Official release · 5.8.3
Learn from consequences. Carry memory forward.
ECHO is a cognitive agent for local simulations and agent tooling. It observes what happens, learns the consequences of actions and reuses that memory to guide future decisions. Run it on CPU, explore its terminal and carry memory between sessions in WSP files.
Imagine an agent exploring a small simulated world. Some objects move on their own; touching them can end the attempt. Experience with one world can help the agent recognize that role in another, even when the object has a new identifier.
A WSP file is like a memory card for a game: save what the agent has learned, then load it into another compatible ECHO agent so it can reuse that experience. The file carries learned records; the receiving agent uses the same memory format and role representation.
In this recorded MiniGrid example, the agent without memory hits an obstacle at step 2. With learned memory, it reaches the green goal at step 33. The red triangle is the agent; the blue circles are moving obstacles. Left: empty memory. Right: learned memory. This is one illustrative run with new object identifiers.
Both agents finish this recorded task: 38 steps without memory, 27 with memory. The agent picks up a key, unlocks a door and collects the target object. Left: empty memory. Right: learned memory. These clips show individual examples; the evaluation totals below summarize many runs.
This is actual output from the installed release, paced for readability. The
small synthetic example records three observations, saves and reloads a WSP file,
and predicts HURT for a new object with the same role. valid: true confirms the
file passes the format and integrity checks. Try these commands in Getting started.
Explore the interactive replay and controls.
- Agents with persistent memory: record consequences, recall learned roles and save or reload the same memory.
- Interactive simulations: follow an agent's observations, map and actions in the browser panel.
- Connected tools: use the terminal with local backends and an optional language-model adviser.
One echoai installation brings the core, memory tools and terminal together.
The source includes the simulation and repository integrations.
Python 3.12+, Git and a Linux, macOS or WSL shell:
git clone https://github.com/navywakura/echoai.git
cd echoai
bash install.sh
export PATH="$HOME/.local/bin:$PATH"
echoai info
echoai demo --memory echo-memory.wsp
echoai memory verify echo-memory.wsp
echoai tuiThe installer creates a dedicated environment. The small synthetic demo learns
three observations, predicts the same consequence for a new object with the
same role and reloads the saved memory. Expect new_object_prediction: "HURT"
and valid: true. Choose a new filename when repeating the demo.
In the terminal, /help lists commands and /demo opens a sample session.
Download the ready-to-install source package from Hugging Face.
Usage · Memory · Simulation · Terminal
Completed evaluations in simulated worlds:
| What improved? | Measured result | In everyday terms |
|---|---|---|
| Remembering danger | Harmful endings 150.0 → 24.2 | Starting with learned memory reduced attempts that ended in hazards such as lava or contact with moving obstacles. |
| Choosing useful improvements | Unfinished tasks 94.3 → 13.8 out of 120; harmful endings 58.6 → 0 | ECHO selected and combined five available policy improvements, completing more tasks safely. |
| Sharing experience | 160/160 worlds completed, compared with 119/160 for isolated agents | Four agents used shared memory to help complete worlds, including tasks where agents cooperate on pressure plates. |
A harmful ending means a simulated attempt ended in a hazard. An unfinished task means the goal was not reached, whether because of a hazard or because the step budget ran out. Decimal counts are averages across repeated evaluations.
How these numbers were measured
- MEMORIA-X: mean harmful endings per evaluation run, pooled across held-out world families in the A1 setting (fixed object identifiers), over 10 runs; learned memory compared with empty memory.
- ACUMULA-3: means over 10 runs, each with a 120-task evaluation and five rounds of selecting and chaining supplied policy pieces.
- ENJAMBRE-1: four agents per team, 20 teams and eight worlds per team; includes pressure-plate tasks designed for cooperation.
These measurements describe those tasks. ECHO 5.8.3 packages the components with common setup and memory commands.
ECHO 4/4.5 established the core and terminal; 5.0/5.1 added simulation, composition and repository tools; 5.5 developed interactive-world tooling; 5.8 brings role memory, chained improvements and cooperation. The WSP persistence work from 5.8.2 is included in this release.
RxLabs / Nav
rxlabs.org · Source code
GPL-3.0-or-later for the main source; component licenses are included.