jamjet-engram-server 0.5.0

Durable memory for AI agents — temporal knowledge graph, hybrid retrieval, SQLite or PostgreSQL.

java-ai-memory.dev · Main repo · JamJet docs · Discord

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Engram is a durable memory layer for AI agents. It extracts facts from conversations, stores them in a temporal knowledge graph, and retrieves them with hybrid semantic + keyword search — backed by a single SQLite file or a PostgreSQL database.

It ships in two shapes:

• jamjet-engram — a Rust library you embed in your own application.
• jamjet-engram-server (this crate) — a standalone binary that speaks MCP over stdio and REST over HTTP, so Claude Desktop, Cursor, and any HTTP client can use it with no code.

Engram is provider-agnostic. Five LLM backends are wired in out of the box, and a sixth — command — lets you shell out to any external script, so you can plug in a provider Engram does not ship natively without touching Rust code:

ENGRAM_LLM_PROVIDER=	What it does
ollama (default)	Local Ollama via /api/chat. Free, no API keys, runs on your laptop.
openai-compatible	Any endpoint that speaks OpenAI's chat-completions protocol — see the long list below.
anthropic	Anthropic Claude via the Messages API.
google	Google Gemini via generateContent with native JSON mode.
command	Shell out to a user-supplied script. Infinite extensibility, zero recompile.
mock	Deterministic tests-only backend — returns empty facts.

Pick one with ENGRAM_LLM_PROVIDER=… — the same binary handles all of them.

State of the project, April 2026. Engram is new — v0.3.2, small community, no public LongMemEval / DMR numbers yet. The architecture below works, the tests pass, the Docker image runs. If you need production-scale memory today, Mem0 Cloud and Zep Cloud are more mature. If you need a tryable, self-hostable, single-binary memory layer that doesn't require Python, Postgres, Qdrant, or Neo4j, Engram is built for you.

Why Engram?

Problem	Engram's answer
Every agent memory library is Python-first	Rust core with native Python, Java, and MCP clients — no sidecar required
Needs Postgres + Qdrant + Neo4j just to try	Single SQLite file (zero infra) or Postgres when you need it
Conversation history is not knowledge memory	Fact extraction pipeline — pulls structured facts out of messages
Old facts drift and contradict each other	Conflict detection + consolidation — decay, promote, dedup, summarize, reflect
Memory recall is either semantic OR keyword	Hybrid retrieval — vector search + SQLite FTS5 in one query
Agents lose memory across processes	Durable by default — SQLite or Postgres, crash-safe, portable
MCP support is an afterthought	MCP-native — 7 tools exposed by a single binary
No time-travel over what the agent knew	Temporal knowledge graph — every fact is scoped and timestamped
Can't isolate memory per user or tenant	First-class scopes — org / user / session built into every query

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Quickstart — 30 seconds

Engram speaks to four LLM providers out of the box: Ollama (default — free, local), OpenAI, Anthropic Claude, and Google Gemini. Pick one.

Option A — Ollama (free, local, zero API keys)

Requirements: Ollama with llama3.2 and nomic-embed-text pulled:

ollama pull llama3.2
ollama pull nomic-embed-text

Docker (easiest):

docker run --rm -i \
  -v engram-data:/data \
  ghcr.io/jamjet-labs/engram-server:0.3.2

The container defaults to Ollama at host.docker.internal:11434 — works out of the box on Docker Desktop for Mac and Windows. Linux users need --add-host=host.docker.internal:host-gateway.

Option B — Anthropic Claude (hosted, highest quality)

docker run --rm -i \
  -e ENGRAM_LLM_PROVIDER=anthropic \
  -e ANTHROPIC_API_KEY=sk-ant-... \
  -v engram-data:/data \
  ghcr.io/jamjet-labs/engram-server:0.3.2

Defaults to claude-haiku-4-5-20251001. Override with -e ENGRAM_ANTHROPIC_MODEL=claude-sonnet-4-6.

Note: Anthropic has no native JSON mode, so Engram parses JSON from text responses. The llm_util::extract_json_payload helper strips markdown fences that Claude occasionally emits.

Option C — Google Gemini

docker run --rm -i \
  -e ENGRAM_LLM_PROVIDER=google \
  -e GOOGLE_API_KEY=AIza... \
  -v engram-data:/data \
  ghcr.io/jamjet-labs/engram-server:0.3.2

Defaults to gemini-flash-latest. Override with -e ENGRAM_GOOGLE_MODEL=gemini-2.5-flash.

