composable-agents

Configure Deep Agent LangGraph agents in YAML and expose them via FastAPI.

composable-agents is a Python framework that lets you declare AI agents as simple YAML files and instantly expose them as a full-featured HTTP API. It is built on deepagents (LangGraph-based Deep Agent) with a strict hexagonal architecture, making every component testable and replaceable.

The server supports multi-agent mode: multiple agents are defined as separate YAML files in an agents/ directory, each thread is bound to a specific agent at creation time, and agents are lazily instantiated on first use.

Quickstart (5 minutes)

Prerequisites

Python 3.11+
UV package manager
An API key for at least one LLM provider (Anthropic, OpenAI, or Google)

Installation

git clone https://github.com/your-org/composable-agents.git
cd composable-agents
uv sync
cp .env.example .env

Edit .env and add your API key:

ANTHROPIC_API_KEY=sk-ant-...
# or
OPENAI_API_KEY=sk-...
# or
GOOGLE_API_KEY=...

Configure your agents

Each agent is a standalone YAML file inside the agents/ directory. A minimal agent only needs a name. Create agents/my-agent.yaml:

name: my-agent

Or use one of the provided examples in the agents/ directory (see Examples).

Validate the configuration

uv run python -m src validate agents/my-agent.yaml

Launch the server

uv run python -m src serve

The API starts on http://localhost:8000. On startup, the server reads the AGENTS_DIR environment variable (default: ./agents) to discover available agents. Agents are not loaded into memory until a thread references them for the first time.

Test with curl

# Health check
curl http://localhost:8000/health

# Create a thread bound to an agent (agent_name must match a YAML filename in agents/)
curl -X POST http://localhost:8000/api/v1/threads \
  -H "Content-Type: application/json" \
  -d '{"agent_name": "my-agent"}'

# Send a message (replace <thread_id> with the id from the previous response)
curl -X POST http://localhost:8000/api/v1/chat/<thread_id> \
  -H "Content-Type: application/json" \
  -d '{"message": "Hello, what can you do?"}'

Multi-Agent Architecture

composable-agents now supports running multiple agents simultaneously. Each agent is defined by a separate YAML file in the agents/ directory.

How it works

Discovery -- On startup, the server scans AGENTS_DIR (default: ./agents) for .yaml files. The filename (without extension) becomes the agent name.
Thread creation -- When creating a thread via POST /api/v1/threads, you specify an agent_name. If no matching YAML file exists, the API returns 404.
Lazy loading -- The agent (LangGraph graph + runner) is created only when a thread first sends a message to it. Subsequent requests reuse the cached runner.
Per-thread binding -- Each thread is permanently bound to its agent. Different threads can use different agents.

Key components

Component	Location	Role
`AgentRegistry` (port)	`src/domain/ports/agent_registry.py`	Abstract interface for retrieving agent runners by name.
`DeepAgentRegistry` (adapter)	`src/infrastructure/deepagent/registry.py`	Scans `agents/` directory, creates and caches runners on demand.
`AgentNotFoundError`	`src/domain/exceptions.py`	Raised when a requested agent name has no corresponding YAML file.

Example: two agents, two threads

# Create a thread using the research assistant agent
curl -X POST http://localhost:8000/api/v1/threads \
  -H "Content-Type: application/json" \
  -d '{"agent_name": "research-assistant"}'
# Returns: {"id": "thread-1-uuid", "agent_name": "research-assistant", ...}

# Create another thread using the code reviewer agent
curl -X POST http://localhost:8000/api/v1/threads \
  -H "Content-Type: application/json" \
  -d '{"agent_name": "code-reviewer"}'
# Returns: {"id": "thread-2-uuid", "agent_name": "code-reviewer", ...}

# Each thread talks to its own agent
curl -X POST http://localhost:8000/api/v1/chat/<thread-1-uuid> \
  -H "Content-Type: application/json" \
  -d '{"message": "Summarize the latest research on transformers."}'

curl -X POST http://localhost:8000/api/v1/chat/<thread-2-uuid> \
  -H "Content-Type: application/json" \
  -d '{"message": "Review this Python function for security issues."}'

Configuration YAML Reference

Every agent is defined by a single YAML file validated against the AgentConfig Pydantic schema.

