Experiment Automation Agents (EAA)

EAA is a Python toolkit for building experiment-facing agents around a shared set of task-manager, tool, memory, and WebUI primitives. The current codebase centers on a LangGraph-backed BaseTaskManager, stateful BaseTool integrations, SQLite-backed checkpointing/WebUI relay, and a small set of concrete task managers for imaging and tuning workflows.

Current Status

• Core agent runtime: src/eaa/core/task_manager/base.py
• Reusable built-in graphs: chat and feedback loop
• Concrete workflows: ROI search, feature tracking, parameter tuning, Bayesian optimization, and analytical task managers under src/eaa/task_manager/
• Tool system: BaseTool, serialized execution, optional approval gates, MCP server/client helpers
• Long-term memory: optional chat-memory layer configured through MemoryManagerConfig
• WebUI: FastAPI + static frontend, connected to the agent through a shared SQLite database

Installation

Option 1: uv (recommended)

uv sync
source .venv/bin/activate
which python

which python should resolve to .venv/bin/python.

Optional extras are defined in pyproject.toml. Common ones are:

• docs for Sphinx documentation
• asksage for AskSage-specific LLM access
• aps_mic for APS microscopy integrations
• postgresql_vector_store for external PostgreSQL/pgvector client dependencies

Install an extra set with:

uv sync --extra docs

Option 2: pip

python -m venv .venv
source .venv/bin/activate
pip install -e .

With extras:

pip install -e .[docs]

Quickstart

The smallest useful setup is a task manager, an LLM config, and one or more tools. This example starts a free-form chat with a simulated image-acquisition tool:

from skimage import data

from eaa.api.llm_config import OpenAIConfig
from eaa.core.task_manager.base import BaseTaskManager
from eaa.tool.imaging.acquisition import SimulatedAcquireImage

llm_config = OpenAIConfig(
    model="your-model-name",
    base_url="https://api.openai.com/v1",
    api_key="your-api-key",
)

acquisition_tool = SimulatedAcquireImage(
    whole_image=data.camera(),
    add_axis_ticks=True,
    show_image_in_real_time=False,
)

task_manager = BaseTaskManager(
    llm_config=llm_config,
    tools=[acquisition_tool],
    session_db_path="session.sqlite",
    use_webui=False,
)

task_manager.run_conversation()

For a workflow-oriented manager, see examples/roi_search.py.

Architecture

• LLMConfig objects describe how the chat model is constructed. The shipped config classes are OpenAIConfig, AskSageConfig, and ArgoConfig.
• BaseTaskManager owns model invocation, tool registration, memory, persistence, and graph execution.
• SerialToolExecutor runs tool calls one at a time. This is intentional: many experiment tools are stateful, should not be driven concurrently, or not thread-safe.
• MemoryManager adds optional long-term memory retrieval/saving on chat turns.
• The WebUI is a separate FastAPI process. It communicates with the agent through the same SQLite database used for WebUI relay state and checkpointing.

Built-In Graphs and Workflows

The reusable graphs shipped in the base runtime are:

• chat_graph for interactive conversation
• feedback_loop_graph for iterative tool-driven workflows

build_task_graph() is available as a subclass hook for custom LangGraph workflows, but the task managers currently in this repository mostly either:

• reuse run_feedback_loop() / run_conversation(), or
• implement an analytical workflow directly in Python while still updating message history and WebUI state through task-manager helpers

WebUI and Checkpointing

Set use_webui=True on the task manager and give it a session_db_path. Then launch the standalone WebUI process against the same SQLite file:

from eaa.gui.chat import run_webui, set_message_db_path

set_message_db_path("session.sqlite")
run_webui(host="127.0.0.1", port=8008)

Checkpointing and the WebUI relay share the same SQLite database. The base task manager exposes:

• run_conversation_from_checkpoint()
• run_feedback_loop_from_checkpoint()
• run_from_checkpoint() for subclasses that implement task_graph

Long-Term Memory

Long-term memory is configured with MemoryManagerConfig. In the current codebase, the built-in memory manager creates a Chroma-backed vector store and can:

• retrieve relevant memories on chat turns
• inject retrieved memories back into the model context
• save new memories when a user message contains trigger phrases such as "remember this" or "keep in mind"

The postgresql_vector_store extra is present in pyproject.toml, but the built-in MemoryManager path in this repository currently wires up Chroma.

MCP Integration

EAA supports both directions of MCP integration.

Expose EAA tools as an MCP server:

from eaa.core.mcp.server import run_mcp_server_from_tools
from eaa.tool.example_calculator import CalculatorTool

run_mcp_server_from_tools(
    tools=CalculatorTool(),
    server_name="Calculator MCP Server",
)

Use an external MCP server as a normal EAA tool:

from eaa.tool.mcp import MCPTool

mcp_tool = MCPTool(
    {
        "mcpServers": {
            "remote_tools": {
                "command": "python",
                "args": ["./path/to/server.py"],
            }
        }
    }
)

Skills

Skills are reusable, markdown-first task packages that EAA can discover and load at runtime. In the current implementation, each skill is a directory with at least a SKILL.md file. Additional markdown files and referenced images can live alongside it.

Bundled skills live under src/eaa/skills/. A typical layout looks like:

my-skill/
  SKILL.md
  references/
    api_reference.md
    figure.png

To use skills, point a task manager at one or more skill directories:

task_manager = BaseTaskManager(
    llm_config=llm_config,
    tools=[acquisition_tool],
    skill_dirs=["./src/eaa/skills", "~/.eaa_skills"],
)

At build time, EAA scans those directories for SKILL.md, turns each skill into a documentation-fetching tool, and makes the discovered skills available inside the agent. In an interactive chat session you can:

• run /skill to list the loaded skills
• run /subtask <task description> to let the agent choose a skill, fetch its docs, and launch a skill-driven subtask flow

If you want to copy the bundled skills out of the package tree, use:

python -m eaa.cli install-skills --destination ~/.eaa_skills

Documentation

Sphinx documentation lives under docs/ and is configured for Read the Docs. Build it locally with:

uv sync --extra docs
source .venv/bin/activate
cd docs
make html

The generated site will be in docs/_build/html/.