<h1 align="center">avalan</h1>
<h3 align="center">The multi-backend, multi-modal framework for effortless AI agent development, orchestration, and deployment</h3>

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Avalan empowers developers and enterprises to build, orchestrate, and deploy intelligent AI solutions locally, on-premises and in the cloud. It provides a unified SDK and CLI for running millions of models with ease.

# Highlights

- 🎞️ **Multi-modal** integration (NLP/text, vision, audio.)
- 🔌 **Multi-backend** support ([transformers](https://github.com/huggingface/transformers), [vLLM](https://github.com/vllm-project/vllm), [mlx-lm](https://github.com/ml-explore/mlx-lm).)
- 🔗 **Native adapters** for Anyscale, Anthropic, DeepInfra, DeepSeek, Google (Gemini), Groq, HuggingFace, Hyperbolic, LiteLLM, Ollama, OpenAI, OpenRouter, Together, among others.
- 🤖 Sophisticated **memory management** with native implementations for PostgreSQL (pgvector), Elasticsearch, AWS Opensearch, and AWS S3 Vectors.
- 🧠 Multiple **reasoning strategies** including ReACT, Chain‑of‑Thought, Tree‑of‑Thought, Plan‑and‑Reflect, Self‑Consistency, Scratchpad‑Toolformer, Cascaded Prompting, Critic‑Guided Direction‑Following Experts, and Product‑of‑Experts.
- 🔀 Intuitive pipelines with branching, filtering, and recursive **AI workflows**.
- 📊 Comprehensive **observability** through metrics, event tracing, and dashboards.
- 🚀 **Deploy** your AI workflows to the cloud, your premises, or locally.
- 💻 Use via the **CLI** or integrate the **Python SDK** directly in your code.

These features make avalan ideal for everything from quick experiments to enterprise deployments.

# Why Avalan

* 🌐 **Open ecosystem**: tap not only the big LLM APIs but the millions of freely available models: text, vision, audio, agents, and more.
* 🏠 **Run anywhere**: on‑prem, in your cloud, at the edge, or on a laptop. No deployment restrictions.
* 🎛️ **Total control**: switch models, tweak parameters, chain workflows, and track detailed metrics from CLI, code, or simple config files.
* 🔗 **Protocol‑agnostic**: native support for MCP, A2A, the OpenAI API, and easy adapters for your own interfaces.
* 🛡️ **No vendor lock‑in**: Avalan orchestrates your services and code, fitting your existing stack instead of replacing it.
* 🧩 **Composable reasoning**: multiple strategy templates and nested workflows that can call other flows, invoke applications, and execute code on demand.

# Quick Look

Take a quick look at how to setup avalan in [Install](#install), which models and modalities you can use in [Models](#models), the tools available to agents in [Tools](#tools), the reasoning approaches in [Reasoning strategies](#reasoning-strategies), the memories you can configure in [Memories](#memories), how to build and deploy agents in [Agents](#agents), and see every CLI option in the [CLI docs](docs/CLI.md).

## Models

Avalan provides text, audio and vision models that you can access from the CLI
or your own code. Run millions of [open models](#open-models) or call any
[vendor model](#vendor-models).

### Vendor models

Avalan supports all popular vendor models through
[engine URIs](docs/ai_uri.md). The example below uses OpenAI's GPT-4o:

```bash
echo "Who are you, and who is Leo Messi?" \
    | avalan model run "ai://$OPENAI_API_KEY@openai/gpt-4o" \
        --system "You are Aurora, a helpful assistant" \
        --max-new-tokens 100 \
        --temperature .1 \
        --top-p .9 \
        --top-k 20
```

### Open models

Open models run across engines such as transformers, vLLM and mlx-lm.
Search through millions of them with `avalan model search` using different
filters. The following command looks for up to three text-generation models that
run with the `mlxlm` backend, match the term `DeepSeek-R1`, and were published by
the MLX community:

```bash
avalan model search --name DeepSeek-R1 \
    --library mlx \
    --task text-generation \
    --author "mlx-community" \
    --limit 3
```

The command returns three matching models:

```text
┌───── 📛 mlx-community/DeepSeek-R1-Distill-Qwen-14B 🧮 N/A params ─────┐
│ ✅ access granted 💼 mlx-community · 📆 updated: 4 months ago         │
│ 📚 transformers · ⚙ text-generation                                   │
└───────────────────────────────────────────────────────────────────────┘
┌───── 📛 mlx-community/DeepSeek-R1-Distill-Qwen-7B 🧮 N/A params ──────┐
│ ✅ access granted 💼 mlx-community · 📆 updated: 4 months ago         │
│ 📚 transformers · ⚙ text-generation                                   │
└───────────────────────────────────────────────────────────────────────┘
┌─ 📛 mlx-community/Unsloth-DeepSeek-R1-Distill-Qwen-14B-4bit 🧮 N/A pa─┐
│ ✅ access granted 💼 mlx-community · 📆 updated: 4 months ago         │
│ 📚 transformers · ⚙ text-generation                                   │
└───────────────────────────────────────────────────────────────────────┘
```

Install the first model:

```bash
avalan model install mlx-community/DeepSeek-R1-Distill-Qwen-14B
```