Field	Type	Default	Description
`name`	`string` (required)	--	Unique agent name (1-100 characters).
`model`	`string`	`"claude-sonnet-4-5-20250929"`	LLM model identifier. See Supported Models.
`model_kwargs`	`dict`	`null`	Extra keyword arguments passed to `init_chat_model()` (e.g. `base_url`, `api_key`).
`system_prompt`	`string`	`null`	Inline system prompt. Mutually exclusive with `system_prompt_file`.
`system_prompt_file`	`string`	`null`	Path to a text file containing the system prompt (resolved relative to the YAML file). Mutually exclusive with `system_prompt`.
`tools`	`list[string]`	`[]`	Python tool references in `module.path:attribute` format.
`middleware`	`list[MiddlewareType]`	`[]`	Middleware to attach. See Middlewares.
`backend`	`BackendConfig`	`{"type": "state"}`	Persistence backend. See Backends.
`hitl`	`HITLConfig`	`{"rules": {}}`	Human-in-the-loop interrupt rules.
`memory`	`list[string]`	`[]`	Paths to memory files (e.g. `"./AGENTS.md"`).
`skills`	`list[string]`	`[]`	Paths to skill directories (e.g. `"./skills/"`).
`subagents`	`list[SubAgentConfig]`	`[]`	Sub-agent definitions for delegation.
`mcp_servers`	`list[McpServerConfig]`	`[]`	MCP server connections. See MCP Servers.
`debug`	`bool`	`false`	Enable debug mode.

SubAgentConfig

Field	Type	Default	Description
`name`	`string` (required)	--	Sub-agent name.
`description`	`string` (required)	--	Description of the sub-agent's role.
`instructions`	`string`	`null`	System prompt / instructions for the sub-agent.
`model`	`string`	`null`	Override model for this sub-agent.
`tools`	`list[string]`	`[]`	Tool references specific to this sub-agent.
`skills`	`list[string]`	`[]`	Skill paths for this sub-agent.
`mcp_servers`	`list[McpServerConfig]`	`[]`	MCP servers for this sub-agent.

McpServerConfig

Field	Type	Default	Description
`name`	`string` (required)	--	Server identifier.
`transport`	`"stdio"` or `"http"` (required)	--	Transport type.
`command`	`string`	`null`	Command to run (required for `stdio` transport).
`args`	`list[string]`	`[]`	Command arguments (for `stdio` transport).
`url`	`string`	`null`	Server URL (required for `http` transport).
`headers`	`dict[string, string]`	`{}`	HTTP headers (for `http` transport).
`env`	`dict[string, string]`	`{}`	Environment variables for the server process. Supports `${VAR_NAME}` syntax for resolving env vars.

HITL Rules

HITL rules map tool names to either a boolean or a detailed rule:

hitl:
  rules:
    write_file: true                  # Simple: interrupt on any call
    execute:                          # Detailed: restrict allowed decisions
      allowed_decisions:
        - approve
        - reject

Allowed decisions: approve, edit, reject.

Supported Models

Provider	Format	Example
Anthropic	`claude-<variant>`	`claude-sonnet-4-5-20250929`
OpenAI	`openai:<model>`	`openai:gpt-4o`
Google	`google_genai:<model>`	`google_genai:gemini-2.0-flash`

The default model is claude-sonnet-4-5-20250929.

For OpenAI-compatible endpoints (OpenRouter, LiteLLM, vLLM, etc.), set the OPENAI_BASE_URL environment variable to point to your endpoint. The OpenAI SDK reads this variable automatically. Alternatively, use model_kwargs in the YAML to set base_url and api_key per-agent.

MCP Servers

Agents can connect to Model Context Protocol (MCP) servers for tool access. MCP servers are defined in the agent's YAML config:

name: mcp-agent
model: claude-sonnet-4-5-20250929
system_prompt: "You are an agent with MCP tool access."
mcp_servers:
  - name: filesystem
    transport: stdio
    command: npx
    args: ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"]

Environment variables in env fields support the ${VAR_NAME} resolution syntax.

