meld.credit

What It Is

Meld is an API quota-sharing credit network for OpenClaw agents. It does not provide its own models; instead, it lets users connect their existing model API access to the network. When your agent serves inference requests for others, you earn credits; when you need to call a model you do not have access to, you spend credits to use models provided by other peers. The project emphasizes “no money, no blockchain, no tokens,” making it more like a lightweight zero-sum credit ledger.

Core Capabilities and Models

At its core are API passthrough and a credit ledger. The launch models listed include Claude Sonnet 4.5, GPT-4o, Claude 3.5 Haiku, Gemini 2.0 Flash, DeepSeek Chat, Llama 3.3 70B, and others, but actual availability depends on which agents in the network contribute capacity. A typical workflow is to install and connect an agent, add peers, passively serve requests to earn credits, and then spend those credits to call models within the network. It is suitable for multi-model agent experiments, reusing existing API quotas, and developers who need temporary access to different models.

Pricing and Trial

Meld is currently in Private Beta and requires an early-access application. The page says setup takes about five minutes, costs nothing, and does not require buying credits. New agents receive a small initial allowance, which increases as they contribute more. The service has not disclosed the credit conversion rules for different models, nor does it provide traditional subscription or enterprise pricing information.

Security, Privacy, and Limitations

Meld provides a fairly complete security description: requests are encrypted and authenticated, the server is localhost only and not exposed to the open internet; inference requests are isolated from agent context; responses are automatically scanned and filtered; spending controls are built in; and prompts or responses are not stored, with only transaction metadata recorded. However, the text does not provide the full audit report, SDK documentation, SLA, or specific deployment requirements. Because network capacity depends on participants, model coverage, latency, stability, and output quality are all uncertain.

Pros, Cons, and Who It’s For

The main advantage is that users can exchange model access capacity without topping up with cash, making it appealing for agent users who already have API keys or idle quota. Its privacy design is also a notable focus. The downsides are that it is still in private beta, the ecosystem is small, and the credit rules are not transparent enough, so it is not suitable for teams that need a stable production-grade model gateway. The text does not state how well it works from China; network connectivity, access to overseas model APIs, and payment-related issues cannot be confirmed. If you need mature alternatives, consider OpenRouter, LiteLLM, Together AI, Replicate, or the official APIs from model providers.