fim-one

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All-in-One Agent Platform for Global × China Enterprises. Wire every system you already run — global SaaS to the China stack — through one agent core.

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[!TIP] ☁️ Skip the setup — try FIM One on Cloud. A managed version is live at cloud.fim.ai — no Docker, no API keys, no config. Sign in and start connecting your systems in seconds. Early access, feedback welcome.

Overview

Global enterprises run a sprawl of systems that don't talk to each other — ERP, CRM, OA, HR, finance, databases, IM platforms across regions. FIM One is the all-in-one agent platform that wires every system you already run into one agent core — global SaaS on one side, the full China stack (Feishu, WeCom, DingTalk, DM, Kingbase, etc.) on the other. One brain. Every system. Global SaaS × China Stack.

graph LR
    ERP <--> Hub["🧠 FIM One Agent Core"]
    Database <--> Hub
    Lark <--> Hub
    Hub <--> CRM
    Hub <--> OA
    Hub <--> API[Custom API]

Screenshots

Dashboard — stats, activity trends, token usage, and quick access to agents and conversations.

Agent Chat — ReAct reasoning with multi-step tool calling against a connected database.

DAG Planner — LLM-generated execution plan with parallel steps and live status tracking.

Demo

Using Agents

Using Planner Mode

Quick Start

Docker (recommended)

git clone https://github.com/fim-ai/fim-one.git
cd fim-one

cp example.env .env
# Edit .env: set LLM_API_KEY (and optionally LLM_BASE_URL, LLM_MODEL)

docker compose up --build -d

Open http://localhost:3000 — on first launch you'll create an admin account. That's it.

docker compose up -d          # start
docker compose down           # stop
docker compose logs -f        # view logs

Local Development

Prerequisites: Python 3.11+, uv, Node.js 18+, pnpm.

git clone https://github.com/fim-ai/fim-one.git && cd fim-one

cp example.env .env           # Edit: set LLM_API_KEY

uv sync --all-extras
cd frontend && pnpm install && cd ..

./start.sh dev                # hot reload: Python --reload + Next.js HMR

For production deployment (Docker, reverse proxy, zero-downtime updates), see the Deployment Guide.

Key Features

Cross-Border Connectivity

• Three delivery modes — Standalone assistant, embedded Copilot, or central Hub; same agent core.
• Any system, one pattern — Connect APIs, databases, MCP servers. Actions auto-register as agent tools with auth injection. Progressive disclosure meta-tools reduce token usage by 80%+ across all tool types.
• Database connectors — PostgreSQL, MySQL, Oracle, SQL Server, and enterprise databases common in China (DM, KingbaseES, GBase, Highgo) that most global platforms can't reach. Schema introspection and AI-powered annotation.
• Three ways to build — Import OpenAPI spec, AI chat builder, or connect MCP servers directly.

Planning & Execution

• Dynamic DAG planning — LLM decomposes goals into dependency graphs at runtime. No hard-coded workflows.
• Concurrent execution — Independent steps run in parallel via asyncio; auto re-plan up to 3 rounds.
• ReAct agent — Structured reasoning-and-acting loop with automatic error recovery.
• Agent harness — Production-grade execution environment: ContextGuard for 5-layer token-budget management, progressive-disclosure meta-tools to keep the tool surface tractable, and self-reflection loops to counter goal drift.
• Hook System — Deterministic enforcement that runs outside the LLM loop. First shipped: FeishuGateHook gates sensitive tool calls behind a human approval card posted to a Feishu group. Extensible to audit logging, read-only-mode guards, and rate limits (v0.9).
• Content guardrails — Three-layer safety: tool-permission hooks (actions), credential / SSRF / MCP-auth checks (protocols), and content guardrails (input/output text). Default jailbreak-phrase detector aborts the turn before the LLM is invoked, saving tokens and surfacing a clear blocked notice in chat. Output guardrails optional via FIM_GUARDRAILS_OUTPUT.
• Auto-routing — Classifies queries and routes to optimal mode (ReAct or DAG). Configurable via AUTO_ROUTING.
• Extended thinking — Chain-of-thought for OpenAI o-series, Gemini 2.5+, Claude.
• Prompt-cache observability — Anthropic prompt-cache read/create token counts captured per turn, surfaced in the chat done payload and logged so operators can verify cache hits and detect relay stations that don't honor the discount.

Workflow & Tools

• Visual workflow editor — 12 node types, drag-and-drop canvas (React Flow v12), import/export as JSON.
• Smart file handling — Uploaded files auto-inlined into context (small) or readable on-demand via read_uploaded_file tool. Intelligent document processing: PDFs, DOCX, and PPTX files get vision-aware processing with embedded image extraction when the model supports vision. Smart PDF mode extracts text from text-rich pages and renders scanned pages as images.
• Universal document conversion — Built-in convert_to_markdown tool turns PDF / Word / Excel / PowerPoint / HTML / images / audio / Outlook .msg / EPUB / YouTube transcripts into clean Markdown via Microsoft MarkItDown. Vision-capable LLMs OCR embedded images and scanned pages — works with Claude, Gemini, Bedrock, and any LiteLLM-supported provider, no per-provider adapter code.
• Pluggable tools — Python, Node.js, shell exec with optional Docker sandbox (CODE_EXEC_BACKEND=docker).
• V4A patch editing — Beyond find_replace, agents can apply line-hunk patches with fuzzy whitespace matching via file_ops.apply_patch — robust to multi-line edits where exact-substring match would be brittle.
• Full RAG pipeline — Jina embedding + LanceDB + hybrid retrieval + reranker + inline [N] citations. Vision-aware ingestion routes scanned PDFs and Office embedded images through the workspace's default vision LLM for OCR.
• Tool artifacts — Rich outputs (HTML previews, files) rendered in-chat.

Messaging Channels (v0.8)

• Org-scoped IM bridge — BaseChannel abstraction for outbound messaging across Slack, Microsoft Teams, Discord, Feishu (Lark), WeCom, and DingTalk. First shipping implementation is Feishu; Slack / Teams / WeCom / Email are next on the v0.9 roadmap.
• Fernet-encrypted credentials — App secrets and encrypt keys encrypted at rest; every inbound callback signature-verified.
• Interactive approval cards — Channel-native GateHook (Feishu today, Slack/Teams next) posts an Approve / Reject card to your group when a sensitive tool call fires; the tool blocks until a group member taps a verdict. Human-in-the-loop approval without a custom workflow engine.
• Configurable approval routing per agent — Three modes (Auto / Inline only / Channel only) with an approver-scope selector (initiator / agent owner / any org member). One audit path stamps approver_user_id and decided_at whether the verdict came from chat or from the channel. Auto mode falls back to inline if no channel is linked, so agents always get a real approval UX.
• Task-completion notifications — Long-running ReAct or DAG agents can push a summary card to the org's channel when work fi