Atac
Record and Replay AI Agent Trajectories.
What is Atac?
Atac is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to record and replay ai agent trajectories.
Record and Replay AI Agent Trajectories.
This server falls under the Developer Tools category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Record and Replay AI Agent Trajectories.
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx atacConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Atac
ATaC (Agent Trajectory and Composition) is a Python framework and MCP server that lets AI agents record, store, and replay their tool-use trajectories as reusable graph workflows. Rather than re-executing a full agent conversation from scratch, ATaC captures the sequence of tool calls an agent makes and packages them into a named, callable graph that other agents or developers can trigger with a single command. This makes it possible to build a library of proven agent workflows, debug agent behavior by inspecting recorded trajectories, and compose complex multi-step automations from smaller, tested building blocks.
Prerequisites
- Python 3.10 or newer
- uv package manager installed (https://docs.astral.sh/uv/)
- LangGraph and LangChain (installed automatically via uv)
- An MCP client such as Claude Desktop or a LangGraph-compatible agent runtime
- API keys for any MCP tools used in your recorded trajectories
Install ATaC using uv
ATaC is distributed as a Python package and is best installed with the uv tool manager, which creates an isolated environment and makes the 'atac' command available globally.
uv tool install atacRegister your tools with the ATaC service
In your agent application, import the ATaC service and register both your local LangChain tools and any MCP tool clients. All registered tools become available to agent-authored graph workflows.
from atac import get_service
from langchain_mcp_adapters.client import MultiServerMCPClient
service = get_service()
# Register local tools
from myapp.tools import search_tool, calculator_tool
service.register_langgraph_tools([search_tool, calculator_tool])
# Register MCP tools
mcp_client = MultiServerMCPClient({"filesystem": {"command": "npx", "args": ["@modelcontextprotocol/server-filesystem", "."]}})
service.register_langgraph_tools(mcp_client.get_tools())Record an agent trajectory
Run your agent as normal through a task. ATaC intercepts tool calls and records them as a trajectory that can be replayed. The recording captures the exact sequence of tool invocations, inputs, and outputs.
Replay a trajectory as a graph workflow
Use service.run_graph to load and execute a previously recorded trajectory. Provide the module path to your graph builder function and the initial state. ATaC replays the tool calls deterministically.
from myapp.bootstrap import get_service
service = get_service()
result = service.run_graph(
"myapp.graphs:build_graph",
{"query": "What is the weather in São Paulo?"}
)Configure ATaC as an MCP server for Claude Desktop
To use ATaC through Claude Desktop, add it to your MCP configuration so Claude can trigger graph executions conversationally.
{
"mcpServers": {
"atac": {
"command": "atac",
"args": ["serve"]
}
}
}Atac Examples
Client configuration
claude_desktop_config.json to run ATaC as an MCP server after installing it with uv.
{
"mcpServers": {
"atac": {
"command": "atac",
"args": ["serve"]
}
}
}Prompts to try
Example prompts for interacting with ATaC's trajectory recording and replay capabilities.
- "List all recorded agent trajectories available in this project."
- "Replay the 'data-extraction' graph workflow with input: {topic: 'AI safety'}."
- "Show me the tool call sequence from the last recorded trajectory."
- "Execute the 'report-generator' graph and return the result."Troubleshooting Atac
'atac' command not found after installation
Run 'uv tool update-shell' to ensure uv's tool bin directory is on your PATH, then open a new terminal. Alternatively verify with 'uv tool list' that atac appears in the installed tools.
MCP tool registration fails with import errors
ATaC requires LangGraph and langchain-mcp-adapters. Install them in the same environment: 'uv pip install langgraph langchain-mcp-adapters'. Confirm versions are compatible by checking ATaC's requirements.
Graph replay produces different results than the original trajectory
Replay is deterministic only for pure tool calls; if your graph includes LLM inference steps, the model may produce different text. Pin the model version and use temperature=0 in your graph definition to improve reproducibility.
Frequently Asked Questions about Atac
What is Atac?
Atac is a Model Context Protocol (MCP) server that record and replay ai agent trajectories. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Atac?
Follow the installation instructions on the Atac GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Atac?
Atac works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Atac free to use?
Yes, Atac is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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