engram

<div align="center"> # Engram: Cognitive Memory Layer for AI Agents **The SQLite of agent memory: embeddable, local-first, cognitively grounded.** [](https://github.com/TAIPANBOX/engram/actions/workflows/ci.yml)    <img src="docs/architecture.png" alt="Engram architecture: observations flow into the memory core, which holds episodic memory, bitemporal semantic facts, and an entity graph, with optional LLM reflection and compression, and recall flowing back out to the agent" width="960"> </div> Engram gives an AI agent a persistent, provenance-tracked memory: raw episodic observations, bitemporal semantic facts that know when they were true, and an entity graph for associative recall, all in one embeddable `.engram` file with no server, no Docker, and no API key required to write a memory. --- ## The problem Every AI agent starts from zero. Ask it something it answered last week - it has no idea. Show it a document it already processed - it processes it again. Tell it Ivan moved to a new company - it still thinks Ivan works at the old one. This happens because agents have no persistent memory. When the conversation ends, everything is gone. The usual fix is to throw a vector database at the problem. Store text, embed it, search by similarity. That helps - but it's not enough. You still can't ask *"what did the agent think in March?"* or *"where did this belief come from?"* or *"show me everything the agent knows about Ivan."* A vector search finds similar text. It doesn't understand time, relationships, or importance. **Engram is memory done properly.** --- ## What Engram does Engram gives your agent a persistent memory that works like a file - one `.engram` file on disk, no server required. You `pip install` it and start using it in two lines: ```python from engram import Engram with Engram(path="./agent.engram") as mem: # Remember something mem.observe("Ivan moved from Acme to Globex last week", actors=["Ivan"]) # Recall it later - even in a completely different session for r in mem.recall("where does Ivan work?", k=3): print(f"[{r.score:.2f}] {r.episode.content}") ``` No server to start. No API key for the store. No Docker. No configuration file. Here is what Engram gives you that a plain vector database does not: **Remembers raw events** - every observation is stored with who was involved, what tags apply, and how important it felt at the time. Search finds the right memories even when the query is phrased differently. **Understands facts** - a background process (no LLM needed at write time) reads your observations and extracts structured knowledge: *Ivan works at Globex*, *Alice is the CTO*. These facts can be queried directly, updated when things change, and traced back to their source. **Knows what happened when** - if Ivan changes jobs, the old fact is not deleted. It is closed with an end date. You can ask what the agent believed in March even if the truth has changed since. **Forgets wisely** - memories that haven't been accessed in a while gradually become less important. Memories that matter (accessed often, emotionally significant) stay sharp. The agent doesn't accumulate noise forever. **Explains itself** - for any fact, you can ask where it came from: which observation triggered it, which LLM run extracted it, with what confidence. **Works with multiple agents** - several agents can share a single `.engram` file. Each has its own private observations; extracted facts and the relationship graph are shared between them. <div align="center"> <img src="assets/diagram.svg" alt="Engram architecture: an agent observes and recalls against one SQLite file holding episodic, semantic and procedural memory with bitemporal facts, spreading activation and decay, exposed over MCP on stdio" width="960"> _{The same service as its room on <a href="https://it-rat.com/services/engram.html">it-rat.com</a> draws it, where the diagram sits next to a simulation you can scrub back and forth.} </div> --- ## Where this fits in the stack Engram is the memory plane of the TAIPANBOX agent-governance stack: it gives agents persistent, provenance-tracked memory, and its own reflection can route through TokenFuse. ```mermaid flowchart TB Agent["AI agent (any framework)"] -->|"LLM call (base-URL swap)"| TF["TokenFuse proxy: spend + enforcement"] TF -->|"POST /v1/decide (PEP)"| WX["Wardryx: policy PDP"] WX -.->|"allow / deny / hold"| TF TF -->|"cheapest model, budget OK"| LLM[("LLM provider")] TF -->|"CallRecords"| CL["TokenFuse Cloud: control plane, incidents, replay, evidence, kill-switch"] TF ==>|"agent-event NDJSON"| BUS{{"agent-event bus + Agent Passport"}} WX ==> BUS ENG["Engram: memory"] -->|"reflect via base_url"| TF ENG ==> BUS BUS ==> IDX["Idryx: identity graph, detectors, Agent-BOM"] BUS ==> QX["Qryx: crypto / PQC, passport + hash-chain scan"] BUS ==> VX["Verdryx: quality / drift"] VX ==>|"quality events"| BUS TF -->|"outcome-tagged traces"| VX MX["Mockryx: pre-prod safety rehearsal"] -->|"hostile scenarios"| TF MX ==>|"sim events"| BUS BUS ==> HX["heraldyx: reads the log, mails you"] HX -->|"one mail, a view and never an action"| OPS["your mailbox"] YOU(["you, in a browser over your own tunnel"]) --> GX[["Genaryx: the console over all of it"]] GX -->|"signed commands: the kill, an approval, a policy"| CL GX -->|"signed commands"| WX GX -.