Conversations API

Endpoints

Method	Path	Description
POST	/:project_id/v1/conversations	Create a conversation
GET	/:project_id/v1/conversations	List conversations
GET	/:project_id/v1/conversations/{id}	Get a conversation
POST	/:project_id/v1/conversations/{id}	Update a conversation
DELETE	/:project_id/v1/conversations/{id}	Delete a conversation
POST	/:project_id/v1/conversations/{id}/items	Add an item
GET	/:project_id/v1/conversations/{id}/items	List items
GET	/:project_id/v1/conversations/{id}/items/{item_id}	Get an item
DELETE	/:project_id/v1/conversations/{id}/items/{item_id}	Delete an item

Conversations are project-scoped. All paths are relative to your project base URL: https://api.erebine.ai/proj_ABC123

Quick Start

Create a conversation, add a system prompt, then use it with the Responses API:

Step 1: Create a conversation

                    curl -X POST https://api.erebine.ai/proj_ABC123/v1/conversations \
  -H "Authorization: Bearer ere_myproject_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "metadata": {"title": "Weather assistant"},
    "items": [
      {
        "type": "message",
        "role": "system",
        "content": "You are a helpful weather assistant."
      }
    ]
  }'
# Returns: {"id": "conv_abc123", ...}
                

Step 2: Add a user message

                    curl -X POST https://api.erebine.ai/proj_ABC123/v1/conversations/conv_abc123/items \
  -H "Authorization: Bearer ere_myproject_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "type": "message",
    "role": "user",
    "content": "What is the weather in Paris?"
  }'
                

Step 3: Generate a response

                    # Responses API uses endpoint-scoped path
curl -X POST https://api.erebine.ai/proj_ABC123/my-endpoint/v1/responses \
  -H "Authorization: Bearer ere_myproject_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-3.1-8b",
    "input": "What is the weather in Paris?",
    "conversation": {"id": "conv_abc123"}
  }'
                

The Responses API automatically loads all conversation items as context and appends the model's response to the conversation.

Lifecycle

The two surfaces meet at the conversation id; the model writes back into the same store you read from.

flowchart LR
    Create["POST /v1/conversations
conv_abc123"]
    AddUser["POST .../items
user message"]
    Respond["POST /v1/responses
conversation: {id: ...}"]
    LoadCtx["Server loads items
as prior context"]
    Generate["Model generates"]
    AutoAppend["Server appends
assistant + function_call items"]
    Harvest["Client reads new items
for call_id, content, ..."]

    Create --> AddUser
    AddUser --> Respond
    Respond --> LoadCtx
    LoadCtx --> Generate
    Generate --> AutoAppend
    AutoAppend --> Harvest
    Harvest -.-> AddUser

Manage Conversations

Create Conversation

POST /:project_id/v1/conversations

Parameter	Type	Description
metadataoptional	object	Up to 16 key-value pairs. Keys max 64 characters, values max 512 characters.
itemsoptional	array	Initial items to add (max 20). Useful for seeding system prompts or conversation history.

Response

                        {
  "id": "conv_abc123def456",
  "object": "conversation",
  "created_at": 1709000000,
  "updated_at": 1709000000,
  "metadata": {"title": "Weather assistant"}
}
                    

List Conversations

GET /:project_id/v1/conversations

Returns a paginated list of conversations. Use cursor-based pagination with after and limit parameters.

Query Parameters

Parameter	Type	Description
limitoptional	integer	Maximum conversations to return (1-100, default 20).
afteroptional	string	Cursor for pagination. Pass the `last_id` from the previous page to get the next page.

curl

                    curl "https://api.erebine.ai/proj_ABC123/v1/conversations?limit=10" \
  -H "Authorization: Bearer ere_myproject_your_api_key"
                

Response

                        {
  "object": "list",
  "data": [
    {
      "id": "conv_abc123def456",
      "object": "conversation",
      "created_at": 1709000000,
      "updated_at": 1709000100,
      "metadata": {"title": "Weather assistant"}
    }
  ],
  "first_id": "conv_abc123def456",
  "last_id": "conv_abc123def456",
  "has_more": false
}
                    

Get Conversation

GET /:project_id/v1/conversations/{conversation_id}

curl

                    curl https://api.erebine.ai/proj_ABC123/v1/conversations/conv_abc123 \
  -H "Authorization: Bearer ere_myproject_your_api_key"
                

