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13 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Aari
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feb287077e
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fix(security): html-escape memory context summaries rendered into the injection prompt (#4119) | ||
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Aari
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158c4f9622
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fix(security): html-escape memory facts rendered into the injection prompt (#4097)
* fix(security): html-escape memory facts rendered into the injection prompt
The lead-agent system prompt declares the <memory> block user-managed and
everything else framework-internal, but the injection renderer _format_fact_line
formats a fact's content, category and correction sourceError raw. Memory is
user-editable via /api/memory, so a fact whose content is
'</memory></system-reminder>...' closes the block and relocates the text after
it out of the user-managed trust zone.
Escape those three fields at render time, mirroring the MEMORY_UPDATE_PROMPT
escaping added for the update-prompt side in #4028/#4060. The fact dict is not
mutated, so stored memory keeps the raw value and the apply path is unaffected.
* fix(memory): stop entity-encoding quotes in injected fact text
The three html.escape() calls in _format_fact_line used the default
quote=True, which also converts " to " and ' to '. These fields
are rendered as element text inside the <memory> block, never inside an
attribute value, so escaping quotes buys no defense here: only <, >, and &
can break out of the surrounding tags, and those are escaped either way.
Ordinary facts ("User's preference", 'Said "use Python"') reached the model
as User's preference / Said "use Python" -- content the
lead-agent prompt declares as user-managed data the model should discuss
freely. Pass quote=False and extend the benign-content test, which used a
string with no quotes and so never exercised this path.
Note the escape in updater.py's consolidation_candidates block renders into
an XML attribute value and correctly keeps quote=True.
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Daoyuan Li
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a2a949b178
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Fix UnboundLocalError in memory injection when facts are empty (#3992)
`format_memory_for_injection` bound `facts_header` / `all_fact_lines` only inside the `if isinstance(facts_data, list) and facts_data:` block, but the structure-aware overflow-truncation path at the end of the function references both unconditionally. When a user's memory has sizeable user-context / history (so `sections` is non-empty and the assembled output exceeds `max_tokens`) but an empty or missing `facts` list, that block is skipped, so the truncation branch hits `UnboundLocalError: cannot access local variable 'all_fact_lines'` and aborts memory injection entirely. Hoist the two initializers to function scope, alongside the existing `guaranteed_line_tokens = 0`, so they are always bound. Behaviour is unchanged when facts are present. Adds a regression test (empty facts + oversized user context) that fails with UnboundLocalError before the fix and truncates gracefully after. |
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Tianye Song
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9097642658
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feat(memory): add memory consolidation to synthesize fragmented facts (#3996)
* feat(memory): add memory consolidation to synthesize fragmented facts When a fact category accumulates many individual entries, the LLM reviews them during the normal memory-update call (same invocation, no extra API cost) and decides whether groups of related facts can be synthesized into a single richer fact. This completes the memory lifecycle: extraction → guaranteed injection → staleness review → consolidation. - Select fragmented categories by min-facts threshold, surface the most fragmented groups first; prompt-layer caps aligned with apply-layer guardrails so the LLM never sees groups it cannot act on - Cap consolidated confidence at source maximum to prevent inflation; reject results below fact_confidence_threshold - Double-consume protection prevents a fact from being merged into multiple consolidation targets - Feature-gated at both prompt and apply time with per-cycle safety caps - Add 26 tests covering candidate selection, normalization, apply guardrails, and prompt integration * fix(memory): address consolidation correctness issues from PR review Six fixes based on maintainer review of #3996: 1. Deduplicate sourceIds in normalization — ["f1","f1"] previously bypassed the ≥2-distinct-sources check; dict.fromkeys collapses it to ["f1"] which is correctly rejected. 2. Run consolidation after max_facts trim — previously, sources were deleted then the merged fact could be evicted by the trim, leaving no record of either. Moving consolidation last ensures source facts exist in the post-trim index before removal. 