Middlewares

Name	Enum Value	Description
Todo List	`todo_list`	Filesystem-based task tracking middleware.
Filesystem	`filesystem`	Gives the agent read/write access to files on disk.
Sub-Agent	`sub_agent`	Enables delegation to sub-agents defined in `subagents`.

Backends

Name	Enum Value	Description
State	`state`	Default in-memory state backend (no extra config).
Filesystem	`filesystem`	Persists agent state to a directory. Accepts `root_dir`.
Store	`store`	LangGraph store-based backend.
Composite	`composite`	Reserved for advanced composite configurations.

Example with a filesystem backend:

backend:
  type: filesystem
  root_dir: "./workspace"

API Reference

All endpoints are prefixed appropriately. The server runs on http://localhost:8000 by default.

Method	Path	Description	Success Status
`GET`	`/health`	Health check	`200`
`POST`	`/api/v1/threads`	Create a new conversation thread (bound to an agent)	`201`
`GET`	`/api/v1/threads`	List all threads	`200`
`GET`	`/api/v1/threads/{thread_id}`	Get a specific thread	`200`
`DELETE`	`/api/v1/threads/{thread_id}`	Delete a thread	`204`
`GET`	`/api/v1/threads/{thread_id}/messages`	List messages in a thread	`200`
`POST`	`/api/v1/chat/{thread_id}`	Send a message and get the full response	`200`
`POST`	`/api/v1/chat/{thread_id}/stream`	Send a message and stream the response (SSE)	`200`
`POST`	`/api/v1/threads/{thread_id}/hitl`	Submit a human-in-the-loop decision	`200`
`GET`	`/api/v1/agents`	List all agent configs from `agents/` directory	`200`
`GET`	`/api/v1/agents/{agent_name}`	Get a specific agent configuration	`200`
`WS`	`/api/v1/ws/{thread_id}`	WebSocket endpoint for streaming chat	--

Error Responses

Status	Condition
`400`	General configuration error
`404`	Thread not found, agent not found, or config file not found
`422`	Validation error (bad request body, invalid config schema)
`502`	Agent execution error (LLM failure)
`500`	Unexpected domain error

curl Examples

1. Health Check

curl http://localhost:8000/health

Response:

{"status": "ok"}

2. List Available Agents

curl http://localhost:8000/api/v1/agents

Response (200):

[
  {
    "name": "code-reviewer",
    "model": "claude-sonnet-4-5-20250929",
    "system_prompt": "You are an expert code reviewer...",
    "tools": [],
    "middleware": ["filesystem", "sub_agent"],
    "backend": {"type": "state", "root_dir": null},
    "hitl": {"rules": {"write_file": true, "execute": {"allowed_decisions": ["approve", "reject"]}}},
    "subagents": [...]
  },
  {
    "name": "example-agent",
    "model": "openai:anthropic/claude-haiku-4.5:nitro",
    "system_prompt": "You are a helpful assistant.",
    "tools": [],
    "middleware": [],
    "backend": {"type": "state", "root_dir": null},
    "hitl": {"rules": {}},
    "subagents": []
  }
]

3. Get a Specific Agent Configuration

curl http://localhost:8000/api/v1/agents/example-agent

Response (200):

{
  "name": "example-agent",
  "model": "openai:anthropic/claude-haiku-4.5:nitro",
  "system_prompt": "You are a helpful assistant.",
  "system_prompt_file": null,
  "tools": [],
  "middleware": [],
  "backend": {"type": "state", "root_dir": null},
  "hitl": {"rules": {}},
  "memory": [],
  "skills": [],
  "subagents": [],
  "mcp_servers": [],
  "debug": false
}

If the agent does not exist:

curl http://localhost:8000/api/v1/agents/nonexistent

Response (404):

{"detail": "Fichier de configuration introuvable: agents/nonexistent.yaml"}

4. Create a Thread

The agent_name must match an existing YAML filename (without the .yaml extension) in the agents/ directory.

