Fuli (复利) is Chinese for compound interest: returns that generate further returns.
Fuli exists to preserve your own taste, personality, and judgment as you work with AI.
We believe people should no longer be tied to doing everything themselves. You should
be free to make decisions, come up with ideas, and take a break.
Fuli works with Codex, Claude Code, and other agent clients that support MCP and Skills.
Connect supported clients to the same Fuli data and continue with the same agent,
carrying forward its recorded work and relevant context.
Local-first collaboration relationship graph for AI agents.
2026-11-15
Estrelas
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Ferramentas
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Disponibilidade
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As AI learns to take on more of the work, Fuli helps you make the transition while
keeping your own judgment at the center.
Fuli is a collaboration layer for long-lived agents across MCP-compatible AI clients. Connect
your tools to the same accessible Fuli data, select the same agent, and continue with its identity,
project responsibilities, and relevant working context. Use fuli connect for standard MCP
configuration, or automatic setup where a client adapter is available (Codex, Claude Code, Cursor, and Claude
Desktop with a compact chat tool profile). See client connections.
A lasting teammate: keep an agent's role, collaboration history, and working memory between tasks.
Context with boundaries: retrieve relevant project knowledge, decisions, and preferences with
their sources, scope, and confirmation status.
A team when needed: coordinate specialist agents and record actual worker execution through
an available, authorized host client or executor.
Every task has an owner: ask for work inside a project directory; Fuli registers the project
and staffs a lead agent. The reply ends with who led and who helped.
Three built-in specialists: Jefa manages projects, Bole handles AR (Agent Resources) for
recruiting and staffing, and Tonborg helps with judgment. Every space starts with all three.
Judgment guided by your preferences:Tonborg reviews items
within your chosen autonomy level and recommends clients and models. Each decision has a log;
a thumbs up, thumbs down or short reason helps guide future judgments.
Agents talk to each other: through the Roundtable, one agent asks another
conversation directly, and every exchange is recorded.
“Long-lived” means persistent identity and context. Fuli assembles that context and coordinates
work; the connected AI tools run it. Client adapters determine which conversations can be captured
and resumed. Connecting another tool does not grant access to another project's private memory.
Fuli separates private conversation journals, agent working memory, and confirmed knowledge so
ongoing collaboration produces reusable understanding. AI retrieves, summarizes, warns, and
executes; humans retain final authority.
Install Fuli, connect a client, and run one actual tool call to verify the connection. See
Installation for full requirements and client connections
for configuration formats, Hooks, and troubleshooting.
Humans retain login, Hook trust, and manual review. Settings showing Connected (connected)
only means the current Fuli entry is present in the client's configuration file — it does not prove
that the client loaded the MCP process, that a Hook delivered context, or that a tool call
succeeded.
1. Install
With Node.js 24.12+ and Docker Compose v2 available (default container mode; see
Installation for macOS/Linux native mode), run:
npm install --global fuli-context
fuli setup
fuli open
The setup wizard shows its plan before changing your environment.
2. Connect a client
Detected Codex, Claude Code, Cursor, or Claude Desktop:fuli setup writes the MCP entry and
supported Hooks/Skills. Quit and reopen apps that read MCP configuration only at launch, then open
a new task. Codex Hook trust requires manual /hooks review in Codex.
Other MCP clients: run fuli connect (or open Settings → Client connection in the
console), merge the fuli entry into the client's MCP configuration while keeping other servers,
and reload or trust the server in that client. fuli connect prints JSON only.
3. First actual tool call and verification
Run fuli status and confirm the management UI and personal graph are ready (default
http://127.0.0.1:2727).
Confirm Fuli tools appear in the client and make one actual tool call inside a project
directory (for example, ask the Agent to load project collaboration preferences or coordinate a
small task). Verify that Fuli resolves the right project (or registers/staffs a lead Agent) and
asks for missing context rather than inventing it.
Optional cross-tool handoff: use the Agent's continuation instruction in another client
connected to the same data and verify that it recovers the same Agent, project, and prior work.
See Agent conversations and collaboration.
For a one-off task, this source version also supports an isolated temporary project when no
registered project matches; reading preferences alone creates nothing, and an ambiguous project
still needs a choice. Fuli retains its task history and follows the existing Agent recruitment and
executor permissions. The Agent directory explains the first-task entry, and task details show a
completion table only after real worker execution is reported — an assigned Agent or an ordinary
local conversation alone is not worker evidence.
4. Troubleshooting
If setup, client detection, or tool calls fail, see
Common failure recovery. Use
fuli setup --yes --connected-only only to refresh clients that still have an existing Fuli MCP
entry (Connected / connected or Configuration changed / changed in Settings); clients
showing Skills only (skills_left), Removed (removed), Not connected
(not_connected), or newly installed clients without a Fuli MCP entry require a full fuli setup.
Agents talk to each other directly, the way a lead asks a teammate or a sub-agent. A Codex
Agent can ask the Agent behind a specific Claude Code conversation, or a FULI Agent that has
never run yet. Nobody creates rooms or invites participants: an Agent calls message_agent,
Fuli wakes the recipient read-only in its own client and returns its answer: Claude Code
forks the conversation; Codex records the exchange in it (or uses a new session while it is
open in the app). A message that cannot be delivered waits in the recipient's inbox for its
next task.
Agents can also keep direct and group chats with agent_chat. Messages are pushed: an Agent
that calls wait gets new messages at once, and a direct message or an @ mention wakes an
Agent that is not waiting. Chats only carry communication; they never change knowledge weights.
Every exchange is kept locally; the console's Roundtable page shows who asked whom, through
which client, and exactly what was said. Tools and limits: Agent Roundtable.