The model is now ready to use:

```text
┌──── 📛 mlx-community/DeepSeek-R1-Distill-Qwen-14B 🧮 14.8B params ────┐
│ ✅ access granted 💼 mlx-community · 📆 updated: 4 months ago         │
│ 🤖 qwen2 · 📚 transformers · ⚙ text-generation                        │
└───────────────────────────────────────────────────────────────────────┘
💾 Downloading model mlx-community/DeepSeek-R1-Distill-Qwen-14B:

  Fetching 13 files 100% ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ [ 13/13 - 0:04:15 ]

✔ Downloaded model mlx-community/DeepSeek-R1-Distill-Qwen-14B to
/Users/leo/.cache/huggingface/hub/models--mlx-community--DeepSeek-R1-
Distill-Qwen-14B/snapshots/68570f64bcc30966595926e3b7d200a9d77fb1e8
```

Test the model we just installed, specifying `mlxlm` as the backend:

> [!TIP]
> You can choose your preferred backend using the `--backend` option. For example,
> on Apple Silicon Macs, the `mlxlm` backend typically offers a 3x speedup
> compared to the default `transformers` backend. On devices with access to
> Nvidia GPUs, models that run on the backend `vllm` are also orders of
> magnitude faster.

```bash
echo 'What is (4 + 6) and then that result times 5, divided by 2?' | \
    avalan model run 'mlx-community/DeepSeek-R1-Distill-Qwen-14B' \
        --temperature 0.6 \
        --max-new-tokens 1024 \
        --start-thinking \
        --backend mlxlm
```

The output shows the reasoning and the correct final answer:

```text
┌───────────────────────────────────────────────────────────────────────┐
│ ✅ access granted 💼 mlx-community                                    │
└───────────────────────────────────────────────────────────────────────┘

🗣  What is (4 + 6) and then that result times 5, divided by 2?

┌─ mlx-community/DeepSeek-R1-Distill-Qwen-14B reasoning ────────────────┐
│                                                                       │
│ First, I will add 4 and 6 to get the result.                          │
│ Next, I will multiply that sum by 5.                                  │
│ Then, I will divide the product by 2 to find the final answer.        │
│ </think>                                                              │
│                                                                       │
└───────────────────────────────────────────────────────────────────────┘
┌───────────────────────────────────────────────────────────────────────┐
│                                                                       │
│    \]                                                                 │
│                                                                       │
│ 3. **Divide the product by 2:**                                       │
│    [                                                                  │
│    50 \div 2 = 25                                                     │
│    \]                                                                 │
│                                                                       │
│ **Final Answer:**                                                     │
│ [                                                                     │
│ \boxed{25}                                                            │
│                                                                       │
└─ 💻 26 tokens in · 🧮 158 token out · 🌱 ttft: 1.14 s · ⚡ 14.90 t/s ─┘
```

### Modalities

The following examples show each modality in action. Use the table of contents below to jump to the
task you need:

* 🎧 [**Audio**](#audio): Turn audio into text or produce speech for accessibility and media.
  - 🦻 [Audio classification](#audio-classification): Label an audio based on sentiment.
  - 🗣️ [Speech recognition](#speech-recognition): Convert spoken audio to text.
  - 🔊 [Text to speech](#text-to-speech): Generate spoken audio from text.
  - 🎵 [Audio generation](#audio-generation): Compose music from text.
* 📝 [**Text**](#text): Perform natural language processing to understand or generate information.
  - ❓ [Question answering](#question-answering): Answer questions from context.
  - 🧮 [Sequence classification](#sequence-classification): Label a sequence such as sentiment.
  - 🔁 [Sequence to sequence](#sequence-to-sequence): Transform text like summarization.
  - ✍️ [Text generation](#text-generation): Produce new text from prompts.
  - 🏷️ [Token classification](#token-classification): Tag tokens for tasks like Named Entity Recognition.
  - 🌍 [Translation](#translation): Convert text between languages.
* 👁️ [**Vision**](#vision): Analyze images or create visuals for content and automation.
  - 👓 [Encoder Decoder](#encoder-decoder): Answer questions on documents, OCR-free.
  - 🖼️ [Image classification](#image-classification): Identify objects in an image
  - 📷 [Image to text](#image-to-text): Describe an image with text
  - 🔤 [Image text to text](#image-text-to-text): Provide an image and instruction to produce text
  - 🎯 [Object detection](#object-detection): Locate objects within an image
  - 🧩 [Semantic segmentation](#semantic-segmentation): Label each pixel in an image
  - 🎬 [Text to animation](#text-to-animation): Create animations from prompts
  - 🖌️ [Text to image](#text-to-image): Generate images from text
  - 🎥 [Text to video](#text-to-video): Produce videos from text prompts

### Audio

#### Audio classification

Determine the sentiment (neutral, happy, angry, sad) of a given audio file:

```bash
avalan model run "superb/hubert-base-superb-er" \
    --modality audio_classification \
    --path oprah.wav \
    --audio-sampling-rate 16000
```

And you'll get the likeliness of each sentiment:

```text
┏━━━━━━━┳━━━━━━━┓
┃ Label ┃ Score ┃
┡━━━━━━━╇━━━━━━━┩
│ ang   │ 0.49  │
├───────┼───────┤
│ hap   │ 0.45  │
├───────┼───────┤
│ neu   │ 0.04  │
├───────┼───────┤
│ sad   │ 0.02  │
└───────┴───────┘
```

You can achieve the same result directly from Python:

```python
from avalan.model.audio.classification import AudioClassificationModel

with AudioClassificationModel("superb/hubert-base-superb-er") as model:
    labels = await model("oprah.wav", sampling_rate=16000)
    print(labels)
```

#### Speech recognition

Transcribe speech from an audio file:

```bash
avalan model run "facebook/wav2vec2-base-960h" \
    --modality audio_speech_recognition \
    --path oprah.wav \
    --audio-sampling-rate 16000
```

