aichain
The provider-agnostic AI toolkit for Python.
Let your coding agent write your agents. Ship the code you tested. Leave any provider with one word. Pipelines and agents, small enough for Claude Code, Codex or Cursor to learn from one file and build on.
from yait_aichain import Model, Skill
skill = Skill(
model = Model("claude-haiku-5-5"), # change this one word to switch providers
input = {"messages": [{"role": "user", "parts": ["Summarise: {text}"]}]},
)
result = skill.run(variables={"text": "..."})
Change "claude-haiku-5-5" to "gpt-6-luna", "gemini-3-flash-preview" or
"grok-4.3" — nothing else changes.
Three promises
Your coding agent writes the agents. The whole contract is one 26 KB
llms.txt, and the plugin for Claude Code, Codex and Cursor
carries it with twenty worked recipes. Measured on tasks the docs were not
written against: on twenty, a small model with the docs passed 15 in all three
tries and Claude Code passed 19; on eleven for what 3.x added, both passed all
eleven; and without the docs the same small model passed none of an earlier
twenty (the measurements).
The code you tested is the code you ship. Only what you inject changes between a laptop and production: where state is stored, whose keys are used, what starts the run. A chain or an agent that waits for a person or a webhook parks, and resumes later in another process. An agent's state is its conversation, so one turn fits one serverless call. Spending stops at a ceiling in dollars, and keys can be resolved per request, per tenant.
No provider owns your code. Switching provider is changing the model name.
When the new one cannot do what you asked — no top_k, no seed, a different
image size — the result says what was adapted or dropped, instead of leaving
you to find out from the output.
Highlights
- Small enough to read in one go.
llms.txtis 26 KB, the skill 4 KB, the whole library about 29,000 lines of Python. - One required dependency,
urllib3— andtomlion Python 3.10, which has no TOML reader of its own. YAML, MCP and local page fetching are extras you install when you use them. - The price of every call. Usage and cost come from the registry on every
result, and
max_costis one budget in dollars shared by a Skill, a Chain, a Pool and an Agent. - A pipeline is data. Skills and Chains save to YAML and load back as they were; API keys are never written.
- A skill for your coding agent — one package for Claude Code, Codex and Cursor.
What you build with it
| Deterministic pipelines | Skill — one model call with a template and an output format. Chain — steps in order, each output passed on by name. Pool — one Skill or Chain over many inputs at once. |
| Agents, by how hard the task is | mode="agile" decides at every turn. mode="waterfall" writes a plan first — from a stronger planner_model if you like — and holds to it. team= lets it delegate to workers with conversations of their own. On τ²-bench a plain loop did best with a weak model and a planned run with a strong one. |
| Tools | Search, fetch, decide, speech, transcription, images, embeddings, reranking, chunking, wait and gate — each made the same way, search("exa"). Any function becomes a tool with @tool; any REST API or MCP server goes into an agent in two lines. |
| Utilities | Vectors for a vector collection, Shell for one folder to browse, read, write and run commands in, MCP and REST for connections. Your code calls their methods; the agent gets only the methods that are tools. |
| Control and measurement | Usage and cost on every result. max_cost — one budget in dollars across every step. Permissions by risk class, and approve= for a person to decide. An event for every step. eval/ runs arms × cases × trials and reports accuracy, Pass^k and cost side by side. |
| Models | 169 models from 16 cloud providers, plus any open-weight model on your own server, where the data never leaves your perimeter. Text and vision are models; image generation is a tool. |
Install
pip install yait-aichain
One required dependency, urllib3 (and tomli on Python 3.10). Extras when you use what needs them:
[yaml] to save and load, [mcp] for MCP servers, [fetch] to read pages
locally — Installation. 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 META_API_KEY="…"
export MINIMAX_API_KEY="…"
export MISTRAL_API_KEY="…"
export OPENAI_API_KEY="…"
export PERPLEXITY_API_KEY="…"
export DASHSCOPE_API_KEY="…"
export RECRAFT_API_TOKEN="…"
export STEPFUN_API_KEY="…"
export XAI_API_KEY="…"
export MIMO_API_KEY="…"
export ZAI_API_KEY="…"
export BRAVE_SEARCH_API_KEY="…"
export COHERE_API_KEY="…"
export EXA_API_KEY="…"
export FIRECRAWL_API_KEY="…"
export GOOGLE_API_KEY="…"
export PARALLEL_API_KEY="…"
export TAVILY_API_KEY="…"
export TYPESAFE_API_KEY="…"
export VOYAGE_API_KEY="…"
Use it from Claude Code, Codex or Cursor
One plugin carries the skill that teaches a coding agent to build on this
library: SKILL.md, llms.txt and twenty worked recipes, generated by
scripts/skill.py from the files this repository tests.
