Project-URL: Source, https://github.com/lm15-dev/lm15-python
Project-URL: Issues, https://github.com/lm15-dev/lm15-python/issues
Project-URL: Changelog, https://github.com/lm15-dev/lm15-python/blob/main/CHANGELOG.md
Keywords: llm,openai,anthropic,gemini,ollama,provider-neutral,ai
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Typing :: Typed
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Provides-Extra: live
Requires-Dist: websockets>=12; extra == "live"
Dynamic: license-file

# lm15

**lm15** is a small, typed, provider-neutral interface for foundation-model requests, responses, streams, tools, media parts, endpoint APIs, errors, and canonical JSON serialization. This repository is its Python reference implementation.

**What lm15 is — and deliberately is not.** lm15 is a low-level foundation library: one canonical representation, exact serde for it, and adapters that translate it to and from each provider's wire format — stdlib-only, with its own HTTP transport (`websockets` is the single optional extra, for live sessions). It is NOT an opinionated user-facing API: no magic `call()`, no automatic tool loops, no DSL. lm15 is meant to be **the dependency** for libraries that want to build their own take on the right way to talk to AI systems in Python — you bring the opinions, lm15 brings every provider.

The public API is the top-level package: `from lm15 import AnthropicLM, Request, Message, ...` (see `lm15/__init__.py` for the full curated surface). Transport plumbing stays under `lm15.transports`, live sessions under `lm15.live`, and the conformance shim under `lm15.vet`.

Every Python block below is executed by an offline regression test with simulated
provider replies. The displayed outputs are captured from live runs; model text,
token counts, and image sizes vary. Related blocks share variables: run them in
order within each section.

Live examples require the named API keys, Ollama running with `qwen3.5:0.8b`
installed, or a valid CLI subscription login as indicated. Hosted calls, including
web search and image generation, may incur charges.

<!-- footprint:generated:start -->
## Footprint

Measured by [`benchmarks/suite/run.py`](benchmarks/suite/run.py) on Python 3.13.3 — methodology and full results in [benchmarks/BENCHMARKS.md](benchmarks/BENCHMARKS.md):

| package | install size | transitive deps | cold import | import RSS |
|---|---:|---:|---:|---:|
| **lm15** | 0.5 MiB | 0 | 152 ms | 16.6 MiB |
| openai | 18.0 MiB | 15 | 468 ms | 35.3 MiB |
| anthropic | 17.1 MiB | 15 | 589 ms | 41.2 MiB |
| google-genai | 37.2 MiB | 24 | 934 ms | 60.8 MiB |
| litellm | 133.0 MiB | 54 | 2298 ms | 161.0 MiB |
| langchain-openai | 63.3 MiB | 35 | 930 ms | 61.0 MiB |
<!-- footprint:generated:end -->

## Install

The current 1.0 release candidate is **1.0.0rc3**. Opt into prereleases to
use the API documented here; without `--pre`, pip selects the older stable
release (0.9.9.post1). To pin this candidate exactly, use `lm15==1.0.0rc3`.

```bash
python3 -m pip install --pre lm15
# Optional extra for websocket live sessions:
python3 -m pip install --pre 'lm15[live]'
```

Or from source, for development:

```bash
git clone https://github.com/lm15-dev/lm15-python && cd lm15-python
python3 -m pip install -e '.[live]'
```

lm15 has zero required dependencies — it is stdlib-only, including its HTTP transports.

## Stability at 1.0

The chat core, credential resolution and model listing have a frozen contract.
Files, batches, standalone media generation, stored-cache resources, live
sessions and Chat Completions ingest ship as **provisional**: they may change
incompatibly in 1.x with a contract change entry. Pin your version when using
them. See [release scope](docs/roadmap.md#what-ships-in-10-and-what-is-stable)
for the existing policy and its limits.

## Cached-prefix routing

`router.cache(prefix)` records the resolved destination in the optional canonical
`CachedPrefix.provider` field. `prefix.model` and `resource.model` remain wire
model names (and must match); `cached.request(...)` emits `provider:wiremodel`.
This metadata survives serialization, including router-local provider names.
Reuse it with the same router configuration/account: it contains no credentials,
endpoint, or provider declaration. The router's bound `router.lm(...)` also accepts
the qualified request and strips only its own provider prefix, once.

Direct `lm.cache` with a bare model and manually constructed values without
`provider` keep the previous unqualified behavior. Explicit own-provider prefixes
retain their route; underscore input aliases normalize to hyphens. A suffix Request
may name the wire model or the same qualified destination, not another provider.
Absent `provider` is omitted from canonical serialization. This routing correction
and its regression sources have not been execution-verified; no new conformance
claim is implied.