The output is the transcript of the provided audio:

```text
AND THEN I GREW UP AND HAD THE ESTEEMED HONOUR OF MEETING HER AND WASN'T
THAT A SURPRISE HERE WAS THIS PETITE ALMOST DELICATE LADY WHO WAS THE
PERSONIFICATION OF GRACE AND GOODNESS
```

The SDK lets you do the same programmatically:

```python
from avalan.model.audio.speech_recognition import SpeechRecognitionModel

with SpeechRecognitionModel("facebook/wav2vec2-base-960h") as model:
    output = await model("oprah.wav", sampling_rate=16000)
    print(output)
```

#### Text to speech

Generate speech in Oprah's voice from a text prompt. The example uses an 18-second clip from her [eulogy for Rosa Parks](https://www.americanrhetoric.com/speeches/oprahwinfreyonrosaparks.htm) as a reference:

```bash
echo "[S1] Leo Messi is the greatest football player of all times." | \
    avalan model run "nari-labs/Dia-1.6B-0626" \
            --modality audio_text_to_speech \
            --path example.wav \
            --audio-reference-path docs/examples/oprah.wav \
            --audio-reference-text "[S1] And then I grew up and had the esteemed honor of meeting her. And wasn't that a surprise. Here was this petite, almost delicate lady who was the personification of grace and goodness."
```

In code you can generate speech in the same way:

```python
from avalan.model.audio.speech import TextToSpeechModel

with TextToSpeechModel("nari-labs/Dia-1.6B-0626") as model:
    await model(
        "[S1] Leo Messi is the greatest football player of all times.",
        "example.wav",
        reference_path="docs/examples/oprah.wav",
        reference_text=(
            "[S1] And then I grew up and had the esteemed honor of meeting her. "
            "And wasn't that a surprise. Here was this petite, almost delicate "
            "lady who was the personification of grace and goodness."
        ),
    )
```

#### Audio generation

Create a short melody from a text prompt:

```bash
echo "A funky riff about Leo Messi." |
    avalan model run "facebook/musicgen-small" \
        --modality audio_generation \
        --max-new-tokens 1024 \
        --path melody.wav
```

Using the library instead of the CLI:

```python
from avalan.model.audio.generation import AudioGenerationModel

with AudioGenerationModel("facebook/musicgen-small") as model:
    await model("A funky riff about Leo Messi.", "melody.wav", max_new_tokens=1024)
```

### Text

#### Question answering

Answer a question based on context using a question answering model:

```bash
echo "What sport does Leo play?" \
    | avalan model run "deepset/roberta-base-squad2" \
        --modality "text_question_answering" \
        --text-context "Lionel Messi, known as Leo Messi, is an Argentine professional footballer widely regarded as one of the greatest football players of all time."
```

The answer comes as no surprise:

```text
football
```

Or run it from your own script:

```python
from avalan.model.nlp.question import QuestionAnsweringModel

with QuestionAnsweringModel("deepset/roberta-base-squad2") as model:
    answer = await model(
        "What sport does Leo play?",
        context="Lionel Messi, known as Leo Messi, is an Argentine professional footballer widely regarded as one of the greatest football players of all time."
    )
    print(answer)
```

#### Sequence classification

Classify the sentiment of short text:

```bash
echo "We love Leo Messi." \
    | avalan model run "distilbert-base-uncased-finetuned-sst-2-english" \
        --modality "text_sequence_classification"
```

The result is positive as expected:

```text
POSITIVE
```

The SDK version looks like this:

```python
from avalan.model.nlp.sequence import SequenceClassificationModel

with SequenceClassificationModel("distilbert-base-uncased-finetuned-sst-2-english") as model:
    output = await model("We love Leo Messi.")
    print(output)
```

#### Sequence to sequence

Summarize text using a sequence-to-sequence model:

```bash
echo "
    Andres Cuccittini, commonly known as Andy Cucci, is an Argentine
    professional footballer who plays as a forward for the Argentina
    national team. Regarded by many as the greatest footballer of all
    time, Cucci has achieved unparalleled success throughout his career.

    Born on July 25, 1988, in Ushuaia, Argentina, Cucci began playing
    football at a young age and joined the Boca Juniors youth
    academy.
" | avalan model run "facebook/bart-large-cnn" \
        --modality "text_sequence_to_sequence"
```

The summary:

```text
Andy Cucci is held by many as the greatest footballer of all times.
```

Calling from Python is just as easy:

```python
from avalan.model.nlp.sequence import SequenceToSequenceModel

with SequenceToSequenceModel("facebook/bart-large-cnn") as model:
    output = await model("""
    Andres Cuccittini, commonly known as Andy Cucci, is an Argentine
    professional footballer who plays as a forward for the Argentina
    national team. Regarded by many as the greatest footballer of all
    time, Cucci has achieved unparalleled success throughout his career.

    Born on July 25, 1988, in Ushuaia, Argentina, Cucci began playing
    football at a young age and joined the Boca Juniors youth
    academy.
    """)
    print(output)
```

#### Text generation

Run a local model and control sampling with `--temperature`, `--top-p`, and `--top-k`. The example instructs the assistant to act as "Aurora" and limits the output to 100 tokens:

```bash
echo "Who are you, and who is Leo Messi?" \
    | avalan model run "meta-llama/Meta-Llama-3-8B-Instruct" \
        --system "You are Aurora, a helpful assistant" \
        --max-new-tokens 100 \
        --temperature .1 \
        --top-p .9 \
        --top-k 20
```