Personal edition; personal knowledge, the graph database, and the management UI all run locally
Fuli Server npm package
In development
A separate server for team-shared deployments; not yet published
fuli-context is currently the only available npm package. The shared layer never contains
personal taste, personality, or judgment preferences.
Philosophy
1. Measure reuse value, not knowledge volume
Fuli is built around one question:
Without Fuli, would the agent have to relearn information that is confirmed, still valid, and
relevant to the current task?
Node count, transcript count, and graph size are not success metrics. The product succeeds when a
past collaboration asset is reused within the correct project, time, provenance, and authority
boundaries—and reduces repeated explanation, correction, or rework.
2. Review every task; do not retain every task
A task may produce:
reusable knowledge such as API conventions, runbooks, release procedures, or constraints;
experience explaining why an approach worked or failed;
a decision trace with options, a human choice, rationale, and later validation;
judgment aids such as preferences, principles, taste, or personality tendencies.
It may also produce only disposable output, a guess, or a one-off choice. In that case,
retain_nothing is the correct result. Every task should perform a knowledge-value checkpoint, but
not every task must become stored knowledge.
3. Human authority outranks agent repetition
An agent may discover candidates, surface conflicts, recommend promotion, or resolve a deferred
conflict when explicitly authorized. It may not turn semantic similarity, repetition, or its own
judgment into human confirmation. Shared-knowledge promotion and sensitive project mutations use a
preview followed by an explicit, one-time, atomic action.
4. Assemble context, then retrieve evidence on demand
Fuli does not inject the complete history at session start. The entry step always loads effective
personal-global preferences, preferences from one exactly matched project, and explicitly
propagating preferences from authorized parent projects. When the current task signals that a
stable prior fact, method, URL, decision, release, deployment, or authentication runbook may matter,
the same step also performs a small bounded recall from that project and its authorized knowledge
sources. The agent uses focused on-demand search when more evidence is needed.
Retrieval alone is not usage; a usage event is recorded only when an item materially affects an
answer, implementation, or decision.
5. Preserve evolution instead of overwriting history
An old approach may have been correct under earlier conditions and later replaced. Fuli preserves
provenance, confirmation authority, time, rationale, revisions, supersession, and negative evidence
so an agent can explain both what applies now and why the past was different. Negative feedback may
lower rank or trigger review, but it never silently erases history.
6. Put shared knowledge in the parent; keep differences in each child
For example, hotel and flight projects can keep their own PRDs, configuration, and domain rules,
while a parent activity-platform project owns shared local-run, mock, test, and deployment runbooks:
When an agent works in the hotel project, it searches hotel-local knowledge first, then follows
explicit outgoing PART_OF or USES_KNOWLEDGE_FROM relationships to authorized sources, up to two
hops. A child item with the same stable key overrides an inherited item. Project-scoped personal
preferences remain exact by default; only preferences explicitly marked descendants or
selected_projects may propagate over the same authorized relationships, with origin project,
path, and distance preserved. Equally near parent conflicts require human judgment and are never
resolved by weight. Generic RELATED_TO relationships do not expand scope automatically; search
returns only a one-time read-only expansion suggestion that the Agent must ask the user to approve.
Similar items from multiple children create only a common-knowledge candidate. A shared preference
across projects without one common parent likewise creates only a personal-global candidate while
preserving each source text, qualifier, and provenance. Current clustering is a lexical heuristic,
not proof of semantic equivalence. A human must choose the content, scope, and rationale before it
can apply.
7. Local first; no personal model in the shared layer
The personal graph stays local by default. Supported client adapters can journal visible
conversations for private agent continuity, separately from structured confirmed knowledge.
Conversation recall is bounded, with a default seven-day inactivity window. Credentials, temporary
logs, and raw command output must not enter the knowledge graph. The team-shared layer contains only
confirmed project or domain knowledge with context and provenance—not personal taste, personality,
or judgment preferences.
Practical use: classification, scope, project resolution, and retrieval
Decide what to store before deciding where to store it
Content type and effective scope are independent dimensions. The first three rows are personal
collaboration preferences and carry a profileAspect. Project facts have no profileAspect and do
not appear on the Personal preferences page.
Type
What belongs here
Example
Taste (taste)
Outcomes, styles, and quality directions the user explicitly likes or rejects across UI, writing, product, architecture, or engineering work
“Use fewer large gradients and avoid cards nested inside cards.”
Personality (personality)
A stable working or collaboration trait explicitly described by the user
“I communicate directly and want the Agent to move work forward proactively.”
Judgment preference (judgment_preference)
Decision conditions, priorities, risk posture, and operational boundaries used when making trade-offs
“Discuss directional ambiguity first; never git push without explicit authorization.”
Four questions provide a quick classifier: “What outcome do I want?” is taste; “How do I work over
the long term?” is personality; “How do I decide under a trade-off?” is judgment preference; and
“How does this system objectively work?” is project knowledge. One-off commands, temporary output,
unverified guesses, raw chat, and credentials are not promoted into confirmed knowledge.
Personality is not a catch-all category for everything learned about a person. Only an explicit,
stable self-description can be confirmed with a human basis. A personality inferred by an Agent
from one behavior must remain pending; it cannot masquerade as human-confirmed. An empty
Personality filter on /preferences therefore usually means there is no qualifying item in the
selected scope, not that capture failed. “Keep copy concise” is more likely taste, while “do not
push without authorization” is more likely a judgment preference. To make the intent explicit,
say: This is a long-term collaboration trait; save it as a personal-global personality preference: I communicate directly. An Agent-inferred candidate can be confirmed, corrected, or invalidated
by the user from /preferences or /flreview.