->|"reads it"| IDX GX -.->|"reads it"| QX GX -.->|"reads it"| VX GX -.->|"reads it"| MX GX -.->|"reads it"| ENG TFP["terraform-provider-taipan"] -->|"budgets + passports as code"| CL ASG[["agent-stack-go: shared Go contract"]] -.->|imported by| IDX ASG -.->|imported by| WX ASG -.->|imported by| MX ASG -.->|imported by| TFP ASG -.->|imported by| HX ASG -.->|imported by| QX SPEC[["agent-passport: the spec"]] -.->|governs| BUS ``` - **Consumes**: agent observations (`observe()`). - **Produces**: `source: engram` events and `why()` provenance, opt-in via the Agent Passport `agent-event` envelope, schema `taipanbox.dev/agent-event/v0.2`. - **Talks to**: **TokenFuse** (reflection's LLM adapter can point `base_url` at TokenFuse); governed by the **agent-passport** `agent://` scope. The full stack is TokenFuse (spend), Wardryx (policy), Engram (memory), Idryx (access), Qryx (crypto), Verdryx (quality), Mockryx (pre-prod), heraldyx (the mail out) and scopyx (governed web egress), on the shared Agent Passport + agent-event contract (agent-stack-go / agent-passport), configured via terraform-provider-taipan and driven from Genaryx, the console over all of it. Trailryx, the record plane, is built and not wired into this yet. Run the whole open stack locally with one command via [**stack-up**](https://github.com/TAIPANBOX/stack-up); the stack's home on the web is [**it-rat.com**](https://it-rat.com). ## Live infrastructure validation Before any public launch, Engram's Anthropic adapter was run against a real Claude model on real Linux infrastructure: three independent runs, zero bugs, zero contradictions, every extracted fact carrying full `why()` provenance back to the observations it came from.  Full write-up and all numbers: [`VALIDATION.md`](VALIDATION.md). ### Running it on a Kubernetes cluster The whole stack was deployed as a five-node k3s cluster on Hetzner, AWS and GCP between 25 and 27 July 2026 (six clusters, all destroyed afterwards). The manifests, the traps and the evidence are public in [stack-k8s](https://github.com/TAIPANBOX/stack-k8s). Engram is deliberately **not** a pod. The console speaks to `engram-mcp` over **stdio**, and a sidecar container cannot be another container's stdin, so the binary has to live inside the console image rather than beside it. That is why `images/console.Dockerfile` is a four-language build. Getting this wrong produces a cluster where the memory tab is permanently empty while every pod reports healthy. The store itself takes an ordinary ReadWriteOnce volume; single-file memory is an asset here, because there is nothing to cluster. To be clear about scope: those runs verified the deployment shape and the service coming up correctly on three clouds. They did not exercise memory under load: the memory plane was deliberately left unseeded on those clusters, so there are no recall or reflection numbers from them, and none are claimed. The numbers in this file remain the ones to read. --- ## What is Engram, technically? <div align="center"> <img src="docs/memory-model.png" alt="The three memory kinds: episodic observations with vector and BM25 recall, bitemporal semantic facts, and the entity relationship graph" width="900"> </div> Engram is a **cognitive memory layer** for AI agents - a single local file (`agent.engram`) built on SQLite. It models three kinds of memory that mirror how human memory works: **Episodic memory** - raw observations stored as they happen, with actors, tags, salience, and emotional weight. No LLM required at write time; writes complete in ~4 ms. **Semantic memory** - structured knowledge extracted from episodes via a background reflection loop: `(subject, predicate, object)` triples with full bitemporal validity. Every fact tracks *when it was true in reality* and *when the system learned it* - independently on two timelines. When Ivan switches jobs, the old fact is closed with `valid_to`, not deleted. You can query what the agent believed in March even if the truth has since changed. **Dynamic importance** - each memory carries a living importance score based on the Ebbinghaus forgetting curve, reinforced by retrieval frequency and emotional weight. Memories below threshold decay and are pruned automatically during reflection. The agent forgets what doesn't matter; critical memories survive. ### What you can actually do with it - **Debug beliefs**: when the agent says "Ivan works at Globex," call `mem.why(fact_id)` to see exactly which episode produced that belief, which reflection run extracted it, which model, and with what confidence. - **Erase a person**: `forget_entity("Ivan")` permanently removes all episodes, facts, and graph edges connected to Ivan - a proper GDPR right-to-be-forgotten. - **Query the past**: `mem.recall("Ivan employer", as_of=datetime(2024, 3, 1))` returns what the agent knew at that exact point in time, not what it knows now. - **Run multiple agents**: a planner and a coder can share one file - each sees its own episodes, both benefit from shared extracted facts. - **Plug into any MCP client**: run `engram-mcp --db ./agent.engram` and Claude Desktop, Claude Code, or Cursor can `remember`, `recall`, `why`, and `forget` against the same store with zero integration code - see [MCP Server](docs/api-reference.md#mcp-server) in the API reference. --- ## Why not just use a vector database? Vector databases (Pinecone, Chroma, Qdrant) store text and find similar text. That is useful, but it is a fraction of what memory requires. They cannot tell you *when* something was true. They cannot explain *why* the agent believes something. They have no concept of facts becoming outdated, of contradictions, or of some memories mattering more than others. And they run as separate servers - you need Docker, a network connection, and an API call just to write a sentence. Engram is not a replacement for a vector database - it includes one, built in, with no separate process. On top of it, Engram adds time, structure, importance, and provenance that vector DBs do not have. Every other solution forces a trade-off. Engram doesn't. | Capability | Pinecone / Chroma / Qdrant | Mem0 | Zep / Graphiti | Letta (MemGPT) | LangChain memory | **Engram** | |---|:---:|:---:|:---:|:---:|:---:|:---:| | Vector similarity search | ✅ | ✅ | ✅ | ✅ | ⚠️ | ✅ | | **Hybrid BM25 + vector recall** | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | | Semantic fact triples (s, p, o) | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | | **Bitemporal validity** (`as_of` time travel) | ❌ | ❌ | ⚠️ | ❌ | ❌ | ✅ | | **Spreading-activation retrieval** | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | | Importance decay (Ebbinghaus) | ❌ | ❌ | ✅ | ⚠️ | ❌ | ✅ | | **Working memory (7±2 scratchpad)** | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | | **Memory compression via LLM** | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | | **Async API** | ❌ | ❌ | ⚠️ | ❌ | ❌ | ✅ | | **Provenance tracking** (`why()`) | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | | GDPR right-to-be-forgotten | ❌ | ⚠️ | ⚠️ | ❌ | ❌ | ✅ | | **Multi-agent shared store** | ❌ | ❌ | ⚠️ | ❌ | ❌ | ✅ | | Embeddable (no server) | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | | Zero config (single file) | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | | MCP-native | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | | LLM required at write time | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | Contradiction detection | ❌ | ⚠️ | ✅ | ⚠️ | ❌ | ✅ | | Fully local (no cloud) | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | **Key advantages over each competitor:** - **vs. Pinecone / Chroma / Qdrant** - Vector DBs are just similarity search. Engram adds time, graph, importance, and provenance on top. They require a separate server process; Engram is a file you open in two lines. - **vs. Mem0** - Mem0 calls an LLM on *every write* (slow, costly, requires API key at write time). Engram writes instantly; reflection runs async in the background. Mem0 has no temporal validity - it cannot tell you what was true in March. - **vs. Zep / Graphiti** - Server-based runtimes with operational overhead. Engram is a Python library you `pip install`. No Docker, no API keys for the store itself, no migration scripts. - **vs. Letta / MemGPT** - Tied to their own agent runtime and hosting model. Engram plugs into *any* framework: LangChain, LlamaIndex, raw API, or your own loop. - **vs. LangChain memory** - LangChain memory is toy-grade: an in-process list or a Redis key. No decay, no graph, no temporal queries, not production-ready for long-running agents. --- ## How Engram works ### Memory that doesn't forget the wrong things Most tools either remember everything forever (noise accumulates) or forget everything when the session ends (nothing persists). Engram does neither. Every memory gets an importance score. Memories you access often, or that carry emotional weight, stay sharp. Memories that sit untouched gradually fade. When the agent runs its background reflection pass, low-importance memories are pruned automatically. The result is a store that stays useful instead of bloating. This is modelled on the [Ebbinghaus forgetting curve](https://en.wikipedia.org/wiki/Forgetting_curve) - the same pattern that describes how humans forget - combined with Hebbian reinforcement from repeated retrieval. ### Facts that know when they were true When you just store text and search it, you lose track of time. "Ivan works at Acme" and "Ivan works at Globex" are just two strings - you don't know which is current, or what changed. Engram extracts structured facts from your observations - triples like *(Ivan, works_at, Globex)* - and tracks two independent timelines for each: - **When it was true in reality** (`valid_from` / `valid_to`) - **When the system learned it** (`recorded_at` / `superseded_at`) When Ivan changes jobs, the old fact is not deleted - it is closed with an end date. The new fact is added alongside it. You can query what the agent believed at any point in the past: ```python # What did the agent think about Ivan's employer in March? mem.recall("Ivan employer", k=5, as_of=datetime(2024, 3, 1, tzinfo=UTC)) # Full fact history - every job Ivan ever had, with dates mem.timeline("Ivan") ``` This two-timeline approach is standard in financial databases and audit systems. In the AI memory space, Engram is the only tool that implements it. ### What a recall actually does <div align="center"> <img src="docs/recall-pipeline.svg" alt="A recall runs two retrievers over one store: the query is embedded and searched by exact vec0 KNN with agent_id as a partition key and ts as a metadata column, and in parallel tokenised and searched by FTS5 BM25 with its terms joined by OR; each pool is min-max normalised and blended 0.5 cosine to 0.5 BM25 into the top k episodes" width="1000"> </div> Two things in that picture are worth spelling out, because both were wrong until recently and neither is visible from the outside. **The filters live inside the scan.