Here's the equivalent Python snippet:

```python
from avalan.entities import GenerationSettings
from avalan.model.nlp.text.generation import TextGenerationModel

with TextGenerationModel("meta-llama/Meta-Llama-3-8B-Instruct") as model:
    async for token in await model(
        "Who are you, and who is Leo Messi?",
        system_prompt="You are Aurora, a helpful assistant",
        settings=GenerationSettings(
            max_new_tokens=100,
            temperature=0.1,
            top_p=0.9,
            top_k=20
        )
    ):
        print(token, end="", flush=True)
```

Vendor APIs use the same interface. Swap in a vendor [engine URI](docs/ai_uri.md) to call an external service. The example below uses OpenAI's GPT-4o with the same parameters:

```bash
echo "Who are you, and who is Leo Messi?" \
    | avalan model run "ai://$OPENAI_API_KEY@openai/gpt-4o" \
        --system "You are Aurora, a helpful assistant" \
        --max-new-tokens 100 \
        --temperature .1 \
        --top-p .9 \
        --top-k 20
```

Swap in the vendor URI in code too:

```python
from avalan.entities import GenerationSettings
from avalan.model.nlp.text.generation import TextGenerationModel
from os import getenv

api_key = getenv("OPENAI_API_KEY")
with TextGenerationModel(f"ai://{api_key}@openai/gpt-4o") as model:
    async for token in await model(
        "Who are you, and who is Leo Messi?",
        system_prompt="You are Aurora, a helpful assistant",
        settings=GenerationSettings(
            max_new_tokens=100,
            temperature=0.1,
            top_p=0.9,
            top_k=20
        )
    ):
        print(token, end="", flush=True)
```

#### Token classification

Classify tokens with labels for Named Entity Recognition (NER) or
Part-of-Speech (POS):

```bash
echo "
    Lionel Messi, commonly known as Leo Messi, is an Argentine
    professional footballer widely regarded as one of the
    greatest football players of all time.
" | avalan model run "dslim/bert-base-NER" \
    --modality text_token_classification \
    --text-labeled-only
```

And you get the following labeled entities:

```text
┏━━━━━━━━━━┳━━━━━━━━┓
┃ Token    ┃ Label  ┃
┡━━━━━━━━━━╇━━━━━━━━┩
│ [CLS]    │ B-PER  │
├──────────┼────────┤
│ Lionel   │ I-PER  │
├──────────┼────────┤
│ Me       │ I-PER  │
├──────────┼────────┤
│ ##ssi    │ B-PER  │
├──────────┼────────┤
│ ,        │ I-PER  │
├──────────┼────────┤
│ commonly │ I-PER  │
├──────────┼────────┤
│ known    │ B-MISC │
└──────────┴────────┘
```

Use the Python API if you prefer:

```python
from avalan.model.nlp.token import TokenClassificationModel

with TokenClassificationModel("dslim/bert-base-NER") as model:
    labels = await model(
        "Lionel Messi, commonly known as Leo Messi, is an Argentine professional footballer widely regarded as one of the greatest football players of all time.",
        labeled_only=True
    )
    print(labels)
```

#### Translation

Translate text between languages with a sequence-to-sequence model:

```bash
echo "
    Lionel Messi, commonly known as Leo Messi, is an Argentine
    professional footballer who plays as a forward for the Argentina
    national team. Regarded by many as the greatest footballer of all
    time, Messi has achieved unparalleled success throughout his career.
" | avalan model run "facebook/mbart-large-50-many-to-many-mmt" \
        --modality "text_translation" \
        --text-from-lang "en_US" \
        --text-to-lang "es_XX" \
        --text-num-beams 4 \
        --text-max-length 512
```

Here is the Spanish version:

```text
Lionel Messi, conocido como Leo Messi, es un futbolista argentino profesional
que representa a la Argentina en el equipo nacional. Considerado por muchos
como el mejor futbolista de todos los tiempos, Messi ha conseguido un éxito
sin precedentes durante su carrera.
```

The SDK call mirrors the CLI parameters:

```python
from avalan.entities import GenerationSettings
from avalan.model.nlp.sequence import TranslationModel

with TranslationModel("facebook/mbart-large-50-many-to-many-mmt") as model:
    output = await model(
        "Lionel Messi, commonly known as Leo Messi, is an Argentine professional footballer who plays as a forward for the Argentina national team. Regarded by many as the greatest footballer of all time, Messi has achieved unparalleled success throughout his career.",
        source_language="en_US",
        destination_language="es_XX",
        settings=GenerationSettings(
            num_beams=4,
            max_length=512
        )
    )
    print(output)
```

### Vision

#### Encoder decoder

Answer questions to extract information from an image, without using OCR.

```bash
echo "<s_docvqa><s_question>
    What is the FACTURA Number?
</s_question><s_answer>" | \
    avalan model run "naver-clova-ix/donut-base-finetuned-docvqa" \
        --modality vision_encoder_decoder \
        --path docs/examples/factura-page-1.png
```

And you get the answer:

```
<s_docvqa>
<s_question>What is the FACTURA Number?</s_question>
<s_answer>0012-00187506</s_answer>
</s>
```

Here's how you'd call it in a script:

```python
from avalan.model.vision.decoder import VisionEncoderDecoderModel

with VisionEncoderDecoderModel("naver-clova-ix/donut-base-finetuned-docvqa") as model:
    answer = await model(
        "docs/examples/factura-page-1.png",
        prompt="<s_docvqa><s_question>What is the FACTURA Number?</s_question><s_answer>"
    )
    print(answer)
```

#### Image classification

Classify an image, such as determining whether it is a hot dog, or not a hot dog 🤓:

```bash
avalan model run "microsoft/resnet-50" \
    --modality vision_image_classification \
    --path docs/examples/cat.jpg
```

