All 32 agent manifests and 17 hands shipped with empty mcp_servers /
skills lists, which the kernel interprets as "no filter" — every
globally-configured MCP server's tools and every installed skill get
injected into the prompt on every LLM call. On a typical instance (9
MCP servers, ~85 MCP tools + ~82 built-in tools) that's ~50k input
tokens per turn spent on definitions the agent never uses.
Changes
-------
32 agents/*/agent.toml:
- mcp_servers: 1-4 per agent. memory wherever state persists across
turns; fetch / exa-search / brave-search only where the prompt
actually calls for web; git / github / filesystem on engineering
agents; gmail / google-calendar / linear / jira on productivity
agents whose prompts mention them.
- skills: per-role allowlist driven by what the system_prompt names
(e.g. coder → rust/python/typescript/git/shell-scripting; devops-
lead → docker/kubernetes/terraform/ansible/ci-cd/helm/prometheus/
sysadmin). Generalists (assistant) keep skills = [] (see "Open
items" below).
- skills_disabled = true on the four short-conversational agents
(hello-world, recipe-assistant, health-tracker, home-automation).
Their system prompts never instruct the LLM to consult any skill,
so loading all 60 was pure waste. They also drop the explicit
max_history_messages override and inherit the kernel default (60).
- max_history_messages tiered by workload shape:
60 short conversational (hello-world, recipe, health-tracker,
home-automation) — inherits the rising kernel default
(`DEFAULT_MAX_HISTORY_MESSAGES = 60`); no override needed.
60 single-turn task agents (writer, translator, doc-writer,
email-assistant, customer-support, sales-assistant, recruit-
er, social-media, personal-finance, tutor, travel-planner,
meeting-assistant, ops, devops-lead, planner) — explicit
override at the same value to lock the cap if the kernel
default moves again.
80 multi-step / tool-heavy (coder, debugger, architect, code-
reviewer, test-engineer, security-auditor, analyst, data-
scientist, academic-researcher, researcher, legal-assistant)
120 coordinators (assistant, orchestrator) — long multi-agent
sessions where prompt-cache continuity is critical
All values sit at or above the kernel default. Pinning lower
would thrash the prompt cache (the failure mode #91 fixed for
the creator hand by *raising* the cap, not lowering it).
17 hands/*/HAND.toml:
- hand-level mcp_servers / skills now declared on every hand, so
every [agents.*] inside inherits a sensible allowlist.
- skills_disabled = true placed on each [agents.*] inside clip and
creator (pure media pipelines that don't benefit from any skill).
HandDefinitionRaw in librefang-hands does NOT have a top-level
skills_disabled field — declaring it at the hand top level would
be silently dropped by serde, so the setting must live on the
AgentManifest of each sub-agent role.
- devteam: expand existing mcp_servers = ["github"] to include
memory / git / filesystem; populate skills with the expected
dev-team expertise (replacing the placeholder skills = []).
- wiki: replace placeholder mcp_servers = [] with [memory, fetch,
filesystem]. Hand-level skills stays [].
- lead: hand-level skills was originally [email-writer, writing-
coach, interview-prep]; interview-prep is for job-interview
preparation, not lead generation. Replaced with data-analyst
(used by the qualification-scoring step in the prompt).
schema.toml: register mcp_servers / skills / max_history_messages on
the agent field schema so machine consumers (RegistrySchema in
librefang-types) see the new top-level fields. The
max_history_messages description now points at
librefang_runtime::agent_loop::DEFAULT_MAX_HISTORY_MESSAGES (60
today) by name, so the schema doesn't go stale when the constant
moves again.
agents/README.md: example block + "Adding a New Agent" checklist
mention the allowlists; max_history_messages example is shown
commented out with a prompt-cache caveat.
Open items
----------
`assistant` (the default user-facing agent) keeps `skills = []`
deliberately. It is the generalist entry point — capping its skill
surface at a small allowlist would defeat its "delegate to any
specialist" job. The trade-off is that this single agent still pays
the full skill-definition load on every turn; operators who want a
strict allowlist for `assistant` can override it after install.
Why not adopt PR #89's approach
-------------------------------
#89 covers similar ground but with three issues this PR avoids:
1. mcp_servers = ["_none"] sentinel. #89's body explicitly notes
it's pending upstream librefang#4808 (mcp_disabled). Shipping a
magic-string today means coming back later to clean it up. This
PR uses real allowlists.
2. max_history_messages = 8 / 12 / 15 / 20. Far below today's
kernel default (60) and #91's direction for long-workflow hands
(80–120). Every turn that hits the cap invalidates the cached
prompt prefix; the cost of cache misses exceeds the saving from
shorter history. This PR uses 60–120.
