The #254 fix changed resolveConventionPath to {name}-{hashCode} and folded
hyphens to underscores, which re-pathed every existing skill (browser-cdp ->
browser_cdp-<hash>) with no migration, orphaning already-created workspaces and
breaking SkillWorkspaceManagerApplyBundleTest. Drop the hash suffix and keep
hyphens: the bare Unicode-preserving sanitized name already prevents the
non-ASCII collision (distinct CJK names map to distinct dirs) and leaves ASCII
kebab-case paths identical to the legacy scheme. Add path regression tests.
Skills with non-ASCII (e.g. Chinese) names collapsed to underscores in the workspace path, so two such skills resolved to the same directory and overwrote each other. Preserve Unicode letters/digits when sanitizing the path so distinct names map to distinct directories. Also write SKILL.md with CREATE_NEW to avoid a TOCTOU race on concurrent uploads, and mount the skills workspace as a named Docker volume so packages survive container restarts.
Closes#254
updateTextContentFromScan / updateBinaryFileFromScan are only reached via
self-invocation from the ingest* methods, so the proxy-based @Transactional
never applied. Each runs a single atomic updateById; remove the misleading
annotation and document why.
Directory-scan ingestion deduped only by content hash, so when a file at a known path changed, the new hash missed the existing raw and a second row was inserted for the same source_path — both rows then generated wiki pages, accumulating duplicates. Make source path the primary dedup key: same path + same hash skips, same path + changed hash updates the existing raw in place (reset to pending, re-process), falling back to the content-hash check only for genuine copies at new paths. Reprocessing reuses the same rawId, so deleteExclusiveBySourceRawId cleans the old pages before regeneration — no duplicate rows and no duplicate pages. findBySourcePath gains LIMIT 1 to tolerate pre-existing duplicates; docs/fix-duplicate-raws.sql remediates existing data.
Closes#271
Gate MarkdownNormalizer behind mate.agent.markdown-normalize-enabled (default
true) so operators can disable the rewrite verbatim if a normalization edge
case ever mangles a legitimate answer.
LLMs routinely emit malformed Markdown (missing heading spaces, glued `---`, unaligned table pipes) that prompt rules cannot reliably prevent. Add a zero-token, regex-only MarkdownNormalizer applied on the FinalAnswerNode convergence path before persistence / channel delivery. It is code-fence aware, idempotent, and conservative (em-dash `---`, `#5`-style refs, stray prose pipes are left untouched). RETURN_DIRECT verbatim output and approval-wait paths return earlier and are unaffected.
Closes#274
Same-message attachment reads failed on Feishu: the content part carried the sandbox-external ~/.mateclaw/media/ path, so read_file/extract_document_text were rejected by WorkspacePathGuard and ChatUploadResolver's basename fallback missed (media names {messageId}_{key} vs chat-uploads {millis}_{fileName}). cacheRecentFile already copies the attachment into the per-conversation data/chat-uploads/ dir; return that absolute path and stamp it onto the current message's image/file/audio/media parts so the path surfaced to the LLM is resolver-reachable. Reuses the existing copy — no extra I/O, no new state. Rich-text post multi-image and other channels sharing the media dir are noted as follow-ups.
Closes#278
Allow knowledge base source paths to be configured as a newline-separated
list of absolute paths or glob patterns rather than a single directory.