Personal-global, project-scoped, and shared
Scope
When to use it
Key write fields
Retrieval rule
Personal-global preference
The behavior should remain the same in unrelated projects
targetKind: "personal"; omit personalProjectId; set profileAspect
Loaded for every task
Project-scoped personal preference
The collaboration behavior belongs to one project or project family
Same fields plus the exact personalProjectId; default to local_only and never inherited by child projects; explicitly use descendants or selected_projects when propagation is intended
Exact project wins; propagation follows only the explicit inheritance mode and authorized relationships, and score never resolves a conflict automatically
Project knowledge
A fact belongs to one project or to a designated parent/source project
targetKind: "personal"; set personalProjectId; omit profileAspect
Search the current project first, then authorized PART_OF / USES_KNOWLEDGE_FROM sources up to two hops
Team-shared knowledge
Human-confirmed project or domain knowledge genuinely needed by a team
targetKind: "project" after preview/review
Visible only from explicitly selected or subscribed public projects; personal preferences never enter this layer
To choose a preference scope, ask: Should this change the Agent's behavior in a completely
unrelated project? If yes, use personal-global. If it applies only to Fuli, use a project-scoped
preference. If it applies to a project family, keep it project-scoped and explicitly choose whether
it may propagate to descendants. Do not turn an objective project fact into a personal-global item
merely because more than one project may reuse it. Put it in an explicit parent or knowledge-source
project and add a directional authorization relation. When global and project-scoped variants have
the same stable attributes.preferenceKey, human/source confirmation authority is resolved first;
at equal authority, the exact project variant overrides the global one.
How Fuli identifies the current project
Fuli matches only stable project_id values already registered under Personal projects; it does
not guess from fuzzy directory similarity. Normally the project_id matches the repository
directory name, so /workspace/fuli maps to fuli. Standard calls pass only the current working
directory, and resolution follows this order:
Find the nearest Git repository root and match its directory name exactly to a registered
project_id.
For a Codex worktree, recover the original repository ID from its .git pointer.
Match the current directory name exactly to a registered project ID.
At a workspace root, match one registered direct child only when exactly one child contains
.git, package.json, pyproject.toml, Cargo.toml, or go.mod.
Multiple candidates return ambiguous; no candidate returns unmatched. Neither state applies
project preferences or guesses a project.
For an unregistered repository with an unambiguous identity, the installed capture skill may call
upsert_personal_project before the first project-scoped write to create a minimal local private
project. It never creates or subscribes to a public project automatically. If a workspace has
multiple candidates, run the Agent from the exact project directory or explicitly register and
select the project first.
Which tool the Agent calls and when
Normal use does not require manual MCP calls: submit a task to the Agent from the project directory.
Fuli includes lifecycle adapters for Claude Code, Codex, and Cursor. When a loaded, trusted entry
hook supplies begin_task_context context, use it without repeating the fallback. Cursor's adapter
starts the durable task through a hook but relies on the Agent to retrieve preferences and role
context. When no hook context was supplied, call the following once at task entry:
{
"tool": "get_collaboration_preferences",
"arguments": {
"projectPath": "/workspace/fuli",
"taskPrompt": "Fix npm publishing and reuse this project's existing release conventions."
}
}
The important part of a matched response resembles:
If entry-time task_knowledge_recall did not answer a stable project fact, search with one to four
short queries centered on an action, artifact, target system, or identifier. Do not use the full
user request verbatim as the only query:
At task end, hook mode calls checkpoint_task_knowledge: use capture_candidates only for a small,
durable, evidence-backed batch; otherwise use retain_nothing. A prompt-fallback Agent applies the
same rule and writes through capture_session_knowledge. Personal preferences set the correct
profileAspect and stable attributes.preferenceKey; project facts omit profileAspect. If
automatic capture is disabled under Settings, automatic writes return capture_disabled and no
category is created: transcripts, checkpoints, knowledge and preference candidates, knowledge usage,
view records, and feedback or corrections reported through an Agent all stop. If the capture
settings cannot be read, automatic writes stop the same way (reason: policy_unavailable). Edits
you submit yourself in the FULI console and an explicit 新项目： request still save, and reading
existing knowledge keeps working. Session turns written while automatic recording is off are skipped
with a content-free marker and are never imported after it comes back on.
Capture can also be limited to projects. projectMode is all (the default), all_except with
excludedProjectIds, or selected with selectedProjectIds; unregisteredDirectories
(skip by default under selected, capture otherwise) decides work outside every registered
project. A directory resolves to its project the same way task entry does, so a worktree of a
repository is that repository's project. A project that is not recorded returns
reason: project_off, an unregistered directory project_unregistered, and every task receipt
ends with the reason and that explicit edits still save, for example
此项目未开启记忆；你明确提交的修改仍会保存. Switching a project off never hides or
deletes what it already recorded; projectsOffSince keeps when it stopped. Agents read the
settings with get_capture_policy (pass projectPath to learn about the current project) and
change them with update_capture_policy only after restating the change and getting the user's
confirmation; the settings page uses PATCH /api/capture-policy. Saying "这次别记" in a session pauses
only that session at once, without a confirmation: the Agent calls update_capture_policy with just
sessionPaused: true and its taskContextToken, and every later receipt in the session says
本会话已暂停记录（自 HH:mm 起） (reason: session_paused). Earlier records stay, workers the session
starts are paused with it, "继续记" (sessionPaused: false) resumes from then on, and nothing written
while paused is recorded later. The pause lives in the local runtime directory and ends with the session.
To turn FULI off for one session altogether, the person types /fuli stop (Claude Code and Cursor, where
setup installs the fuli command Skill) or fuli stop (any client; Codex accepts no custom slash command).