Update Conversation

POST /:project_id/v1/conversations/{conversation_id}

Updates the conversation metadata. The new metadata replaces the existing metadata entirely.

curl

                    curl -X POST https://api.erebine.ai/proj_ABC123/v1/conversations/conv_abc123 \
  -H "Authorization: Bearer ere_myproject_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "metadata": {"title": "Updated title", "category": "support"}
  }'
                

Delete Conversation

DELETE /:project_id/v1/conversations/{conversation_id}

Soft-deletes the conversation. It is excluded from list queries but retained for auditing.

curl

                    curl -X DELETE https://api.erebine.ai/proj_ABC123/v1/conversations/conv_abc123 \
  -H "Authorization: Bearer ere_myproject_your_api_key"
                

Response

                        {
  "id": "conv_abc123def456",
  "object": "conversation",
  "deleted": true
}
                    

Manage Items

Add Item

POST /:project_id/v1/conversations/{conversation_id}/items

Parameter	Type	Description
typerequired	string	Item type: `"message"`, `"function_call"`, or `"function_call_output"`.
roleconditional	string	Required (non-null) for `message` type. By convention one of `"user"`, `"assistant"`, `"system"`, or `"tool"`; the server stores the value as-is and does not currently reject other strings, but downstream consumers (including the Responses API) expect the conventional values.
contentrequired	string	Text content of the item. Required at decode time for every item type. For `function_call` items the call's semantic payload is in `arguments`; supply an empty string or a short label for `content`.
call_idrecommended	string	Recommended for `function_call` and `function_call_output` items. Links a function call to its output so the Responses API can pair them in subsequent turns. Not enforced by the server today, but omitting it will break tool-result correlation.
namerecommended	string	Function name. Recommended for `function_call` items. Not enforced by the server today, but the model will not be able to resolve the tool without it.
argumentsrecommended	string	Serialized JSON arguments. Recommended for `function_call` items. Not enforced by the server today.

For function_call_output items, the call_id must match the one the model chose; harvest it from the conversation rather than inventing it. See Multi-Turn with Function Calling below for the harvest pattern.

Message Item

curl

                    curl -X POST https://api.erebine.ai/proj_ABC123/v1/conversations/conv_abc123/items \
  -H "Authorization: Bearer ere_myproject_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "type": "message",
    "role": "user",
    "content": "Hello, how are you?"
  }'
                

Response

                        {
  "id": "item_xyz789abcdef",
  "object": "conversation.item",
  "type": "message",
  "role": "user",
  "content": "Hello, how are you?",
  "call_id": null,
  "name": null,
  "arguments": null,
  "sequence_number": 1,
  "created_at": 1709000100
}
                    

Function Call Output Item

curl

                    curl -X POST https://api.erebine.ai/proj_ABC123/v1/conversations/conv_abc123/items \
  -H "Authorization: Bearer ere_myproject_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "type": "function_call_output",
    "call_id": "call_abc123",
    "content": "{\"temperature\": 18, \"condition\": \"sunny\"}"
  }'
                

List Items

GET /:project_id/v1/conversations/{conversation_id}/items

Returns items ordered by sequence number (ascending). Supports cursor-based pagination with after and limit parameters.

Query Parameters

Parameter	Type	Description
limitoptional	integer	Maximum items to return (1-100, default 20).
afteroptional	string	Cursor for pagination. Pass the `last_id` from the previous page.

curl

                    curl "https://api.erebine.ai/proj_ABC123/v1/conversations/conv_abc123/items?limit=50" \
  -H "Authorization: Bearer ere_myproject_your_api_key"
                

Response

                        {
  "object": "list",
  "data": [
    {
      "id": "item_xyz789abcdef",
      "object": "conversation.item",
      "type": "message",
      "role": "user",
      "content": null,
      "call_id": null,
      "name": null,
      "arguments": null,
      "sequence_number": 0,
      "created_at": 1709000100
    }
  ],
  "first_id": "item_xyz789abcdef",
  "last_id": "item_xyz789abcdef",
  "has_more": false
}
                    

Note: The content field is null in list responses for efficiency. Use the Get Item endpoint to retrieve full content for a specific item.

