openclawbrain 12.1.1

Memory graph for AI agents that learns what to retrieve — and what to suppress.

OpenClawBrain

Your retrieval routes become the prompt — assembled by learned routing, not top-k similarity.

Current release: v12.1.0 Website: https://openclawbrain.ai

Setup: Setup Guide

OpenClaw Integration (start here if you run OpenClaw)

OpenClawBrain is designed to be the memory layer for OpenClaw agents.

• Guide: docs/openclaw-integration.md

Quickstart (OpenClaw users):

pip install openclawbrain
openclawbrain init --workspace ~/.openclaw/workspace --output ~/.openclawbrain/main
python3 -m openclawbrain.socket_server --state ~/.openclawbrain/main/state.json

Production Deployment (socket):

Use LaunchAgent/systemd to keep the socket server running:

python3 -m openclawbrain.socket_server --state ~/.openclawbrain/main/state.json

macOS (~/Library/LaunchAgents/com.openclawbrain.daemon.plist):

<key>ProgramArguments</key>
<array>
  <string>/usr/bin/python3</string>
  <string>-m</string>
  <string>openclawbrain.socket_server</string>
  <string>--state</string>
  <string>/Users/YOU/.openclawbrain/main/state.json</string>
</array>

Linux (/etc/systemd/system/openclawbrain-daemon.service):

[Service]
ExecStart=/usr/bin/python3 -m openclawbrain.socket_server --state /home/YOUR_USER/.openclawbrain/main/state.json

python3 -m openclawbrain.socket_client --socket ~/.openclawbrain/main/daemon.sock --method health --params "{}"

OpenClawBrain learns from your agent feedback, so wrong answers get suppressed instead of resurfacing. It builds a memory graph over your workspace, remembers what worked, and routes future answers through learned paths.

• Zero dependencies. Pure Python 3.10+.

• Works offline with built-in hash embeddings.

• Builds a state.json brain from your workspace.

• Queries follow learned routes instead of only similarity matches.

• Positive feedback (+1) strengthens routes, negative (-1) creates inhibitory edges.

• Over time, less noise appears and recurring mistakes are less likely.

• OpenClawBrain integrates with your agent's file-based workspace through incremental sync, constitutional anchors, and automatic compaction.

• See the context lifecycle for details.

Install

pip install openclawbrain

See also: Setup Guide for a complete local configuration walkthrough.

Why OpenClawBrain

• Static retrieval vs learned routing: OpenClawBrain continuously updates node-to-node edges so good routes strengthen and bad routes decay.
• No correction propagation vs inhibitory edges: incorrect context can be actively suppressed and forgotten less often than in similarity-only systems.
• Bulk context load vs targeted traversal: context windows stay focused (roughly 52KB → 3-13KB in typical sessions) by following likely retrieval routes.
• No structural maintenance vs prune/merge/compact: OpenClawBrain includes scheduled maintenance commands to keep the graph healthy and compact.
• No protection vs constitutional anchors: anchor critical nodes with authority so operational instructions do not drift.

5-minute quickstart (A→B learning story)

# 1. Build a brain from the sample workspace
openclawbrain init --workspace examples/sample_workspace --output /tmp/brain

# 2. Check state health
openclawbrain doctor --state /tmp/brain/state.json
# output
# PASS: python_version
# PASS: state_file_exists
# PASS: state_json_valid
# Summary: 8/9 checks passed

# 3. Query (text output includes node IDs)
openclawbrain query "how do I deploy" --state /tmp/brain/state.json --top 3 --json
# output (abbrev.)
# {"fired": ["deploy.md::0", "deploy.md::1", "deploy.md::2"], ...}

# 4. Teach it (good path)
openclawbrain learn --state /tmp/brain/state.json --outcome 1.0 --fired-ids "deploy.md::0,deploy.md::1"
# output
# {"edges_updated": 2, "max_weight_delta": 0.155}

# 5. Inject a correction
openclawbrain inject --state /tmp/brain/state.json \
  --id "fix::1" --content "Never skip CI for hotfixes" --type CORRECTION

# 5b. Add new knowledge (no correction needed, just a new fact)
openclawbrain inject --state /tmp/brain/state.json \
  --id "teaching::monitoring-tip" \
  --content "Check Grafana dashboards before every deploy" \
  --type TEACHING

# 6. Query again and see the route change
openclawbrain query "can I skip CI" --state /tmp/brain/state.json --top 3
# output
# fix::1
# ~~~~~~
# Never skip CI for hotfixes
# ...