3. Fix count= attribute in consolidation prompt — advertised the full category size but listed only max_sources IDs; now uses min(len(group), max_sources) to match what the LLM can act on. 4. Exempt staleness_protected_categories from consolidation candidates — mirrors the existing staleness-review contract so correction facts are never surfaced for merging. 5. Strip and default category in consolidation normalization — " " or " preference " are now normalised, matching _normalize_memory_update_fact. 6. Propagate sourceError from source facts into consolidated fact — correction context is no longer silently lost on merge. * fix(memory): add apply-time guardrails and tests for consolidation P1: mirror the staleness-pass defense-in-depth pattern — build allowed_source_ids from _select_consolidation_candidates at apply time so a protected-category or below-threshold fact proposed by the LLM is rejected regardless of model behavior. P2a: test that LLM-returned confidence is capped at max source confidence and that a capped result below fact_confidence_threshold is rejected. P2b: test that factsToConsolidate with consolidation_enabled=False is a no-op at apply time (35 tests, all pass). * fix(memory): address three correctness issues from second review round 1. Default consolidation_enabled=False — consolidation is lossy (source content is permanently replaced, only consolidatedFrom IDs preserved); new lossy features default to off. config.example.yaml updated to match. 2. Unify confidence coercion between prompt and apply — _build_consolidation_section now calls _coerce_source_confidence(fact) instead of an inline 0.0-default coercion, so a null-confidence fact renders with 0.50 in the LLM prompt and is capped at 0.50 at apply time (same value, same function). 3. Preserve staleness clock on merge — consolidated fact now carries the newest source's createdAt (not now) so aged information does not gain a fresh staleness-review window just by being consolidated; consolidatedAt is added as an explicit audit field. Three regression tests added (default=false, null-confidence consistency, createdAt policy); all guardrail tests now set consolidation_enabled=True explicitly so they test the guardrail, not the feature flag. 38 tests pass. * fix(memory): harden createdAt comparison and confidence handling 1. createdAt max via _parse_fact_datetime — replaces string max() which crashes on non-string createdAt (numeric unix timestamps) and sorts Z/+00:00 mixed formats incorrectly. Mirrors how staleness computes age. 2. Remove dead min(..., 1.0) — _coerce_source_confidence already clamps each source confidence to [0, 1], so max(source_confidences) ≤ 1.0 by contract; the outer min could never bind. 3. Clamp raw_llm_conf to [0, 1] before applying the source cap — out-of- range values like 1.5 are safe today (pinned by the cap) but defensively clamped first so the invariant holds even if the cap is ever loosened. 4. Doc: expand the apply-time guardrails comment to call out the protected- category exclusion via allowed_source_ids — this is the central safety property ("explicit user feedback is never silently merged away"). 5. Test: add test_confidence_fallback_to_max_source_when_llm_omits_field covering the else-branch (LLM omits confidence → uses max_source_conf). 6. Fix lint: reorder imports in test file (stdlib before third-party). 39 tests, all pass. --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
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Tianye Song
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28f2b07b79
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feat(memory): add staleness review to prune silently-outdated facts (#3860)
* feat(memory): add staleness review to prune silently-outdated facts
Facts created long ago may become outdated without any future conversation
explicitly contradicting them ("Silent Staleness"). This adds a staleness
review mechanism that surfaces aged facts to the LLM during the normal
memory-update call so it can semantically judge whether each is still valid.
- New MemoryConfig fields: staleness_review_enabled, staleness_age_days,
staleness_min_candidates, staleness_max_removals_per_cycle,
staleness_protected_categories
- New STALENESS_REVIEW_PROMPT section injected into MEMORY_UPDATE_PROMPT
when enough stale candidates exist
- New staleFactsToRemove output field in the LLM response schema
- Safety cap limits max removals per cycle, keeping lowest-confidence
entries when the LLM returns more than the cap
- Correction facts (category=correction) are protected by default
- Observability via structured logging of each removal with reason
- 32 unit tests covering parsing, selection, triggers, formatting,
normalization, safety cap, and integration
* fix(memory): add deterministic guardrail for staleness removals
_apply_updates previously removed any fact id the LLM returned in
staleFactsToRemove without verifying it was in the actual staleness
candidate set. An LLM slip could silently delete protected-category
facts (e.g. correction) or fresh facts, defeating the stated guarantee.
