yait-aichain 1.3.5

One interface. 8 providers. 63 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.

---

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 63 models from 8 providers — 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="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

---

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

63 models total. Full list: model registry →

---

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-4-8"),
    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

---

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, APIError

try:
    skill.run()
except RateLimitError as e:
    wait(e.retry_after)        # honours the Retry-After header
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/ — 18 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
17	17_chain_human_input.py	Pause a chain for human approval, then resume
18	18_agent_external_trigger.py	Suspend an agent; a webhook resumes it (cross-process)

---

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

Provider	Text	Vision	Image gen	Env var
Anthropic	Claude Fable 5, Opus / Sonnet / Haiku 4	✓	—	ANTHROPIC_API_KEY
OpenAI	GPT-5.5, GPT-5.4, GPT-4o	✓	ChatGPT-Image, GPT-Image-2	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	✓	Wan 2.2 image	DASHSCOPE_API_KEY

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

---

License

MIT

---

Changelog

1.3.5

Model registry refresh — Google image models.

• Gemini image generation is now GA: gemini-3.1-flash-image and gemini-3-pro-image (the -preview suffixes are gone), plus the new gemini-2.5-flash-image. These are the migration target for Google's discontinued Imagen 4 endpoints (imagen-4.0-*), which this library never referenced.

1.3.4

Agent engine, token accounting, and transport fixes from the audit.

• Token budget is enforced within a step (after the action and execution calls, not only between steps), so one step can no longer overshoot max_tokens; agile replans are capped; tokens from an unparseable orchestrator reply are still counted.
• Honest success: a run fails if any executed step ended in an execution error (not only the last), and the final output is the last executed step's output (even None) rather than a stale earlier value.
• Agent LLM calls go through the send() seam (consistent with Skill; async providers work).
• Usage: Skill.last_usage resets each run (no stale value after a failure); chain.last_usage includes Agent-step tokens; NetworkError is retried within a model when max_retries > 0.
• Robustness: token extraction tolerates usage: null / non-numeric; the DeepSeek-reasoner gate matches the name exactly; Google embeddings accept GOOGLE_API_KEY or GOOGLE_AI_API_KEY.
• Unified HTTP transport: one make_http() factory builds the urllib3 manager for both model and tool clients and honours HTTPS_PROXY / HTTP_PROXY — a proxy now applies to tool traffic (search, fetch, REST, …), not just LLM calls.

1.3.3

Durable-run (suspend/resume) correctness fixes from the audit.

• Resume no longer re-runs already-attempted steps: skipped/failed steps get a terminal status and the resume cursor skips past them; a terminal error during a resumed run clears the parked document, so a duplicate trigger is a no-op (no double side effects).
• A nested Agent that pauses (Wait/Gate) inside a Chain now propagates the suspension up — chain.run() returns a SuspendedResult instead of reporting a failed step, and chain.resume() continues the child agent run.
• RunContext is now real: exposed as chain.context / agent.context during a run, persisted in the run document, and restored on resume (Agent.run / Agent.resume accept context= too).
• FileStore: a non-JSON-serialisable variable/output at a suspend point raises a clear error instead of a raw TypeError.

1.3.2

Correctness & safety fixes from a code audit.

• Qwen image: a terminal task failure (FAILED / timeout / no result) now raises a non-retryable TaskFailedError, so a retry or model fallback can't silently submit a second billable generation.
• SSRF hardening: RestApiTool now blocks private / loopback / metadata targets (opt out with AICHAIN_ALLOW_PRIVATE_URLS); the Qwen TTS audio download is guarded and no longer follows redirects.
• VectorDB: ChromaBackend.delete refuses an empty ids+filter (which would wipe the whole collection), matching Qdrant.
• FileStore: fsync before the atomic rename (durable parked runs); a corrupt run file raises a clear error instead of a raw JSONDecodeError.
• convertToHTML writes through the confined output path, consistent with the other convert tools.

1.3.1

Image-generation fixes and additions.

• Qwen text-to-image now works. DashScope serves the wan models only through its native asynchronous task API, so a new request lifecycle (submit → poll → download) runs behind a send() seam on the client and returns the image in the same shape the synchronous path does. Registry updated to the available wan2.2-t2i-flash / wan2.2-t2i-plus (the old wanx2.1-* ids 404'd).
• New OpenAI image models: chatgpt-image-latest (the always-current model used by ChatGPT — fast, recommended default) and gpt-image-2.
• Transparent backgrounds for gpt-image-* / chatgpt-image-*: background and output_format are forwarded from the output format (background: "transparent", output_format: "png"), so these models can emit real PNG alpha. The response_format param is correctly omitted for the chatgpt-image-* family (it returns base64 natively and rejects the param).