The prompt hook answers the line itself, so the model never sees it: from then on that session gets no
injected memory, preferences or recall, its Agent's own FULI reads (searches, Agent memory, conversations)
are refused, it records nothing and starts no collaboration (workers, loans, other Agents, role work), while other sessions, the local service and existing memories stay as they are.
fuli start turns it back on within the existing settings (a pause or a stricter global, client or project
setting still applies), fuli restart also gives the next turn the full context again, and fuli status
shows the session and the default; nothing said while FULI was off is recorded later. Context already sent
to the model cannot be taken back, so isolation needs a new session. Only a task token shows which session a
tool call is for (Claude Code keeps its tool connection across /clear; Codex and Cursor connections name no
session), so while any session of a client is off FULI refuses that client's tool calls without a task token,
rather than serve memory to a session that may be the stopped one. Agents pass the taskContextToken they
already have (entry, searches and team reads take it), so other windows keep working. The same switch runs from a shell the
client started (!fuli session stop), so an Agent with shell access can run it too (the same trust as
access to your files); the console and fuli mcp --call still read for you, and fuli session default off makes new sessions start with FULI
off until started; sessions already open keep theirs.
Run these local contract tests to check that the README and project-path behavior still match the
implementation:
npm run test:node -- test/acceptance-docs.test.js test/project-path-context.test.js
Agent interaction sequence
This is the simplified lifecycle for a normal task. The Chinese
detailed sequence diagram also covers inheritance, usage counting,
write previews, and source markers.
sequenceDiagram
autonumber
actor U as User
participant A as Agent
participant L as Lifecycle integration
participant F as Fuli MCP
participant G as Local graph
U->>A: Submit a task in the current project
alt Claude Code / Codex (hook context supplied)
L->>F: begin_task_context(projectPath, taskPrompt)
F-->>A: Preferences, exact project, bounded recall, and task token
else No hook context (prompt fallback, including Cursor)
A->>F: get_collaboration_preferences(projectPath, taskPrompt)
F-->>A: Preferences, exact project, and bounded recall
end
A->>A: Inspect task_knowledge_recall
opt Historical context is needed and recall did not answer it
A->>F: search_current_project_knowledge with focused queries
F->>G: Search child first, then authorized parents
G-->>A: Bounded content, status, provenance, and history
opt Knowledge materially affects the result
A->>F: record_knowledge_usage
end
end
A->>A: Complete the implementation, validation, or answer
opt The task creates a decision, contradiction, or stale signal
A->>F: Record rationale or knowledge feedback
end
opt Something is worth keeping (candidates, a decision, a meaningful work log)
A->>F: checkpoint_task_knowledge
F->>G: capture_candidates
end
alt Loaded and trusted Claude Code / Codex Stop hook
L->>F: verify_task_checkpoint
F-->>L: Close a routine task as retain_nothing / reported, otherwise remind
else Cursor or an Agent without a Stop hook
Note over A,F: Same contract. A routine fallback task closes at the next task entry
end
A-->>U: Return the result and any Fuli sources actually used
Read-only external knowledge
One knowledge-base connection can be bound to one or more existing personal projects without
granting Fuli write access to the source. Each target keeps independent retrieval mode, sync
cursor, and error state. The Connections page supports multi-project assignment, connection
checks, manual synchronization, disconnection, and a per-project conflict switch.
Live only; for compatible endpoints or adapters in front of systems such as RAGFlow
Trusted custom code
Local ESM sync / retrieve contract
Explicitly installed trusted code, not a sandbox
Connect one or more knowledge bases to a project
One personal project can have multiple knowledge connections, and one connection can target
multiple personal projects. Each submission adds a connection instead of replacing an existing
one. Source configuration is stored once, while retrieval mode, synchronization state, and errors
are isolated per target. Use Bind projects on an existing connection to add or remove targets.
Create or confirm the target under Personal projects.
If the source needs a token, set it in the environment that starts Fuli, then start or restart
the service. The binding stores only the environment-variable name. For example, run
export PROJECT_KB_TOKEN='...' and then fl restart.
Open http://127.0.0.1:2727/connections. Under External knowledge, enter a connection name,
connector, one or more bound projects, and retrieval mode.
For MCP HTTP, enter the final endpoint URL (redirects to another origin are not followed) and an
optional token environment-variable name. For MCP stdio,
enter its command and arguments. For Notion, enter the token environment-variable name and Page
IDs or Data Source IDs. For Feishu/Lark, enter the token environment-variable name, region, and
an explicit Space ID, root-node token, or node tokens. For RAG Retrieval API, enter a
Dify-compatible endpoint, knowledge-base IDs, and an optional token environment-variable name.
A custom connector takes a trusted local ESM module and source JSON.
Select Add connection and then Check. A successful mirror or hybrid binding can be synced;
a live binding reads its source when an Agent query runs.
After creation, use Bind projects to add or remove projects and select a mode per project.
The project graph shows an External knowledge source node and a Uses external knowledge edge for
each assignment. This is a read-only projection of connection configuration. Live bodies are still
retrieved only when an Agent invokes search_connected_knowledge; showing the node does not mirror
content locally.
Retrieval modes differ as follows:
live: read the third party at query time without storing a body mirror in Fuli; the source must
be available.
mirror: manually mirror read-only content into the bound personal project and query the graph.
hybrid: use both the local mirror and live source results; recommended when the connector
supports both capabilities.
Bindings support live, mirror, and hybrid modes. Mirror content is written only to the bound
personal project as observed, pending, restricted, local-only knowledge. Credentials are read
from named environment variables and are not stored in binding JSON. Source writes are never part
of the connector contract. Documents that look like credential material are skipped individually,
reported by count, and never enter the graph or Agent context.
Conflict policy
The conflict policy is stored per personal project and applies across that project's personal
graph, all external bindings, and explicitly selected public projects:
Ask in the Agent conversation is the default. The Agent presents conflicting content, source,
and scope separately so the user can choose.
Allow an Agent decision lets the Agent select the stronger or fresher source for the current
response and requires it to explain the basis. It never confirms, invalidates, overwrites, or
writes back any knowledge.
