Total Agent Memory

Find past solutions/lessons from OTHER projects whose feature set overlaps with the given problem text (Jaccard similarity).

Run the eval harness: recall_at_k, prevention_rate, latency percentiles. Loads scenarios from evals/scenarios/*.json by default.

v8.0: classify task into L1-L4 complexity + suggested phases.

BEFORE editing a file, call this to surface past errors, lessons, and related rules for that file path. Returns risk_score ∈ [0, 1].

Parse a file or directory into semantic AST chunks (functions, classes, methods) across 8 languages. Returns chunk count + sample.

Record a temporal fact assertion (subject, predicate, object). Supersedes any prior assertion with same (s,p) and different object — full history is preserved. Use for evolving architectural decisions.

Point-in-time query: return fact assertions valid at `timestamp` (ISO 8601). Omit timestamp for currently-valid facts.

Close a currently-valid fact assertion. History is retained.

Full chronological history of assertions for a subject.

Structured error capture: file, error, root_cause, fix, pattern. After N (default 3) errors share the same pattern, a prevention rule is auto-synthesized into the rules table.

List recent user prompts from the intents table, newest first. Filter by project and/or session. Max 500 rows.

Generate and verify a cited answer using the configured reasoning LLM. Evidence carries recording dates; when records about the same subject disagree, the latest one gives the current value and the answer names the value it replaced. First runs negative retrieval: a contradiction-seeking second search; a score >= 0.60 hands both sides to the reader and answers with a caveat (MEMORY_CONTRADICTION_POLICY=abstain refuses instead), 0.30-0.60 answers with a caveat (see `negative`); MEMORY_CONTRADICTION_SCORER=jev scores the pairs with TypeSafe's Jev instead of the LLM. Up to one missing-relation retrieval and eight LLM calls including inversion retry and bounded quote repair. Explicit project required. Citation offsets refer to returned evidence content. Ordinary recall remains local.

Associative recall — brain-like spreading activation through knowledge graph. Finds memories through concept resonance, not keyword search. In 'composition' mode, finds minimum set of memories covering all needed concepts.

List or search concepts in the knowledge graph.

Find and merge duplicate/similar knowledge records. Keeps the longest version, supersedes shorter duplicates. Reduces noise in recall results.

v11.0 W2-G: report the consolidation daemon state — per-project last-run, active locks, recent activity. Use to verify the idle-project worker is making progress without interfering with active work.

Build optimal context for a query. Combines: spreading activation + knowledge graph + episodes + skills + self-model. The 'brain thinking' tool.

Delete a knowledge record. By default a soft delete: the record leaves search results and vectors but stays in the database. hard=true erases it for good, with its earlier versions, derived rows and its text in the raw call log (use for personal data or anything that must not be kept).

v11.0 W1-F: resolve a mention to its canonical entity within a project+type. Cross-session coreference via name/alias index + embedding cosine. Returns canonical_id, matched_via, and is_new flag. Pronouns return -1.

Find past episodes (experiences). Search by concepts, outcome, project, or impact.

Save an episode — narrative of WHAT HAPPENED and HOW. Not just facts, but the journey: what was tried, what failed, what worked.

v11.0 Phase 8: runs contradiction_detector against a labelled fixture. Requires balanced/deep mode (LLM). Returns {status: 'not_implemented', ...} if module is unavailable.

v11.0 Phase 8: verifies entity_dedup canonicalization is stable across repeated saves of variant tag spellings.

v11.0 Phase 8: run the LongMemEval-style recall+prevention scenario suite (loaded from evals/scenarios/) against the live store. Forces MEMORY_MODE=fast by default. Returns {scenarios_total, scenarios_passed, recall_at_5, recall_at_10, latency_ms, mode, llm_calls_during_eval, network_calls_during_eval}.

v11.0 Phase 8: large-context recall scenario. Saves N records and queries them at the tail. Reuses eval_harness scenarios tagged 'long_context' if present.

v11.0 Phase 8: generic recall benchmark on a dataset path or a small built-in fixture. Same payload shape as memory_eval_locomo.

v11.0 Phase 8: temporal recall using temporal_kg + temporal_filter. Returns {status: 'not_implemented', ...} when modules are missing.

v11.0: same as memory_search_fast but returns a per-tier breakdown (fts/semantic/graph/fuzzy/hyde with raw scores, the merged RRF list, rerank_applied flag, embedding_space). Use to debug why a record did or didn't surface for a query.

Before re-reading files to answer a codebase question, look up earlier exploration conclusions. Results are ranked by question similarity; each carries status 'fresh' (every source file is byte-identical to when the conclusion was recorded: reuse it) or 'stale' (changed_files / missing_files list what to re-read).