# 7. Re-check health for a quick signal
openclawbrain health --state /tmp/brain/state.json

Correcting mistakes (the main workflow)

When your agent retrieves wrong context, teach OpenClawBrain in one command:

openclawbrain inject --state brain/state.json \
  --id "correction::42" \
  --content "Never show API keys in chat messages" \
  --type CORRECTION

What happens:

1. OpenClawBrain creates a new node with your correction text
2. It connects that node to the most related workspace chunks
3. It adds inhibitory edges — negative-weight links that suppress those chunks
4. Next query touching that topic: the correction appears, the bad route is dampened

To add knowledge without suppressing anything, use --type TEACHING instead.

Adding new knowledge (no rebuild needed)

When you learn something that isn't in any workspace file, inject it directly:

openclawbrain inject --state brain/state.json \
  --id "teaching::codex-spark" \
  --content "Use Codex CLI with gpt-5.3-codex-spark for coding tasks — free on Pro plan" \
  --type TEACHING

TEACHING nodes connect to related workspace chunks just like CORRECTION nodes, but without inhibitory edges — they add knowledge instead of suppressing it.

Three injection types:

• CORRECTION — creates inhibitory edges that suppress related wrong paths
• TEACHING — adds knowledge with normal positive connections
• DIRECTIVE — same as TEACHING (use for standing instructions)

For agent frameworks that need to correlate corrections with earlier queries, see examples/correction_flow/ for the fired-node logging pattern.

You can also reinforce good retrievals:

# After a query returns helpful context, strengthen those paths
openclawbrain learn --state brain/state.json --outcome 1.0 \
  --fired-ids "deploy.md::0,deploy.md::1"

Or weaken bad ones:

openclawbrain learn --state brain/state.json --outcome -1.0 \
  --fired-ids "monitoring.md::2"

What it looks like in practice

# Before learning
openclawbrain query "how do we handle incidents" --state /tmp/brain/state.json --top 3

# After one good learn on the best route
openclawbrain learn --state /tmp/brain/state.json --outcome 1.0 --fired-ids "incidents.md::0,deploy.md::1"

# After one negative learn on a bad route
openclawbrain learn --state /tmp/brain/state.json --outcome -1.0 --fired-ids "monitoring.md::2,incidents.md::0"

# Query again to observe new routing
openclawbrain query "incident runbook for deploy failures" --state /tmp/brain/state.json --top 4

How it compares

	Plain RAG	OpenClawBrain
Retrieval	Similarity search	Learned graph traversal
Feedback	None	learn +1/-1 updates edge weights
Wrong answers	Can keep resurfacing	Inhibitory edges suppress them
Adding knowledge	Re-index/re-embed	inject --type TEACHING (no rebuild)
Over time	Same results for same query	Routes become habitual behavior
Dependencies	Vector DB or service	Zero dependencies

How OpenClawBrain differs from related tools

	OpenClawBrain	Plain RAG	Reflexion	MemGPT
What it learns	Retrieval routes	Nothing	Reasoning via self-reflection text	Memory read/write policies
Negative feedback	Inhibitory edges suppress bad paths	None	None (additive only)	None
New knowledge	inject node (no rebuild)	Re-embed corpus	Add to reflection prompt	Update tier config
Integration	Standalone library, any agent	Vector DB required	Tied to agent loop	Tied to agent architecture
Cold start	Hash embeddings, no API key	Needs embedding service	Needs prior episodes	Needs configured tiers
State	Single state.json file	External DB	Prompt history	Multi-tier storage

Real embeddings + LLM routing (OpenAI)

Offline hash embeddings work for trying the product, but real deployments generally use:

• Embeddings: text-embedding-3-small (1536-dim)
• LLM routing/scoring: gpt-5-mini

from openai import OpenAI
from openclawbrain import split_workspace, VectorIndex

client = OpenAI()


def embed(text):
    return client.embeddings.create(
        model="text-embedding-3-small", input=[text]
    ).data[0].embedding


def llm(system, user):
    return client.chat.completions.create(
        model="gpt-5-mini",
        messages=[{"role": "system", "content": system}, {"role": "user", "content": user}],
    ).choices[0].message.content

graph, texts = split_workspace("./workspace", llm_fn=llm)
index = VectorIndex()
for nid, content in texts.items():
    index.upsert(nid, embed(content))

See examples/openai_embedder/ for a complete example.