From the project directory, an Agent can be asked: Search the current project and connected knowledge bases for the payment callback contract; list conflicting sources and ask me to decide.
search_connected_knowledge keeps personal-graph evidence, each external binding, and explicitly
selected public projects as separate source sets. Public-project aggregation is Beta. When an
Agent finds a material conflict, the default policy is to show it in the conversation and ask the
user; the optional Agent-decision policy applies to the current response only and cannot confirm,
invalidate, or rewrite any source. Direct external-to-public binding, reviewed public promotion,
scheduled sync, webhooks, and full deletion reconciliation remain TODO.
Core mechanisms covered by implementation and automated tests include:
a local personal space, exact project scope, and selective parent inheritance;
preference confirmation, deferred conflict resolution, revision history, and source markers;
bounded task-prompt recall, focused on-demand retrieval, and material-usage auditing;
task entry and completion checkpoints, plus local Claude Code, Codex, and Cursor lifecycle adapters;
decision options, rationale, and validation results supplied during initial capture;
knowledge-usage events, negative feedback, and content-generation isolation;
persistent scoped knowledge reviews with pause/resume watermarks;
common-knowledge discovery and preview-token-protected atomic promotion;
read-only multi-project MCP, Notion, Feishu/Lark, Dify-compatible RAG Retrieval API, and trusted-code bindings with connected retrieval.
New in 0.20 (public preview 0.20.0-beta, see the release notes):
Projects and people: unassigned conversations go to Astro, Jefa triages them, and when a topic keeps coming back (three conversations over at least two days) Astro asks whether to make it a project with its own people. Every project has a brief and every Agent an occupation, which Bole recruits by.
Tasks: assign without starting (backlog), and stop, retry, or review any run from the task details. A temporary failure is retried automatically only when no prompt was sent.
Decisions: an Agent that needs you asks in the conversation first. A request that must wait for you goes to the Decisions page, and your reply reaches the Agent that asked.
Roundtable: besides asks (message_agent), Agents have direct and group chats (agent_chat) with push delivery and wait; the console shows them live.
Memory between projects: weighted links (related, frequent, close) say how much one project may draw on another. Weights never grant access.
Lower token cost: a smaller task entry, shared rules read on demand, long preference lists read in pages, and a Stop hook that closes routine tasks without another model turn.
Console: six entries (Workbench, Agents, Conversations, Projects, Memory, Settings), a phone layout, and Alt/Option-click explanations for any status or term.
Windows: an installed build with Update-Fuli, Rollback-Fuli, and Uninstall-Fuli that keeps your data; the console's 「Update to vX」 downloads the release's verified Windows package and runs the same update in one click.
The following must not be presented as already proven:
the A/B thresholds in FULI_ALIGNMENT_BENCHMARK.md are acceptance conventions; claims that Fuli
reduces explanation or rework require a sufficiently large set of controlled, real paired tasks;
benchmark projects and conversations are explicitly labeled MOCK / synthetic data, not user or
production data;
the decision tool can include validation during initial capture, but it does not yet expose a
dedicated operation for appending immutable validation results to an existing Decision;
Installed files do not prove host loading, trust, or execution. Claude Code and Codex adapters
generate entry/Stop hook configuration; Codex trust review remains a user action. Cursor uses
task-entry hooks and bounded Stop reminders, not an equivalent unconditional Stop gate. Ordinary
input in each real client still requires live acceptance;
selected public projects in connected retrieval are Beta; external-to-public promotion and
background delta synchronization are not implemented.
Installation
Requirements:
Node.js 24.12 or later;
the default container mode needs Docker Compose v2 through Docker Desktop, Rancher Desktop, or a compatible runtime;
macOS and Linux can use native mode without a VM; it requires Java 21 and uv (Python 3.12);
on a memory-constrained Mac, use
fuli setup --runtime-mode native --memory-profile low --adaptive-memory. Container mode remains the compatibility default and is never changed automatically.
Install globally and initialize:
npm install --global fuli-context
fuli setup
Or use the one-line installer, which checks the Node.js version first:
curl -fsSL https://xn--8ovp9s.xn--m8txu.com/fuli/install.sh | sh
On Windows PowerShell: irm https://xn--8ovp9s.xn--m8txu.com/fuli/install.ps1 | iex.
fuli setup first shows its plan and asks for confirmation. It then checks the container runtime,
initializes local Graphiti / Neo4j, creates a personal space, installs the companion Agent Skills,
and registers the fuli MCP with detected agents. The default setup connects only the personal
Provider; it does not simulate a team-shared service.
Open the management UI after setup:
fuli open
The default URL is http://127.0.0.1:2727. On Windows, Hyper-V, WSL and winnat reserve port ranges that change
between boots (netsh interface ipv4 show excludedportrange protocol=tcp lists them). A first setup picks ports outside
those ranges, a console that cannot start on a reserved port says which range holds it, and the Windows tray's
Use another port moves the console to a free nearby port, keeps a backup of runtime-settings.json and restarts Fuli.
Open Agent sessions keep showing the old console address in Fuli links until they restart.
The Settings page manages all seven local Fuli ports, graph runtime mode, LAN access, automatic capture,
Agent access, UI language, and the resource refresh interval. Run fuli restart after saving port,
runtime, or LAN changes;
the refresh interval applies immediately. The same page polls real memory and disk usage for the
management service, Providers, Neo4j, application files, and local data. Memory is sampled on every
poll; disk usage is sampled at most once per minute, with separate timestamps. Missing container metrics
are shown as partial instead of being replaced with mock values. Native Provider and Neo4j processes
are included. Browser-tab memory is excluded; shared container-VM overhead is excluded in container mode.
CLI
The global package provides two equivalent commands: fuli and the short alias fl.
CLI help, prompts, status output, and errors are always in English and do not follow the web or
system locale. User data such as paths and space names is displayed unchanged.
