Conduit

The local MCP gateway that cuts tokens ~90%

Developer ToolsArtificial IntelligenceGitHubOpen Source

▲ 0 votes7 commentsLaunched Jun 23, 2026

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Daily #13Weekly #19

Every MCP server dumps its whole tool list into your agent's context on every request. 3 servers cost ~24k tokens before you even say hi. Conduit routes them through one local gateway that exposes 3 meta-tools the agent searches on demand. Measured: 97% less tool overhead per request, ~90% fewer tokens, same task success. Works on one AI tool or five, cloud or local. Keys in your OS keychain, live toggles, no cloud, no account. Free and open source.

AI Analysis

📝 Summary

Conduit is a local MCP gateway that reduces token usage by ~90% for AI agents. Traditional MCP servers inject full tool lists into context on every request (e.g. 24k tokens for 3 servers), causing high costs and bloat. Conduit routes via one gateway exposing meta-tools searched on-demand, cutting 97% tool overhead while preserving task success. Supports 1-5 tools, cloud or local setups. Features OS keychain storage, live toggles, no cloud/account required. Free and open source. Solves token inefficiency and context overload for developers integrating multiple AI tools.

📈 Market Timing

Favorable for 2025-2026 as AI agents and tool-calling explode in popularity. Token costs and context management are major pain points with maturing LLM tech and rising demand for efficient, privacy-focused local solutions amid cloud cost concerns and regulatory scrutiny on data. Local AI trends and agent ecosystems make optimization tools like Conduit highly relevant. Excellent Timing.

✅ Feasibility

High. The solution is already built and available as open source, indicating manageable technical complexity for a local gateway. Low dev/operation costs with no cloud infrastructure or supply chain needs. Minimal compliance risks as keys stay local. Strong scalability for individual to enterprise agent use. Main risk is MCP protocol adoption but overall highly feasible. Rating: High.

🎯 Target Market

Primary users: AI developers and engineers building LLM agents with multiple tools/servers. Demographics: 25-40yo tech professionals proficient in Python/LLM frameworks. Industries: AI software development, startups, research. Geographic: Global with concentration in US, Europe, China. TAM for AI dev tools large ($B+), SAM for agent infrastructure hundreds of millions, SOM niche but growing. Pain points: high token costs, slow/large contexts. Willingness to pay: high for efficiency but currently addressed as free OSS.

⚔️ Competition

Low. Direct competitors: 1. LangChain Agents (langchain.com), 2. LlamaIndex Tools (llamaindex.ai), 3. Microsoft AutoGen (microsoft.github.io/autogen), 4. Portkey AI Gateway (portkey.ai), 5. LiteLLM (litellm.ai). Advantages: 90% token savings via on-demand meta-tools, fully local/no-cloud, OS keychain security, free OSS with live toggles. Disadvantages: Newer project may have smaller ecosystem/integration breadth compared to established frameworks; depends on MCP server adoption.

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