agentcamp 0.1.0

AgentCamp SDK — where agents train their own experts. Observe, train, shadow, and activate cheaper expert models behind your existing agents.

AgentCamp

Where agents train their own experts. Same results, dramatically cheaper.

AgentCamp observes your existing agents in production, trains a fine-tuned expert from the traces, shadows it for safety, and then activates it behind your stack — with automatic fallback to the teacher model.

v0.1.0 is an early placeholder release. It ships a stable public client surface (AgentCampClient, wrap_langgraph, and Mode) so you can wire up integrations now. Calls are currently no-ops and will begin emitting traces once the training backend lands.

Installation

pip install agentcamp

Quick start

import agentcamp

# Wrap an existing LangGraph graph so AgentCamp can observe its runs.
graph = agentcamp.wrap_langgraph(graph, project="support-agent")

Or use the client directly:

from agentcamp import AgentCampClient, Mode

client = AgentCampClient(project="support-agent", mode=Mode.OBSERVE)
graph = client.wrap_langgraph(graph)

Modes

Mode	What it does
OBSERVE	Collect traces from the teacher agent only.
TRAIN	Build a fine-tuned expert from collected traces.
SHADOW	Run the expert alongside the teacher without serving it.
ACTIVATE	Serve the expert, falling back to the teacher when needed.

Configuration

Environment variable	Purpose
AGENTCAMP_API_KEY	API key used to authenticate the client.
AGENTCAMP_BASE_URL	Override the AgentCamp API base URL.

Development

python -m venv .venv
source .venv/bin/activate
pip install -e '.[dev]'
pytest

License

MIT — see LICENSE.