Agents with Memory¶

By default, each request is independent — the agent doesn’t remember previous messages. Memory fixes that.

How it works¶

ABI-Core uses thread_id in invoke() to maintain conversation history. The LLM sees all previous messages in the same thread.

from abi_core.agent.llm_provider import invoke

# First message
result = await invoke(config.LLM_CONFIG, "My name is Ana", thread_id="session-001")

# Second message — the LLM remembers "Ana"
result = await invoke(config.LLM_CONFIG, "What's my name?", thread_id="session-001")
# → "Your name is Ana"

Add memory to your agent¶

The key is passing context_id as the thread_id. Edit your step:

@agent.step(name="chat_with_memory")
async def chat_with_memory(text, context_id):
    """Respond with conversation memory."""
    from abi_core.agent.llm_provider import invoke
    from prompts import CHAT_PROMPT

    result = await invoke(
        config.LLM_CONFIG,
        CHAT_PROMPT.format(text=text),
        thread_id=context_id,  # This enables memory
    )
    return {"response": result}

And your task passes the context_id through:

@agent.task(name="chat", task_id="task-chat")
async def chat(query):
    data = json.loads(query) if isinstance(query, str) else query
    text = data.get("text", "")
    context_id = data.get("context_id", "default")

    result = await agent.execute_step(
        "chat_with_memory", text=text, context_id=context_id
    )
    yield AgentResponse.result(result)

Test it¶

# First message
curl -X POST http://localhost:8002/stream \
  -H "Content-Type: application/json" \
  -d '{"query": "My name is Carlos", "context_id": "session-42"}'

# Second message — same context_id
curl -X POST http://localhost:8002/stream \
  -H "Content-Type: application/json" \
  -d '{"query": "What is my name?", "context_id": "session-42"}'
# → "Your name is Carlos"

How context_id works¶

Same context_id = same conversation (agent remembers)
Different context_id = fresh conversation (no memory)
The web interface generates a context_id per session automatically

Memory is in-process¶

The default memory (LangGraph MemorySaver) lives in the agent’s process memory. If the container restarts, memory is lost. For persistent memory, store conversations in the Semantic Layer using MCPToolkit.

Next step¶

👉 Testing Agents