** `agent_id` is a vec0 partition key and the timestamp is a metadata column, so "this agent's episodes" and "as of March" are conditions the index applies while it searches. They used to be applied afterwards, in a join, where a filter can only cut into a top-k that has already been chosen: in a shared store where one agent held most of the episodes, the other agent's `recall(k=5)` returned **nothing at all** rather than its own five nearest. It also means scoped recall no longer grows with the store. An agent holding five episodes pays for five, whether the shared file holds a thousand or a hundred thousand: 0.05 ms against 0.09 ms across that whole range. **The keyword side ORs its terms.** FTS5 reads a space between words as an implicit AND, so joining a question's tokens with spaces demands that every one of them appear in a single episode. For "what was the name of the restaurant I mentioned when we talked about my anniversary dinner?" that is sixteen words; it matched nothing, and the blend quietly reduced to plain cosine. Hybrid mode was cosine under another name for any query longer than a few words. What exposed it was a benchmark run scoring both modes to three identical decimal places. ### Three ways to search Engram ships three retrieval modes behind the same API: **`mode="hybrid"`** (default) - combines keyword search (BM25) with semantic search, then blends the scores. It is the default because on LongMemEval-S it beats pure vector search at every k measured, by most at the thing that matters to an agent: putting the specific turn that holds the answer in front of it (0.830 against 0.772 at k=5). Questions carry names, places and specifics, and lexical matching catches those exactly where an embedding blurs them into neighbours. It is also faster per query, 10 ms against 16 ms. The blend is configurable: ```python # BM25 keyword + cosine vector, weighted blend results = mem.recall("Alice CTO Globex", k=5, mode="hybrid") # More weight on exact keywords, less on semantics results = mem.recall("quarterly budget", k=5, mode="hybrid", vector_weight=0.3, fts_weight=0.7) ``` **`mode="cosine"`** - pure semantic vector search, no keyword component. Finds memories that mean the same thing as your query even when they share no words with it. **`mode="spreading"`** - follows relationship edges between memories. If Ivan is connected to Project X in the graph, a query about Ivan can surface Project X episodes even if they share no words or meaning. One memory activates its associates, like human associative recall. Technically: spreading activation runs BFS over Hebbian-weighted graph edges, ranking results by `α·cosine_similarity + β·graph_activation + γ·importance_score`. ### How well it recalls, measured All 500 questions of [LongMemEval-S](https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned) (ICLR 2025, MIT). Each question carries its own history of 30 to 60 sessions; every turn is ingested as one episode, 246 738 in total, and the question is asked against that store. No LLM anywhere in the loop. | mode | session@5 | session@10 | turn@5 | turn@10 | ms/query | |---|---|---|---|---|---| | **`hybrid`** (default, `0.5 / 0.5`) | **0.970** | **0.982** | **0.830** | **0.894** | 10 | | `cosine` | 0.956 | 0.978 | 0.772 | 0.862 | 16 | The dataset marks both the sessions that hold the evidence and the individual turns, so there are two honest numbers, and the second one needs a caveat spelled out in the [API reference](docs/api-reference.md#recall-accuracy): 59% of questions flag more than one evidence turn, so counting a hit when any of them is retrieved is an upper bound on what the model was handed. **Session recall** says the right conversation came back. **Turn recall** says one of the 896 flagged turns did, out of 246 738 - that is what the agent actually reads, and it is 14 points lower. A memory system quoting one unqualified "R@k" is quoting the first; ask which. The blend behind hybrid was swept rather than assumed: every weighting from pure BM25 to pure vector was scored in the same pass, both ends came out worse than the middle, and the default is now `0.5 / 0.5`, which leads the previous `0.7 / 0.3` on all four metrics by about five questions out of five hundred. The margin is small and said to be small; the shape of the curve is the part that matters. Cosine reproduced its figures to three decimals across two independent six-hour passes over the whole dataset. Cosine is ahead on exactly one cut, `multi-session` at k=10 (0.992 against 0.985), which is in the [API