1.3.0

Durable, resumable runs — the serverless core. A Chain or Agent can pause until an external signal arrives (a human, a webhook, a cron tick) and resume later, even in a different process. All additive; existing programs are unchanged.

• Wait / Gate suspend tools — pause a run until a signal arrives; on resume the signal drives the step. Wait is a leaf (its output is the signal); Gate wraps any tool behind an approval decision.
• Chain.resume(run_id, signal) / Agent.resume(run_id, signal) — continue a suspended run from where it paused; completed steps are not re-run.
• Self-contained run documents in a pluggable StateStore: InMemoryStore (default, process-local) and FileStore (survives restart); subclass for S3/DynamoDB/any KV. The store holds only suspended runs and a resume is idempotent — a duplicate trigger is a no-op.
• run() returns a falsy SuspendedResult (carrying run_id and awaiting) when paused instead of a final result. RunContext (tenant, metadata) can be passed to run() for per-request context.

1.2.6

• Security hardening: SSRF guard on outbound URL tools (private/loopback ranges blocked; opt out with AICHAIN_ALLOW_PRIVATE_URLS=1), URL-scheme allow-list, and output-path confinement (AICHAIN_OUTPUT_ROOT). Safe-class checks on tool instantiation during chain load.

1.2.5

• Correctness fixes across the model and tooling layer: response parsing, search/MCP timeouts, table chunking when a header exceeds the chunk size, and assorted edge cases surfaced by analysis.
• Refreshed the model registry to current provider catalogues and June 2026 pricing.

1.2.3

Two-tier model layer: format is code (by API family), provider is data. Changing provider — one word in Model("…") — changes only data, never the request format or any model code. Behaviour is byte-for-byte unchanged (guarded by characterisation tests).

• Model is a single, thin, data-driven class: it resolves the provider from the name and delegates the wire format to the matching family client. The provider is exposed as model._provider.
• Provider settings, model capabilities and prices live in data — one file per provider under models/providers/*.toml.
• The protocol layer (clients/) owns the wire format. Five family clients cover all eight providers: OpenAIClient (openai, xai, kimi, deepseek), PerplexityClient, QwenClient, AnthropicClient, GoogleClient.
• Removed the per-provider Model subclasses and client classes; use Model("name") (or an explicit provider/ prefix for custom names).
• Fixed Qwen region endpoints (us, hk) and base URL.

1.2.1

• Removed leaked application code from the library (skills/summarise.py, tools/section_context.py) — an app-specific pipeline that did not belong in the general-purpose library.

1.2.0

Mechanism 1 — "LLM layer as data". All additive; the minimal program is unchanged.

• Usage on every result — normalised input_tokens / output_tokens / total_tokens across providers; additive; skill.last_usage and chain.last_usage sum across steps.
• Cost estimation from a per-model price table (usage.cost; None when a model is unpriced).
• Exception hierarchy under APIError (RateLimitError, AuthenticationError, InvalidRequestError, NotFoundError, ServerError, NetworkError).
• provider/model routing — Model("openai/gpt-4o"); unlocks custom / fine-tuned names.
• Model fallback chain — Skill(model=[primary, backup, …]) advances on a transient failure; a non-transient failure propagates immediately.
• registry.refresh(provider) — diffs the registry against the provider's live list_models().

1.1.0

Foundation repaired: five features that never worked in the installed package are revived, plus fragility closed across the library. 69 regression tests.

• Fixed Chain.load(), Agent inside Chain, the Qwen embedder/reranker, and Agent persistent memory — each previously crashed or was dead code.
• Honest success=False on token-budget exhaustion and exhausted step retries.
• Hardened LLM-response and agent-JSON parsing; timeouts on all search tools and the MCP bridge; delete(ids=[]) no longer wipes a collection.
• Chunker contract fixes; thread-safe Chain.run(); fixed from yait_aichain.tools import *.
• POST retry policy (429/503); Anthropic auto-raises max_tokens above the thinking budget; gpt-5 / o-series parameter correctness; Chroma v2; Qdrant string IDs; batched VectorDB upsert; safe prompt templating.
• Security: the Google API key moved from the query string to the x-goog-api-key header.

1.0.0

Initial release — Skill, Chain, Pool, Agent, Tool / MCPTools, VectorDB, Reranker, and 8 providers (Anthropic, OpenAI, Google, xAI, Perplexity, Kimi, DeepSeek, Qwen).