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_nameandapi_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
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 Explanationsdocs/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):
- Command-line arguments
- TOML configuration file (
-c,DRAGITER_CONFIG_FILE, or~/.config/dragiter/config.toml) - Environment variables (
DRAGITER_*) - 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 and per-section
chunk_substitutions - Sequential or batched processing of material chunks
- Context-window estimation via
chars_per_token,max_context_tokensandmax_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.