curl -X POST http://localhost:8000/api/v1/threads \
  -H "Content-Type: application/json" \
  -d '{"agent_name": "example-agent"}'

Response (201):

{
  "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "agent_name": "example-agent",
  "messages": [],
  "created_at": "2025-01-15T10:30:00.000000",
  "updated_at": "2025-01-15T10:30:00.000000"
}

If the agent name does not match any YAML file:

curl -X POST http://localhost:8000/api/v1/threads \
  -H "Content-Type: application/json" \
  -d '{"agent_name": "nonexistent-agent"}'

Response (404):

{"detail": "Agent introuvable: nonexistent-agent"}

5. Send a Message

curl -X POST http://localhost:8000/api/v1/chat/a1b2c3d4-e5f6-7890-abcd-ef1234567890 \
  -H "Content-Type: application/json" \
  -d '{"message": "Explain the hexagonal architecture pattern in 3 sentences."}'

Response (200):

{
  "role": "ai",
  "content": "Hexagonal architecture separates core business logic from external concerns...",
  "timestamp": "2025-01-15T10:30:05.000000",
  "tool_calls": null,
  "tool_call_id": null
}

6. Stream a Message (SSE)

curl -N -X POST http://localhost:8000/api/v1/chat/a1b2c3d4-e5f6-7890-abcd-ef1234567890/stream \
  -H "Content-Type: application/json" \
  -d '{"message": "Write a haiku about programming."}'

Response (Server-Sent Events):

data: Lines
data:  of
data:  code
data:  align
data: ...

7. List All Threads

curl http://localhost:8000/api/v1/threads

Response (200):

[
  {
    "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
    "agent_name": "example-agent",
    "messages": [],
    "created_at": "2025-01-15T10:30:00.000000",
    "updated_at": "2025-01-15T10:30:00.000000"
  },
  {
    "id": "b2c3d4e5-f6a7-8901-bcde-f12345678901",
    "agent_name": "research-assistant",
    "messages": [],
    "created_at": "2025-01-15T10:31:00.000000",
    "updated_at": "2025-01-15T10:31:00.000000"
  }
]

8. Get a Specific Thread

curl http://localhost:8000/api/v1/threads/a1b2c3d4-e5f6-7890-abcd-ef1234567890

9. List Messages in a Thread

curl http://localhost:8000/api/v1/threads/a1b2c3d4-e5f6-7890-abcd-ef1234567890/messages

Response (200):

[
  {
    "role": "human",
    "content": "Explain the hexagonal architecture pattern in 3 sentences.",
    "timestamp": "2025-01-15T10:30:00.000000",
    "tool_calls": null,
    "tool_call_id": null
  },
  {
    "role": "ai",
    "content": "Hexagonal architecture separates core business logic from external concerns...",
    "timestamp": "2025-01-15T10:30:05.000000",
    "tool_calls": null,
    "tool_call_id": null
  }
]

10. HITL -- Approve a Pending Tool Call

When the agent is configured with HITL rules and a tool call is interrupted, submit a decision:

curl -X POST http://localhost:8000/api/v1/threads/a1b2c3d4-e5f6-7890-abcd-ef1234567890/hitl \
  -H "Content-Type: application/json" \
  -d '{
    "tool_call_id": "call_abc123",
    "action": "approve"
  }'

Response (200):

{
  "role": "ai",
  "content": "Action approved. Proceeding with file write.",
  "timestamp": "2025-01-15T10:31:00.000000",
  "tool_calls": null,
  "tool_call_id": null
}

11. HITL -- Reject a Pending Tool Call

curl -X POST http://localhost:8000/api/v1/threads/a1b2c3d4-e5f6-7890-abcd-ef1234567890/hitl \
  -H "Content-Type: application/json" \
  -d '{
    "tool_call_id": "call_abc123",
    "action": "reject",
    "reason": "This operation is too risky for production."
  }'

