Migrating to LibreFang

This guide covers migrating from OpenClaw (and other frameworks) to LibreFang. The migration engine handles config conversion, agent import, memory transfer, channel re-configuration, and skill scanning.

Quick Migration
What Gets Migrated
Manual Migration Steps
Config Format Differences
Tool Name Mapping
Provider Mapping
Feature Comparison

Quick Migration

Run a single command to migrate your entire OpenClaw workspace:

librefang migrate --from openclaw

This auto-detects your OpenClaw workspace at ~/.openclaw/ and imports everything into ~/.librefang/.

Options

# Specify a custom source directory
librefang migrate --from openclaw --source-dir /path/to/openclaw/workspace

# Dry run -- see what would be imported without making changes
librefang migrate --from openclaw --dry-run

Migration Report

After a successful migration, a migration_report.md file is saved to ~/.librefang/ with a summary of everything that was imported, skipped, or needs manual attention.

Other Frameworks

LangChain and AutoGPT migration support is planned:

librefang migrate --from langchain   # Coming soon
librefang migrate --from autogpt     # Coming soon

What Gets Migrated

Item	Source (OpenClaw)	Destination (LibreFang)	Status
Config	`~/.openclaw/config.yaml`	`~/.librefang/config.toml`	Fully automated
Agents	`~/.openclaw/agents/*/agent.yaml`	`~/.librefang/agents/*/agent.toml`	Fully automated
Memory	`~/.openclaw/agents/*/MEMORY.md`	`~/.librefang/agents/*/imported_memory.md`	Fully automated
Channels	`~/.openclaw/messaging/*.yaml`	`~/.librefang/channels_import.toml`	Automated (manual merge)
Skills	`~/.openclaw/skills/`	Scanned and reported	Manual reinstall
Sessions	`~/.openclaw/agents/*/sessions/`	Not migrated	Fresh start recommended
Workspace files	`~/.openclaw/agents/*/workspace/`	Not migrated	Copy manually if needed

Channel Import Note

Channel configurations (Telegram, Discord, Slack) are exported to a channels_import.toml file. You must manually merge the [channels] section into your ~/.librefang/config.toml.

Skills Note

OpenClaw skills (Node.js) are detected and listed in the migration report but not automatically converted. After migration, reinstall skills using:

librefang skill install <skill-name-or-path>

LibreFang automatically detects OpenClaw-format skills and converts them during installation.

Manual Migration Steps

If you prefer migrating by hand (or need to handle edge cases), follow these steps:

1. Initialize LibreFang

librefang init

This creates ~/.librefang/ with a default config.toml.

2. Convert Your Config

Translate your config.yaml to config.toml:

OpenClaw (~/.openclaw/config.yaml):

provider: anthropic
model: claude-sonnet-4-20250514
api_key_env: ANTHROPIC_API_KEY
temperature: 0.7
memory:
  decay_rate: 0.05

LibreFang (~/.librefang/config.toml):

[default_model]
provider = "anthropic"
model = "claude-sonnet-4-20250514"
api_key_env = "ANTHROPIC_API_KEY"

[memory]
decay_rate = 0.05

[network]
listen_addr = "127.0.0.1:4545"

3. Convert Agent Manifests

Translate each agent.yaml to agent.toml:

OpenClaw (~/.openclaw/agents/coder/agent.yaml):

name: coder
description: A coding assistant
provider: anthropic
model: claude-sonnet-4-20250514
tools:
  - read_file
  - write_file
  - execute_command
tags:
  - coding
  - dev

LibreFang (~/.librefang/agents/coder/agent.toml):

name = "coder"
version = "0.4.0"
description = "A coding assistant"
author = "librefang"
module = "builtin:chat"
tags = ["coding", "dev"]

[model]
provider = "anthropic"
model = "claude-sonnet-4-20250514"

[capabilities]
tools = ["file_read", "file_write", "shell_exec"]
memory_read = ["*"]
memory_write = ["self.*"]

4. Convert Channel Configs

OpenClaw (~/.openclaw/messaging/telegram.yaml):

type: telegram
bot_token_env: TELEGRAM_BOT_TOKEN
default_agent: coder
allowed_users:
  - "123456789"

LibreFang (add to ~/.librefang/config.toml):

[channels.telegram]
bot_token_env = "TELEGRAM_BOT_TOKEN"
default_agent = "coder"
allowed_users = ["123456789"]

5. Import Memory

Copy any MEMORY.md files from OpenClaw agents to LibreFang agent directories:

cp ~/.openclaw/agents/coder/MEMORY.md ~/.librefang/agents/coder/imported_memory.md

The kernel will ingest these on first boot.