reference](docs/api-reference.md#recall-accuracy) along with the per-type breakdown. The per-question records behind every number are in [`benchmarks/results/`](benchmarks/results/) with the snippet to recompute them, so the table can be checked rather than believed. Reproduce it yourself: ```bash engram-bench longmemeval --data ./longmemeval_s_cleaned.json --k 5,10 \ --checkpoint ./lme.jsonl --resume ``` ### A scratchpad for the agent's current task Engram also provides `WorkingMemory` - a small, fast, in-memory scratchpad for whatever the agent is actively thinking about. It holds a fixed number of items (default 7, matching the average human working memory capacity). When it fills up, the least-recently-used item is dropped - and if you pass an Engram store, it is automatically saved to long-term memory before being evicted: ```python from engram import WorkingMemory wm = WorkingMemory(capacity=5, engram=mem) # evicted items → long-term store wm.set("task", "Summarise the quarterly report") wm.set("context", "Revenue grew 12% YoY - needs explanation") item = wm.get("task") # read + promote to most-recently-used item = wm.peek("context") # read without changing eviction order wm.flush() # write everything to long-term store + clear ``` ### Background reflection (the agent's "sleep") LLM calls in Engram never block writes. The reflection loop runs asynchronously - while the agent keeps working: 1. Group recent observations by entity or topic 2. Call the LLM to extract structured facts (`Ivan works_at Globex`) 3. Detect contradictions - same subject and predicate, different value 4. Close superseded facts with an end date (newest value wins; an identical re-extraction supersedes silently) 5. Recompute importance scores 6. Prune memories below threshold ```python thread = mem.reflect_async() # starts in background, returns immediately thread.join() # wait only when you need the results print(f"{thread.result.facts_extracted} facts, {thread.result.cost_tokens} tokens") ``` ### Compressing old memories When a store grows large, `compress()` groups low-importance observations into batches and asks the LLM to summarise each batch into a single paragraph. The originals are hard-deleted; the summary is stored in their place, with a `summary_of` pointer to what it replaced: ```python result = mem.compress( max_episodes=1000, # only compress when store exceeds this importance_threshold=0.3, # target: episodes below this importance score batch_size=20, # observations per LLM call ) print(f"Removed {result.episodes_removed} episodes → {result.summaries_created} summaries") ``` Compression is lossy by design. Run `reflect()` first to extract facts from episodes before compressing them - facts survive compression, raw text does not. --- ## Under the hood - technical details ### Bitemporal validity Every fact carries *two* independent timelines: ``` valid_from / valid_to → when the fact was TRUE in reality recorded_at / superseded_at → when the system LEARNED it ``` ### Hybrid BM25 + cosine recall Three retrieval modes unified in one API: ``` mode="hybrid" → FTS5 BM25 + cosine, normalised and blended (default) mode="cosine" → pure vector similarity (semantic) mode="spreading" → cosine KNN seeds → BFS over Hebbian graph ``` ### Importance scoring formula ``` importance(m, t) = salience(m) × exp(−λ × (t − last_access(m))) # Ebbinghaus forgetting curve + α × log(1 + access_count(m)) # Hebbian reinforcement + β × emotional_weight(m) # affective weight ``` Parameters `λ`, `α`, `β` are configurable via `DecayConfig`. ### Spreading-activation graph traversal ``` query → seed memories (cosine KNN) ↓ graph edges (Hebbian weights - reinforced by co-access) ↓ activated neighbors (activation × decay per hop) ↓ rank by: α·similarity + β·activation + γ·importance ``` ### Working memory - Miller's 7±2 law Fixed-capacity LRU cache backed by `collections.OrderedDict`. Evicted items optionally written to long-term store via `observe()`. Capacity default of 7 matches the average human working memory span (Miller, 1956). --- ## Install ```bash pip install engdbram # LLM-powered reflection (optional, pick one): pip install 'engdbram[anthropic]' # Claude pip install 'engdbram[openai]' # OpenAI or any OpenAI-compatible # Integrations: pip install 'engdbram[mcp]' # MCP server (Claude Desktop, Cursor, etc.) pip install 'engdbram[langchain]' # LangChain retriever + chat history pip install 'engdbram[llamaindex]' # LlamaIndex memory buffer # Everything: pip install 'engdbram[anthropic,mcp,langchain,llamaindex]' ``` > The distribution name on PyPI is **`engdbram`** (the `engram` name is squatted). The import name is still `engram`, so application code is unaffected: `from engram import Engram`. **Requirements:** Python 3.11+, no system dependencies. `fastembed` downloads the ONNX embedding model (~64 MB) on first use; all subsequent calls are local, including with the network unplugged. --- ## Quickstart ### Basic usage ```python from engram import Engram mem = Engram(path="./agent.engram") # or ":memory:" for ephemeral # Store an observation - instant, no LLM needed ep_id = mem.observe( "Alice presented the Q3 roadmap to the exec team", actors=["Alice"], tags=["work", "roadmap"], salience=0.8, # 0-1, subjective