dragiter - Deterministic Context Iterator

A focused command-line tool for structured, reproducible LLM workflows.

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dragiter treats large language models the way a well-mannered Unix utility treats everything else: as a stage in a pipeline. You supply material, a prompt template and (optionally) a loop file; it assembles deterministic requests and writes the results where you tell it to. No chat interface, no hidden state, no surprises.

Why it exists

Assembling context for an LLM by hand is a peculiar form of digital drudgery. dragiter replaces that ritual with something closer to proper engineering:

• Structured context assembly - glob patterns and regular expressions carve documents into the exact chunks the model should see.
• Prompt templates as code - system instructions, material formatting and synthesis logic live in plain TOML files that can be version-controlled, reviewed and shared.
• Batch iteration without custom scripts - a simple text file or JSONL loop drives repeated runs with different parameters.
• Provider independence - any OpenAI-compatible endpoint (local Ollama, xAI Grok, Google, LiteLLM proxies, …) works by changing three settings: base_url, model_name and api_key.

If you prefer deterministic behaviour, explicit file routing and the ability to put an entire AI workflow under version control, this tool is for you.

Installation

pip install dragiter

Requires Python ≥ 3.11.

Compatibility

2026.9.9 is a breaking release for resource files. The singular TOML key regex_pattern is no longer accepted. A section that still sets it aborts collection and names the section. Use regex_patterns as a list of strings:

regex_patterns = ['^##\s+', '^###\s+']

## Quick Start

Keep *resources* (what the model should know) separate from *prompts* (what you want it to do).

### 1. Extract the examples

```bash
dragiter-gen-examples .
cd examples/01_md_sample

2. Always simulate first

dragiter -s -p 01_prompt_md.toml -r 01_resource_md.toml -l 01_loop_md.txt

No network calls, no tokens spent - just a clear view of the assembled prompts and file routing.

3. Run against a local Ollama instance

dragiter -v -c config-ollama.toml -p 01_prompt_md.toml -r 01_resource_md.toml -l 01_loop_md.txt

The -v flag is advisable with local models; they can take their time and the silence is otherwise rather disconcerting.

Documentation

Documentation follows the Diátaxis framework and is deliberately split:

dragiter-gen-docs .

• docs/manual.md - Tutorial, How-to guides and Explanations
• docs/reference.md - Complete technical reference (flags, schemas, placeholders, defaults, activity log)
• docs/info.txt - Concise man-page summary (dragiter --info)

Configuration in brief

Settings are resolved in this strict order (highest priority first):

1. Command-line arguments
2. TOML configuration file (-c, DRAGITER_CONFIG_FILE, or ~/.config/dragiter/config.toml)
3. Environment variables (DRAGITER_*)
4. Built-in defaults

A value set by a higher-priority source cannot be overridden by a lower one.

Notable capabilities (all present in the code)

• Regex-based document chunking with optional include/exclude filters
• Sequential or batched processing of material chunks
• Context-window estimation via chars_per_token, max_context_tokens and max_output_tokens
• JSONL loops that expose every object key as a template placeholder
• Standard input support ({STDIN} placeholder or automatic use with -t)
• Activity tracing to JSONL for auditing
• Retry logic with configurable delay and maximum attempts (transient 5xx and connection drops; 504 / gateway timeout / runner crash are terminal)
• Optional mutual TLS (client certificate + key)
• Output modes: exclusive create (x), overwrite (w), append (a)

Tool Chaining (the Unix way)

Because dragiter does one job cleanly it composes with the rest of the terminal. Standard input is fully supported:

# Pipe live data straight into a prompt that contains the {STDIN} placeholder
curl -s https://example-competitor.com/pricing \
  | dragiter -p summarize_pricing.toml -r web_resources.toml -o pricing_report.txt

The same pattern works with database exports, log files or any other tool that can produce a stream.

Acknowledgements

This project would still be an elegant collection of unfinished ideas without the tireless pair-programming assistance of Grok and Gemini. Their code reviews and occasional refusal to let dubious design pass were invaluable.

Equal thanks are due to the broader Python community, whose libraries and documentation remain the foundation of tools like this one.

Happy automating.