Command
Purpose
fuli --help / fuli -h
List every public command and its available options
fuli --version / fuli -v
Print the installed Fuli CLI version
fuli setup [options]
Initialize the Provider, UI, agent integrations, and Skills; safe to rerun
fuli start [options]
Check Agent integrations, then start the Provider and UI; optionally open a browser, rebuild containers, or enable LAN access
fuli stop [--data-dir DIR]
Stop services without deleting data
fuli restart [options]
Restart local services using the same runtime options as start
fuli status [--json] [--data-dir DIR] [--port PORT]
Show UI, personal graph, and shared-service status; --json emits machine-readable output
fuli open [--data-dir DIR]
Open the current management UI in the default browser
fuli graph export --output DIR [--mode container|native]
Export graph data as a checksummed, copyable offline bundle
fuli graph import --input DIR [--target-mode container|native] [--yes]
Verify and replace target graph data, preserving an automatic pre-import rollback bundle
fuli graph migrate --to native [--yes]
Move a container graph to native mode, accepted only when node and relationship counts match
fuli update [setup options]
Update the npm package and refresh local integrations
fuli uninstall [--yes] [--data-dir DIR]
Remove agent integrations and services while preserving knowledge data and Neo4j volumes
The command verifies discovery, protocol version, and token scope before atomically updating the
local runtime configuration with mode 0600. It never prints the token, token-file path, or remote
principal ID. Non-loopback services must use HTTPS. The current fuli-workspace-v1 adapter enables
discovery, explicit subscriptions, and queries; publishing, contributions, and review remain off
until those protocol mappings are implemented.
start and restart accept --data-dir DIR, --personal-space NAME, --port PORT, --open,
--rebuild, --lan, and --no-lan. When port or LAN flags are omitted, the saved Settings values
are used. LAN mode listens on private IPv4 addresses and prints the reachable URLs, the
fuli username, and a temporary access code generated for that start. The default remains
loopback-only; internal Providers and Neo4j ports are never exposed by --lan. LAN mode uses
HTTP Basic Auth and is intended only for trusted home or office Wi-Fi, not public deployment.
Before starting, fuli start performs a read-only check of detected Agent MCP, Skill, Codex bootstrap,
and Claude lifecycle integrations. If any are missing or outdated, it still starts the local services
and tells you to run fuli setup; it never applies setup changes implicitly.
setup and update accept:
Option
Purpose
--yes
Skip confirmation for unattended setup
--codex-only
Configure Codex only
--data-dir DIR
Use a specific data and configuration directory
--personal-space NAME
Set the personal-space name; defaults to Personal
--port PORT
Set the management UI port; defaults to 2727
--runtime-mode container|native
Select containers or native processes; defaults to container, with native mode currently on macOS/Linux
--memory-profile low|balanced
Select Neo4j's memory budget; fresh installs default to balanced
--adaptive-memory
Enable on-demand wake and staged idle sleep, coordinated by the management service
--no-adaptive-memory
Disable idle sleep and keep graph services running
--skip-agents
Do not change agent configuration or Skills
--connected-only
Refresh only clients that already have a Fuli MCP entry; used automatically by update
--no-start
Initialize the Provider without starting the UI
--allow-provider-downgrade
Accept a lower native Provider version, for example moving from a local rc build to a public beta; back up the data folder first
--personal-only
Use only the personal Provider; this is the default
--with-dev-public
Start a development shared Provider for local integration work only
The low profile uses a 128 MiB initial heap, 256 MiB maximum heap, and 64 MiB page cache.
balanced uses 256 MiB, 512 MiB, and 256 MiB respectively. The low profile does not add a hard
container memory limit: it lowers steady memory use while leaving room for Neo4j's native memory.
It may cause more garbage collection and disk reads under bulk writes, large traversals, or high
concurrency, but it does not change transaction semantics, storage format, or data volumes. If those
workloads become slow or fail for lack of heap, run fuli setup --memory-profile balanced --yes.
The selected profile is saved and reused by later setup, update, start, and restart commands.
Adaptive memory is a lifecycle policy independent of the Neo4j memory profile. When enabled, the
lightweight management service stays available. Real MCP tools, MCP resources, and graph requests
acquire a runtime lease and wake services on demand. After the final lease is released, the personal
Provider stops after 60 idle seconds by default and Neo4j stops after 180 idle seconds. Volumes are
preserved and the next request restores the same data. Active calls renew their leases and cannot be
interrupted by idle timers; a crashed client lease expires after at most 180 seconds. These durations
are current product defaults, not measured hardware thresholds.
This mode trades idle memory for first-request cold-start latency and additional disk reads. Health,
runtime-status, and resource polling do not wake the graph, and fuli status treats intentional sleep
as healthy. The management service must remain running to wake graph services, so --no-start only
saves the policy for a later start. In container mode the policy stops only Fuli Provider and Neo4j
containers; it does not shut down Rancher Desktop, Docker Desktop, Kubernetes, or the container VM
itself. Native mode directly stops the corresponding Provider and Neo4j processes, so no shared VM
overhead remains while idle.
Jefa (project manager), Bole (AR, Agent Resources), and Tonborg (judgment) are built-in roles in every
space and are never hired. Bole's workbench shows staffing, current work, and recruitment history;
Tonborg's workbench shows decisions and feedback. Jefa's task board is built into FULI and needs no
installation; Manage assigned projects on a role's card or workbench sets which projects it manages.
The console has no hiring step: a custom employee package is recruited through the recruit_employee Agent
tool, which reuses a durable identity with separate project assignments. An installed employee workbench and
API share the FULI port; existing FULI MCP clients discover and call its tools. Recruitment does not start a
model or rewrite client settings. See the employee package and extension contract.
