Declarative LLM orchestration. Define workflows in YAML — each action gets its own model, context window, schema, and pre-check gate. The framework handles DAG resolution, parallel execution, batch processing, and output validation.
[!WARNING] Experimental — Under active development. Expect breaking changes. Open an issue with feedback.
actions:
- name: extract_features
intent: "Extract key product features from listing"
model_vendor: anthropic # Each action picks its own model
model_name: claude-sonnet-4-20250514
- name: generate_description
dependencies: [extract_features]
model_vendor: openai # Mix vendors in one pipeline
model_name: gpt-4o-mini
context_scope:
observe:
- extract_features.features # See only what it needs
drop:
- source.raw_html # Don't waste tokens on noise
Install
pip install agent-actions
Quick start
agac init my_project && cd my_project # scaffold a project
agac init example contract_reviewer my_project # or start from an example
agac run -a my_workflow # execute
Why not just write Python?
You will, until you have 15 steps, 3 models, batch retry, and a teammate asks what your pipeline does.
| Capability | Agent Actions | Python script | n8n / Make |
|---|---|---|---|
| Per-step model selection | YAML field | Manual wiring | Per-node config |
| Context isolation per step | observe / drop |
You build it | Not available |
| Pre-check guards (skip before LLM call) | guard: |
If-statements | Post-hoc branching |
| Parallel consensus (3 voters + merge) | 2 lines of YAML | Custom code | Many nodes + JS |
| Schema validation + auto-repair | Built in | DIY | Not available |
| Batch processing (1000s of records) | Built in | For-loops | Loop nodes |
| The YAML is the documentation | Yes | No | Visual graph |
Examples
| Example | Pattern | Key Features |
|---|---|---|
| Review Analyzer | Parallel consensus | 3 independent scorers, vote aggregation, guard on quality threshold |
| Contract Reviewer | Map-reduce | Split clauses, analyze each, aggregate risk summary |
| Product Listing Enrichment | Tool + LLM hybrid | LLM generates copy, tool fetches pricing, LLM optimizes |
| Book Catalog Enrichment | Multi-step enrichment | BISAC classification, marketing copy, SEO metadata, reading level |
| Incident Triage | Parallel consensus | Severity classification, impact assessment, team assignment, response plan |
| Support Resolution | Non-JSON pipeline | Classify, route, and draft responses using output_field — works with any model including local Ollama |
Providers
| Provider | Batch | Provider | Batch |
|---|---|---|---|
| OpenAI | Yes | Groq | Yes |
| Anthropic | Yes | Cohere | Online only |
| Google Gemini | Yes | Ollama (local) | Yes |
Switch providers per-action by changing model_vendor.
Key capabilities
- Pre-flight validation — schemas, dependencies, templates, and credentials checked before any LLM call
- Batch processing — route thousands of records through provider batch APIs
- User-defined functions — Python tools for pre/post-processing and custom logic
- Expectations — declare rules the output must satisfy; failures are regenerated with the rule's feedback
- Observability — per-action timing, token counts, and structured event logs
- Interactive docs —
agac docsbuilds and serves a visual workflow dashboard
Documentation
- Full docs — guides, tutorials, reference
- Configuration — YAML schema reference
- CLI — all commands and options
Contributing
git clone https://github.com/Muizzkolapo/agent-actions.git && cd agent-actions
uv sync
pytest
See CONTRIBUTING.md. Report bugs via Issues.
License
1 maintainer
Metadata
1 maintainer
Release files for agent-actions 1.0.0
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| File | Size | Uploaded | |
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| agent_actions-1.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 14.0 MB
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