causal-memory
★ 99
vs
kaeru
★ 40
causal-memory vs kaeru
causal-memory: Causal-memory is an agent memory system with a causal core, uniquely modeling both excitatory and inhibitory connections for robust long-term retention. It enables agents to understand not just what happened, but why it worked or failed, and anticipate outcomes, thereby preventing repeated mistakes.; kaeru: Kaeru is a local-first, cross-agent cognitive engine for LLM agents, providing a typed property graph and a recollection layer for long-term memory. It enables multi-session and multi-agent continuity, allowing agents to maintain full context, follow provenance, and build on shared knowledge over time.
01
TL;DR
causal-memoryMemory & Context · ★ 99
Preventing Repeated Mistakes: Agents can recall past `decision → outcome` causal edges, avoiding re-fixing the same bug or repeating past failed architectural choices.
kaeruMemory & Context · ★ 40
An agent needs to recall its thought process and project context from a previous session.
02
Side-by-Side Comparison
Field
causal-memorykaeruCategory
Memory & Context
Memory & Context
Stars
★ 99
★ 40
License
Apache-2.0
MIT
Install Command
git clone https://github.com/JingxuanC/causal-memory.git && cd causal-memory && cargo build --release
git clone https://github.com/kaerulabs/kaeru.git && cd kaeru && cargo build --release
Updated
1w ago
2d ago
Open Source
Yes
Yes
Tags
Agent Memory, Causal Reasoning, Long-term Memory
LLM Agents, Knowledge Graph, Bi-temporal Database
03
Features
causal-memory01Causal Core with Excitatory and Inhibitory Connections: Models 'caused', 'enabled', and 'prevented' relationships using typed causal semantics and negative activation spread (GABA analogue).
02Immutable Consolidation & Q-value Dynamics: Employs hippocampus-style SWR (Sleep-Wake Replay) consolidation with LTP/LTD/GC, Q-value dynamics, and novelty-entropy triggers for robust memory evolution.
03Intervention & Counterfactual Query: Enables forward simulation to predict outcomes before action and contrastive analysis to compare outcomes of different alternatives.
04Compaction Survival: Stores causal information in an external SQLite database, making it immune to context window compaction and preserving critical 'why' knowledge.
05Unified Memory Graph: Integrates facts, temporal state, and decision-outcome causal edges into a single graph, accessible via RRF-fused retrieval (BM25 + semantic).
kaeru01Two-tier graph (operational/hippocampus and archival/cortex) for active thinking and settled knowledge.
02Bi-temporal nature with native assertion/retraction history for time-travel queries and non-destructive conflict resolution.
03Per-initiative scoping and layered re-entry to restore project context across sessions and access archival knowledge on demand.
04Optional cross-agent sharing through a cloud tier, allowing trusted teams to share settled knowledge with safety gates.
05Structural recall based on exact name lookup, typed graph traversal, and FTS fuzzy fallback, rather than primarily vector embeddings.
04
Use Cases
causal-memory↳Preventing Repeated Mistakes: Agents can recall past `decision → outcome` causal edges, avoiding re-fixing the same bug or repeating past failed architectural choices.
↳Proactive Warning and Intervention: Before committing risky actions (e.g., `git push --no-verify`), the system can fire DANGER warnings based on recorded past negative outcomes.
↳Root Cause Analysis: Trace multi-hop backward causal chains to identify the ultimate root cause of failures or unexpected outcomes.
↳Informed Decision Making: Agents can perform counterfactual comparisons between alternative actions, leveraging known outcomes to choose the best path.
↳Cross-Task Lesson Transfer: Discover and apply meta-edges (e.g., `similar_to`, `repeated`) to transfer lessons and patterns across different tasks or sessions.
kaeru↳An agent needs to recall its thought process and project context from a previous session.
↳A team of agents needs to share and build upon a collective, long-term knowledge base.
↳An agent wants to capture new observations or hypotheses within a specific project scope.
↳Tracing the provenance and history of a piece of knowledge or a decision made by an agent.
FAQ
FAQ
What is the difference between causal-memory and kaeru?
Both causal-memory and kaeru are in the Memory & Context category. causal-memory has 99 stars, while kaeru has 40 stars.
Which is better, causal-memory or kaeru?
The best choice depends on your use case. Choose causal-memory if Preventing Repeated Mistakes: Agents can recall past `decision → outcome` causal edges, avoiding re-fixing the same bug or repeating past failed architectural choices., and kaeru if An agent needs to recall its thought process and project context from a previous session..
Is causal-memory free or open source?
Yes, causal-memory is open source on GitHub (Apache-2.0).
Is kaeru free or open source?
Yes, kaeru is open source on GitHub (MIT).
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