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mini-a

Your goals. Your LLM. One command.

A minimalist autonomous agent framework built on OpenAF. Connect any LLM, use 25+ built-in MCP servers, and orchestrate delegated multi-agent workflows with proxy-backed tools, streaming, validation-model overrides, and worker registration from the terminal, a web UI, or your own code.

Track recent releases, new capabilities, and migration notes in What's New.

See It in Action

{: .section}

Web UI showing a multi-step task with streaming output

Web interface with real-time streaming

Console interactive goal execution

Interactive console mode

10+ LLM Providers
25+ MCP Servers
100+ Parameters
50-70% Cost Savings
4 Interfaces

Features

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Any LLM, Your Choice

OpenAI, Google, Anthropic, Ollama, AWS Bedrock, GitHub Models — switch providers with one config line.

Cut Costs by 70%

Dual-model architecture routes simple tasks to cheaper models automatically. Pay less, get the same results.

Separate Execution from Validation

Use a dedicated validation model in deep-research flows, globally with OAF_VAL_MODEL or per run with modelval=....

25+ MCP Servers, Ready to Run

Time, finance, databases, web, email, Kubernetes, office docs, OpenAF helpers, and more ship as built-in MCP servers.

Multi-Agent Orchestration

Enable delegation to split goals into subtasks and run them across local child agents, remote workers, or self-registering worker pools.

40-60% Fewer Tokens

Automatic context optimization, conversation compaction, and smart summarization keep costs low.

Proxy and Script-Friendly Tools

Aggregate tools behind one `proxy-dispatch` interface or expose them through a localhost bridge for programmatic MCP calls from generated scripts.

Console. Web. Library. Docker.

Use it as a CLI tool, web app, JavaScript library, or Docker container — whatever fits your workflow.

Inspectable Reasoning Output

Use showthinking=true to surface XML-tagged thinking blocks as thought logs when your provider returns them.

Secure by Default

Shell access off by default, prompt normalization, untrusted-input labeling, prompt-size limits, read-only mode, and encrypted key storage.

Quick Example

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1 Configure your model 
export OAF_MODEL="(type: openai, model: gpt-5.2, key: '...')"
2 Run mini-a 
mini-a useshell=true
3 Give it a goal 
> list all JavaScript files in this project and count the lines of code in each
<script>AsciinemaPlayer.create("{{ '/assets/images/screenshots/s3-quick-start.cast' | relative_url }}", document.getElementById('player-s3'), { cols: 127, rows: 24, autoPlay: true, loop: true, fit: 'width' });</script>

How It Works

{: .section} mini-a follows a simple loop: understand the goal → plan steps → execute tools → validate results → report back.

┌─────────┐     ┌─────────┐     ┌──────────┐     ┌──────────┐
│  Your   │────▶│  LLM    │────▶│  Tools   │────▶│  Result  │
│  Goal   │     │  Engine │     │  (MCP)   │     │  Output  │
└─────────┘     └─────────┘     └──────────┘     └──────────┘
                     │                │
                     ▼                ▼
                ┌──────────┐     ┌──────────┐
                │ Planning │     │  Shell   │
                │ & Memory │     │ Commands │
                └──────────┘     └──────────┘

mini-a architecture diagram showing entry points, orchestrator, and subsystems

Ready to Get Started?

Install mini-a in under a minute and run your first autonomous task.