DeepRecall v2 — OpenClaw Skill

Pure-Python recursive memory for persistent AI agents. Implements the Anamnesis Architecture: "The soul stays small, the mind scales forever."

Description

DeepRecall gives AI agents infinite memory by recursively querying their own memory files through a manager→workers→synthesis RLM loop — entirely in Python. No Deno runtime, no fast-rlm subprocess, no vector database. Just markdown files and HTTP calls to any OpenAI-compatible LLM endpoint.

When the agent needs to recall something, DeepRecall:

1. Scans the workspace for memory files (scoped by category)
2. Indexes file metadata — headers, topics, dates, people
3. Manager selects the most relevant files from the index
4. Workers (parallel) extract exact verbatim quotes from each file
5. Synthesis combines quotes into a cited, grounded answer

Workers are constrained by anti-hallucination prompts to return only verbatim quotes. The synthesis step cites every claim with (filename:line).

Installation

pip install deep-recall

Or install from source:

git clone https://github.com/Stefan27-4/DeepRecall
cd DeepRecall && pip install .

Dependencies

• httpx (preferred) or requests — HTTP client for LLM calls
• PyYAML — config parsing
• Python ≥ 3.10
• An LLM provider configured in OpenClaw

v2 breaking change: Deno and fast-rlm are no longer required. The entire RLM loop runs in-process as pure Python.

Quick Start

from deep_recall import recall

result = recall("What did we decide about the project architecture?")
print(result)

API

recall(query, scope, workspace, verbose, config_overrides) → str

The primary entry point. Runs the full manager→workers→synthesis loop.

from deep_recall import recall

result = recall(
    "Find all mentions of budget discussions",
    scope="memory",          # "memory" | "identity" | "project" | "all"
    verbose=True,            # print progress to stdout
    config_overrides={
        "max_files": 5,      # max files the manager can select
    },
)

Returns: A string containing the recalled information with source citations, or a [DeepRecall] status message if no files/results were found.

recall_quick(query, verbose) → str

Fast, cheap recall scoped to identity files. Best for simple lookups.

from deep_recall import recall_quick

name = recall_quick("What is my human's name?")

Equivalent to recall(query, scope="identity", config_overrides={"max_files": 2}).

recall_deep(query, verbose) → str

Thorough recall across all workspace files. Best for cross-referencing.

from deep_recall import recall_deep

summary = recall_deep("Summarize all decisions from March")

Equivalent to recall(query, scope="all", config_overrides={"max_files": 5}).

CLI

python deep_recall.py \x3Cquery> [scope]

# Examples
python deep_recall.py "What was the first project we worked on?"
python deep_recall.py "Find budget discussions" all

Scopes

Scopes control which files DeepRecall searches. Narrower scopes are faster and cheaper.

File Categories

DeepRecall classifies discovered files into categories:

• soul — SOUL.md, IDENTITY.md — who the agent IS (always in context)
• mind — MEMORY.md, USER.md, TOOLS.md, HEARTBEAT.md, AGENTS.md — compact orientation
• long-term — memory/LONG_TERM.md — full detailed memories, grows forever
• daily-log — memory/YYYY-MM-DD.md — raw daily logs
• workspace — everything else (project files, configs, docs)

Configuration

DeepRecall reads your existing OpenClaw setup — no additional config files needed.

Provider Resolution

Provider, API key, and model are resolved automatically from:

1. ~/.openclaw/openclaw.json — primary model setting
2. ~/.openclaw/agents/main/agent/models.json — provider base URLs
3. ~/.openclaw/credentials/ — cached tokens (e.g. GitHub Copilot)
4. Environment variables — fallback (ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY, etc. (18+ providers supported, all optional))

Supported Providers (20+)

Anthropic · OpenAI · Google (Gemini) · GitHub Copilot · OpenRouter · Ollama · DeepSeek · Mistral · Together · Groq · Fireworks · Cohere · Perplexity · SambaNova · Cerebras · xAI · Minimax · Zhipu (GLM) · Moonshot (Kimi) · Qwen

Auto Model Pairing

The manager and synthesis steps use your primary model. Workers use a cheaper sub-agent model automatically:

config_overrides

Pass overrides via the config_overrides parameter:

recall("query", config_overrides={
    "max_files": 5,       # max files manager can select (default: 3)
})

Skill Files

Memory Layout

Recommended workspace structure for the Anamnesis Architecture:

~/.openclaw/workspace/
├── SOUL.md              # Identity — always in context, never grows
├── IDENTITY.md          # Core agent facts
├── MEMORY.md            # Compact index (~100 lines), auto-loaded each session
├── USER.md              # About the human
├── AGENTS.md            # Agent behavior rules
├── TOOLS.md             # Tool-specific notes
└── memory/
    ├── LONG_TERM.md     # Full memories — grows forever, searched via DeepRecall
    ├── 2026-03-05.md    # Daily raw log
    ├── 2026-03-04.md
    └── ...

⚠️ Privacy Notice

DeepRecall reads your workspace memory files and sends their contents to your configured LLM provider (Anthropic, OpenAI, Gemini, etc.) to perform recall. This is how it works — there is no local-only mode.

What gets sent:

• File metadata (names, headings, topics) → to the manager LLM
• Full file contents of selected files → to worker LLMs
• This may include personal notes, daily logs, project files

What is NOT sent:

• API keys and credentials (read locally for auth, never in prompts)
• Files outside your workspace

Credentials used locally:

• ~/.openclaw/openclaw.json and ~/.openclaw/credentials/* — to resolve your LLM provider
• Env vars (ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY, etc. (18+ providers supported, all optional)) — as fallback if no OpenClaw config found

Recommended Memory Architecture

DeepRecall works best with a two-tier memory system:

Tier 1: MEMORY.md (The Index)

• Auto-loaded every session — keep it SMALL (~100 lines)
• Contains: quick reference facts, active projects, key metrics, a table of contents pointing to LONG_TERM.md sections
• Think of it as your orientation file — "what do I need to know right now?"
• If it grows past ~120 lines, move details to LONG_TERM.md

Tier 2: memory/LONG_TERM.md (The Encyclopedia)

• Never auto-loaded — searched via DeepRecall when needed
• Contains: full context, decisions with reasoning, timestamps, bugs with fixes, architecture details
• Grows forever — never delete, only append
• The devil is in the details — "the diary entry, not the Wikipedia summary"

Tier 3: memory/YYYY-MM-DD.md (Daily Logs)

• Raw notes of what happened each day
• Distilled into LONG_TERM.md at end of day

Nightly Sync Routine

At the end of each day (or via cron/heartbeat):

1. Read today's daily log
2. Append key events, decisions, lessons, bugs, metrics to LONG_TERM.md
3. Update MEMORY.md index table if new topics appeared

⚠️ Setting this up? Ask your human before restructuring existing memory files. Show them this recommendation and let them decide how to organize their agent's memory.

License

MIT — see LICENSE.