composio vs conductor
Two of the top projects in Agent and MCP starters, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
composio
TypeScript and Python SDKs that give agents authenticated tools across 1000+ apps
conductor
Durable workflow engine that orchestrates microservices and LLM agents from JSON
Verdict
composio ranks higher, 92 to 91. Of the 4 score parts we measure for both, composio leads on 1, conductor on 1 and 2 are even.
- Adoptionconductor leads by 1 point, 93 to 92: close to even.
- FreshnessEven at 100.
- MaintenanceEven at 86.
- Agent-readycomposio leads by 15 points, 85 to 70: a wide gap.
Pick composio if you are
Developers wiring agents to third-party apps with managed per-user auth
- Adapters for about 16 frameworks, including OpenAI Agents, Claude Agent SDK, LangChain and Mastra
- Per-user sessions handle authentication, so the agent does not manage OAuth flows
Pick conductor if you are
Backend teams needing durable orchestration for services and AI agent loops
- Every step is persisted, with retry, restart, rerun, pause and terminate controls on executions
- Workflows are versioned JSON; running executions stay pinned to the version they started on
| What we compare | composio | conductor |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 92, widely used | 93, widely used |
| Momentum | Not scored for this kind | Not scored for this kind |
| Freshness | 100, active | 100, active |
| Maintenance | 86, healthy | 86, healthy |
| Agent-ready | 85, ready | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 31k | 32.5k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Nov 2026 | Nov 2026 |
| Last release | Nov 2026 | Oct 2026 |
| Language | TypeScript | Java |
| Docker | No | Yes |
| GPU | Not needed | Not stated |
| arm64 or Apple Silicon | Mentioned | Mentioned |
composio
Composio is an SDK monorepo (TypeScript, Python, and a CLI) that creates per-user sessions exposing pre-authenticated toolkits to an agent. Sessions default to meta tools that discover, authenticate and execute app tools at runtime, and can also be served through a hosted MCP endpoint. Provider adapters convert the tools to the formats of OpenAI, Anthropic, LangChain, Vercel AI SDK, Google ADK, CrewAI and others.
Who it is for: Developers wiring agents to third-party apps with managed per-user auth
Strengths
- Adapters for about 16 frameworks, including OpenAI Agents, Claude Agent SDK, LangChain and Mastra
- Per-user sessions handle authentication, so the agent does not manage OAuth flows
- Meta tools load tools at runtime instead of putting hundreds of definitions in context
- Each session can also be exposed as a hosted MCP URL for Claude, Cursor or other clients
Weaknesses
- Requires a COMPOSIO_API_KEY from the hosted dashboard; the README documents no self-hosted option
- The SDK is open source, but toolkits and auth run through Composio's service
- Provider coverage is uneven: Vercel and Mastra are TypeScript only, CrewAI and AutoGen Python only
- Pi provider is experimental and ships from a separate package
- TypeScript, OpenAI, Anthropic, Google GenAI, LangChain
- Needs Composio API key (hosted service), Node 22+ for TypeScript SDK, Python 3.10+ for Python SDK
conductor
Conductor is a Java workflow orchestration server, originally from Netflix, that persists every task and runs versioned JSON workflow graphs. Workers in Java, Python, Go, JavaScript, C#, Ruby or Rust poll for tasks, while built-in tasks cover LLM chat, MCP tool discovery and calls, human approval, loops, forks and sub-workflows. It runs on Redis, Postgres or MySQL, with Elasticsearch or OpenSearch for indexing.
Who it is for: Backend teams needing durable orchestration for services and AI agent loops
Strengths
- Every step is persisted, with retry, restart, rerun, pause and terminate controls on executions
- Workflows are versioned JSON; running executions stay pinned to the version they started on
- Native LLM_CHAT_COMPLETE, LIST_MCP_TOOLS and CALL_MCP_TOOL tasks for agent loops
- Multiple persistence backends (Redis, Postgres, MySQL) and polyglot worker SDKs
Weaknesses
- Requires Java 25+ and a separate index store such as Elasticsearch or OpenSearch
- Orchestration is declarative JSON, not code, which suits some teams less than code-first engines
- Ruby and Rust SDKs are marked incubating
- Not a ready-made agent app; you build agents by composing workflow tasks
- Java, OpenAI (via llmProvider)
- Needs Java 25+, Node.js 16+ (CLI), Redis, Postgres or MySQL, Elasticsearch or OpenSearch
- Docker
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