- Each non-blank, non-# line is treated as one path or glob pattern
- Plain paths (no wildcards) retain the existing recursive-scan behaviour
filtered by SUPPORTED_EXTENSIONS
- Glob patterns (e.g. /data/ocr/**/*.txt) walk from the fixed-prefix base
and apply Java's PathMatcher against each candidate's absolute path
- Patterns whose filename segment explicitly specifies an extension
(*.txt, *.{xlsx,csv}) skip the SUPPORTED_EXTENSIONS secondary filter,
respecting the user's explicit choice (key for OCR pipelines that
produce .txt output and should ignore the original PDF scans)
- Candidates collected across multiple patterns are deduplicated by
resolved absolute path so overlapping patterns don't double-count
- Symlink-escape check uses each pattern's own validated scan root
- parseSourcePatterns / extractBasePath moved into WikiSourcePathValidator
to eliminate a static circular reference between the two services
- Frontend watcher panel: single-line <input> replaced with <textarea>
supporting multiline editing; i18n updated with example patterns
- No DB schema change; fully backward-compatible with existing single-path
configs stored in sourceDirectory
Closes #(pending issue)
finishRunAndPublish updated the run row with status and finished_at but dropped the LLM token usage, so mate_cron_job_run.token_usage stayed NULL and the scheduler history always showed 0. Write the prompt+completion token total (already computed for the assistant message row) to the run row, guarded so non-LLM paths (reminder direct-push with a null ChatResult) leave the column untouched.
Closes#262
The skill meta-tools (listAvailableSkills, readSkillFile, listSkillFiles, load_skill, runSkillScript) queried the full skill catalog without checking the calling agent's bindings, so an agent scoped to a subset of skills could still read, load, or execute any skill via direct tool calls. Resolve the agent's bound skill ids from the tool context and filter/deny accordingly: a null binding set means no restriction (backward compatible), a non-null set (including empty) restricts access to that set — matching the system-prompt catalog filtering.
Closes#264
Surface directory-scan failures to the user via toast and render ScanResult.errors[]; expose MATE_WIKI_ALLOWED_SOURCE_ROOTS as a Docker env entry with blank-entry filtering in the path validator; set C.UTF-8 locale in the runtime image so non-ASCII file names decode correctly during scans.
Fixes#259
Add a single global-proxy switch that routes the backend's outbound traffic
through a configured HTTP/HTTPS/SOCKS proxy, for deployments that cannot reach
overseas APIs directly or must use a unified egress.
- ProxyManager installs the proxy via a default ProxySelector (honored by
java.net.http and HttpURLConnection), the proxy system properties, and a
--proxy-server arg for the browser tool; restores direct egress when
disabled. One switch covers LLM, web search, media generation, channels,
MCP and the browser.
- SOCKS applies to the HttpURLConnection-based egress only; the java.net.http
LLM/streaming path uses an HTTP proxy, and the UI states this.
- New Settings -> Network Proxy page: enable toggle, address, bypass list,
test-connection, and a coverage summary. Config stored as key/value in
mate_system_setting (no migration).
refs #109
Streaming chat ran render tools on an async thread with no bound request,
so download links lost their host and arrived without a domain. Resolve the
host on the request thread and carry it through ChatOrigin/ToolContext;
falls back to a configurable public-base-url, then a relative path.
Agents now have a per-agent primary wiki KB stored on
mate_agent.primary_kb_id. KBs remain workspace-shared — selecting one in
the agent editor only chooses the default wiki target for that agent, it
does not change the KB's ownership or visibility.
Backend
- AgentEntity: add primary_kb_id field (FieldStrategy.ALWAYS so the UI
can clear it back to "no primary")
- AgentController#update: switch body to Map<String, Object> so we can
tell "field missing" apart from "explicit null" via containsKey, then
convertValue back to AgentEntity
- WikiKnowledgeBaseService:
- new resolvePrimaryKb(agentId): prefers agent.primary_kb_id when it
points to a workspace-visible KB; falls back to legacy
kb.agent_id marker, then to most-recently-updated workspace KB
- listByAgentId now returns the full workspace set (KBs are
workspace-shared under the new model)
- update(id, name, description) no longer touches agent_id
- WikiController: new GET /knowledge-bases/bindable for the UI picker;
PUT /knowledge-bases/{id} no longer reads agentId
- WikiKnowledgeBaseEntity: add FieldStrategy.ALWAYS on embeddingModelId
and configContent so explicit nulls actually unbind/clear instead of
being silently skipped by MyBatis-Plus's NOT_NULL default
- Migrations V129 (H2 + MySQL): add primary_kb_id column + index, backfill
from legacy kb.agent_id, MySQL uses INFORMATION_SCHEMA guard +
PREPARE/EXECUTE for idempotency
- WikiKnowledgeBaseServiceTest: 13 cases, all passing
Frontend
- Agents.vue: new "Knowledge Base" tab, radio-select bindable KBs
- API: listBindableKBs() + Agent.primaryKbId typed string | number | null
- IDs handled as strings throughout (Snowflake-safe)
- i18n keys for the new tab in zh-CN and en-US
The previous private-repo support inlined the access token into the
clone URL and then logged that URL on success — leaking the token to
log files, container stdout, and any IOException thrown when the clone
failed. The token also appeared in the process command line, visible
to anyone with shell access via `ps`.