Config Format Differences

Aspect	OpenClaw	LibreFang
Format	YAML	TOML
Config location	`~/.openclaw/config.yaml`	`~/.librefang/config.toml`
Agent definition	`agent.yaml`	`agent.toml`
Channel config	Separate files per channel	Unified in `config.toml`
Tool permissions	Implicit (tool list)	Capability-based (tools, memory, network, shell)
Model config	Flat (top-level fields)	Nested (`[model]` section)
Agent module	Implicit	Explicit (`module = "builtin:chat"` / `"wasm:..."` / `"python:..."`)
Scheduling	Not supported	Built-in (`[schedule]` section: reactive, continuous, periodic, proactive)
Resource quotas	Not supported	Built-in (`[resources]` section: tokens/hour, memory, CPU time)
Networking	Not supported	OFP protocol (`[network]` section)

Tool Name Mapping

Tools were renamed between OpenClaw and LibreFang for consistency. The migration engine handles this automatically.

OpenClaw Tool	LibreFang Tool	Notes
`read_file`	`file_read`	Noun-first naming
`write_file`	`file_write`
`list_files`	`file_list`
`execute_command`	`shell_exec`	Capability-gated
`web_search`	`web_search`	Unchanged
`fetch_url`	`web_fetch`
`browser_navigate`	`browser_navigate`	Unchanged
`memory_search`	`memory_recall`
`memory_recall`	`memory_recall`
`memory_save`	`memory_store`
`memory_store`	`memory_store`
`sessions_send`	`agent_send`
`agent_message`	`agent_send`
`agents_list`	`agent_list`
`agent_list`	`agent_list`

New Tools in LibreFang

These tools have no OpenClaw equivalent:

Tool	Description
`agent_spawn`	Spawn a new agent from within an agent
`agent_kill`	Terminate another agent
`agent_find`	Search for agents by name, tag, or description
`memory_store`	Store key-value data in shared memory
`memory_recall`	Recall key-value data from shared memory
`task_post`	Post a task to the shared task board
`task_claim`	Claim an available task
`task_complete`	Mark a task as complete
`task_list`	List tasks by status
`event_publish`	Publish a custom event to the event bus
`schedule_create`	Create a scheduled job
`schedule_list`	List scheduled jobs
`schedule_delete`	Delete a scheduled job
`image_analyze`	Analyze an image
`location_get`	Get location information

Tool Profiles

OpenClaw's tool profiles map to explicit tool lists:

OpenClaw Profile	LibreFang Tools
`minimal`	`file_read`, `file_list`
`coding`	`file_read`, `file_write`, `file_list`, `shell_exec`, `web_fetch`
`messaging`	`agent_send`, `agent_list`, `memory_store`, `memory_recall`
`research`	`web_fetch`, `web_search`, `file_read`, `file_write`
`full`	All 10 core tools

Provider Mapping

OpenClaw Name	LibreFang Name	API Key Env Var
`anthropic`	`anthropic`	`ANTHROPIC_API_KEY`
`claude`	`anthropic`	`ANTHROPIC_API_KEY`
`openai`	`openai`	`OPENAI_API_KEY`
`gpt`	`openai`	`OPENAI_API_KEY`
`groq`	`groq`	`GROQ_API_KEY`
`ollama`	`ollama`	(none required)
`openrouter`	`openrouter`	`OPENROUTER_API_KEY`
`deepseek`	`deepseek`	`DEEPSEEK_API_KEY`
`together`	`together`	`TOGETHER_API_KEY`
`mistral`	`mistral`	`MISTRAL_API_KEY`
`fireworks`	`fireworks`	`FIREWORKS_API_KEY`

