treelang 0.10.0

LLM function calling on steroids using Abstract Syntax Trees.

🌲 treelang

Turn your toolboxes into executable Abstract Syntax Trees (ASTs) that Large Language Models can plan in a single shot. treelang lets you express arbitrarily complex workflows, keep sensitive values out of the LLM, and reuse the resulting programs as shareable, cacheable trees.

Highlights

• One LLM call, full plan – generate an AST for a complete solution without the expensive function-call loop.
• Complex workflows – conditionals, higher-order functions (map, filter, reduce), and nested tool invocations all live in one tree.
• Secure + green – the LLM never sees tool results; you evaluate nodes locally while controlling cost and compliance.
• Model Context Protocol native – ships with an MCP client provider but can work with any tool registry through the ToolProvider abstraction.
• Composable outputs – turn ASTs into callable tools (AST.tool) or serialize/describe them for sharing, caching, or review.

What you can build

• Enterprise copilots that must orchestrate dozens of tools with branching logic.
• Automations that need to fan out over datasets (e.g., score/map/filter large collections asynchronously).
• Reusable skills: persist an AST, describe it with EvalResponse.describe(), and redeploy it as a tool on your MCP server.
• LLM evaluation loops: ask for the tree (EvalType.TREE) to inspect reasoning before execution, or walk it immediately for answers.

Quick start

Requirements

• Python 3.12+
• An OpenAI API key (OPENAI_API_KEY) and optional OPENAI_MODEL override (defaults to gpt-4o-2024-11-20)
• A source of tools: an MCP server, or your own provider implementing treelang.ai.provider.ToolProvider

Install

pip install treelang

Wire up tools (MCP)

import asyncio
from contextlib import AsyncExitStack

from mcp import ClientSession
from mcp.client.streamable_http import streamable_http_client

from treelang.ai.arborist import EvalType, OpenAIArborist
from treelang.ai.provider import MCPToolProvider


async def build_arborist(stack: AsyncExitStack) -> OpenAIArborist:
    try:
        await stack.__aenter__()
        # Connect to a streamable HTTP server
        read_stream, write_stream, _ = await stack.enter_async_context(
            streamable_http_client("http://localhost:8000/mcp")
        )

        # Create a session using the client streams
        session = await stack.enter_async_context(
            ClientSession(read_stream, write_stream)
        )

        # Initialize the connection
        await session.initialize()

        # Create the Arborist
        provider = MCPToolProvider(session)
        arborist = OpenAIArborist(provider=provider, model="gpt-4o")

        return arborist

    except Exception:
        print ("Error building the Arborist")
        await stack.aclose()
        raise

Ask a question (MCP)

async def run():
    stack = AsyncExitStack()
    arborist = await build_arborist(stack)

    response = await arborist.eval(
        query="Compare next weekend flights BOS➜SFO and summarize the cheapest option.",
        type=EvalType.WALK,  # change to EvalType.TREE to inspect the JSON AST instead
    )
    print(response.content)  # fully-evaluated tool output
    if (stack):
        await stack.aclose()

if __name__ == "__main__":
    asyncio.run(run())

Use response.jsontree with AST.parse() or AST.repr() if you want to log, cache, or transform the raw tree.

Tree-first workflow

1. Generate – OpenAIArborist assembles an AST using your available tools.
2. Inspect – represent the tree as JSON, describe it with EvalResponse.describe(), or pretty-print it using AST.repr().
3. Evaluate – AST.eval(tree, provider) asynchronously executes every node; the LLM never sees intermediate values.
4. Package – await AST.tool(tree, provider) turns a tree into a callable tool so you can add it back to your MCP server.

Architecture at a glance

• Arborist (treelang/ai/arborist.py) – orchestrates LLM calls, maintains history via the optional Memory interface, and decides whether to return ASTs or walked results.
• Tool providers (treelang/ai/provider.py) – abstract how tools are discovered/invoked. We ship an MCP client implementation and a template for custom providers.
• Selectors (treelang/ai/selector.py) – plug in your own tool filtering logic; AllToolsSelector ships by default.
• Trees (treelang/trees/tree.py) – immutable node classes plus helpers such as async traversal, repr generation, and turning trees into callable tools.

Resources & examples

• Cookbook notebooks (cookbook/) walk through building trees, call patterns, and evaluation strategies.
• Reproducible benchmark (evaluation/eval.py) runs versioned deterministic cases without credentials, records machine-readable quality and resource metrics, and checks them against committed regression baselines. A separate manual/scheduled live workflow records comparable model quality, latency, token, and cost evidence without exposing credentials to pull requests. See evaluation/README.md for commands and baseline policy.
• Unit tests (tests/) cover the AST core and are a good reference for expected behavior when extending nodes.

Contributing & local development

We actively welcome contributions—see CONTRIBUTING.md for the full workflow.

Maintainers publish signed, provenance-attested releases through PyPI Trusted Publishing. See RELEASING.md for versioning, promotion, publishing, and verification instructions.