Now intersect stale_ids_to_remove with _select_stale_candidates before
the safety cap, making the protection independent of both model behavior
and the staleness_review_enabled flag.
Add three regression tests:
- test_protected_category_fact_refused_at_apply
- test_non_aged_fact_refused_at_apply
- test_guardrail_runs_when_staleness_review_disabled
* docs(memory): sync AGENTS.md staleness config + simplify datetime parsing
Address reviewer feedback from PR #3860:
- Add staleness workflow step and 5 new config fields to backend/AGENTS.md
- Simplify _parse_fact_datetime: drop manual Z→+00:00 replace, Python 3.12+ fromisoformat handles Z natively
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
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Tianye Song
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b990da785f
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feat(memory): add guaranteed injection for correction facts with graceful fallback (#3592)
* feat(memory): add guaranteed injection for correction facts with graceful fallback When the token budget is tight, high-value facts (e.g. user corrections) can be silently evicted by lower-priority regular facts. This change: - Introduces configurable 'guaranteed_categories' (default: [correction]) whose facts draw from a separate 'guaranteed_token_budget', ensuring they are never dropped due to budget pressure. - Adds a graceful fallback to confidence-only ranking when the guaranteed-category path raises an unexpected exception. - Refactors fact selection into a header-agnostic helper (_select_fact_lines) with explicit token accounting in the caller, eliminating double-counting of separators. - Emits a single 'Facts:' header regardless of whether both guaranteed and regular facts are present. - Extends the final safety truncation limit to account for the additional guaranteed budget so guaranteed facts survive end-to-end. * refactor(memory): address review feedback on guaranteed injection - Restore strict break-on-overflow in `_select_fact_lines` to preserve the caller's confidence-ordered ranking; add a regression test locking in the invariant that a shorter lower-confidence fact never slips ahead of a skipped higher-confidence one. - Account for the inter-group `\n` separator between guaranteed and regular fact blocks in the regular budget (1-token precision fix). - Clarify docstrings on `format_memory_for_injection` and `MemoryConfig.guaranteed_token_budget` to distinguish the common *displacement* case (total stays within `max_tokens`) from the rarer *additive* case (safety-truncation ceiling raised when guaranteed lines alone would overflow). * fix(memory): address P1 safety truncation + P2s from review - Structure-aware safety truncation: Facts block is now a protected suffix so guaranteed-category facts can never be silently discarded by a prefix-cut on overflow. Only the preceding (user/history) sections are eligible for truncation. - Extend the same protected-suffix treatment to the except/fallback path by returning fact lines alongside the formatted section from _fallback_format_facts, avoiding string parsing. - Single inter-section separator: facts section no longer embeds its own leading \n\n; the final "\n\n".join(sections) is the single source of truth for section-to-section spacing. - Bare string for guaranteed_categories now raises TypeError instead of silently iterating single characters. - Category-less / malformed facts no longer default-promote into the guaranteed "context" pool — only facts with an explicit category field qualify. - Lift valid_facts pre-filter outside the try so the fallback path reuses it instead of re-doing validation work. - MemoryConfigResponse + DeerFlowClient.get_memory_config now expose guaranteed_categories / guaranteed_token_budget. - config.example.yaml: document the two new fields and bump config_version from 12 to 13. - Add regression tests for every finding. --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
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Ryker_Feng
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167ef4512f
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feat(memory): add memory.token_counting config to avoid tiktoken network dependency (#3429) (#3465)