Switch to git's GIT_CONFIG_COUNT/KEY/VALUE environment variables, which
inject `http.extraHeader: Authorization: Bearer <token>` into the child
process without ever touching argv or the repo URL. The URL stays
pristine, so the existing INFO log and error message are safe.
Other changes:
- Resolve token from `mateclaw.skill.github-token` property first, then
fall back to GITHUB_TOKEN env var. Keeps the original deployment
contract while letting admins manage the credential via configuration.
- Tighten the host check (prefix match on `https://github.com/` etc.)
so a crafted URL like `https://evil.com/?u=github.com/...` cannot
trick the fetcher into forwarding the token to a third party.
- Set GIT_TERMINAL_PROMPT=0 so a bad token fails fast instead of
blocking on an interactive password prompt.
stopWebSocket() only nullified the wsClient reference without calling
disconnect() on the SDK client. This left the old WebSocket connection's
pingLoop thread and ExecutorService running, leaking file descriptors
and threads on each reconnect. Over time, accumulated leaks prevented
new connections from being established, causing the Feishu channel to
silently stop receiving messages.
Fix: use reflection to access the SDK's protected `conn` field and call
close(1000) on the OkHttp WebSocket, triggering the SDK's onClosed →
disconnect() cleanup chain.
Note: oapi-sdk 2.7.1 adds a public close() method that would make this
reflection unnecessary. Consider upgrading as a follow-up.
Closes#220
IM channels (Feishu, DingTalk, WeCom, etc.) and the WebChat widget were
calling saveMessage without token usage parameters, causing promptTokens
and completionTokens to default to 0. This made the Token Statistics
module report significantly lower numbers than actual usage.
Root cause: the _usage_final event (containing promptTokens /
completionTokens) emitted by the agent graph at stream end was not being
captured in these paths, unlike ChatController's StreamAccumulator which
already handles it correctly.
Fix: capture _usage_final events in doOnNext handlers for:
- ChannelMessageRouter sync path (non-streaming IM adapters)
- ChannelMessageRouter streaming path (DingTalk, etc.)
- WebChatController SSE stream
Refs #214 (remaining String-API paths covered by follow-up).
Some OpenAI-compatible providers (LM Studio's built-in server, certain
strict-mode vLLM / SGLang deployments) reject 400 "System message must
be at the beginning" when SystemMessages appear after user / assistant
/ tool messages. The reasoning loop currently emits four SystemMessage
segments — main prompt at index 0, skill catalog inserted at index 1,
progress-ledger snapshot and stale-reminder appended at the end of
nonHistoryPrefix after the runtime-context UserMessage. The latter two
violate the strict shape, so conversations on LM Studio 400 on the
first turn (reported in #218).
Add MessageNormalizer: collects every SystemMessage in the outbound
prompt regardless of position, joins their text with a blank-line
separator, and emits a single SystemMessage at index 0. Non-system
messages keep their relative order, so AssistantMessage(tool_calls) ↔
ToolResponseMessage adjacency is preserved verbatim (required by strict
pair validators).
Wire it into doStreamCall as the first pre-egress step so every node
(reasoning, step-execution, summarizing, plan-generation, limit-exceeded)
inherits the fix without per-node changes, and any future node that
emits multiple SystemMessages stays compliant.