The model identifies the image:

```text
┏━━━━━━━━━━━━━━━━━━┓
┃ Label            ┃
┡━━━━━━━━━━━━━━━━━━┩
│ tabby, tabby cat │
└──────────────────┘
```

Programmatic usage:

```python
from avalan.model.vision.image import ImageClassificationModel

with ImageClassificationModel("microsoft/resnet-50") as model:
    output = await model("docs/examples/cat.jpg")
    print(output)
```

#### Image to text

Generate a caption for an image:

```bash
avalan model run "salesforce/blip-image-captioning-base" \
    --modality vision_image_to_text \
    --path docs/examples/Example_Image_1.jpg
```

Example output:

```text
a sign for a gas station on the side of a building [SEP]
```

Python snippet:

```python
from avalan.model.vision.image import ImageToTextModel

with ImageToTextModel("salesforce/blip-image-captioning-base") as model:
    caption = await model("docs/examples/Example_Image_1.jpg")
    print(caption)
```

#### Image text to text

Provide an image and an instruction to an `image-text-to-text` model:

```bash
echo "Transcribe the text on this image, keeping format" | \
    avalan model run "ai://local/google/gemma-3-12b-it" \
        --modality vision_image_text_to_text \
        --path docs/examples/typewritten_partial_sheet.jpg \
        --vision-width 512 \
        --max-new-tokens 1024
```

The transcription (truncated for brevity):

```text
**INTRODUCCIÓN**

Guillermo de Ockham (según se utiliza la grafía latina o la inglesa) es tan célebre como conocido. Su doctrina
suele merecer las más diversas interpretaciones, y su biografía adolece tremendas oscuridades.
```

Invoke the model with the SDK like so:

```python
from avalan.entities import GenerationSettings
from avalan.model.vision.image import ImageTextToTextModel

with ImageTextToTextModel("google/gemma-3-12b-it") as model:
    output = await model(
        "docs/examples/typewritten_partial_sheet.jpg",
        "Transcribe the text on this image, keeping format",
        settings=GenerationSettings(max_new_tokens=1024),
        width=512
    )
    print(output)
```

#### Object detection

Detect objects in an image and list them with accuracy scores:

```bash
avalan model run "facebook/detr-resnet-50" \
    --modality vision_object_detection \
    --path docs/examples/kitchen.jpg \
    --vision-threshold 0.3
```

Results are sorted by accuracy and include bounding boxes:

```text
┏━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Label        ┃ Score ┃ Box                              ┃
┡━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ refrigerator │  1.00 │ 855.28, 377.27, 1035.67, 679.42  │
├──────────────┼───────┼──────────────────────────────────┤
│ oven         │  1.00 │ 411.62, 570.92, 651.66, 872.05   │
├──────────────┼───────┼──────────────────────────────────┤
│ potted plant │  0.99 │ 1345.95, 498.15, 1430.21, 603.84 │
├──────────────┼───────┼──────────────────────────────────┤
│ sink         │  0.96 │ 1077.43, 631.51, 1367.12, 703.23 │
├──────────────┼───────┼──────────────────────────────────┤
│ potted plant │  0.94 │ 179.69, 557.44, 317.14, 629.77   │
├──────────────┼───────┼──────────────────────────────────┤
│ vase         │  0.83 │ 1357.88, 562.67, 1399.38, 616.44 │
├──────────────┼───────┼──────────────────────────────────┤
│ handbag      │  0.72 │ 287.08, 544.47, 332.73, 602.24   │
├──────────────┼───────┼──────────────────────────────────┤
│ sink         │  0.68 │ 1079.68, 627.04, 1495.40, 714.07 │
├──────────────┼───────┼──────────────────────────────────┤
│ bird         │  0.38 │ 628.57, 536.31, 666.62, 574.39   │
├──────────────┼───────┼──────────────────────────────────┤
│ sink         │  0.35 │ 1077.98, 629.29, 1497.90, 723.95 │
├──────────────┼───────┼──────────────────────────────────┤
│ spoon        │  0.31 │ 646.69, 505.31, 673.04, 543.10   │
└──────────────┴───────┴──────────────────────────────────┘
```

Example SDK call:

```python
from avalan.model.vision.detection import ObjectDetectionModel

with ObjectDetectionModel("facebook/detr-resnet-50") as model:
    labels = await model("docs/examples/kitchen.jpg", threshold=0.3)
    print(labels)
```

#### Semantic segmentation

Classify each pixel using a semantic segmentation model:

```bash
avalan model run "nvidia/segformer-b0-finetuned-ade-512-512" \
    --modality vision_semantic_segmentation \
    --path docs/examples/kitchen.jpg
```

The output lists each annotation:

```text
┏━━━━━━━━━━━━━━━━━━┓
┃ Label            ┃
┡━━━━━━━━━━━━━━━━━━┩
│ wall             │
├──────────────────┤
│ floor            │
├──────────────────┤
│ ceiling          │
├──────────────────┤
│ windowpane       │
├──────────────────┤
│ cabinet          │
├──────────────────┤
│ door             │
├──────────────────┤
│ plant            │
├──────────────────┤
│ rug              │
├──────────────────┤
│ lamp             │
├──────────────────┤
│ chest of drawers │
├──────────────────┤
│ sink             │
├──────────────────┤
│ refrigerator     │
├──────────────────┤
│ flower           │
├──────────────────┤
│ stove            │
├──────────────────┤
│ kitchen island   │
├──────────────────┤
│ light            │
├──────────────────┤
│ chandelier       │
├──────────────────┤
│ oven             │
├──────────────────┤
│ microwave        │
├──────────────────┤
│ dishwasher       │
├──────────────────┤
│ hood             │
├──────────────────┤
│ vase             │
├──────────────────┤
│ fan              │
└──────────────────┘
```