CLI Reference

Command	Description
init	Build a brain from workspace files
query	Traverse graph and return context
learn	Apply outcome feedback to fired edges
merge	Suggest and apply node merges
anchor	Set/list/remove constitutional authority on nodes
connect	Connect learning nodes to workspace neighborhoods
maintain	Run structural maintenance (health, decay, prune, merge)
compact	Compact old daily notes into graph nodes
sync	Incremental re-embed after file changes
inject	Add CORRECTION/TEACHING/DIRECTIVE nodes
replay	Replay session queries into brain
health	Show graph health metrics
journal	Show event journal
doctor	Run diagnostic checks
info	Show brain info (nodes, edges, embedder)
daemon	Start persistent worker (JSON-RPC over stdio, state loaded once)

Persistent Worker (openclawbrain daemon)

For production use, run OpenClawBrain as a long-lived daemon so the graph stays hot in memory and query paths avoid repeated startup+reload overhead.

Why this matters:

• First load initializes state.json once, then keeps the process and index warm.
• Saves about 100-800ms per call versus shelling out per query (production measure: ~504ms per warm query path on Mac Mini M4 Pro).
• Reduces memory churn and tail latency under steady traffic.

Start it with:

openclawbrain daemon --state ~/.openclawbrain/main/state.json

Protocol:

• Transport: stdin/stdout with newline-delimited JSON (NDJSON).
• Each request is a single JSON object with id, method, and params.
• Each response is a single JSON object with the same id and either result or error.

Example request/response:

echo '{"id":"req-1","method":"query","params":{"query":"how to deploy","top_k":4,"chat_id":"telegram:123"}}' | openclawbrain daemon --state ~/.openclawbrain/main/state.json

{"id":"req-1","result":{"fired_nodes":["a"],"context":"...","seeds":[["a",0.96]],"embed_query_ms":1.1,"traverse_ms":2.4,"total_ms":3.5}}

Supported methods (all 10):

• query: run route traversal and return fired_nodes, context, timing, and seeds.
• learn: apply outcomes (+1/-1) to existing edges and return edges_updated.
• inject: add TEACHING/CORRECTION/DIRECTIVE nodes and connect them to related workspace chunks.
• correction: atomically apply negative feedback to last-fired nodes and inject a CORRECTION node.
• maintain: run maintenance ops and return health/merge summary fields.
• health: return current health metrics for the loaded graph.
• info: return state metadata and object counts.
• save: persist current in-memory state to disk immediately.
• reload: reload state.json without restarting.
• shutdown: persist pending writes and exit cleanly.
• query/learn/maintain/health/info responses include embed_query_ms, traverse_ms, and total_ms timing fields where applicable.
• query/learn/inject/correction are the only mutation-capable methods; the daemon is the single source of truth for those changes while state is hot in memory.

Current limitations:

• Per-chat mutation APIs remain scoped through request payloads (chat_id) and adapter-layer bookkeeping.
• Concurrent writers are serialized by the socket transport lock and one active request at a time.

Production timing (Mac Mini M4 Pro, OpenAI embeddings):

• MAIN (1,158 nodes): 397ms embed + 107ms traverse = 504ms total
• PELICAN (582 nodes): 634ms embed + 51ms traverse = 685ms total
• BOUNTIFUL (285 nodes): 404ms embed + 27ms traverse = 431ms total

See examples/ops/client_example.py for a Python client and docs/architecture.md for protocol details.

True Policy Gradient (apply_outcome_pg)

apply_outcome_pg implements a full REINFORCE policy-gradient update.

• It updates all outgoing edges for each visited node on the fired trajectory, not only traversed edges.
• It uses the update: Δw = (η(z-b)γ^ℓ)/τ · (𝟙[j=a] - π(j|i)) where:
  • η = learning rate
  • z = outcome reward
  • b = baseline
  • γ = discount
  • ℓ = trajectory depth
  • τ = temperature
  • π(j|i) = action probability from softmax (including STOP)
  • 𝟙[j=a] = 1 for the taken action, else 0
• Conservation property: for each source node i, outgoing updates sum to zero, so total outgoing mass is preserved.
• Use apply_outcome_pg when you want smoother, probability-based updates across alternatives; use apply_outcome for a simpler sparse update that only touches traversed edges.

from openclawbrain import apply_outcome_pg, LearningConfig

config = LearningConfig(learning_rate=0.1, temperature=1.0, baseline=0.0)
updates = apply_outcome_pg(graph, fired_nodes=["a", "b", "c"], outcome=1.0, config=config)

Full derivation: https://jonathangu.com/openclawbrain/gu2016/

Write policy summary

Situation	Action
Durable fact	Edit file → sync re-embeds
Correction	Edit file + daemon correction method
Soft teaching	openclawbrain inject --type TEACHING
Wrong retrieval	daemon correction (graph-only, no rebuild)
New rule	Edit AGENTS.md or SOUL.md

Production stats (current)

• MAIN: 1,160 nodes, 2,551 edges, 43 learnings
• PELICAN: 555 nodes, 2,211 edges, 181 learnings
• BOUNTIFUL: 289 nodes, 1,101 edges, 35 learnings
• CORMORANT: 1,672 nodes, ~7,100 edges, 22 learnings (first external user!)