The transformation is semantically equivalent on permissive providers
(OpenAI, DashScope, Ollama, DeepSeek, Kimi, Doubao, GLM) — the merged
token sequence matches what they would have seen across N SystemMessages
— and safe on non-OpenAI protocols (Anthropic, Vertex / Gemini), whose
adapters already extract SystemMessages into a top-level system field
and receive an identical payload.
Kill switch: -Dmateclaw.llm.message-normalizer.enabled=false reverts to
the prior behavior for emergency rollback.
Tests: 11 unit tests on MessageNormalizer cover empty / no-system /
canonical / mid-list / tail / blanks / tool-pair preservation / Prompt
option-reference preservation / kill switch. 1 wiring test pins the
call site in doStreamCall. Full vip.mate.agent.** suite (504 tests)
stays green.
Closes#218.
1. Relative parent traversal in shell commands (HIGH)
validateShellCommand only scanned absolute path tokens, so commands
like `cat ../mateclaw/CLAUDE.md`, `cd .. && cat foo`, or
`ln -sf ../bar breakout` had no absolute path to trip the check.
From a workspace cwd that's a real escape — `..` segments resolve
against the JVM cwd at file-tool time and reach anywhere the user
can read.
Add a second pass: any token containing `..` as a path segment is
resolved against the workspace root via root.resolve(token).
normalize(); reject when the result falls outside. In-workspace
traversal like `subdir/../sibling` normalizes back inside and
passes. Identifiers without slashes (e.g. version strings with
`1.2..3`) are not treated as paths.
2. Shell validation and process working directory used different
context sources (MEDIUM)
execute_shell_command validated with the explicit ToolContext, but
buildShellProcess called WorkspacePathGuard.getWorkingDirectory()
(no-arg), which only sees the ThreadLocal fallback. Today the
ToolExecutionExecutor sets both so the discrepancy is latent, but
a future direct Spring AI invocation passing only ToolContext would
validate against one basePath and exec against another. Thread ctx
through buildShellProcess and call getWorkingDirectory(ctx) so
validation and execution agree on a single source of truth.
3. Absolute agent override could disable workspace scoping (MEDIUM)
resolveAgentBasePath accepted an absolute override verbatim, even
when it pointed outside the workspace root. An admin (or any
account with agent-edit permission) could set workspaceBasePath="/"
or another team's repo and bypass workspace boundaries entirely.
When a workspace has its own basePath, require absolute overrides
to sit underneath it. The caller in build() catches the rejection,
logs WARN, and falls back to the workspace basePath so chat stays
available rather than crashing agent construction. When the
workspace has no basePath there's no boundary to enforce, so legacy
behavior is preserved.
Test coverage: WorkspacePathGuardShellTest grows from 17 to 23 (six
new cases for `cd ..`, relative parent traversal, relative symlink
escape, deeper traversal, in-workspace normalization, and the
identifier false-positive guard). AgentGraphBuilderBasePathResolutionTest
grows from 7 to 10 (three new cases for in-workspace absolute,
outside-workspace absolute rejection, and no-workspace legacy
behavior). All 45 sandbox-area tests pass with no regressions.
* feat(agent): optional agent-level workspace basePath override
Add workspaceBasePath field to AgentEntity that optionally overrides
the workspace-level basePath. When set, the agent uses its own directory;
when null, it inherits the workspace's basePath (existing behavior).
- AgentEntity: new workspaceBasePath field with ALWAYS update strategy
- AgentGraphBuilder: agent-level override takes priority over workspace
- Flyway migration V121 for H2 and MySQL
- UI: form input in basic tab with i18n (zh-CN, en-US)
* fix(agent): rename migration V121→V125 to avoid Flyway conflict with upstream
Upstream already has V121__tool_disclosure_tier.sql. Rename our
migration to V125 (next available after V124).
* fix(agent): make MySQL V125 migration idempotent
Use INFORMATION_SCHEMA check before ADD COLUMN to avoid
"Duplicate column name" error on re-deploy.