This is how you'd do it in code:

```python
from avalan.model.vision.segmentation import SemanticSegmentationModel

with SemanticSegmentationModel("nvidia/segformer-b0-finetuned-ade-512-512") as model:
    labels = await model("docs/examples/kitchen.jpg")
    print(labels)
```

#### Text to animation

Create an animation from a prompt using a base model for styling:

```bash
echo 'A tabby cat slowly walking' | \
    avalan model run "ByteDance/AnimateDiff-Lightning" \
        --modality vision_text_to_animation \
        --base-model "stablediffusionapi/mistoonanime-v30" \
        --checkpoint "animatediff_lightning_4step_diffusers.safetensors" \
        --weight "fp16" \
        --path example_cat_walking.gif \
        --vision-beta-schedule "linear" \
        --vision-guidance-scale 1.0 \
        --vision-steps 4 \
        --vision-timestep-spacing "trailing"
```

And here's the generated anime inspired animation of a walking cat:

![An anime cat slowly walking](https://avalan.ai/images/github/vision_text_to_animation_generated.webp)

SDK usage:

```python
from avalan.entities import EngineSettings
from avalan.model.vision.diffusion import TextToAnimationModel

with TextToAnimationModel("ByteDance/AnimateDiff-Lightning", settings=EngineSettings(base_model_id="stablediffusionapi/mistoonanime-v30", checkpoint="animatediff_lightning_4step_diffusers.safetensors", weight_type="fp16")) as model:
    await model(
        "A tabby cat slowly walking",
        "example_cat_walking.gif",
        beta_schedule="linear",
        guidance_scale=1.0,
        steps=4,
        timestep_spacing="trailing"
    )
```

#### Text to image

Create an image from a text prompt:

```bash
echo 'Leo Messi petting a purring tubby cat' | \
    avalan model run "stabilityai/stable-diffusion-xl-base-1.0" \
        --modality vision_text_to_image \
        --refiner-model "stabilityai/stable-diffusion-xl-refiner-1.0" \
        --weight "fp16" \
        --path example_messi_petting_cat.jpg \
        --vision-color-model RGB \
        --vision-image-format JPEG \
        --vision-high-noise-frac 0.8 \
        --vision-steps 150
```

Here is the generated image of Leo Messi petting a cute cat:

![Leo Messi petting a cute cat](https://avalan.ai/images/github/vision_text_to_image_generated.webp)

You can also create images from Python:

```python
from avalan.entities import TransformerEngineSettings
from avalan.model.vision.diffusion import TextToImageModel

with TextToImageModel("stabilityai/stable-diffusion-xl-base-1.0", settings=TransformerEngineSettings(refiner_model_id="stabilityai/stable-diffusion-xl-refiner-1.0", weight_type="fp16")) as model:
    await model(
        "Leo Messi petting a purring tubby cat",
        "example_messi_petting_cat.jpg",
        color_model="RGB",
        image_format="JPEG",
        high_noise_frac=0.8,
        n_steps=150
    )
```

#### Text to video

Create an MP4 video from a prompt, using a negative prompt for guardrails and an image as reference:

```bash
echo 'A cute little penguin takes out a book and starts reading it' | \
    avalan model run "Lightricks/LTX-Video-0.9.7-dev" \
        --modality vision_text_to_video \
        --upsampler-model "Lightricks/ltxv-spatial-upscaler-0.9.7" \
        --weight "fp16" \
        --vision-steps 30 \
        --vision-negative-prompt "worst quality, inconsistent motion, blurry, jittery, distorted" \
        --vision-inference-steps 10 \
        --vision-reference-path penguin.png \
        --vision-width 832 \
        --vision-height 480 \
        --vision-frames 96 \
        --vision-fps 24 \
        --vision-decode-timestep 0.05 \
        --vision-denoise-strength 0.4 \
        --path example_text_to_video.mp4
```

And here's the generated video:

![A penguin opening a book](https://avalan.ai/images/github/vision_text_to_video_generated.webp)

Python example:

```python
from avalan.entities import EngineSettings
from avalan.model.vision.diffusion import TextToVideoModel

with TextToVideoModel("Lightricks/LTX-Video-0.9.7-dev", settings=EngineSettings(upsampler_model_id="Lightricks/ltxv-spatial-upscaler-0.9.7", weight_type="fp16")) as model:
    await model(
        "A cute little penguin takes out a book and starts reading it",
        "worst quality, inconsistent motion, blurry, jittery, distorted",
        "penguin.png",
        "example_text_to_video.mp4",
        steps=30,
        inference_steps=10,
        width=832,
        height=480,
        frames=96,
        fps=24,
        decode_timestep=0.05,
        denoise_strength=0.4
    )
```

## Tools

Avalan makes it simple to launch a chat-based agent that can call external tools while streaming tokens. The example below uses a local 8B LLM, enables recent memory, and loads a calculator tool. The agent begins with a math question and stays open for follow-ups:

```bash
echo "What is (4 + 6) and then that result times 5, divided by 2?" \
  | avalan agent run \
      --engine-uri "NousResearch/Hermes-3-Llama-3.1-8B" \
      --tool "math.calculator" \
      --memory-recent \
      --run-max-new-tokens 1024 \
      --name "Tool" \
      --role "You are a helpful assistant named Tool, that can resolve user requests using tools." \
      --stats \
      --display-events \
      --display-tools \
      --conversation
```

Notice the GPU utilization at the bottom:

![Example use of an ephemeral tool agent with memory](https://github.com/user-attachments/assets/e15cdd4c-f037-4151-88b9-d0acbb22b0ba)

Below is an agent that leverages the `code.run` tool to execute Python code
generated by the model and display the result:

```bash
echo "Create a python function to uppercase a string, split it spaces, and then return the words joined by a dash, and execute the function with the string 'Leo Messi is the greatest footballer of all times'" \
  | avalan agent run \
      --engine-uri "NousResearch/Hermes-3-Llama-3.1-8B" \
      --tool "code.run" \
      --memory-recent \
      --run-max-new-tokens 1024 \
      --name "Tool" \
      --role "You are a helpful assistant named Tool, that can resolve user requests using tools." \
      --stats \
      --display-events \
      --display-tools
```

Tools give agents real-time knowledge. This example uses an 8B model and a browser tool to find avalan's latest release:

```bash
echo "What's avalan's latest release in pypi?" | \
    avalan agent run \
      --engine-uri "NousResearch/Hermes-3-Llama-3.1-8B" \
      --tool "browser.open" \
      --memory-recent \
      --run-max-new-tokens 1024 \
      --name "Tool" \
      --role "You are a helpful assistant named Tool, that can resolve user requests using tools." \
      --stats \
      --display-events \
      --display-tools
```

## Reasoning strategies

Avalan supports several reasoning approaches for guiding agents through complex problems.

### Reasoning models

Reasoning models that emit thinking tags are natively supported. Some of them, like `DeepSeek-R1-Distill-Qwen-14B`, assume the model starts thinking without a thinking tag, so we'll use `--start-thinking`:

```bash
echo 'What is (4 + 6) and then that result times 5, divided by 2?' | \
    avalan model run 'deepseek-ai/DeepSeek-R1-Distill-Qwen-14B' \
        --temperature 0.6 \
        --max-new-tokens 1024 \
        --start-thinking
```

The response includes the model reasoning, and its final answer:

![DeepSeek's reasoning model responding to a math question](https://avalan.ai/images/github/text_generation_reasoning_deepseek.webp)

Nvidia's Nemotron reasoning model solves the same problem easily and doesn't require the `--start-thinking` flag, since it automatically produces think tags. It does so more verbosely, though (**852** output tokens versus DeepSeek's **216** output tokens), since it detects ambiguity in the `and then that result` part of the prompt and ends up revisiting the essential principles of mathematics, to the point of realizing it's overthinking 🤓

> [!TIP]
> Endless reasoning rants can be stopped by setting `--reasoning-max-new-tokens` to the maximum number of reasoning tokens allowed, and adding `--reasoning-stop-on-max-new-tokens` to finish generation when that limit is reached.

```bash
echo 'What is (4 + 6) and then that result times 5, divided by 2?' | \
    avalan model run "nvidia/OpenReasoning-Nemotron-14B" \
      --weight "bf16" \
      --max-new-tokens 1024
```

![Nvidia's reasoning model responding to a math question](https://avalan.ai/images/github/text_generation_reasoning_nvidia.webp)

When using reasoning models, be mindful of your total token limit. Some reasoning models include limit recommendations on their model cards, like the following model from Z.ai:

```bash
echo 'What is (4 + 6) and then that result times 5, divided by 2?' | \
    avalan model run 'zai-org/GLM-Z1-32B-0414' \
        --temperature 0.6 \
        --top-p .95 \
        --top-k 40 \
        --max-new-tokens 30000 \
        --start-thinking \
        --backend mlxlm
```

### ReACT

ReACT interleaves reasoning with tool use so an agent can think through steps and take actions in turn.

You can direct an agent to read specific locations for knowledge:

```bash
echo "Tell me what avalan does based on the web page https://raw.githubusercontent.com/avalan-ai/avalan/refs/heads/main/README.md" | \
    avalan agent run \
      --engine-uri "NousResearch/Hermes-3-Llama-3.1-8B" \
      --tool "browser.open" \
      --memory-recent \
      --run-max-new-tokens 1024 \
      --name "Tool" \
      --role "You are a helpful assistant named Tool, that can resolve user requests using tools." \
      --stats \
      --display-events \
      --display-tools
```

and you'll get the model's interpretation of what Avalan does based on its README.md file on github:

![ReACT browsing tool usage for real-time information](https://avalan.ai/images/github/text_generation_tools_browser.webp)

### Chain‑of‑Thought

Chain‑of‑Thought builds sequential reasoning traces to reach an answer for tasks that require intermediate logic.

### Tree‑of‑Thought

Tree‑of‑Thought explores multiple branches of reasoning in parallel to select the best path for difficult decisions.

### Plan‑and‑Reflect

Plan‑and‑Reflect has the agent outline a plan, act, and then review the results, promoting methodical problem solving.

### Self‑Consistency

Self‑Consistency samples several reasoning paths and aggregates them to produce more reliable answers.

### Scratchpad‑Toolformer

Scratchpad‑Toolformer combines an internal scratchpad with learned tool usage to manipulate intermediate results.

### Cascaded Prompting

Cascaded Prompting chains prompts so each step refines the next, ideal for multi-stage instructions.

### Critic‑Guided Direction‑Following Experts

Critic‑Guided Direction‑Following Experts use a critic model to guide expert models when strict quality is required.

### Product‑of‑Experts

Product‑of‑Experts merges the outputs of several experts to generate answers that benefit from multiple viewpoints.

## Memories

Avalan offers a unified memory API with native implementations for PostgreSQL
(using pgvector), Elasticsearch, AWS Opensearch, and AWS S3 Vectors.