importance at encoding emotional_valence=0.2, # -1 (negative) … +1 (positive) ) # Semantic recall results = mem.recall("Alice roadmap", k=5) for r in results: print(f"[score={r.score:.2f}] {r.episode.content}") # Assert facts directly (no LLM) mem.assert_fact("Ivan", "works_at", "Globex", confidence=0.95) mem.close() ``` ### Async API ```python import asyncio from engram import AsyncEngram, ObserveInput async def main(): async with AsyncEngram(path="./agent.engram") as mem: # All methods are async - event loop never blocked by ONNX or SQLite await mem.observe("Alice joined Globex as CTO", actors=["Alice"]) await mem.observe_many([ ObserveInput(content="Q3 planning complete", tags=["planning"]), ObserveInput(content="Ivan submitted architecture proposal", actors=["Ivan"]), ]) results = await mem.recall("who joined Globex?", k=3) for r in results: print(f"[{r.score:.2f}] {r.episode.content}") await mem.assert_fact("Alice", "role", "CTO") facts = await mem.timeline("Alice") asyncio.run(main()) ``` ### Working memory scratchpad ```python from engram import Engram, WorkingMemory with Engram(path="./agent.engram") as mem: # 5-slot scratchpad; evicted items automatically saved to long-term memory wm = WorkingMemory(capacity=5, engram=mem) wm.set("goal", "Draft the board presentation") wm.set("context", "Q3 revenue up 12%, but CAC increased") wm.set("constraint", "Must fit 10 slides, no more") task = wm.get("goal") # promotes to most-recently-used note = wm.peek("constraint") # reads without changing LRU order print(f"Current slots: {len(wm)} / {wm.capacity}") wm.flush() # write everything to long-term store + clear ``` ### Hybrid recall ```python with Engram(path="./agent.engram") as mem: # BM25 keyword match + cosine vector search, blended results = mem.recall("Alice quarterly roadmap", k=5, mode="hybrid") # Tune the blend weights results = mem.recall( "exact phrase match needed", k=5, mode="hybrid", vector_weight=0.3, # less semantic fts_weight=0.7, # more keyword ) ``` ### Bulk import with observe_many When loading historical context, `observe_many()` runs a single ONNX inference pass for the whole batch and commits all rows in one transaction - about 2× faster than calling `observe()` in a loop: ```python from engram import Engram, ObserveInput items = [ ObserveInput( content="Alice joined Globex as CTO", actors=["Alice"], tags=["hr"], salience=0.9, ), ObserveInput(content="Q3 planning session concluded", tags=["planning"]), ObserveInput(content="Ivan submitted the architecture proposal", actors=["Ivan"]), ] with Engram(path="./agent.engram") as mem: ids = mem.observe_many(items) print(f"Inserted {len(ids)} episodes") ``` ### Async reflection with Claude ```python from engram import Engram, AnthropicAdapter mem = Engram( path="./agent.engram", llm=AnthropicAdapter(model="claude-haiku-4-5-20251001"), ) mem.observe("Ivan said he finally joined Globex last Monday") mem.observe("The team shipped v2 of the payment service") # Trigger reflection in the background thread = mem.reflect_async() # Keep doing agent work… results = mem.recall("Ivan career", k=5) thread.join() run = thread.result print(f"Facts: {run.facts_extracted} Contradictions resolved: {run.contradictions_resolved}") print(f"Tokens used: {run.cost_tokens}") ``` ### Memory compression ```python from engram import Engram, AnthropicAdapter mem = Engram( path="./agent.engram", llm=AnthropicAdapter(model="claude-haiku-4-5-20251001"), ) # Compress episodes with low importance into LLM summaries result = mem.compress( max_episodes=500, # no-op if store is smaller than this importance_threshold=0.3, # episodes below this score are candidates batch_size=20, # episodes per LLM call ) print(f"Compressed {result.episodes_removed} episodes → {result.summaries_created} summaries") print(f"Tokens used: {result.cost_tokens}") mem.close() ``` ### Time travel ```python from datetime import datetime, UTC # What did the agent know about Ivan in March 2024? past_results = mem.recall( "Ivan employer", k=5, as_of=datetime(2024, 3, 1, tzinfo=UTC), ) # Full fact timeline for an entity for fact in mem.timeline("Ivan"): end = fact.valid_to.date() if fact.valid_to else "now" print(f"[{fact.valid_from.date()} → {end}] Ivan {fact.predicate} {fact.object}") ``` ### Multi-agent shared store Multiple agents can read and write to the same `.engram` file. Episodes are scoped per agent; facts and the entity graph are shared. ```python from engram import Engram # Each agent has its own episode scope. The id is an opaque key to the store, # and an `agent://<trust-domain>/<name>` identifier is what the Agent Passport # event log needs, so writing it that way from the start costs nothing. planner = Engram(path="./team.engram", agent_id="agent://acme.example/planner") coder = Engram(path="./team.engram", agent_id="agent://acme.example/coder") planner.observe("Decided to migrate to PostgreSQL", tags=["arch"]) coder.observe("Started migration branch: feat/pg-migration", tags=["dev"]) # Each agent recalls only its own episodes by default planner_results = planner.recall("migration", k=5) # Cross-agent search when needed all_results = planner.recall("migration", k=10, cross_agent=True) # Inspect who's written to the shared file with