Open PTC Agent

English | 中文

Getting Started | CLI Reference | Configuration | Changelog | Roadmap

Demo: Analyzing 2 years of NVDA, AMD & SPY stock data (15,000+ lines of raw JSON) using DeepSeek V3.2

What is Programmatic Tool Calling?

This project is an open source implementation of Anthropic recently introduced Programmatic Tool Calling (PTC), which enables agents to invoke tools with code execution rather than making individual JSON tool calls. This paradigm is also featured in their earlier engineering blog Code execution with MCP.

Why PTC?

1. LLMs are exceptionally good at writing code! They excel at understanding context, reasoning about data flows, and generating precise logic. PTC lets them do what they do best - write code that orchestrates entire workflows rather than reasoning through one tool call at a time.

2. Traditional tool calling returns full results to the model's context window. Suppose fetching 1 year of daily stock prices for 10 tickers. This means 2,500+ OHLCV data points polluting context - tens of thousands of tokens just to compute a portfolio summary. With PTC, code runs in a sandbox, processes data locally, and only the final output returns to the model. Result: 85-98% token reduction.

3. PTC particularly shines when working with large volumes of structured data, time series data (like financial market data), and scenarios requiring further data processing - filtering, aggregating, transforming, or visualizing results before returning them to the model.

How It Works

User Task
    |
    v
+-------------------+
|    PTCAgent       |  Tool discovery -> Writes Python code
+-------------------+
    |       ^
    v       |
+-------------------+
|  Daytona Sandbox  |  Executes code
|  +-------------+  |
|  | MCP Tools   |  |  tool() -> process / filter / aggregate -> dump to data/ directory
|  | (Python)    |  |
|  +-------------+  |
+-------------------+
    |
    v
+-------------------+
|Final deliverables |  Files and data can be downloaded from sandbox
+-------------------+

Built on LangChain DeepAgents - This project uses many components from DeepAgents and cli feature was bootstrapped from deepagent-cli. Special thanks to the LangChain team!

Sandbox environment provided by Daytona.

What's New

• Interactive CLI - New ptc-agent command for terminal-based interaction with session persistence, plan mode, themes, and rich UI
• Background Subagent Execution - Subagents run asynchronously with Task IDs (Task-1, Task-2, etc.). The main agent continues working while subagents execute in parallel. Completed results are cached and the agent is notified to retrieve them via task_output()
• Task Monitoring - wait() blocks until task(s) complete; task_output() retrieves results or shows progress
• Agent Skills - Extensible capabilities via the open Agent Skills standard
• Vision/Multimodal Support - New view_image tool enables vision-capable LLMs to analyze images from URLs, base64 data, or sandbox files

Features