3. Doubling max_llm_tokens_per_hour (coder 200k→500k, assistant
300k→500k) widens the per-agent budget — the opposite direction
from #87's "reduce per-call cost" goal. Left to the operator's
instance-specific tuning.
Refs librefang/librefang-registry#87, librefang/librefang-registry#89
10 KiB
10 KiB
Agents Registry
Agent templates for LibreFang. Each entry is a ready-to-install agent definition with a pre-configured system prompt, model settings, capability declarations, and routing aliases.
These are the reference agents shipped with the registry. You can install them as-is, override individual fields (model, system_prompt, tools) after installation, or use them as base templates inside a Hand.
File Format
Each agent lives in its own subdirectory containing a single agent.toml:
agents/
├── coder/
│ └── agent.toml
├── orchestrator/
│ └── agent.toml
└── ...
agent.toml format
name = "coder" # must match directory name
version = "0.4.3-beta3-20260314"
description = "Expert software engineer. Reads, writes, and analyzes code."
author = "librefang"
module = "builtin:chat" # runtime module — builtin:chat for all current agents
# Resource allowlists — keep these small. The kernel injects every entry's
# tools/skills into the agent's prompt on every LLM call, so an empty list
# (the default "all available" semantics) on an instance with many MCP
# servers and skills can burn 30-60k tokens per turn on definitions the
# agent never uses.
mcp_servers = ["memory", "git", "github", "filesystem"] # [] = all available
skills = ["rust-expert", "python-expert", "git-expert"] # [] = all available
# max_history_messages = 60 # OPT-IN override; omit to inherit kernel default
# (default rises with the kernel; setting too low
# thrashes the prompt cache — measure before pinning)
[metadata.routing]
aliases = ["write code", "fix bug", "implement feature"] # exact activation phrases
weak_aliases = ["refactor", "patch", "code change"] # keyword hints
[model]
provider = "default" # default = use LibreFang's configured primary provider
model = "default"
api_key_env = "GEMINI_API_KEY" # optional override: use this key env var
max_tokens = 8192
temperature = 0.3
system_prompt = """You are Coder, an expert software engineer..."""
[[fallback_models]] # optional: try these providers on failure
provider = "default"
model = "default"
api_key_env = "GROQ_API_KEY"
[schedule] # optional: continuous or cron activation
continuous = { check_interval_secs = 120 }
[resources]
max_llm_tokens_per_hour = 200000
max_concurrent_tools = 10
[capabilities]
tools = ["file_read", "file_write", "file_list", "shell_exec", "web_search", "web_fetch",
"memory_store", "memory_recall"]
network = ["*"] # "*" = all, or list specific domains
memory_read = ["*"]
memory_write = ["self.*"] # "self.*" = own namespace only
shell = ["cargo *", "rustc *", "git *", "npm *", "python *"] # shell command allowlist
agent_spawn = false
agent_message = [] # agents this agent may message
[i18n.zh]
name = "编码工程师"
description = "资深软件工程师:阅读、编写与分析代码。"
Installing and Using Agents
# List all available agent templates
librefang catalog agents
# Install an agent from the registry
librefang agent install coder
# Install with a custom name
librefang agent install coder --name my-coder
# List installed agents
librefang agent list
# Send a message to an installed agent
librefang agent message coder "Implement a binary search function in Rust"
# Remove an agent
librefang agent remove my-coder
Agents can also be used as base templates in a Hand by setting base = "coder" in HAND.toml.