12. HITL -- Edit and Approve a Pending Tool Call

curl -X POST http://localhost:8000/api/v1/threads/a1b2c3d4-e5f6-7890-abcd-ef1234567890/hitl \
  -H "Content-Type: application/json" \
  -d '{
    "tool_call_id": "call_abc123",
    "action": "edit",
    "edits": {"filename": "safe_output.txt", "content": "sanitized content"}
  }'

13. Delete a Thread

curl -X DELETE http://localhost:8000/api/v1/threads/a1b2c3d4-e5f6-7890-abcd-ef1234567890

Response: 204 No Content

WebSocket

Connect to the WebSocket endpoint and send JSON messages:

const ws = new WebSocket("ws://localhost:8000/api/v1/ws/<thread_id>");
ws.onopen = () => ws.send(JSON.stringify({ message: "Hello" }));
ws.onmessage = (event) => {
  if (event.data === "[END]") {
    console.log("Response complete");
  } else {
    process.stdout.write(event.data);
  }
};

Architecture

composable-agents follows a strict hexagonal architecture (ports and adapters). The domain layer has zero dependencies on frameworks or infrastructure.

                    +---------------------------+
                    |      HTTP / WebSocket     |
                    |   (FastAPI application)   |
                    +------------+--------------+
                                 |
                    +------------+--------------+
                    |       Use Cases           |
                    | (application/use_cases/)  |
                    +------+----------+---------+
                           |          |
               +-----------+          +-----------+
               |                                  |
    +----------+---------+            +-----------+---------+
    |   Domain Ports     |            |   Domain Entities   |
    | (abstract classes) |            | AgentConfig, Thread |
    +----------+---------+            |   Message           |
               |                      +---------------------+
    +----------+---------+
    |  Infrastructure    |
    | (adapters)         |
    +--------------------+
    | - DeepAgentRunner  |
    | - DeepAgentRegistry|
    | - YamlConfigLoader |
    | - InMemoryThreads  |
    +--------------------+

File Tree

composable-agents/
  agents/                              # YAML agent configuration files
    example-agent.yaml                 #   Basic example agent
    minimal.yaml                       #   Minimal agent (name only)
    mcp-agent.yaml                     #   Agent with MCP server tools
    research-assistant.yaml            #   Research assistant with tools
    code-reviewer.yaml                 #   Code reviewer with HITL + subagents
  src/
    main.py                            # FastAPI app creation and lifespan
    config.py                          # Pydantic Settings (env vars)
    dependencies.py                    # Dependency injection wiring
    application/
      requests/
        chat.py                        # Request models (ChatRequest, CreateThreadRequest, HITLDecisionRequest)
      routes/
        health.py                      # GET /health
        threads.py                     # CRUD /api/v1/threads
        chat.py                        # POST /api/v1/chat/{id} and /stream
        hitl.py                        # POST /api/v1/threads/{id}/hitl
        agents.py                      # GET /api/v1/agents
        websocket.py                   # WS /api/v1/ws/{id}
      use_cases/
        send_message.py                # Invoke agent synchronously
        stream_message.py              # Stream agent response
        hitl_decision.py               # Approve / reject / edit HITL decisions
        load_agent_config.py           # Load and validate a YAML config
        thread_management.py           # Create / get / list / delete threads
    domain/
      entities/
        agent_config.py                # AgentConfig, BackendConfig, HITLConfig, SubAgentConfig
        mcp_server_config.py           # McpServerConfig, McpTransportType
        message.py                     # Message (role, content, timestamp, tool_calls)
        thread.py                      # Thread (id, agent_name, messages, timestamps)
        tracing_config.py              # TracingConfig, TracingProviderType
      ports/
        agent_config_loader.py         # Abstract: load config from file
        agent_registry.py              # Abstract: get_runner(name), list_agents(), close()
        agent_runner.py                # Abstract: invoke, stream, HITL operations
        mcp_tool_loader.py             # Abstract: load MCP tools
        thread_repository.py           # Abstract: CRUD for threads
        tracing_provider.py            # Abstract: tracing lifecycle
      exceptions.py                    # DomainError hierarchy (incl. AgentNotFoundError)
    infrastructure/
      env_utils.py                     # ${VAR_NAME} environment variable resolution
      deepagent/
        adapter.py                     # DeepAgentRunner (LangGraph adapter)
        factory.py                     # create_agent_from_config (resolves tools, middleware, backend)
        registry.py                    # DeepAgentRegistry (lazy loading + caching from agents/ dir)
        example_tools.py               # Example tools: current_time, word_count
      mcp/
        adapter.py                     # LangchainMcpToolLoader
      memory_thread/
        adapter.py                     # InMemoryThreadRepository
      yaml_config/
        adapter.py                     # YamlAgentConfigLoader
      tracing/
        langfuse_adapter.py            # Langfuse tracing provider
        phoenix_adapter.py             # Phoenix tracing provider
        noop_adapter.py                # No-op tracing provider (default)
  tests/
    conftest.py
    doubles/
      fake_agent_runner.py             # FakeAgentRunner for unit tests
      fake_agent_config_loader.py      # FakeAgentConfigLoader for unit tests
      fake_agent_registry.py           # FakeAgentRegistry for unit tests
      fake_mcp_tool_loader.py          # FakeMcpToolLoader for unit tests
      fake_tracing_provider.py         # FakeTracingProvider for unit tests
    unit/
      test_agent_config.py
      test_deep_agent_runner.py
      test_env_utils.py
      test_factory.py
      test_factory_mcp_integration.py
      test_hitl.py
      test_langfuse_adapter.py
      test_load_agent_config_use_case.py
      test_mcp_adapter.py
      test_mcp_lifecycle.py
      test_mcp_server_config.py
      test_noop_tracing.py
      test_phoenix_adapter.py
      test_routes.py
      test_runner_tracing.py
      test_send_message.py
      test_thread.py
      test_thread_management.py
      test_tracing_config.py
      test_tracing_di.py
      test_tracing_lifecycle.py
      test_yaml_loader.py
  .env.example                         # Environment variable template
  pyproject.toml                       # Project metadata and dependencies
  CONTRIBUTING.md                      # Contributor guide
  uv.lock                             # Lockfile