Claude Code
/plugin marketplace add yaitio/aichain
/plugin install yait-aichain@yait
Codex — add the repository as a marketplace, then install yait-aichain
from it:
codex plugin marketplace add yaitio/aichain
Cursor — Customize → From GitHub Repository → yaitio/aichain, then
install yait-aichain.
The skill alone works too: copy plugin/skills/yait-aichain/ into
~/.agents/skills/, which Codex and Cursor both read, or into
~/.claude/skills/ for Claude Code.
Learn more
| Start | Quickstart — five minutes, four steps · Concepts — six ideas, one screen each · Migrating to 4.0 |
| Pipelines | Skill · Chain · Pool · Pause and resume |
| Agents | The loop and its modes · Driving the loop · An agent as a chain step |
| Tools | How tools work · Search · Fetch · Decide · Chunk, embed, rerank · Speech and images |
| Utilities | Vectors, Shell, REST, MCP |
| Control | Cost and budget · Permissions · Observability · Eval |
| Models | Models and options · Private models · the registry |
| Production | From laptop to production · Build with your coding agent |
| API | every class, function and parameter, generated from the code |
Examples
→ examples/ — one concept per file
| # | File | What it shows |
|---|---|---|
| 01 | 01_skill.py |
Run a prompt against a model. |
| 02 | 02_skill_models.py |
Same prompt, three providers. |
| 03 | 03_skill_multimodal.py |
Text → Image → Text, three providers. |
| 04 | 04_skill_save_load.py |
Save a skill to YAML, reload and run it. |
| 05 | 05_tool_fetch.py |
A web page's text from its URL. |
| 06 | 06_tool_mcp.py |
An MCP server as a utility: discover its tools, call them. |
| 07 | 07_tool_custom.py |
Make any function a tool, and use it in a Chain. |
| 08 | 08_chain.py |
Two skills in sequence, two different providers. |
| 09 | 09_chain_tool_skill.py |
Tool + Skill in one chain. |
| 10 | 10_chain_save_load.py |
Save a chain to YAML, reload and run it. |
| 11 | 11_pool.py |
Run the same skill in parallel for multiple inputs. |
| 12 | 12_pool_chain.py |
Chain as a Pool runner. |
| 13 | 13_agent.py |
Basic agent with one tool. |
| 14 | 14_agent_tools.py |
Agent with multiple tools, picks autonomously. |
| 15 | 15_agent_orchestrator.py |
Orchestrator agent spawns sub-agents. |
| 16 | 16_debug.py |
Inspect intermediate steps in Chain, Pool, and Agent. |
| 17 | 17_chain_human_input.py |
Human-in-the-loop: pause a chain for manual input. |
| 18 | 18_chain_external_trigger.py |
Pause a run until an EXTERNAL trigger resumes it. |
| 19 | 19_image_edit.py |
Image → image (editing) across providers, one tool. |
| 20 | 20_observability.py |
The step boundary: hooks, events, and an approval gate. |
| 21 | 21_multi_turn.py |
Directed multi-turn reasoning in ONE Skill. |
| 22 | 22_goal_mode.py |
A stop condition the harness can verify |
| 23 | 23_private_model.py |
Run a model on hardware you control. |
| 24 | 24_scaffold.py |
Scaffold — the model designs the team, then runs it |
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.
1 maintainer
Metadata
1 maintainer
Release files for yait-aichain 4.0.0
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Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| yait_aichain-4.0.0.tar.gz | 470.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| yait_aichain-4.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 920.4 kB
Release files / yait_aichain-4.0.0.tar.gz
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