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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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.

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 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

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

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 finishes. Configurable per-agent in Settings → Agent → Notifications.
• 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. Multi-worker support via WORKERS=N with a Redis interrupt broker for cross-worker relay.
• 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%.
• Stripe billing & per-user quotas — Optional Pro-plan upgrade via Stripe Checkout + Customer Portal. Quota chain (per-user override → plan tier → system default) with 0 for unlimited. Admin feature flag gates the entire pipeline; private deployments without Stripe stay clean.
• Evaluation Center — Test-dataset management, parallel eval runs with LLM-graded judgments, per-case pass/fail/latency/token results viewer with auto-polling.
• Conversation recovery — Synthetic tool_result rows persist after interrupted turns; clients auto-reconnect dropped SSE streams via /chat/resume with exponential backoff and a "Reconnecting…" indicator.
• 6 languages — EN, ZH, JA, KO, DE, FR. Translations are fully automated — single glossary drives every LLM translation call (JSON, MDX, README), pre-commit hook refuses manual edits to generated locale files.
• 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, audit, and rate limits; Channels carry outbound notifications and approval cards to external IM platforms. See Connector Architecture, Hook System, and Channels for details.

Configuration

FIM One works with any OpenAI-compatible provider:

Provider	LLM_API_KEY	LLM_BASE_URL	LLM_MODEL
OpenAI	sk-...	(default)	gpt-4o
DeepSeek	sk-...	https://api.deepseek.com/v1	deepseek-chat
Anthropic	sk-ant-...	https://api.anthropic.com/v1	claude-sonnet-4-6
Ollama (local)	ollama	http://localhost:11434/v1	qwen2.5:14b

Minimal .env:

LLM_API_KEY=sk-your-key
# LLM_BASE_URL=https://api.openai.com/v1   # default
# LLM_MODEL=gpt-4o                         # default
JINA_API_KEY=jina_...                       # unlocks web tools + RAG

Full reference: Environment Variables

Tech Stack

Layer	Technology
Backend	Python 3.11+, FastAPI, SQLAlchemy, Alembic, asyncio
Frontend	Next.js 14, React 18, Tailwind CSS, shadcn/ui, React Flow v12
AI / RAG	OpenAI-compatible LLMs, Jina AI (embed + search), LanceDB
Database	SQLite (dev) / PostgreSQL (prod)
Messaging	BaseChannel abstraction (Slack, Teams, Discord, Feishu/Lark, WeCom, DingTalk), Fernet-encrypted credentials, HMAC signature verification
Infra	Docker, uv, pnpm, SSE streaming

Development

uv sync --all-extras          # install dependencies
pytest                         # run tests
pytest --cov=fim_one           # with coverage
ruff check src/ tests/         # lint
mypy src/                      # type check
bash scripts/setup-hooks.sh    # install git hooks (enables auto i18n)

Roadmap

See the full Roadmap for version history and planned features.

FAQ

Common questions about deployment, LLM providers, system requirements, and more — see the FAQ.

Contributing

We welcome contributions of all kinds — code, docs, translations, bug reports, and ideas.

Pioneer Program: The first 100 contributors who get a PR merged are recognized as Founding Contributors with permanent credits, a badge, and priority issue support. Learn more →

Quick links:

• Contributing Guide — setup, conventions, PR process
• Development Conventions — type safety, testing, and code quality standards
• Good First Issues — curated for newcomers
• Open Issues — bugs & feature requests

Security: To report a vulnerability, please open a GitHub issue with the [SECURITY] tag. For sensitive disclosures, contact us via Discord DM.

Activity

Contributors

Thanks to these wonderful people (emoji key):

This project follows the all-contributors specification. Contributions of any kind welcome!

About FIM

FIM One is developed by FIM. FIM is a brand shared by two independent companies: FIM Labs Pte. Ltd. (Singapore) and Beijing FIM Network Technology Co., Ltd. (Beijing, founded 2013).

License

FIM One Source Available License. This is not an OSI-approved open source license.

Permitted: internal use, modification, distribution with license intact, embedding in non-competing applications.

Restricted: multi-tenant SaaS, competing agent platforms, white-labeling, removing branding.

For commercial licensing inquiries, please open an issue on GitHub.

See LICENSE for full terms.

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🌐 Website · 📖 Docs · 📋 Changelog · 🐛 Report Bug · 💬 Discord · 🐦 Twitter · 🏆 Product Hunt