Examples

Minimal Agent

agents/minimal.yaml -- the simplest possible agent. Uses all defaults (Claude Sonnet, no tools, state backend).

name: minimal-agent

Example Agent (OpenAI-compatible endpoint)

agents/example-agent.yaml -- a basic agent using an OpenAI-compatible model via OpenRouter.

name: example-agent
model: "openai:anthropic/claude-haiku-4.5:nitro"
system_prompt: "You are a helpful assistant."

MCP Agent

agents/mcp-agent.yaml -- an agent connected to an MCP filesystem server.

name: mcp-agent
model: claude-sonnet-4-5-20250929
system_prompt: "You are an agent with MCP tool access."
mcp_servers:
  - name: filesystem
    transport: stdio
    command: npx
    args: ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"]

Research Assistant with Tools

agents/research-assistant.yaml -- an agent with custom tools and filesystem persistence.

name: research-assistant
model: "claude-sonnet-4-5-20250929"
system_prompt: |
  You are a research assistant specialized in technical documentation.
  Always cite your sources and provide structured summaries.
tools:
  - "src.infrastructure.deepagent.example_tools:current_time"
  - "src.infrastructure.deepagent.example_tools:word_count"
middleware:
  - filesystem
backend:
  type: filesystem
  root_dir: "./workspace"
debug: false

Code Reviewer with HITL and Subagents

agents/code-reviewer.yaml -- a multi-agent system with human-in-the-loop approval.

name: code-reviewer
model: "claude-sonnet-4-5-20250929"
system_prompt: |
  You are an expert code reviewer. Analyze code for correctness,
  performance, security, and maintainability.
middleware:
  - filesystem
  - sub_agent
backend:
  type: state
hitl:
  rules:
    write_file: true
    execute:
      allowed_decisions:
        - approve
        - reject
subagents:
  - name: security-auditor
    description: "Specialized in security vulnerability analysis"
    instructions: "Focus on OWASP Top 10 and common security patterns"
  - name: performance-analyst
    description: "Specialized in performance optimization"
    instructions: "Analyze time complexity, memory usage, and bottlenecks"

Environment Variables

Configured via .env file or environment variables. See .env.example.

