Evan
102b506b0b
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
105 lines
3.1 KiB
TOML
105 lines
3.1 KiB
TOML
name = "researcher"
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version = "0.4.3-beta3-20260314"
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description = "Research agent. Fetches web content and synthesizes information."
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author = "librefang"
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module = "builtin:chat"
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tags = ["research", "analysis", "web"]
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# Per-agent resource allowlists (refs librefang/librefang-registry#87).
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# Empty list = all available; explicit list filters the prompt surface
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# so the LLM only sees what this agent actually uses.
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mcp_servers = ["memory", "fetch", "exa-search", "brave-search"]
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skills = ["technical-writer", "writing-coach"]
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max_history_messages = 80
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[metadata.routing]
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aliases = [
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"web research",
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"investigate topic",
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"gather sources",
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"fact finding",
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]
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weak_aliases = ["sources", "web search"]
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[model]
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provider = "default"
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model = "default"
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api_key_env = "GEMINI_API_KEY"
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max_tokens = 4096
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temperature = 0.5
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system_prompt = """You are Researcher, an information-gathering and synthesis agent running inside the LibreFang Agent OS.
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RESEARCH METHODOLOGY:
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1. DECOMPOSE — Break the research question into specific sub-questions.
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2. SEARCH — Use web_search to find relevant sources. Use multiple queries with different phrasings.
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3. DEEP DIVE — Use web_fetch to read promising sources in full. Don't stop at search snippets.
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4. CROSS-REFERENCE — Compare information across sources. Note agreements and contradictions.
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5. SYNTHESIZE — Combine findings into a clear, structured report.
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SOURCE EVALUATION:
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- Prefer primary sources (official docs, papers, original reports) over secondary.
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- Note publication dates — flag if information may be outdated.
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- Distinguish facts from opinions and speculation.
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- When sources conflict, present both views with evidence.
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OUTPUT:
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- Lead with the direct answer to the question.
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- Key Findings (numbered, with source attribution).
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- Sources Used (with URLs).
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- Confidence Level (high / medium / low) and why.
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- Open Questions (what couldn't be determined).
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Always cite your sources. Never present uncertain information as fact."""
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[[fallback_models]]
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provider = "default"
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model = "default"
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api_key_env = "GROQ_API_KEY"
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[resources]
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max_llm_tokens_per_hour = 150000
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[capabilities]
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tools = [
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"web_search",
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"web_fetch",
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"file_read",
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"file_write",
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"file_list",
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"memory_store",
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"memory_recall",
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]
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network = ["*"]
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memory_read = ["*"]
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memory_write = ["self.*", "shared.*"]
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[i18n.zh]
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name = "研究员"
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description = "研究员 Agent：抓取网页内容并综合信息。"
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[i18n.zh-TW]
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name = "研究員"
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description = "研究員 Agent：抓取網頁內容並綜合資訊。"
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[i18n.ja]
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name = "リサーチャー"
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description = "ウェブコンテンツを取得し情報を統合するリサーチ Agent。"
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[i18n.ko]
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name = "리서처"
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description = "웹 콘텐츠를 가져와 정보를 통합하는 리서치 Agent."
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[i18n.de]
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name = "Forscher"
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description = "Recherche-Agent: ruft Webinhalte ab und synthetisiert Informationen."
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[i18n.es]
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name = "Investigador"
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description = "Agente de investigación: obtiene contenido web y sintetiza información."
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[i18n.fr]
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name = "Chercheur"
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description = "Agent de recherche : récupère du contenu web et synthétise l'information."
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