* feat(tool): add send_file tool for sending existing server files as IM attachments
Adds a new built-in tool that reads a file from the server and stashes it
in GeneratedFileCache so the channel adapter (Feishu, DingTalk, etc.)
automatically sends it as a native attachment. This fills the gap where
agents had no way to send existing server files to users — ReadFileTool
only reads text, and render tools only generate new files.
- New SendFileTool with path validation, MIME detection, 20MB limit
- Added "send_file" to tool allowlist in AgentBindingService
- Added i18n error messages (zh-CN + en-US)
* fix(tool): send_file returns URL in scrubber-detectable format
The previous JSON return format caused the LLM to reply with just
"status: sent" without echoing the /api/v1/files/generated/{id} URL.
GeneratedFileScrubber only scans the LLM's final text output, so the
file was never delivered as a native attachment.
Changed to match GeneratedFileLink's format: returns a markdown link
with explicit instructions for the LLM to echo the URL verbatim.
The Lark SDK throws HandlerNotFoundException for any event type without
a registered handler. This exception is caught internally by the SDK's
WebSocket client, which then sends a 500 response to the Feishu server.
The server may close the connection as a result, and the exception is
swallowed — never reaching the application layer.
Added empty handlers for all remaining IM event types:
- P2MessageReadV1 (read receipts)
- P2MessageRecalledV1 (message recall)
- P2ChatMemberBotDeletedV1 (bot removed from chat)
- P2ChatMemberUserAddedV1 / UserDeletedV1 / UserWithdrawnV1
- P2ChatUpdatedV1 (chat info update)
- P2ChatDisbandedV1 (chat disbanded)
- P2ChatAccessEventBotP2pChatEnteredV1 (bot entered p2p chat)
Also added explicit logback config for com.lark.oapi at WARN level
to ensure SDK internal errors are not silently filtered.
Refs: larksuite/oapi-sdk-java#185
Some providers (notably SiliconFlow) return "network connection error" in the response body when their backend is overloaded or the upstream model connection is disrupted. classifyError() had no pattern for this string, so it fell through to UNKNOWN (non-retryable), surfacing the raw error to the user on the first failure instead of running the exponential-backoff recovery. Adds the pattern to the SERVER_ERROR classifier and a friendly message mapping in extractUserFriendlyError(); bumps MAX_RETRIES from 5 to 10 so sustained wiki batch load can ride out provider flaps without surfacing an error to the channel user.
Closes#178
Closes#174
Model identifiers like 'Qwen/Qwen3-Embedding-8B' or
'Pro/deepseek-ai/DeepSeek-V3' carry forward slashes that Spring MVC
decodes from %2F before path matching, so even with the frontend's
encodeURIComponent the request never reaches the handler and 404s out.
The two affected endpoints take modelId as a request param instead:
DELETE /{providerId}/models/{modelId} -> DELETE /{providerId}/models?modelId=...
POST /{providerId}/models/{modelId}/test -> POST /{providerId}/models/test?modelId=...
modelApi.removeProviderModel / testModel in the UI follow suit, passing
the id via axios params so axios handles the URL encoding consistently.
providerId stays as a path variable — provider ids are kebab-case and
never contain slashes.
Closes#175
ModelConfigController.testEmbedding() previously caught and stringified
the exception's getMessage() into the response body without writing
anything to the server log. Operators investigating an Embedding test
failure saw only the truncated client-side message — root causes like
the DashScope-native vs OpenAI-compat routing bug (#166) or the
requireApiKey gap (#167) were invisible server-side.
Add @Slf4j to the controller and log.error the full stack trace
alongside the failing modelId, so future Embedding test regressions are
diagnosable from the server log without redeploying with debug
breakpoints.
Closes#169
ModelConfigService.validateModel() flagged a duplicate when re-adding a
manually-typed (provider, modelName) pair that happened to match a row
with deleted=1 in mate_model_config. The user-visible symptom: adding
'dashscope/qwen3-plus' fails with 'model identifier already exists',
yet the management page shows no such model.