Variable	Default	Description
`AGENTS_DIR`	`./agents`	Directory containing agent YAML configuration files.
`ANTHROPIC_API_KEY`	--	API key for Anthropic models.
`OPENAI_API_KEY`	--	API key for OpenAI models.
`GOOGLE_API_KEY`	--	API key for Google models.
`OPENAI_BASE_URL`	`https://api.openai.com/v1`	Base URL for OpenAI-compatible endpoints. Set to use OpenRouter, LiteLLM, vLLM, etc.
`HOST`	`0.0.0.0`	Server bind host.
`PORT`	`8000`	Server bind port.

Tracing Variables

Variable	Default	Description
`TRACING_PROVIDER`	`none`	Tracing backend: `none`, `langfuse`, or `phoenix`.
`TRACING_ENABLED`	`false`	Enable/disable tracing.
`TRACING_PROJECT_NAME`	`composable-agents`	Project name for the tracing backend.
`LANGFUSE_HOST`	`https://cloud.langfuse.com`	Langfuse server URL.
`LANGFUSE_PUBLIC_KEY`	--	Langfuse public key.
`LANGFUSE_SECRET_KEY`	--	Langfuse secret key.
`PHOENIX_COLLECTOR_ENDPOINT`	`http://localhost:6006`	Phoenix collector endpoint.
`PHOENIX_API_KEY`	--	Phoenix API key.
`LANGCHAIN_API_KEY`	--	LangChain/LangSmith API key.
`LANGCHAIN_PROJECT`	`composable-agents`	LangChain/LangSmith project name.

Development

Install dependencies (including dev tools)

uv sync

Run the test suite

uv run pytest tests/ -v

Run tests with coverage

uv run pytest tests/ -v --cov=src

Lint

uv run ruff check .

Type check

uv run mypy src/

Validate all agent YAML files

for f in agents/*.yaml; do uv run python -m src validate "$f"; done

Optional Dependencies

The project provides optional dependency groups for tracing support:

# Langfuse tracing only
uv sync --extra langfuse

# Phoenix tracing only
uv sync --extra phoenix

# All tracing providers
uv sync --extra tracing

Contributing

See CONTRIBUTING.md for details on:

Project architecture and dependency rules
How to add custom tools, middlewares, and backends
How the YAML schema works
Running tests and linting
Code style conventions

License

This project does not currently include a license file. Contact the maintainers for licensing information.

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
agents		agents
src		src
tests		tests
.env.example		.env.example
.gitignore		.gitignore
.python-version		.python-version
CONTRIBUTING.md		CONTRIBUTING.md
README.md		README.md
pyproject.toml		pyproject.toml
uv.lock		uv.lock

SoluDevTech/composable-agents

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composable-agents

Quickstart (5 minutes)

Prerequisites

Installation

Configure your agents

Validate the configuration

Launch the server

Test with curl

Multi-Agent Architecture

How it works

Key components

Example: two agents, two threads

Configuration YAML Reference

SubAgentConfig

McpServerConfig

HITL Rules

Supported Models

MCP Servers

Middlewares

Backends

API Reference

Error Responses

curl Examples

1. Health Check

2. List Available Agents

3. Get a Specific Agent Configuration

4. Create a Thread

5. Send a Message

6. Stream a Message (SSE)

7. List All Threads

8. Get a Specific Thread

9. List Messages in a Thread

10. HITL -- Approve a Pending Tool Call

11. HITL -- Reject a Pending Tool Call

12. HITL -- Edit and Approve a Pending Tool Call

13. Delete a Thread

WebSocket

Architecture

File Tree

Examples

Minimal Agent

Example Agent (OpenAI-compatible endpoint)

MCP Agent

Research Assistant with Tools

Code Reviewer with HITL and Subagents

Environment Variables

Tracing Variables

Development

Install dependencies (including dev tools)

Run the test suite

Run tests with coverage

Lint

Type check

Validate all agent YAML files

Optional Dependencies

Contributing

License

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