Record the conclusion of a codebase exploration together with the SHA-256 of every file read to reach it. Call it after answering a structural question by reading files (how X is wired, where Y is decided). Paths are stored relative to `root` (default: the server's working directory); files outside root are refused. Up to 64 files of at most 32 MB each. Recording the same question again for the project replaces the earlier memo.

Export all knowledge as JSON for backup or migration. Includes knowledge, sessions, relations and the agent action log.

Get pending session transcripts for knowledge extraction. Previous sessions are auto-captured on exit. Use action='list' to see pending, 'get' to read transcript, then save knowledge via memory_save, then 'complete' to mark as processed.

Apply retention policy: archive stale records (>180d, never recalled, low confidence), purge very old archived records (>365d), prune the agent action log past TAM_ACTION_LOG_RETENTION_DAYS. Keeps memory clean.

Batched fetch by ID — complement to memory_recall(mode='index'). Returns full content for ONLY the IDs the caller chose after inspecting an index. Typical 3-layer flow: recall(mode='index') → pick IDs → memory_get(ids=[...]).

Query the unified knowledge graph. Returns neighborhood of a node: connected rules, skills, memories, concepts, entities.

Reindex CLAUDE.md rules and skills into the knowledge graph. Run after modifying CLAUDE.md or adding new skills.

Knowledge graph statistics: nodes, edges, communities, top concepts, health metrics.

View version history for a knowledge record. Shows the chain of superseded versions (newest → oldest), enabling time-travel through knowledge evolution.

Build local passage indexes before evidence searches. Repeat using next_after_id until remaining=0.

Save a lightweight observation (auto-capture). No dedup, no ChromaDB — fast and cheap. Use for tracking file changes, tool usage, and session activity. Observations auto-cleanup after 30 days.

v11.0: dump in-process telemetry counters (search_total_ms, embed_ms, fts_ms, vector_ms, llm_calls, network_calls) plus persistent embedding_cache stats. Use to verify the fast hot path stays clean.

v11.0: re-encode every record (or every record in a given embedding space) and update the binary + float32 vectors. Idempotent. Pass embedding_space='code' to refresh only code rows after switching the code embedder. Returns {rebuilt: int, skipped: int}.

v11.0: drop and rebuild the SQLite FTS5 virtual table from `knowledge` rows. Useful after migrations or content_type column changes that the FTS triggers didn't see. Returns {rebuilt: int}.

Search ALL memory: decisions, solutions, facts, lessons from ALL past sessions. 6-stage pipeline: FTS5+BM25 → semantic → fuzzy → graph → (optional) CrossEncoder → (optional) MMR. Default: hybrid mode (BM25 + semantic + RRF). Use BEFORE starting any task. v11.0: routes to fast hot path when MEMORY_MODE=fast (default). Use memory_search_fast / memory_explain_search for explicit fast routing.

v11.0 W1-B: IRCoT-style iterative retrieval. Decomposes the query into sub-questions, retrieves per sub-question, and asks a planner LLM whether more retrieval is needed. Best for multi-hop questions. Returns unified evidence + provenance per iteration.

Run reflection (the 'sleep' process). Consolidates knowledge, finds patterns, generates skill proposals, updates self-model.

Create a typed relation between two knowledge records. Enriches graph expansion in Tier 4 search. Types: causal, solution, context, related, contradicts.

Activity report for a project (or all projects) over a period: today (period=day), this week (week, ISO Monday-Sunday), this month (month), all time (all) or custom since/until dates; offset=-1 gives the previous day/week/month ('last week'). Sections: summary numbers with deltas against the previous equal period, key decisions with their WHY, solutions and fixes, errors with recurring patterns and lessons, open next steps and pitfalls from session summaries, most touched files, entities/technologies, and a day-by-day timeline. Every item carries source IDs (#id -> memory_get). Built deterministically from stored records, no LLM; include_llm_summary=true adds an optional paragraph from the configured LLM. Periods use the local timezone (or tz). save=true writes the Markdown to <memory dir>/reports/<project>/<period>-<date>.md. Use it when the user asks what happened, for a status/progress report, a weekly summary or a retrospective.

Save knowledge explicitly. Types: decision (MUST include WHY in context), solution, lesson, fact, convention. Saving the same words again (case and punctuation aside) replaces the stored record, so it carries the latest date; any other text, including a changed value, is stored as a new record. v10: a quality gate scores the record before save; below-threshold records are rejected with a `rejected_by_quality_gate: true` response (override with MEMORY_QUALITY_GATE_ENABLED=false). Use `importance` to surface critical decisions at recall time (boosts the final RRF score). v11.0: routes to fast hot path when MEMORY_MODE=fast (default). Use memory_save_fast for explicit fast routing.

v11.0: same as memory_save but routes through the fast hot path (skip_quality=True, no LLM, no async-blocking). Use when you want to bypass the v10 quality gate without flipping the env flag.