## Quickstart

```python
import os

from lm15 import Config, Message, OpenAILM, Request

lm = OpenAILM(api_key=os.environ["OPENAI_API_KEY"])

response = lm.complete(
    Request(
        model="gpt-4.1-mini",
        system="You are terse.",
        messages=(Message.user("Say hello in three words."),),
        config=Config(max_tokens=50, temperature=0.2),
    )
)

print(response.text)
print(response.finish_reason)
print(response.usage.total_tokens)
```

```output
Hello there, friend.
stop
27
```

The mental model is one straight line:

```text
Message parts → Message → Request → ProviderLM → Response
                              │
                              └── stream() → StreamEvent → materialized Response
```

## One Request, every provider

The exact same `Request` shape drives the three first-party adapters:

```python
import os

from lm15 import AnthropicLM, GeminiLM, Message, Request

providers = [
    AnthropicLM(api_key=os.environ["ANTHROPIC_API_KEY"]),
    GeminiLM(api_key=os.environ["GEMINI_API_KEY"]),
]

for lm in providers:
    response = lm.complete(
        Request(
            model={
                "anthropic": "claude-sonnet-4-5",
                "gemini": "gemini-3-flash-preview",
            }[lm.provider],
            messages=(Message.user("Say hello."),),
        )
    )
    print(lm.provider, response.text)
```

```output
anthropic Hello! 👋 How can I help you today?
gemini Hello! How can I help you today?
```

And the same shape reaches every OpenAI-compatible server through `OpenAIChatLM`, the Chat Completions dialect adapter. A compat preset name — `"ollama"`, `"groq"`, `"openrouter"`, `"deepseek"`, `"zai"`, `"moonshotai"`, `"meta"`, `"vllm"`, `"sglang"`, ... — bundles that server's wire-format quirks *and* its default `base_url`, so a local Ollama is one constructor argument away:

```python
from lm15 import Config, Message, OpenAIChatLM, Request

lm = OpenAIChatLM(api_key="ollama", compat="ollama")  # base_url -> http://localhost:11434/v1

response = lm.complete(
    Request(
        model="qwen3.5:0.8b",
        messages=(Message.user("Say hello in five words or fewer."),),
        config=Config(max_tokens=80, extensions={"reasoning_effort": "none"}),
    )
)

print(response.text)
```

```output
Hello! I am Qwen3.5, the latest large language model developed by Tongyi Lab. How can I assist you today?
```

To switch to `compat="groq"` or `compat="openrouter"`, supply that server's key
**and a model ID it supports**; an Ollama model ID is not portable between servers.
Pass an explicit `base_url` to change a preset's address. Server-specific knobs
ride in `Config.extensions` and pass through verbatim. For an unlisted server,
see [custom compatibility policies](docs/connecting-openai-compatible-servers.md).

## Streaming

`stream()` yields typed `StreamEvent` objects. Text arrives as `StreamDeltaEvent(delta=TextDelta(...))`, and the stream is normalized across providers: exactly one `StreamEndEvent` ends the stream, carrying `finish_reason` and `usage` (mapping rule MAP-3).

```python
import os

from lm15 import Message, OpenAILM, Request, StreamDeltaEvent, TextDelta

lm = OpenAILM(api_key=os.environ["OPENAI_API_KEY"])
request = Request(
    model="gpt-4.1-mini",
    messages=(Message.user("Write one short sentence about Montreal."),),
)

for event in lm.stream(request):
    if isinstance(event, StreamDeltaEvent) and isinstance(event.delta, TextDelta):
        print(event.delta.text, end="", flush=True)
```

```output
Montreal is a vibrant city in Canada known for its rich culture and beautiful architecture.
```

To consume a stream into a full `Response` (this makes a second request, rather
than reusing the stream above):

```python
from lm15 import materialize_response

response = materialize_response(lm.stream(request), request)
print(response.text)
```

```output
Montreal is a vibrant cultural hub in Canada known for its historic architecture and diverse cuisine.
```

The materialized `Response` is identical in shape to one from `complete()` — same `message`, `finish_reason`, `usage`, and `provider_data`.

