{"id":"evalstate-fast-agent","name":"fast-agent","af_score":60.0,"security_score":74.8,"reliability_score":38.8,"what_it_does":"fast-agent is a Python CLI-first framework for interacting with LLMs and building coding agents, workflows, and evaluation pipelines. It supports MCP servers/clients (including stdio and streamable HTTP/SSE transports), shell-mode, interactive TUI prompts, and Python agent definitions (decorator-based) that can call MCP tools and chain workflows. It also includes MCP OAuth (PKCE) integration and optional MCP ping support.","best_when":"You want a CLI and Python framework to orchestrate LLMs with MCP tool ecosystems, including interactive workflows and/or running locally with configurable transports and OAuth for MCP connections.","avoid_when":"You need a stable, standardized REST/GraphQL/SDK surface for programmatic usage by other systems; you mainly want a turnkey managed cloud service with predictable SLAs.","last_evaluated":"2026-03-29T15:04:11.561899+00:00","has_mcp":true,"has_api":false,"auth_methods":["CLI/config-based OAuth integration for MCP (PKCE with local callback / redirect flow)","OS keychain-backed token persistence via keyring (with in-memory fallback)"],"has_free_tier":false,"known_gotchas":["CLI-first workflow: programmatic integration may require adopting their Python API/decorators or invoking the CLI rather than using a standardized REST interface.","MCP transport and OAuth behavior can vary by environment (e.g., keychain availability uses in-memory token storage), which may affect repeatability across sessions.","LLM/model/provider differences (and provider-specific model query overrides) may lead to inconsistent outputs if not pinned/configured carefully.","Chained/parallel tool calling can amplify failures if upstream MCP servers are unreliable; behavior under partial tool failures is not described in the provided material."],"error_quality":0.0}