aichain

The simplest way to build AI pipelines. 11 cloud providers + your own private server. 1 interface. Zero lock-in.

from yait_aichain import Model, Skill

skill = Skill(
    model  = Model("claude-sonnet-4-6"),   # change this one word to switch providers
    input  = {"messages": [{"role": "user", "parts": ["Summarise: {text}"]}]},
)

result = skill.run(variables={"text": "..."})

Change "claude-sonnet-4-6" to "gpt-4o", "gemini-2.5-pro", or "grok-3" — nothing else changes.

Why aichain?

Every major AI library makes you choose: LangChain is too complex, LlamaIndex is RAG-only, CrewAI is agents-only, AutoGen requires a PhD to configure.

aichain covers the full stack with the simplest interface:

All of these work identically across 90 models from 11 cloud providers — plus any open-weight model you host yourself, where the data never leaves your perimeter — with one line to swap any of them.

Install

pip install yait-aichain

Optional extras:

pip install markitdown   # file/URL → Markdown
pip install pyyaml       # save & load Skills and Chains
pip install fastmcp      # MCP server integration

API keys — only for the providers you use:

export ANTHROPIC_API_KEY="…"
export BFL_API_KEY="…"
export DEEPSEEK_API_KEY="…"
export GOOGLE_AI_API_KEY="…"
export MOONSHOT_API_KEY="…"
export OPENAI_API_KEY="…"
export PERPLEXITY_API_KEY="…"
export DASHSCOPE_API_KEY="…"
export RECRAFT_API_TOKEN="…"
export REVE_API_KEY="…"
export XAI_API_KEY="…"
export BRAVE_SEARCH_API_KEY="…"
export COHERE_API_KEY="…"
export GOOGLE_API_KEY="…"
export SERPAPI_API_KEY="…"
export VOYAGE_API_KEY="…"

Every provider, one syntax

from yait_aichain.models import Model

Model("claude-sonnet-4-6")   # Anthropic
Model("gpt-4o")              # OpenAI
Model("gemini-2.5-flash")    # Google
Model("grok-3")              # xAI
Model("sonar-pro")           # Perplexity
Model("kimi-k2.5")           # Kimi
Model("deepseek-chat")       # DeepSeek
Model("qwen-max")            # Qwen
Model("private/llama3.3")    # your own server — vLLM, Ollama, LM Studio, …

88 cloud models total (full list: model registry →). The private provider needs no key and accepts whatever your server serves — the prompts and the answers stay inside your perimeter (private models →).

Core concepts

Skill — one prompt, any model

skill  = Skill(model=Model("gpt-4o-mini"), input={...})
result = skill.run(variables={"topic": "neural networks"})

Chain — sequential steps, automatic variable flow

chain = Chain(steps=[
    (fetch_tool,    "page"),
    (summarise,     "summary"),
    (translate,     "result"),
])
result = chain.run(variables={"url": "https://…", "language": "French"})
print(chain.history)   # full audit trail

Pool — parallel execution

pool    = Pool(summarise_skill, items=[{"text": t} for t in documents], max_flows=10)
results = pool.run()                    # all documents processed simultaneously

print(pool.status)   # {PENDING: 0, RUNNING: 0, DONE: 50, FAILED: 0}
print(pool.history)  # per-item: status, output, error, duration

Agent — autonomous reasoning

from yait_aichain.agent import Agent, step_count

agent  = Agent(
    model        = Model("claude-opus-4-8"),
    tools        = [searchPerplexity(), convertToMD()],
    mode         = "agile",
    stop_when    = [step_count(10)],
)
result = agent.run("Compare the top 3 vector databases.")
print(result.output)
print(f"steps={result.steps_taken}  tokens={result.tokens_used:,}")

Full RAG pipeline

from yait_aichain.tools.embedding import Embedding
from yait_aichain.tools.vectordb  import VectorDB, vectorChunk, vectorUpsert, vectorQuery
from yait_aichain.tools.reranking import Reranker

store    = VectorDB("chroma", "docs", embedder=Embedding("cohere/embed-v4.0"))
reranker = Reranker("cohere/rerank-v3.5")

# Ingest
chunks = vectorChunk(max_chars=800).run(my_document)
vectorUpsert(store).run([{"id": f"c{i}", **c} for i, c in enumerate(chunks)])

# Query → rerank → answer
pipeline = Chain(steps=[
    (vectorQuery(store),  "candidates", {"input": "{question}", "options": {"n": 20}}),
    (reranker,            "context",    {"input": "{candidates}",
                                         "options": {"query": "{question}", "top_n": 5}}),
    answer_skill,
])
answer = pipeline.run(variables={"question": "How does KV caching work?"})

Vector DB providers: Chroma · Qdrant · Pinecone Reranking providers: Cohere · Voyage · Qwen

Cost, routing & resilience

These are all opt-in — the minimal program above is unchanged.

Token usage & cost — every result carries normalised usage:

skill.run()
skill.last_usage.input_tokens   # 1240
skill.last_usage.total_tokens   # 1310
skill.last_usage.cost           # 0.0032  (USD, None if the model has no price)

chain.run()
chain.last_usage.cost           # summed across all Skill steps

Explicit provider routing — pick the provider with a provider/model prefix; it also unlocks custom / fine-tuned names the auto-detector can't recognise:

Model("openai/gpt-4o")                 # same as Model("gpt-4o")
Model("openai/ft:gpt-4o:acme:42")      # custom name, explicit provider

Fallback chain — pass a list; a transient failure (rate limit / server / network) advances to the next model. A real error (bad key, bad request) still raises immediately:

skill = Skill(
    model = [Model("claude-sonnet-4-6"), Model("gpt-4o")],  # primary, then backup
    input = {"messages": [{"role": "user", "parts": ["..."]}]},
)

Typed errors — catch a specific failure mode, or APIError for all:

from yait_aichain import (RateLimitError, AuthenticationError,
                          InsufficientCreditsError, APIError)

try:
    skill.run()
except RateLimitError as e:
    wait(e.retry_after)        # honours the Retry-After header
except InsufficientCreditsError:
    ...                        # out of credits/quota — top up (not a key problem)
except AuthenticationError:
    ...

Keep the model list fresh — diff the registry against a provider's live roster:

from yait_aichain.models import registry
registry.refresh("openai")     # → {"new": [...], "removed": [...], ...}

Built-in tools

Search

searchPerplexity · searchBrave · searchSerp · searchOpenAI

Convert

convertToMD · convertToHTML · convertToPDF · TTS(provider) · STT(provider)

Embeddings

Embedding("openai/text-embedding-3-small")
Embedding("cohere/embed-v4.0")
Embedding("voyage/voyage-3-large")

Examples

→ examples/ — one concept per file

Persist and reload

skill.save("skills/translator.yaml")
skill = Skill.load("skills/translator.yaml")   # API key from env, not file

chain.save("chains/research.yaml")
chain = Chain.load("chains/research.yaml")

Supported providers

Image editing — instruction edit, preserves the subject: OpenAI · Google · xAI · Qwen · FLUX Kontext (place / restyle / recompose while keeping the original object)
Image-to-image — whole-image variation (restyle, not subject-preserving): Recraft (imageToImage, strength-controlled)
Embedding: OpenAI · Cohere · Voyage · Google · Qwen
Reranking: Cohere · Voyage · Qwen
Vector DB: Chroma · Qdrant · Pinecone

License

MIT

Changelog

Every release is in CHANGELOG.md — what changed, and why it changed. Releases before 2.0 are recorded incompletely; the file says which ones and why they were not reconstructed.