FastClaw
A self-hosted multi-agent runtime for operating isolated, configurable chat agents from one control plane.
What does this agent do, and when should you use it?
FastClaw is a lightweight Go AI agent runtime positioned as an Agent Factory for creating, managing, and running agents. Each agent can have its own SOUL.md, memory, skills, tools, model overrides, sessions, and channel bindings, managed through a web dashboard, CLI, and HTTP APIs. The runtime handles LLM communication, tool execution, session isolation, and Docker or E2B sandboxing; SQLite is the default store, with Postgres documented for multi-pod deployments. It exposes Telegram, Discord, and Slack channels plus an OpenAI-compatible chat-completions endpoint, making it a fit for teams building a self-hosted agent backend or application integration layer.
On first launch, fastclaw opens a setup wizard to configure an LLM provider and create a default agent; the dashboard can edit system files such as SOUL.md, IDENTITY.md, and MEMORY.md, and configure models, skills, IM channels, and schedules. For incoming chats, the runtime stores sessions isolated by agent × user and by channel/chatID, and can invoke built-in exec, read_file, write_file, list_dir, web_fetch, web_search, and memory_search tools. With sandboxing enabled, Docker or E2B environments hydrate skills and workspaces, then sync sandbox-side files back to durable storage after each tool call. Applications can call streaming /v1/chat/completions, provision isolated end users through POST /v1/users or X-Fastclaw-End-User, while administrators manage resources through /api/agents, /api/config, /api/skills/install, and /api/apikeys.
- A SaaS team provisioning a separate bot for each customer can use a user API key and `X-Fastclaw-End-User` to lazily create users with isolated sessions, memory, and files.
- An internal platform team operating several role-specific assistants can give every agent its own SOUL.md, model settings, skills, and private skill directory.
- A team delivering a focused support or business bot in Telegram, Discord, or Slack can bind that channel to an agent and keep individual chat threads separate.
- An application developer who needs a chat-agent backend can call the streaming OpenAI-compatible `/v1/chat/completions` endpoint instead of driving the web chat UI.
- A team that needs agents to run code or manipulate files while constraining execution in hosted deployments can choose Docker or E2B sandboxing with `FASTCLAW_DEPLOY=hosted`.
What are this agent's strengths and limitations?
- A single Go binary combines the dashboard, CLI, runtime, session management, and HTTP API, reducing the amount of infrastructure a team must assemble.
- Agent-level model overrides, skills, identity files, channels, and user-partitioned sessions and memory support multiple independently configured roles in one deployment.
- SQLite provides a simple default path, while Postgres, Redis, object storage, and Kubernetes are explicitly documented for scaled deployments.
- Docker and E2B sandboxing include skill/workspace synchronization; hosted mode can force exec and file calls inside the sandbox.
- The FastClaw Community License adds restrictions: hosting multi-tenant SaaS for unrelated organizations requires a commercial license, and dashboard branding may not be removed or modified.
- LLM providers, bot channels, and external web search/fetch require network access and relevant provider or bot credentials; the supplied material does not establish service costs or quotas.
- On self-hosted installations, host-shell execution is the default unless the model chooses `exec(sandbox:true)`; deployments exposed to untrusted IM users need `FASTCLAW_SANDBOX_ENFORCE=1` for hosted-style enforcement.
- Multi-pod operation requires Postgres, Redis when shared channel leases and message streams are needed, and S3-compatible object storage for distributed skill/file hydration.
How do you install or deploy this agent?
Install:curl -fsSL https://raw.githubusercontent.com/fastclaw-ai/fastclaw/main/install.sh | bash
Run for the first time:fastclaw
Configure an LLM provider in the wizard; it creates a default agent. To create an OpenAI-backed agent from the CLI, make OPENAI_API_KEY available in the environment, then run:fastclaw agents init alpha --provider openai --model openai/gpt-4o-mini --api-key-env OPENAI_API_KEY
The dashboard is at http://localhost:18953; use fastclaw daemon start for background execution.
How do you use this agent?
Use fastclaw agents ls to inspect agents, fastclaw agents config alpha set temperature 0.7 to change agent configuration, and fastclaw agents files put alpha SOUL.md ./SOUL.md to upload an identity file. In the dashboard, open an agent to test chat, install private skills, bind Telegram/Discord/Slack, inspect sessions, and manage cron jobs created by that agent. For application integration, send OpenAI-compatible streaming requests to /v1/chat/completions; a multi-tenant app uses a user API key and X-Fastclaw-End-User: <your-app-user-id>.
How does this agent compare with similar options?
The repository topics label FastClaw as an OpenClaw alternative; the supplied material does not provide a verifiable feature-by-feature comparison.