Get Item

GET /:project_id/v1/conversations/{conversation_id}/items/{item_id}

Returns the item with full content loaded from tiered storage.

curl

                    curl https://api.erebine.ai/proj_ABC123/v1/conversations/conv_abc123/items/item_xyz789 \
  -H "Authorization: Bearer ere_myproject_your_api_key"
                

Delete Item

DELETE /:project_id/v1/conversations/{conversation_id}/items/{item_id}

Soft-deletes the item. It is excluded from future conversation context but the sequence number gap is preserved.

curl

                    curl -X DELETE https://api.erebine.ai/proj_ABC123/v1/conversations/conv_abc123/items/item_xyz789 \
  -H "Authorization: Bearer ere_myproject_your_api_key"
                

Response

                        {
  "id": "item_xyz789abcdef",
  "object": "conversation.item",
  "deleted": true
}
                    

Delete response shape. Both delete endpoints return the resource type in object ("conversation" or "conversation.item"), not a synthetic ".deleted" suffix. Match on the deleted: true flag, not on the object string.

Responses API Integration

Pass a conversation parameter when creating a response on the Responses API to use the conversation's items as context. The model's output is automatically appended to the conversation.

curl

                    curl -X POST https://api.erebine.ai/proj_ABC123/my-endpoint/v1/responses \
  -H "Authorization: Bearer ere_myproject_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-3.1-8b",
    "input": "What is the weather in Paris?",
    "conversation": {"id": "conv_abc123"}
  }'
                

Note that conversations are project-scoped (at /:project_id/v1/conversations) while the Responses API is endpoint-scoped (at /:project_id/:endpoint_slug/v1/responses).

How It Works

The conversation's active items are loaded and prepended to the input as context, oldest-first, up to 2000 items per request.
The model generates a response using that history plus the new input.
The model's output items (messages, function calls) are automatically appended to the conversation.
The conversation's updated_at timestamp is refreshed.

The history is not trimmed to fit. If the loaded conversation plus the new input exceeds the model's context window, the request returns 400 context_length_exceeded under the default truncation: "disabled". Erebine does not drop your oldest turns and answer from a shortened conversation. The same applies to the 2000-item load bound: a conversation with more than 2000 items cannot be replayed in one request, so under "disabled" it returns 400 context_length_exceeded rather than an answer built from part of it. Send truncation: "auto" to have middle items dropped to fit -- which also selects the most recent 2000 items -- or branch the conversation. The context-window check applies only when the model's context length is resolvable; the item bound always applies.

Use one prior-context source, not both. conversation and previous_response_id are mutually exclusive. A request carrying both returns 400 invalid_request_error (code invalid_request, param conversation); neither source is applied. Use conversation for explicit state management and previous_response_id for simple response chaining.

Storage Tiers

Conversation content is stored using the platform's standard two-tier storage architecture. For details on storage tiers, encryption, retention, and billing, see Storage.

Conversation Storage Limits

Limit	Value
Max items per conversation	100
Max individual item size	1 MB
Max metadata keys per conversation	16
Max metadata key length	64 characters
Max metadata value length	512 characters
Max total metadata size	16 KB

Error Handling

HTTP Status	Error Code	Description
400	`invalid_request`	Missing or invalid parameters (e.g., missing role for message items).
400	`invalid_request`	Initial items array exceeds 20 items.
401	`authentication_error`	Invalid or missing API key.
404	`not_found`	Conversation or item not found.
429	`rate_limit_exceeded`	Too many requests. Check the `Retry-After` header.

Examples

Python (requests)

Python

                    import requests

headers = {
    "Authorization": "Bearer ere_myproject_your_api_key",
    "Content-Type": "application/json"
}
base = "https://api.erebine.ai/proj_ABC123/v1"

# Create a conversation with a system prompt
conv = requests.post(f"{base}/conversations", headers=headers, json={
    "metadata": {"title": "Demo chat"},
    "items": [{
        "type": "message",
        "role": "system",
        "content": "You are a helpful assistant."
    }]
}).json()
print(f"Created conversation: {conv['id']}")

# Add a user message
requests.post(
    f"{base}/conversations/{conv['id']}/items",
    headers=headers,
    json={
        "type": "message",
        "role": "user",
        "content": "What is the capital of France?"
    }
)

# List conversations
convs = requests.get(f"{base}/conversations?limit=10", headers=headers).json()
for c in convs["data"]:
    print(f"Conversation: {c['id']}")