* feat(memory): add memory.token_counting config to avoid tiktoken network dependency (#3429) Add a `memory.token_counting` option (`tiktoken` | `char`) so deployments in network-restricted environments can opt out of tiktoken entirely. In `char` mode the memory-injection budget uses a network-free character-based estimate and never triggers the BPE download from openaipublic.blob.core.windows.net, which could otherwise block for tens of minutes (see #3402). Also harden the default `tiktoken` path: - cache an in-flight LOADING sentinel so concurrent callers fall back immediately instead of spawning more blocking get_encoding threads when the first load is still running (e.g. under the 5s startup warm-up timeout); - cache failures with a timestamp and retry after a cooldown so a transient network outage self-heals back to accurate counting without a restart; - skip startup warm-up entirely in char mode. The new config is surfaced via the memory config API and config.example.yaml (config_version bumped). Default remains `tiktoken`, so existing deployments are unaffected. * fix(memory): use CJK-aware char token estimate and address review feedback - Replace the flat len(text)//4 fallback with a CJK-aware estimate so Chinese/Japanese/Korean memory content does not over-fill the injection budget - Document the internal tiktoken retry cooldown and char-mode escape hatch - Sync CLAUDE.md / config.example.yaml / MEMORY_IMPROVEMENTS.md wording - Fix MemoryConfigResponse mocks/assertions and add CJK estimate tests |
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Willem Jiang
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519200728a
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fix(middleware): offload memory injection off event loop to prevent tiktoken blocking (#3402) (#3411)
* fix(middleware): offload memory injection off event loop to prevent tiktoken blocking (#3402) DynamicContextMiddleware.abefore_agent() called _inject() synchronously on the asyncio event loop. The first time memory is injected (second request), _inject() → format_memory_for_injection() → _count_tokens() → tiktoken.get_encoding("cl100k_base") needs to download the BPE data from openaipublic.blob.core.windows.net. In network-restricted environments this download blocks until the OS TCP timeout (~26 min), starving ALL concurrent handlers including /api/v1/auth/me. Fix: - abefore_agent now uses asyncio.to_thread(self._inject, state) so file I/O and tiktoken never block the event loop. - Extract _get_tiktoken_encoding() with a module-level cache so tiktoken.get_encoding() is called at most once per encoding name. - Add warm_tiktoken_cache() startup helper; gateway lifespan pre-warms the cache via asyncio.to_thread so the first request never triggers a cold download. - _count_tokens falls back to len(text) // 4 on any encoding failure. Tests: - tests/test_tiktoken_cache_and_count_tokens.py (12 tests): cache hit/miss, fallback paths, warm-up helper. - tests/blocking_io/test_dynamic_context_middleware.py (2 tests): Blockbuster gate verifies abefore_agent does not block the event loop; async/sync parity check. Fixes #3402 * Apply suggestions from code review Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> * fix the lint error * fix(memory): use future annotations to avoid NameError when tiktoken is absent Add `from __future__ import annotations` to prompt.py so that tiktoken.Encoding type hints are never evaluated at runtime. Without this, environments where tiktoken is not installed could raise NameError on the module-level cache and function return annotations. Addresses Copilot review comment on PR #3411. * fix(middleware): bound abefore_agent injection with timeout to prevent hung requests Wrap the asyncio.to_thread(self._inject) offload in asyncio.wait_for() with a 5-second cap. If the startup warm-up failed silently (e.g. network blip during deploy), a cold tiktoken BPE download on the first request can block until the OS TCP timeout (~26 min). The bounded timeout ensures the request degrades gracefully (no memory/date context for that turn) rather than hanging. Adds test_abefore_agent_returns_none_on_timeout to the blocking-IO regression anchors. Addresses review feedback from xg-gh-25 on PR #3411. --------- Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> |
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ppyt
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5664b9d413
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fix: inject longTermBackground into memory prompt (#1734)
The format_memory_for_injection function only processed recentMonths and earlierContext from the history section, silently dropping longTermBackground. The LLM writes longTermBackground correctly and it persists to memory.json, but it was never injected into the system prompt — making the user's long-term background invisible to the AI. Add the missing field handling and a regression test. Co-authored-by: ppyt <14163465+ppyt@users.noreply.github.com> |
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AochenShen99
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0cdecf7b30
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feat(memory): structured reflection + correction detection in MemoryMiddleware (#1620) (#1668)
* feat(memory): add structured reflection and correction detection * fix(memory): align sourceError schema and prompt guidance --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
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DanielWalnut
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d119214fee
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feat(harness): integration ACP agent tool (#1344)