The project itself runs hard-delete via deleteById(), so the user-facing
delete path doesn't create deleted=1 rows. The stale rows come from
schema migrations (V44, V81) that intentionally tombstone bogus catalog
entries — for instance V81 sets deleted=1 on the non-existent
'qwen3-plus' (id=1000000172) so it stays out of routing but preserves
the id for audit. ModelConfigEntity has no @TableLogic, and the project
has no global logic-delete-field config, so LambdaQueryWrapper queries
do not auto-append the deleted filter; the migration tombstones leak
into the validate-model query.
Add an explicit .eq(getDeleted, 0) to the uniqueness check so migration
tombstones don't block legitimate re-adds.
Follow-up: several other queries in ModelConfigService share the same
oversight (list/get methods), and a future migration could drop the
tombstones entirely to align with the V20 hard-delete posture.
Closes#168
The native DashScope provider exposes both chat and embedding models, but
DASHSCOPE_NATIVE_ALLOW_PREFIXES only listed chat families
(qwen-/qwen2-/qwen3-/deepseek-/baichuan/yi-/llama). When a user manually
added text-embedding-v1/v2/v3/v4 to the dashscope provider,
assertModelIdAcceptable() rejected the id because no allow prefix matched.
Add 'text-embedding-' to the allow-list and broaden the doc comment from
"native chat protocol" to "native protocol (chat or embedding)" so the
intent is clear.
Discovery probing is chat-based and will still mark embedding entries
probeOk=false; surfacing them as discoverable embedding suggestions is a
separate follow-up.
Closes#167
EmbeddingModelFactory.buildOpenAi() hard-failed on any provider whose API
key was empty or unusable, so keyless providers like Ollama and OpenCode
(declared with requireApiKey=false) could pass the chat connectivity test
but bounce when the same provider's embedding model was tested.
Mirror the chat path in OpenAiCompatibleChatModelBuilder.buildOpenAiApi:
- If requireApiKey is not explicitly false, an unusable key still throws.
- If requireApiKey == false, the key check is skipped and an empty string
is passed to OpenAiApi.builder() so no Authorization: Bearer header is
attached to the outgoing request.
Closes#166
EmbeddingModelFactory used EmbeddingProtocol.fromProviderId() to pick the
embedding protocol, which substring-matches 'dashscope' / 'qwen' / 'aliyun'
in the providerId. The dashscope-compat provider carries 'dashscope' in its
id but runs in OpenAI compatible mode (chatModel='OpenAIChatModel',
baseUrl='https://dashscope.aliyuncs.com/compatible-mode/v1'). Routing it to
DASHSCOPE_EMBEDDING made DashScopeApi build its native path against the
compat base, producing 404s on every embedding call.
Switch to the chatModel column instead — the same signal ModelProtocol
.fromChatModel() uses for the chat path. chatModel='DashScopeChatModel'
takes the native protocol; everything else (including dashscope-compat)
takes OpenAI-compatible.
EmbeddingProtocol.fromProviderId() is retained for reference but is no
longer called; future callers should follow the chatModel pattern.
Closes#162
require_mention=true previously degraded to a no-op when botPrefix was unset:
shouldProcess() returned true for all messages and checkAccess() fell through
unconditionally, so any group message would be answered — including ones where
the @mention targeted another user.
FeishuChannelAdapter now consults the Feishu SDK's mentions field directly:
- WebSocket: read EventMessage.getMentions(); webhook: read mentions[] from the
JSON payload. In both paths each mention's id.open_id is compared against the
bot's own open_id.
- Bot open_id is fetched lazily via /open-apis/bot/v3/info and cached on the
adapter instance. If the call fails the message is allowed through, matching
the previous behaviour.
- The require_mention gate is applied at the top of handleFeishuMessage so 1:1
chats are unaffected.
Tests: 15 unit cases covering null/empty inputs, bot mentioned, only-other
mentioned, bot among multiple mentions, and malformed payloads.