Option D — Any OpenAI-compatible endpoint

docker run --rm -i \
  -e ENGRAM_LLM_PROVIDER=openai-compatible \
  -e OPENAI_API_KEY=sk-... \
  -v engram-data:/data \
  ghcr.io/jamjet-labs/engram-server:0.3.2

Defaults to OpenAI itself (https://api.openai.com/v1, gpt-4o-mini). Change one env var to point at any of these providers without recompiling:

Provider	ENGRAM_OPENAI_BASE_URL=	Note
OpenAI	https://api.openai.com/v1	default
Azure OpenAI	https://<resource>.openai.azure.com/openai/deployments/<deployment>
Groq	https://api.groq.com/openai/v1	very fast inference
Together.ai	https://api.together.xyz/v1
Mistral	https://api.mistral.ai/v1
DeepSeek	https://api.deepseek.com/v1
Perplexity	https://api.perplexity.ai
OpenRouter	https://openrouter.ai/api/v1	one key, many models
Fireworks	https://api.fireworks.ai/inference/v1
vLLM (self-hosted)	http://your-host:8000/v1
LM Studio	http://localhost:1234/v1	local desktop app
LocalAI	http://localhost:8080/v1	self-hosted
Ollama /v1 compat layer	http://localhost:11434/v1	alternate path for Ollama
Your corporate LLM gateway	whatever URL your infra team exposes

openai is accepted as a backwards-compatible alias for openai-compatible.

Option E — command (shell out to anything)

For providers Engram does not ship natively — an internal model behind a custom RPC, a raw SOAP endpoint, a local inference binary, a quick wrapper over a Python SDK, or tomorrow's new provider — use the command backend. Engram will spawn your script per extraction call, pipe a JSON request to its stdin, and read a JSON response from its stdout.

The contract. Your script reads one JSON object from stdin:

{"system": "<extraction prompt>", "user": "<conversation text>", "structured": true}

Then writes one JSON value to stdout. Either a bare value:

{"facts": [{"text": "...", "entities": [...], "confidence": 0.95, "category": "..."}]}

Or an envelope (useful for surfacing errors):

{"content": {"facts": [...]}}
{"error": "rate limited, try again"}

Exit 0 on success, non-zero (with stderr) on failure.

Example Python wrapper (~15 lines):

#!/usr/bin/env python3
# my-llm.py — wraps any Python SDK for Engram.
import json, sys
from my_llm_sdk import chat  # your SDK

req = json.loads(sys.stdin.read())
resp = chat(
    system=req["system"],
    user=req["user"],
    json_mode=req.get("structured", False),
)
sys.stdout.write(json.dumps(resp))

Point Engram at it:

docker run --rm -i \
  -e ENGRAM_LLM_PROVIDER=command \
  -e ENGRAM_LLM_COMMAND="python /scripts/my-llm.py" \
  -v $(pwd)/scripts:/scripts \
  -v engram-data:/data \
  ghcr.io/jamjet-labs/engram-server:0.3.2

Timeout is 120 seconds by default — override with ENGRAM_LLM_COMMAND_TIMEOUT=30.

Security: the command provider runs arbitrary commands as the Engram process user. Never use it in a multi-tenant deployment where untrusted users can set ENGRAM_LLM_COMMAND. It is a local and single-tenant feature.

Option F — cargo install (all providers same binary)

cargo install jamjet-engram-server
engram serve                                            # ollama (default)
ENGRAM_LLM_PROVIDER=anthropic ANTHROPIC_API_KEY=... engram serve
ENGRAM_LLM_PROVIDER=command ENGRAM_LLM_COMMAND="python /path/to/wrapper.py" engram serve

Option G — REST mode for testing

engram serve --mode rest --port 9090

curl -X POST http://localhost:9090/v1/memory \
  -H 'content-type: application/json' \
  -d '{
    "user_id": "alice",
    "messages": [
      {"role": "user", "content": "I am allergic to peanuts and I love sourdough."}
    ]
  }'

curl 'http://localhost:9090/v1/memory/recall?q=food%20allergies&user_id=alice'

No server. No config. No Python sidecar. One binary.

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MCP client configuration

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json)

{
  "mcpServers": {
    "engram": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-v", "engram-data:/data",
        "ghcr.io/jamjet-labs/engram-server:0.3.2"
      ]
    }
  }
}

Cursor / any MCP-aware IDE

Point it at the same docker run command, or at a locally-installed engram serve binary. After restart, eleven tools are available to the model (seven memory_* + four messages_*).

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MCP tools

Memory tools (7)

Tool	What it does
memory_add	Extract and store facts from conversation messages (calls the LLM for extraction)
memory_recall	Semantic search over stored facts, scoped by user_id / org_id
memory_context	Assemble a token-budgeted context block for an LLM prompt, with tier-aware selection
memory_search	Keyword search over facts (SQLite FTS5 / Postgres full-text)
memory_forget	Soft-delete a fact by ID with an optional reason
memory_stats	Aggregate counts: total facts, valid facts, entities, relationships
memory_consolidate	Run a consolidation cycle — decay stale facts, promote high-value ones, dedup near-duplicates

All scoped by (org_id, user_id, session_id) — org is the coarsest, session the finest.

