trellis-pipelines 0.2.0

Modular agentic pipeline system for document and data workflows

Trellis

Modular agentic pipeline system for document and data workflows.

Trellis lets you define multi-step AI pipelines as declarative YAML — ingest PDFs, fetch SEC filings, call LLMs, extract structured fields, fan out over lists — and run them from the CLI, a REST API, or directly from Python.

Documentation · Installation · Pipeline DSL Reference · Examples

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Install

pip install trellis-pipelines

With uv:

uv add trellis-pipelines

Requires Python 3.12+. Set at least one LLM provider key before running pipelines:

export ANTHROPIC_API_KEY=sk-ant-...   # or OPENAI_API_KEY, etc.

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Quickstart

CLI

# Validate a pipeline file
trellis validate examples/pipelines/pdf_summarize.yaml

# Run a pipeline
trellis run examples/pipelines/pdf_summarize.yaml

# Pass runtime parameters
trellis run examples/pipelines/fetch_10k_parametrized.yaml \
  --params '{"ticker": "AAPL", "year": "2024"}'

# Override the LLM model for all tasks
trellis run examples/pipelines/pdf_summarize.yaml --model anthropic/claude-haiku-4-5

A pipeline file

pipeline:
  id: pdf_summarize
  goal: "Load a PDF and summarize key points"
  tasks:
    - id: ingest_pdf
      tool: ingest_document
      inputs:
        path: "https://example.com/report.pdf"

    - id: extract_content
      tool: extract_from_texts
      inputs:
        document: "{{ingest_pdf.output}}"
        prompt: "Extract the main topics and key metrics"

    - id: summarize
      tool: llm_job
      inputs:
        prompt: |
          Summarize in 5 bullet points for a busy executive:
          {{extract_content.output}}
        max_tokens: 256

Dependencies are inferred from {{task_id.output}} references — no explicit wiring needed.

Python

import asyncio
from trellis.models.pipeline import PipelineSpec
from trellis.execution.orchestrator import Orchestrator

spec = PipelineSpec.from_yaml("examples/pipelines/pdf_summarize.yaml")
orchestrator = Orchestrator()
result = asyncio.run(orchestrator.run_pipeline(spec))

print(result.outputs)

REST API

# Start the server (requires trellis-pipelines[api])
pip install "trellis-pipelines[api]"
uvicorn trellis_api.main:app --reload

curl -X POST http://localhost:8000/pipelines/run \
  -H "Content-Type: application/json" \
  -d '{
    "pipeline": {
      "id": "hello",
      "goal": "Say hello",
      "tasks": [
        {
          "id": "greet",
          "tool": "llm_job",
          "inputs": { "prompt": "Say hello in one sentence." }
        }
      ]
    }
  }'

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Key features

• Declarative YAML DSL — flat task list, dependencies inferred from template references
• Template resolution — {{task_id.output}}, {{params.key}}, {{session.key}}, {{item}} (fan-out)
• Fan-out / parallel_over — scatter a task over a list, collect results automatically
• await barriers — explicit synchronization across parallel branches
• Retry & backoff — per-task retry with exponential backoff and jitter
• Structured extraction — extract_fields with JSON Schema, extract_from_texts for free-form
• PDF + web ingestion — ingest_document (PDF/HTML), fetch_url, search_web
• Multi-tenancy — tenant-scoped blackboard (store / {{session.key}}) for stateful workflows
• CLI, REST API, and Python SDK — three interfaces, one engine

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Project structure

trellis/            # Core: models, execution engine, tool registry
trellis_api/        # FastAPI REST server (optional extra: [api])
trellis_cli/        # Typer CLI
trellis_mcp/        # MCP server adapter (roadmap)
examples/           # Example pipelines and data
docs/               # MkDocs source
tests/              # Test suite

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Optional extras

Extra	What it adds
trellis-pipelines[api]	FastAPI + uvicorn REST server
trellis-pipelines[dev]	pytest, ruff, mypy, black, isort
trellis-pipelines[all]	All extras

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Documentation

• Installation
• Quickstart
• Pipeline DSL Reference
• Tools & Registry
• CLI reference
• REST API reference

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License

MIT — see LICENSE or https://opensource.org/license/mit.