## Tools: the full round-trip

lm15 distinguishes **function tools** that your application executes from **provider-native built-in tools** like web search. Here is the complete function-tool round-trip — model asks, you run your function, you answer back:

```python
import os

from lm15 import Config, FunctionTool, Message, OpenAILM, Request, ToolChoice

lm = OpenAILM(api_key=os.environ["OPENAI_API_KEY"])

def get_weather(city: str) -> str:
    return f"Sunny and 22°C in {city}."

weather_tool = FunctionTool(
    name="get_weather",
    description="Get the current weather for a city.",
    parameters={
        "type": "object",
        "properties": {"city": {"type": "string"}},
        "required": ["city"],
    },
)

messages = (Message.user("What is the weather in Montreal?"),)
request = Request(
    model="gpt-4.1-mini",
    messages=messages,
    tools=(weather_tool,),
    config=Config(tool_choice=ToolChoice(mode="required", parallel=False)),
)

response = lm.complete(request)
for call in response.tool_calls:
    print(call.name, call.input)
```

```output
get_weather {'city': 'Montreal'}
```

This example requires one tool call and disables parallel calls, so the next
block can consume that single call. With the default automatic tool choice,
a model may answer without calling a tool; do not index an empty `tool_calls`.

Now run your function and hand the result back. The model's tool-call turn is
`response.message`; your answer is `Message.tool(call_id, result)`. The final
request disables further tool calls so this demonstration ends with an answer:

```python
call = response.tool_calls[0]
result = get_weather(**call.input)

messages = (*messages, response.message, Message.tool(call.id, result))
final = lm.complete(Request(
    model="gpt-4.1-mini",
    messages=messages,
    tools=(weather_tool,),
    config=Config(tool_choice=ToolChoice(mode="none")),
))
print(final.text)
```

```output
The weather in Montreal is currently sunny with a temperature of 22°C.
```

lm15 will never run the loop for you — that's your layer. This is the whole loop.

Built-in tools are provider-executed; you just declare them and read the results (citations come back as typed parts):

```python
from lm15 import BuiltinTool, Message, Request

response = lm.complete(
    Request(
        model="gpt-4.1-mini",
        messages=(Message.user("Where will the 2028 Summer Olympics be held? One sentence, cite a source."),),
        tools=(BuiltinTool("web_search"),),
    )
)

print(response.text)
for citation in response.citations:
    print(citation.title, citation.url)
```

```output
The 2028 Summer Olympics will be held in Los Angeles, California, United States, from July 14 to 30, 2028. ([en.wikipedia.org](https://en.wikipedia.org/wiki/2028_Summer_Olympics?utm_source=openai))
2028 Summer Olympics https://en.wikipedia.org/wiki/2028_Summer_Olympics?utm_source=openai
```

## Async

Every adapter has an async mirror — `AsyncOpenAILM`, `AsyncAnthropicLM`, `AsyncGeminiLM`, `AsyncOpenAIChatLM`, `AsyncClaudeCodeLM`, `AsyncOpenAICodexLM` — with the same constructor fields, the same canonical `Request` in, and the same `Response`/stream events out. `await` is the only difference: `complete()` is `async def`, and `stream()` is an `async for`-able iterator of the same events.

```python
import asyncio

from lm15 import (
    AsyncOpenAIChatLM,
    Config,
    Message,
    Request,
    StreamDeltaEvent,
    TextDelta,
)

async def main() -> None:
    async with AsyncOpenAIChatLM(api_key="ollama", compat="ollama") as lm:
        request = Request(
            model="qwen3.5:0.8b",
            messages=(Message.user("Name two colors."),),
            config=Config(max_tokens=80, extensions={"reasoning_effort": "none"}),
        )

        response = await lm.complete(request)
        print(response.text)

        async for event in lm.stream(request):
            if isinstance(event, StreamDeltaEvent) and isinstance(event.delta, TextDelta):
                print(event.delta.text, end="", flush=True)
        print()

asyncio.run(main())
```

```output
Two common names for the color are:

1. **Primary Color** (e.g., Red)
2. **Secondary Color** (e.g., Blue)

*(Note: In some color theory frameworks, all three—are called primaries—but "primary" alone and "secondary" alone are just two valid distinct names as requested.)*
Two common colors are **black** and **white**. These are among the most fundamental in art and design for their versatility and clarity in representation.
```

Async adapters also provide non-chat operations where the provider supports
them, including files, batch jobs, image/speech generation, and live sessions.
Unsupported provider/endpoint combinations raise `UnsupportedFeatureError`.
Use `with` for sync clients and `async with` for async clients to close their
connections when finished.

## Local subscription adapters

The ordinary provider adapters use API keys that callers pass explicitly: `OpenAILM(api_key=...)`, `AnthropicLM(api_key=...)`, and `GeminiLM(api_key=...)`.

lm15 also has explicit local-developer subscription adapters for users who are already signed in to provider CLIs. These adapters do not read API-key environment variables. They read local OAuth credentials created by the CLI and send provider-specific OAuth headers.