# List items in a conversation
items = requests.get(
    f"{base}/conversations/{conv['id']}/items?limit=50",
    headers=headers
).json()
for item in items["data"]:
    print(f"[{item['role']}] {item['content']}")

# Delete a conversation
requests.delete(f"{base}/conversations/{conv['id']}", headers=headers)
                

Node.js (fetch)

JavaScript

                    const base = "https://api.erebine.ai/proj_ABC123/v1";
const headers = {
    "Authorization": "Bearer ere_myproject_your_api_key",
    "Content-Type": "application/json"
};

// Create a conversation with a system prompt
const convRes = await fetch(`${base}/conversations`, {
    method: "POST",
    headers,
    body: JSON.stringify({
        metadata: { title: "Demo chat" },
        items: [{
            type: "message",
            role: "system",
            content: "You are a helpful assistant."
        }]
    })
});
const conv = await convRes.json();
console.log(`Created conversation: ${conv.id}`);

// Add a user message
await fetch(`${base}/conversations/${conv.id}/items`, {
    method: "POST",
    headers,
    body: JSON.stringify({
        type: "message",
        role: "user",
        content: "What is the capital of France?"
    })
});

// List conversations
const listRes = await fetch(`${base}/conversations?limit=10`, { headers });
const list = await listRes.json();
list.data.forEach(c => console.log(`Conversation: ${c.id}`));

// List items in a conversation
const itemsRes = await fetch(
    `${base}/conversations/${conv.id}/items?limit=50`,
    { headers }
);
const items = await itemsRes.json();
items.data.forEach(item => console.log(`[${item.role}] ${item.content}`));

// Delete a conversation
await fetch(`${base}/conversations/${conv.id}`, {
    method: "DELETE",
    headers
});
                

Multi-Turn with Function Calling (curl)

When the Responses API decides to call a tool, it auto-appends a function_call item to the conversation. That appended item carries the call_id the model chose; you must read it from the conversation before posting the matching function_call_output. Do not invent or hardcode call_id values.

curl

                    # 1. Create conversation
curl -X POST https://api.erebine.ai/proj_ABC123/v1/conversations \
  -H "Authorization: Bearer ere_myproject_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{"metadata": {"title": "Tool demo"}}'
# Returns: {"id": "conv_abc123", ...}

# 2. Generate response with tools (auto-appends a function_call item to the conversation)
curl -X POST https://api.erebine.ai/proj_ABC123/my-endpoint/v1/responses \
  -H "Authorization: Bearer ere_myproject_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-3.1-8b",
    "input": "What is the weather in Paris?",
    "conversation": {"id": "conv_abc123"},
    "tools": [{
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "Get weather for a city",
        "parameters": {"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]}
      }
    }]
  }'

# 3. Harvest the assistant's call_id from the conversation.
# The Responses API auto-appended a function_call item; list the conversation
# items and pick the most recent function_call entry. Its call_id is the value
# the model chose and is the only value that will correlate with the output.
ASSISTANT_CALL_ID=$(curl -s \
  "https://api.erebine.ai/proj_ABC123/v1/conversations/conv_abc123/items?limit=100" \
  -H "Authorization: Bearer ere_myproject_your_api_key" \
| jq -r '[.data[] | select(.type == "function_call")] | last | .call_id')
echo "captured call_id: $ASSISTANT_CALL_ID"

# 4. Post the function output using the captured call_id (NOT a hardcoded one)
curl -X POST https://api.erebine.ai/proj_ABC123/v1/conversations/conv_abc123/items \
  -H "Authorization: Bearer ere_myproject_your_api_key" \
  -H "Content-Type: application/json" \
  -d "{
    \"type\": \"function_call_output\",
    \"call_id\": \"$ASSISTANT_CALL_ID\",
    \"content\": \"{\\\"temperature\\\": 18, \\\"condition\\\": \\\"sunny\\\"}\"
  }"

# 5. Continue with the function result in context
curl -X POST https://api.erebine.ai/proj_ABC123/my-endpoint/v1/responses \
  -H "Authorization: Bearer ere_myproject_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-3.1-8b",
    "input": "Great, now summarize the weather.",
    "conversation": {"id": "conv_abc123"}
  }'
                

Note on the list response. List Items returns content: null for efficiency. call_id, name, type, and sequence_number are populated in the list response, so harvesting the call_id does not require a follow-up Get Item call. If you also need the function arguments payload, fetch the specific item by id.