New Providers in LibreFang

Provider	Description
`vllm`	Self-hosted vLLM inference server
`lmstudio`	LM Studio local models

Feature Comparison

Feature	OpenClaw	LibreFang
Language	Node.js / TypeScript	Rust
Config format	YAML	TOML
Agent manifests	YAML	TOML
Multi-agent	Basic (message passing)	First-class (spawn, kill, find, workflows, triggers)
Agent scheduling	Manual	Built-in (reactive, continuous, periodic, proactive)
Memory	Markdown files	SQLite + KV store + semantic search + knowledge graph
Session management	JSONL files	SQLite with context window tracking
LLM providers	~5	11 (Anthropic, OpenAI, Groq, OpenRouter, DeepSeek, Together, Mistral, Fireworks, Ollama, vLLM, LM Studio)
Per-agent models	No	Yes (per-agent provider + model override)
Security	None	Capability-based (tools, memory, network, shell, agent spawn)
Resource quotas	None	Per-agent token/hour limits, memory limits, CPU time limits
Workflow engine	None	Built-in (sequential, fan-out, collect, conditional, loop)
Event triggers	None	Pattern-matching event triggers with templated prompts
WASM sandbox	None	Wasmtime-based sandboxed execution
Python runtime	None	Subprocess-based Python agent execution
Networking	None	OFP (LibreFang Protocol) peer-to-peer
API server	Basic REST	REST + WebSocket + SSE streaming
WebChat UI	Separate	Embedded in daemon
Channel adapters	Telegram, Discord	Telegram, Discord, Slack, WhatsApp, Signal, Matrix, Email
Skills/Plugins	npm packages	TOML + Python/WASM/Node.js, FangHub marketplace
CLI	Basic	Full CLI with daemon auto-detect, MCP server
MCP support	No	Built-in MCP server (stdio)
Process supervisor	None	Health monitoring, panic/restart tracking
Persistence	File-based	SQLite (agents survive restarts)

Troubleshooting

Migration reports "Source directory not found"

The migration engine looks for ~/.openclaw/ by default. If your OpenClaw workspace is elsewhere:

librefang migrate --from openclaw --source-dir /path/to/your/workspace

Agent fails to spawn after migration

Check the converted agent.toml for:

Valid tool names (see the Tool Name Mapping table)
A valid provider name (see the Provider Mapping table)
Correct module field (should be "builtin:chat" for standard LLM agents)

Skills not working

OpenClaw Node.js skills must be reinstalled:

librefang skill install /path/to/openclaw/skills/my-skill

The installer auto-detects OpenClaw format and converts the skill manifest.

Channel not connecting

After migration, channels are exported to channels_import.toml. You must merge them into your config.toml manually:

cat ~/.librefang/channels_import.toml
# Copy the [channels.*] sections into ~/.librefang/config.toml

Then restart the daemon:

librefang start

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Migrating to LibreFang

Table of Contents

Quick Migration

Options

Migration Report

Other Frameworks

What Gets Migrated

Channel Import Note

Skills Note

Manual Migration Steps

1. Initialize LibreFang

2. Convert Your Config

3. Convert Agent Manifests

4. Convert Channel Configs

5. Import Memory

Config Format Differences

Tool Name Mapping

New Tools in LibreFang

Tool Profiles

Provider Mapping

New Providers in LibreFang

Feature Comparison

Troubleshooting

Migration reports "Source directory not found"

Agent fails to spawn after migration

Skills not working

Channel not connecting

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MIGRATION.md

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MIGRATION.md

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Migrating to LibreFang

Table of Contents

Quick Migration

Options

Migration Report

Other Frameworks

What Gets Migrated

Channel Import Note

Skills Note

Manual Migration Steps

1. Initialize LibreFang

2. Convert Your Config

3. Convert Agent Manifests

4. Convert Channel Configs

5. Import Memory

Config Format Differences

Tool Name Mapping

New Tools in LibreFang

Tool Profiles

Provider Mapping

New Providers in LibreFang

Feature Comparison

Troubleshooting

Migration reports "Source directory not found"

Agent fails to spawn after migration

Skills not working

Channel not connecting