deltagency
execute, then delegate
while most AI agent frameworks today are conversation-obsessed and production-hostile, deltagency cuts through the noise by implementing a task-based, Ollama-native AI agent framework with zero bloat.
in contrast to layers of abstractions of other frameworks, the goal is to provide a clean, simple interface to work with and make the workflow as deterministic as possible.
vision
deltagency aims to provide:
- modular components instead of rigid hierarchies
- optimization for input → output workflows, not endless chat
- direct Ollama integration with full API control
- declarative YAML agent definitions
- minimal dependencies, maximum control
architecture
deltagency is built with a simple but structured approach.
its architecture combines clean design layers with a flexible relay pattern for orchestration.
design layers
the core of deltagency is organized in clear layers.
each layer has a single responsibility, making agents easier to build, debug, and extend.
- communication bridge → direct Ollama API integration
- agent executor → task execution
- relay nodes → composition of workflow made of multiple agents
- relay runner → the control loop that executes the nodes
the relay pattern
traditional frameworks mix execution with decision-making, creating debugging nightmares
deltagency introduces the relay pattern, which separates concerns:
- execute → agent completes its specialized task
- delegate → agent passes results or decides the next step
- clean → context is released, resources freed
# build any orchestration pattern
A ↔ B (consultation loop) → C (finalize)
Router → {SQL_Expert | XSS_Expert | Auth_Expert}
Coordinator ↔ {Worker1, Worker2, Worker3}
one pattern, infinite workflows
why it’s different
most frameworks optimize for conversations or heavy orchestration.
deltagency is different:
- task-focused → deterministic workflows, predictable outputs
- modular → composable, reusable components
- ollama-native → direct API integration, no wrappers
- lightweight → only requests, PyYAML, jsonschema
- developer-friendly → YAML configs and a clean API
status
deltagency is under active development.
early results:
- better modularity
- cleaner configurations
- equal accuracy with less overhead
code will be open-sourced once production stability is reached.
in summary
deltagency is a lightweight, task-first framework for building modular AI agents.
it focuses on deterministic workflows, direct Ollama integration, and minimal dependencies.
the goal is simple: remove overhead and give developers predictable, controllable agents.
case studies
case study: deltagency vs autogen
comparative analysis against autogen AI agents