All Agents (33 total)
Development
| Name | Description | Key Tools |
|---|---|---|
| architect | System architect. Designs software architectures, evaluates trade-offs, creates technical specifications. | file_read, file_write, web_search, web_fetch |
| code-reviewer | Senior code reviewer. Reviews PRs, identifies issues, suggests improvements with production standards. | file_read, shell_exec, web_search |
| coder | Expert software engineer. Reads, writes, and analyzes code. | file_read, file_write, shell_exec, web_search |
| debugger | Expert debugger. Traces bugs, analyzes stack traces, performs root cause analysis. | file_read, shell_exec, web_search |
| devops-lead | DevOps lead. Manages CI/CD, infrastructure, deployments, monitoring, and incident response. | shell_exec, file_read, web_search |
| ops | DevOps agent. Monitors systems, runs diagnostics, manages deployments. | shell_exec, file_read, web_search |
| test-engineer | Quality assurance engineer. Designs test strategies, writes tests, validates correctness. | file_read, file_write, shell_exec |
Research and Analysis
| Name | Description | Key Tools |
|---|---|---|
| academic-researcher | Academic research agent. Searches scholarly papers, summarizes findings, and generates literature reviews. | web_search, web_fetch, file_write |
| analyst | Data analyst. Processes data, generates insights, creates reports. | file_read, web_search, web_fetch |
| data-scientist | Data scientist. Analyzes datasets, builds models, creates visualizations, performs statistical analysis. | file_read, file_write, shell_exec |
| researcher | Research agent. Fetches web content and synthesizes information. | web_search, web_fetch, memory_store |
Writing and Documentation
| Name | Description | Key Tools |
|---|---|---|
| doc-writer | Technical writer. Creates documentation, README files, API docs, tutorials, and architecture guides. | file_read, file_write, web_fetch |
| writer | Content writer. Creates documentation, articles, and technical writing. | file_read, file_write, web_search |
Orchestration
| Name | Description | Key Capabilities |
|---|---|---|
| orchestrator | Meta-agent that decomposes complex tasks, delegates to specialist agents, and synthesizes results. | agent_spawn, agent_send, agent_list, agent_kill |
| planner | Project planner. Creates project plans, breaks down epics, estimates effort, identifies risks and dependencies. | file_read, file_write, web_search |
Business and Operations
| Name | Description | Key Tools |
|---|---|---|
| customer-support | Customer support agent for ticket handling, issue resolution, and customer communication. | memory_store, memory_recall, web_search |
| email-assistant | Email triage, drafting, scheduling, and inbox management agent. | memory_store, memory_recall, file_write |
| legal-assistant | Legal assistant agent for contract review, legal research, compliance checking, and document drafting. | file_read, file_write, web_search |
| meeting-assistant | Meeting notes, action items, agenda preparation, and follow-up tracking agent. | memory_store, memory_recall, file_write |
| recruiter | Recruiting agent for resume screening, candidate outreach, job description writing, and hiring pipeline management. | web_search, file_read, memory_store |
| sales-assistant | Sales assistant agent for CRM updates, outreach drafting, pipeline management, and deal tracking. | memory_store, memory_recall, web_search |
| security-auditor | Security specialist. Reviews code for vulnerabilities, checks configurations, performs threat modeling. | file_read, shell_exec, web_search |
Personal Productivity
| Name | Description | Key Tools |
|---|---|---|
| assistant | General-purpose assistant agent. The default agent for everyday tasks, questions, and conversations. | file_read, file_write, web_search, memory_store |
| health-tracker | Wellness tracking agent for health metrics, medication reminders, fitness goals, and lifestyle habits. | memory_store, memory_recall, file_write |
| hello-world | A friendly greeting agent that can read files, search the web, and answer everyday questions. | file_read, web_search, web_fetch |
| home-automation | Smart home control agent for IoT device management, automation rules, and home monitoring. | shell_exec, memory_store, web_fetch |
| personal-finance | Personal finance agent for budget tracking, expense analysis, savings goals, and financial planning. | file_read, memory_store, web_search |
| recipe-assistant | Cooking assistant that helps with recipes, meal plans, ingredient substitutions, and portion adjustments. | web_search, memory_recall, file_write |
| social-media | Social media content creation, scheduling, and engagement strategy agent. | web_fetch, web_search, file_write |
| translator | Multi-language translation agent for document translation, localization, and cross-cultural communication. | file_read, file_write, web_fetch |
| travel-planner | Trip planning agent for itinerary creation, booking research, budget estimation, and travel logistics. | web_search, web_fetch, memory_store |
| tutor | Teaching and explanation agent for learning, tutoring, and educational content creation. | web_search, memory_recall, file_write |
Capability Reference
| Capability field | Values | Effect |
|---|---|---|
tools |
list of tool names | Which built-in tools the agent may invoke |
network |
["*"] or domain list |
Outbound HTTP domain allowlist |
memory_read |
["*"] or namespace list |
Which memory namespaces the agent can read |
memory_write |
["self.*"] or ["*"] |
Which memory namespaces the agent can write |
shell |
glob patterns | Shell command allowlist (e.g. "cargo *") |
agent_spawn |
true / false |
Whether the agent can spawn child agents |
agent_message |
["*"] or agent name list |
Which agents this agent may send messages to |
Adding a New Agent
- Create
agents/<name>/agent.toml—namemust match the directory name. - Set
module = "builtin:chat"unless you have a custom runtime module. - Write a focused
system_prompt— clear role definition, methodology, and constraints. - Declare only the tools and capabilities the agent actually needs.
- Set
mcp_serversandskillsto the minimum the prompt actually references — leaving them empty falls back to "all available", which on a populated instance bloats every LLM call with tool definitions the agent never uses. - Add
[metadata.routing]aliases so the router can activate the agent by intent. - Run
python scripts/validate.py. - Submit a PR.
See CONTRIBUTING.md for the full guide.