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Multi-provider LLM and message queue abstraction for distributed agent systems

Project description

teenyfactories

Agent runtime for TeenyFactories. Postgres-backed pub/sub, multi-provider LLM calls, vector search.

pip install --pre teenyfactories
import teenyfactories as tf

@tf.on_state('documents', 'loaded').do
def analyse(item):
    tf.collection('documents').set(item['key'], state='analysed')

while True:
    tf.run_pending()
    tf.sleep(1)

Writing a row with a given state fires NOTIFY {factory}.{collection}.{state}. Every subscriber to that pair gets the row. That's the framework.

Public API

Group Names
Pub/sub on_state, on_message, send_message, run_pending
Data collection (.set, .add, .get, .get_all, .remove, .count, .exists, .vector_search)
LLM call_llm, embed
MCP add_mcp_server, add_mcp_tool
Schedule on_schedule.every(N).<unit>.do(handler)
Logging log_debug, log_info, log_warn, log_error, log_persona
Time / IDs get_timestamp, get_timestamp_utc, generate_unique_id
Util sleep, PROJECT_NAME, FACTORY_PREFIX, __version__

Anything not in this list is not part of the supported surface — don't reach into submodules.

How it plugs in

A factory is a directory:

my_factory/
├── factory.yml
└── agents/
    ├── poller.py
    └── enricher.py

Each agents/*.py runs in its own container based on ghcr.io/teenyfactories/agent:dev — which has this library pre-installed. Single-factory mode runs each file as a docker compose service; multi-factory mode runs them under the orchestrator. Either way, agent code is just import teenyfactories as tf + handlers + main loop.

Full setup, compose templates, environment variable reference: see the core repo.

Versioning

PEP 440 dev pre-releases: 0.1.0.devYYYYMMDD. pip install teenyfactories will NOT pick these up — you need --pre (latest dated build) or an explicit pin (teenyfactories==0.1.0.dev20260512).

License

MIT.

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