Ecosystem report · 43 tracked · data as of Aug 17, 2026

State of the ecosystem.

What moved across every tracked OpenClaw alternative, how the architecture cohorts actually compare, and where the security and deployment posture sits. Numbers are computed from the same data the profiles use — no figure on this page is written by a model.

Measured · tables built at compile timeAI-written summary · Aug 16, 2026Summary written against older data

Tracked

43

projects in every table below

New stars

5,063

across 7 days

Flat

13

gained nothing in the window

Hardened

12

security ≥ 85 and shell risk ≤ 4

The short version

Five things worth knowing before you open the index. Everything below expands on them with the full tables.

  1. 01Hermes Agent pulled most of this cycle's new stars, but OpenClaw still holds the largest total.
  2. 02Twelve projects were flat or declining, so growth is concentrated in a few names.
  3. 03Edge and minimalist runtimes are the largest cohort and carry the highest median security score.
  4. 04Compiled runtimes sandbox better than scripting ones, which matters if you run untrusted tool calls.
  5. 05Most projects are local-first with optional cloud, and MIT remains the most common license.

What moved

Star movement, Aug 10, 2026 → Aug 17, 2026.

Measured from the daily star history for 43 of 43 tracked projects. Absolute growth favours the large repos, so the relative column is listed beside it.

AI readingMost of this cycle's star growth went to Hermes Agent, with OpenClaw still holding the largest total count by a wide margin. Twelve projects were flat or declining, so attention is concentrating rather than lifting the whole field. Smaller projects like Ouroboros and Carapace moved fastest by percentage, which can signal niche adoption rather than broad momentum. If you are picking a tool with a community behind it, the gap between the top names and the long tail is widening.

13 of 43 projects gained nothing over the window.

Architecture

Three cohorts, assigned by rule.

Rules are applied in order and the first match wins, so all 43 projects land in exactly one cohort and none can appear twice. Medians are computed per cohort.

AI readingEdge and minimalist runtimes form the largest cohort at 20 projects, with low memory and fast boot times, and they carry the highest median security score. Team and multi-tenant options are fewer but suit collaborative setups where multi-user isolation matters. Full-runtime assistants sit between the two, offering broader capability at higher footprint. If you want something small and embeddable, the edge cohort is where to look; if you need multi-user support, the team cohort is thinner but purpose-built.

Edge & minimalist20 of 43

Single-binary runtimes small enough for a tiny VPS, an ARM board, or an always-on background process.

Rule: Memory ≤ 20 MB and boot ≤ 50 ms

Mem
10.5 MB
Boot
22.5 ms
Sec
85

+ 15 more in this cohort

Team & multi-tenant9 of 43

Heavier runtimes that carry shared workspaces, tenant separation, or channel fan-out for a group.

Rule: Multi-user declared, above the edge footprint

Mem
80 MB
Boot
200 ms
Sec
65

+ 4 more in this cohort

Full-runtime assistant14 of 43

Single-operator assistants that keep the reference feature surface and pay for it in memory and boot time.

Rule: Everything else

Mem
80 MB
Boot
150 ms
Sec
67.5

+ 9 more in this cohort

Security

The runtime split, in real numbers.

Averages and medians over the five security sub-scores every profile carries. Sandboxing and telemetry safety are protection axes where higher is better; shell risk runs the other way. How the axes are scored.

AI readingCompiled runtimes average a higher security score than scripting runtimes, with better sandboxing and lower shell risk across the median. Scripting runtimes show higher shell risk and weaker sandboxing, which matters when agents execute tool calls without supervision. Nine projects still allow unsupervised shell access, so reviewing that capability before deployment is worthwhile. Hardened projects are a minority, so if runtime isolation is a hard requirement, the compiled cohort is the safer place to start.

CohortProjectsAvg securitySandboxingShell riskTelemetry
Compiled runtimesRust, Go, Zig, C, C++, Kotlin19797 / 104 / 108 / 10
Scripting runtimesPython, TypeScript, JavaScript and friends24675.5 / 107 / 107 / 10

Median score

75 / 100

Across all 43 tracked projects

Hardened

12

Security ≥ 85 with shell risk ≤ 4

Open shell

9

Shell risk ≥ 8, largely unsupervised execution

Posture

Where the data actually runs, and under what licence.

AI readingThirty-four of the tracked projects are local-first, and thirty make cloud optional, so most of the ecosystem works without external dependencies. Ten projects require cloud connectivity, which limits where they can be deployed. MIT is the most common license at twenty projects, followed by Apache-2.0, with a few copyleft options and ten under unknown terms. For developers who need self-hosted or air-gapped deployment, the majority of the field supports that posture without much friction.

Local-first

34 / 43

Cloud required

10 / 43

Cloud optional

30 / 43

Multi-user

14 / 43

Licences

  • MIT20
  • Apache-2.010
  • AGPL-3.02
  • GPL-3.01
  • Not published10

Runtimes by median footprint

Python11 projects · median 75 MB
Rust11 projects · median 15 MB
TypeScript10 projects · median 80 MB
Go4 projects · median 8.5 MB
C2 projects · median 1.2 MB
JavaScript1 project · median 80 MB
JSON1 project · median 80 MB
Kotlin1 project · median 75 MB
Shell1 project · median 1.5 MB
Zig1 project · median 1 MB

19 projects run on a compiled runtime and 24 on a scripting runtime — the split that drives most of the footprint and containment difference above.

Next

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