Engram(path="./team.engram") as global_view: # ['agent://acme.example/coder', 'agent://acme.example/planner'] print(global_view.list_agents()) planner.close() coder.close() ``` ### Backup and export ```python # Hot backup - safe to call while the store is open. It copies the whole file, # so it is refused on an instance scoped to an agent_id; open the file unscoped. mem.backup("./agent_backup.engram") # Portable JSON export (episodes, facts, entities, edges). A scoped instance # exports its own episodes and edges; facts and entities are shared by design. doc = mem.export_json("./agent_dump.json") print(f"Exported {doc['counts']['episodes']} episodes, {doc['counts']['facts']} facts") # Import into another store with Engram(path="./new_store.engram") as dst: counts = dst.import_json("./agent_dump.json") # merge=True skips duplicate ids instead of raising counts = dst.import_json("./agent_dump.json", merge=True) ``` ### GDPR right-to-be-forgotten ```python # Permanently erase a single episode mem.forget(episode_id) # Permanently erase a single semantic fact mem.forget_fact(fact_id) # Erase everything about a person: episodes, facts, graph edges result = mem.forget_entity("Ivan") print(f"Deleted {result.episodes_deleted} episodes, {result.facts_deleted} facts") ``` --- ## CLI Engram ships a command-line interface for inspecting and operating stores without writing code: ``` engram inspect <path> [--agent-id ID] engram recall <path> <query> [--k K] [--mode cosine|spreading|hybrid] [--as-of DATE] [--agent-id ID] [--cross-agent] engram timeline <path> <entity> engram observe <path> <content> [--actors NAME...] [--tags TAG...] [--salience F] [--valence F] [--agent-id ID] engram reflect <path> [--llm anthropic|openai] [--model MODEL] [--base-url URL] [--agent-id ID] engram forget <path> (--episode ID | --entity NAME) [--agent-id ID] engram list-agents <path> ``` ```bash # Inspect a store engram inspect ./agent.engram # Store: ./agent.engram (1.4 MB) # Episodes: 1842 (vec index: 1842) # Facts: 234 (active: 198, superseded: 36) # Entities: 41 # Reflections: 12 (last: 2025-05-11 09:14 UTC) # Recall (cosine, hybrid, or spreading) engram recall ./agent.engram "Ivan employer" --k 3 engram recall ./agent.engram "Ivan employer" --mode hybrid --k 5 # Recall as of a past date engram recall ./agent.engram "Ivan employer" --as-of 2024-03-01 # Observe from the command line engram observe ./agent.engram "Alice promoted to VP Engineering" --actors Alice --tags hr # Run reflection engram reflect ./agent.engram --llm anthropic --model claude-haiku-4-5-20251001 # Forget an entity (GDPR) engram forget ./agent.engram --entity Ivan # Multi-agent: list all agents engram list-agents ./team.engram # Recall scoped to one agent engram recall ./team.engram "migration" --agent-id agent://acme.example/coder ``` --- ## API reference Every method on `Engram` and `AsyncEngram`, all six LLM adapters, the LangChain / LlamaIndex / MCP integrations, the on-disk schema, and the benchmark numbers live in a dedicated reference doc so this README stays readable. Start there for exact signatures, return types, and copy-paste examples for `observe`, `recall`, `assert_fact`, `reflect`, `timeline`, `why`, `forget_entity`, `compress`, `WorkingMemory`, and everything else. See the full [API reference](docs/api-reference.md). --- ## Development ```bash git clone https://github.com/taipanbox/engram cd engram python -m venv .venv && source .venv/bin/activate pip install -e '.[dev]' pytest -x # run tests, stop on first failure ruff check . --fix # lint + auto-fix ruff format . # format mypy engram # type check (strict) ``` ### Test coverage (510 tests, 517 with the encryption extra) ``` tests/ test_schema.py schema + SQLite migrations (incl. backward compat) test_observe.py observe() + embeddings test_recall.py cosine recall test_hybrid_recall.py hybrid BM25 + cosine recall, FTS index population test_smoke.py end-to-end Engram class test_importance.py decay formula test_decay.py decay background job + access log test_store_facts.py fact CRUD + assert_fact() test_reflection.py reflection loop (stub LLM), cost_tokens, reflect_async test_graph.py entity/edge CRUD + spreading recall test_bitemporal.py as_of + timeline (incl. naive-datetime boundaries) test_forget.py forget(), forget_entity(), GDPR cascade test_cli.py all CLI subcommands + --agent-id + --cross-agent test_multiagent.py agent_id scoping, shared facts, cross-agent recall test_performance.py observe_many correctness + batch decay + LRU cache test_export.py export_json / import_json round-trip, merge mode, agent-scoped export test_backup.py backup() - hot copy, openable as Engram, refused on a scoped instance test_working_memory.py WorkingMemory LRU, eviction, flush, spillover test_async_engram.py AsyncEngram - all async methods + concurrency safety test_compress.py compress() - LLM summarisation, batching, no-op paths test_encryption.py SQLCipher encryption-at-rest + rekey() test_llm_adapters.py all LLM adapters + response-parsing edge cases test_integrations.py LangChain, LlamaIndex test_mcp_server.py MCP server (engram-mcp): remember/recall/why/forget/stats, agent pooling, procedural rejection, reflect() not exposed test_events.py Agent Passport