* refactor: extract shared utils to break harness→app cross-layer imports Move _validate_skill_frontmatter to src/skills/validation.py and CONVERTIBLE_EXTENSIONS + convert_file_to_markdown to src/utils/file_conversion.py. This eliminates the two reverse dependencies from client.py (harness layer) into gateway/routers/ (app layer), preparing for the harness/app package split. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * refactor: split backend/src into harness (deerflow.*) and app (app.*) Physically split the monolithic backend/src/ package into two layers: - **Harness** (`packages/harness/deerflow/`): publishable agent framework package with import prefix `deerflow.*`. Contains agents, sandbox, tools, models, MCP, skills, config, and all core infrastructure. - **App** (`app/`): unpublished application code with import prefix `app.*`. Contains gateway (FastAPI REST API) and channels (IM integrations). Key changes: - Move 13 harness modules to packages/harness/deerflow/ via git mv - Move gateway + channels to app/ via git mv - Rename all imports: src.* → deerflow.* (harness) / app.* (app layer) - Set up uv workspace with deerflow-harness as workspace member - Update langgraph.json, config.example.yaml, all scripts, Docker files - Add build-system (hatchling) to harness pyproject.toml - Add PYTHONPATH=. to gateway startup commands for app.* resolution - Update ruff.toml with known-first-party for import sorting - Update all documentation to reflect new directory structure Boundary rule enforced: harness code never imports from app. All 429 tests pass. Lint clean. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * chore: add harness→app boundary check test and update docs Add test_harness_boundary.py that scans all Python files in packages/harness/deerflow/ and fails if any `from app.*` or `import app.*` statement is found. This enforces the architectural rule that the harness layer never depends on the app layer. Update CLAUDE.md to document the harness/app split architecture, import conventions, and the boundary enforcement test. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat: add config versioning with auto-upgrade on startup When config.example.yaml schema changes, developers' local config.yaml files can silently become outdated. This adds a config_version field and auto-upgrade mechanism so breaking changes (like src.* → deerflow.* renames) are applied automatically before services start. - Add config_version: 1 to config.example.yaml - Add startup version check warning in AppConfig.from_file() - Add scripts/config-upgrade.sh with migration registry for value replacements - Add `make config-upgrade` target - Auto-run config-upgrade in serve.sh and start-daemon.sh before starting services - Add config error hints in service failure messages Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix comments * fix: update src.* import in test_sandbox_tools_security to deerflow.* Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: handle empty config and search parent dirs for config.example.yaml Address Copilot review comments on PR #1131: - Guard against yaml.safe_load() returning None for empty config files - Search parent directories for config.example.yaml instead of only looking next to config.yaml, fixing detection in common setups Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: correct skills root path depth and config_version type coercion - loader.py: fix get_skills_root_path() to use 5 parent levels (was 3) after harness split, file lives at packages/harness/deerflow/skills/ so parent×3 resolved to backend/packages/harness/ instead of backend/ - app_config.py: coerce config_version to int() before comparison in _check_config_version() to prevent TypeError when YAML stores value as string (e.g. config_version: "1") - tests: add regression tests for both fixes Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix: update test imports from src.* to deerflow.*/app.* after harness refactor Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * feat(harness): add tool-first ACP agent invocation (#37) * feat(harness): add tool-first ACP agent invocation * build(harness): make ACP dependency required * fix(harness): address ACP review feedback * feat(harness): decouple ACP agent workspace from thread data ACP agents (codex, claude-code) previously used per-thread workspace directories, causing path resolution complexity and coupling task execution to DeerFlow's internal thread data layout. This change: - Replace _resolve_cwd() with a fixed _get_work_dir() that always uses {base_dir}/acp-workspace/, eliminating virtual path translation and thread_id lookups - Introduce /mnt/acp-workspace virtual path for lead agent read-only access to ACP agent output files (same pattern as /mnt/skills) - Add security guards: read-only validation, path traversal prevention, command path allowlisting, and output masking for acp-workspace - Update system prompt and tool description to guide LLM: send self-contained tasks to ACP agents, copy results via /mnt/acp-workspace - Add 11 new security tests for ACP workspace path handling Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * refactor(prompt): inject ACP section only when ACP agents are configured The ACP agent guidance in the system prompt is now conditionally built by _build_acp_section(), which checks get_acp_agents() and returns an empty string when no ACP agents are configured. This avoids polluting the prompt with irrelevant instructions for users who don't use ACP. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix lint * fix(harness): address Copilot review comments on sandbox path handling and ACP tool - local_sandbox: fix path-segment boundary bug in _resolve_path (== or startswith +"/") and add lookahead in _resolve_paths_in_command regex to prevent /mnt/skills matching inside /mnt/skills-extra - local_sandbox_provider: replace print() with logger.warning(..., exc_info=True) - invoke_acp_agent_tool: guard getattr(option, "optionId") with None default + continue; move full prompt from INFO to DEBUG level (truncated to 200 chars) - sandbox/tools: fix _get_acp_workspace_host_path docstring to match implementation; remove misleading "read-only" language from validate_local_bash_command_paths Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(acp): thread-isolated workspaces, permission guardrail, and ContextVar registry P1.1 – ACP workspace thread isolation - Add `Paths.acp_workspace_dir(thread_id)` for per-thread paths - `_get_work_dir(thread_id)` in invoke_acp_agent_tool now uses `{base_dir}/threads/{thread_id}/acp-workspace/`; falls back to global workspace when thread_id is absent or invalid - `_invoke` extracts thread_id from `RunnableConfig` via `Annotated[RunnableConfig, InjectedToolArg]` - `sandbox/tools.py`: `_get_acp_workspace_host_path(thread_id)`, `_resolve_acp_workspace_path(path, thread_id)`, and all callers (`replace_virtual_paths_in_command`, `mask_local_paths_in_output`, `ls_tool`, `read_file_tool`) now resolve ACP paths per-thread P1.2 – ACP permission guardrail - New `auto_approve_permissions: bool = False` field in `ACPAgentConfig` - `_build_permission_response(options, *, auto_approve: bool)` now defaults to deny; only approves when `auto_approve=True` - Document field in `config.example.yaml` P2 – Deferred tool registry race condition - Replace module-level `_registry` global with `contextvars.ContextVar` - Each asyncio request context gets its own registry; worker threads inherit the context automatically via `loop.run_in_executor` - Expose `get_deferred_registry` / `set_deferred_registry` / `reset_deferred_registry` helpers Tests: 831 pass (57 for affected modules, 3 new tests) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(sandbox): mount /mnt/acp-workspace in docker sandbox container The AioSandboxProvider was not mounting the ACP workspace into the sandbox container, so /mnt/acp-workspace was inaccessible when the lead agent tried to read ACP results in docker mode. Changes: - `ensure_thread_dirs`: also create `acp-workspace/` (chmod 0o777) so the directory exists before the sandbox container starts — required for Docker volume mounts - `_get_thread_mounts`: add read-only `/mnt/acp-workspace` mount using the per-thread host path (`host_paths.acp_workspace_dir(thread_id)`) - Update stale CLAUDE.md description (was "fixed global workspace") Tests: `test_aio_sandbox_provider.py` (4 new tests) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(lint): remove unused imports in test_aio_sandbox_provider Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix config --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> |
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haoliangxu
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3af709097e
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fix: normalize structured LLM content in serialization and memory updater (#1215)
* fix: normalize ToolMessage structured content in serialization
When models return ToolMessage content as a list of content blocks
(e.g. [{"type": "text", "text": "..."}]), the UI previously displayed
the raw Python repr string instead of the extracted text.
Replace str(msg.content) with the existing _extract_text() helper in
both _serialize_message() and stream() to properly normalize
list-of-blocks content to plain text.
Fixes #1149
Also fixes the same root cause as #1188 (characters displayed one per
line when tool response content is returned as structured blocks).
Added 11 regression tests covering string, list-of-blocks, mixed,
empty, and fallback content types.
* fix(memory): extract text from structured LLM responses in memory updater
When LLMs return response content as list of content blocks
(e.g. [{"type": "text", "text": "..."}]) instead of plain strings,
str() produces Python repr which breaks JSON parsing in the memory
updater. This caused memory updates to silently fail.