### Claude Code subscription auth

Use `ClaudeCodeLM.from_claude_code()` when Claude Code is installed and logged in as the same OS user:

```python
from lm15 import ClaudeCodeLM, Config, Message, Request

lm = ClaudeCodeLM.from_claude_code()

response = lm.complete(
    Request(
        model="claude-fable-5",
        messages=(Message.user("Say hello briefly."),),
        config=Config(max_tokens=128),
    )
)

print(response.text)
```

The default credential path is `~/.claude/.credentials.json`. If the credential is missing or expired, run Claude Code and log in again (`claude`, then `/login` if prompted).

`ClaudeCodeLM` always prepends the Claude Code system prompt required by this OAuth route:

```text
You are Claude Code, Anthropic's official CLI for Claude.
```

If `Request.system` is also provided, lm15 keeps both: the required Claude Code prompt comes first, then the caller's system instruction.

Fable 5 note: Fable may spend part of `max_tokens` on hidden thinking, so a too-small budget can return no visible text with `finish_reason="length"`. Use `Config(max_tokens=128)` or higher for non-trivial prompts.

### OpenAI Codex / ChatGPT subscription auth

Use `OpenAICodexLM.from_codex_cli()` when Codex CLI is installed and signed in with ChatGPT:

```python
from lm15 import Message, OpenAICodexLM, Request

lm = OpenAICodexLM.from_codex_cli()

response = lm.complete(
    Request(
        model="gpt-5.5",
        messages=(Message.user("Say hello briefly."),),
    )
)

print(response.text)
```

The default credential path is `~/.codex/auth.json`. `OpenAICodexLM` reads the local ChatGPT OAuth access token and account id from that file, then calls the Codex subscription endpoint. The Codex subscription backend is streaming-first, so `complete()` internally streams and materializes a normal `Response`.

Current Codex route note: lm15 intentionally omits max-token fields here because the verified local Codex route accepts the request shape without them; set output limits in your application layer if you need a hard cap.

These subscription adapters are intended for local interactive development, not server or CI deployments. Treat the credential files as secrets; do not print or log their bearer tokens.

## Media and non-chat endpoints

Multimodal input uses typed media parts (`ImagePart`, `AudioPart`, `DocumentPart`, ...):

```python
import os

from lm15 import ImagePart, Message, OpenAILM, Request, TextPart

lm = OpenAILM(api_key=os.environ["OPENAI_API_KEY"])

request = Request(
    model="gpt-4.1-mini",
    messages=(
        Message.user([
            TextPart("Describe this image in a few words."),
            ImagePart(
                url="https://raw.githubusercontent.com/github/explore/main/topics/react/react.png",
                media_type="image/png",
                detail="low",
            ),
        ]),
    ),
)

print(lm.complete(request).text)
```

```output
This image shows a blue atom symbol with a central nucleus and three elliptical electron orbits.
```

**Provisional in 1.0:** the standalone generation and resource APIs below
may change in 1.x. This does not change the frozen status of media parts
used inside chat messages.

Non-chat endpoints have separate request/response types — `ImageGenerationRequest`, `SpeechGenerationRequest`, `FileUploadRequest`, `BatchRequest`, `LiveConfig` — and generated media comes back as the same typed parts you send in:

```python
import base64
from lm15 import ImageGenerationRequest

img = lm.image_generate(ImageGenerationRequest(
    model="gpt-image-1",
    prompt="a minimal line drawing of a fox reading a book",
    size="1024x1024",
    extensions={"quality": "low"},
))
part = img.images[0]  # an ImagePart, same shape as image *inputs*
print(part.media_type, len(base64.b64decode(part.data)), "bytes")
```

```output
image/png 1115289 bytes
```

## Canonical JSON serialization

The serde functions convert every public lm15 type to canonical JSON-compatible dicts and back, exactly — this is the wire format the conformance corpus pins:

```python
from lm15 import Message, Request
from lm15.serde import request_from_dict, request_to_dict

request = Request(model="gpt-4.1-mini", messages=(Message.user("Hi"),))
wire = request_to_dict(request)
round_tripped = request_from_dict(wire)
print(round_tripped == request)
```

```output
True
```