NDJSON event exporter: schema validation, fail-open on I/O error, skip-on-empty agent_id, warn-on- nonconforming agent_id, off-by-default, bulk and cascade paths (observe_many, forget_entity, compress), the v0.2 envelope version and a chain that continues across it test_gates_are_wired.py the gate scripts run in the workflows that ship test_benchmarks.py benchmark infrastructure ``` --- ## Status - [x] v0.1 - SQLite schema, `observe()`, `recall()` (cosine) - [x] v0.2 - Importance scoring + Ebbinghaus decay - [x] v0.3 - Reflection loop (async LLM fact extraction) - [x] v0.4 - Entity graph + spreading-activation retrieval - [x] v0.5 - Bitemporal queries (`as_of`, `timeline()`) - [x] v0.6 - MCP server, LangChain + LlamaIndex adapters - [x] v1.0 - Benchmarks, docs, production polish - [x] v1.1 - `forget()` / GDPR right-to-be-forgotten - [x] v1.2 - CLI (`engram inspect`, `recall`, `timeline`, `observe`, `reflect`, `forget`, `list-agents`) - [x] v1.3 - Multi-agent shared memory (`agent_id`, `cross_agent`, `list_agents()`) - [x] v2.0 - Batch decay (21×), `observe_many()` (2×), embedding LRU cache - [x] v2.0.1 - WAL journal mode + 32 MB page cache (4× faster commits, concurrent reads/writes) - [x] v2.1 - Hybrid recall (FTS5 BM25 + cosine), `WorkingMemory`, `AsyncEngram`, `compress()`, `backup()`, `export_json` / `import_json` - [x] v2.1.1 - GitHub Actions CI, `DATA_FLOW.md`, tunable `k_inner` / `candidate_limit`, adapter history hydration, PyPI distribution renamed to `engdbram` - [x] v2.1.2 - Multi-agent isolation hardening (per-agent `prune`, FTS cleanup), hybrid `as_of`, FTS5 query safety, embedder normalization, prompt-injection hardening, async API parity (`timeline(as_of=)`, `recall(k_inner=, candidate_limit=)`), tag-triggered PyPI publishing via OIDC - [x] v2.2.0 - Correctness pass (bitemporal `as_of` UTC coercion, `contradictions()` identical-fact fix, `import_json` re-embed + FTS, reflection abort rollback, LLM response-parse guards), thread-safety (`Store` + embedder locks for `reflect_async` / `AsyncEngram`), per-agent edge & decay scoping, PEP 561 `py.typed`, CI matrix (Python 3.13 + encryption job), release gated on tests, `migrate()` fails loudly on embedder-dimension mismatch, `pip-audit` CI job - [x] v2.2.1 - MCP server tool surface (`engram-mcp`): stdio transport, optional `[mcp]` extra, `remember`/`recall`/`why`/`forget`/`stats` tools with structured semantic params and per-call `agent_id`, `reflect()` deliberately not exposed; public `forget_fact()` API (sync + async) for erasing a single semantic fact; opt-in [Agent Passport](https://github.com/TAIPANBOX/agent-passport) NDJSON event exporter (`events_path` / `ENGRAM_EVENTS_PATH`, `engram-mcp --events` / `ENGRAM_MCP_EVENTS`) emitting `memory_written`, `memory_forgotten`, `reflection_run`, `contradiction_found` - [x] v2.3.0 - `agent_id` is a vec0 partition key and `ts` a metadata column, so scoped and `as_of` recall resolve inside the KNN scan (scoped recall could return nothing at all before, and no longer grows with the store); recall accuracy published on LongMemEval-S, all 500 questions, with the per-question records in `benchmarks/results/`; `mode="hybrid"` is the default after beating cosine at every k measured, and the BM25 query it depends on no longer AND-joins its terms into matching nothing; `engram-bench scale` and `engram-bench longmemeval`; `observe(timestamp=)`; atomic `forget_entity()`; `compress()` keeps its summary inside the period it summarises - [x] v2.4.0 - the hybrid blend measured instead of assumed: `engram-bench longmemeval --sweep` scores every weighting in one pass, and the default moved from `0.7 / 0.3` to `0.5 / 0.5`, which leads on all four metrics by about five questions in five hundred; both ends of the range are clearly worse than the middle, which is the evidence hybrid mode rests on - [x] v2.4.1 - the numbers made re-readable and the headline table made honest: the LongMemEval checkpoint now records the rank of every evidence and gold-session hit plus each question's evidence count, so a smaller k and the strict "every flagged turn retrieved" reading are recomputable from a finished run instead of costing another six-hour pass; the published turn-recall figure carries the caveats it needed (21 of 500 questions flag no turn at all, so the figure over the 479 answerable ones is 0.866 at k=5, and 59% flag more than one, so "at least one retrieved" is an upper bound); and the headline table, which had gone on showing the `0.7 / 0.3` figures under the word "default" for one release after 2.4.0 moved it, was corrected everywhere from one run --- ## Contributing PRs welcome. Please: 1. Open an issue first for non-trivial changes. 2. Follow [Conventional Commits](https://www.conventionalcommits.org/) (`feat:`, `fix:`, `refactor:`). 3. Run `pytest -x && ruff check . && mypy engram` before submitting. 4. Keep PRs small - one logical change per PR. See [CONTRIBUTING.md](CONTRIBUTING.md) for the full development guide. --- ## License Apache-2.0 - see [LICENSE](LICENSE). - Architecture rationale and design decisions: [DESIGN.md](DESIGN.md) - Release notes by version: [CHANGELOG.md](CHANGELOG.md) - Read / write paths and on-disk guarantees: [DATA_FLOW.md](DATA_FLOW.md)