ollie

A Go library for building agentic systems. Provides a sandboxed execute_code tool, a common LLM backend interface, MCP server support, and a skill system for domain-specific capabilities.

Works well with anvillm, which provides a skill system, tool scripts, and multi-agent infrastructure.

The reference frontend is ollie-tui, a terminal UI built on top of this library.

Packages

pkg/agent/           — Core interface, agent loop, session management
pkg/backend/         — Backend interface + implementations (Ollama, OpenAI, Anthropic, Copilot, Kiro)
pkg/config/          — Config struct and loader
pkg/mcp/             — MCP client
pkg/tools/           — Server and Dispatcher interfaces; tool definitions
pkg/tools/execute/   — execute.Server: execute_code, execute_tool, execute_pipe
pkg/tools/reasoning/ — reasoning.Server: reasoning_think, reasoning_plan

Install

mk

No build step — ollie-core is a library.

Configuration

Config file: ~/.config/ollie/config.json

{
  "mcpServers": {
    "my-server": {
      "command": "my-mcp-server",
      "args": [],
      "env": {
        "API_TOKEN": "${API_TOKEN}"
      }
    }
  },
  "hooks": {
    "agentSpawn": "notify-send ollie started",
    "userPromptSubmit": "",
    "stop": ""
  }
}

MCP server env values support ${VAR} expansion from the parent environment.

Sandbox config: ~/.config/ollie/sandbox.yaml

Controls landrun sandboxing for execute_code. Created automatically with defaults on first run. See the file header for documentation.

Tools

Five built-in tools across two servers:

• execute_code — run inline shell code in a sandbox
• execute_tool — run a named tool script from OLLIE_TOOLS_PATH (default: ~/.config/ollie/tools)
• execute_pipe — chain steps as a pipeline
• reasoning_think — externalize intermediate reasoning
• reasoning_plan — decompose a goal into ordered steps; persists to task backend if available, otherwise queued via fallback backend or in-context only

File operations go through execute_code using standard shell tools (cat, grep, sed, ed, ssam if plan9port is available, etc.).

MCP server tools are discovered at startup and available alongside the built-ins.

Each tool server exports a Decl function that returns a func() tools.Server factory. execute.Decl(cwd) accepts a working directory used as cmd.Dir for sandboxed commands and for {CWD} expansion in the sandbox config; pass "" to fall back to os.Getwd(). Frontends register servers by passing Decl results to tools.NewDispatcherFunc. Adding a new tool means implementing tools.Server, exporting a Decl function, and registering it — no frontend changes required.

Skills

Skills are domain-specific knowledge files served from the ollie 9P mount (sk/ directory).

# Discover
ls ${OLLIE_9MOUNT:-$HOME/mnt/ollie}/sk/
grep -li <keyword> ${OLLIE_9MOUNT:-$HOME/mnt/ollie}/sk/*.md

# Load
cat ${OLLIE_9MOUNT:-$HOME/mnt/ollie}/sk/<name>.md

Skills are sourced from OLLIE_SKILLS_PATH (default: ~/.config/ollie/skills/). The sk/ directory in the mount exposes them as flat <name>.md files.

Integrations

9beads-mcp provides task persistence using 9beads — enabling the agent to track, list, and manage tasks across sessions.

License

GPLv3