Agents can also grow small Agent apps; in this version an approved app runs
unsandboxed with your account's permissions, after you confirm it with fl app approve in a terminal.
Project Agent identities remain control-plane records rather than one resident process per identity.
Roles now keep versioned, project-private working memory in the same Neo4j Provider. Task entry
restores one owner across connected MCP clients; lifecycle checkpoints survive separate
MCP processes. See role memory, host hooks and acceptance boundaries.
Supported Codex and Claude Code hooks also journal visible conversations. Once unfolded messages pass
a size threshold (64 KB by default), older ones fold into a digest; recovery stays within a byte budget. Specialist loans and artifact verification share the same Provider.
See conversation continuity and collaboration
for client coverage, model capability configuration and acceptance limits.
Physical executors share leases by executor ID. Fuli starts and stops only executors with an explicitly
injected managed lifecycle adapter; host-owned external executors such as Codex are never started or
killed implicitly. The current minimum-memory combination is:
The two modes use separate data directories. Switching preserves the old mode's data and never
merges changes in the background. To migrate, export while the source mode is still active, then
install or switch to the target mode and import:
Or run the same steps as one command, from a package that has the native runtime (on Windows the
installed Fuli desktop package):
fuli graph migrate --to native
It stops the running console, counts and exports the container graph, sets up native mode, imports,
and accepts the result only when every label and relationship-type count matches. On any failure it
switches back to container mode. The container volume is only read and is left stopped, never removed.
Client integrations are not changed.
Swap container and native for the reverse direction. Export briefly stops the source database
and resumes only services that were actually running beforehand. Import validates the manifest and
every dump's SHA-256 before stopping the target. It preserves the old target under backups/graph
inside the data directory and attempts an immediate rollback if loading fails. Let active Agent graph
writes finish before either operation.
After loading, Fuli uses the target installation's bootstrap token to rotate local Provider access
credentials and atomically updates the runtime configuration; plaintext access tokens are never added
to the bundle. Unmanaged external Workspace connections are preserved for a same-device migration.
Reconnect their credentials separately after moving the bundle to another device.
The bundle is a directory containing manifest.json, personal.dump, and an optional
workspace.dump; it can be copied to another disk or machine. Container mode transfers files through
the Docker API, so the backup location does not need to be shared with Rancher. Dumps use the pinned
Neo4j 5.26 format and are intended for the two Fuli runtime modes or a compatible Neo4j 5.26 setup;
they are not a generic CSV/JSON interchange format for arbitrary databases.
Update:
fuli update
The update flow shows the running CLI, Node, PATH launchers, and npm installation target. If the
current version is already latest, it exits without reinstalling or restarting services. Otherwise,
it verifies that it will not downgrade, stops the old services, installs the latest version, and
refreshes only clients with an existing Fuli MCP entry. Existing knowledge, configuration backups,
and Neo4j volumes remain intact. Reuse any custom setup options during updates.
Reconnect Fuli in already-open clients, or restart those clients and open a new task, to load the
new MCP process. To repair existing integrations that still have a Fuli MCP entry without upgrading,
run fuli setup --yes --connected-only. Clients that are skills-only, removed, or newly added
(no Fuli MCP entry) require a full fuli setup.
Automated uninstall does not permanently delete the personal graph, which allows a later install to
reuse the same data.
Agent integrations and primary tools
Fuli installs the capturing-session-knowledge, grilling-project, operating-fuli, and flreview Skills for
supported agents. /flreview asks FULI for ranked candidates, then the invoking Agent generates a
fresh interactive review artifact. Codex rejects custom slash commands before any hook runs, so there the person
types fuli review (or flreview) on its own line; the Codex prompt hook passes that exact line to the model with
the installed Skill named, and in a session where FULI is off it answers the line itself without starting a review. FULI remains the durable source of truth and write surface; no
permanent review tab or one-question-at-a-time chat flow is required.
operating-fuli makes Agents inspect the structured interface-parity catalog before changing data,
verify writes through matching read tools, and treat browser automation as visual verification only.
See the Agent interface architecture.
Fuli setup generates Claude Code UserPromptSubmit/Stop MCP hooks, a Codex
UserPromptSubmit MCP hook plus a local Stop command, and Cursor
sessionStart/beforeSubmitPrompt/stop hooks. The Codex Stop command first blocks an unfinished
checkpoint; if the same token is still unfinished after one continuation, it records only
retain_nothing to prevent an infinite loop and never invents knowledge or Agent memory. Codex
bootstrap and Cursor instructions retain the fallback for tasks without supplied lifecycle context;
they must not duplicate supplied preferences. Installing configuration never approves host trust.
Preference content remains in local Fuli as the single source of truth and is not copied into agent
configuration.
FULI also exposes read-only fuli:// resources for each local personal project and for global taste.
Agents with MCP mention support can select them from the @ picker; a project resource selects one
exact project and never expands the search to other projects or RELATED_TO projects.
Remote Claude / Cowork connector upstream
fuli remote-mcp serves a least-privilege Streamable HTTP MCP endpoint bound to one exact personal
project. It uses the distinct claude client attribution, requires an owner-only bearer-token file,
rejects cross-origin requests, and listens on loopback only:
On POSIX systems the CLI opens the token nonblocking with no-follow semantics and requires mode
0600. Windows does not provide the same O_NOFOLLOW or POSIX mode guarantees through Node.js, so
the token must be a regular file in a directory protected by Windows ACLs and must not be a link.
Startup fails closed unless the project exists in the configured personal space. The upstream
allows at most eight concurrent sessions and evicts sessions idle for 15 minutes; tune those bounds
with --max-sessions and --session-idle-ttl-seconds when needed. Idle means no completed MCP
application request: a silent standalone SSE notification stream does not pin a slot forever and
may be closed at the TTL, after which the client must reconnect and initialize a new session.