Message store tools (4)

Tool	What it does
messages_save	Save chat messages for a conversation (optionally triggers fact extraction)
messages_get	Get all messages for a conversation by ID
messages_list	List all conversation IDs
messages_delete	Delete all messages for a conversation

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REST API

Thirteen endpoints, rooted at /v1/memory. Full OpenAPI-style surface:

Method	Path	Handler
GET	/health	Liveness probe
POST	/v1/memory	Add messages (fact extraction)
GET	/v1/memory/recall?q=…&user_id=…	Semantic recall
POST	/v1/memory/context	Token-budgeted context assembly
GET	/v1/memory/search?q=…&user_id=…	Keyword search
GET	/v1/memory/stats	Aggregate statistics
POST	/v1/memory/consolidate	Trigger consolidation
DELETE	/v1/memory/facts/:id	Forget a fact
DELETE	/v1/memory/users/:id	GDPR user-data delete
POST	/v1/memory/messages	Save messages for a conversation
GET	/v1/memory/messages	List conversation IDs
GET	/v1/memory/messages/:id	Get messages for a conversation
DELETE	/v1/memory/messages/:id	Delete a conversation

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Configuration

All settings flow through CLI flags or environment variables. Env vars are the recommended way to configure the Docker image.

Core

CLI flag	Env var	Default	Notes
--db	ENGRAM_DB_PATH	engram.db	SQLite file path or postgres://… connection URL
--extract-on-save	ENGRAM_EXTRACT_ON_SAVE	true	Enable fact extraction when saving chat messages
--mode	ENGRAM_MODE	mcp	mcp (stdio) or rest (HTTP)
--port	ENGRAM_PORT	9090	HTTP port in REST mode
--llm-provider	ENGRAM_LLM_PROVIDER	ollama	ollama, openai-compatible (alias openai), anthropic, google, command, mock
--embedding-provider	ENGRAM_EMBEDDING_PROVIDER	ollama	ollama or mock (cloud embedding providers are planned)
--embedding-model	ENGRAM_EMBEDDING_MODEL	nomic-embed-text	Ollama embedding model
--embedding-dims	ENGRAM_EMBEDDING_DIMS	768	Must match the embedding model's output

Ollama

CLI flag	Env var	Default
--ollama-url	ENGRAM_OLLAMA_URL	http://localhost:11434 (Docker image: http://host.docker.internal:11434)
--ollama-llm-model	ENGRAM_OLLAMA_LLM_MODEL	llama3.2

OpenAI-compatible (OpenAI, Groq, Together, Mistral, DeepSeek, Azure, vLLM, …)

CLI flag	Env var	Default
--openai-api-key	OPENAI_API_KEY	(required when provider is openai-compatible)
--openai-base-url	ENGRAM_OPENAI_BASE_URL	https://api.openai.com/v1 — change this to switch provider, see the big table above
--openai-model	ENGRAM_OPENAI_MODEL	gpt-4o-mini

Anthropic

CLI flag	Env var	Default
--anthropic-api-key	ANTHROPIC_API_KEY	(required when provider is anthropic)
--anthropic-base-url	ENGRAM_ANTHROPIC_BASE_URL	https://api.anthropic.com
--anthropic-model	ENGRAM_ANTHROPIC_MODEL	claude-haiku-4-5-20251001

Google Gemini

CLI flag	Env var	Default
--google-api-key	GOOGLE_API_KEY	(required when provider is google)
--google-base-url	ENGRAM_GOOGLE_BASE_URL	https://generativelanguage.googleapis.com/v1beta
--google-model	ENGRAM_GOOGLE_MODEL	gemini-flash-latest

Command (shell-out)

CLI flag	Env var	Default
--llm-command	ENGRAM_LLM_COMMAND	(required when provider is command) — run via sh -c
--llm-command-timeout	ENGRAM_LLM_COMMAND_TIMEOUT	120 — seconds before the child is killed

The mock backend returns empty facts and deterministic byte-cycled vectors. It exists for tests and CI. The server prints a clear warning on startup if it detects mock mode in use. API keys and command contents are never echoed to stdout or logs — the server only describes provider model at base_url or command \<first 60 chars>``.

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Database Backend

Engram supports SQLite (default) and PostgreSQL backends. The backend is selected by the --db URL:

# SQLite (default)
engram serve --mode rest --db engram.db

# PostgreSQL
engram serve --mode rest --db postgres://user:pass@localhost:5432/engram

When using PostgreSQL, tables are created automatically on first startup.

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Chat Message Store

Engram can store raw chat messages alongside extracted facts. This enables Spring AI ChatMemoryRepository integration.

