fim-one

| Mode | What it is | Access | | -------------- | ------------------------------------------------------- | ----------------------- | | Standalone | General-purpose AI assistant — search, code, KB | Portal | | Copilot | AI embedded in a host system's UI | iframe / widget / embed | | Hub | Central AI orchestration across all connected systems | Portal / API |

graph LR
    ERP <--> Hub["🔗 FIM One Hub"]
    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

| Command | What starts | URL | | ---------------- | --------------------------------- | ------------------------------ | | ./start.sh | Next.js + FastAPI | localhost:3000 (UI) + :8000 | | ./start.sh dev | Same, with hot reload | Same | | ./start.sh dev:api | API only, dev mode (hot reload) | localhost:8000 | | ./start.sh dev:ui | Frontend only, dev mode (HMR) | localhost:3000 | | ./start.sh api | FastAPI only (headless) | localhost:8000/api |

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

Key Features

Connector Hub

• 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).
• 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.

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.
• Pluggable tools — Python, Node.js, shell exec with optional Docker sandbox (CODE_EXEC_BACKEND=docker).
• Full RAG pipeline — Jina embedding + LanceDB + hybrid retrieval + reranker + inline [N] citations.
• 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.
• Browse-and-pick UI — No copying raw channel IDs from the vendor console; the portal calls the IM platform's API and shows a group picker.

Platform

• Multi-tenant — JWT auth, org isolation, admin panel with usage analytics and connector metrics.
• Marketplace — Publish and subscribe to agents, connectors, KBs, skills, workflows.
• Global skills (SOPs) — Reusable operating procedures loaded for every user; progressive mode cuts tokens ~80%.
• 6 languages — EN, ZH, JA, KO, DE, FR. Translations are fully automated.
• First-run setup wizard, dark/light theme, command palette, streaming SSE, DAG visualization.

Deep dive: Architecture · Hook System · Channels · Execution Modes · Why FIM One · Competitive Landscape

Architecture

graph TB
    subgraph app["Application Layer"]
        a["Portal · API · iframe · Feishu · Slack · WeCom · DingTalk · Teams · Email · Contract Systems · Custom Webhooks"]
    end
    subgraph mid["FIM One"]
        direction LR
        m1["Connectors<br/>+ MCP Hub"] ~~~ m2["Orch Engine<br/>ReAct / DAG"] ~~~ m3["RAG /<br/>Knowledge"] ~~~ m5["Hook System<br/>+ Channels"] ~~~ m4["Auth /<br/>Admin"]
    end
    subgraph biz["Business Systems"]
        b["ERP · CRM · OA · Finance · Databases · Contract Mgmt · Custom APIs"]
    end
    app --> mid --> biz

Each connector and channel is a standardized bridge — the agent doesn't know or care whether it's talking to SAP, a custom contract system, or a Feishu group. The Hook System runs platform code outside the LLM loop for approvals,