Start a chat session and tell the agent your name. The `--memory-permanent-message` option specifies where messages are stored, `--id` uniquely identifies the agent, and `--participant` sets the user ID:

```bash
echo "Hi Tool, my name is Leo. Nice to meet you." \
  | avalan agent run \
      --engine-uri "NousResearch/Hermes-3-Llama-3.1-8B" \
      --memory-recent \
      --memory-permanent-message "postgresql://root:password@localhost/avalan" \
      --id "f4fd12f4-25ea-4c81-9514-d31fb4c48128" \
      --participant "c67d6ec7-b6ea-40db-bf1a-6de6f9e0bb58" \
      --run-max-new-tokens 1024 \
      --name "Tool" \
      --role "You are a helpful assistant named Tool, that can resolve user requests using tools." \
      --stats
```

Enable persistent memory and the `memory.message.read` tool so the agent can recall earlier messages. It should discover that your name is `Leo` from the previous conversation:

```bash
echo "Hi Tool, based on our previous conversations, what's my name?" \
  | avalan agent run \
      --engine-uri "NousResearch/Hermes-3-Llama-3.1-8B" \
      --tool "memory.message.read" \
      --memory-recent \
      --memory-permanent-message "postgresql://root:password@localhost/avalan" \
      --id "f4fd12f4-25ea-4c81-9514-d31fb4c48128" \
      --participant "c67d6ec7-b6ea-40db-bf1a-6de6f9e0bb58" \
      --run-max-new-tokens 1024 \
      --name "Tool" \
      --role "You are a helpful assistant named Tool, that can resolve user requests using tools." \
      --stats
```

Agents can use knowledge stores to solve problems. Index the rules of the "Truco" card game directly from a website. The `--dsn` parameter sets the store location and `--namespace` chooses the knowledge namespace:

```bash
avalan memory document index \
    --participant "c67d6ec7-b6ea-40db-bf1a-6de6f9e0bb58" \
    --dsn "postgresql://root:password@localhost/avalan" \
    --namespace "games.cards.truco" \
    "sentence-transformers/all-MiniLM-L6-v2" \
    "https://trucogame.com/pages/reglamento-de-truco-argentino"
```

## Agents

You can easily create AI agents from configuration files. Let's create one to handle gettext translations.
Create a file named [agent_gettext_translator.toml](https://github.com/avalan-ai/avalan/blob/main/docs/examples.agent_gettext_translator.toml)
with the following contents:

```toml
[agent]
role = """
You are an expert translator that specializes in translating gettext
translation files.
"""
task = """
Your task is to translate the given gettext template file,
from the original {{source_language}} to {{destination_language}}.
"""
instructions = """
The text to translate is marked with `msgid`, and it's quoted.
Your translation should be defined in `msgstr`.
"""
rules = [
    """
    Ensure you keep the gettext format intact, only altering
    the `msgstr` section.
    """,
    """
    Respond only with the translated file.
    """
]

[template]
source_language = "English"
destination_language = "Spanish"

[engine]
uri = "meta-llama/Meta-Llama-3-8B-Instruct"

[run]
use_cache = true
max_new_tokens = 1024
skip_special_tokens = true
```

You can now run your agent. Let's give it a gettext translation template file,
have our agent translate it for us, and show a visual difference of what the
agent changed:

```bash
icdiff locale/avalan.pot <(
    cat locale/avalan.pot |
        avalan agent run docs/examples/agent_gettext_translator.toml --quiet
)
```

![diff showing what the AI translator agent modified](https://avalan.ai/images/github/agent_gettext_translator.webp)

There are more agent, NLP, multimodal, audio, and vision examples in the
[docs/examples](https://github.com/avalan-ai/avalan/blob/main/docs/examples)
folder.

### Serving agents

Serve your agents on an OpenAI API–compatible endpoint:

```bash
avalan agent serve docs/examples/agent_tool.toml -vvv
```

Or build an agent from inline settings and expose its OpenAI API endpoints:

```bash
avalan agent serve \
    --engine-uri "NousResearch/Hermes-3-Llama-3.1-8B" \
    --tool "math.calculator" \
    --memory-recent \
    --run-max-new-tokens 1024 \
    --name "Tool" \
    --role "You are a helpful assistant named Tool, that can resolve user requests using tools." \
    -vvv
```

You can call your tool streaming agent's OpenAI-compatible endpoint just like
the real API; simply change `--base-url`:

```bash
echo "What is (4 + 6) and then that result times 5, divided by 2?" | \
    avalan model run "ai://openai" --base-url "http://localhost:9001/v1"
```

# Install

On macOS, install avalan with Homebrew:

```bash
brew tap avalan-ai/avalan
```

In other environments, use [Poetry](https://python-poetry.org/) to install
avalan with the `all` extra:

```bash
poetry install avalan --extras all
```

> [!TIP]
> If you have access to Nvidia GPUs, add the `nvidia` extra to benefit from the
> `vllm` backend and quantized models:
>
> ```bash
> poetry install avalan --extras all --extras nvidia
> ```

> [!TIP]
> If you are running on Apple Silicon Macs, add the `apple` extra to benefit
> from the `mlxlm` backend:
>
> ```bash
> poetry install avalan --extras all --extras apple
> ```

> [!TIP]
> On macOS, sentencepiece may fail to build. Ensure the Xcode CLI is installed
> and install the required Homebrew packages:
>
> `xcode-select --install`
> `brew install cmake pkg-config protobuf sentencepiece`

> [!TIP]
> If you need transformer loading classes that are not yet released, install
> the development version of transformers:
> `poetry install git+https://github.com/huggingface/transformers --no-cache`


