MCP Servers · evalstate

fast-agent

fast-agent is a Python framework for building and running LLM-powered agents with native support for Model Context Protocol (MCP) servers, skills, and multiple LLM providers. It provides both CLI and programmatic interfaces for creating agents that can interact with tools, external services, and workflows.

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Source: GitHub — github.com/evalstate/fast-agent

3.9k

GitHub stars

413

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	evalstate/fast-agent
Owner	evalstate
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	3.9k
Forks	413
Open issues	24
Latest release	v0.9.0 (2026-07-04)
Last updated	2026-07-06
Source	https://github.com/evalstate/fast-agent

What fast-agent is

A Python-based agent framework with CLI-first design, MCP 1.0 support (including Sampling and Elicitations), declarative YAML configuration, structured outputs, vision/PDF support, and native integrations with Anthropic, OpenAI, Google, Azure, Ollama, and others via TensorZero. Includes shell mode, OAuth credential management, and MCP transport diagnostics.

Quickstart

Get the fast-agent source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/evalstate/fast-agent.git2cd fast-agent3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

LLM-Powered Development & Coding Agents

Build agents that can read, analyze, and generate code with shell access, LSP support, and integrated development tools. Excellent for automated code generation and refactoring workflows.

Multi-Agent Workflow Orchestration

Chain agents together with MCP server dependencies to create complex workflows (e.g., research → content creation → publishing). Simple YAML configuration enables rapid composition without boilerplate.

Interactive Agent Development & Prototyping

Rapid experimentation with different LLM providers and MCP servers via CLI without code changes. Model strings support inline query overrides for testing (reasoning modes, context windows, web search toggles).

Implementation considerations

License is Apache 2.0 (permissive OSI license); commercial use is permitted with standard conditions. No proprietary restrictions identified.
Framework is actively maintained (pushed 2026-07-06, v0.9.0 released 2026-07-04), but pre-1.0 status means API and configuration formats may change. Assess migration risk for long-lived deployments.
Multi-provider model support requires managing API keys and credentials; OAuth KeyRing integration mitigates some secrets management burden, but review for compliance (e.g., HIPAA, SOC2 if applicable).
MCP server connectivity via stdio and HTTP(S) with OAuth support; test transport reliability and latency for your MCP server topology before production deployment.
Shell mode and tool execution capabilities present code execution attack surface; validate agent instructions and constrain permissions if agents run untrusted input.

When to avoid it — and what to weigh

Production High-Frequency Real-Time Systems — v0.9.0 indicates early-stage (pre-1.0). Infrastructure-grade reliability, horizontal scaling, and backward compatibility guarantees are not yet proven. Deployment complexity and production SLA support unknown.
Closed-Source / On-Premise Only Constraints — Framework integrates multiple cloud LLM providers and relies on external MCP servers. No clear isolation or airgapped deployment model documented.
Minimal Dependency / Lightweight Requirements — Includes prompt_toolkit, rich, and potentially large transitive dependency trees from TensorZero multi-provider support. No vendoring or minimal installation mode evident.
Teams Without Python Expertise — Core abstractions and workflow definitions require Python understanding. CLI provides some abstraction, but configuration and extension are Python-centric.

License & commercial use

Apache License 2.0 (SPDX: Apache-2.0). Permissive OSI license permitting commercial use, modification, and distribution with standard attribution and liability disclaimers. No copyleft obligations.

Commercial use is explicitly permitted under Apache 2.0. No proprietary licensing, per-seat restrictions, or SaaS usage prohibitions identified. Note: License covers the framework itself; ensure your LLM provider agreements (OpenAI, Anthropic, Google, etc.) permit commercial use of their APIs. MCP servers and skills may have separate license terms; audit dependencies before commercial deployment.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Framework supports OAuth with KeyRing-based credential storage for secure token management. Shell mode (`-x` flag) enables arbitrary shell execution, presenting command injection risk if agent instructions are untrusted or user input is unsanitized—strict input validation and permission scoping required. Multi-provider LLM support means secrets for Anthropic, OpenAI, Google, Azure, Ollama stored locally; review KeyRing backend security (OS-dependent). MCP server connections over HTTP(S) with OAuth; ensure TLS and certificate validation. No explicit mention of input sanitization, rate limiting, or audit logging for agent actions. Framework maturity (v0.9.0) suggests security hardening may be ongoing; security disclosure process not documented.

Alternatives to consider

LangChain / LangGraph

Mature (1.0+), production-proven agent orchestration. Broader ecosystem of integrations. Better documentation and commercial support options. Higher abstraction overhead and heavier dependency footprint.

OpenAI Swarm / Anthropic Agents SDK

Native, opinionated frameworks from respective LLM providers. Lightweight and tightly integrated with Anthropic/OpenAI models. Narrower scope (single provider focus), less multi-provider flexibility and MCP ecosystem support.

Purpose-built for multi-agent teams and role-based workflows. Simpler abstraction for hierarchical agent coordination. Less flexible for custom tooling and MCP integration; smaller community compared to LangChain.

Software development agency

Build on fast-agent with DEV.co software developers

Start with `uvx fast-agent-mcp@latest -x` for an interactive demo, or review the docs at https://fast-agent.ai. Ideal for R&D, coding agents, and multi-agent workflow prototyping. Pre-1.0 maturity—assess breaking change risk before critical production use.

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Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

mcp-agent fastmcp openai-agents-python

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fast-agent FAQ

Can I use fast-agent with local/open-source LLMs?

Yes. Ollama, llama.cpp, and generic provider (Qwen, Deepseek via TensorZero) are supported. Use `fast-agent model llamacpp` to configure llama.cpp or `--model generic.qwen2.5` for Ollama. No cloud LLM required.

What is MCP and why does fast-agent emphasize it?

Model Context Protocol (MCP) is a standard for connecting LLMs to external tools, services, and data sources. fast-agent has end-to-end MCP 1.0 support (Sampling, Elicitations) and ships with MCP transport diagnostics, making it the first framework with complete, tested MCP feature coverage.

Is this suitable for production use?

Requires careful evaluation. v0.9.0 is pre-1.0; breaking changes possible in minor updates. Active maintenance is a positive signal. No documented SLA, security hardening roadmap, or production deployment case studies in excerpt. Recommended for piloting and evaluation before critical production workloads.

Do I need to write Python code, or can I configure agents declaratively?

Both. Agents can be defined with simple `@fast.agent()` decorators (minimal Python). Complex workflows use YAML (`fast-agent.yaml`) for MCP server and skill configuration. Core logic is Python, but CLI and YAML abstract away boilerplate.

Software development & web development with DEV.co

DEV.co helps companies turn open-source tools like fast-agent into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your mcp servers stack.

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