Shutdown gives bounded requests and session cleanup one shared grace deadline before forcing the
remaining HTTP connections closed. When a reverse proxy supplies a
browser Origin or preserves its public Host, add the exact values with
--allowed-origin https://connector.example --allowed-host connector.example. Otherwise configure
the proxy to rewrite Host to the loopback upstream authority. Arbitrary Origin and Host values are
rejected. Each MCP session may read private context and memory only for the Agent selected by its
task entry. A checkpoint token is also bound to both the upstream's configured project and the MCP
session that created it, so neither another project process nor a peer session can reuse it.
This is an internal upstream, not a public deployment. Claude custom connectors originate from
Anthropic's cloud and therefore require a public HTTPS endpoint with OAuth 2.1 in front of this
listener. The proxy must validate user identity, token audience and scopes, then replace the external
token with the internal bearer; never expose the Fuli Provider or Neo4j ports. Local lifecycle hooks
do not run in claude.ai, so the MCP initialization instructions request task entry and checkpointing,
but only a real connected Claude conversation can prove that behavior.
When a task needs a taste or judgment recommendation, get_user_taste_skill generates a bounded,
read-only user-taste Skill projection from the effective personal profile and prior task data.
The projection labels evidence status and scope, returns task-matched recommendations, and is
regenerated as preferences are added or revised. It never overwrites a user-authored taste Skill;
the durable graph remains the source of truth.
Tool
Purpose
begin_task_context
Hook entry: resolve the task, run bounded recall when signaled, and create a task token
get_collaboration_preferences
Fallback entry: read effective preferences and bounded task recall
get_user_taste_skill
Generate the current evidence-labeled taste Skill projection and task recommendations
search_current_project_knowledge
Search the child first, then authorized knowledge sources
search_knowledge_graph
Run a general query within an explicit bounded scope
search_connected_knowledge
Search the graph, project-bound read-only sources, and selected public projects without merging provenance
record_knowledge_usage
Record a citation or application that materially affected the result
record_knowledge_feedback
Preserve rejection, failed validation, contradiction, or stale evidence
record_decision_trace
Store options, rejected alternatives, rationale, and optional initial validation
capture_session_knowledge
Store a small structured batch of durable candidates
checkpoint_task_knowledge
Complete review with capture_candidates or retain_nothing
discover_common_knowledge_candidates
Read-only discovery of possible parent-project knowledge
preview_common_knowledge_promotion
Preview a human-confirmed promotion
apply_common_knowledge_promotion
Apply the preview atomically with a one-time token
resolve_deferred_preference_conflict
Resolve an AI-deferred conflict only when a task needs it
start_knowledge_review
Start or resume an exact personal review scope
list_knowledge_review_candidates
Rank candidates by time, conflicts, weight, and cross-session repetition
record_knowledge_review_progress
Save keep, update, invalidate, skip, or defer outcomes
finish_knowledge_review
Pause, or complete and advance the next-review watermark
Privacy and safety boundaries
Personal knowledge is written to the local Provider.
Agents distill structured knowledge separately from private, adapter-visible conversation journals.
Tokens, cookies, private keys, credentials, private contact details, temporary logs, and raw command
output must not enter the graph.
Graphiti's remote-LLM path is disabled; embeddings are currently computed locally.
Searches are bounded by personal space and project scope instead of mixing every project.
External connectors never write to third-party sources; raw credentials are rejected from stored
binding configuration.
Shared promotion requires auditable human confirmation. Agent usage evidence can promote an item
only as far as agent_confirmed.
Fuli is not a real-time monitoring or Git service; current state must be read from the relevant
source system.
Local services
Service
Default loopback address
Availability
Fuli management UI
127.0.0.1:2727
Always
Personal Provider
127.0.0.1:8787
Always
Personal Neo4j Browser / Bolt
127.0.0.1:8060 / 127.0.0.1:7687
Always
Development shared Provider
127.0.0.1:8788
--with-dev-public only
Development shared Neo4j Browser / Bolt
127.0.0.1:7475 / 127.0.0.1:7688
--with-dev-public only
These seven ports can be changed in Settings. Provider and Neo4j bindings remain loopback-only even
when the management UI is started in LAN mode.
To access the UI from another device on the same Wi-Fi, run fuli start --lan. Starting LAN mode
from a loopback-only runtime safely restarts only the management UI and preserves Provider and
graph data. Disable LAN in Settings and run fuli restart, or run fuli restart --no-lan, to return
to loopback-only mode. Starting LAN mode again rotates the temporary access code.
If a port is occupied, Docker Compose is unavailable, or the container runtime is not running,
setup stops before modifying agent configuration and reports the cause.
npm install
npm test
npm run test:package
npm run test:external-knowledge:live
The external-knowledge live test temporarily downloads two public official documentation
repositories, validates read-only synchronization and retrieval, then removes the complete
temporary directory. It is network-dependent and is not part of the default test suite.
npm run test:package builds the web UI, creates a real npm tarball, installs it into an isolated
global prefix, and verifies fuli / fl, version output, help output, and the packaged UI. Tests,
QA screenshots, and internal design documents are excluded from the npm package.
Ainda não há lista de ferramentas. Servidores remotos recebem uma após a primeira verificação de saúde.
Perguntas frequentes
Perguntas
Como instalo Fuli Context?
Copie nesta página a configuração stdio para Cursor, Claude Desktop, Claude Code ou VS Code. Ela usa a instalação do publicador, não um proxy do PlugMCP.
Fuli Context precisa de uma chave de API?
Este registro não lista nenhuma variável de ambiente obrigatória.
Qual transporte Fuli Context usa?
Fuli Context roda localmente via stdio.
Quando Fuli Context foi verificado pela última vez?
Última verificação: 2026-11-15. Local-first collaboration relationship graph for AI agents.