Changes:
- Add _extract_text() helper in updater.py for safe content normalization
- Use _extract_text() instead of str(response.content) in update_memory()
- Fix format_conversation_for_update() to handle plain strings in list content
- Fix subagent executor fallback path to extract text from list content
- Replace print() with structured logging (logger.info/warning/error)
- Add 13 regression tests covering _extract_text, format_conversation,
and update_memory with structured LLM responses
* fix: address Copilot review - defensive text extraction + logger.exception
- client.py _extract_text: use block.get('text') + isinstance check (prevent KeyError/TypeError)
- prompt.py format_conversation_for_update: same defensive check for dict text blocks
- executor.py: type-safe text extraction in both code paths, fallback to placeholder instead of str(raw_content)
- updater.py: use logger.exception() instead of logger.error() for traceback preservation
* Apply suggestions from code review
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* fix: preserve chunked structured content without spurious newlines
* fix: restore backend unit test compatibility
---------
Co-authored-by: Exploreunive <Exploreunive@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
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DanielWalnut
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76803b826f
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refactor: split backend into harness (deerflow.*) and app (app.*) (#1131)
* refactor: extract shared utils to break harness→app cross-layer imports Move _validate_skill_frontmatter to src/skills/validation.py and CONVERTIBLE_EXTENSIONS + convert_file_to_markdown to src/utils/file_conversion.py. This eliminates the two reverse dependencies from client.py (harness layer) into gateway/routers/ (app layer), preparing for the harness/app package split. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * refactor: split backend/src into harness (deerflow.*) and app (app.*) Physically split the monolithic backend/src/ package into two layers: - **Harness** (`packages/harness/deerflow/`): publishable agent framework package with import prefix `deerflow.*`. Contains agents, sandbox, tools, models, MCP, skills, config, and all core infrastructure. - **App** (`app/`): unpublished application code with import prefix `app.*`. Contains gateway (FastAPI REST API) and channels (IM integrations). Key changes: - Move 13 harness modules to packages/harness/deerflow/ via git mv - Move gateway + channels to app/ via git mv - Rename all imports: src.* → deerflow.* (harness) / app.* (app layer) - Set up uv workspace with deerflow-harness as workspace member - Update langgraph.json, config.example.yaml, all scripts, Docker files - Add build-system (hatchling) to harness pyproject.toml - Add PYTHONPATH=. to gateway startup commands for app.* resolution - Update ruff.toml with known-first-party for import sorting - Update all documentation to reflect new directory structure Boundary rule enforced: harness code never imports from app. All 429 tests pass. Lint clean. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * chore: add harness→app boundary check test and update docs Add test_harness_boundary.py that scans all Python files in packages/harness/deerflow/ and fails if any `from app.*` or `import app.*` statement is found. This enforces the architectural rule that the harness layer never depends on the app layer. Update CLAUDE.md to document the harness/app split architecture, import conventions, and the boundary enforcement test. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat: add config versioning with auto-upgrade on startup When config.example.yaml schema changes, developers' local config.yaml files can silently become outdated. This adds a config_version field and auto-upgrade mechanism so breaking changes (like src.* → deerflow.* renames) are applied automatically before services start. - Add config_version: 1 to config.example.yaml - Add startup version check warning in AppConfig.from_file() - Add scripts/config-upgrade.sh with migration registry for value replacements - Add `make config-upgrade` target - Auto-run config-upgrade in serve.sh and start-daemon.sh before starting services - Add config error hints in service failure messages Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix comments * fix: update src.* import in test_sandbox_tools_security to deerflow.* Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: handle empty config and search parent dirs for config.example.yaml Address Copilot review comments on PR #1131: - Guard against yaml.safe_load() returning None for empty config files - Search parent directories for config.example.yaml instead of only looking next to config.yaml, fixing detection in common setups Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: correct skills root path depth and config_version type coercion - loader.py: fix get_skills_root_path() to use 5 parent levels (was 3) after harness split, file lives at packages/harness/deerflow/skills/ so parent×3 resolved to backend/packages/harness/ instead of backend/ - app_config.py: coerce config_version to int() before comparison in _check_config_version() to prevent TypeError when YAML stores value as string (e.g. config_version: "1") - tests: add regression tests for both fixes Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix: update test imports from src.* to deerflow.*/app.* after harness refactor Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> |