## Error normalization

Provider-specific HTTP/API errors are normalized into one lm15 error hierarchy, so callers handle `AuthError`, `RateLimitError`, `ContextLengthError`, ... identically across providers:

```python
import os

from lm15 import AuthError, Message, OpenAILM, ProviderError, RateLimitError, Request

lm = OpenAILM(api_key="not a key")

try:
    lm.complete(Request(model="gpt-4.1-mini", messages=(Message.user("Hi"),)))
except AuthError as exc:
    print("Check API key:", exc.env_keys)
except RateLimitError as exc:
    print("Retry later:", exc.retry_after)
except ProviderError as exc:
    print(exc.provider, exc.provider_code, exc.status, exc.request_id)
```

```output
Check API key: ('OPENAI_API_KEY',)
```

Rate/capacity failures also expose `error.rate_limit_headers`: bounded,
read-only provider evidence (limits, remaining balances, resets and wait hints).
`str(error)` includes an advisory summary; no automatic retry or endpoint
switch is added. See [Understanding “no capacity”](docs/error-diagnostics.md),
including streaming errors and Azure request IDs.

## Model metadata

`ModelRegistry.discover()` hydrates optional, advisory model metadata (pricing, context windows, capability hints) from installed catalog packages via the `lm15.model_catalogs` entry-point group — the `aimo` catalog is one such package. Hydrated metadata never changes what an adapter sends: requests are byte-identical with or without it. See [docs/model-hydration.md](docs/model-hydration.md) for the contract.

## Design notes

- [docs/design-rationale.md](docs/design-rationale.md) — why `config=Config(...)` instead of kwargs, why there is no automatic tool loop, why request `extensions` and response `provider_data` are different names on purpose.
- `lm15-contract/docs/serde-rules.md` — the canonical JSON omission and round-trip rules (normative; [docs/serde-rules.md](docs/serde-rules.md) forwards there).
- `lm15-contract/docs/mapping-rules.md` — the provider mapping invariants MAP-1..MAP-10 (normative; [docs/mapping-rules.md](docs/mapping-rules.md) forwards there).
- Behavior is pinned by a cross-language conformance corpus: the sibling `lm15-contract` repository is the spec; this package is the reference implementation, not the authority — the full story is in [How lm15 is specified](https://lm15-dev.github.io/lm15-python/how-lm15-is-specified/).

## Documentation

The full documentation site — getting started, guides, runnable cookbooks, API reference, the specification pages, benchmarks, and the [roadmap](https://lm15-dev.github.io/lm15-python/roadmap/) — lives at **[lm15-dev.github.io/lm15-python](https://lm15-dev.github.io/lm15-python/)**.

## Contributing

Fixture and conformance workflows, the doc-drift checker, the provider adapter development guide, and the useful-commands cheat sheet live in [CONTRIBUTING.md](CONTRIBUTING.md).

## Reporting bugs and security issues

- **Bugs and unexpected behavior**: open an issue on the [issue tracker](https://github.com/lm15-dev/lm15-python/issues). Include the lm15 version, the provider and endpoint involved, and a minimal reproduction. Wire-level evidence (the exact request/response bytes) makes triage much faster.
- **Security vulnerabilities**: never open a public issue. Follow [SECURITY.md](SECURITY.md) to report privately.

## Project repositories

lm15 is a multi-repository project under the [lm15-dev](https://github.com/lm15-dev) organization:

| Repository | Status | Role |
| --- | --- | --- |
| [lm15-contract](https://github.com/lm15-dev/lm15-contract) | active | The authority: canonical spec, fixture corpus, goldens, and the language-neutral conformance harness. |
| [lm15-python](https://github.com/lm15-dev/lm15-python) | active | This repository — the Python reference implementation. |
| [lm15-ts](https://github.com/lm15-dev/lm15-ts) | rebuilding | TypeScript implementation, rebuilt from the contract module by module. |
| [lm15-go](https://github.com/lm15-dev/lm15-go) | rebuilding | Go implementation, rebuilt from the contract module by module. |
| [lm15-rs](https://github.com/lm15-dev/lm15-rs) | rebuilding | Rust implementation, rebuilt from the contract module by module. |
| [lm15-jl](https://github.com/lm15-dev/lm15-jl) | rebuilding | Julia implementation, rebuilt from the contract module by module. |
| [spec](https://github.com/lm15-dev/spec) | archived | Superseded by lm15-contract. |
| [curl-fixtures](https://github.com/lm15-dev/curl-fixtures), [cross-sdk-curl-tests](https://github.com/lm15-dev/cross-sdk-curl-tests) | archived | Moved into the conformance corpus. |

`lm15-contract` is the source of truth for behavior; each implementation repo must pass its conformance harness at the SHA pinned in its `CONTRACT_PIN` file.