REST Endpoints:

Method	Endpoint	Description
POST	/v1/memory/messages	Save messages for a conversation
GET	/v1/memory/messages	List conversation IDs
GET	/v1/memory/messages/{id}	Get messages for a conversation
DELETE	/v1/memory/messages/{id}	Delete a conversation

MCP Tools: messages_save, messages_get, messages_list, messages_delete

Fact extraction on message save is controlled by --extract-on-save (default: true).

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Embedding the library

If you don't want a separate process, depend on jamjet-engram directly:

[dependencies]
jamjet-engram = "0.3.2"

use engram::{Memory, OllamaEmbeddingProvider, OllamaLlmClient, Scope, ExtractionConfig, Message};

let embedding = Box::new(OllamaEmbeddingProvider::new());
let memory = Memory::open("sqlite:engram.db?mode=rwc", embedding).await?;

let scope = Scope::user("default", "alice");
let messages = vec![Message {
    role: "user".into(),
    content: "I am allergic to peanuts.".into(),
}];

let llm = Box::new(OllamaLlmClient::new());
let fact_ids = memory
    .add_messages(&messages, scope.clone(), llm, ExtractionConfig::default())
    .await?;

// Later, recall
let facts = memory.recall(&engram::memory::RecallQuery {
    query: "food allergies".into(),
    scope: Some(scope),
    max_results: 5,
    as_of: None,
    min_score: None,
}).await?;

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Other languages

Engram is part of the JamJet ecosystem. These clients speak to engram-server over REST:

Language	Package	Install
Python	jamjet (includes EngramClient)	pip install jamjet
Java	dev.jamjet:jamjet-sdk (includes EngramClient)	Maven Central
Spring Boot	dev.jamjet:engram-spring-boot-starter	Maven Central — zero-config @Bean EngramMemory
Rust	jamjet-engram (embed directly)	cargo add jamjet-engram

See java-ai-memory.dev for how Engram compares to LangChain4j ChatMemory, Spring AI ChatMemory, Koog, Embabel, Google ADK Memory Bank, the Mem0 community wrapper, and Zep — with honest notes on where Engram fits and where the alternatives are more mature.

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Architecture

┌──────────────────────────────────────────────────────────┐
│           Clients: Claude Desktop, Cursor, JamJet,        │
│           Python SDK, Java SDK, Spring Boot, cURL         │
├─────────────────┬────────────────────────────────────────┤
│   MCP (stdio)   │           REST (HTTP + JSON)            │
├─────────────────┴────────────────────────────────────────┤
│                     engram-server                         │
│                 (jamjet-engram-server)                    │
├──────────────────────────────────────────────────────────┤
│                       engram (lib)                        │
│   Extraction pipeline  │  Hybrid retrieval  │  Scopes     │
│   Conflict detection   │  Consolidation     │  Context    │
├──────────────────────────────────────────────────────────┤
│    LlmClient trait        │    EmbeddingProvider trait    │
│    Ollama · Mock          │    Ollama · Mock              │
├──────────────────────────────────────────────────────────┤
│                       Storage                             │
│   SQLite or PostgreSQL (facts, entities, FTS, vectors)   │
└──────────────────────────────────────────────────────────┘

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What Engram is not

• Not just a chat-history store. Engram now includes a message store (see Chat Message Store), but its primary value is the fact extraction and knowledge graph layer on top. If all you need is the last N messages of a conversation, a framework's built-in window may be simpler.
• Not a state checkpointer. If you need to snapshot agent execution state for resume and replay, that's what LangGraph, Koog persistence, or JamJet's own durable runtime does. Pair them with Engram — they solve different problems.
• Not a managed service. No hosted plane, no auth layer beyond scopes, no SLA. Bring your own process manager.
• Not benchmarked yet. LongMemEval and DMR numbers are on the roadmap. Until they exist, treat comparative claims with the skepticism they deserve.

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Roadmap

Status	Item
✅	Fact extraction pipeline + SQLite storage
✅	Hybrid retrieval (vector + FTS5 with prefix matching)
✅	Consolidation engine (decay, promote, dedup, summarize, reflect)
✅	MCP stdio server (7 tools)
✅	REST API (9 endpoints)
✅	Multi-provider LLM backends: Ollama, OpenAI-compatible (OpenAI/Azure/Groq/Together/Mistral/vLLM/LM Studio/…), Anthropic, Google, command shell-out
✅	Python, Java, Spring Boot clients
✅	Docker image, MCP Registry publish
✅	Postgres backend (in parallel to SQLite)
✅	Chat message store + Spring AI ChatMemoryRepository integration
📋	LongMemEval + DMR benchmark scores
📋	Quarkus extension
📋	Cloud embedding providers (OpenAI text-embedding-3, Google text-embedding-004)

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License

Apache 2.0 — see LICENSE.

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