Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A lightweight cli for running single-purpose AI agents. Define focused agents in TOML, trigger them from anywhere; pipes, git hooks, cron, or the terminal.
| Date | Stars |
|---|---|
| 2026-07-31 | 832 |
| 2026-08-01 | 832 |
| 2026-08-06 | 832 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Axe  A CLI tool for managing and running LLM-powered agents. ## Why Axe? Most AI tooling assumes you want a chatbot. A long-running session with a massive context window doing everything at once. But that's not how good software works. Good software is small, focused, and composable. Axe treats LLM agents the same way Unix treats programs. Each agent does one thing well. You define it in a TOML file, give it a focused skill, and run it from the command line. Pipe data in, get results out. Chain agents together. Trigger them from cron, git hooks, or CI. Whatever you already use. No daemon, no GUI, no framework to buy into. Just a binary and your configs. ## Overview Axe orchestrates LLM-powered agents defined via TOML configuration files. Each agent has its own system prompt, model selection, skill files, context files, working directory, persistent memory, and the ability to delegate to sub-agents. Axe is the executor, not the scheduler. It is designed to be composed with standard Unix tools — cron, git hooks, pipes, file watchers — rather than reinventing scheduling or workflow orchestration. ## Features - **Multi-provider support** — Anthropic, OpenAI, Ollama (local models), OpenCode, and AWS Bedrock - **TOML-based agent configuration** — declarative, version-controllable agent definitions - **Sub-agent delegation** — agents can call other agents via LLM tool use, with depth limiting and parallel execution - **Persistent memory** — timestamped markdown logs that carry context across runs - **Memory garbage collection** — LLM-assisted pattern analysis and trimming - **Skill system** — reusable instruction sets that can be shared across agents - **Stdin piping** — pipe any output directly into an agent (`git diff | axe run reviewer`) - **Local agent directories** — auto-discovers agents from `<cwd>/axe/agents/` before the global config, or use `--agents-dir` to point anywhere - **Dry-run mode** — inspect resolved context without calling the LLM - **JSON output** — structured output with metadata for scripting - **Built-in tools** — file operations (read, write, edit, list) sandboxed to working directory; shell command execution; URL fetching; web search - **Output allowlist** — restrict `url_fetch` and `web_search` to specific hostnames; private/reserved IPs are always blocked (SSRF protection) - **Token budgets** — cap cumulative token usage per agent run via `[budget]` config or `--max-tokens` flag - **MCP tool support** — connect to external MCP servers for additional tools via SSE or streamable-HTTP transport - **Configurable retry** — exponential, linear, or fixed backoff for transient provider errors (429, 5xx, timeouts) - **Minimal dependencies** — four direct dependencies (cobra, toml, mcp-go-sdk, x/net); all LLM calls use the standard library ## Installation Requires Go 1.25+. **Pre-built binaries** (no Go required) are available for Linux, macOS, and Windows on the [GitHub Releases page](https://github.com/jrswab/axe/releases/latest). Install via Go: ```bash go install github.com/jrswab/axe@latest ``` > If this fails with `invalid go version`, your Go toolchain is older than 1.25. Upgrade from [go.dev/dl](https://go.dev/dl/) or download a pre-built binary instead. Or build from source: ```bash git clone https://github.com/jrswab/axe.git cd axe go build . ``` ## Quick Start Initialize the configuration directory: ```bash axe config init ``` This creates the directory structure at `$XDG_CONFIG_HOME/axe/` with a sample skill and a default `config.toml` for provider credentials. Scaffold a new agent: ```bash axe agents init my-agent ``` Edit its configuration: ```bash axe agents edit my-agent ``` Run the agent: ```bash axe run my-agent ``` Pipe input from other tools: ```bash git diff --cached | axe run pr-reviewer cat error.log | axe run log-analyzer ``` ## Examples The [`examples/`](examples/) directory contains ready-to-run agents you can copy into your config
Excerpt of 22,223 characters
Read on GitHub
119
5
1
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:69d623762652e5ee, topic:ai-agents, desc:ai agents
matched fp:69d623762652e5ee, topic:llm
matched fp:69d623762652e5ee, topic:developer-tools
matched fp:69d623762652e5ee, topic:automation