AI Agent Prototype
Local DeepSeek Coding Agent
A local DeepSeek-powered coding agent MVP built as a learning project. It connects a CLI, a tool-calling loop, workspace-aware file tools, shell/test execution, git diff inspection, and a Rich terminal UI, with local safety checks around file writes and risky commands.
Period
Jun 2026
Tools / Tech
PythonDeepSeekTyperRichTool CallingPytest
Why I built it
I built this to understand the mechanics behind coding agents: how tasks become tool calls, how observations feed back into the loop, how workspace context is reset, and how local automation can be kept under control.
Links
What it includes
- Implements a basic tool-calling loop where the model can plan, call tools, receive observations, edit files, run tests, check diffs, and summarize results.
- Provides workspace-aware tools for listing, reading, writing, editing, and searching files, plus shell/test execution and git diff output.
- Includes a Rich terminal UI and Typer CLI commands for run, ui, chat, diff, and test workflows.
- Supports configurable modes such as fast, balanced, smart, max, and custom to experiment with speed and loop-budget tradeoffs.
- Adds practical safety checks for workspace-only writes, sensitive-file refusal, dangerous command blocking, confirmations for destructive commands, timeouts, output limits, and repeated-tool limits.
What I worked on
- Organized the agent prototype across CLI dispatch, terminal UI, configuration loading, LLM client/tool schemas, tool execution, patch application, and safety policy.
- Implemented local tools for filesystem operations, unified-diff patch application, shell/test execution with command checks, and git diff inspection.
- Documented the build path in docs/AGENT_BUILD_GUIDE.md so the project can also serve as a learning artifact for coding-agent internals.
- Added tests around agent behavior, configuration, filesystem tools, LLM handling, entry points, patch parsing, safety policy, shell tools, and UI behavior.
What I Learned
- Learned that even a small coding agent depends heavily on clear tool limits, useful observations, context reset, safety checks, and understandable error handling.
- Practiced designing local automation with explicit workspace limits instead of allowing broad file or shell access.
- Built a clearer understanding of the gap between a chat wrapper and a tool-using coding assistant.