yait-aichain 1.0.1

One interface. 8 providers. 57 models. Text, images, vision, RAG, agents, parallel pipelines.

aichain

The simplest way to build AI pipelines. 8 providers. 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.

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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:

Need	aichain primitive
Call any LLM	Skill
Chain steps together	Chain
Run tasks in parallel	Pool
Autonomous reasoning	Agent
Vector search + RAG	VectorDB + vectorQuery
Rerank results	Reranker
Call any tool or MCP server	Tool / MCPTools

All of these work identically across 57 models from 8 providers — with one line to swap any of them.

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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="sk-ant-…"
export OPENAI_API_KEY="sk-…"
export GOOGLE_AI_API_KEY="AIza…"
export XAI_API_KEY="xai-…"
export PERPLEXITY_API_KEY="pplx-…"
export MOONSHOT_API_KEY="sk-…"      # Kimi
export DEEPSEEK_API_KEY="sk-…"
export DASHSCOPE_API_KEY="sk-…"     # Qwen
export COHERE_API_KEY="…"           # embeddings + reranking
export VOYAGE_API_KEY="…"           # embeddings + reranking

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8 providers, 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

57 models total. Full list: model registry →

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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

agent  = Agent(
    orchestrator = Model("claude-opus-4-6"),
    tools        = [searchPerplexity(), convertToMD()],
    mode         = "agile",
    max_steps    = 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

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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")

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Examples

→ examples/ — 16 focused examples, one concept each

#	File	What it shows
01	01_skill.py	The minimum viable aichain program
02	02_skill_models.py	Same prompt, Claude + GPT + Gemini
03	03_skill_multimodal.py	Text → image → vision, three providers
04	04_skill_save_load.py	Save to YAML, reload anywhere
05	05_tool_convert.py	URL → Markdown
06	06_tool_mcp.py	Connect an MCP server, discover + call tools
07	07_tool_custom.py	Build your own Tool, plug into a Chain
08	08_chain.py	GPT writes → Claude reviews
09	09_chain_tool_skill.py	Fetch page → summarise
10	10_chain_save_load.py	Save/reload a full pipeline
11	11_pool.py	5 topics, all in parallel
12	12_pool_chain.py	Chain-per-item, all in parallel
13	13_agent.py	Autonomous agent, one tool
14	14_agent_tools.py	Agent picks its own tools
15	15_agent_orchestrator.py	Orchestrator spawns sub-agents
16	16_debug.py	Inspect Chain history, Pool status, Agent steps

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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")

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Supported providers

Provider	Text	Vision	Image gen	Env var
Anthropic	Claude Opus / Sonnet / Haiku 4	✓	—	ANTHROPIC_API_KEY
OpenAI	GPT-5, GPT-4.1, GPT-4o, o1, o3, o4-mini	✓	DALL-E 3, GPT-Image-1	OPENAI_API_KEY
Google	Gemini 2.5 Pro / Flash, 3.x	✓	Gemini image models	GOOGLE_AI_API_KEY
xAI	Grok 4, Grok 3	✓	Grok-Imagine	XAI_API_KEY
Perplexity	Sonar Pro, Sonar, Deep Research	—	—	PERPLEXITY_API_KEY
Kimi	K2.5, K2, K2 Turbo, K2 Thinking	✓	—	MOONSHOT_API_KEY
DeepSeek	DeepSeek-V3, DeepSeek-R1	—	—	DEEPSEEK_API_KEY
Qwen	Qwen-Max, Qwen3, QwQ	✓	Wanx image models	DASHSCOPE_API_KEY

Embedding: OpenAI · Cohere · Voyage · Google · Qwen
Reranking: Cohere · Voyage · Qwen
Vector DB: Chroma · Qdrant · Pinecone

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License

MIT