Traversal defaults

Setting	Default	Purpose
beam_width	8	Frontier size per hop (wider = reaches farther routes)
max_hops	30	Safety ceiling; damping controls convergence
fire_threshold	0.01	Minimum score required to fire a candidate node
reflex_threshold	0.6	Edges with weight >= 0.6 auto-follow (no route function)
habitual_range	0.2 - 0.6	Edges in this band run through route function
inhibitory_threshold	-0.01	Edges at or below suppress targets
max_fired_nodes	None	Hard stop on fired node count
max_context_chars	None	Hard stop on rendered traversal context
edge_damping	0.3	Per-reuse decay (weight × 0.3^k)

from openclawbrain import traverse, TraversalConfig

result = traverse(
    graph,
    seeds,
    config=TraversalConfig(max_context_chars=20000, max_fired_nodes=30),
)

query and query_brain.py honor these budgets and stop as soon as any termination condition is met.

External benchmarks

External retrieval benchmarks are optional and use separately downloaded datasets. OpenClawBrain ships a quick-start workflow for MultiHop-RAG and HotPotQA in benchmarks/external/README.md, but the datasets are not in the repository.

Quick run (from project root):

mkdir -p benchmarks/external
curl -L https://huggingface.co/datasets/yixuantt/MultiHopRAG/raw/main/MultiHopRAG.json -o benchmarks/external/multihop_rag.json
curl -L https://curtis.ml.cmu.edu/datasets/hotpot/hotpot_dev_distractor_v1.json -o benchmarks/external/hotpotqa_dev_distractor.json
python3 benchmarks/external/run_multihop_rag.py --limit 50
python3 benchmarks/external/run_hotpotqa.py --limit 50

Python API

from openclawbrain import (
    split_workspace,
    traverse,
    apply_outcome,
    inject_node,
    inject_correction,
    inject_batch,
    VectorIndex,
    HashEmbedder,
    TraversalConfig,
    save_state,
    load_state,
    ManagedState,
    measure_health,
    replay_queries,
    score_retrieval,
)

State lifecycle

• Where it lives: a single state.json file (portable, version-controllable)
• How big: ~180KB for 20 nodes (hash), ~60MB for 1,600 nodes (OpenAI embeddings)
• When to rebuild: after major workspace restructuring or embedder changes
• Embedder changes: OpenClawBrain stores the embedder name + dimension in state metadata and hard-fails on mismatch — no silent corruption
• Merging: use openclawbrain merge to consolidate similar nodes as the graph grows

Cost control

• Free tier: hash embeddings work offline with zero API calls. Good for trying OpenClawBrain and small workspaces.
• Budget tier: use OpenAI text-embedding-3-small (~$0.02/1M tokens). Embed once at init, cache in state.json.
• LLM routing: optional. gpt-5-mini for routing/scoring decisions. Only called during query, not at rest.
• Batch init: openclawbrain init embeds all workspace files in one batch call. Subsequent queries reuse cached vectors.
• Upgrade path: start with hash, switch to real embeddings later by rebuilding state with openclawbrain init.

Optional: warm start from sessions

If you have prior conversation logs, replay them:

openclawbrain replay --state /tmp/brain/state.json --sessions ./sessions/

Skip this if you are just getting started.

Production experience

Three brains run in production on a Mac Mini M4 Pro:

Brain	Nodes	Edges	Learning Corrections	Sessions Replayed
MAIN	1,142	2,814	43	215
PELICAN	512	1,984	181	183
BOUNTIFUL	273	1,073	35	300

Design Tenets

• No network calls in core.
• No secret discovery (no dotfiles, no keychain lookup).
• Embedder identity stored in state metadata; hard-fail on dimension mismatch.
• One canonical state format (state.json).
• Traversal defaults are budget-first for safety: beam_width=8, max_hops=30, fire_threshold=0.01.

Paper + links

jonathangu.com/openclawbrain — 8 deterministic simulations + production deployment data.

• PyPI: pip install openclawbrain
• GitHub: jonathangu/openclawbrain
• ClawHub: clawhub install openclawbrain
• Benchmarks: python3 benchmarks/run_benchmark.py (deterministic per commit; timings vary by machine)