Maki

Maki is a Python framework for multi-agent LLM applications developed by Bowl of Data, an open-source AI research community. It lets developers build tool-using agents that run on local hardware through Ollama, on hosted APIs like OpenAI and Anthropic, or a combination of both — using the same agent code throughout. The project is licensed under MIT and is in early 0.x development, self-described as deliberately small and focused.

What It Is

Maki sits in the agent framework category: it provides the scaffolding to define role-based agents, wire them to LLM backends, equip them with plugins, and orchestrate them in multi-step workflows. Its distinguishing design choice is treating local inference (Ollama) as a first-class backend equal to hosted APIs, rather than an afterthought. All backends — MakiLLama, MakiOpenAI, MakiAnthropic, MakiOpenRouter, and HFBackend — implement the same abstract LLMBackend contract, so swapping inference providers requires changing only one object.

Architecture and Core Layers

The framework is organized into four layers on top of a shared infrastructure layer:

• LLM Backends — Ollama, OpenAI, Anthropic, OpenRouter, and an in-process HuggingFace Transformers backend (requires manual install of torch, transformers, accelerate)
• Agent System — Agent composes PluginHandler and ReasoningEngine mixins; AgentManager orchestrates agents via WorkflowTask and WorkflowState with dependency resolution, retries, parallel batching, and checkpoint/resume
• Distributed Layer — AgentServer (FastAPI) exposes agents over HTTP via maki serve; AgentProxy provides a remote-agent client with circuit-breaking; DistributedAgentManager mixes local and remote agents in the same workflow
• Infrastructure — a hardened Connector with URL validation, private-address blocking, and DNS pinning; shared data classes; typed exceptions; runtime config; and structured logging

The base install has only three dependencies: requests, httpx, and python-dotenv. All other capabilities are opt-in extras.

Security and Guardrails

Maki ships with guardrails enabled by default rather than as an optional add-on. URLs sourced from content (web pages, feeds) are checked against private and reserved address ranges at connect time, including redirect hops. Plugins are fail-closed: a model can only call methods a plugin explicitly declares in ALLOWED_METHODS, and destructive operations — file writes, FTP transfers, trades — remain disabled unless the agent is instantiated with allow_dangerous_tools=True. The trading plugin runs in paper mode unless live trading is explicitly opted into.

Built-in Plugins

Maki includes 16 built-in plugins across several categories:

• File system: directory_reader, file_reader, file_writer, json_reader
• Web: web_search (RSS, HackerNews, Reddit, GitHub Trending, Lobste.rs), web_to_md, provider_updates, trend_search
• Market data and trading: alpaca_data, alpaca_news, alpaca_trading, alpaca_stream
• Memory: obsidian_memory (persistent note-based memory via Obsidian vault), rag_memory (retrieval-augmented memory with pluggable vector backends)
• Vision: image_classifier, ocr

Current Status

The repository was created in May 2025 and was last pushed in September 2026, indicating active development. The project self-identifies as version 0.x and explicitly notes it is young and deliberately small. The README acknowledges it is not suited for teams needing a large catalog of third-party integrations, hosted tracing, or a large ecosystem — positioning it as a focused local-first framework for developers who want control over inference and security defaults. The test suite covers 900+ tests across backends, agents, workflows, plugins, connectors, the distributed layer, and security behavior.