recall-echo

Persistent three-layer memory system for pulse-null entities. Gives AI agents long-term recall across sessions — curated facts, recent session context, and searchable conversation archives.

Why

LLM coding agents start every session from zero. Built-in memory is typically a single flat file with no session continuity, no short-term vs long-term distinction, and no searchable history. Memory management that depends on the agent remembering to save things is circular.

recall-echo makes the memory lifecycle mechanical. When running as a pulse-null plugin, archival and checkpointing happen automatically. The agent writes to MEMORY.md during sessions. Everything else is handled by the system.

Architecture

recall-echo provides a three-layer memory model:

┌──────────────────────────────────────────────────────┐
│              MEMORY ARCHITECTURE                      │
│                                                       │
│  Layer 1: CURATED (always in context)                 │
│  ┌───────────┐                                        │
│  │ MEMORY.md │  Facts, preferences, patterns          │
│  └───────────┘  Distilled & maintained by the agent   │
│                                                       │
│  Layer 2: SHORT-TERM (FIFO rolling window)            │
│  ┌───────────────┐                                    │
│  │ EPHEMERAL.md  │  Last N session summaries          │
│  └───────────────┘  Appended on archive, auto-trimmed │
│                                                       │
│  Layer 3: LONG-TERM (searched on demand)              │
│  ┌─────────────┐    ┌────────────────────────────┐    │
│  │ ARCHIVE.md  │───→│ conversations/             │    │
│  └─────────────┘    │  conversation-001.md       │    │
│                     │  conversation-002.md       │    │
│                     │  ...                       │    │
│                     └────────────────────────────┘    │
│                     YAML frontmatter + markdown       │
│                     LLM-summarized or algorithmic     │
└──────────────────────────────────────────────────────┘

All paths are relative to an entity root directory:

{entity_root}/memory/
├── MEMORY.md                 # Layer 1 — curated facts (≤200 lines)
├── EPHEMERAL.md              # Layer 2 — rolling session window (default 5)
├── ARCHIVE.md                # Layer 3 — conversation index
├── conversations/            # Layer 3 — full conversation archives
│   ├── conversation-001.md
│   ├── conversation-002.md
│   └── ...
└── .recall-echo.toml         # Optional configuration

How It Works

recall-echo operates in two modes:

As a pulse-null Plugin

recall-echo is a native pulse-null plugin implementing the Plugin trait from echo-system-types. It fills the required Memory role (exactly one per entity).

• pulse-null calls archive::archive_session() at session end — creates a conversation archive with LLM-generated summary, updates ARCHIVE.md index, appends to EPHEMERAL.md
• pulse-null calls checkpoint::create_checkpoint() before context compaction — preserves conversation state before details are lost
• Health checks report memory directory state (Healthy / Degraded / Down)
• Setup wizard prompts for entity_root during pulse-null init

use recall_echo::RecallEcho;

// pulse-null creates the plugin via factory:
let plugin = recall_echo::create(&config, &ctx).await?;
// plugin.role() == PluginRole::Memory

As a Standalone CLI

For administration and use outside pulse-null:

recall-echo init [entity_root]         # Create memory directory structure
recall-echo status [entity_root]       # Health check with dashboard
recall-echo search <query>             # Line-level archive search
recall-echo search <query> --ranked    # File-ranked relevance search
recall-echo distill [entity_root]      # Analyze MEMORY.md, suggest cleanup
recall-echo consume [entity_root]      # Output EPHEMERAL.md content

Installation

cargo install

cargo install recall-echo
recall-echo init

Prebuilt binaries

Download from GitHub Releases for:

• x86_64-unknown-linux-gnu
• aarch64-unknown-linux-gnu
• x86_64-apple-darwin
• aarch64-apple-darwin (Apple Silicon)

tar xzf recall-echo-<target>.tar.gz
./recall-echo init

From source

git clone https://github.com/dnacenta/recall-echo.git
cd recall-echo
cargo build --release
./target/release/recall-echo init

Commands

recall-echo init

Create the memory directory structure under entity_root. Creates memory/ with MEMORY.md, EPHEMERAL.md, ARCHIVE.md, and conversations/. Idempotent — never overwrites existing files.

recall-echo status

Health check with a dashboard showing memory usage, ephemeral state, archive count, recent sessions, and health assessment. Color-coded bars show MEMORY.md capacity (green → yellow → red at 75% / 90%).

recall-echo — healthy

  MEMORY.md:    142/200 lines (71%)
  EPHEMERAL.md: 3 entries
  Archives:     23 conversations

recall-echo search

Search conversation archives.

recall-echo search "auth middleware"              # line-level matches
recall-echo search "auth middleware" -C 3          # with 3 lines of context
recall-echo search "auth middleware" --ranked      # ranked by relevance
recall-echo search "auth middleware" --ranked --max-results 5

Ranked search scores files by match count, word coverage, and recency.

recall-echo distill

Analyze MEMORY.md and suggest cleanup. Identifies sections over 30 lines that could be extracted to topic files (e.g., memory/debugging.md) with references left in MEMORY.md.

recall-echo consume

Output EPHEMERAL.md content wrapped in memory markers. Used by hooks or scripts that need to inject recent session context into an agent's input.

Archive Format

Conversation archives use YAML frontmatter with markdown content:

---
log: 5
date: "2026-03-06T10:30:00Z"
session_id: "abc123"
message_count: 34
duration: "30m"
source: "session"
topics: ["auth", "jwt", "middleware"]
---

## Summary
Summary of the conversation with key outcomes.

**Decisions**: Chose JWT for authentication.
**Action Items**: Implement token refresh endpoint.

### User
(message content)

### Assistant
(message content)

## Tags
**Files**: src/auth.rs, src/middleware.rs
**Tools**: Read, Edit, Bash

Summaries are LLM-generated when a provider is available (via pulse-null), with silent fallback to algorithmic extraction.

Configuration

Optional .recall-echo.toml in the memory directory:

[ephemeral]
max_entries = 5    # Rolling window size (1-50, default 5)

All settings have sensible defaults. Missing file or invalid values fall back silently.

Contributing

See CONTRIBUTING.md for branch naming, commit conventions, and workflow.

License

AGPL-3.0